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Abstract

The effect of artificial food colors (AFCs) on child behavior has been studied for more than 35 years, with accumulating evidence from imperfect studies. This article summarizes the history of this controversial topic and testimony to the 2011 Food and Drug Administration Food Advisory Committee convened to evaluate the current status of evidence regarding attention-deficit/hyperactivity disorder (ADHD). Features of ADHD relevant to understanding the AFC literature are explained: ADHD is a quantitative diagnosis, like hypertension, and some individuals near the threshold may be pushed over it by a small symptom increment. The chronicity and pervasiveness make caregiver ratings the most valid measure, albeit subjective. Flaws in many studies include nonstandardized diagnosis, questionable sample selection, imperfect blinding, and nonstandardized outcome measures. Recent data suggest a small but significant deleterious effect of AFCs on children's behavior that is not confined to those with diagnosable ADHD. AFCs appear to be more of a public health problem than an ADHD problem. AFCs are not a major cause of ADHD per se, but seem to affect children regardless of whether or not they have ADHD, and they may have an aggregated effect on classroom climate if most children in the class suffer a small behavioral decrement with additive or synergistic effects. Possible biological mechanisms with published evidence include the effects on nutrient levels, genetic vulnerability, and changes in electroencephalographic beta-band power. A table clarifying the Food and Drug Administration and international naming systems for AFCs, with cross-referencing, is provided.

Electronic supplementary material

The online version of this article (doi:10.1007/s13311-012-0133-x) contains supplementary material, which is available to authorized users.

Keywords: ADHD, Food dyes, Artificial food colors, Hyperactivity, Child behavior, FDA

In March 2011, the United States (U.S.) Food and Drug Administration (FDA) Food Advisory Committee held a hearing on the behavioral effects of synthetic food dyes, technically known as artificial food colors (AFCs). The focus of the meeting was on attention-deficit/hyperactivity disorder (ADHD), so discussion of that disorder was requested as background for understanding the data on AFCs. The controversial committee decision (8-6 vote) was not to recommend banning AFCs or requiring a warning label. This article summarizes relevant background information, further needs for research, and interim conclusions.

AFC Classification Systems

To make them more identifiable, AFCs have both a common name and an official number that may differ from country to country. The International Numbering System (INS) is the world standard for classifying everything associated with food and uses the numbers 100 to 199 for color additives (approved for use or not) from the Codex Alimentarius ("Book of Food") established in 1963 by the Food and Agriculture Organization (FAO) and World Health Organization (WHO). The safety of AFCs and other food additives is regulated by the Joint FAO/WHO Expert Committee on Food Additives ([JECFA] established in 1955.) The European Union (EU) uses the INS and adds an "E" prefix ("E" for "Europe") for both natural and synthetic food colors approved by the European Food Safety Authority ([EFSA] established in 2002). Unlike the EU, in the U.S. the FDA separates synthetically produced colors or AFCs, which are batch-tested for safety ("certified colors," N = 9) from those derived from natural sources in which individual batch testing is not required ("exempt from certification," N = 29). There are 7 certified food colors ("FD&C colors"; FD&C = Food, Drugs & Cosmetics) approved for widespread use in the U.S.: brilliant blue (Blue No. 1b), indigotine (Blue No. 2), fast green (Green No. 3), tartrazine (Yellow No. 5b), sunset yellow (Yellow No. 6b), erythrosine (Red No. 3) and allura red (Red No. 40b). Two other AFCs are approved for specific limited use: citrus red (Citrus Red No. 2) (to color orange rinds) and Orange B (to color weiner/sausage casings). Table lists these 9 AFCs with FD&C, INS, and EU "E" numbers and approval status for each classification and JECFA acceptable daily intakes (ADIs).

Table 1

History of the Food-Dye/AFC-ADHD Controversy

Food coloring with natural substances has been used since approximately 1500 B.C. in Ancient Egypt, and has been regulated from the time of England's King Edward I, in the 13 th century, to control their unsafe and fraudulent use (for more detail see Burrows [ 1] for a fascinating review of the history of food coloring and regulation). The 1856 development of synthetic dyes from petroleum or coal (so-called "coal-tar colors," most of which are still in use today) by the English chemist Sir William Henry Perkin ushered in a series of acts in Europe and the U.S. regulating food colors. The FDA was established in1938 and the Joint FAO/WHO in 1955. Since then, the role of the FDA has been to ensure AFCs safety and prevent its fraudulent use in making food appear better or of greater value than it really is. Although not without criticism, the FDA has dramatically revolutionized AFCs from a time when highly toxic color additives were indiscriminately and widely used to the present when a "food colorant with a 1 in 19 billion chance of causing cancer is legally considered too dangerous" [ 1, p.405].

One of the more current controversies in the field of AFCs is concerned with their effect on children's behavior. Although the idea that food allergies or hypersensitivities lead to behavior and learning problems dates back to the 1920s [ 2], a specific hypothesis regarding this relationship was not developed until the 1970s. In 1973, Dr. Benjamin Feingold [ 3] presented an article at the annual meeting of the American Medical Association, proposing that pediatric hyperactivity and learning problems were due to certain foods and food additives. Based on his own clinical observations, he believed that his patients were often sensitive to foods containing natural salicylates, AFCs, and flavors, and he devised a diet (the "Kaiser Permanente" or "K-P" diet) free of these substances [ 4, 5]. By 1977, [ 6, 7] Feingold also eliminated 2 preservatives (butylated hydroxytoluene and butylated hydroxyanisole, which he thought also led to hyperactivity [ 6] and claimed that 60 to 70 % of the children he treated improved [ 7].

Feingold's [ 8] initial presentation generated a great deal of attention from the media, public, and professionals, and led to his best-selling book, Why Your Child is Hyperactive. However, his work was generally criticized by the medical and pharmaceutical fields [ 9, 10], and in 1975, the food industry, represented by the National Advisory Committee of the Nutrition Foundation, declared, "No controlled studies have demonstrated that hyperkinesis is related to the ingestion of food additives" [ 11]. However, Feingold's work was accepted by many parents, who formed the still-existing Feingold Association of the United States (FAUS) (established in 1976). The first study on AFCs was conducted in 1976 by Conners et al. [ 12] at the University of Pittsburgh. After several additional studies, the National Institutes of Health held a conference on defined diets and hyperactivity in 1982 and concluded that further studies were needed [ 13]. One year later a meta-analysis by Kavale and Forness [ 14] (of 23 controlled group studies) concluded that the overall effect size (ES, 0.11) was too small to be important and the results did not support the K-P diet as a treatment for hyperactivity. This led to a decrease in interest in the K-P diet and the effect of AFCs until 2004 when Schab and Trinh [ 15] published their meta-analysis of 15 double-blind, placebo-controlled studies and found an ES of 0.28 between AFCs and the placebo on ADHD symptoms, in parent ratings, but not teacher or observer ratings, and only when children were preselected as diet responders; however, this pertained to all of the subjects, regardless of whether they were initially hyperactive or not.

Typical Elimination Studies and Examples

Besides a few studies that actually single-blinded an elimination and the "placebo" regular diet, there are many others following a different model, similar in some ways to a drug discontinuation study. Modern studies often start with an oligoantigenic or "few foods" diet for everyone. If there is improvement, then foods or components are openly added back 1 at a time to find the offenders. Then what follows is a double-blind challenge with offenders that can be blinded. Fortunately, AFCs are 1 of the easier things to blind; they are tasteless and can be camouflaged by dark food or drink. Preservatives are also easy to blind, which unfortunately makes it tempting to challenge blindly with mixes of the 2 classes of ingredients. In any event, the ease of blinding makes AFCs (and preservatives) better studied than other dietary components, but they should not be considered the whole problem. For details, see Stevens et al. [ 16].

A few exemplary highlights are these: Egger et al. [ 17], in a sample of 76 hyperactive children, found 48 foods with evidence of deterioration on being added back. Colors and preservatives were the most frequent offenders, but were not the sole problem in most children. In a sample of 200 selected from 800 hyperactive children, 150 openly improved with the elimination of AFCs and deteriorated on their resumption; there were 34 of these children who entered a double-blind challenge with 6 doses of tartrazine and placebo. Twenty-two of the 34 children clearly reacted with irritability, restlessness, and sleep disturbance [ 18]. This study illustrates the denominator problem in trying to determine the prevalence of AFC sensitivity in ADHD: were the 22 proven reactors 60 % of 34, or 15 % of 150, or 11 % of 200, or 2.5 % of 800? If we take 11 % as the most plausible, it highlights another interesting finding in this study: 20 nonhyperactive controls were also challenged at the same time, and 2 of those, or 10 %, also clearly reacted [ 16]. Thus, the controls showed a similar rate of reactivity as the hyperactive children. This presages the findings of the Southampton studies.

Southampton Studies

In 2004 and 2007, there were 3 landmark studies published from Stevenson & colleagues at Southampton University, as reported in 2 articles [ 19, 20]. Two are in preschool [ 19, 20], and 1 in the 8- to 9-year-olds [ 20]. All 3 samples included more than 100 participants each (N = 277, 153, and 144). The samples were nonclinical, acquired in an epidemiological manner (the first invited all the preschoolers on the Isle of Wight). The children were classified as hyperactive or not based on a rating scale and, in the first study [ 19], as atopic or not by a skin prick test. No other diagnostic assessment was done. However, severity assessments were done on double-blind challenges of an AFC mix and placebo using several measures. What was called "hyperactivity" included all 18 Diagnostic and Statistical Manual, fourth edition (DSM-IV) [ 21] ADHD symptoms, not just the hyperactive symptoms. The authors refrained from calling it an ADHD measure because they had not formally diagnosed any of the children.

All children were given an elimination diet free of AFCs and preservatives for 2 weeks, then challenged with mixed fruit juice with or without a mix of AFCs (sunset yellow, tartrazine, carmoisine, ponceau 4R, quinoline yellow, and/or allura red AC) and 45 mg Na benzoate. The first preschool study [ 19] used 5 mg each of sunset yellow, tartrazine, carmoisine, and ponceau 4R (20 mg total). The second preschool study [ 20] used 2 mixes and 2 doses besides the placebo. Mix A had the same AFCs and total dose as the first study, but different proportions (2.5 mg carmoisine, 7.5 mg tartrazine, 5 mg each of SY, and ponceau 4R). Mix B had 7.5 mg each of sunset yellow, carmoisine, quinoline yellow, and allura red AC, totaling 30 mg. The 2 doses for the 8- to 9-year-olds [ 20] were: mix A was the same as the preschool mix A, but 1.25 times as large, totaling 25 mg of AFC. Mix B was the same as preschool mix B, but 2.08 times as large, totaling 62 mg AFC, approximately the 2010 per capita daily consumption of AFCs in the U.S. There was a 1-week placebo washout between challenges with the placebo or AFC/benzoate mix, which were administered in random order.

The blinding was excellent. Preliminary adult tasting panels could not distinguish challenge from the placebo drink after inspecting, opening the sealed bottles, and tasting. Parent guesses of order (placebo-AFC vs AFC-placebo) in the first preschool study [ 19] reflected chance: half guessed correctly, exactly what would be expected by chance for a binary guess.

The main outcome measure for the first study [ 19] was "hyperactivity," a composite of overactivity, inattention, and impulsiveness, rated by parents in all studies and by teachers in the second and third studies [ 20]. The first study [ 19] also had an aggregated test of hyperactivity, composited from psychologist observation and task performance. The second and third studies [ 20] also had classroom observation, and for the 8- to 9-year-olds the Conners Continuous Performance Test. For the second and third studies, a global hyperactivity score was derived from all the measures and used as the primary outcome measure.

Results of the First Study

In the first study [ 19], parent ratings, but not the aggregated psychologist's score, showed a significantly greater increase in "hyperactivity" on active challenge than on placebo by a small-medium difference. Importantly, there was no association with ADHD (defined by rating scale score) or atopy (defined by skin prick). Thus the deleterious effect applied to all children.

Results of 2nd preschool study

Mix A but not Mix B showed a significantly greater increase than placebo in the primary outcome, the Global Hyperactivity score (d = 0.2). Again, there was no interaction with designation of child as hyperactive. Thus this study replicated the first study in a different sample [ 20].

Results for 8-9 year-olds

On intent-to-treat analysis, Mix B (higher dose) but not Mix A showed a significantly greater increase than placebo in Global Hyperactivity score. On analysis of those with 85 % compliance, both mixes showed a significant effect. The effect size was small, d = 0.12-0.2. Again, there was no interaction with baseline hyperactivity; the results of all 3 studies showed a small significant effect for all children, not just those meeting criterion A of DSM-IV ADHD [ 20]. This suggests that food AFCs are more of a public health problem than an ADHD problem.

The results of these studies led to some significant changes in the field of public health, with the United Kingdom government requesting that food manufacturers avoid these additives in favor of natural food colors and flavors, and the EU asking manufacturers to voluntarily remove several AFCs from foods and beverages or list the following warning on the label: "[this AFC] may have an adverse effect on activity and attention in children" [ 22]. In the U.S., the Southampton studies inspired a petition to the FDA from the Center for Science in the Public Interest (CSPI) [ 23] and, along with media interest and congressional support, led the FDA Food Advisory Committee to review the evidence on AFCs and ADHD and have a public hearing on March 30-31, 2011. This committee was given three documents prior to this meeting. One described the charge: "to consider available relevant data on the possible association between consumption of certified color additives in food and hyperactivity in children, and to advise FDA as to what action, if any, is warranted to ensure consumer safety" [ 24]. Another described the FDA's history of food color regulation [ 25], and the third was a literature review of publications on AFCs and ADHD [ 26]. During the hearing the committee heard two days of testimony from several reviewers, experts on ADHD and food colors, members of the public, and representatives of advocacy groups and industry. The committee was given 5 questions. On question #2, "Do the current relevant data support FDA's conclusion, as set forth in the September 1, 2010 Interim Toxicology Review Memorandum, that a causal relationship between consumption of certified color additives in food and hyperactivity or other adverse effects on behavior in children in the general population has not been established?" the committee members voted 79 % yes; 21 % no [ 27]." On question #4, "Should additional information be disclosed on the product label of food containing certified color additives to ensure their safe use? The Committee members voted 43 % yes; 57 % no." [ 27]. Finally, on question #5, the need for additional studies, "The Committee members voted 93 % yes; 7 % no" [ 27].

In 2012 Weiss [ 28] reviewed the FDA's decision [ 27] and noted four flaws in the process. 1). The FDA review confined itself to the relationship between AFCs and the clinical diagnosis of ADHD rather than broader behavioral problems. Weiss stated this was important because most children, not just those with ADHD, consume AFCs; few of the studies investigated a DSM-IV [ 21] diagnosis of ADHD; nearly all of the studies examined short-term rather than long-term effects expected of a chronic disease like ADHD; and narrow-band measures of ADHD would not identify non-ADHD symptoms caused by AFCs (e.g., irritability & sleep problems). 2). The FDA looked for large numbers of children to be affected by AFCs rather than recognize the importance of smaller but still vulnerable subpopulations. 3). The FDA judged McCann et al.'s [ 20] ES of 0.18 (in the range of many studies on AFCs & ADHD) as of "low magnitude." Weiss [ 28] estimated such an ES as equivalent to a loss of three IQ points, and concluded "Most observers would not consider this to be a value of "rather low magnitude" (p. 3). 4). The FDA committee's conclusion that further research was needed before taking preventive action did not consider the implications for institutional review board (IRB) approval for studies with documented risk and the cost of studies examining each of the certified AFCs.

Since the FDA hearing two more reviews have been published: Stevens, Kuczwk, Burgess, Hurt and Arnold [ 16] and Nigg et al. [ 2]. In their review of "35 years of research," Stevens et al. [ 16] noted scientists have examined Feingold's hypotheses using 3 types of diets: (1) the K-P diet, (2) an elimination diet followed by AFC challenges, and (3) an oligoantigenic or few-foods diet followed by AFC and natural food challenges. From their review of four K-P diet studies, Stevens et al [164] concluded there are a small proportion (11 %-33 %) of children with hyperactivity whose functioning at home and school is improved by the K-P diet. From their review of 11 elimination diet-AFC challenge studies with children and with animals, Stevens et al [ 16] concluded most studies suggest that AFC challenges (mixed or with just tartrazine), compared with placebo, cause significant behavioral changes in ADHD subpopulations, the general pediatric population and in laboratory animals.. From their review of seven oligoantigenic/few-foods elimination diet-AFC/natural food challenges, Stevens et al [ 16] concluded all studies reported high response rates to various elimination diets (>70 %) and most parents reported more hyperactivity when challenged with offending foods/AFCs than placebo, with AFCs and preservatives the most likely to cause reactions, but no child responded only to AFCs.

Most recently, Nigg et al. [ 2] published a meta-analysis examining studies of dietary restriction and AFCs. For restriction diets they identified 14 open-label trials with a random-effects-weighted response rate of 47.4 %, which would set the upper limit on response rates for such diets (the rate could be lower considering that some of the response could be nonspecific effects). Five double-blind randomized placebo-challenge studies were also identified for restricted diets with an ES = 0.29, which Nigg et al. [ 2] noted was 1/3 the ES for medication and equivalent to a clinically meaningful change from the 50th to 62nd percentile. For AFCs 24 double-blind, placebo-controlled crossover studies were included and produced an ES for parent reports of 0.18, reduced to 0.12 after adjustment for potential publication bias. This effect was reliable in studies of food colors without preservatives (ES = .21) but not for studies of only FDA-approved food colors. Teacher/observer reports had a nonsignificant ES of 0.07 for studies in general, but when restricted to AFCs, it rose to 0.22 and survived correction. Psychometric tests of attention had an ES of 0.27, which also survived correction. Nigg and colleagues [ 2] concluded that "Although the evidence is too weak to justify action recommendations absent a strong precautionary stance, it is too substantial to dismiss" (p. 96) and "Although these average effect sizes are small in clinical terms, they could be quite substantial from the perspective of population-wide prevention efforts" (p. 96.).

Summary of Prevalent Flaws in Published Research on Dietary Sensitivities Relevant to ADHD

Although there were many studies with various controls, all were flawed in some way. First, diagnosis often does not follow DSM criteria. This is partly because many studies predated the current DSM-IV operational criteria [ 21]. But even some studies conducted after DSM-IV criteria [ 21] were available do not follow them. Frequently the "diagnosis" is a rating scale threshold or clinical impression. Another problem is that blinding is frequently imperfect, sometimes nonexistent, and its validity rarely examined in the studies of dietary sensitivities in which AFCs have been examined along with other suspected dietary components. However, the blinding is usually better for AFCs than other dietary components tested (e.g., the blinding was excellent in the Southampton studies [ 19, 20]). When AFCs are tested, they are often done as a mix rather than a single AFC (or possibly even mixed with a preservative), so that often it is difficult to attribute effects accurately to particular AFCs.

Admixture of preservatives in challenges is especially vexing because they require a different standard of evidence for removing them from the food supply. Removal of preservatives would carry an economic and public health cost in terms of food spoilage and possible food poisoning, whereas AFCs are purely cosmetic, so that removal would not seem to have an economic or public health cost. Thus it would be important to distinguish AFC effects from preservative effects. Other limitations of studies include the wide variety of AFC doses, duration of exposure to AFCs, and the timing of post-challenge testing.

Clarifications about ADHD Relevant to Evaluating the AFC Behavioral Literature

There are several aspects of ADHD diagnosis and evaluation relevant to evaluating the impact of AFCs and, relatedly, to the FDA preliminary review of materials. First, ADHD is a phenomenological, not causal, diagnosis [ 29]. Syndromes that meet the diagnostic criteria may be considered ADHD regardless of cause. In fact, there are probably many causes for a common phenotype.

Moreover, the phenotype itself is pleomorphic with four main variations recognized in the Diagnostic and Statistical Manual-Fourth Edition-Text Revision [ 29]: predominantly inattentive type, predominantly hyperactive-impulsive type, combined type, and "not otherwise specified." Although the literature is not conclusive, there appears to be some variation in pharmacologic and other treatment response by type (e.g., [ 30]) and it would not be surprising if there were some variation in etiology. Nevertheless, the distinctions among types, despite being operationally/definitionally clear, are both fuzzy and arbitrary: Of the 9 inattentive and 9 hyperactive-impulsive symptoms, it is possible to have 6 inattentive symptoms and 5 hyperactive-impulsive symptoms and be classified as inattentive type, only one symptom short of being combined type with 6 of each. In fact, persons with 9 inattentive symptoms and 6 hyperactive-impulsive symptoms would be diagnosed as combined type even though they would have a greater preponderance of inattentive symptoms than the person with 6 and 5 respectively. Finally, two people could theoretically have the inattentive type with only 3 symptoms in common and up to 7 symptoms different (3 inattentive and 4 hyperactive-impulsive). Further, the phenotype defined by ICD-10 [ 31] hyperkinetic disorder and hyperkinetic conduct disorder is different yet; only 145 of the 579 children in the Multimodal Treatment Study of ADHD (the MTA) with combined-type ADHD met the ICD-10 criteria [ 32].

Diagnostic Procedure

The diagnosis requires 5 criteria, several of which are relevant to understanding the effect of AFCs: symptom count and severity; impairment; pervasiveness across settings; chronicity (6 months or more, starting young); and not better explained by another mental disorder [ 29].

Analogy to Hypertension

Because of the first criterion (symptom count and severity), ADHD is a dimensional diagnosis, like hypertension. Although everyone has some blood pressure (BP), too much is a problem. Cardiologists have struggled with how to set the threshold for problematic BP and have changed the benchmarks or thresholds from time to time, as psychiatrists have done for ADHD (DSM-IIIR [ 33] required 8 of 14 symptoms rather than the current 6 of 9). The current threshold for pre-hypertension is ≥80 or more diastolic BP and for hypertension is ≥90 or more diastolic [ 34]. Wherever it is set, some people will be on the cusp, such that a few mm change in BP will nudge them into a different category (e.g., going from 78 to 82 diastolic gives one pre-hypertension and from 88 to 92 gives one hypertension). Risk factors like stress, excess salt, or obesity could nudge some people over the BP threshold. As a corollary, one does not need diagnosable hypertension to be harmed by stress, excess salt, or obesity, which makes the prevention of those problems applicable to the general population. Similarly, a cause with only a small effect (as found for AFC in group averages) can nudge someone into the diagnosable range for ADHD (e.g., from 5 to 6 symptoms), and one does not need to have diagnosable ADHD to be harmed by a cause with a small deleterious effect, making the preventive implications widely applicable.

Chronicity and Pervasiveness Criteria Affect Measurement

One criticism of the studies of AFC effects on behavior is that they mainly show an effect on parent ratings, not so much on objective tests or on clinic observations. This criticism appears vacuous when we consider the diagnostic criteria of chronicity and pervasiveness, which require a consistent pattern of behavior over time, in more than one setting. Therefore severity and change (improvement or deterioration) cannot be adequately measured or appreciated in short time fragments in an artificial setting. They depend on caregiver ratings, or at least caregiver informants, usually parent and teacher, who know the child well and in various settings. These are subjective, but the most valid [ 35]. In recognition of this fact, the FDA has approved indications for ADHD drugs on the basis of parental information as the primary outcome.

Multifactorial Causes of ADHD

Genetics and epigenetics are perhaps the best-documented causes. Numerous family studies, including twin studies, have shown heritability up to 80 %, and this is backed up by replicated candidate genes such as the 7-repeat allele of DRD4 and the 10-repeat allele of DAT1 [ 36]. However, this should not translate to genetic determinism, because genes are expressed by interaction with the environment, including diet. A good example is phenylketonuria (PKU), a well-established 100 % genetic disorder, lack of the gene for the enzyme to metabolize phenylalanine to tyrosine, resulting in a toxic alternative metabolic pathway for phenylalanine. Because the disease develops only in the presence of phenylalanine in the diet, it is 100 % environmental as well as 100 % heritable [ 37]. Thus the fact that ADHD is 80 % heritable means that it is between 20 % and 100 % environmental. We can see that heritability could partly be genes for vulnerability to specific environmental factors, such as AFCs, insecticides, environmental chemicals, infections, parasites, trauma, poor diet, etc.

First Suspected Cause: MBD

Minimal brain damage or minimal brain dysfunction (MBD) was posited as a cause from the beginning because the syndrome was noted to be a sequela of infections (von Economo's encephalitis, intrauterine rubella, childhood infections), perinatal "reproductive casualty" (e.g., kernicterus), head trauma, and lead poisoning [ 38]. More modern additions to the list of suspects might include insecticide residues, industrial, construction, and consumer product chemical pollutants, and AFCs.

Increased Prevalence and Putative Causes

The estimated prevalence of ADHD has increased since 1970, when it was 3-5 %, a figure that was still quoted as late as the 1994 DSM-IV [ 21]. More recent estimates have ranged up to 10-12 % [ 39]. Part of this, increase, of course, is increased recognition and more liberal diagnostic practice. Nevertheless, there may be an actual increase beyond diagnostic inflation. Many possible causes have been suspected. Those with genes for vulnerability may be exposed to more stresses, insults, or lack of developmental opportunities, which are likely to bring out the problems. The past century has seen multiple environmental changes. The educational setting has changed from one-room schools and self-contained classrooms with individualized instruction and close school-home cooperation to group classes with less discipline and home cooperation at the same time that the information age has ratcheted up the amount and difficulty of material to be learned. Parents working outside the home reduce the amount of one-to-one task-oriented adult mentorship that was available on a farm or in a small family business. Social breakdown of neighborhoods, families, and mores deprive children of the structure that is so helpful to youngsters vulnerable to developing ADHD. Electronic pastimes have reduced the amount of cerebellar exercise, which some suspect may be necessary for optimal cognition. Finally, there are new chemicals in the environment that could stress neurophysiology.

Environmental Contaminants and ADHD Symptoms

A good bit of research, especially in the 1960s and 70s, established the role of subclinical lead burden in cognitive and behavioral problems [ 40], to the extent that lead was eventually removed from gasoline Interestingly, the inverse correlation of intelligence quotient with subclinical blood lead levels [ 41] was of the same magnitude as the effect on hyperactivity recently reported for AFCs + Na benzoate in the Southampton studies [ 19, 20]). Lead contamination had been around for decades (albeit perhaps increasing Post-World War II because of increasing use of leaded gasoline), but there are other environmental contaminants of more recent origin. For example, insecticides have been associated with ADHD symptom severity: In one study, organophosphate insecticide residues and metabolites in children's urine doubled the risk of ADHD [ 42]. The source was suspected to be residues on fruits and vegetables. In another study, maternal gestational serum insecticide level had an association with the child's later behavior at age 5 [ 43]. Other modern sources of possible toxicity are industrial, construction, and consumer product chemicals. For instance, children whose cord blood had a PCB level in the top 25 % had 1.76 times the risk of ADHD symptoms as those whose cord blood had the bottom 25 % of PCB levels [ 44]. In another study polyfluoralkyl levels in adolescents age 12-15 were associated with ADHD symptoms [ 45]. These chemicals, of course, have come into common use in the past 50-60 years. Another environmental chemical class recently prevalent in children's food is artificial coloring. Figure shows the increase in per capita consumption of AFCs since 1950, taken from FDA data.

AFC Interaction with Nutrient

Other changes have occurred in the foods available to children. Mineral content of fruits and vegetables has decreased since the 1930s [ 46] with intensive agriculture that mainly fertilizes with nitrogen, phosphorus, and potassium, largely neglecting other essential elements. Further, food is processed much more extensively, which changes its nutrient content in known and unknown ways, and such processed foods may be associated with vulnerability to depression [ 47]. One of the known changes in children's diets, of course, is addition of artificial AFCs. There have been repeated reports of low blood and other tissue levels of iron, zinc, magnesium, and omega-3 fatty acids. A couple of interesting studies by Ward [ 48, 49] may tie some of this together.

The first study [ 48] was done with 10 hyperactive children whose parents said their behavior was sensitive to food AFCs and 10 healthy control children. At baseline, the hyperactive children had lower serum, urine, and nail zinc than the controls. After a 3-day additive-free diet, half of each group consumed a tartrazine challenge consisting of a commercial drink containing tartrazine, and half drank a similar orange drink without tartrazine. The serum and saliva Zn went down nonsignificantly and urine zinc went up significantly in the hyperactive children who drank tartrazine, but not in controls. Behavioral observations correlated with the amount of zinc change. In 1997, Ward [ 49] replicated these findings in 47 more children that parents reported to react to food AFC, again with age- and sex-matched controls. This time 23 children were given 50 mg tartrazine (slightly less than the 60 mg per capita daily consumption of AFC in 2010), 12 were given amaranth, and 12 sunset yellow. Five controls were given each of the 3 AFCs (N = 15 controls). Again serum zinc went down nonsignificantly, urine Zn went up significantly in hyperactive children given tartrazine and sunset yellow (not amaranth), but not in controls. Again, zinc changes from AFC challenge were associated with behavioral deterioration. Both studies suggest zinc wasting (excessive excretion) from tartrazine (and sunset yellow), possibly by chelation. This raises a question of what other nutrients may be wasted or otherwise interfered with by various AFCs. It also shows a possible mechanism for AFC to affect the brain without crossing the blood-brain barrier, given that Zn is essential for normal brain function [ 50],. This is relevant to the argument that AFC should not affect behavior because only Blue #1 crosses a mature blood-brain barrier in appreciable amounts.

Genetic Basis of AFC Sensitivity

Genetic tests were done in the second and third Southampton studies [ 20] on 6 candidate genetic variants, 2 histamine and 4 dopamine-relevant polymorphisms [ 51]. Three gene polymorphisms moderated the effect of AFC mixes on the Global Hyperactivity score. Both histamine polymorphisms were significant: HNMT Thr105Ile for both ages and HNMT T939C for the 8-9 year olds. There was one significant Dopamine polymorphism, DAT1 in 8-9 year-olds. COMT val108met, ADRA C1291G, and DRD4 rs740373 were not significant moderators. For the moderating effect of HNMT T939C, the C allele is protective against AFC effect, and absence of the C allele, the marker for vulnerability to the AFC effect, occurred in about 60 % of the children, supporting the public health import of the findings.

Other Biological Evidence: Brain Topographical Mapping

In another study [ 52], Brain Electrical Activity Mapping was done with and without a provoking food (including AFCs) during the preceding weeks and the same day in a crossover design, with blind interpretation of the EEG. With the provoking food, but not without it, there was an increase in frontotemporal Beta-1 band activity and behavioral symptoms. Unfortunately, the challenges were not blind, so one could not rule out the possibility that the parents' knowing might have influenced the child's EEG.

Additional Laboratory Evidence of Biological Effect

Selected examples of laboratory results, mainly in animals, include erythrosine-induced inhibition of serotonergic activity in rats [ 53] , corticosterone effects of erythrosine in rats [ 54] (another way in which AFCs could affect brain function without crossing the blood-brain barrier), changes in liver function tests from AFC mixtures in rats [ 55], and human mast cell degranulation with tartrazine, releasing histamine [ 56]. Further details may be found in Stevens et al [ 16].

Conclusions

Additional research should attend to the following points: Sample selection and characterization should consider specialty clinics vs. general mental health clinics vs. normal "controls" to address the denominator problem, and there should be careful diagnosis by DSM criteria, including both patients with ADHD and controls without, or controls with other diagnoses, to confirm the public health breadth of the risk. Age effects should be examined: Do adolescents and adults outgrow the sensitivity? Challenges should be "unbundled" to examine individual AFCs as well as various mixes, and especially examine AFCs separate from preservatives. Dose effects need more systematic exploration, especially in light of the ascending curve of daily consumption. How much is too much? How much is consumed by the highest-ingesting children? Careful blinding should be incorporated as much as possible. This should include double-blinding to prevent unwitting telegraphing of expectation by the research staff and testing of blinding validity for all those who should be blinded. Standard scales and observations should be used for comparison across studies and with studies of other interventions.

Three kinds of interactions should be systematically examined: interaction with nutrients, interaction with medications, and interactions between/among AFCs. A special question in light of the Southampton demonstration of a widespread effect on the general population [ 19, 20] is the effects on a whole classroom as well as individual children. If most of the children in a classroom deteriorate behaviorally by a small amount every day, is the aggregate and cumulative effect on the classroom climate greater than the sum of its parts?

Finally, samples need to be large enough to detect the small population effect sizes demonstrated in the Southampton studies [ 19, 20]. The 3-figure Southampton samples were barely sufficient to detect the statistically small but clinically important effects noted. An analogy is the subclinical-lead-burden literature, where it apparently took a sample size of about a thousand to demonstrate the effect convincingly. There is great danger of Type II error here, and statistical expertise needs to be involved in the early stages of planning any such study. Such large samples raise an interesting question about cost and who should pay for it. Some would argue that industry should sponsor studies to prove safety, similar to what is needed for drug approval. Others might argue that these AFCs have already been approved and are in use, and it is up to public health advocates to test whether they are unsafe. Perhaps the cost should be shared between industry and public health agencies.

Interim Working Conclusions

While awaiting the results of such further research, the following conclusions seem reasonable:

AFCs are not a main cause of ADHD, but they may contribute significantly to some cases, and in some cases may additively push a youngster over the diagnostic threshold.

There are several threads of evidence for a biological mechanism.

Although there is probably not an immune-mediated reaction, a direct release of histamine may be involved.

By affecting nutrients and other metabolism in the periphery, AFCs could affect the brain without crossing the blood-brain barrier.

The deleterious effect does not appear to be confined to ADHD (a general effect has been replicated). Therefore AFCs may be more a general public health problem than an ADHD problem

A small deleterious effect regardless of diagnosis was replicated and a possible mechanism (related to histamine genes) identified

The magnitude of reported effect is reminiscent of subclinical lead poisoning (<10 mcg/dL): r ~ 0.11 after correction for social factors [ 41], which led to the eventual removal of lead from gasoline.

Per capita daily consumption of AFCs quadrupled in the last 50 yrs.

"The dose alone makes the poison" --Paracelsus

There may conceivably be a possible deleterious effect on classroom climate from most children deteriorating slightly, thus additively or even synergistically impairing the learning atmosphere.

The current status of evidence is inconclusive "but too substantial to dismiss."(2) Until safety can be better determined, we suggest minimizing children's exposure to AFCs. With the current concerns about childhood obesity, there appears to be no need to make food look more attractive than its natural color.

Electronic supplementary material

(PDF 510 kb)

Required Author Forms

Disclosure forms provided by the authors are available with the online version of this article.

Footnotes

¹ This article developed from the first author's testimony to the 2011 FDA Food Advisory Committee on the behavioral effects of food AFCs, which is incorporated in updated summary.

References

3. Feingold BF. Adverse reactions to food additives. Presented at: The American Medical Association Annual Meeting; June 24-28, 1973; Chicago, IL.

7. Feingold BF. Hyperkinesis and learning disabilities linked to the ingestion of artificial food colors and flavors. Speech to: American Academy of Pediatrics; November 8, 1977; New York, NY.

8. Feingold BF. Why Your Child is Hyperactive. New York: Random House; 1975.

11. Feingold BF. A view from the other side. Speech to: Newspaper Food Editors and Writers Association; June 8, 1977; Milwaukee, WI.

13. Defined diets and childhood hyperactivity. NIH Consens Statement, 1982:4:1-11.

21. Diagnostic and statistical manual of mental disorders, 4th ed. Washington, DC: American Psychiatric Association, 2000.

24. FDA/CFSAN Food Advisory Committee, 2011. Center for Food Safety and Applied Nutrition, March 30-31, 2011, Food Advisory Committee Meeting

29. Diagnostic and statistical manual of mental disorders, 4th ed., text revision. Washington, DC: American Psychiatric Association, 2000.

31. World Health Organization. ICD-10: International statistical classification of diseases and related health problems, 10th revised ed. New York, NY, 2008.

33. Diagnostic and statistical manual of mental disorders, 3rd ed. Washington, DC: American Psychiatric Association, 1980.

39. Akinbami LJ, Xiang L, Pastor PN, Reuben CA. Attention deficit hyperactivity disorder among children aged 5-17 years in the United States, 1998-2009. NCHS Data Brief 2011;70:xx.

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Artificial Food Colors and Attention-Deficit/Hyperactivity Symptoms: Conclusions to Dye for

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The effect of artificial food colors (AFCs) on child behavior has been studied for more than 35 years, with accumulating evidence from imperfect studies. This article summarizes the history of this controversial topic and testimony to the 2011 Food and Drug Administration Food Advisory Committee convened to evaluate the current status of evidence regarding attention-deficit/hyperactivity disorder (ADHD).

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We investigated whether minority children attending U.S. elementary and middle schools are disproportionately represented in special education. We did so using hazard modeling of multiyear longitudinal data and extensive covariate adjustment for potential child-, family-, and state-level confounds. Minority children were consistently less likely than otherwise similar White, English-speaking children to be identified as disabled and so to receive special education services. From kindergarten entry to the end of middle school, racial- and ethnic-minority children were less likely to be identified as having (a) learning disabilities, (b) speech or language impairments, (c) intellectual disabilities, (d) health impairments, or (e) emotional disturbances. Language-minority children were less likely to be identified as having (a) learning disabilities or (b) speech or language impairments.

Keywords at-risk students, disparities, educational policy, elementary schools, hazard modeling, language minorities, longitudinal, middle schools, minority disproportionate representation, racial and ethnic minorities, secondary data analysis, special education, survey research

Racial-, ethnic-, and language-minority schoolchildren in the United States have repeatedly been reported to be overidentified as disabled and so disproportionately overrepresented in special education (e.g., Artiles, 2003; Dunn, 1968; Harry, Arnaiz, Klinger, & Sturges, 2008; Oswald, Coutinho, Best, & Singh, 1999; Sullivan & Bal, 2013). These findings have led to characterizations of special education as "discriminatory" ( Skiba, Poloni-Staudinger, Simmons, Feggins-Azziz, & Chung, 2005, p. 142), having "systemic bias" ( Oswald, Coutinho, Best, & Nguyen, 2001, p. 361), constituting "a new legalized form of structural segregation and racism" ( Blanchett, 2006, p. 25), and "another manifestation of institutionalized racism" ( Codrington & Fairchild, 2012, p. 6). Federal legislation and policies have been enacted to reduce minority disproportionate representation (MDR) in special education (e.g., Posney, 2007; U.S. Department of Education, Office of Civil Rights, 2009). For example, the U.S. Congress observed that "more minority children continue to be served in special education than would be expected from the percentage of minority students in the general school population" (p. 118 of Statute 2651, Public Law 108-446).

Local educational agencies (LEAs) are currently required to report to the U.S. Department of Education whether minority children are significantly overrepresented in special education. If overrepresentation is observed, the LEAs are required to allocate 15% of their Part B funds for early intervening services to children from minority groups being overidentified as disabled. The federal government is currently considering enacting further compliance monitoring policies ( Government Accountability Office, 2013; U.S. Department of Education, 2014). Calls are also being made to strengthen existing monitoring policies ( Skiba, Albrech, & Losen, 2013), including requiring LEAs to report any disproportionality instead of reporting disproportionality resulting only from incorrectly applied procedures or policies ( Albrecht, Skiba, Losen, Chung, & Middelberg, 2012).

Yet existing theoretical and empirical work is contradictory as to whether minority children in the United States are disproportionately represented in special education. Some mechanisms have been theorized to result in overidentification (e.g., Harry et al., 2008); others in underidentification (e.g., Coll, Crnic, Lamberty, & Waski, 1996; Hibel, Farkas, & Morgan, 2010). Some empirical studies find that minority children are overidentified as disabled (e.g., Oswald, et al., 1999; Sullivan & Bal, 2013). Others find that minority children are underidentified (e.g., Hibel et al., 2010; Morgan, Farkas, Hillemeier, & Maczuga, 2012; Morgan, Staff, Hillemeier, Farkas, & Maczuga, 2013; Shifrer, Muller, & Callahan, 2011; Sullivan, 2013) as well as less likely to be referred for services ( Delgado & Scott, 2006). Below, we briefly survey this contradictory theoretical and empirical work. We then identify methodological and substantive limitations in the existing empirical studies to justify the current study.

Mechanisms theorized to result in racial-, ethnic-, and language-minority children being overrepresented in special education include cultural, linguistic, and racial bias. For example, minority children are believed to experience systemic prejudice that results in their abilities and behaviors being unjustifiably characterized as problematic and atypical ( Coutinho & Oswald, 2000; Harry et al., 2008; Hays, Prosek, & McLeod, 2010; Lorsen & Orfield, 2002; O'Connor & Fernandez, 2006), resulting in segregation into special education ( Blanchett, 2006; Reid & Knight, 2006).1 Prior to possibly becoming sensitive to minority overrepresentation, teachers may have been more likely to refer minority students for special education (e.g., Hosp & Reschly, 2003), although early evidence of this tendency is also contradictory ( Skiba et al., 2008; Tobias, Cole, Zibrin, & Bodlakova, 1982; Tobias, Zibrin, & Mennell, 1983). Schools have been theorized to use academic and behavioral standards aligned more with White, English-speaking, middle-class, privileged populations ( Blanchett, Klingner, & Harry, 2009). Racial-, ethnic-, and language-minority children may be less likely to meet these standards as a result of receiving lower-quality educations provided at underresourced schools (e.g., Peske & Haycock, 2006) and so more likely to be identified for special education. Minority children in the United States are also more likely to be exposed to biological and environmental factors in early childhood (e.g., fetal alcohol syndrome, low birth weight, lead exposure, poverty) that disproportionately increase their risk for impaired cognitive, academic, and behavioral functioning and disability ( Annie E. Casey Foundation, 2014; Donovan & Cross, 2002; Mann, McCartney, & Park, 2007).

Consistent with mechanisms theorized to result in overrepresentation, Skiba et al.'s (2005) analyses of cross-sectional state-level data and statistical control for school- and district-level confounds indicated that Black and Hispanic children were overrepresented in special education. This was the case for multiple disability conditions, including intellectual disabilities, emotional disturbances, speech or language impairments, and learning disabilities. Oswald et al.'s (1999) analyses of cross-sectional, nationally representative, and district-level data with statistical control for district-level educational, economic, and demographic confounds indicated that minorities were overrepresented in special education. Minority children were overidentified as having intellectual disabilities and emotional disturbances. Sullivan and Bal's (2013) cross-sectional analyses of an urban school district and statistical control for child- and school-level potential confounds indicated that Black children were more likely to be identified for special education services. Follow-up analyses indicated that Black children were overidentified for learning disabilities, although they were no more likely than White children to be identified as having cognitive impairments, speech or language impairments, other health impairments, or emotional disturbances and were underidentified as having intellectual disabilities. Samson and Lesaux's (2009) analyses of a nationally representative, longitudinal data set with statistical control for individual- and family-level potential confounds indicated that language-minority children were overrepresented in special education by third grade, although they were underrepresented during both kindergarten and first grade. Sullivan and Bal (2013) also reported that Hispanic children were underrepresented in special education.

Other mechanisms have been theorized to result in racial-, ethnic-, and language-minority children being underrepresented in special education ( Hibel et al., 2010; Morgan, Farkas, et al., 2012; Rosenberg, Zhang, & Robinson, 2008; Samson & Lesaux, 2009), including socioeconomic, linguistic, and/or cultural obstacles that constrain access by minority families to special education services ( Danesco, 1997; Coll et al., 1996; Harry, 1992; O'Hara, 2003; Pena & Fiestas, 2009). Some minority families may prefer to rely on the social support of extended families to assist their children and so may not agree to evaluation requests for special education ( Coll et al., 1996). Cultural factors may also result in some minority groups attributing their children's perceived lower academic behavioral functioning to systemic prejudice or other nonbiological explanations ( Danesco, 1997; Yeh, Forness, Ho, McCabe, & Hough, 2004; Yeh, Hough, McCabe, Lau, & Garland, 2004). The stigma associated with disability identification may further reduce a racial-, ethnic-, or language-minority family's willingness to have their child identified as disabled ( Hervey-Jumper, Dougan, & Franco, 2008; O'Hara, 2003; Zuckerman et al., 2014), particularly for conditions considered highly stigmatizing (e.g., intellectually disabled). Minority families may experience fewer interactions with pediatricians and other health professionals who often diagnose disorders (e.g., autism) during early childhood ( Palfrey, Singer, Walker, & Butler, 1987). This may occur as a result of less access to health care ( Flores & Committee on Pediatric Research, 2010; Inkelas, Raghavan, Larson, Kuo, & Ortega, 2007) as well as language barriers ( Flores & Tomany-Korman, 2008; Zuckerman et al., 2014). Children attending disadvantaged schools may also be less likely to be identified as displaying atypical academic or behavioral functioning ( Delpit, 1995). For example, Hibel et al. (2010) theorized that minority children experience "frog pond" effects in regard to special education eligibility. Specifically, attending poorly resourced schools may result in referrals for special education only for those children displaying unusually low academic achievement or behavior relative to other, lower-performing children attending these same schools.

Consistent with mechanisms posited to result in underrepresentation, Hibel et al.'s (2010) analyses of a nationally representative sample followed from kindergarten entry to the end of fifth grade indicated that, following extensive statistical control, Black, Hispanic, and Asian children were less likely to receive special education services than otherwise similar White children. Follow-up analyses indicated that minority children were less likely to be identified as having learning disabilities, speech or language impairments, and intellectual disabilities. Morgan, Farkas, et al.'s (2012) analyses of a population-based, longitudinal sample of preschool-age children found that those who were Black or from homes where a language other than English was spoken were less likely than otherwise similar White children or those from English-speaking homes to be identified as having developmental delays or disabilities. Shifrer et al.'s (2011) analyses of a nationally representative sample of high school students with statistical control for individual-, family-, and school-level potential confounds indicated that Blacks were less likely than otherwise similar Whites to be identified as having learning disabilities. Yeh, Forness, et al.'s (2004) analyses of a large sample of children receiving mental health services with statistical control for child- and family-level factors indicated that Hispanic children were less likely to receive special education services as a result of being underidentified as having emotional disorders.

Extant Empirical Work's Methodological and Substantive Limitations

The relatively fewer studies using extensive covariate adjustment consistently find that racial-, ethnic-, and language-minority children are instead underidentified as disabled (e.g., Hibel et al., 2010; Morgan et al., 2013; Morgan, Farkas, et al., 2012; Shifrer et al., 2011; Yeh, Forness, et al., 2004). Covariate adjustment has sometimes resulted in the estimates reversing direction from over- to underrepresentation ( Hibel et al., 2010; Shifrer et al., 2011). Estimates reported by those education researchers using extensive covariate adjustment for child-, family-, and school-level control variables are also consistent with estimates reported by public health researchers, who often use covariate adjustment when investigating disparities in disability identification and treatment (for a review, see Flores & Committee on Pediatric Research, 2010). For example, children who are Black have been found to be less likely to be diagnosed with autism, learning disabilities, and attention-deficit/hyperactivity disorder following covariate adjustment for IQ, prior academic achievement and behavior, maternal education, and additional factors (e.g., Bussing, Zima, Gary, & Garvan, 2003; Mandell, Listerud, Levy, & Pinto-Martin, 2002; Morgan et al., 2013; Pastor & Reuben, 2005). Furthermore, those minorities who receive a diagnosis have been reported to be disproportionately less likely than otherwise similar White children to make use of empirically based treatments for their disorders ( Morgan et al., 2013).

We systematically investigated whether and to what extent racial-, ethnic-, or language-minority children in the United States were over- or underidentified as disabled relative to otherwise similar White, English-speaking children as they attended elementary and middle schools. To address methodological and substantive limitations in the existing knowledge base, we (a) analyzed longitudinal data collected on a nationally representative sample followed from kindergarten entry to the end of middle school, (b) used hazard modeling to estimate over-time dynamics of disability identification across five specific conditions, and (c) extensively corrected for child- and family-level variables that might confound the directional estimates of disproportionality uniquely attributable to children's status as racial, ethnic, or language minorities. Collectively, these analyses should yield rigorously derived estimates of the likelihood that racial-, ethnic-, or language-minority children in the United States are disproportionately over- or underidentified as having disabilities throughout elementary and middle school and so whether they are more or less likely to be receiving special education services than otherwise similar White, English-speaking children.

The study's sample included children participating in the Early Childhood Longitudinal Study-Kindergarten Cohort, 1998-1999 (ECLS-K; Tourangeau, Lê, Nord, & Sorongon, 2009). The ECLS-K is a nationally representative cohort of U.S. schoolchildren who entered kindergarten in the fall of 1998 and were then surveyed during the spring of kindergarten, fall and spring of first grade (with data from only a random subsample collected during the fall of first grade), and the springs of third, fifth, and eighth grade. Data were collected from children, parents, and teachers. Our hazard modeling analyzed data from a sample of 20,100 kindergarten children with complete information on race/ethnicity, gender, and age at the fall-of-kindergarten survey wave. Table 1 displays the means of the study's criterion and predictor variables. Additional detail regarding the ECLS-K's study design is available from the National Center for Education Statistics (NCES; see http://nces.ed.gov/ecls/kindergarten.asp). Our analyses were weighted to produce results generalizable to the population of U.S. schoolchildren who entered kindergarten in 1998 and who continued through school until at least to the end of eighth grade.2

Descriptive Statistics for Selected Variables (Weighted), N = 20,100

We identified students receiving special education services as a result of having one of five Individuals with Disabilities Education Act (IDEA)-eligible disabilities. In kindergarten and in first, third, fifth, and eighth grades, a child's special education teacher was asked, "For which of the following disabilities did this student receive special education or related services this school year?" Response options included (a) learning disability, (b) speech or language impairment, (c) mental retardation, (d) health impairment (e.g., attention-deficit/hyperactivity disorder, Tourette's syndrome), and (e) serious emotional disturbance.3 These five disability categories were the most frequently identified reasons why children were receiving special education services in the ECLS-K. The percentage of all children participating in the ECLS-K with these IDEA-eligible identified disabilities ranged from a high of 6.4% (speech or language impairment) to a low of 0.7% (intellectual disability)

Parents reported on their child's gender and race/ethnicity. We analyzed four racial and ethnic categories. These were (a) Hispanic, (b) non-Hispanic Black, (c) non-Hispanic White, and (d) other race/ethnicity (e.g., including Asian, American Indian, Alaskan Native). The mother's marital status was reported in fall kindergarten.

The mother's age at the time of birth was calculated from the child's and mother's ages recorded at the date of interview in spring of kindergarten. We considered mothers as being younger mothers if they gave birth when they were 17 years old or younger. We considered mothers as being older mothers if they gave birth when they were 39 years old or older. Parents reported whether the child was born at low birth weight (i.e., less than 5 lbs.) and whether the child had health insurance. We used whether a language other than English was the language spoken during the ECLS-K home interview as an indicator of language-minority status.4 We also included the ECLS-K's multivariate measure of the family's socioeconomic status (SES). This included the mother's and father's education levels and occupations along with household income. To allow for nonlinear effects of SES, our analyses used dummy variables for SES quintiles. Dummy variables for the states of the children's schools were also included as controls. Sample members were drawn from almost all states. These time-invariant variables were measured during the spring of kindergarten.

The frequencies of children's externalizing and self-regulatory behaviors were rated independently by their general education teachers in kindergarten and first, third, and fifth grades using the Social Rating Scale (SRS). The SRS is a modified version of the Social Skills Rating Scale (SSRS; Gresham & Elliott, 1990), a psychometrically validated behavioral measure. The test-retest correlation of the original SSRS was .85 ( Gresham & Elliott, 1990). Correlational and factor analyses support the measure's construct validity ( Feng & Cartledge, 1996; Furlong & Karno, 1995). NCES subsequently modified the SSRS (e.g., adding some items, expanding the response format from a 3-point to a 4-point scale; Pollack, Atkins-Burnett, Najarian, & Rock, 2005). Teachers used a frequency scale to rate how often children in their class displayed a particular social skill or behavior (i.e., 1 = never; 4 = very often). The Externalizing Problem Behaviors subscale measured the frequency of a child's acting-out behaviors, including how frequently the child argued with a teacher, fought, showed anger, acted impulsively, and disturbed the classroom. This subscale's reliabilities ranged from .86 to .89 across the survey waves. The Approaches to Learning subscale measured how frequently a child self-regulated and managed his or her behavior while completing classroom tasks. These self-regulatory behaviors included how well a child paid attention, kept belongings organized, worked independently, showed eagerness to learn new things, easily adapted to changes in routine, and persisted in completing tasks. This subscale's reliabilities ranged from .89 to .91 across the survey waves. We averaged the ratings if more than one teacher provided them in a given year. The SRS was not administered in eighth grade. To statistically control for the frequency of an eighth-grade child's self-regulatory and externalizing problem behaviors, we imputed the two SRS subscale ratings obtained from the prior (i.e., fifth-grade) survey wave. Prior work has established that elementary schoolchildren's behavioral functioning is stable over time (e.g., Lin et al., 2013; Shaw, Lacourse, & Nagin, 2005).

During each survey wave, children's academic achievement was estimated by averaging their scores from general measures of both their reading and mathematics achievement. Each of these two achievement tests was individually administered, untimed, and extensively psychometrically validated. Scores on the measures are relative to those of all other children participating in the ECLS-K. Item response theory and routing procedures were used for both measures to maximize academic content coverage and to obtain scale scores specifically designed by NCES to be comparable across the ECLS-K's survey waves. The Reading Test evaluated a range of reading skills across time (e.g., sight word reading, decoding, vocabulary, reading comprehension). After first grade, the emphasis on basic reading skills was decreased, and greater emphasis was given to reading comprehension skills. All items were field-tested. Theta reliabilities for the kindergarten to eighth-grade assessments ranged from .87 to .96 ( NCES, 2009). As an indication of construct validity, the Reading Test scores of first-grade students correlated at .85 or above with their scores on the Kaufman Test of Educational Achievement Reading subtest ( Rock & Pollack, 2002). The Mathematics Test evaluated a wide range of skills over time (e.g., place value, ordinality, sequence, basis operations, measurement, fractions). Reliabilities of the Mathematics Test's theta scores are in the mid .90s for each of the assessment waves ( NCES, 2009; Pollack et al., 2005). As with the Reading Test, there was little differential item functioning, nor was there evidence of floor or ceiling effects. We standardized scores on the time-varying measures for ease of interpretation.

We used discrete-time logit (hazard) regression models for event history analysis to identify factors predicting children's identification as disabled from kindergarten to eighth grade ( Singer & Willet, 2003). Importantly, we estimated separate regressions for each of the five disability diagnoses under study. (Each of these is a dichotomous variable for diagnosed with the particular disability or not.) These diagnoses are mutually exclusive at each time point. These models estimated the hazard of a child being identified with the specific disability being examined, conditional upon not having been previously identified with this specific disability. Once identified, the child was censored from the sample for this specific disability condition but not from the regressions for the other disability conditions. Thus, for example, if a child was diagnosed with speech or language impairment in first grade, then that child was censored from the subsequent speech-or-language-impairment condition regressions in third, fifth, and eighth grades. However, the child may subsequently have been identified for another disability condition (e.g., learning disability, emotional disturbance) in the regressions for the other four disability conditions. Thus, children's risk for disability identification was fully modeled across the five disability conditions and time periods examined.

Hazard models are advantageous over models that predict disability identification at only a single time point. This is because hazard models more appropriately model disability identification as a dynamic, over-time process. These models measure the duration until diagnosis, thus allowing differential rates of diagnosis for different racial, ethnic, and language-use groups to be appropriately modeled. These models also allow for the use of time-varying covariates. Use of time-varying covariates better corrects for potentially confounding variables occurring at the same time that children were identified as disabled, thereby allowing for more closely matched White, English-speaking children when estimating the risk of being identified as disabled throughout elementary and middle school that is uniquely attributable to children's status as racial, ethnic, or language minorities.

We first prepared the data by using SAS 9.3 (SAS Institute, Cary, NC) and then converted the data file to Stata using Version SE 12.1 (StataCorp, College Station, TX). We then modeled the data using the Stata add-on procedure dthaz. We estimated two hazard models for each of the study's five specific disability conditions. For Model 1, we included only race or ethnicity and time as predictors for the specific disability condition. Using time as a predictor allowed us to model the probability of receiving special education services for each of the disability conditions from kindergarten entry to the end of eighth grade. Model 1 results show the relative frequency of disability diagnosis for children from the racial and ethnic groups before these relative frequencies are adjusted for covariates such as individual child-level academic achievement or behavioral functioning or family-level SES, which may be associated with disability diagnosis and on which the groups of children differ. In Model 2, we then included additional individual child- and family-level covariates measured in kindergarten, as well as time-varying measures of children's behavioral and academic functioning measured from kindergarten to eighth grade, for each specific disability condition. This relatively large number of control variables was necessary so that our directional estimates of the likelihood of disability identification uniquely attributable to children's status as racial, ethnic, and language minorities were adjusted so as to make the children as otherwise similar as possible. We reported these estimates as odds ratios (ORs). Odds are the ratio of the probability that an event occurs to the probability that it does not. For our estimates of disparities in children's disability identification, the OR is (a) the odds that one particular group (e.g., children who are Black) will experience versus not experience an event (e.g., being identified as having learning disabilities) versus (b) the odds of another group (e.g., children who are White) will experiences versus not experience the same event (see Szumilas, 2010, for a brief primer). Odds coefficients of 1.0 indicate no effect of the predictor variable on the outcome. Odds above 1.0 indicate a positive effect; those below 1.0, a negative effect.

Table 2 shows weighted estimates from the discrete-time logit regression modeling of disability identification for each of the five specific conditions. Model 1's results vary considerably and do not yield any evidence that racial- and ethnic-minority children are statistically significantly overidentified as having disabilities. Instead, and prior to extensive covariate adjustment, Model 1's results indicate that racial and ethnic minorities are underidentified as having speech or language impairments as well as health impairments.

Discrete-Time Logistic Regression Models (Adjusted Odds Ratios) of IEP Categories

Model 2 extensively adjusts for potentially confounding factors, including family-level SES and individual child-level academic achievement and behavioral functioning. Doing so indicates that racial- and ethnic-minority children are less likely than otherwise similar White children to be identified as having disabilities, and these results are statistically significant. For example, Black children are 58% (covariate adjusted OR = .42), 63% (covariate adjusted OR = .37), 57% (covariate adjusted OR = .43), and 77% (covariate adjusted OR = .23) less likely than otherwise similar White children to be identified as having learning disabilities, speech or language impairments, intellectual disabilities, and health impairments, respectively. Black children are 64% (covariate adjusted OR = .36) less likely to be identified as having emotional disturbances than otherwise similar White children.

Hispanic children are 29% (covariate adjusted OR = .71) less likely than White children to be identified as having learning disabilities, 33% (covariate adjusted OR = .67) less likely to be identified as having speech or language impairments, and 73% (covariate adjusted OR = .27) less likely to be identified as having health impairments. These results indicate that Hispanic children are identified with disabilities more frequently than Black children but significantly less often than otherwise similar White children. Model 2's results also indicate that language minorities are 28% (1 − 0.72) and 40% (1 − 0.60) less likely than otherwise similar children from English-speaking households to be identified as having learning disabilities or speech or language impairments, respectively. However, these children are not significantly more or less likely than other children to be identified as having intellectual disabilities, health impairments, or emotional disturbances.

Model 2's results also indicate that factors other than children's status as racial, ethnic, or language minorities are associated with their risk of disability identification. Children from families without health insurance are less likely to be identified as having speech or language impairments. Particularly notable as well is that higher academic achievement and greater behavioral self-regulation consistently lower children's likelihood of being identified as disabled. More frequent externalizing problem behaviors decrease children's likelihood of being identified as having learning disabilities or speech or language impairments. These behaviors increase children's likelihood of being identified as having health impairments or emotional disturbances. Collectively, the findings suggest that U.S. schools are likely to identify children as disabled on the basis of their academic achievement, behavioral self-regulation, and externalizing problem behaviors.

Figures 1 and 2 display the covariate-adjusted hazard and survival functions, respectively, of each of the specific disability conditions.5 The hazard functions plot the probabilities of being identified for the specific disability condition for the particular time period for those children who have not yet been identified with the specific condition. The survival curves measure, at each time period, the percentage of children who have not as yet been identified with the specific disability condition. Thus, for example, for two groups of 1,000 otherwise similar White and Black children who begin in kindergarten, the survival function indicates that approximately 99% will be estimated not to have been identified with a learning disability by the spring of first grade. In contrast, 97% of the Black children but 94.5% of the White children will have survived not being identified as having learning disabilities by the end of eighth grade.

These estimates are adjusted for each condition's Model 2 control variables. Figure 1 shows that young children's likelihood of being identified as having learning disabilities steadily increases and then peaks in fifth grade, after which point it begins to decline. In contrast, children's likelihood of being identified as having speech or language impairments declines steadily over time. Intellectual disabilities display no clear over-time pattern. Children's likelihood of being identified as having health impairments or emotional disturbances increases over time. Some of these rates are very low as a result of relatively low prevalence of the disabilities in the general population of U.S. schoolchildren. Yet the estimated rates also continue to indicate that, after extensive covariate adjustment, Hispanic and Black children are less likely than otherwise similar White children to be identified with disabilities while attending elementary and middle schools in the United States.

Contrary to federal legislative and policy efforts to reduce MDR in special education, our study failed to find any evidence that racial-, ethnic-, or language-minority children in the United States are being disproportionately overrepresented in special education. This lack of overrepresentation was evident both prior to and following extensive covariate adjustment. Instead, results from our analyses of longitudinal and nationally representative data consistently indicate that racial-, ethnic-, and language-minority schoolchildren in the United States are less likely than otherwise similar White, English-speaking schoolchildren to be identified as disabled and so are comparatively underrepresented in special education. Minority children's underrepresentation was evident across the entire elementary and middle school time periods. Racial and ethnic minority children in the United States are less likely to receive special education services as a result of being identified as having learning disabilities, speech or language impairments, intellectual disabilities, health impairments, or emotional disturbances. Language-minority children are less likely to be identified as having learning disabilities or speech or language impairments. Our estimates of minority disproportionate underrepresentation were extensively corrected for potential confounding variables, including individual child-level academic achievement and behavioral functioning as well as family-level SES.

Study's Contributions and Implications

Long-standing and ongoing federal legislation and policymaking have attempted to reduce minority disproportionate overrepresentation in special education. This is because, to date, the extant education research has largely reported that minority children are more likely to be identified as having disabilities. This has led to characterizations of special education as "discriminatory" ( Skiba et al., 2005, p. 142), maintaining "a new legalized form of structural segregation and racism" ( Blanchett, 2006, p. 25), and constituting "institutionalized racism" (Codrington & Fairchild, 2102, p. 6). Yet, and despite these characterizations, our analyses of a longitudinal sample of U.S. schoolchildren followed from kindergarten entry to the end of middle school failed to yield any evidence of minority overrepresentation in special education. Instead, and consistent with those few studies by education researchers employing extensive covariate adjustment for individual student-, family-, and school-level variables (e.g., Hibel et al., 2010; Morgan, Farkas, et al., 2012; Shifrer et al., 2011), as well as with studies by public health researchers who have also used extensive covariate adjustment ( Bussing et al., 2003; Flores & Committee for Pediatric Research, 2010; Mandell et al., 2002), our study's analyses indicated that minority children are underidentified as having disabilities and so are less likely to be receiving special education services than otherwise similar White, English-speaking children in the United States. This study's findings are not consistent with characterizations of special education as racist or discriminatory due to predispositions to label racial-, ethnic-, or language-minority children as disabled in order to segregate them in classrooms separate from their White, English-speaking classmates. Nor are they consistent with long-standing and ongoing federal legislation and policies designed to reduce minority overrepresentation in special education.

Our findings also extend the field's knowledge base by establishing that, although lower health care access and non-English use are each predictive of a lower likelihood of disability identification, neither factor fully explains the underidentification attributable to children's status as racial, ethnic, or language minorities ( Zuckerman et al., 2014). Our results also suggest that future investigations of minority disproportionate representation are likely to report biased estimates of minority disproportionate representation if they fail to account for the strong potential confounding variables of individual child-level academic achievement and behavioral functioning. These factors, as well as other sociodemographic characteristics (e.g., age, gender) are both consistently and uniquely related to U.S. schoolchildren's likelihood of being identified as disabled. Prior research has reported that the directionality of disproportionate representation varies depending on the type of disability condition under investigation, although these studies have often not accounted for individual child-level academic achievement and behavioral functioning ( Donovan & Cross, 2002; Skiba et al, 2005; Sullivan, 2011; Sullivan & Bal, 2013). Those studies adjusting for individual- and family-level as well as school-level potential confounds consistently indicate the minority children are underrepresented in special education, with this underrepresentation evident both generally and for specific disability conditions and both prior to and following school entry ( Hibel et al., 2010; Morgan, Farkas, et al., 2012; Shifrer et al., 2011). Our results indicate that racial- and ethnic-minority children who are otherwise similar to White children are consistently less likely to be identified as disabled whether the specific condition being investigated is learning disabilities, speech or language impairments, intellectual disabilities, health impairments, or emotional disturbances. Our study extends this prior work by establishing that the underrepresentation is evident across multiple disability conditions and over time when examined in the same cohort followed from the beginning of elementary school to the end of middle school. Thus, and consistent with mechanisms suggesting that sociocultural factors may collectively result in barriers to access to services ( Coll et al., 1996; Danesco, 1997; Harry, 1992; O'Hara, 2003), our findings suggest that racial- and ethnic-minority children experience underrepresentation across time and for a wide range of disability conditions.

In contrast, our findings indicate that language-minority children's underrepresentation is specific to the language-related disorders of learning disabilities and speech or language impairments, possibly as a result of these children's lower proficiency in English as well as a lack of availability and use of proper diagnostic assessments for these types of disorders with non-English speakers ( Figueroa & Newsome, 2006; Hibel & Jasper, 2012; Linan-Thompson, 2010). Elementary schoolchildren who are language minorities have also been hypothesized to follow a specific over-time dynamic of disproportionate representation, in which they are initially underidentified as having disabilities but then begin to be overidentified as teachers begin attributing the children's lower achievement to factors other than limited English proficiency ( Hibel & Jasper, 2012; Samson & Lesaux, 2009). Yet, and possibly due to our use of a wider set of covariates, we find no evidence of this dynamic. Instead, we find that, following extensive statistical control, including for individual-level academic achievement and behavioral functioning, language-minority children continue to be underidentified for learning disabilities and speech or language impairments throughout elementary and middle school.

Our findings support policies and practices that result in increased use by practitioners of culturally and linguistically sensitive special education evaluation methods. Use of these methods may be necessary to ensure that special education eligibility procedures do not result in unwarranted overrepresentation of White, English-speaking children. This overrepresentation may result from teachers, school psychologists, and other professionals being differentially responsive to White, English-speaking parents. Pediatricians have already been reported to be more likely to solicit developmental concerns about children from White parents ( Guerrero, Slusser, Barreto, Rosales, & Kuo, 2011). Minority children have been reported to be less likely to be using empirically validated treatments than White children ( Morgan et al., 2013). From a social justice standpoint, if special education's eligibility procedures are being disproportionately responsive to children from White, English-speaking families, then children from racial-, ethnic-, or language-minority families may not be experiencing the same access to potentially beneficial special education services for which they are legally entitled. This in turn may maintain or exacerbate the nation's long-standing educational inequalities, including achievement gaps. Minority children's lack of treatment for undiagnosed disorders and disabilities has been hypothesized to at least partially account for these achievement gaps ( Basch, 2011). Educational professionals should be attentive to cultural and language barriers that may be keeping minority children from being appropriately evaluated and identified for disabilities. Use of interpreters as well as appropriately translated written materials may assist with this process ( Brotanek, Rocha, & Flores, 2008). Due-process pamphlets describing disability eligibility procedures are often written in less accessible language, particularly for parents of lower-SES backgrounds ( Fitzgerald & Watkins, 2006), who are more likely to be minorities ( U.S. Census Bureau, 2012). School-to-community outreach programs may be another possible method of addressing cultural and linguistic barriers to disability identification and treatment. Such outreach programs have helped reduce racial and ethnic disparities in children's health and health care access ( Flores, 2009; Madsen, Thompson, Adkins, & Crawford, 2013).

For policymakers, our results suggest that current federal educational legislation and policymaking designed to minimize overidentification of minorities in special education may be misdirected ( Government Accountability Office, 2013; U.S. Department of Education, 2014), including the reallocation of Part B funding to early intervening services designed to reduce minority overrepresentation in special education. Our analyses of a nationally representative and multiyear data set failed to yield any evidence that minority overrepresentation is currently occurring. Instead, we consistently found that racial-, ethnic-, and language-minority schoolchildren are comparatively underidentified as having disabilities and so are underrepresented in special education as they attend elementary and middle schools in the United States. Consequently, federal legislation and policies may be inadvertently exacerbating educational inequities by reducing access to special education services for eligible schoolchildren who are racial, ethnic, or language minorities. Federal legislation and policies that lower cultural and language barriers resulting in these racial, ethnic, and language disparities may be necessary to ensure that all U.S. schoolchildren with disabilities are provided with the special education services for which they are legally entitled.

Notes

Costello, E. J., Angold, A., Burns, B. J., Stangl, D. K., Tweed, D. L., Erkanli, A., Worthman, C. M. (1996). The Great Smoky Mountains study of youth: Goals, design, methods, and the prevalence of , DSM-III-R disorders. Archives of General Psychiatry, 53, 1129-1136. doi:10.1001/archpsyc.1996.01830120067012 Google Scholar Crossref, Medline

Finkel, S. E. (1995). Causal analysis with panel data. Thousand Oaks, CA: Sage. , Google Scholar Crossref

Furlong, M., Karno, M. (1995). Review of the Social Skills Rating System. In Conoley, J. C., Impara, J. C. (Eds.), Twelfth mental measurements yearbook (pp. 697-969). Lincoln, NE: Buros Institute of Mental Measurement. Google Scholar

Oswald, D. P., Coutinho, M. J., Best, A. M., Nguyen, N. (2001). Impact of socio-demographic characteristics on the identification rates of minority students as having mental retardation. Mental Retardation, 39, 351-367. , Google Scholar Crossref, Medline

Posney, A. (2007, Aptil). Memorandum on disproportionality of racial and ethnic groups in special education (OSEP 07-09) sent to state directors of special education. Retrieved from http://www2.ed.gov/policy/speced/guid/idea/letters/revpolicy/tpmeth.html Google Scholar

Singer, J. D., Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford. , Google Scholar Crossref

Authors

RICHARD MATTISON, MD, is a professor of psychiatry at Pennsylvania State Milton S. Hershey Medical Center, Hershey, PA 17033; rmattison@hmc.psu.edu . His work examines psychiatric disorders in special education students.

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Minorities Are Disproportionately Underrepresented in Special Education

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We investigated whether minority children attending U.S. elementary and middle schools are disproportionately represented in special education. We did so using hazard modeling of multiyear longitudinal data and extensive covariate adjustment for potential child-, family-, and state-level confounds. Minority children were consistently less likely than otherwise similar White, English-speaking children to be identified as disabled and so to receive special education services.

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Pediatric Integrative Medicine Approaches to Attention Deficit Hyperactivity Disorder (ADHD)

Program in Integrative Medicine, University of Kansas Medical Center, Kansas City, KS, 66160, USA

Author to whom correspondence should be addressed.

Received: 30 May 2014; in revised form: 22 July 2014 / Accepted: 15 August 2014 / Published: 27 August 2014

Abstract
: Attention deficit hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in children and is increasing in prevalence. There has also been a related increase in prescribing stimulant medication despite some controversy whether ADHD medication makes a lasting difference in school performance or achievement. Families who are apprehensive about side effects and with concerns for efficacy of medication pursue integrative medicine as an alternative or adjunct to pharmacologic and cognitive behavioral treatment approaches. Integrative medicine incorporates evidence-based medicine, both conventional and complementary and alternative therapies, to deliver personalized care to the patient, emphasizing diet, nutrients, gut health, and environmental influences as a means to decrease symptoms associated with chronic disorders. Pediatric integrative medicine practitioners are increasing in number throughout the United States because of improvement in patient health outcomes. However, limited funding and poor research design interfere with generalizable treatment approaches utilizing integrative medicine. The use of research designs originally intended for drugs and procedures are not suitable for many integrative medicine approaches. This article serves to highlight integrative medicine approaches in use today for children with ADHD, including dietary therapies, nutritional supplements, environmental hygiene, and neurofeedback.

pediatric; integrative; medicine; approach; ADHD; nutrient; diet; microbiome; environment; neurofeedback

1. Introduction

Attention deficit hyperactivity disorder (ADHD) is characterized as a psychiatric condition of heightened impulsivity, inattention, and hyperactivity [ 1]. ADHD is the most common neurodevelopment disorder in children, affecting approximately 11% of children between 4 and 17 years of age in the United States. Increased public awareness and escalation of inciting risk factors has been accompanied by an increased rate of ADHD diagnosis, along with a rise in medication use [ 2]. The definitive etiopathogenesis of ADHD remains elusive due to its complex, multifactorial nature. ADHD has strong genetic and environmental influences [ 3]. However, the increased prevalence of ADHD is likely due to genetics but rather these environmental factors. Nutritional status, oxidative stress, neurotransmitter and endocrine dysregulation, neurological abnormalities in fronto-striatal and basal ganglia network, history of physical and emotional trauma, and environmental toxicity have all been implicated in ADHD [ 4, 5, 6, 7, 8, 9]. In order to identify the most appropriate treatment, healthcare providers should identify these risk factors with a thorough evaluation of the child with ADHD.

It is crucial that children with ADHD are treated appropriately as they can develop significant psychosocial, educational, and neuropsychological impairment [ 10]. They are also at risk for not achieving their highest potential in education and employment as adults [ 11]. Behavioral therapy and medications are central to the management of ADHD, resulting in greater improvements in academic performance, reduction of behavioral problems, and higher parental satisfaction [ 12, 13]. Limited access to mental health professionals interferes with the utility of psychosocial therapy. Only one-third of youths treated for ADHD received both psychosocial therapy and medications, and less than 10% received psychosocial interventions alone [ 14]. Children with ADHD also exhibit several comorbid disorders, resulting in decreased response to treatment.

Medication for ADHD is associated with its own set of problems. Twenty to 35% of patients with ADHD do not respond to medication. In addition, medications have significant adverse effects with the most common side effects including delayed onset of sleep and decreased appetite both known to potentiate symptoms of ADHD. This results in discontinuation of medication as a result of side effects and the parents' perception that the medication is ineffective; another factor in medication cessation is the cost burden for families [ 15, 16]. Thus, families are seeking different approaches to treatment, including complementary and alternative therapies (CAM).

The 2007 National Health Interview Survey discovered that approximately 12 percent of children in the United States have used or been given a CAM therapy [ 17]. More than 50% of Children with ADHD have been reported to use CAM therapies, but only 11% of parents discuss CAM therapies with their child's physician [ 18]. As physicians are reluctant to incorporate CAM therapies because they question their scientific efficacy, there is continued need for research and education of these CAM therapies. There is a subset of pediatricians who utilize both conventional and CAM therapies, a field known as integrative medicine, in order to improve outcomes in children with ADHD. Integrative medicine incorporates evidence-based medicine, both conventional and complementary and alternative therapies, to deliver personalized care to the patient, emphasizing diet, nutrients, gut health, and environmental influences as a means to decrease symptoms associated with chronic disorders. Pediatric integrative medicine (PIM) is a fairly new subspecialty and is defined as a relationship-centered practice that utilizes the best evidence in therapeutic approaches to achieve optimal health for children [ 19]. This review is designed to educate healthcare professionals on a PIM approach to ADHD to help initiate discussions of CAM use with patients and families.

2. Diet

What children eat has a profound effect on their health. Although controversial, dietary therapies have been suggested to play a major role in ADHD and should be considered in the evaluation and management of children with ADHD [ 20, 21, 22]. Nutritional deficiencies from a "Western" diet, food insecurity, artificial food additives and dyes, and food sensitivities and allergies have been implicated in ADHD [ 23, 24, 25, 26, 27, 28]. Most research related to dietary constituents has focused on restricted elimination diets (RED), sugar restriction, and artificial food color exclusion diets in the treatment of ADHD. A research review identified that dietary constituents can significantly worsen ADHD symptoms in 17 of 23 controlled studies [ 29]. As oxidative stress, or inflammation, is an underlying risk factor in ADHD, modulation of systemic inflammation through nutrition may have potential in decreasing symptoms of youth with ADHD [ 30]. A Western diet that is high in omega-6 fatty acids, sodium, and sugar intake can induce inflammation, promoting upregulation of pro-inflammatory TH17 cells, cytokines, and IL-10 [ 31].

2.1. Restricted Elimination Diet (RED)

Although adherence is difficult and time consuming, RED has been shown to benefit children with ADHD. The Impact of Nutrition in Children with ADHD (INCA) was a double-blind crossover research study that showed children ages 4-8 years old improve on the abbreviated Conners' scale by 11.6 after being placed on a tailored RED for five weeks [ 26]. Furthermore, 63% of the children relapsed with ADHD symptoms after a food challenge. The INCA study found that IgG blood levels did not correlate with ADHD symptoms. This study was well designed with good methodology, large sample size, and correlation with IgG food antigen blood tests. In addition, the diets were specifically tailored to each participant, which helped improve adherence [ 26]. Due to the recurrence of ADHD symptoms after a five-week RED, more research trials need to investigate how long children with ADHD need to stay on a RED in order to prevent recurrence of symptoms during a food challenge. Some review articles evaluating RED to treat ADHD have discussed that the diet may only influence some aspects of ADHD, such as behavior, and that it should only be done in a select few and for a short period of time of 2-3 weeks [ 20]. However, antibodies have a half-life between 22-96 days and more improvement in symptoms may be seen with a three-month RED [ 32]. After approximately 3-6 months, those children who show improvements in behavior and a decrease in ADHD symptoms on RED should introduce one food at a time every one to two weeks until offending foods or food items are identified.

2.2. Junk Food Diet

PIM physicians begin with a whole foods, non-processed diet for children with ADHD [ 33]. Children with ADHD symptoms are at increased risk for becoming obese in adolescence and this is correlated with poor dietary choices and physical inactivity [ 34]. Children today are exposed to aggressive advertising for highly processed and sugary foods, influencing their eating behaviors [ 35]. A diet high in processed foods and sugar, otherwise known as "junk food," has been studied in children with ADHD, though very little evidence has demonstrated associations. One study demonstrated a modest association between children who ate junk food and hyperactivity symptoms, but attenuated after adjustment for confounders [ 36]. In two separate research studies, sucrose was associated with an increase in motor activity and was shown to reduce attention in children with ADHD, but not in normal children [ 37, 38]. There has been little evidence to support an association between sugar intake and behavioral problems [ 38, 39]. These research studies have not found any association on sugar consumption and ADHD, but cannot rule out a small effect on subsets of children with ADHD [ 38, 39]. In addition, these research studies used artificial sweeteners as the placebo group. This may confound the results because artificial sweeteners have been implicated in neuropsychiatric dysfunction [ 40, 41]. These few research studies also evaluated response to acute effects of sugar consumption, but did not evaluate chronic effects. Further research should evaluate not just sucrose consumption, but excess and chronic carbohydrate consumption and its effect on ADHD symptomology as providers report improvements in behavior with a whole foods diet.

3. Gluten-Related Disorders

Gluten-free diets have been implemented for children with neuropsychiatric disorders, including autism and ADHD [ 7]. Gluten-related disorders, such as celiac disease (CD) and gluten sensitivity, may be associated with ADHD. CD results in gluten-triggered autoimmune intestinal villi destruction in genetically susceptible individuals. Non-celiac gluten sensitivity (NCGS) is a condition in which gluten ingestion leads to morphological or symptomatic manifestations despite the absence of CD. NCGS and CD are triggered by the ingestion of gluten, or more specifically gliadin, the protein components of wheat, rye, and barley. Exposure results in a variable degree of intestinal damage that can induce intestinal permeability, resulting in extra-intestinal manifestations. These extra-intestinal symptoms can masquerade as behavioral and psychiatric disorders.

In the 1950s and 1960s, physicians first began reporting neurologic and psychotic symptoms in patients with CD [ 42]. There has been emerging evidence to show that individuals with gluten-related disorders, such as CD and NCGS, have neurologic, psychiatric and mood disorders [ 43, 44, 45]. Gluten-free diets are becoming widely accepted in the United States [ 46]. Furthermore, avoiding gluten is common among non-CD children who exhibit nonspecific behavioral and gastrointestinal symptoms [ 47]. However, there are few research trials done specifically in regards to gluten and its association with ADHD. Dr. Helmut Neiderhofer, MD, PhD and Dr. Klaus Pittschieler, MD demonstrated an increased prevalence of CD in ADHD patients and also noted improved ADHD symptoms after implementation of gluten-free diet in children with CD [ 48, 49]. Inattention has been found to be associated in children with CD compared to healthy children, though a conflicting study by Gungor et al. did not show any association between CD and ADHD [ 48, 50, 51]. However, it still remains that gluten withdrawal in neuropsychiatric disorders results in improved mood, focus and attention, as well as a decrease in disruptive behaviors [ 52, 53].

The mechanism of action behind gluten's effect on neuropsychiatric symptoms is still inconclusive, but gastrointestinal immune dysregulation can lead to chronic pro-inflammatory immune dysregulation. This chronic inflammation may be one of the underlying links in the development of ADHD [ 7, 30]. It is also hypothesized that gluten can induce gastrointestinal inflammation, thereby limiting absorption of nutrients and disrupting neurotransmitter metabolism [ 54].

Despite conflicting evidence of CD and its association with ADHD, clinicians should focus on a broad array of symptoms when evaluating children with ADHD, including both gastrointestinal and neuropsychiatric symptoms to rule out CD or NCGS as an exacerbating condition. Screening for and diagnosing CD and NCGS remains a challenge. A research trial assessed the usefulness of screening for CD when children presented with common CD symptoms (e.g., poor appetite, stomach ache, nausea, bloating, tiredness, hard stools, loose stools and lactose intolerance) [ 55]. The study results showed that screening children based on only these symptoms did not discriminate between undiagnosed children with CD-associated symptoms and asymptomatic CD children. Determination of NCGS is difficult due to lack of consensus on specific biomarkers, as opposed to CD. Positive effects after gluten withdrawal and return of symptoms with gluten challenge are the main diagnostic criteria [ 56, 57, 58]. In most patients with CD and NCGS, the gastrointestinal and extra-intestinal symptoms will reverse on a gluten-free diet [ 59].

If the decision is made to choose a gluten-free diet, this should be implemented by a team of healthcare professionals, including a trained dietitian, as it is easy to replace wheat/gluten-containing products with gluten-free products high in refined carbohydrates, but low in fiber and nutrients. And if a gluten-free diet is not a good fit, a whole foods diet rich in fresh unprocessed foods is ideal. Most patients with ADHD would benefit from a whole foods diet.

4. Micronutrients

The value of nutrition is increasingly becoming recognized as a means for achieving optimal health, as poor diet and nutritional intake are implicated in chronic disorders [ 60]. Most studies in ADHD and nutrients have focused on polyunsaturated fatty acids and a few minerals, including magnesium, zinc and iron. However, there are many more micronutrients than those that have been predominantly studied. Micronutrients are utilized as cofactors in enzymatic reactions and play a large role in metabolism, neurotransmission, cognitive function, immune function, and detoxification [ 61, 62]. Instead of looking at only a few micronutrients, a comprehensive but focused nutrient panel may be done for those children who have failed conventional treatment approaches or for those families who are exploring other options besides medication. A comprehensive micronutrient panel may include vitamins, minerals, fatty acids, and amino acids, as the quality of our nutrient supply from food has declined [ 63].

4.1. Zinc

Zinc is known to a play a role in neuropsychiatric disorders and is a micronutrient recognized as participating in metabolism relevant to neurotransmitters, hormones, nutrients, and immune function [ 64, 65]. It also contributes to the structure and function of the brain, forming neural pathways affecting neurotransmission [ 66]. Zinc deficiency can thus have a significant impact on attention, motor activity, cognition, and behavior. Approximately 12%-66% of the world's population is at risk of zinc deficiency, and is seen especially in children with ADHD who are vulnerable to poor zinc status [ 67, 68]. Although a Cochrane review of 13 randomized trials did not demonstrate a positive effect of zinc supplementation on mental and motor development, there have been several review articles discussing positive improvements in ADHD symptoms with zinc supplementation [ 69, 70, 71]. There have been three positive randomized controlled studies on zinc supplementation versus placebo in children with ADHD. Bilici et al . demonstrated that zinc decreased motor activity and improved impulsivity, but did not increase attention [ 72]. Akhondzadeh's et al . showed improvement in total ADHD scores with the addition of elemental zinc 13 mg/day to methylphenidate 1 mg/kg/day versus placebo and methylphenidate [ 73]. In a study by Arnold and colleagues, children on zinc supplementation were able to lower their dose of amphetamine, though zinc did not improve inattention or other ADHD symptoms more than placebo [ 74]. There is encouraging support for zinc supplementation in a subset of children with ADHD, depending on symptoms and if used in combination with stimulants.

4.2. Iron

Iron status is commonly evaluated in children with neuropsychiatric disorders likely due to its role as a cofactor in monoaminergic neurotransmitter metabolism [ 75]. Research has suggested that iron status should be investigated in order to optimize treatment outcomes with stimulant medication [ 76]. With the prevalence of sleep disturbance in ADHD, Cortese and colleagues found that serum ferritin levels below 45 microg/L may provide evidence to supplement iron in those children with sleep disorder [ 77]. There are some significant and non-significant associations between serum ferritin levels and ADHD symptoms, but most of these studies focused on serum ferritin levels without other iron indices. As inflammation and oxidative stress have been implicated in the etiopathogenesis of ADHD, serum ferritin is an inflammatory marker when elevated, which may complicate its evaluation. In addition, excess inflammation and low levels of glutathione, the main body's detoxifier and antioxidant, can result in iron dysregulation [ 78]. It may be worthwhile then to evaluate a complete blood count with differential and serum ferritin with iron panel to help guide management in children with ADHD, especially when associated with more complex symptoms, including sleep disturbance.

4.3. Vitamin B6

Insufficient Vitamin B6, also known as pyridoxine or pyridoxal-5-phosphate, affects metabolism of polyunsaturated fatty acids, hemoglobin synthesis, and neurotransmission [ 62]. It was later found that pyridoxine is a cofactor in neurotransmitter metabolism including serotonin, glutamate/GABA, and dopamine. Because pyridoxine has a positive effect on children with ADHD, it is recommended to supplement in order to regulate and normalize ADHD behaviors [ 79]. In fact, a pharmaceutical medication, metadoxine, is an ion-pair salt of pyridoxine that has shown to improve inattentive symptoms, but only studied in the adult population with ADHD [ 80]. By regulating neurotransmission, vitamin B6 may improve executive function and symptoms of ADHD. Vitamin B6 is generally safe without serious side effects and should be considered in the evaluation of children with ADHD.

4.4. Magnesium

Magnesium is the fourth most abundant mineral and is involved as a cofactor in over 300 enzymatic reactions in the body, including fatty acid, glucose and energy metabolism [ 62]. Some of these metabolic processes play a vital role in neuronal function and neurotransmitter metabolism [ 81]. Some investigators have proposed magnesium supplementation is advantageous for ADHD symptoms [ 33, 82, 83, 84]. Magnesium acts as a neuroprotectant from excessive excitatory neurotransmitters, such as glutamate [ 85]. Several studies have found low erythrocyte or RBC (red blood cell) magnesium in children with ADHD [ 68, 82, 84, 86]. Although a systematic review of magnesium therapy for treating ADHD did not show a significant improvement in symptoms, most of these studies had methodological limitations, including many that were not double-blind randomized controlled clinical trials, and magnesium was measured inappropriately [ 87]. Therefore, RBC magnesium, a sensitive biomarker for magnesium deficiency, should be incorporated into future clinical trials and may be important in the work-up for children with ADHD. Appropriate magnesium supplementation may be a useful adjunct for children on stimulant medication.

4.5. Polyunsaturated Fatty Acids

Polyunsaturated fatty acid (PUFAs) disturbance has also been implicated in ADHD, as they are necessary for nerve cell membrane fluidity and neuronal function to support neurotransmission. However, the typical American diet contains an imbalance of omega-6 to omega-3 PUFAs. An elevated Omega-6 to Omega-3 ratio is implicated in inflammation [ 88, 89]. Omega-6 fatty acids derived from canola oil, corn oil, soybean oil, and other vegetable fats are inflammatory in nature as they metabolize downstream to arachidonic acid. Arachidonic acid subsequently produces inflammatory prostaglandins and leukotrienes. Gamma-linolenic acid, an n-6 fatty acid, is the exception to the rule which has been shown to be anti-inflammatory in nature [ 90]. Limiting restaurant and take-out food is also important in balancing PUFAs to reduce inflammatory omega-6 fats because of the prevalence of use and the tendency to damage fats during high heat cooking conditions.

Omega-3 fatty acids, found in flaxseeds, chia seeds, and fatty fish, metabolize downstream to anti-inflammatory prostaglandins and leukotrienes [ 89, 91]. With the increased prevalence of farmed fish, there are less omega-3s produced than are found in their wild counterparts [ 92]. Dr. Susan Carlson, PhD and her team have found that DHA, an n-3 fatty acid, is essential in neurocognitive development of children. Her research suggests that DHA should be supplemented throughout the lifecycle due to its importance in neuropsychiatric disorders [ 93].

Practitioners, including dietitians, can help guide families of children with ADHD to increase balanced portions of healthy fats and decrease the inflammatory fats. As research has identified elevated inflammatory markers in children and adolescents with ADHD, maintaining a healthy omega-6 to omega-3 ratio with a diet containing healthy balances of PUFAs, saturated fats, and phospholipids may be a promising integrative medicine approach to treatment of ADHD [ 94].

However, some reports using placebo-controlled randomized controlled trials (RCTs) evaluating PUFAs efficacy in treating ADHD differ in conclusions [ 95, 96, 97, 98, 99]. Research studies using PUFAs for ADHD that did not reach statistical significance may likely be due to the different combinations, doses, and frequency of PUFA mixes (ALA/LA/EPA/DHA/GLA ratios), in addition to using a placebo with nutritional properties, such as olive oil and vitamin C [ 97, 98, 99, 100]. The Cochrane review demonstrated that a subgroup of children with ADHD did show improvement with EPA, DHA, and a small amount of GLA [ 101]. A meta-analysis also demonstrated an effect size of 0.31 for omega-3 fatty acid supplements [ 95]. When compared to methylphenidate with an effect size of 0.78, Omega-3 fatty acids as a single supplement are approximately 40% as effective as methylphenidate [ 95, 102]. Also, most of these studies did not evaluate phospholipids, such as phosphatidylcholine, essential in neurodevelopment and has been shown to enhance the effect of omega-3 supplements [ 103].

In clinical practice, evaluation of fatty acids using a comprehensive serum fatty acid panel or RBC fatty acid panel to guide supplementation may be an effective strategy for optimizing treatment outcomes. Fatty acid synthesis and metabolism also involves several other micronutrients as cofactors for the desaturase and elongase enzymes and these may be evaluated by laboratory testing [ 62]. Individuals vary in need depending on dietary intake and metabolism of fatty acids. PUFAs and associated micronutrient supplementation should be tailored to the individual using guidelines as reports of adverse events exist in the research literature, such as over supplementation of omega-3 fatty acids may increase bleeding time [ 104]. However, following a healthcare practitioner's advice on adding PUFAs and monounsaturated fats from fish, flax and chia seeds, nuts, avocado, extra virgin olive oil, and healthy saturated fats from coconut oil is likely advantageous to a child with ADHD.

4.6. Carnitine

Carnitine is a fatty acid transporter that transfers long chain fatty acids across mitochondrial membrane for beta-oxidation. Acetyl l-carnitine is an abundant short chain ester of carnitine, which has an effect on brain metabolism and neurotransmission, specifically the cholinergic and dopaminergic pathways [ 105, 106]. Three placebo-controlled trials on carnitine supplementation in participants with ADHD from 2002 to current have been conducted [ 107, 108, 109]. However, only one of these small trials showed an improvement after supplementation with carnitine in attention and decrease in aggression in boys with ADHD [ 108]. Two studies did not demonstrate a significant benefit, but one study found that it did attenuate the side effects of irritability and headaches of methylphenidate [ 106, 108]. However, these two studies did not distinguish between subtypes of ADHD and did not evaluate carnitine levels at baseline and post-treatment. It has to be considered that these children may not have any nutritional need for carnitine supplementation. Carnitine needs further evaluation in future research studies to adequately assess effects in children with ADHD.

4.7. Vitamin D

Vitamin D is now implicated in ADHD [ 110]. Vitamin D is essential for normal brain development and regulates both the innate and adaptive immune system enhancing neuroprotective mechanisms against inflammation [ 111, 112]. A case-control study in Qatar of 1,331 children ages 5-18 years old demonstrated a higher prevalence of vitamin D deficiency in the ADHD children compared to controls [ 110]. In 2001, a study done by Taylor et al. surveyed parents to compare symptoms of their child with ADHD when they were engaging in leisure activities in a windowless room versus in an outdoor setting where natural production of vitamin D will occur. The survey results demonstrated that the outdoor setting was more likely to be chosen by parents to reduce inattentive symptoms [ 113]. Sunlight and vitamin D may be considered a major participant in improving certain symptoms of ADHD and a decrease in outdoor activity with less sunlight exposure may have contributed to the higher prevalence of vitamin D deficiency in children with ADHD. Supplementing children with vitamin D guided by lab results may be a safe and effective strategy to help improve symptoms.

4.8. Iodine

For women of childbearing age, iodine insufficiency is approximately 15% in the United States. Pregnant women in the United States are at slight risk of iodine insufficiency especially in the first trimester [114. Iodine deficiency can be measured by a low urine iodine of less than 100 microg/L [ 62] In women who are planning to become pregnant and are found to be deficient, supplemental iodine may needed to optimize iodine status to ensure healthy cognitive development of the fetus. In addition, psychomotor development and cognitive performance declines in children with iodine insufficiency and iodine status should regularly be evaluated in children with ADHD. A synthesis of the research on iodine deficiency and its relationship to other disease processes other than thyroid disorders has associated iodine deficiency with the occurrence of ADHD [ 115].

The recommended daily allowance of up to 150 micrograms/day is meant to prevent goiter or thyroid disease [ 115]. It should be considered that this is not the optimal dose for all individuals who have different metabolic needs and thyroid function. Serial urine iodine measurements should be done until iodine status normalization. More studies are needed to evaluate iodine status and replacement strategies in children with ADHD, as subnormal levels of iodine can have an impact on a child's ability to focus and concentrate on school tasks.

5. Gut Microbiome

Humans have 100 trillion microbes residing in the gastrointestinal tract, some of which may be commensal, while others are pathologic [ 116]. Consumption of these healthy commensal microbes known as probiotics, have been shown to modulate brain activity [ 117]. Research has shown that there is a gut microbiome-mind-body continuum in which the enteric microbiota interact with the neuroendocrine system [ 118]. The gut microbiome essentially has its own nervous system, as it produces neuroendocrine hormones that transmit information across the microbiome [ 119]. There is increasing evidence that gut bacteria can modulate mood and behavior via the gut-brain axis [ 120]. Most of these studies have been done in germ-free animals who exhibited changes in mental status and behavior when exposed to pathogenic bacteria, probiotics, or antibiotics. The establishment of a healthy gut microbiome takes place early in development. Infants who were born via cesarean delivery had less microflora in their intestinal tract than those infants born by vaginal delivery [ 121]. This is consistent with Cesarean birth as a major risk factor of ADHD [ 122]. Additional evaluation of gut microbiome in ADHD children is therefore warranted.

As part of the practice at University of Kansas Pediatric Integrative Medicine, evaluation of children with neurodevelopment and mood disorders includes stool analysis for intestinal dysbiosis. Medications, such as antibiotics, can disrupt the gut microbiome by reducing diversity and allowing overgrowth of some pathogenic microorganisms [ 123]. Gut microbiota, such as Candida, can invade the intestine as a pathogen and may lead to deterioration in ADHD symptoms [ 124]. A Western diet, even in a short-term period, can alter the gut microbiome, again demonstrating the importance of diet and nutrition as a valuable therapy in ADHD [ 125]. The evidence surrounding the association with healthy gut microbiome and its relationship with psychiatric and behavioral disorders is increasing. This will present new opportunities for interventions with pre- and probiotics, as they are already in common use among families who use integrative therapies.

6. Environmental Toxicity

Industry produces over 83,000 chemicals greater than one million pounds per year with little information about the potential effects on neurodevelopment [ 126, 127]. Approximately 1,000 of these chemicals can affect the nervous system, and at least 200 of these are known neurotoxins [ 128]. Children are highly susceptible to environmental chemicals, especially during preconception and as an infant through breast milk. Exposures to chemicals can alter epigenetic programming, leading to disruptions in neurodevelopment [ 129]. Recent research studies have now shown that transgenerational changes may even occur when grandparents of the child are exposed to environmental chemicals. Most testing has been done on animals, but these research studies still provide sufficient results to indicate toxic effects in human neurodevelopment [ 129]. Children also have a larger body surface area with higher respirations leaving them more susceptible to even low-dose chemical exposures through breathing, dermal contact, and food compared to adults. In a cohort of 607 children aged 7-11 years, investigators found an association between low-level prenatal organochlorine exposure via cord blood and ADHD behaviors in childhood [ 130]. The risk of ADHD-like behaviors in these children increased by 26% to 92%, according to the level of organochlorine exposure [ 130]. Organochlorines, including polychlorinated biphenyls (PCBs), are environmentally persistent chemicals that cross the placenta and have been shown to alter neurodevelopment [ 131]. Organophosphates, widely used as pesticides, remain another important avenue for neurodevelopment toxicity in children [ 132]. Urinary metabolites of organophosphates were obtained from the National Health and Nutrition Examination Survey (NHANES) on 1,139 children. The analyses demonstrated that the children with higher levels of exposure to organophosphates were more likely to have a diagnosis of ADHD [ 132]. In children, fruits and vegetables may be the most important source of chronic pesticide exposure [ 133]. This was demonstrated in a study by comparing organic vs. conventional diets in children, attesting that organic diets have a protective effect against exposure to organophosphate pesticides [ 133]. Recommendations to avoid or wash thoroughly the "dirty dozen" conventional produce and eating more of the "Clean 15" may be a beneficial nutritional approach for children with ADHD [ 134, 135].

In addition to obtaining a thorough diet history in patients with ADHD, environmental exposure history needs to be evaluated and addressed. Some families may have a history of using herbicides, insecticides, chemicals including cleaning supplies, and volatile organic compounds, such as air fresheners in their homes. Education is of utmost importance to keep chemical exposure at a minimum. In fact, healthy housing initiatives are gaining popularity among medical communities to help control and prevent chronic disease in children. Advising families to opt for more natural solutions, such as "green" cleaning products that do not contain toxic industrial chemicals and using sticky traps for pests may be healthier alternatives. Limiting plastic products, such as plastic water bottles and microwaveable plastic containers, may also protect against ADHD, as there are positive associations between ADHD and bisphenol A (BPA) and phthalates commonly found in plastics [ 136].

Of the heavy metals, lead toxicity has been associated with ADHD and other neuropsychiatric disorders. It is well known that lead can cause cognitive impairment and attention dysregulation [ 137, 138]. In a study of 236 children diagnosed with ADHD, low levels of lead exposure demonstrated an increased risk of ADHD [ 137]. In 2012, the Advisory Committee on Childhood Lead Poisoning Prevention of the Centers for Disease Control and Prevention (CDC) issued updated and stricter guidelines due to long-term effects on neurodevelopment of even low-level lead exposure [ 138]. Increasing the awareness of healthcare providers, families and the community to environmental chemical and heavy metal exposures is vital to the adjunctive management of these children with ADHD.

7. Neurofeedback

One of the most promising treatments in ADHD is neurofeedback, which is an EEG-based form of biofeedback. Neurofeedback is a safe, non-invasive therapy that restores the brain's ability to function through altering brain wave patterns by operant conditioning [ 139]. The individual can achieve self-regulation by responding to feedback from real-time audio-visual information. Quantitative EEG studies suggest that children with ADHD exhibit slower brainwave patterns in brain regions, such as the prefrontal cortex, associated with attention and cognitive executive function which is consistent with single photon emission computed tomography (SPECT) and positron emission tomography (PET) studies where decreased metabolism in prefrontal regions is identified [ 140]. In 1976, Lubar and Shouse were the first to report positive changes in a hyperkinetic child after training the Sensorimotor EEG rhythm (SMR: 12-14 Hz) [ 141]. Lubar then went on to develop protocols that inhibited theta (slow waves) and rewarded beta (fast waves involved in focus and attention). The FDA announced in July 2013 the approval of diagnosing ADHD by measuring the theta/beta ratio which has been shown to be higher in children with ADHD than normal children [ 142]. However, excessive theta/beta ratio may only characterize a subgroup of ADHD, approximately 26% of pediatric ADHD patients, compared with 2.5% in healthy controls [ 143, 144, 145]. Some studies have concluded that responders to stimulant medications have an initial excess of slow wave (theta) activity. After treatment with stimulant medication, increased beta waves and reduced theta waves result [ 146]. Stimulant medications may work not only through regulating neurotransmission, but also by affecting brain waves. A quantitative EEG (i.e., brain map), utilized as initial baseline testing of brainwaves, should also be considered in future research studies as an evaluation tool for identifying responders and non-responders to ADHD medication [ 147]. Other identified EEG phenotypes of ADHD have also been reported including, excess beta, excess alpha, low alpha peak frequency and hypercoherence [ 143].

In 2004, Slow Cortical Potential (SCP) neurofeedback was found to improve ADHD symptoms [ 148]. SCP neurofeedback rewards changes in the polarity of the EEG and was initially used to treat epilepsy [ 149]. SCP neurofeedback enhances regulation of attention through slowly balancing cortical activation and inhibition. Children with ADHD have a significant reduction in the ability to regulate SCPs. In a RCT trial by Gevensleben and his colleagues, 102 children with ADHD participated in neurofeedback and the control group participated in computerized attention skills training. The neurofeedback group completed one block of 18 theta/beta training sessions and one block of 18 SCP training sessions. At a six-month follow-up, reductions in inattention and hyperactivity/impulsivity were about 25%-35% in the neurofeedback group compared to 10%-15% in the control group with a group effect of 4.72 for inattention and 3.45 for hyperactivity/impulsivity [ 150]. SCP neurofeedback is recognized as an efficacious treatment for ADHD, though future studies still need to identify a sham control and improve efforts to blind participants, parents, teachers, and technicians [ 151].

A recent RCT research study on 23 children with ADHD carried out 40 sessions of theta/beta training sessions or methylphenidate and compared the two therapies with a six-month follow-up. Although this study had a limitation of small sample size, and 8 out of 12 participants in the neurofeedback group started medication, neurofeedback training was comparable to methylphenidate in reducing ADHD primary symptoms and associated functional impairment. Academic performance significantly improved in the neurofeedback group compared to the group receiving methylphenidate [ 152].

Despite promising results among research studies and anecdotal evidence, neurofeedback treatment is not yet accepted as a standard therapy for children with ADHD. This is possibly due to limitations of scientific research in neurofeedback accompanied by the difficulty in conducting a well-designed randomized controlled trial. Sham neurofeedback groups are designed as control groups in neurofeedback research. However, attention by neurofeedback technicians may result in positive gains in the control group. Subjective treatment effects can threaten the validity of neurofeedback research and these include participants, parents, and teachers' expectations of beneficial outcomes, provider training and experience, environmental stressors surrounding the participant, and participants' engagement during training. In a research update by Loo and Makeig, although the evidence is growing for scientific quality of research in neurofeedback, more research is needed before neurofeedback can be recommended as a first-line, stand-alone treatment modality [ 143].

In addition, neurofeedback participants must be encouraged to strive toward mastering engagement and regulation [ 153]. Participants should be rewarded during neurofeedback therapy to further enhance operant conditioning learning. This raises a question about the feasibility of conducting double-blind placebo-controlled trials. Neurofeedback protocols should be guided based on the individual's baseline qualitative EEG (QEEG), as one child with ADHD may have a different QEEG than another child with ADHD. Hence, protocols used in research may not be applicable to the individual and therefore, neurofeedback, may need to be considered a personalized therapy for improved clinical outcomes in ADHD.

8. Future Research

Some of the research related to integrative therapies for children with ADHD has shown mixed results. Of the integrative medicine approaches, the best evidence is for restricted elimination diets, neurofeedback, omega-3 fatty acid supplements, vitamin B6, and zinc. More rigorous and applicable research trials need to be funded in order to identify effective integrative therapies as standard practice. Some researchers suspect the "placebo effect" is responsible for the improvement seen with some integrative therapies. On the other hand, the "placebo effect" can account for perceived improvement from medications and other conventional therapies.

Unfortunately, research is limited in integrative medicine approaches to ADHD. This may be due to funding of and difficulty in designing randomized controlled trials (RCTs), considered the gold standard of evaluating treatment interventions. Evidence-based medicine was initially designed for drugs and procedures, but has been applied to nutrients and other integrative therapies. The high internal validity of RCTs can impede its external validity, meaning that it may not be generalizable to real-life situations, and applicability to the individual may be questionable. Hence, RCTs may not be the best tool to evaluate integrative therapies compared to other types of study designs, such as a pragmatic study design. For example, a single neurofeedback protocol for the general population may not be appropriate for the individual due to differences in brainwaves, genetics, diet, and environmental conditions. Pragmatic trials may be more efficacious in assessing integrative therapeutic approaches in real-life settings, compared to the controlled environments that do not take place outside of the research trial [ 154]. Evidence-based medicine was initially designed for drugs and procedures, but has been applied to nutrients. To effectively evaluate nutrients, research trials need to be redesigned to account for the difference between drugs and nutrients [ 155]. The best evidence, however, for an integrative intervention may be from outcome trials.

9. Conclusions

With adverse effects of and poor outcomes in long-term safety studies evaluating ADHD medications, more families are turning to integrative modalities to help decrease symptoms and improve quality of life in their children with ADHD. Integrative medicine may provide an alternative approach to treating ADHD, especially for those classified as non-responders. Because a significant percentage of the population with ADHD is classified as non-responsive, integrative medicine approaches to ADHD have gained popularity among families and practitioners. Because research is limited and outcomes show conflicting results, it is difficult for conventional practitioners to advise ADHD patients and their families when questions about integrative therapies arise. Partnering with an experienced pediatrician of integrative medicine may provide an outlet for treatment options and a trusted source for unanswered questions. Since the numbers of pediatricians of integrative medicine are growing, there is an expanding workforce that can provide guidance to conventional practitioners and families and help with design of future clinical trials in ADHD. Healthcare providers need to be aware of integrative modalities in use by families of children with ADHD in order to keep open communication when providing advice on possible benefits and/or limitations based on current research. This will help foster improved communication between families and patients with ADHD and their practitioners.

Acknowledgments

No grants or funds were received for this review article.

Author Contributions

Anna Esparham, MD is the lead author of this review article with expertise in pediatric integrative medicine for children with ADHD. She is a board-certified pediatrician with a fellowship in integrative medicine at the University of Kansas Medical Center Integrative Medicine (KU IM). She provided contributions to this article through organizing, directing, writing and editing the major sections.

Randall Evans, MS, RD, LD and is a dietitian at KU IM with a Master's degree in dietetics and integrative medicine with extensive experience in dietary interventions for parents of children with ADHD to decrease excess sugar and increase whole foods for lifestyle change. He provided guidance on the section of "junk food diets" with attention to gaps in the scientific literature.

Leigh Wagner, MS, RD, LD is a dietitian at KU IM with a Master's in dietetics and integrative medicine with experience in restricted elimination diets (RED) for young children with ADHD. She advised on the section of RED through synthesis of the most compelling scientific evidence.

Jeanne Drisko, MD is the founder and program director of KU IM with board certification in neurofeedback. She contributed to this paper through writing and advising the review of the entire manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Pediatric Integrative Medicine Approaches to Attention Deficit Hyperactivity Disorder (ADHD)

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Attention deficit hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in children and is increasing in prevalence. There has also been a related increase in prescribing stimulant medication despite some controversy whether ADHD medication makes a lasting difference in school performance or achievement.

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Abstract

Attention-deficit/hyperactivity disorder (ADHD) affects nearly 10% of children in the United States, and the prevalence of this disorder has increased steadily over the past decades. The cause of ADHD is unknown, although recent studies suggest that it may be associated with a disruption in dopamine signaling whereby dopamine D ₂ receptors are reduced in reward-related brain regions. This same pattern of reduced dopamine-mediated signaling is observed in various reward-deficiency syndromes associated with food or drug addiction, as well as in obesity. While genetic mechanisms are likely contributory to cases of ADHD, the marked frequency of the disorder suggests that other factors are involved in the etiology. In this article, we revisit the hypothesis that excessive sugar intake may have an underlying role in ADHD. We review preclinical and clinical data suggesting overlaps among ADHD, sugar and drug addiction, and obesity. Further, we present the hypothesis that the chronic effects of excessive sugar intake may lead to alterations in mesolimbic dopamine signaling, which could contribute to the symptoms associated with ADHD. We recommend further studies to investigate the possible relationship between chronic sugar intake and ADHD.

ADHD, sucrose, fructose, high-fructose corn syrup, reward-deficiency syndrome, dopamine, D Keywords: ₂ receptor, obesity

Introduction

The Centers for Disease Control and Prevention recently reported that nearly 1 in 10 children in the United States aged 4 to 17 years have parent-diagnosed attention-deficit/hyperactivity disorder (ADHD), representing 5.4 million children, half of whom are actively receiving medication. Attention-deficit/hyperactivity disorder is diagnosed by specific criteria (such as the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision [DSM-IV-TR]), including hyperactivity and inattention, an inability to focus, becoming distracted easily, and making careless errors. Other features include impulsiveness, emotional lability, fidgeting, and excessive talking. Attention-deficit/hyperactivity disorder is commonly associated with learning disorders and impaired school performance; it may also affect socialization and have psychiatric manifestations (eg, mood disorders, conduct disorders, and bipolar manifestations). Furthermore, manifestations of ADHD commonly continue into adulthood, affecting 3% to 5% of the adult population. Adults with ADHD, compared with adults without ADHD, have an increased risk for substance abuse (16% vs 4%, respectively) and antisocial behavior (18% vs 2%, respectively). Treatment consists of behavior-modification programs and pharmacotherapy with stimulant drugs (eg, amphetamine or methylphenidate) that increase extracellular levels of both dopamine and norepinephrine, or the selective norepinephrine reuptake inhibitor atomoxetine. While these treatment approaches often improve symptoms, complete resolution of symptoms is rare and cure is infrequent.

Identifying the etiology of ADHD is paramount for developing better ways to prevent and treat the disorder. A number of studies suggest that ADHD may have a genetic basis, and there is increasing evidence to suggest that this may relate to polymorphisms in genes involved in dopamine neurotransmission. Indeed, there is increasing evidence to suggest that ADHD may involve alterations in mesolimbic dopamine signaling (). For example, the polymorphism DRD2-TAQ-IA, which results in low striatal D ₂ receptors, also results in increased risk for alcohol and opioid addiction, obesity, and ADHD. Nevertheless, while the importance of genetics in ADHD is not disputed, the few genetic linkages identified to date can account for only a small percentage of ADHD cases. Thus, it is important for us to consider other possible factors that might cause or predispose individuals to develop ADHD.

One theory among lay people is that sugar consumption may have a role in ADHD, and parents are often of the opinion that acute ingestion of sugar may cause bouts of hyperactivity in their children, followed by sedation and inactivity. Nevertheless, studies performed in the 1980s appeared to rule out sugar as a likely cause of ADHD. However, while prior studies on sucrose intake and ADHD were outstanding in design, they evaluated the acute effects of sugar on symptoms of ADHD and also mostly compared the effects of sucrose with the effects of non-nutritive sweet tastes. In contrast, because ADHD is a chronic disorder, we present the hypothesis that the chronic effects of excessive sugar intake could be a mechanism associated with ADHD. We further hypothesize that sweet taste (provided by sugar or artificial sweeteners) is sufficient to affect the mesolimbic dopamine system in a way that could result in behaviors common to ADHD.

Early Studies of Sugar and ADHD

Some early studies supported the concept that increased intake of added sugars may have a role in ADHD. For example, a study by Prinz et al reported that hyperactive children who ingested more sucrose showed greater hyperactivity. However, elegant studies led by Wolraich and others provided convincing evidence that sugar (sucrose) intake is not related to symptoms of ADHD. For example, the administration of sugar for 3 weeks was no different from the administration of aspartame or saccharine in inducing signs of ADHD in children thought to be sensitive to sucrose. In another study, children with "sugar sensitivity" were rated for hyperactivity by parents who were told that their children were administered aspartame or sucrose. The parents rated children who received sucrose as having worse behavior; however, in actuality, both groups received aspartame. In another study, the administration of sucrose resulted in similar behavior as the administration of aspartame in hyperactive boys. The inability to document an effect of added sugars on hyperactivity, even in children thought to be sensitive to the stimulatory effects of sugar (mostly when compared with other sweet tastes, such as aspartame), has largely discredited the sugar hypothesis of ADHD. Indeed, a meta-analysis of clinical trials conducted 15 years ago concluded that sugar is not the cause of ADHD.

Hypothesis: Chronic Sugar Intake May Cause Symptoms of ADHD

Our basic hypothesis is shown in . In essence, we suggest that in some subjects, the initiating process that leads to the development of ADHD is excessive sugar (or sweetener) intake, resulting in enhanced dopamine release. Over weeks to months, this leads to a reduction in D ₂ receptors and D ₂ receptor-mediated signaling. In response, sugar intake increases. However, over time, the dopamine response to sugar slowly decreases, and the intervening periods are associated with a reduction in striatal dopamine levels. As a consequence, frontal lobe sensitivity to natural rewards is reduced, resulting in the development of behaviors such as overeating and ADHD.

Chronic Sugar Intake and ADHD Show Changes in Dopamine and D₂ Receptor Signaling, Similar to Drug Addiction

Recurrent stimulation of dopamine in the ventral striatum (nucleus accumbens) and dorsal striatum (caudate/putamen) by drugs such as cocaine or heroin can lead to addiction-like behaviors. Although signaling via both D ₁ and D ₂ receptor subtypes is involved in addiction, most human studies are based on analysis of D ₂ receptors, as they correlate with features of addiction and can be quantified using positron emission tomography (PET) scanning with raclopride C11 ([ ¹¹C]raclopride), which binds selectively to D ₂ receptors. Such studies have shown that D ₂ receptors are reduced in reward-related brain regions in cocaine and heroin addicts. A reduced number of nucleus accumbens D ₂ receptors prior to drug exposure also predicts cocaine self-administration in rats. Collectively, these studies suggest that recurrent release of dopamine may result in a downregulation of striatal D ₂ receptors, predisposing that individual to the development of drug addiction. The concept that fewer D ₂ receptors may increase vulnerability to addiction is also supported by the finding that subjects with the DRD2-TAQ-IA polymorphism have reduced D ₂ receptor density and are at increased risk for alcohol and opiate addiction.

The mechanism by which low striatal D ₂ receptors leads to addictive behavior may relate to the known relationship between dopamine signaling and cortical control mechanisms. The dorsolateral prefrontal cortex and medial prefrontal cortex are involved in controlling behavior and motivation, and are altered in subjects with drug addiction. The observation that subjects with low D ₂ receptors due to polymorphisms in DRD2-TAQ-IA have altered prefrontal lobe metabolism and show a learning disability with the inability to avoid actions with negative consequences also supports a role for a causal connection between D ₂ receptor density and cortical control behavioral mechanisms. The additional observation that morbidly obese subjects also show altered prefrontal metabolism that correlates with low D ₂ receptors and addictive behaviors further supports this important link.

Sucrose is a potent stimulus for dopamine release. In rats, the ingestion of sucrose results in an immediate increase in extracellular dopamine in the nucleus accumbens, and both sucrose intake and extracellular dopamine are enhanced if presynaptic reuptake of dopamine is blocked. The increase in dopamine may increase behavioral responses that can lead to further ingestion of sucrose. For example, mice with genetically high dopamine levels show enhanced incentive performance for sucrose, increase their food and water intake, learn quicker, resist distractions, and proceed more efficiently to their goals. While they have a greater "desire" response to sucrose, they do not appear to have a better liking (satisfaction) response to the sucrose.

Although the acute effects of dopamine may enhance performance, the issue is whether desensitization of dopamine-stimulated responses occurs with repeated sugar administration. Much of our knowledge about the effects of sucrose on addiction-like behaviors in laboratory rats has come from studies by the laboratory of the late Bart Hoebel at Princeton University (Princeton, NJ), who developed a model of sugar overeating in rats by limiting their daily exposure to sucrose. Specifically, rats offered sucrose 12 h/day for approximately 3 weeks escalated their daily intake of sucrose and binge ate sucrose on daily access. In these rats, administration of naloxone (an opioid antagonist) resulted in signs of opiate-like withdrawal (eg, teeth chattering, head shakes, and forepaw tremor) and signs of anxiety. Signs of withdrawal are also observed if the sucrose and food are withheld. Also, rats that have a history of bingeing on sucrose show a heightened sensitivity to drugs of abuse. Thus, repeated intermittent sugar exposure may lead to a "sugar addiction" that involves behaviors similar to those seen with classical drug addiction.

The effect of intermittent sugar consumption on nucleus accumbens dopamine release differs from what normally occurs in response to ingestion of a palatable food. While palatable foods release dopamine, this effect is more closely associated with the novelty of the food, and the release of dopamine attenuates with subsequent exposure to the food. However, when rats repeatedly overeat sugar (ie, on a daily basis for 1 month), they continue to release dopamine from the nucleus accumbens on ingesting or tasting sucrose. Nevertheless, the observation that the dopamine response remains similar over time despite progressively higher intakes of sucrose suggests some desensitization.

Consistent with desensitization, chronic sucrose intake in rats is associated with a reduction in nucleus accumbens D ₂ receptor mRNA expression compared with control rats. There is also decreased D ₂ receptor binding in this region, a finding that has also been seen in a more restrictive intermittent sucrose exposure paradigm. Striatal D ₂ receptors are also reduced in rats consuming a sucrose-containing, cafeteria-style diet for 40 days, and these rats progressively increased their food intake and developed obesity. These rats exhibited a higher reward threshold in response to electrical stimulation of the nucleus accumbens, suggesting that they might need to eat more of the sucrose-rich diet to achieve a comparable reward. In addition, these rats became progressively resistant to punishment (foot shock) paired with eating. These effects were amplified by knocking down the striatal D ₂ receptors in rats on the sucrose-rich diet. Collectively, these studies suggest that repeated exposure to sugar may affect the mesolimbic dopaminergic response to palatable food, possibly due, in part, to lower D ₂ receptors.

Brain dopamine signaling is also altered in patients with ADHD. Adults with ADHD show fewer D ₂-like receptors in the left ventral striatum (involved in reward behavior), left midbrain, and left hypothalamus (involved in memory) compared with healthy adults, and the reduction in D ₂ receptors correlated with degree of inattention. Furthermore, there is reduced glucose metabolism in the prefrontal cortex of adults with ADHD, consistent with a loss of frontal control mechanisms. Finally, there is also a reduction in dopamine metabolites in cerebral spinal fluid samples obtained from children with ADHD. Thus, ADHD has a similar dopamine biosignature as that observed with sucrose or drug addiction, with both showing a downregulation of striatal D ₂ receptors. Attention-deficit/hyperactivity disorder is also associated with a reduction in frontal lobe sensitivity to natural rewards and greater symptoms of inattention, and although this has not been shown in animals chronically fed sucrose, the observation that a genetic reduction in striatal D ₂ receptors is associated with altered frontal lobe behavioral mechanisms suggests that chronic sugar ingestion may have similar effects. The general link between dopamine D ₂ receptors and frontal lobe control mechanisms has led Volkow et al to propose that recurrent stimulation of dopamine release may lead to desensitization of the postsynaptic dopamine signaling pathways, which, in turn, reduces inhibitory signals generated by the frontal cortex, resulting in impulsive behavior and loss of emotional control, and symptoms of ADHD. Our contribution is primarily to suggest that this link may be due to chronic sugar intake. If true, chronic sugar intake should correlate with an increased prevalence of ADHD.

The Prevalence of Chronic Sugar Intake and ADHD Have Increased in Parallel

Sugar intake and ADHD have increased in parallel in recent years. The intake of added sugars in the United Kingdom and United States has increased remarkably over the past 2 centuries, with a marked acceleration in the past 40 years in association with the introduction of high-fructose corn syrup (HFCS). Today, intake of added sugars accounts for 15% to 20% of daily caloric intake in adults; in 10% of adults and in 25% of children, the intake of added sugars may be > 25% of their diets.

The prevalence of ADHD is difficult to assess, as definitions have varied over the years and because there are few large-population studies. However, studies published in the early 20th century on childhood psychiatric disorders focused on aphasia, dyslexia, and autism. Reports of the hyperkinetic child or of abnormal fatigue in children are relatively limited through the first half of the 20th century. Beginning in the late 1960s and 1970s, one can observe a dramatic increase in publications on children with ADHD, which at this time was termed "minimal brain dysfunction." Estimates from as recent as 1990 suggested that approximately 2% to 5% of US school children have hyperactivity syndrome. More recently, the National Survey of Children's Health consisted of a randomized, national, and cross-sectional survey of > 70 000 households with children between the ages of 4 and 17 years, which was conducted in both 2003 and 2007. These data show a > 20% increase in parent-reported ADHD between 2003 and 2007, increasing from 7.8% to 9.5% of children (consisting of an increase from 11.0% to 13.2% in boys and 4.4% to 5.6% in girls). The National Health Interview Survey also reported an increase in ADHD between 1997 and 2006 at a rate of 3% per year.

The increasing prevalence of ADHD is compatible with the known increases in sugar consumption in the United States. Although, to our knowledge, no studies have directly assessed whether a correlation exists between the prevalence of ADHD and sugar intake, there are a few reports linking ADHD with sugar consumption. Parents of children with ADHD report sleep disturbances that are associated with increased sugar intake. Further, preschool-aged children who consumed a diet rich in "junk food" with a high sugar content were more likely to exhibit hyperactivity at age 7 years compared with children who ate less junk food.

Chronic Sugar Intake and ADHD Are Both Associated with Obesity

The marked increase in sugar intake has been epidemiologically and physiologically linked with the increase in obesity and metabolic syndrome. Attention-deficit/hyperactivity disorder is also associated with obesity. In 1 study of children with ADHD aged 3 to 18 years, 29% had a body mass index (BMI) >85th percentile, which is twice the frequency observed in the normal population. Another study found that nearly 20% of 5- to 14-year-old boys with ADHD had a BMI > 90th percentile. In a study of Chinese teenagers (aged 13-17 years) with ADHD, the frequency of obesity was 1.4-fold greater than the frequency of being lean.

Adults with ADHD are also commonly obese. In one study of adults with ADHD, the likelihood of being overweight was 1.58 (odds ratio [OR], 1.58; 95% confidence interval [CI], 1.05, 2.38) and for obesity the OR was 1.81 (95% CI, 1.14, 2.64). Another study found that ADHD and hyperactivity were associated with both obesity and hypertension in young adults. Conversely, obese subjects are also at increased risk for ADHD. Among children hospitalized for obesity, ADHD was diagnosed in > 50% of cases. Further, in obese adults undergoing bariatric surgery, ADHD has been found in 27% of patients, and the frequency was even higher (42%) in those with morbid obesity (BMI > 40 kg/m ²).

There are several potential explanations for the association between ADHD and obesity. First, characteristics associated with ADHD, such as depression or binge eating, may result in obesity. The converse may also be true, that the presence of ADHD may interfere with the ability to lose weight via diet programs or following bariatric surgery. A final explanation, which we are proposing in this article, could be that sugar intake may be driving both ADHD and the risk for obesity. Davis also recently implicated dietary intake of fats and sugars in the pathogenesis of ADHD, particularly if ingested during pregnancy (which she described as a fetal sugar spectrum disorder).

Obesity Has a Dopamine Biosignature Similar to That of ADHD and Chronic Sucrose Ingestion

Striatal D ₂ receptor availability is chronically decreased in obese subjects as determined by PET scanning with [ ¹¹C]raclopride. Obese subjects also have fewer striatal D ₂ receptors, which correlates with reduced glucose metabolism in the frontal and somatosensory cortices. Obese individuals also have a reduction in dorsal striatal response, measured by functional magnetic resonance imaging (fMRI), to palatable food intake, consistent with a lower dopamine response and/or lower D ₂ receptors. Thus, obese individuals may overeat to compensate for impaired reward responses. While obese individuals typically show a reduced dopaminergic response to the intake of palatable food, they may show an enhanced response to the sight of food. Binding of [ ¹¹C]raclopride to D ₂ receptors decreases in the dorsal striatum when subjects who are pretreated with methylphenidate see appetizing food, and this expressed desire for food is consistent with an acute release of dopamine and occupation (stimulation) of D ₂ receptors. Further, increased dopaminergic response to food stimuli has been found to correlate with binge-eating behavior in obese subjects. Thus, a reduced dopamine-stimulated D ₂ receptor-mediated response to food may result in the need to eat more palatable food (to promote dopamine responses) and a greater desire and heightened dopamine activation in response to sight of food (possibly resulting from the inhibition of frontal cortex-dependent executive control).

Dietary-induced obese animals show low basal dopamine levels that increase in response to palatable food, but not to standard rodent food. Other studies suggest that cholecystokinin-deficient Otsuka Long Evans Tokushima Fatty (OLETF) rats, which are obese, have decreased D ₂ receptor binding in the nucleus accumbens shell, and that D ₂ receptor activation contributes to the avidity for sucrose in obese OLETF rats.

How Could Chronic, Excessive Sugar Intake Cause Abnormalities in Dopamine and D₂ Receptor Signaling?

Sucrose likely activates dopamine release in the brain via several mechanisms. One way involves activation of sweet receptors (T1R2 and T1R3) present in the tongue and gut. Sweet taste from either sucrose or sucralose will elicit taste preference and a dopaminergic response in the nucleus accumbens. The importance of taste receptors has also been suggested by the use of sham feeding in which a gastric fistula minimizes the absorption of food. Under these circumstances, sucrose can still increase extracellular dopamine in the nucleus accumbens. However, the taste receptor is not the only mechanism for inducing dopamine release in sucrose-fed rats. Hence, mice lacking functional taste receptors (trpm5 −/− knockout mice in which signaling via the sweet taste receptors is prevented) still show a dopamine response and preference for sucrose, whereas the dopamine response to sucralose is eliminated. Likewise, mice genetically lacking the T1R3 in their taste buds and gut continue to show a preference for sucrose even if it is provided via gastric infusion. The observation that artificial sugars, such as sucralose, can stimulate dopamine in the nucleus accumbens of normal mice may provide one explanation as to why earlier studies comparing sucrose with aspartame showed no difference in ADHD symptoms.

The observation that mice lacking sweet receptors continue to prefer sucrose and manifest an increased striatal dopamine response suggests that sucrose may have effects on mesolimbic dopamine signaling as a consequence of its metabolism. Sucrose is degraded by sucrase in the gut to fructose and glucose, which are then absorbed and metabolized. Thus, the effects of sucrose, as well as HFCS, likely relate to the metabolic effects of glucose and/or fructose. Studies led largely by Ackroff et al suggest that rats demonstrate taste preference for both glucose (and its polymers [Polycose]) and fructose, even if they are given these sugars postorally (which is performed by coupling the administration with an orally flavored substance). Intake of both glucose and fructose can be reduced by the injection of dopamine receptor antagonists in the nucleus accumbens. Studies assessing "sugar addiction" have been performed using glucose, and the findings suggest that if glucose is provided intermittently, it can induce an addiction-like syndrome, with bingeing behavior, withdrawal-like symptoms in response to naloxone, and a downregulation of D ₂ receptors. These data suggest that both glucose and fructose can elicit dopamine responses that might have relevance to understanding ADHD.

While fructose and glucose show some similarity in their effects, studies suggest that they may mediate their effects on taste preferences via different pathways. In rats, for example, aqueous glucose is preferred over fructose due to stronger postoral mechanisms, whereas fructose may elicit a stronger oral response. Fructose and glucose also differ markedly in their metabolism (). Unlike glucose, fructose readily induces intracellular phosphate and adenosine tri-phosphate (ATP) depletion during its metabolism, as the initial phosphorylation of fructose to fructose-1-phosphate by fructokinase results in the rapid consumption of ATP. In contrast, during glucose metabolism, ATP depletion never occurs, as there is a negative feedback system that prevents excessive phosphorylation. The decrease in intracellular phosphate that occurs during fructose metabolism also results in the stimulation of adenosine monophosphate (AMP) deaminase, which converts AMP to inosine monophosphate (IMP) and eventually to uric acid. Uric acid is rapidly generated in the liver with a rise in serum uric acid that peaks within 1 hour after fructose ingestion. In addition, some studies suggest that fructose may be metabolized in the hypothalamus; if so, it should also result in intracellular uric acid generation at this site.

Acutely increasing uric acid in rats has been reported to increase extracellular dopamine in the substantia nigra. Theoretically, this should inhibit dopamine neuronal firing due to stimulation of inhibitory somatodendritic D ₂ autoreceptors on the dopamine neurons. However, acutely increasing uric acid also stimulates locomotor activity. This observation suggests that dopamine in the terminal fields is also elevated and stimulates postsynaptic D ₁ and D ₂ receptors to cause locomotor activation. In turn, persistent receptor activation could result in downregulation of dopamine receptors in striatum. Uric acid may increase dopamine by blocking metabolism of dopamine to its oxidative end product, dihydroxyphenylacetic acid.

Additional lines of evidence support a possible role for uric acid in ADHD. First, children with ADHD have higher serum uric acid levels than controls. Specifically, in a study of 40 girls and 50 boys (aged 3.5-4.5 years), serum uric acid levels correlated with hyperactivity, short attention span, impulsivity, and anger control. Low-level lead intoxication has been linked with increased risk for ADHD, and lead intoxication is another mechanism for increasing uric acid levels. Attention-deficit/hyperactivity disorder is also much more common in boys than girls, which is consistent with the fact that boys have higher uric acid levels than girls.

Fructose is thought by some to be the critical component in sucrose and HFCS that drives obesity and metabolic syndrome. Fructose may induce obesity via several mechanisms, including by failing to stimulate leptin secretion compared with glucose and by inducing insulin and leptin resistance, the latter resulting in impaired leptin signaling to the hypothalamus. Because insulin and leptin inhibit dopamine signaling, the induction of resistance to these hormones might facilitate increased dopamine signaling. Fructose can also induce ATP depletion in the liver, and ATP depletion in the liver has been shown to stimulate hunger. Fructose also lowers ATP in the hypothalamus, activates AMP kinase, and inhibits acetyl-CoA carboxylase (by phosphorylating it), which lowers malonyl-CoA, resulting in increased POMC (pro-opiomelanocortin) and hunger. Furthermore, a recent study using fMRI reported that glucose increased cortical activation in reward-control areas, whereas fructose had opposing effects. Thus, it remains possible that fructose and glucose may have distinct mechanisms by which they alter dopamine signaling.

While these latter studies implicate fructose as a key factor in how sucrose may be related to obesity and ADHD, the binge behavior and dopamine signaling that can be induced by intermittent exposure to glucose could also play a major contributory role. Clearly, more studies are needed to determine the role of these 2 sugars alone and in combination as they may be related to behaviors associated with ADHD.

Conclusion

We postulate that sugar acutely increases dopamine, which, over time, leads to a reduced number of D ₂ receptors and possibly a reduction in extracellular dopamine itself, leading to desensitization of this dopamine signaling axis. These effects would not be due to the acute effects of sugar, but rather would occur over weeks to months with chronically elevated and intermittent sugar ingestion (). If this is true, then children with ADHD may ingest more sugar than other children in an attempt to correct the dopamine-deficient state, resulting in excessive sugar intake that could result in "sugar addiction" and increase their risk for obesity. These children would manifest with slightly higher uric acid levels, reflecting the increased sugar intake. Caregivers may consider that the acute effects of sugar are the cause of ADHD. However, the administration of sugar over days to weeks would be unlikely to induce greater symptoms of ADHD, especially if sucrose intake is compared with artificial sweeteners that can also elicit a dopamine response. Therefore, a potential causal relationship between sucrose and ADHD could have been missed in prior studies.

The observation that ADHD represents a dopamine-deficient state could explain why treatments that increase dopamine levels in the nucleus accumbens, such as amphetamine and methylphenidate, improve symptoms, at least acutely. However, based on the increasing evidence for D ₂ receptor desensitization/downregulation as a mechanism underlying ADHD, one might expect these drugs to have an enhanced potential to cause addiction. Indeed, this issue has been raised with modafinil, which increases extracellular dopamine and has been used to treat narcolepsy. Dopamine receptor agonists have also been reported to lead to gambling and addictive behavior in subjects with Parkinson's disease. Further studies evaluating the role of dopamine receptor agonists in ADHD are needed.

We recommend specific experimental and clinical studies to test our hypothesis (). If it is determined that ADHD is a consequence of the marked increase in intake of added sugars, then public health measures to reduce sugar intake are indicated, especially in young children (aged < 7 years), who are most predisposed to developing ADHD. As ADHD can be associated with impaired school performance, antisocial behavior, and drug addiction, the importance of such an approach could be far reaching.

Table 1

1. Reevaluate the relationship between chronic excessive intake of sugar in childhood and later development of ADHD and binge eating.

2. Determine if individuals with low sugar ingestion in childhood are relatively protected from developing ADHD.

3. Evaluate the effect of chronic excessive sugar intake on development of ADHD-like symptoms in experimental animals.

4. Conduct studies to determine the effect of fructose versus glucose in inducing a reduction in striatal D ₂ receptors.

5. Conduct studies to separate the effect of artificial sweeteners from that of nondietary sugars in inducing a long-term reduction in D ₂ receptors and inhibition of frontal lobe control mechanisms.

Acknowledgments

The authors received supported from the National Institutes of Health (NIH), NIH HL-68607 (RJJ), K05 DA015050 (NRZ), K01 DA031230, and the National Eating Disorders Foundation (NMA). The authors thank Miaoyuan Wang for her assistance with preparing the figures.

Footnotes
Conflict of Interest Statement
Richard J. Johnson, MD and Takuji Ishimoto, MD have a patent application on inhibition of fructokinase as a mechanism to treat sugar craving. Richard J. Johnson is the author of The Sugar Fix (Rodale and Simon and Schuster, 2008 and 2009). Mark S. Gold, MD, David R. Johnson, PhD, Miguel A. Lanaspa, PhD, Nancy R. Zahniser, PhD, and Nicole M. Avena, PhD disclose no conflicts of interest.

References

2. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 4. Washington, DC: American Psychiatric Association; 2000. Text Revision.

55. Baker S. Fatigue in school children. Educational Reviews. 1898;15:34-39.

58. Anastopoulos AD, Barkley RA. Biological factors in Attention Deficit-Hyperactivity Disorder. The Behavior Therapist. 1988;11:47-53.

73. Wang GJ, Geliebter A, Volkow ND, et al. Enhanced striatal dopamine release during food stimulation in binge eating disorder [published online ahead of print February 24, 2011] Obesity (Silver Spring) [ PMC free article]

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Attention-Deficit/Hyperactivity Disorder: Is it Time to Reappraise the Role of Sugar Consumption?

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Attention-deficit/hyperactivity disorder (ADHD) affects nearly 10% of children in the United States, and the prevalence of this disorder has increased steadily over the past decades. The cause of ADHD is unknown, although recent studies suggest that it may be associated with a disruption in dopamine signaling whereby dopamine D 2 receptors are reduced in reward-related brain regions.

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In 1973, at a meeting of the American Medical Association, a retired pediatric allergist proposed a hypothesis that seemed ludicrous at the time. He claimed that at least some of the children labeled as hyperactive, or hyperkinetic, or afflicted with minimal brain dysfunction actually possessed an elevated sensitivity to certain elements of the diet. He followed with a book directed at the general public ( Feingold 1975). He singled out food additives for special treatment. Feingold named artificial flavors and colors as primary culprits but also indicted some preservatives. Adopt a diet free of these offending ingredients, he advised, and many of the unsettling behavioral problems exhibited by these children will wane. Neither Feingold nor his critics defined hyperactivity in current terms of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders [DSM-IV ( American Psychiatric Association 2000)]. The DSM-IV identifies three types of attention-deficit and/or hyperactivity disorder (AD/HD): predominantly inattentive (attention deficit disorder; ADD), predominantly hyperactive (attention-deficit/hyperactivity disorder; ADHD), and combined subtype (the most common). Boyle et al. (2011) give the proportion of U.S. children diagnosed with ADHD as 6.69%.

Feingold's claims drew wide public attention and also provoked a series of studies directed at his hypotheses. Some tested elimination diets free of additives and other substances, such as salicylates, that Feingold linked to hyperactivity (e.g., Conners et al. 1976; Harley et al. 1978a, 1978b). Others focused on artificial colors because they represented only a small fraction of the additives in the food supply and could be manipulated more easily (e.g., Swanson and Kinsbourne 1980; Weiss et al. 1980; Williams et al. 1978). Most of these adopted the tactic of challenging children with one or a blend of food colors and a placebo. By the early 1980s, enough evidence about the Feingold hypothesis had accrued that even some of its most severe critics viewed it as plausible in some respects. For example, Stare et al. (1980) observed that "challenge experiments indicate that the symptoms of a small subgroup of all hyperactive children appear to be sensitive to the artificial food colors in their diet."

After the early 1980s, interest in assessing behavioral reactions to food colors abated. During the intervening years, occasional studies both supporting and contradicting Feingold's assertions about food colors made their way into the literature. From the point of view of the U.S. Food and Drug Administration (FDA), the positive studies taken together failed to constitute enough evidence to require regulatory action (FDA 2010). The data available up to 1982 ( Weiss 1982) were considered not substantial enough to cause any shift in the FDA position. Debates about the behavioral toxicity of food colors continued but did not arouse singular interest.

The debate reignited with publication of a large study conducted by a group at the University of Southampton in the United Kingdom ( McCann et al. 2007). It enrolled about 300 preschool and elementary school children who were challenged by a blend of food colors and sodium benzoate in a double-blind design employing a variety of behavioral measures. The study used two different mixtures, and the amounts chosen were based on estimates of intake by the British Food Standards Agency and probably are close to U.S. levels. Mix A included 20 mg artificial food colorings for 3-year-old children and 24.98 mg for 8- to 9-year old children. Mix B included 30 mg for the younger children and 62.4 mg for the older children. The doses for the 3-year-old children corresponded roughly to the amounts found in 112 g of candy.

The behavioral measures used by McCann et al. (2007), combined into a single score (as well as some components of the total score), demonstrated statistically significant adverse responses in both groups of children to the food color challenge. Although some of these measures are used in ADHD research and diagnosis, the Southampton study was aimed not at ADHD but at the more general question of behaviors evoked by food colors. Neither was the study aimed at the question of sensitivity to food colors in ADHD children; the subjects came from the general population of school children.

Discussion

Because of the uniqueness and size of the study by McCann et al. (2007), it drew renewed attention to the food color debate, which was introduced to the environmental health community by an article in Environmental Health Perspectives ( Barrett 2007), followed by a letter from Weiss (2008). Barrett (2007) solicited a response from a spokesperson for the FDA (Mike Herndon), who replied as follows:

However, we have no reason at this time to change our conclusions that the ingredients that were tested in this study that currently are permitted for food use in the United States are safe for the general population.

The article by McCann et al. (2007) elicited a petition to the FDA from the Center for Science in the Public Interest (CSPI), a public interest group that earlier had called for a ban on food colors (CSPI 2008). This petition, together with congressional interest and media publicity, led to an FDA decision to review the food color literature and to hold a public hearing before its established Food Advisory Committee. The hearing was held on 30-31 March 2011. After listening to testimony from FDA reviewers and the public, the committee concluded that the evidence was too inconclusive to link food colors to hyperactivity and too insufficient to recommend warning labels for products containing artificial food colors. (I testified before the committee that the available evidence indicated a connection between adverse behavioral responses and food color consumption.)

As described by the FDA Food Advisory Committee (2011a), the FDA framed the question put to the advisory committee primarily in the form, "Are food colors a cause of hyperactivity?" Only as a secondary question did the FDA ask if food colors might be a source of other kinds of adverse behavioral responses. Although the food color question was framed quite narrowly by the FDA, it is representative of many of the questions that confront the environmental health sciences. What kind of data-and how much data-does it take to render an outcome conclusive enough for action? The committee decision and the FDA's current view (as quoted by Barrett 2007) signify a group of persistent questions pertaining both to environmental health science and to regulatory practices. In this commentary, I try to place the FDA committee decision in this broader context.

Identifying the appropriate measures. The FDA described the committee's mission in these terms (FDA Food Advisory Committee 2011a):

The task before this Food Advisory Committee is to consider available relevant data on the possible association between consumption of synthetic color additives in food and hyperactivity in children, and to advise FDA as to what action, if any, is warranted to ensure consumer safety.

The charge did not explicitly conform to the DSM-IV definition of ADHD, which is multifaceted, so the charge was somewhat ambiguous.

Two review documents were contracted for and submitted to the Food Advisory Committee before the meeting: a background document, describing the FDA's history of food color regulation (FDA Food Advisory Committee 2011a), and a literature review of publications about the connections between food colors and hyperactivity (FDA Food Advisory Committee 2011b). These documents provided the basis for the review presented to the committee by the FDA Office of Food Additive Safety.

In their review the FDA apparently decided to focus on Feingold's 35-year-old hypothesis ( Feingold 1975) rather than on the broader environmental issue of whether food colors may induce adverse behavioral responses. This is a broader issue because, as noted above, most U.S. children, not just those diagnosed with ADHD, consume synthetic food colors in their diet.

Moreover, few of the artificial food color challenge studies did so to test the hypothesis that food colors cause ADHD as defined by the DSM-IV. No one, of course, can specify any predominant cause of ADHD. It is clearly a multicausal disorder as well as one with notable variation in expression. The food color literature is aimed mostly at the short-term effects of challenges, not chronic disease. Although the questionnaires, rating scales, and performance assays prominent in ADHD research have proven useful in challenge studies, they do not encompass all the behaviors evoked by food colors. Swanson and Kinsbourne (1980) found that performance on a paired-associate learning task deteriorated after administration of a color mixture challenge. Goyette et al. (1978; see also Conners et al. 1976) identified 3 of the 16 children they assessed as responders by their performance on a visual tracking task. Even the FDA review observed that measures confined to ADHD symptoms may not reflect responses evoked by food colors. It noted the following in discussing a study by Rowe and Rowe (1994):

The behavioral effects elicited by the tartrazine challenges, however, involved irritability, fidgetiness and sleep problems which are not typically representative of hyperactivity related behaviors. Several other investigators also reported behavioral responses to color challenge that were not particularly characteristic of ADHD. (FDA Food Advisory Committee 2011b)

By narrowing the scope of the committee's task to a judgment of whether artificial food colors are associated with ADHD, the FDA Food Advisory Committee (2011b) effectively eliminated a much more relevant and important question: Is there evidence that food colors are behaviorally toxic to the general population of children?

The large investment by the National Institute of Environmental Health Sciences (NIEHS) in bisphenol A research is, in many ways, a design for answering questions of similar scope. Bisphenol A is often labeled as "estrogenic." Had the NIEHS bisphenol A initiative been restricted to this question ( Spivey 2009), it might have limited its breadth only to questions bearing on the chemical's alleged estrogenic properties. The NIEHS, however, recognized the scope of associations between bisphenol A exposure and health effects, including those such as obesity and externalizing behavior in young girls, that could not be linked firmly to estrogenicity, if at all. Analogously, if questions about the adverse health effects of airborne particulates had been restricted to lung function, the superficially obvious target organ, the association with cardiovascular function, its primary adverse effect, would have been overlooked.

One possible source of the FDA review's misleading charge may be its limited view of brain-behavior relationships. In summarizing its findings, the FDA Food Advisory Committee (2011a) offered the following statement:

For certain susceptible children with attention deficit/hyperactivity disorder and other problem behaviors, however, the data suggest that their condition may be exacerbated by exposure to a number of substances in food, including, but not limited to, synthetic color additives. Findings from relevant clinical trials indicate that the effects on their behavior appear to be due to a unique intolerance to these substances and not to any inherent neurotoxic properties.

This statement surely does not mean to assert that the central nervous system is not the essential substrate for behavior or that behavior is a phenomenon independent of the brain. Its roots perhaps may be found in how toxicology was practiced in the past, when pathology-overt tissue damage-was far more important than function in assessing chemical safety.

Identifying special populations. The literature on behavioral toxicity of food additives is replete with observations by investigators-and by much of the applicable data-that not all children are sensitive to additives in general, or food colors in particular, at common dietary levels. Indeed, not even Feingold asserted that all hyperactive children were sensitive to food additives. In a convincing example of such findings, Rowe and Rowe (1994), in a double-blind controlled challenge study with tartrazine, identified a subgroup of 24 children within their sample of 54 that responded consistently on each occasion that they consumed a color rather than a placebo capsule. Moreover, these children displayed a clear dose-response function, with the higher doses eliciting higher scores on their 30-item behavior inventory, including five clusters of related behaviors: a) irritability/control, b) sleep disturbances, c) restlessness, d) aggression, and e) attention span.

The FDA review, however, seemed to insist that proving a connection between food color ingestion and adverse behavioral effects requires a uniformity of response in the sample under study that is virtually impossible to achieve in the diverse human population. For example:

Generally, the various reported findings across these 10 reviewed post-1982 portion of Group I trials, suggests that certain susceptible subgroups of problem behavior children with and without ADHD and, possibly, certain susceptible children from the general population without particular behavioral problems may exhibit a unique intolerance to artificial food colors resulting in typically small to moderate adverse behavioral changes which may not necessarily be characteristic of the ADHD syndromes. (FDA Food Advisory Committee 2011b).

Such a rejection of evidence stemming from data suggesting a subpopulation of children with enhanced sensitivity to food colors is perplexing. The FDA review implies that, because such a subpopulation may represent only a small proportion of children (hardly a proven proposition), it does not represent a significant health problem. Such a contention is inconsistent with the tenets of public health. Much of biomedical research, including environmental health research, is devoted to identifying and treating especially sensitive or vulnerable subpopulations. The underlying health goals of the Human Genome Project surely embraced that perspective. FDA drug warnings often are directed at special subpopulations. Finally, the FDA view on how this question pertains to food colors is an outlier among federal agencies. Note how the U.S. Environmental Protection Agency (2011) interpreted the Clean Air Act:

The National Ambient Air Quality Standards (NAAQS) are designed to protect the most vulnerable populations from outdoor air pollutants. Identifying these groups more precisely and understanding why they are more susceptible is of great importance to scientists and policy makers.

Effect size. In its critique of McCann et al. (2007), the FDA is somewhat dismissive of the results, at least as conveyed by this statement:

Whatever behavioral changes [in the Southampton study] may have occurred were apparently of rather low magnitude (effect size of 0.18). This would suggest that the type of treatment effects reported in this study, even though the investigators referred to increases in levels of "hyperactivity," were not the disruptive excessive hyperactivity behaviors of ADHD but more likely the type of overactivity exhibited occasionally by the general population of preschool and school age children. (FDA Food Advisory Committee 2011b).

This is a puzzling statement because an important facet of an ADHD diagnosis is excessive or inappropriate activity. Also, the DSM-IV lists six kinds of hyperactivity, not just the variation described above. The term "occasionally," in this context, is at least equally puzzling. Respiratory infections are also occasional events for most children. If a survey were to find, say, a significant rise in the incidence of such infections among a group of schoolchildren, questions would be asked and actions possibly taken. This question, in fact, is the theme of many reports in environmental health.

The more significant paradox about the passage above by the FDA Food Advisory Committee (2011b) is its view that an effect size of 0.18 [in the range of many of the published studies (see Schab and Trinh 2004; Stevens et al. 2011)] can be considered trivial. Effect size is often used to gauge the importance or strength of a finding; therefore, how it applies to McCann et al. (2007) -and its interpretation-is worth examining with a more familiar example.

Consider . For an IQ (intelligence quotient) distribution with a mean of 100 and and an SD of 15 (which describes a standardized IQ test such as the Stanford-Binet), 2.3% of the population will receive a score of < 70, a score that many school districts will view as warranting remedial attention. Now, define effect size, as used by McCann et al. (2007) and typically in the psychological literature, in terms of the standardized mean difference:

(mean 1 - mean 2) ÷ (pooled SD).

If an environmental exposure shifts the mean by 3%, equivalent to an effect size of 0.2, to a mean of 97, 3.6% of the population represented by the distribution will have a score < 70. Based on Census 2000 counts, the U.S. government (Childstats.gov 2011) estimates that there are 76 million children 0-17 years of age in the nation. Of these, 1.75 million would be presumed to have an IQ score of < 70, given a mean of 100. A shift of the mean IQ to 97 would indicate that 2.74 million children would have an IQ < 70 (an increase of 990,000 children). Most observers would not consider this to be a value of "rather low magnitude."

presents another set of implications based on an effect size of 0.2, or a 3% shift in IQ. It depicts the calculations by Herrnstein and Murray (1994) of the broader social consequences of a population IQ increase of 3%, which was converted into the effects of a corresponding decrease in IQ by Weiss and Bellinger (2006). Although some of the presuppositions of these authors have aroused controversy, the relationships between IQ scores and lifetime earnings (e.g., Grosse et al. 2002), and how income influences the outcomes shown in , lends credibility to the calculations.

One other aspect of effect size calculations that the FDA review failed to consider is how such values are influenced by population heterogeneity. In their , Weiss and Bellinger (2006) showed how effect size calculations can be distorted if the sample population contains two subpopulations. Assume that the sample population consists of 70% nonresponders and 30% responders, and that the mean of the responders is shifted by 1 SD when presented with a challenge, such a food color. Under these conditions, it would require a total sample of 265 subjects to achieve an effect size of 1.0 as defined above. It is easy to see how true effects of a food color challenge to an unselected population can be missed if the sample size is small or if a minority of the sample consists of responders. Given such circumstances, it made sense for some investigators, such as Rowe and Rowe (1994), to screen subjects for responsiveness to an elimination diet before undertaking the tartrazine challenge portion of their study. It is analogous to the cancer bioassay strategy of using high doses to identify carcinogenic potential in reasonably small samples of rodents.

Conclusions: The View from Environmental Health

The food color issue is emblematic of many questions in environmental health. What is the border between "inconclusive" and "conclusive" evidence? How is a susceptible population identified, and how large must it be for it to be seen as significant for public health? How broadly (or narrowly) should an outcome or criterion be defined and still remain relevant? From these standpoints, the FDA's review, current position, charge to the Food Advisory Committee, and view of the issue's future reflect a somewhat narrow vision. It is revealed in the conclusion by both the committee and the review (FDA FAC 2011c) that further research on the topic is necessary (committee members voted 93% yes and 7% no, when asked if more research was needed). This is hardly a statement to evoke disagreement, but consider the way such studies would have to be carried out. They would require institutional review board (IRB) approval. How would the investigator address the question of risk? How likely is it that an IRB would approve a study design in which the investigator states that, according to the published literature and the FDA, some children respond to a food color challenge with adverse behavioral effects? What, then, asks the IRB, is the purpose of the study? The difficulty in devising an argument for conducting a study that would satisfy most IRBs reveals the flaws in the FDA's current position.

The next phase of the protocol would prove at least equally daunting. Parents would have to provide informed consent. As with the argument to the IRB, the parent would have to be made aware that food color challenges have been reported to induce adverse behavioral effects in some children. Would more than a small proportion of parents agree to have their child included?

Consider the cost of such a study. The Southampton study ( McCann et al. 2007) enrolled about 300 children, about 150 of nursery-school age and about 150 in elementary school, and it used a blend of food colors. According to the principal investigator, the study cost about $1 million to complete. If the FDA demands that each of the certified colors be studied individually, the total cost would reach $7 million. Additional studies are unlikely ever to be performed on the scale of that performed by the Southampton investigators.

If the FDA had approached the food color question from an environmental health perspective, it would have enlisted a broad sample of scientists from a variety of relevant disciplines to examine the question. Its model would have been that exemplified by the April 2011 issue of Environmental Health Perspectives, which highlighted the health effects of airborne particulate matter. That issue of the journal contained articles addressing topics such as differential susceptibility in populations, the effects of different particulate matter components on mortality, cardiovascular effects, coronary heart disease, and respiratory health.

Had the FDA approached the food color question with the breadth of inquiry adopted in 1977, when a select committee reviewed the toxicity of food additives permitted under the GRAS (Generally Regarded as Safe) criteria ( Siu et al. 1977), it would have arrived at a different conclusion. That committee, whose deliberations were supported by the FDA, looked beyond the simple question of "safety." It enlisted public comment early in the process; made the penultimate draft of its report available to the public via the Federal Register and solicited comments; noted the importance of "psychotoxicology" in food safety evaluation; and emphasized the unique risks to neonates, a vulnerable group not considered in the FDA review but that is exposed to food colors.

If the FDA had given consideration to how the food color question might effectively be resolved, it might have adopted the vision described by NIEHS in the promise of Green Chemistry for Environmental Endocrine Disruptors, a meeting held in March 2011 in Sausalito, California ( Schug 2011). In a parallel fashion, the FDA might have called for a "green chemistry" approach to synthetic food colors.

In defense of the FDA position, one might argue that the narrow scope of the review and committee charge simply were products of the CSPI petition (CSPI 2008). But the agency has had 35 years since the Feingold book ( Feingold 1975) and 37 years since the GRAS report ( Siu et al. 1977) to address the neurobehavioral toxicity of food colors. Perhaps a regulatory agency is not capable of being proactive. However, the British Food Standards Agency has advised parents to consider eliminating artificial food colors from the diet, and the European Union has called for eliminating six colors or listing on the label the warning that "[the color] may have an adverse effect on activity and attention in children" ( Food Standards Agency 2011).

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Synthetic Food Colors and Neurobehavioral Hazards: The View from Environmental Health Research

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In 1973, at a meeting of the American Medical Association, a retired pediatric allergist proposed a hypothesis that seemed ludicrous at the time. He claimed that at least some of the children labeled as hyperactive, or hyperkinetic, or afflicted with minimal brain dysfunction actually possessed an elevated sensitivity to certain elements of the diet.

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Developing Mixed Methods Research with Dr. John W. Creswell

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We investigated whether minority children attending U.S. elementary and middle schools are disproportionately represented in special education. We did so using hazard modeling of multiyear longitudinal data and extensive covariate adjustment for potential child-, family-, and state-level confounds. Minority children were consistently less likely than otherwise similar White, English-speaking children to be identified as disabled and so to receive special education services. From kindergarten entry to the end of middle school, racial- and ethnic-minority children were less likely to be identified as having (a) learning disabilities, (b) speech or language impairments, (c) intellectual disabilities, (d) health impairments, or (e) emotional disturbances. Language-minority children were less likely to be identified as having (a) learning disabilities or (b) speech or language impairments.

Keywords at-risk students, disparities, educational policy, elementary schools, hazard modeling, language minorities, longitudinal, middle schools, minority disproportionate representation, racial and ethnic minorities, secondary data analysis, special education, survey research

Racial-, ethnic-, and language-minority schoolchildren in the United States have repeatedly been reported to be overidentified as disabled and so disproportionately overrepresented in special education (e.g., Artiles, 2003; Dunn, 1968; Harry, Arnaiz, Klinger, & Sturges, 2008; Oswald, Coutinho, Best, & Singh, 1999; Sullivan & Bal, 2013). These findings have led to characterizations of special education as "discriminatory" ( Skiba, Poloni-Staudinger, Simmons, Feggins-Azziz, & Chung, 2005, p. 142), having "systemic bias" ( Oswald, Coutinho, Best, & Nguyen, 2001, p. 361), constituting "a new legalized form of structural segregation and racism" ( Blanchett, 2006, p. 25), and "another manifestation of institutionalized racism" ( Codrington & Fairchild, 2012, p. 6). Federal legislation and policies have been enacted to reduce minority disproportionate representation (MDR) in special education (e.g., Posney, 2007; U.S. Department of Education, Office of Civil Rights, 2009). For example, the U.S. Congress observed that "more minority children continue to be served in special education than would be expected from the percentage of minority students in the general school population" (p. 118 of Statute 2651, Public Law 108-446).

Local educational agencies (LEAs) are currently required to report to the U.S. Department of Education whether minority children are significantly overrepresented in special education. If overrepresentation is observed, the LEAs are required to allocate 15% of their Part B funds for early intervening services to children from minority groups being overidentified as disabled. The federal government is currently considering enacting further compliance monitoring policies ( Government Accountability Office, 2013; U.S. Department of Education, 2014). Calls are also being made to strengthen existing monitoring policies ( Skiba, Albrech, & Losen, 2013), including requiring LEAs to report any disproportionality instead of reporting disproportionality resulting only from incorrectly applied procedures or policies ( Albrecht, Skiba, Losen, Chung, & Middelberg, 2012).

Yet existing theoretical and empirical work is contradictory as to whether minority children in the United States are disproportionately represented in special education. Some mechanisms have been theorized to result in overidentification (e.g., Harry et al., 2008); others in underidentification (e.g., Coll, Crnic, Lamberty, & Waski, 1996; Hibel, Farkas, & Morgan, 2010). Some empirical studies find that minority children are overidentified as disabled (e.g., Oswald, et al., 1999; Sullivan & Bal, 2013). Others find that minority children are underidentified (e.g., Hibel et al., 2010; Morgan, Farkas, Hillemeier, & Maczuga, 2012; Morgan, Staff, Hillemeier, Farkas, & Maczuga, 2013; Shifrer, Muller, & Callahan, 2011; Sullivan, 2013) as well as less likely to be referred for services ( Delgado & Scott, 2006). Below, we briefly survey this contradictory theoretical and empirical work. We then identify methodological and substantive limitations in the existing empirical studies to justify the current study.

Mechanisms theorized to result in racial-, ethnic-, and language-minority children being overrepresented in special education include cultural, linguistic, and racial bias. For example, minority children are believed to experience systemic prejudice that results in their abilities and behaviors being unjustifiably characterized as problematic and atypical ( Coutinho & Oswald, 2000; Harry et al., 2008; Hays, Prosek, & McLeod, 2010; Lorsen & Orfield, 2002; O'Connor & Fernandez, 2006), resulting in segregation into special education ( Blanchett, 2006; Reid & Knight, 2006).1 Prior to possibly becoming sensitive to minority overrepresentation, teachers may have been more likely to refer minority students for special education (e.g., Hosp & Reschly, 2003), although early evidence of this tendency is also contradictory ( Skiba et al., 2008; Tobias, Cole, Zibrin, & Bodlakova, 1982; Tobias, Zibrin, & Mennell, 1983). Schools have been theorized to use academic and behavioral standards aligned more with White, English-speaking, middle-class, privileged populations ( Blanchett, Klingner, & Harry, 2009). Racial-, ethnic-, and language-minority children may be less likely to meet these standards as a result of receiving lower-quality educations provided at underresourced schools (e.g., Peske & Haycock, 2006) and so more likely to be identified for special education. Minority children in the United States are also more likely to be exposed to biological and environmental factors in early childhood (e.g., fetal alcohol syndrome, low birth weight, lead exposure, poverty) that disproportionately increase their risk for impaired cognitive, academic, and behavioral functioning and disability ( Annie E. Casey Foundation, 2014; Donovan & Cross, 2002; Mann, McCartney, & Park, 2007).

Consistent with mechanisms theorized to result in overrepresentation, Skiba et al.'s (2005) analyses of cross-sectional state-level data and statistical control for school- and district-level confounds indicated that Black and Hispanic children were overrepresented in special education. This was the case for multiple disability conditions, including intellectual disabilities, emotional disturbances, speech or language impairments, and learning disabilities. Oswald et al.'s (1999) analyses of cross-sectional, nationally representative, and district-level data with statistical control for district-level educational, economic, and demographic confounds indicated that minorities were overrepresented in special education. Minority children were overidentified as having intellectual disabilities and emotional disturbances. Sullivan and Bal's (2013) cross-sectional analyses of an urban school district and statistical control for child- and school-level potential confounds indicated that Black children were more likely to be identified for special education services. Follow-up analyses indicated that Black children were overidentified for learning disabilities, although they were no more likely than White children to be identified as having cognitive impairments, speech or language impairments, other health impairments, or emotional disturbances and were underidentified as having intellectual disabilities. Samson and Lesaux's (2009) analyses of a nationally representative, longitudinal data set with statistical control for individual- and family-level potential confounds indicated that language-minority children were overrepresented in special education by third grade, although they were underrepresented during both kindergarten and first grade. Sullivan and Bal (2013) also reported that Hispanic children were underrepresented in special education.

Other mechanisms have been theorized to result in racial-, ethnic-, and language-minority children being underrepresented in special education ( Hibel et al., 2010; Morgan, Farkas, et al., 2012; Rosenberg, Zhang, & Robinson, 2008; Samson & Lesaux, 2009), including socioeconomic, linguistic, and/or cultural obstacles that constrain access by minority families to special education services ( Danesco, 1997; Coll et al., 1996; Harry, 1992; O'Hara, 2003; Pena & Fiestas, 2009). Some minority families may prefer to rely on the social support of extended families to assist their children and so may not agree to evaluation requests for special education ( Coll et al., 1996). Cultural factors may also result in some minority groups attributing their children's perceived lower academic behavioral functioning to systemic prejudice or other nonbiological explanations ( Danesco, 1997; Yeh, Forness, Ho, McCabe, & Hough, 2004; Yeh, Hough, McCabe, Lau, & Garland, 2004). The stigma associated with disability identification may further reduce a racial-, ethnic-, or language-minority family's willingness to have their child identified as disabled ( Hervey-Jumper, Dougan, & Franco, 2008; O'Hara, 2003; Zuckerman et al., 2014), particularly for conditions considered highly stigmatizing (e.g., intellectually disabled). Minority families may experience fewer interactions with pediatricians and other health professionals who often diagnose disorders (e.g., autism) during early childhood ( Palfrey, Singer, Walker, & Butler, 1987). This may occur as a result of less access to health care ( Flores & Committee on Pediatric Research, 2010; Inkelas, Raghavan, Larson, Kuo, & Ortega, 2007) as well as language barriers ( Flores & Tomany-Korman, 2008; Zuckerman et al., 2014). Children attending disadvantaged schools may also be less likely to be identified as displaying atypical academic or behavioral functioning ( Delpit, 1995). For example, Hibel et al. (2010) theorized that minority children experience "frog pond" effects in regard to special education eligibility. Specifically, attending poorly resourced schools may result in referrals for special education only for those children displaying unusually low academic achievement or behavior relative to other, lower-performing children attending these same schools.

Consistent with mechanisms posited to result in underrepresentation, Hibel et al.'s (2010) analyses of a nationally representative sample followed from kindergarten entry to the end of fifth grade indicated that, following extensive statistical control, Black, Hispanic, and Asian children were less likely to receive special education services than otherwise similar White children. Follow-up analyses indicated that minority children were less likely to be identified as having learning disabilities, speech or language impairments, and intellectual disabilities. Morgan, Farkas, et al.'s (2012) analyses of a population-based, longitudinal sample of preschool-age children found that those who were Black or from homes where a language other than English was spoken were less likely than otherwise similar White children or those from English-speaking homes to be identified as having developmental delays or disabilities. Shifrer et al.'s (2011) analyses of a nationally representative sample of high school students with statistical control for individual-, family-, and school-level potential confounds indicated that Blacks were less likely than otherwise similar Whites to be identified as having learning disabilities. Yeh, Forness, et al.'s (2004) analyses of a large sample of children receiving mental health services with statistical control for child- and family-level factors indicated that Hispanic children were less likely to receive special education services as a result of being underidentified as having emotional disorders.

Extant Empirical Work's Methodological and Substantive Limitations

The relatively fewer studies using extensive covariate adjustment consistently find that racial-, ethnic-, and language-minority children are instead underidentified as disabled (e.g., Hibel et al., 2010; Morgan et al., 2013; Morgan, Farkas, et al., 2012; Shifrer et al., 2011; Yeh, Forness, et al., 2004). Covariate adjustment has sometimes resulted in the estimates reversing direction from over- to underrepresentation ( Hibel et al., 2010; Shifrer et al., 2011). Estimates reported by those education researchers using extensive covariate adjustment for child-, family-, and school-level control variables are also consistent with estimates reported by public health researchers, who often use covariate adjustment when investigating disparities in disability identification and treatment (for a review, see Flores & Committee on Pediatric Research, 2010). For example, children who are Black have been found to be less likely to be diagnosed with autism, learning disabilities, and attention-deficit/hyperactivity disorder following covariate adjustment for IQ, prior academic achievement and behavior, maternal education, and additional factors (e.g., Bussing, Zima, Gary, & Garvan, 2003; Mandell, Listerud, Levy, & Pinto-Martin, 2002; Morgan et al., 2013; Pastor & Reuben, 2005). Furthermore, those minorities who receive a diagnosis have been reported to be disproportionately less likely than otherwise similar White children to make use of empirically based treatments for their disorders ( Morgan et al., 2013).

We systematically investigated whether and to what extent racial-, ethnic-, or language-minority children in the United States were over- or underidentified as disabled relative to otherwise similar White, English-speaking children as they attended elementary and middle schools. To address methodological and substantive limitations in the existing knowledge base, we (a) analyzed longitudinal data collected on a nationally representative sample followed from kindergarten entry to the end of middle school, (b) used hazard modeling to estimate over-time dynamics of disability identification across five specific conditions, and (c) extensively corrected for child- and family-level variables that might confound the directional estimates of disproportionality uniquely attributable to children's status as racial, ethnic, or language minorities. Collectively, these analyses should yield rigorously derived estimates of the likelihood that racial-, ethnic-, or language-minority children in the United States are disproportionately over- or underidentified as having disabilities throughout elementary and middle school and so whether they are more or less likely to be receiving special education services than otherwise similar White, English-speaking children.

The study's sample included children participating in the Early Childhood Longitudinal Study-Kindergarten Cohort, 1998-1999 (ECLS-K; Tourangeau, Lê, Nord, & Sorongon, 2009). The ECLS-K is a nationally representative cohort of U.S. schoolchildren who entered kindergarten in the fall of 1998 and were then surveyed during the spring of kindergarten, fall and spring of first grade (with data from only a random subsample collected during the fall of first grade), and the springs of third, fifth, and eighth grade. Data were collected from children, parents, and teachers. Our hazard modeling analyzed data from a sample of 20,100 kindergarten children with complete information on race/ethnicity, gender, and age at the fall-of-kindergarten survey wave. Table 1 displays the means of the study's criterion and predictor variables. Additional detail regarding the ECLS-K's study design is available from the National Center for Education Statistics (NCES; see http://nces.ed.gov/ecls/kindergarten.asp). Our analyses were weighted to produce results generalizable to the population of U.S. schoolchildren who entered kindergarten in 1998 and who continued through school until at least to the end of eighth grade.2

Descriptive Statistics for Selected Variables (Weighted), N = 20,100

We identified students receiving special education services as a result of having one of five Individuals with Disabilities Education Act (IDEA)-eligible disabilities. In kindergarten and in first, third, fifth, and eighth grades, a child's special education teacher was asked, "For which of the following disabilities did this student receive special education or related services this school year?" Response options included (a) learning disability, (b) speech or language impairment, (c) mental retardation, (d) health impairment (e.g., attention-deficit/hyperactivity disorder, Tourette's syndrome), and (e) serious emotional disturbance.3 These five disability categories were the most frequently identified reasons why children were receiving special education services in the ECLS-K. The percentage of all children participating in the ECLS-K with these IDEA-eligible identified disabilities ranged from a high of 6.4% (speech or language impairment) to a low of 0.7% (intellectual disability)

Parents reported on their child's gender and race/ethnicity. We analyzed four racial and ethnic categories. These were (a) Hispanic, (b) non-Hispanic Black, (c) non-Hispanic White, and (d) other race/ethnicity (e.g., including Asian, American Indian, Alaskan Native). The mother's marital status was reported in fall kindergarten.

The mother's age at the time of birth was calculated from the child's and mother's ages recorded at the date of interview in spring of kindergarten. We considered mothers as being younger mothers if they gave birth when they were 17 years old or younger. We considered mothers as being older mothers if they gave birth when they were 39 years old or older. Parents reported whether the child was born at low birth weight (i.e., less than 5 lbs.) and whether the child had health insurance. We used whether a language other than English was the language spoken during the ECLS-K home interview as an indicator of language-minority status.4 We also included the ECLS-K's multivariate measure of the family's socioeconomic status (SES). This included the mother's and father's education levels and occupations along with household income. To allow for nonlinear effects of SES, our analyses used dummy variables for SES quintiles. Dummy variables for the states of the children's schools were also included as controls. Sample members were drawn from almost all states. These time-invariant variables were measured during the spring of kindergarten.

The frequencies of children's externalizing and self-regulatory behaviors were rated independently by their general education teachers in kindergarten and first, third, and fifth grades using the Social Rating Scale (SRS). The SRS is a modified version of the Social Skills Rating Scale (SSRS; Gresham & Elliott, 1990), a psychometrically validated behavioral measure. The test-retest correlation of the original SSRS was .85 ( Gresham & Elliott, 1990). Correlational and factor analyses support the measure's construct validity ( Feng & Cartledge, 1996; Furlong & Karno, 1995). NCES subsequently modified the SSRS (e.g., adding some items, expanding the response format from a 3-point to a 4-point scale; Pollack, Atkins-Burnett, Najarian, & Rock, 2005). Teachers used a frequency scale to rate how often children in their class displayed a particular social skill or behavior (i.e., 1 = never; 4 = very often). The Externalizing Problem Behaviors subscale measured the frequency of a child's acting-out behaviors, including how frequently the child argued with a teacher, fought, showed anger, acted impulsively, and disturbed the classroom. This subscale's reliabilities ranged from .86 to .89 across the survey waves. The Approaches to Learning subscale measured how frequently a child self-regulated and managed his or her behavior while completing classroom tasks. These self-regulatory behaviors included how well a child paid attention, kept belongings organized, worked independently, showed eagerness to learn new things, easily adapted to changes in routine, and persisted in completing tasks. This subscale's reliabilities ranged from .89 to .91 across the survey waves. We averaged the ratings if more than one teacher provided them in a given year. The SRS was not administered in eighth grade. To statistically control for the frequency of an eighth-grade child's self-regulatory and externalizing problem behaviors, we imputed the two SRS subscale ratings obtained from the prior (i.e., fifth-grade) survey wave. Prior work has established that elementary schoolchildren's behavioral functioning is stable over time (e.g., Lin et al., 2013; Shaw, Lacourse, & Nagin, 2005).

During each survey wave, children's academic achievement was estimated by averaging their scores from general measures of both their reading and mathematics achievement. Each of these two achievement tests was individually administered, untimed, and extensively psychometrically validated. Scores on the measures are relative to those of all other children participating in the ECLS-K. Item response theory and routing procedures were used for both measures to maximize academic content coverage and to obtain scale scores specifically designed by NCES to be comparable across the ECLS-K's survey waves. The Reading Test evaluated a range of reading skills across time (e.g., sight word reading, decoding, vocabulary, reading comprehension). After first grade, the emphasis on basic reading skills was decreased, and greater emphasis was given to reading comprehension skills. All items were field-tested. Theta reliabilities for the kindergarten to eighth-grade assessments ranged from .87 to .96 ( NCES, 2009). As an indication of construct validity, the Reading Test scores of first-grade students correlated at .85 or above with their scores on the Kaufman Test of Educational Achievement Reading subtest ( Rock & Pollack, 2002). The Mathematics Test evaluated a wide range of skills over time (e.g., place value, ordinality, sequence, basis operations, measurement, fractions). Reliabilities of the Mathematics Test's theta scores are in the mid .90s for each of the assessment waves ( NCES, 2009; Pollack et al., 2005). As with the Reading Test, there was little differential item functioning, nor was there evidence of floor or ceiling effects. We standardized scores on the time-varying measures for ease of interpretation.

We used discrete-time logit (hazard) regression models for event history analysis to identify factors predicting children's identification as disabled from kindergarten to eighth grade ( Singer & Willet, 2003). Importantly, we estimated separate regressions for each of the five disability diagnoses under study. (Each of these is a dichotomous variable for diagnosed with the particular disability or not.) These diagnoses are mutually exclusive at each time point. These models estimated the hazard of a child being identified with the specific disability being examined, conditional upon not having been previously identified with this specific disability. Once identified, the child was censored from the sample for this specific disability condition but not from the regressions for the other disability conditions. Thus, for example, if a child was diagnosed with speech or language impairment in first grade, then that child was censored from the subsequent speech-or-language-impairment condition regressions in third, fifth, and eighth grades. However, the child may subsequently have been identified for another disability condition (e.g., learning disability, emotional disturbance) in the regressions for the other four disability conditions. Thus, children's risk for disability identification was fully modeled across the five disability conditions and time periods examined.

Hazard models are advantageous over models that predict disability identification at only a single time point. This is because hazard models more appropriately model disability identification as a dynamic, over-time process. These models measure the duration until diagnosis, thus allowing differential rates of diagnosis for different racial, ethnic, and language-use groups to be appropriately modeled. These models also allow for the use of time-varying covariates. Use of time-varying covariates better corrects for potentially confounding variables occurring at the same time that children were identified as disabled, thereby allowing for more closely matched White, English-speaking children when estimating the risk of being identified as disabled throughout elementary and middle school that is uniquely attributable to children's status as racial, ethnic, or language minorities.

We first prepared the data by using SAS 9.3 (SAS Institute, Cary, NC) and then converted the data file to Stata using Version SE 12.1 (StataCorp, College Station, TX). We then modeled the data using the Stata add-on procedure dthaz. We estimated two hazard models for each of the study's five specific disability conditions. For Model 1, we included only race or ethnicity and time as predictors for the specific disability condition. Using time as a predictor allowed us to model the probability of receiving special education services for each of the disability conditions from kindergarten entry to the end of eighth grade. Model 1 results show the relative frequency of disability diagnosis for children from the racial and ethnic groups before these relative frequencies are adjusted for covariates such as individual child-level academic achievement or behavioral functioning or family-level SES, which may be associated with disability diagnosis and on which the groups of children differ. In Model 2, we then included additional individual child- and family-level covariates measured in kindergarten, as well as time-varying measures of children's behavioral and academic functioning measured from kindergarten to eighth grade, for each specific disability condition. This relatively large number of control variables was necessary so that our directional estimates of the likelihood of disability identification uniquely attributable to children's status as racial, ethnic, and language minorities were adjusted so as to make the children as otherwise similar as possible. We reported these estimates as odds ratios (ORs). Odds are the ratio of the probability that an event occurs to the probability that it does not. For our estimates of disparities in children's disability identification, the OR is (a) the odds that one particular group (e.g., children who are Black) will experience versus not experience an event (e.g., being identified as having learning disabilities) versus (b) the odds of another group (e.g., children who are White) will experiences versus not experience the same event (see Szumilas, 2010, for a brief primer). Odds coefficients of 1.0 indicate no effect of the predictor variable on the outcome. Odds above 1.0 indicate a positive effect; those below 1.0, a negative effect.

Table 2 shows weighted estimates from the discrete-time logit regression modeling of disability identification for each of the five specific conditions. Model 1's results vary considerably and do not yield any evidence that racial- and ethnic-minority children are statistically significantly overidentified as having disabilities. Instead, and prior to extensive covariate adjustment, Model 1's results indicate that racial and ethnic minorities are underidentified as having speech or language impairments as well as health impairments.

Discrete-Time Logistic Regression Models (Adjusted Odds Ratios) of IEP Categories

Model 2 extensively adjusts for potentially confounding factors, including family-level SES and individual child-level academic achievement and behavioral functioning. Doing so indicates that racial- and ethnic-minority children are less likely than otherwise similar White children to be identified as having disabilities, and these results are statistically significant. For example, Black children are 58% (covariate adjusted OR = .42), 63% (covariate adjusted OR = .37), 57% (covariate adjusted OR = .43), and 77% (covariate adjusted OR = .23) less likely than otherwise similar White children to be identified as having learning disabilities, speech or language impairments, intellectual disabilities, and health impairments, respectively. Black children are 64% (covariate adjusted OR = .36) less likely to be identified as having emotional disturbances than otherwise similar White children.

Hispanic children are 29% (covariate adjusted OR = .71) less likely than White children to be identified as having learning disabilities, 33% (covariate adjusted OR = .67) less likely to be identified as having speech or language impairments, and 73% (covariate adjusted OR = .27) less likely to be identified as having health impairments. These results indicate that Hispanic children are identified with disabilities more frequently than Black children but significantly less often than otherwise similar White children. Model 2's results also indicate that language minorities are 28% (1 − 0.72) and 40% (1 − 0.60) less likely than otherwise similar children from English-speaking households to be identified as having learning disabilities or speech or language impairments, respectively. However, these children are not significantly more or less likely than other children to be identified as having intellectual disabilities, health impairments, or emotional disturbances.

Model 2's results also indicate that factors other than children's status as racial, ethnic, or language minorities are associated with their risk of disability identification. Children from families without health insurance are less likely to be identified as having speech or language impairments. Particularly notable as well is that higher academic achievement and greater behavioral self-regulation consistently lower children's likelihood of being identified as disabled. More frequent externalizing problem behaviors decrease children's likelihood of being identified as having learning disabilities or speech or language impairments. These behaviors increase children's likelihood of being identified as having health impairments or emotional disturbances. Collectively, the findings suggest that U.S. schools are likely to identify children as disabled on the basis of their academic achievement, behavioral self-regulation, and externalizing problem behaviors.

Figures 1 and 2 display the covariate-adjusted hazard and survival functions, respectively, of each of the specific disability conditions.5 The hazard functions plot the probabilities of being identified for the specific disability condition for the particular time period for those children who have not yet been identified with the specific condition. The survival curves measure, at each time period, the percentage of children who have not as yet been identified with the specific disability condition. Thus, for example, for two groups of 1,000 otherwise similar White and Black children who begin in kindergarten, the survival function indicates that approximately 99% will be estimated not to have been identified with a learning disability by the spring of first grade. In contrast, 97% of the Black children but 94.5% of the White children will have survived not being identified as having learning disabilities by the end of eighth grade.

These estimates are adjusted for each condition's Model 2 control variables. Figure 1 shows that young children's likelihood of being identified as having learning disabilities steadily increases and then peaks in fifth grade, after which point it begins to decline. In contrast, children's likelihood of being identified as having speech or language impairments declines steadily over time. Intellectual disabilities display no clear over-time pattern. Children's likelihood of being identified as having health impairments or emotional disturbances increases over time. Some of these rates are very low as a result of relatively low prevalence of the disabilities in the general population of U.S. schoolchildren. Yet the estimated rates also continue to indicate that, after extensive covariate adjustment, Hispanic and Black children are less likely than otherwise similar White children to be identified with disabilities while attending elementary and middle schools in the United States.

Contrary to federal legislative and policy efforts to reduce MDR in special education, our study failed to find any evidence that racial-, ethnic-, or language-minority children in the United States are being disproportionately overrepresented in special education. This lack of overrepresentation was evident both prior to and following extensive covariate adjustment. Instead, results from our analyses of longitudinal and nationally representative data consistently indicate that racial-, ethnic-, and language-minority schoolchildren in the United States are less likely than otherwise similar White, English-speaking schoolchildren to be identified as disabled and so are comparatively underrepresented in special education. Minority children's underrepresentation was evident across the entire elementary and middle school time periods. Racial and ethnic minority children in the United States are less likely to receive special education services as a result of being identified as having learning disabilities, speech or language impairments, intellectual disabilities, health impairments, or emotional disturbances. Language-minority children are less likely to be identified as having learning disabilities or speech or language impairments. Our estimates of minority disproportionate underrepresentation were extensively corrected for potential confounding variables, including individual child-level academic achievement and behavioral functioning as well as family-level SES.

Study's Contributions and Implications

Long-standing and ongoing federal legislation and policymaking have attempted to reduce minority disproportionate overrepresentation in special education. This is because, to date, the extant education research has largely reported that minority children are more likely to be identified as having disabilities. This has led to characterizations of special education as "discriminatory" ( Skiba et al., 2005, p. 142), maintaining "a new legalized form of structural segregation and racism" ( Blanchett, 2006, p. 25), and constituting "institutionalized racism" (Codrington & Fairchild, 2102, p. 6). Yet, and despite these characterizations, our analyses of a longitudinal sample of U.S. schoolchildren followed from kindergarten entry to the end of middle school failed to yield any evidence of minority overrepresentation in special education. Instead, and consistent with those few studies by education researchers employing extensive covariate adjustment for individual student-, family-, and school-level variables (e.g., Hibel et al., 2010; Morgan, Farkas, et al., 2012; Shifrer et al., 2011), as well as with studies by public health researchers who have also used extensive covariate adjustment ( Bussing et al., 2003; Flores & Committee for Pediatric Research, 2010; Mandell et al., 2002), our study's analyses indicated that minority children are underidentified as having disabilities and so are less likely to be receiving special education services than otherwise similar White, English-speaking children in the United States. This study's findings are not consistent with characterizations of special education as racist or discriminatory due to predispositions to label racial-, ethnic-, or language-minority children as disabled in order to segregate them in classrooms separate from their White, English-speaking classmates. Nor are they consistent with long-standing and ongoing federal legislation and policies designed to reduce minority overrepresentation in special education.

Our findings also extend the field's knowledge base by establishing that, although lower health care access and non-English use are each predictive of a lower likelihood of disability identification, neither factor fully explains the underidentification attributable to children's status as racial, ethnic, or language minorities ( Zuckerman et al., 2014). Our results also suggest that future investigations of minority disproportionate representation are likely to report biased estimates of minority disproportionate representation if they fail to account for the strong potential confounding variables of individual child-level academic achievement and behavioral functioning. These factors, as well as other sociodemographic characteristics (e.g., age, gender) are both consistently and uniquely related to U.S. schoolchildren's likelihood of being identified as disabled. Prior research has reported that the directionality of disproportionate representation varies depending on the type of disability condition under investigation, although these studies have often not accounted for individual child-level academic achievement and behavioral functioning ( Donovan & Cross, 2002; Skiba et al, 2005; Sullivan, 2011; Sullivan & Bal, 2013). Those studies adjusting for individual- and family-level as well as school-level potential confounds consistently indicate the minority children are underrepresented in special education, with this underrepresentation evident both generally and for specific disability conditions and both prior to and following school entry ( Hibel et al., 2010; Morgan, Farkas, et al., 2012; Shifrer et al., 2011). Our results indicate that racial- and ethnic-minority children who are otherwise similar to White children are consistently less likely to be identified as disabled whether the specific condition being investigated is learning disabilities, speech or language impairments, intellectual disabilities, health impairments, or emotional disturbances. Our study extends this prior work by establishing that the underrepresentation is evident across multiple disability conditions and over time when examined in the same cohort followed from the beginning of elementary school to the end of middle school. Thus, and consistent with mechanisms suggesting that sociocultural factors may collectively result in barriers to access to services ( Coll et al., 1996; Danesco, 1997; Harry, 1992; O'Hara, 2003), our findings suggest that racial- and ethnic-minority children experience underrepresentation across time and for a wide range of disability conditions.

In contrast, our findings indicate that language-minority children's underrepresentation is specific to the language-related disorders of learning disabilities and speech or language impairments, possibly as a result of these children's lower proficiency in English as well as a lack of availability and use of proper diagnostic assessments for these types of disorders with non-English speakers ( Figueroa & Newsome, 2006; Hibel & Jasper, 2012; Linan-Thompson, 2010). Elementary schoolchildren who are language minorities have also been hypothesized to follow a specific over-time dynamic of disproportionate representation, in which they are initially underidentified as having disabilities but then begin to be overidentified as teachers begin attributing the children's lower achievement to factors other than limited English proficiency ( Hibel & Jasper, 2012; Samson & Lesaux, 2009). Yet, and possibly due to our use of a wider set of covariates, we find no evidence of this dynamic. Instead, we find that, following extensive statistical control, including for individual-level academic achievement and behavioral functioning, language-minority children continue to be underidentified for learning disabilities and speech or language impairments throughout elementary and middle school.

Our findings support policies and practices that result in increased use by practitioners of culturally and linguistically sensitive special education evaluation methods. Use of these methods may be necessary to ensure that special education eligibility procedures do not result in unwarranted overrepresentation of White, English-speaking children. This overrepresentation may result from teachers, school psychologists, and other professionals being differentially responsive to White, English-speaking parents. Pediatricians have already been reported to be more likely to solicit developmental concerns about children from White parents ( Guerrero, Slusser, Barreto, Rosales, & Kuo, 2011). Minority children have been reported to be less likely to be using empirically validated treatments than White children ( Morgan et al., 2013). From a social justice standpoint, if special education's eligibility procedures are being disproportionately responsive to children from White, English-speaking families, then children from racial-, ethnic-, or language-minority families may not be experiencing the same access to potentially beneficial special education services for which they are legally entitled. This in turn may maintain or exacerbate the nation's long-standing educational inequalities, including achievement gaps. Minority children's lack of treatment for undiagnosed disorders and disabilities has been hypothesized to at least partially account for these achievement gaps ( Basch, 2011). Educational professionals should be attentive to cultural and language barriers that may be keeping minority children from being appropriately evaluated and identified for disabilities. Use of interpreters as well as appropriately translated written materials may assist with this process ( Brotanek, Rocha, & Flores, 2008). Due-process pamphlets describing disability eligibility procedures are often written in less accessible language, particularly for parents of lower-SES backgrounds ( Fitzgerald & Watkins, 2006), who are more likely to be minorities ( U.S. Census Bureau, 2012). School-to-community outreach programs may be another possible method of addressing cultural and linguistic barriers to disability identification and treatment. Such outreach programs have helped reduce racial and ethnic disparities in children's health and health care access ( Flores, 2009; Madsen, Thompson, Adkins, & Crawford, 2013).

For policymakers, our results suggest that current federal educational legislation and policymaking designed to minimize overidentification of minorities in special education may be misdirected ( Government Accountability Office, 2013; U.S. Department of Education, 2014), including the reallocation of Part B funding to early intervening services designed to reduce minority overrepresentation in special education. Our analyses of a nationally representative and multiyear data set failed to yield any evidence that minority overrepresentation is currently occurring. Instead, we consistently found that racial-, ethnic-, and language-minority schoolchildren are comparatively underidentified as having disabilities and so are underrepresented in special education as they attend elementary and middle schools in the United States. Consequently, federal legislation and policies may be inadvertently exacerbating educational inequities by reducing access to special education services for eligible schoolchildren who are racial, ethnic, or language minorities. Federal legislation and policies that lower cultural and language barriers resulting in these racial, ethnic, and language disparities may be necessary to ensure that all U.S. schoolchildren with disabilities are provided with the special education services for which they are legally entitled.

Notes

Costello, E. J., Angold, A., Burns, B. J., Stangl, D. K., Tweed, D. L., Erkanli, A., Worthman, C. M. (1996). The Great Smoky Mountains study of youth: Goals, design, methods, and the prevalence of , DSM-III-R disorders. Archives of General Psychiatry, 53, 1129-1136. doi:10.1001/archpsyc.1996.01830120067012 Google Scholar Crossref, Medline

Finkel, S. E. (1995). Causal analysis with panel data. Thousand Oaks, CA: Sage. , Google Scholar Crossref

Furlong, M., Karno, M. (1995). Review of the Social Skills Rating System. In Conoley, J. C., Impara, J. C. (Eds.), Twelfth mental measurements yearbook (pp. 697-969). Lincoln, NE: Buros Institute of Mental Measurement. Google Scholar

Oswald, D. P., Coutinho, M. J., Best, A. M., Nguyen, N. (2001). Impact of socio-demographic characteristics on the identification rates of minority students as having mental retardation. Mental Retardation, 39, 351-367. , Google Scholar Crossref, Medline

Posney, A. (2007, Aptil). Memorandum on disproportionality of racial and ethnic groups in special education (OSEP 07-09) sent to state directors of special education. Retrieved from http://www2.ed.gov/policy/speced/guid/idea/letters/revpolicy/tpmeth.html Google Scholar

Singer, J. D., Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford. , Google Scholar Crossref

Authors

RICHARD MATTISON, MD, is a professor of psychiatry at Pennsylvania State Milton S. Hershey Medical Center, Hershey, PA 17033; rmattison@hmc.psu.edu . His work examines psychiatric disorders in special education students.

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Minorities Are Disproportionately Underrepresented in Special Education

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We investigated whether minority children attending U.S. elementary and middle schools are disproportionately represented in special education. We did so using hazard modeling of multiyear longitudinal data and extensive covariate adjustment for potential child-, family-, and state-level confounds. Minority children were consistently less likely than otherwise similar White, English-speaking children to be identified as disabled and so to receive special education services.

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Simple changes in nutrition - like cutting back on sugary snacks - could bring out the sweeter side in your child. It may even help her control challenging ADHD symptoms like impulsivity and inattention.

Boy with ADHD sits at table with stack of sugary donuts in front of him

Chances are, you've had the following chat with the doctor of your child with ADHD - probably just before the holidays...

"Every time Johnny has lots of sugar in his diet, his symptoms of ADHD worsen, and he becomes irritable and hyper. I dread this season because Johnny turns it into unhappy days for everyone."

Your doctor leans back in his leather chair and says, "What your child eats has nothing to do with his behavior! There is no research that supports a link between sugar and ADHD."

Think again.

How Does Sugar Impact ADHD?

While some studies have found no correlation between refined sugar and increased hyperactivity in children with attention deficit hyperactivity disorder ( ADHD or ADD), other studies on nutrition suggest that some kids with ADHD are "turned on" by copious amounts of sweet stuff.

A study conducted by the University of South Carolina concluded that the more sugar hyperactive children consumed, the more destructive and restless they became. A study conducted at Yale University indicates that high-sugar diets may increase inattention in some kids with ADHD.
[Free Guide: What to Eat (And Avoid) to Improve ADHD Symptoms]
So where does this leave you, in between birthday cupcakes, holiday treats, and summer ice cream? Don't panic. The first thing to do is to determine whether sugar affects your child the way it affected children in the studies. Start by removing as much sugar, corn syrup, and other natural sweeteners as you can from his diet for 10 days - a difficult but doable task!

On the eleventh day, give him a sugar test, stirring a tablespoon of sugar into 100 percent pure fruit juice. Does he kick it up a gear or three in terms of hyperactivity? Does he have less ability to focus? If yes, you have your answer.

Healthy Holidays

It's time to go on sugar alert. But how can you possibly cut back on sweets during the holidays? Here are some strategies:

Set a good example at Halloween by giving out sugar-free chewing gum - or inedible items, like fancy pencils or nickels or dimes. If candy is a must, then look for white peppermints sweetened with sorbitol.
[Cutting Back on Sugar? Amp Up These Vitamins and Minerals Instead!]
Manage the candy your child does bring home from trick or treating. You can make a deal beforehand about exactly how much Halloween candy he can consume each day. Or you can "buy" the candy from your child, so he can purchase something else he really wants.

Substitute nuts, a platter of fresh veggies, or colorful fruits with tasty dips for the chocolate-covered cherries and candy corn served at gatherings.

Instead of sugary cookies, try Pepperidge Farm Chessmen - one of the few commercial varieties on the market low in sugar and free of food dyes.

Serve chilled punch, made with 100 percent fruit juice, in holiday cups. Avoid fruit "drinks" or "cocktails," both of which are higher in sugar. When serving juice, accompany it with sandwiches made of meats or poultry on whole-grain bread. The protein in the meat and the fiber in the whole grain will help maintain steady blood sugar levels.

Sounds like a lot of work, and it is. But if your child is less hyperactive and inattentive, everyone benefits!

Sugar by Any Other Name

The following ingredients are all code words for sugar:

corn sweetener

corn syrup

corn syrup solids

dehydrated cane juice

dextrin

dextrose

maltodextrin; malt syrup; maltose

molasses

rice syrup

saccharose

sorghum or sorghum syrup

sucrose

¹ Prinz, Robert, et al. "Dietary Correlates of Hyperactive Behavior in Children." Journal of Consulting and Clinical Psychology, vol. 48, no. 6, 1980, pp. 760-769.
² Jones, T W, et al. "Enhanced Adrenomedullary Response and Increased Susceptibility to Neuroglycopenia: Mechanisms Underlying the Adverse Effects of Sugar Ingestion in Healthy Children." The Journal of Pediatrics, vol. 126, no. 2, 1995, pp. 171-177.

Updated on October 31, 2019

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The Sugar Wars: How Food Impacts ADHD Symptoms

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Simple changes in nutrition - like cutting back on sugary snacks - could bring out the sweeter side in your child. It may even help her control challenging ADHD symptoms like impulsivity and inattention. Chances are, you've had the following chat with the doctor of your child with ADHD - probably just before the holidays...

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Abstract

The effect of artificial food colors (AFCs) on child behavior has been studied for more than 35 years, with accumulating evidence from imperfect studies. This article summarizes the history of this controversial topic and testimony to the 2011 Food and Drug Administration Food Advisory Committee convened to evaluate the current status of evidence regarding attention-deficit/hyperactivity disorder (ADHD). Features of ADHD relevant to understanding the AFC literature are explained: ADHD is a quantitative diagnosis, like hypertension, and some individuals near the threshold may be pushed over it by a small symptom increment. The chronicity and pervasiveness make caregiver ratings the most valid measure, albeit subjective. Flaws in many studies include nonstandardized diagnosis, questionable sample selection, imperfect blinding, and nonstandardized outcome measures. Recent data suggest a small but significant deleterious effect of AFCs on children's behavior that is not confined to those with diagnosable ADHD. AFCs appear to be more of a public health problem than an ADHD problem. AFCs are not a major cause of ADHD per se, but seem to affect children regardless of whether or not they have ADHD, and they may have an aggregated effect on classroom climate if most children in the class suffer a small behavioral decrement with additive or synergistic effects. Possible biological mechanisms with published evidence include the effects on nutrient levels, genetic vulnerability, and changes in electroencephalographic beta-band power. A table clarifying the Food and Drug Administration and international naming systems for AFCs, with cross-referencing, is provided.

Electronic supplementary material

The online version of this article (doi:10.1007/s13311-012-0133-x) contains supplementary material, which is available to authorized users.

Keywords: ADHD, Food dyes, Artificial food colors, Hyperactivity, Child behavior, FDA

In March 2011, the United States (U.S.) Food and Drug Administration (FDA) Food Advisory Committee held a hearing on the behavioral effects of synthetic food dyes, technically known as artificial food colors (AFCs). The focus of the meeting was on attention-deficit/hyperactivity disorder (ADHD), so discussion of that disorder was requested as background for understanding the data on AFCs. The controversial committee decision (8-6 vote) was not to recommend banning AFCs or requiring a warning label. This article summarizes relevant background information, further needs for research, and interim conclusions.

AFC Classification Systems

To make them more identifiable, AFCs have both a common name and an official number that may differ from country to country. The International Numbering System (INS) is the world standard for classifying everything associated with food and uses the numbers 100 to 199 for color additives (approved for use or not) from the Codex Alimentarius ("Book of Food") established in 1963 by the Food and Agriculture Organization (FAO) and World Health Organization (WHO). The safety of AFCs and other food additives is regulated by the Joint FAO/WHO Expert Committee on Food Additives ([JECFA] established in 1955.) The European Union (EU) uses the INS and adds an "E" prefix ("E" for "Europe") for both natural and synthetic food colors approved by the European Food Safety Authority ([EFSA] established in 2002). Unlike the EU, in the U.S. the FDA separates synthetically produced colors or AFCs, which are batch-tested for safety ("certified colors," N = 9) from those derived from natural sources in which individual batch testing is not required ("exempt from certification," N = 29). There are 7 certified food colors ("FD&C colors"; FD&C = Food, Drugs & Cosmetics) approved for widespread use in the U.S.: brilliant blue (Blue No. 1b), indigotine (Blue No. 2), fast green (Green No. 3), tartrazine (Yellow No. 5b), sunset yellow (Yellow No. 6b), erythrosine (Red No. 3) and allura red (Red No. 40b). Two other AFCs are approved for specific limited use: citrus red (Citrus Red No. 2) (to color orange rinds) and Orange B (to color weiner/sausage casings). Table lists these 9 AFCs with FD&C, INS, and EU "E" numbers and approval status for each classification and JECFA acceptable daily intakes (ADIs).

Table 1

History of the Food-Dye/AFC-ADHD Controversy

Food coloring with natural substances has been used since approximately 1500 B.C. in Ancient Egypt, and has been regulated from the time of England's King Edward I, in the 13 th century, to control their unsafe and fraudulent use (for more detail see Burrows [ 1] for a fascinating review of the history of food coloring and regulation). The 1856 development of synthetic dyes from petroleum or coal (so-called "coal-tar colors," most of which are still in use today) by the English chemist Sir William Henry Perkin ushered in a series of acts in Europe and the U.S. regulating food colors. The FDA was established in1938 and the Joint FAO/WHO in 1955. Since then, the role of the FDA has been to ensure AFCs safety and prevent its fraudulent use in making food appear better or of greater value than it really is. Although not without criticism, the FDA has dramatically revolutionized AFCs from a time when highly toxic color additives were indiscriminately and widely used to the present when a "food colorant with a 1 in 19 billion chance of causing cancer is legally considered too dangerous" [ 1, p.405].

One of the more current controversies in the field of AFCs is concerned with their effect on children's behavior. Although the idea that food allergies or hypersensitivities lead to behavior and learning problems dates back to the 1920s [ 2], a specific hypothesis regarding this relationship was not developed until the 1970s. In 1973, Dr. Benjamin Feingold [ 3] presented an article at the annual meeting of the American Medical Association, proposing that pediatric hyperactivity and learning problems were due to certain foods and food additives. Based on his own clinical observations, he believed that his patients were often sensitive to foods containing natural salicylates, AFCs, and flavors, and he devised a diet (the "Kaiser Permanente" or "K-P" diet) free of these substances [ 4, 5]. By 1977, [ 6, 7] Feingold also eliminated 2 preservatives (butylated hydroxytoluene and butylated hydroxyanisole, which he thought also led to hyperactivity [ 6] and claimed that 60 to 70 % of the children he treated improved [ 7].

Feingold's [ 8] initial presentation generated a great deal of attention from the media, public, and professionals, and led to his best-selling book, Why Your Child is Hyperactive. However, his work was generally criticized by the medical and pharmaceutical fields [ 9, 10], and in 1975, the food industry, represented by the National Advisory Committee of the Nutrition Foundation, declared, "No controlled studies have demonstrated that hyperkinesis is related to the ingestion of food additives" [ 11]. However, Feingold's work was accepted by many parents, who formed the still-existing Feingold Association of the United States (FAUS) (established in 1976). The first study on AFCs was conducted in 1976 by Conners et al. [ 12] at the University of Pittsburgh. After several additional studies, the National Institutes of Health held a conference on defined diets and hyperactivity in 1982 and concluded that further studies were needed [ 13]. One year later a meta-analysis by Kavale and Forness [ 14] (of 23 controlled group studies) concluded that the overall effect size (ES, 0.11) was too small to be important and the results did not support the K-P diet as a treatment for hyperactivity. This led to a decrease in interest in the K-P diet and the effect of AFCs until 2004 when Schab and Trinh [ 15] published their meta-analysis of 15 double-blind, placebo-controlled studies and found an ES of 0.28 between AFCs and the placebo on ADHD symptoms, in parent ratings, but not teacher or observer ratings, and only when children were preselected as diet responders; however, this pertained to all of the subjects, regardless of whether they were initially hyperactive or not.

Typical Elimination Studies and Examples

Besides a few studies that actually single-blinded an elimination and the "placebo" regular diet, there are many others following a different model, similar in some ways to a drug discontinuation study. Modern studies often start with an oligoantigenic or "few foods" diet for everyone. If there is improvement, then foods or components are openly added back 1 at a time to find the offenders. Then what follows is a double-blind challenge with offenders that can be blinded. Fortunately, AFCs are 1 of the easier things to blind; they are tasteless and can be camouflaged by dark food or drink. Preservatives are also easy to blind, which unfortunately makes it tempting to challenge blindly with mixes of the 2 classes of ingredients. In any event, the ease of blinding makes AFCs (and preservatives) better studied than other dietary components, but they should not be considered the whole problem. For details, see Stevens et al. [ 16].

A few exemplary highlights are these: Egger et al. [ 17], in a sample of 76 hyperactive children, found 48 foods with evidence of deterioration on being added back. Colors and preservatives were the most frequent offenders, but were not the sole problem in most children. In a sample of 200 selected from 800 hyperactive children, 150 openly improved with the elimination of AFCs and deteriorated on their resumption; there were 34 of these children who entered a double-blind challenge with 6 doses of tartrazine and placebo. Twenty-two of the 34 children clearly reacted with irritability, restlessness, and sleep disturbance [ 18]. This study illustrates the denominator problem in trying to determine the prevalence of AFC sensitivity in ADHD: were the 22 proven reactors 60 % of 34, or 15 % of 150, or 11 % of 200, or 2.5 % of 800? If we take 11 % as the most plausible, it highlights another interesting finding in this study: 20 nonhyperactive controls were also challenged at the same time, and 2 of those, or 10 %, also clearly reacted [ 16]. Thus, the controls showed a similar rate of reactivity as the hyperactive children. This presages the findings of the Southampton studies.

Southampton Studies

In 2004 and 2007, there were 3 landmark studies published from Stevenson & colleagues at Southampton University, as reported in 2 articles [ 19, 20]. Two are in preschool [ 19, 20], and 1 in the 8- to 9-year-olds [ 20]. All 3 samples included more than 100 participants each (N = 277, 153, and 144). The samples were nonclinical, acquired in an epidemiological manner (the first invited all the preschoolers on the Isle of Wight). The children were classified as hyperactive or not based on a rating scale and, in the first study [ 19], as atopic or not by a skin prick test. No other diagnostic assessment was done. However, severity assessments were done on double-blind challenges of an AFC mix and placebo using several measures. What was called "hyperactivity" included all 18 Diagnostic and Statistical Manual, fourth edition (DSM-IV) [ 21] ADHD symptoms, not just the hyperactive symptoms. The authors refrained from calling it an ADHD measure because they had not formally diagnosed any of the children.

All children were given an elimination diet free of AFCs and preservatives for 2 weeks, then challenged with mixed fruit juice with or without a mix of AFCs (sunset yellow, tartrazine, carmoisine, ponceau 4R, quinoline yellow, and/or allura red AC) and 45 mg Na benzoate. The first preschool study [ 19] used 5 mg each of sunset yellow, tartrazine, carmoisine, and ponceau 4R (20 mg total). The second preschool study [ 20] used 2 mixes and 2 doses besides the placebo. Mix A had the same AFCs and total dose as the first study, but different proportions (2.5 mg carmoisine, 7.5 mg tartrazine, 5 mg each of SY, and ponceau 4R). Mix B had 7.5 mg each of sunset yellow, carmoisine, quinoline yellow, and allura red AC, totaling 30 mg. The 2 doses for the 8- to 9-year-olds [ 20] were: mix A was the same as the preschool mix A, but 1.25 times as large, totaling 25 mg of AFC. Mix B was the same as preschool mix B, but 2.08 times as large, totaling 62 mg AFC, approximately the 2010 per capita daily consumption of AFCs in the U.S. There was a 1-week placebo washout between challenges with the placebo or AFC/benzoate mix, which were administered in random order.

The blinding was excellent. Preliminary adult tasting panels could not distinguish challenge from the placebo drink after inspecting, opening the sealed bottles, and tasting. Parent guesses of order (placebo-AFC vs AFC-placebo) in the first preschool study [ 19] reflected chance: half guessed correctly, exactly what would be expected by chance for a binary guess.

The main outcome measure for the first study [ 19] was "hyperactivity," a composite of overactivity, inattention, and impulsiveness, rated by parents in all studies and by teachers in the second and third studies [ 20]. The first study [ 19] also had an aggregated test of hyperactivity, composited from psychologist observation and task performance. The second and third studies [ 20] also had classroom observation, and for the 8- to 9-year-olds the Conners Continuous Performance Test. For the second and third studies, a global hyperactivity score was derived from all the measures and used as the primary outcome measure.

Results of the First Study

In the first study [ 19], parent ratings, but not the aggregated psychologist's score, showed a significantly greater increase in "hyperactivity" on active challenge than on placebo by a small-medium difference. Importantly, there was no association with ADHD (defined by rating scale score) or atopy (defined by skin prick). Thus the deleterious effect applied to all children.

Results of 2nd preschool study

Mix A but not Mix B showed a significantly greater increase than placebo in the primary outcome, the Global Hyperactivity score (d = 0.2). Again, there was no interaction with designation of child as hyperactive. Thus this study replicated the first study in a different sample [ 20].

Results for 8-9 year-olds

On intent-to-treat analysis, Mix B (higher dose) but not Mix A showed a significantly greater increase than placebo in Global Hyperactivity score. On analysis of those with 85 % compliance, both mixes showed a significant effect. The effect size was small, d = 0.12-0.2. Again, there was no interaction with baseline hyperactivity; the results of all 3 studies showed a small significant effect for all children, not just those meeting criterion A of DSM-IV ADHD [ 20]. This suggests that food AFCs are more of a public health problem than an ADHD problem.

The results of these studies led to some significant changes in the field of public health, with the United Kingdom government requesting that food manufacturers avoid these additives in favor of natural food colors and flavors, and the EU asking manufacturers to voluntarily remove several AFCs from foods and beverages or list the following warning on the label: "[this AFC] may have an adverse effect on activity and attention in children" [ 22]. In the U.S., the Southampton studies inspired a petition to the FDA from the Center for Science in the Public Interest (CSPI) [ 23] and, along with media interest and congressional support, led the FDA Food Advisory Committee to review the evidence on AFCs and ADHD and have a public hearing on March 30-31, 2011. This committee was given three documents prior to this meeting. One described the charge: "to consider available relevant data on the possible association between consumption of certified color additives in food and hyperactivity in children, and to advise FDA as to what action, if any, is warranted to ensure consumer safety" [ 24]. Another described the FDA's history of food color regulation [ 25], and the third was a literature review of publications on AFCs and ADHD [ 26]. During the hearing the committee heard two days of testimony from several reviewers, experts on ADHD and food colors, members of the public, and representatives of advocacy groups and industry. The committee was given 5 questions. On question #2, "Do the current relevant data support FDA's conclusion, as set forth in the September 1, 2010 Interim Toxicology Review Memorandum, that a causal relationship between consumption of certified color additives in food and hyperactivity or other adverse effects on behavior in children in the general population has not been established?" the committee members voted 79 % yes; 21 % no [ 27]." On question #4, "Should additional information be disclosed on the product label of food containing certified color additives to ensure their safe use? The Committee members voted 43 % yes; 57 % no." [ 27]. Finally, on question #5, the need for additional studies, "The Committee members voted 93 % yes; 7 % no" [ 27].

In 2012 Weiss [ 28] reviewed the FDA's decision [ 27] and noted four flaws in the process. 1). The FDA review confined itself to the relationship between AFCs and the clinical diagnosis of ADHD rather than broader behavioral problems. Weiss stated this was important because most children, not just those with ADHD, consume AFCs; few of the studies investigated a DSM-IV [ 21] diagnosis of ADHD; nearly all of the studies examined short-term rather than long-term effects expected of a chronic disease like ADHD; and narrow-band measures of ADHD would not identify non-ADHD symptoms caused by AFCs (e.g., irritability & sleep problems). 2). The FDA looked for large numbers of children to be affected by AFCs rather than recognize the importance of smaller but still vulnerable subpopulations. 3). The FDA judged McCann et al.'s [ 20] ES of 0.18 (in the range of many studies on AFCs & ADHD) as of "low magnitude." Weiss [ 28] estimated such an ES as equivalent to a loss of three IQ points, and concluded "Most observers would not consider this to be a value of "rather low magnitude" (p. 3). 4). The FDA committee's conclusion that further research was needed before taking preventive action did not consider the implications for institutional review board (IRB) approval for studies with documented risk and the cost of studies examining each of the certified AFCs.

Since the FDA hearing two more reviews have been published: Stevens, Kuczwk, Burgess, Hurt and Arnold [ 16] and Nigg et al. [ 2]. In their review of "35 years of research," Stevens et al. [ 16] noted scientists have examined Feingold's hypotheses using 3 types of diets: (1) the K-P diet, (2) an elimination diet followed by AFC challenges, and (3) an oligoantigenic or few-foods diet followed by AFC and natural food challenges. From their review of four K-P diet studies, Stevens et al [164] concluded there are a small proportion (11 %-33 %) of children with hyperactivity whose functioning at home and school is improved by the K-P diet. From their review of 11 elimination diet-AFC challenge studies with children and with animals, Stevens et al [ 16] concluded most studies suggest that AFC challenges (mixed or with just tartrazine), compared with placebo, cause significant behavioral changes in ADHD subpopulations, the general pediatric population and in laboratory animals.. From their review of seven oligoantigenic/few-foods elimination diet-AFC/natural food challenges, Stevens et al [ 16] concluded all studies reported high response rates to various elimination diets (>70 %) and most parents reported more hyperactivity when challenged with offending foods/AFCs than placebo, with AFCs and preservatives the most likely to cause reactions, but no child responded only to AFCs.

Most recently, Nigg et al. [ 2] published a meta-analysis examining studies of dietary restriction and AFCs. For restriction diets they identified 14 open-label trials with a random-effects-weighted response rate of 47.4 %, which would set the upper limit on response rates for such diets (the rate could be lower considering that some of the response could be nonspecific effects). Five double-blind randomized placebo-challenge studies were also identified for restricted diets with an ES = 0.29, which Nigg et al. [ 2] noted was 1/3 the ES for medication and equivalent to a clinically meaningful change from the 50th to 62nd percentile. For AFCs 24 double-blind, placebo-controlled crossover studies were included and produced an ES for parent reports of 0.18, reduced to 0.12 after adjustment for potential publication bias. This effect was reliable in studies of food colors without preservatives (ES = .21) but not for studies of only FDA-approved food colors. Teacher/observer reports had a nonsignificant ES of 0.07 for studies in general, but when restricted to AFCs, it rose to 0.22 and survived correction. Psychometric tests of attention had an ES of 0.27, which also survived correction. Nigg and colleagues [ 2] concluded that "Although the evidence is too weak to justify action recommendations absent a strong precautionary stance, it is too substantial to dismiss" (p. 96) and "Although these average effect sizes are small in clinical terms, they could be quite substantial from the perspective of population-wide prevention efforts" (p. 96.).

Summary of Prevalent Flaws in Published Research on Dietary Sensitivities Relevant to ADHD

Although there were many studies with various controls, all were flawed in some way. First, diagnosis often does not follow DSM criteria. This is partly because many studies predated the current DSM-IV operational criteria [ 21]. But even some studies conducted after DSM-IV criteria [ 21] were available do not follow them. Frequently the "diagnosis" is a rating scale threshold or clinical impression. Another problem is that blinding is frequently imperfect, sometimes nonexistent, and its validity rarely examined in the studies of dietary sensitivities in which AFCs have been examined along with other suspected dietary components. However, the blinding is usually better for AFCs than other dietary components tested (e.g., the blinding was excellent in the Southampton studies [ 19, 20]). When AFCs are tested, they are often done as a mix rather than a single AFC (or possibly even mixed with a preservative), so that often it is difficult to attribute effects accurately to particular AFCs.

Admixture of preservatives in challenges is especially vexing because they require a different standard of evidence for removing them from the food supply. Removal of preservatives would carry an economic and public health cost in terms of food spoilage and possible food poisoning, whereas AFCs are purely cosmetic, so that removal would not seem to have an economic or public health cost. Thus it would be important to distinguish AFC effects from preservative effects. Other limitations of studies include the wide variety of AFC doses, duration of exposure to AFCs, and the timing of post-challenge testing.

Clarifications about ADHD Relevant to Evaluating the AFC Behavioral Literature

There are several aspects of ADHD diagnosis and evaluation relevant to evaluating the impact of AFCs and, relatedly, to the FDA preliminary review of materials. First, ADHD is a phenomenological, not causal, diagnosis [ 29]. Syndromes that meet the diagnostic criteria may be considered ADHD regardless of cause. In fact, there are probably many causes for a common phenotype.

Moreover, the phenotype itself is pleomorphic with four main variations recognized in the Diagnostic and Statistical Manual-Fourth Edition-Text Revision [ 29]: predominantly inattentive type, predominantly hyperactive-impulsive type, combined type, and "not otherwise specified." Although the literature is not conclusive, there appears to be some variation in pharmacologic and other treatment response by type (e.g., [ 30]) and it would not be surprising if there were some variation in etiology. Nevertheless, the distinctions among types, despite being operationally/definitionally clear, are both fuzzy and arbitrary: Of the 9 inattentive and 9 hyperactive-impulsive symptoms, it is possible to have 6 inattentive symptoms and 5 hyperactive-impulsive symptoms and be classified as inattentive type, only one symptom short of being combined type with 6 of each. In fact, persons with 9 inattentive symptoms and 6 hyperactive-impulsive symptoms would be diagnosed as combined type even though they would have a greater preponderance of inattentive symptoms than the person with 6 and 5 respectively. Finally, two people could theoretically have the inattentive type with only 3 symptoms in common and up to 7 symptoms different (3 inattentive and 4 hyperactive-impulsive). Further, the phenotype defined by ICD-10 [ 31] hyperkinetic disorder and hyperkinetic conduct disorder is different yet; only 145 of the 579 children in the Multimodal Treatment Study of ADHD (the MTA) with combined-type ADHD met the ICD-10 criteria [ 32].

Diagnostic Procedure

The diagnosis requires 5 criteria, several of which are relevant to understanding the effect of AFCs: symptom count and severity; impairment; pervasiveness across settings; chronicity (6 months or more, starting young); and not better explained by another mental disorder [ 29].

Analogy to Hypertension

Because of the first criterion (symptom count and severity), ADHD is a dimensional diagnosis, like hypertension. Although everyone has some blood pressure (BP), too much is a problem. Cardiologists have struggled with how to set the threshold for problematic BP and have changed the benchmarks or thresholds from time to time, as psychiatrists have done for ADHD (DSM-IIIR [ 33] required 8 of 14 symptoms rather than the current 6 of 9). The current threshold for pre-hypertension is ≥80 or more diastolic BP and for hypertension is ≥90 or more diastolic [ 34]. Wherever it is set, some people will be on the cusp, such that a few mm change in BP will nudge them into a different category (e.g., going from 78 to 82 diastolic gives one pre-hypertension and from 88 to 92 gives one hypertension). Risk factors like stress, excess salt, or obesity could nudge some people over the BP threshold. As a corollary, one does not need diagnosable hypertension to be harmed by stress, excess salt, or obesity, which makes the prevention of those problems applicable to the general population. Similarly, a cause with only a small effect (as found for AFC in group averages) can nudge someone into the diagnosable range for ADHD (e.g., from 5 to 6 symptoms), and one does not need to have diagnosable ADHD to be harmed by a cause with a small deleterious effect, making the preventive implications widely applicable.

Chronicity and Pervasiveness Criteria Affect Measurement

One criticism of the studies of AFC effects on behavior is that they mainly show an effect on parent ratings, not so much on objective tests or on clinic observations. This criticism appears vacuous when we consider the diagnostic criteria of chronicity and pervasiveness, which require a consistent pattern of behavior over time, in more than one setting. Therefore severity and change (improvement or deterioration) cannot be adequately measured or appreciated in short time fragments in an artificial setting. They depend on caregiver ratings, or at least caregiver informants, usually parent and teacher, who know the child well and in various settings. These are subjective, but the most valid [ 35]. In recognition of this fact, the FDA has approved indications for ADHD drugs on the basis of parental information as the primary outcome.

Multifactorial Causes of ADHD

Genetics and epigenetics are perhaps the best-documented causes. Numerous family studies, including twin studies, have shown heritability up to 80 %, and this is backed up by replicated candidate genes such as the 7-repeat allele of DRD4 and the 10-repeat allele of DAT1 [ 36]. However, this should not translate to genetic determinism, because genes are expressed by interaction with the environment, including diet. A good example is phenylketonuria (PKU), a well-established 100 % genetic disorder, lack of the gene for the enzyme to metabolize phenylalanine to tyrosine, resulting in a toxic alternative metabolic pathway for phenylalanine. Because the disease develops only in the presence of phenylalanine in the diet, it is 100 % environmental as well as 100 % heritable [ 37]. Thus the fact that ADHD is 80 % heritable means that it is between 20 % and 100 % environmental. We can see that heritability could partly be genes for vulnerability to specific environmental factors, such as AFCs, insecticides, environmental chemicals, infections, parasites, trauma, poor diet, etc.

First Suspected Cause: MBD

Minimal brain damage or minimal brain dysfunction (MBD) was posited as a cause from the beginning because the syndrome was noted to be a sequela of infections (von Economo's encephalitis, intrauterine rubella, childhood infections), perinatal "reproductive casualty" (e.g., kernicterus), head trauma, and lead poisoning [ 38]. More modern additions to the list of suspects might include insecticide residues, industrial, construction, and consumer product chemical pollutants, and AFCs.

Increased Prevalence and Putative Causes

The estimated prevalence of ADHD has increased since 1970, when it was 3-5 %, a figure that was still quoted as late as the 1994 DSM-IV [ 21]. More recent estimates have ranged up to 10-12 % [ 39]. Part of this, increase, of course, is increased recognition and more liberal diagnostic practice. Nevertheless, there may be an actual increase beyond diagnostic inflation. Many possible causes have been suspected. Those with genes for vulnerability may be exposed to more stresses, insults, or lack of developmental opportunities, which are likely to bring out the problems. The past century has seen multiple environmental changes. The educational setting has changed from one-room schools and self-contained classrooms with individualized instruction and close school-home cooperation to group classes with less discipline and home cooperation at the same time that the information age has ratcheted up the amount and difficulty of material to be learned. Parents working outside the home reduce the amount of one-to-one task-oriented adult mentorship that was available on a farm or in a small family business. Social breakdown of neighborhoods, families, and mores deprive children of the structure that is so helpful to youngsters vulnerable to developing ADHD. Electronic pastimes have reduced the amount of cerebellar exercise, which some suspect may be necessary for optimal cognition. Finally, there are new chemicals in the environment that could stress neurophysiology.

Environmental Contaminants and ADHD Symptoms

A good bit of research, especially in the 1960s and 70s, established the role of subclinical lead burden in cognitive and behavioral problems [ 40], to the extent that lead was eventually removed from gasoline Interestingly, the inverse correlation of intelligence quotient with subclinical blood lead levels [ 41] was of the same magnitude as the effect on hyperactivity recently reported for AFCs + Na benzoate in the Southampton studies [ 19, 20]). Lead contamination had been around for decades (albeit perhaps increasing Post-World War II because of increasing use of leaded gasoline), but there are other environmental contaminants of more recent origin. For example, insecticides have been associated with ADHD symptom severity: In one study, organophosphate insecticide residues and metabolites in children's urine doubled the risk of ADHD [ 42]. The source was suspected to be residues on fruits and vegetables. In another study, maternal gestational serum insecticide level had an association with the child's later behavior at age 5 [ 43]. Other modern sources of possible toxicity are industrial, construction, and consumer product chemicals. For instance, children whose cord blood had a PCB level in the top 25 % had 1.76 times the risk of ADHD symptoms as those whose cord blood had the bottom 25 % of PCB levels [ 44]. In another study polyfluoralkyl levels in adolescents age 12-15 were associated with ADHD symptoms [ 45]. These chemicals, of course, have come into common use in the past 50-60 years. Another environmental chemical class recently prevalent in children's food is artificial coloring. Figure shows the increase in per capita consumption of AFCs since 1950, taken from FDA data.

AFC Interaction with Nutrient

Other changes have occurred in the foods available to children. Mineral content of fruits and vegetables has decreased since the 1930s [ 46] with intensive agriculture that mainly fertilizes with nitrogen, phosphorus, and potassium, largely neglecting other essential elements. Further, food is processed much more extensively, which changes its nutrient content in known and unknown ways, and such processed foods may be associated with vulnerability to depression [ 47]. One of the known changes in children's diets, of course, is addition of artificial AFCs. There have been repeated reports of low blood and other tissue levels of iron, zinc, magnesium, and omega-3 fatty acids. A couple of interesting studies by Ward [ 48, 49] may tie some of this together.

The first study [ 48] was done with 10 hyperactive children whose parents said their behavior was sensitive to food AFCs and 10 healthy control children. At baseline, the hyperactive children had lower serum, urine, and nail zinc than the controls. After a 3-day additive-free diet, half of each group consumed a tartrazine challenge consisting of a commercial drink containing tartrazine, and half drank a similar orange drink without tartrazine. The serum and saliva Zn went down nonsignificantly and urine zinc went up significantly in the hyperactive children who drank tartrazine, but not in controls. Behavioral observations correlated with the amount of zinc change. In 1997, Ward [ 49] replicated these findings in 47 more children that parents reported to react to food AFC, again with age- and sex-matched controls. This time 23 children were given 50 mg tartrazine (slightly less than the 60 mg per capita daily consumption of AFC in 2010), 12 were given amaranth, and 12 sunset yellow. Five controls were given each of the 3 AFCs (N = 15 controls). Again serum zinc went down nonsignificantly, urine Zn went up significantly in hyperactive children given tartrazine and sunset yellow (not amaranth), but not in controls. Again, zinc changes from AFC challenge were associated with behavioral deterioration. Both studies suggest zinc wasting (excessive excretion) from tartrazine (and sunset yellow), possibly by chelation. This raises a question of what other nutrients may be wasted or otherwise interfered with by various AFCs. It also shows a possible mechanism for AFC to affect the brain without crossing the blood-brain barrier, given that Zn is essential for normal brain function [ 50],. This is relevant to the argument that AFC should not affect behavior because only Blue #1 crosses a mature blood-brain barrier in appreciable amounts.

Genetic Basis of AFC Sensitivity

Genetic tests were done in the second and third Southampton studies [ 20] on 6 candidate genetic variants, 2 histamine and 4 dopamine-relevant polymorphisms [ 51]. Three gene polymorphisms moderated the effect of AFC mixes on the Global Hyperactivity score. Both histamine polymorphisms were significant: HNMT Thr105Ile for both ages and HNMT T939C for the 8-9 year olds. There was one significant Dopamine polymorphism, DAT1 in 8-9 year-olds. COMT val108met, ADRA C1291G, and DRD4 rs740373 were not significant moderators. For the moderating effect of HNMT T939C, the C allele is protective against AFC effect, and absence of the C allele, the marker for vulnerability to the AFC effect, occurred in about 60 % of the children, supporting the public health import of the findings.

Other Biological Evidence: Brain Topographical Mapping

In another study [ 52], Brain Electrical Activity Mapping was done with and without a provoking food (including AFCs) during the preceding weeks and the same day in a crossover design, with blind interpretation of the EEG. With the provoking food, but not without it, there was an increase in frontotemporal Beta-1 band activity and behavioral symptoms. Unfortunately, the challenges were not blind, so one could not rule out the possibility that the parents' knowing might have influenced the child's EEG.

Additional Laboratory Evidence of Biological Effect

Selected examples of laboratory results, mainly in animals, include erythrosine-induced inhibition of serotonergic activity in rats [ 53] , corticosterone effects of erythrosine in rats [ 54] (another way in which AFCs could affect brain function without crossing the blood-brain barrier), changes in liver function tests from AFC mixtures in rats [ 55], and human mast cell degranulation with tartrazine, releasing histamine [ 56]. Further details may be found in Stevens et al [ 16].

Conclusions

Additional research should attend to the following points: Sample selection and characterization should consider specialty clinics vs. general mental health clinics vs. normal "controls" to address the denominator problem, and there should be careful diagnosis by DSM criteria, including both patients with ADHD and controls without, or controls with other diagnoses, to confirm the public health breadth of the risk. Age effects should be examined: Do adolescents and adults outgrow the sensitivity? Challenges should be "unbundled" to examine individual AFCs as well as various mixes, and especially examine AFCs separate from preservatives. Dose effects need more systematic exploration, especially in light of the ascending curve of daily consumption. How much is too much? How much is consumed by the highest-ingesting children? Careful blinding should be incorporated as much as possible. This should include double-blinding to prevent unwitting telegraphing of expectation by the research staff and testing of blinding validity for all those who should be blinded. Standard scales and observations should be used for comparison across studies and with studies of other interventions.

Three kinds of interactions should be systematically examined: interaction with nutrients, interaction with medications, and interactions between/among AFCs. A special question in light of the Southampton demonstration of a widespread effect on the general population [ 19, 20] is the effects on a whole classroom as well as individual children. If most of the children in a classroom deteriorate behaviorally by a small amount every day, is the aggregate and cumulative effect on the classroom climate greater than the sum of its parts?

Finally, samples need to be large enough to detect the small population effect sizes demonstrated in the Southampton studies [ 19, 20]. The 3-figure Southampton samples were barely sufficient to detect the statistically small but clinically important effects noted. An analogy is the subclinical-lead-burden literature, where it apparently took a sample size of about a thousand to demonstrate the effect convincingly. There is great danger of Type II error here, and statistical expertise needs to be involved in the early stages of planning any such study. Such large samples raise an interesting question about cost and who should pay for it. Some would argue that industry should sponsor studies to prove safety, similar to what is needed for drug approval. Others might argue that these AFCs have already been approved and are in use, and it is up to public health advocates to test whether they are unsafe. Perhaps the cost should be shared between industry and public health agencies.

Interim Working Conclusions

While awaiting the results of such further research, the following conclusions seem reasonable:

AFCs are not a main cause of ADHD, but they may contribute significantly to some cases, and in some cases may additively push a youngster over the diagnostic threshold.

There are several threads of evidence for a biological mechanism.

Although there is probably not an immune-mediated reaction, a direct release of histamine may be involved.

By affecting nutrients and other metabolism in the periphery, AFCs could affect the brain without crossing the blood-brain barrier.

The deleterious effect does not appear to be confined to ADHD (a general effect has been replicated). Therefore AFCs may be more a general public health problem than an ADHD problem

A small deleterious effect regardless of diagnosis was replicated and a possible mechanism (related to histamine genes) identified

The magnitude of reported effect is reminiscent of subclinical lead poisoning (<10 mcg/dL): r ~ 0.11 after correction for social factors [ 41], which led to the eventual removal of lead from gasoline.

Per capita daily consumption of AFCs quadrupled in the last 50 yrs.

"The dose alone makes the poison" --Paracelsus

There may conceivably be a possible deleterious effect on classroom climate from most children deteriorating slightly, thus additively or even synergistically impairing the learning atmosphere.

The current status of evidence is inconclusive "but too substantial to dismiss."(2) Until safety can be better determined, we suggest minimizing children's exposure to AFCs. With the current concerns about childhood obesity, there appears to be no need to make food look more attractive than its natural color.

Electronic supplementary material

(PDF 510 kb)

Required Author Forms

Disclosure forms provided by the authors are available with the online version of this article.

Footnotes

¹ This article developed from the first author's testimony to the 2011 FDA Food Advisory Committee on the behavioral effects of food AFCs, which is incorporated in updated summary.

References

3. Feingold BF. Adverse reactions to food additives. Presented at: The American Medical Association Annual Meeting; June 24-28, 1973; Chicago, IL.

7. Feingold BF. Hyperkinesis and learning disabilities linked to the ingestion of artificial food colors and flavors. Speech to: American Academy of Pediatrics; November 8, 1977; New York, NY.

8. Feingold BF. Why Your Child is Hyperactive. New York: Random House; 1975.

11. Feingold BF. A view from the other side. Speech to: Newspaper Food Editors and Writers Association; June 8, 1977; Milwaukee, WI.

13. Defined diets and childhood hyperactivity. NIH Consens Statement, 1982:4:1-11.

21. Diagnostic and statistical manual of mental disorders, 4th ed. Washington, DC: American Psychiatric Association, 2000.

24. FDA/CFSAN Food Advisory Committee, 2011. Center for Food Safety and Applied Nutrition, March 30-31, 2011, Food Advisory Committee Meeting

29. Diagnostic and statistical manual of mental disorders, 4th ed., text revision. Washington, DC: American Psychiatric Association, 2000.

31. World Health Organization. ICD-10: International statistical classification of diseases and related health problems, 10th revised ed. New York, NY, 2008.

33. Diagnostic and statistical manual of mental disorders, 3rd ed. Washington, DC: American Psychiatric Association, 1980.

39. Akinbami LJ, Xiang L, Pastor PN, Reuben CA. Attention deficit hyperactivity disorder among children aged 5-17 years in the United States, 1998-2009. NCHS Data Brief 2011;70:xx.

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Artificial Food Colors and Attention-Deficit/Hyperactivity Symptoms: Conclusions to Dye for

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The effect of artificial food colors (AFCs) on child behavior has been studied for more than 35 years, with accumulating evidence from imperfect studies. This article summarizes the history of this controversial topic and testimony to the 2011 Food and Drug Administration Food Advisory Committee convened to evaluate the current status of evidence regarding attention-deficit/hyperactivity disorder (ADHD).

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Could a high-protein, low-sugar, no-additive diet plan can actually help your child manage ADHD symptoms? Find out in this video.

Red dye #40. Gluten and casein. Refined sugar. Dairy. Artificial preservatives. Each of these may lead to increase hyperactivity, decreased focus, and other health and behavior complications in some children with ADHD. But each child is different, and what exacerbates symptoms in one may not cause any discernible difference in another.

Here are some of the most common dietary triggers for ADHD symptoms; work with your child's doctor to determine the best way to test your child's sensitivities to these five.

5 Things Children with ADHD Should NOT Eat
1. Artificial Flavoring and Preservatives
Roughly 5 percent of children with ADHD are sensitive to chemicals in food. For these kids, the Feingold Diet - or another like it - may improve symptoms. That means NO:

Artificial food colors

Flavorings

Sweeteners

Preservatives

Salicylates

2. Sugar
Processed sugar is a simple carbohydrate that provides quick bursts of energy and burns out quickly.

Kids with ADHD are particularly susceptible to bouts of hyperactivity, inattentiveness, and impulsivity after consuming it.

Sugar hides in various disguises:
3. Food Dyes
Artificial food colors may lead to hyperactivity, irritability, and sleep disorders in some kids with ADHD. Experts recommend eliminating Red #40, Blue #2, Yellow #6, and sodium benzoate, then watching for behavioral changes. The following natural food colorings are OK:
4. Pesticides
Dr. Sandy Newmark, founder of the Center for Pediatric Integrative Medicine, recommends that anyone with ADHD limit pesticide exposure as much as possible.

Buy organic food whenever possible and avoid the "dirty dozen" with the highest levels of pesticides.
5. Trigger Foods
Children can have a sensitivity to certain foods - dairy, wheat, corn, soy, eggs, nuts, and citrus are common culprits - that can exacerbate symptoms.

Try eliminating these foods, and watch behavior carefully.

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Could a high-protein, low-sugar, no-additive diet plan can actually help your child manage ADHD symptoms? Find out in this video. Red dye #40. Gluten and casein. Refined sugar. Dairy. Artificial preservatives. Each of these may lead to increase hyperactivity, decreased focus, and other health and behavior complications in some children with ADHD.

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International Institute for Qualitative Methodology (IIQM)

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Advances in Mixed Methods Research Webinar - John Creswell

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Please ensure you have JavaScript enabled in your browser. If you leave JavaScript disabled, you will only access a portion of the content we are providing. Here's how.

Key Info

Make a list to keep track of ALL the books, magazines, and websites you read as you follow your background research plan. Later this list of sources will become your bibliography.

Most teachers want you to have at least three written sources of information.

Write down, photocopy, or print the following information for each source you find. You can use the Science Buddies Bibliography Worksheet to help you.

Collect this information for each printed source:

Collect this information for each Web Site:

author name

title of the publication (and the title of the article if it's a magazine or encyclopedia)

date of publication

the place of publication of a book

the publishing company of a book

the volume number of a magazine or printed encyclopedia

the page number(s)

author and editor names (if available)

title of the page (if available)

the company or organization who posted the webpage

the Web address for the page (called a URL)

the last date you looked at the page

The bibliographic information for different types of resources are located in different places, so you may need to do some detective work to get all of the information for your bibliography. Try looking in these places:

the title page of a book, encyclopedia or dictionary

the heading of an article

the front, second, or editorial page of the newspaper

the contents page of a journal or magazine

the header (at the top) or footer (at the bottom) of a Web site

When it is time to turn in your Bibliography, type all of your sources into a list. Use the examples in MLA Format Examples orAPA Format Examples as a template to insure that each source is formatted correctly.

List the sources in alphabetical order using the author's last name. If a source has more than one author, alphabetize using the first one. If an author is unknown, alphabetize that source using the title instead.

Overview

A bibliography is a listing of the books, magazines, and Internet sources that you use in designing, carrying out, and understanding your science fair project. But, you develop a bibliography only after first preparing a background research plan - a road map of the research questions you need to answer. Before you compose your bibliography, you will need to develop your background research plan.

With your background research plan in hand, you will find sources of information that will help you with your science fair project. As you find this information it will be important for you to write down where the sources are from. You can use the Bibliography Worksheet to help you, just print out a few copies and take them with you to the library. As you find a source, write in all of the necessary information. This way, when you are typing your bibliography you won't need to go back to the library and find any missing information. The more information you write down about your source, the easier it will be for you to find if you want to read it again.

When you are writing your report, you will use the sources in your bibliography to remind you of different facts and background information you used for your science fair project. Each time you use some information from a source, you will need to cite the source that it came from. To cite a source, simply put the author's name and the date of the publication in parentheses (Author, date) in your text. If the person reading your report wants to find the information and read more about it, they can look up the reference in your bibliography for more detail about the source. That is why each source you use must be listed in a detailed bibliography with enough information for someone to go and find it by themselves.

Your bibliography should include a minimum of three written sources of information about your topic from books, encyclopedias, and periodicals. You may have additional information from the Web if appropriate.

Examples of Bibliography Formats

There are standards for documenting sources of information in research papers. Even though different journals may use a slightly different format for the bibliography, they all contain the same basic information. The most basic information that each reference should have is the author's name, the title, the date, and the source.

Different types of sources have different formatting in the bibliography. In American schools, the two most commonly used guidelines for this formatting are published by the MLA (Modern Language Association) and the APA (American Psychological Association).

The MLA guidelines call for the bibliography to be called Works Cited. Science Buddies has summarized some of the most common MLA formats for your use: MLA Format Examples.

The APA guidelines call for the bibliography to be called the Reference List. Science Buddies has summarized some of the most common APA formats for your use: APA Format Examples.

Your teacher will probably tell you which set of guidelines to use.

On the Science Buddies website we use the following guidelines:

APA format for online sources

MLA format for all other sources

APA (author, date, page) format for citations in our articles

Getting Started

Download and print the Science Buddies Bibliography Worksheet. Keep several copies with you and fill in the information as you do your research. When you are finished, type the information from the worksheet into a formatted bibliography using the examples listed above.

Bibliography Checklist

What Makes a Good Bibliography?

For a Good Bibliography, You Should Answer "Yes" to Every Question

Have you included at least 3 sources of written information on your subject? (If you include Web pages, they should be in addition to the written sources.)

Yes / No

Have you included complete information to identify each of your sources (author's name, the title, the date, and where it was published)?

Yes / No

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It has to be acknowledged that it is difficult to explain positivism research philosophy in a precise and succinct manner. This is because there are vast differences between settings in which positivism is used by researchers. The number variations in explaining positivism may be equal to the number of authors who addressed the area of research philosophy. Nevertheless, in its essence, positivism is based on the idea that science is the only way to learn about the truth. The text below explains positivism research philosophy with the focus on business studies in particular.

Positivism: Introduction

As a philosophy, positivism adheres to the view that only "factual" knowledge gained through observation (the senses), including measurement, is trustworthy. In positivism studies the role of the researcher is limited to data collection and interpretation in an objective way. In other words, the researcher is an objective analyst and she distances herself from personal values in conducting the study. In these types of studies research findings are usually observable and quantifiable.

Positivism depends on quantifiable observations that lead to statistical analyses. It has been a dominant form of research in business and management disciplines for decades. It has been noted that "as a philosophy, positivism is in accordance with the empiricist view that knowledge stems from human experience. It has an atomistic, ontological view of the world as comprising discrete, observable elements and events that interact in an observable, determined and regular manner"[1].

Moreover, in positivism studies the researcher is independent form the study and there are no provisions for human interests within the study. Crowther and Lancaster (2008)[2] argue that as a general rule, positivist studies usually adopt deductive approach, whereas inductive research approach is usually associated with a phenomenology philosophy. Moreover, positivism relates to the viewpoint that researcher needs to concentrate on facts, whereas phenomenology concentrates on the meaning and has provision for human interest.

Researchers warn that "if you assume a positivist approach to your study, then it is your belief that you are independent of your research and your research can be purely objective. Independent means that you maintain minimal interaction with your research participants when carrying out your research."[3] In other words, studies with positivist paradigm are based purely on facts and consider the world to be external and objective.

The five main principles of positivism research philosophy can be summarized as the following:

1. There are no differences in the logic of inquiry across sciences.

2. The research should aim to explain and predict.

3. Research should be empirically observable via human senses. Inductive reasoning should be used to develop statements (hypotheses) to be tested during the research process.

4. Science is not the same as the common sense. The common sense should not be allowed to bias the research findings.

5. Science must be value-free and it should be judged only by logic.

The following are a few examples for studies that adhere to positivism research philosophy:

A study into the impact of COVID-19 pandemic on the equity of fashion brands in North America.

An analysis of effects of foreign direct investment in information technology industry on GDP growth in

A study of relationship between diffusion of innovation of mobile applications and saturation of applications in

The following Table 1 illustrates ontology, epistemology, axiology and typical research methods associated with positivism research philosophy:

Real, external, independent

One true reality

(universalism)

Granular (things)

Ordered

Scientific method

Observable and measurable facts

Law-like generalizations

Numbers

Causal explanation and prediction as contribution

Value-free research

Researcher is detached, neutral and independent of what is researched

Researcher maintains objective stance

Typically deductive, highly structured, large samples, measurement, typically quantitative method of analysis, but a range of data can be analysed

Table 1 Ontology, epistemology, axiology and typical research methods associated with positivism research philosophy
Science as an Underlying Ground for Positivism

Positivism often involves the use of existing theory to develop hypotheses to be tested during the research process. Positivist researchers tend to use highly structured research methodology in order to allow the replication of the same study in the future. Science can be specified as a cornerstone in positivism research philosophy. Specifically, positivism relies on the following aspects of science.

1. Science is deterministic. Scientific approach is based on the assumption that X causes Y under certain circumstances. The role of researcher when following the scientific approach is to discover specific nature of cause and effect relationships.

2. Science is mechanistic. Mechanical nature of scientific approach can be explained in a way that researchers develop hypotheses to be proved or disproved via application of specific research methods. This leads to the fact that

3. Science uses method. Chosen methods are applied mechanically in order to operationalize theory or hypothesis. Application of methodology involves selection of sample, measurements, analysis and reaching conclusions about hypotheses.

4. Science deals with empiricism. In other words, science only deals with what can be seen or measured. From this perspective, science can be assessed as objective.

Differences between Positivism and Interpretivism

The key features of positivism and social constructionism philosophical approaches are presented in the following Table 2 by Ramanathan (2008)[4].

Research progresses through
Hypotheses and deductions

Gather rich data from which ideas are induced

Table 2 Differences between positivism and social constructionism
Alternatively, the differences between positivist and phenomenology paradigms are best illustrated by Easterby-Smith et al. (2008)[5] in the following manner:

Basic notions

The world is perceived as external and objective

Independency of the observer

Value-free approach to science

The world is perceived to be socially constructed and subjective

Observer is considered a part of the object of observation

Human interests drives science

Responsibilities of researcher

Focusing on facts

Causalities and fundamental laws are searched

Phenomenon are reduced to the simplest elements

Hypotheses formulation and testing them

To be focusing on meanings

Aiming to understand the meaning of events

Exploring the totality of each individual case

Ideas are developed by induction from data

Most suitable research methods

Concepts have to be operationalized

Using several methods in order to different aspects of phenomena

Samples have to be large

Small samples are analyzed in a greater depth or over longer period of time

Table 3 Positivist and phenomenology paradigms
Shortcomings of Positivism

Positivism as an epistemology is associated with the following set of disadvantages:

Firstly, positivism relies on experience as a valid source of knowledge. However, a wide range of basic and important concepts such as cause, time and space are not based on experience. There might be many additional factors that have impacted research findings and positivism research philosophy fails to acknowledge the effect of these factors.

Secondly, positivism assumes that all types of processes can be perceived as a certain variation of actions of individuals or relationships between individuals.

Thirdly, adoption of positivism in business studies and other studies can be criticized for reliance on status quo. In other words, research findings in positivism studies are only descriptive, thus they lack insight into in-depth issues.
My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance contains discussions of theory and application of research philosophy. The e-book also explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in this e-book in simple words.
John Dudovskiy

[1] Collins, H. (2010) "Creative Research: The Theory and Practice of Research for the Creative Industries" AVA Publications, p.38

[2] Crowther, D. & Lancaster, G. (2008) "Research Methods: A Concise Introduction to Research in Management and Business Consultancy" Butterworth-Heinemann

[3] Wilson, J. (2010) "Essentials of Business Research: A Guide to Doing Your Research Project" SAGE Publications

[4] Ramanathan, R. (2008) "The Role of Organisational Change Management in Offshore Outsourcing of Information Technology Services" Universal Publishers

[5] Easterby-Smith, M, Thorpe, R. & Jackson, P. (2008) "Management Research" 3rd ed,SAGE Publications Ltd., London

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It has to be acknowledged that it is difficult to explain positivism research philosophy in a precise and succinct manner. This is because there are vast differences between settings in which positivism is used by researchers. The number variations in explaining positivism may be equal to the number of authors who addressed the area of research philosophy.