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Although there are problems and risks with administering psychotropics for children, research has proven that they have the ability to restructure brain abnormalities.

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This topic comes by request on the Savvy Psychologist Facebook page from listener Anita M. of Detroit. Anita works with foster kids and, too often, sees disadvantaged kids who have been on a cocktail of psychiatric medications from as early as age 6. She asks, does such early use alter a child's brain or body? And have the effects of lifelong psychiatric medication been studied?

Childhood mental illness (and resulting medication) is equally overblown and under-recognized. Approximately 21% of American kids - that's 1 in 5 - will battle a diagnosable mental illness before they reach the age of 17, whether or not they actually get treatment.

The problem is anything but simple. Some childhood illnesses - ADHD and autism, for example - often get misused as "grab-bag" diagnoses when something's wrong but no one knows what. This leads to overdiagnosis and sometimes, overmedicating. Other illnesses, like substance abuse, get overlooked or written off as rebellion or experimentation, leading to underdiagnosis and kids slipping through the cracks.

But the most common problem is inconsistent diagnosis. For example, a 2008 study found that fewer than half of individuals diagnosed with bipolar disorder actually had the illness, while 5% of those diagnosed with something completely different actually had bipolar disorder.

But let's get back to Anita's questions: Does early psychotropic medication alter a child's brain? The short answer is yes, but the long answer might be different than you think.

A 2012 review from Stanford researchers analyzed over 50 studies that used neuroimaging - that is, MRI, fMRI, magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI), and anything else that takes before-and-after pictures of the brain - to examine the brains of kids with a variety of mental illnesses: anorexia, ADHD, autism, bipolar disorder, depression, OCD, and schizophrenia. They found that overall, medication does indeed affect brain structure and function to a degree detectable by imaging.

But probably not in the way you expect.

Do Psychiatric Drugs Alter the Brain?
Usually, when we think "brain changes" we think it means something bad, like damage or stunting. But it's important to remember that untreated mental illness can also harm brain development. Early medication can help prevent the illness from getting worse or becoming neurally entrenched.

Let's look at the anorexia group, for example. Before medication and therapy, teenagers with untreated anorexia displayed different brain activation than a control group, but after 7 months of treatment, the differences disappeared. As the anorexic group regained weight and got better, their brains changed to be more like "normal."

Likewise, a 2012 study on kids and adults with ADHD revealed that, over time, treatment has a positive effect on brain structure. As a group, kids who received treatment had fewer brain structure abnormalities than those who were left untreated.

Now, does all this mean it's acceptable to put 6-year-olds on antipsychotics?

The answer? We don't know.

But it's no coincidence that medication overload starts at age 6, because this is when kids start school. When one child constantly disrupts the learning of 20-30 others, medication unfortunately can get called in as a neutralizing ray.

But, just like adults shouldn't substitute coffee for sleep, or alcohol for relaxation, psychiatric medication should never be a substitute for teaching kids self-regulation, dealing with a stressful family situation, or to sedate a child who is causing problems.
>> Continue reading on QuickAndDirtyTips.com

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Should Kids Take Psychiatric Medication?

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We all know (or were) the kid who makes us wish for the invention of a Ritalin blow dart. But is it a good idea to put kids on psychiatric meds? And once they’re on, how long is long enough?

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Tolerability, Safety, and Benefits of Risperidone in Children and Adolescents with Autism: 21-Month Follow-up After 8-Week Placebo-Controlled Trial | Journal of Child and Adolescent Psychopharmacology

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If the address matches an existing account you will receive an email with instructions to reset your password

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This study discusses the short and long term effects of different drugs on kids.

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Open Peer Review reports

Background

Animal studies have shown that methylphenidate (MPH) and fluoxetine (FLX) have different effects on dopaminergic and serotonergic system in the developing brain compared to the developed brain. The effects of Psychotropic drugs On the Developing brain (ePOD) study is a combination of different approaches to determine whether there are related findings in humans.

Methods/Design

Animal studies were carried out to investigate age-related effects of psychotropic drugs and to validate new neuroimaging techniques. In addition, we set up two double-blind placebo controlled clinical trials with MPH in 50 boys (10-12 years) and 50 young men (23-40 years) suffering from ADHD (ePOD-MPH) and with FLX in 40 girls (12-14 years) and 40 young women (23-40 years) suffering from depression and anxiety disorders (ePOD-SSRI). Trial registration numbers are: Nederlands Trial Register NTR3103 and NTR2111. A cross-sectional cohort study on age-related effects of these psychotropic medications in patients who have been treated previously with MPH or FLX (ePOD-Pharmo) is also ongoing. The effects of psychotropic drugs on the developing brain are studied using neuroimaging techniques together with neuropsychological and psychiatric assessments of cognition, behavior and emotion. All assessments take place before, during (only in case of MPH) and after chronic treatment.

Discussion

The combined results of these approaches will provide new insight into the modulating effect of MPH and FLX on brain development.

The brain in development is dependent on the emergence of critical developmental processes (i.e. synaptogenesis, [], and therefore sensitive to pharmacological interventions. Treating children and adolescents with serotonergic (5-HTergic) or dopaminergic (DAergic) drugs like fluoxetine (FLX) and methylphenidate (MPH), is therefore likely to have influence on the maturation of the brain.

For the 5-HTergic system, FLX (a selective serotonin reuptake inhibitor (SSRI), registered for the treatment of depression in children aged 8 years and older, is known to increase extracellular levels of 5-HT by blocking the serotonin transporter (SERT). However, animal studies have demonstrated that periadolescent 5-HT pharmacological manipulations can lead to abnormal outgrowth of the 5-HT system [, ]. Experiments by our group have shown that chronic treatment with FLX results in a significant increase in prefrontal and hypothalamic 5-HT transporter (SERT; +30%, p < 0.01) in juvenile-treated rats, but not in adult treated rats []. These findings are in accordance with Wegerer and Bock who have also shown that this effect persists into adulthood, long after discontinuation of treatment with SSRIs [, ]. Recently it was confirmed that FLX administration upregulates SERT long-lastingly, also in non-human primates []. These preclinical studies suggest that 5-HT manipulations have an impact on the regulation of 5-HT outgrowth which is dependent on the age of exposure.

For the DAergic system, recent animal studies with MPH, a DA reuptake inhibitor and stimulant drug frequently prescribed in the treatment of attention deficit hyperactivity disorder (ADHD), have demonstrated that these effects are also age-dependent. For instance, early treatment with MPH led to a considerable (-50%) reduction of dopamine transport density (DAT) in rat striatum when compared to non-treated animals, whereas no effects were observed in adult animals []. These alterations in the DA system have been shown to result in behavioral abnormalities. For example, young rats treated with MPH show more anxiety- and depression-related behavior in adulthood than adult rats treated with MPH [].

There is some clinical evidence for related findings in humans. For example, after concerns about increased suicide risk among children and adolescents treated with SSRIs, the Food and Drug Administration and European Medicines Agency (EMEA) stated in 2003-2004 that SSRIs were contraindicated for treating depression in children and adolescents. Furthermore, in the NIMH Collaborative Multisite Multimodal Treatment Study of Children With Attention-Deficit/Hyperactivity Disorder (MTA) children who received behavioral therapy had a lower rate of diagnoses of anxiety or depression (4.3%) than the children who were treated with MPH (19.1%) thus indicating a (transient) increase in the occurrence of emotional disorders six to eight years after treatment with MPH []. Age-related differences have also been found between adolescent and adult patients on fMRI studies, with adolescent patients treated with MPH showing more activity in the prefrontal cortex after treatment than adult patients [].

Thus, evidence is slowly emerging that the long-term effects of drug exposure are delayed and come to expression once the vulnerable system reaches maturation (i.e., typically during adulthood). This phenomenon is known as 'neuronal imprinting' and occurs when the effects of drug exposure outlast the drug itself []. Still, very little is known on exposure during later brain development. Most (clinical) studies are hampered by the fact that they are retrospective in design, and therefore the findings could be caused by other factors on which the groups differed. As pointed out by Shaw and colleagues: '....the ideal study design for this question would be a randomized trial comparing cortical growth in children on psychostimulants against an unmedicated comparison group-but this would be both logistically and ethically challenging' []. Notwithstanding this challenge, we have set up three studies (the effects of Psychotropic drugs On the Developing brain 'ePOD' project): two randomized controlled trials (RCTs) and a retrospective cohort study, investigating the possibility of the existence of neuronal imprinting in children medicated with these drugs while using several modalities to assess neurocognitive development. Here we report on the objectives and methods of these studies.

General design of the ePOD project

Only a long-term prospective study in patients randomly assigned to MPH or SSRIs and placebo conditions can determine unequivocally whether the (adverse) effects of these medications on the neurotransmitter systems interact with the age when these drugs are prescribed. To this purpose we designed two RCTs, one with MPH and one with FLX. However, it would not be ethical to deprive subjects in a placebo setting from treatment for extensive periods of time. Therefore, in addition to the RCTs, which will last 4 months, we investigate the long-term effects (at least 7 years) in a cohort study based on medical prescription (the ePOD-Pharmo study). The three sub-studies of the ePOD project include:

ePOD-MPH: A 16 week RCT with MPH in 100 medication naïve ADHD patients. This RCT involves three separate NPA and MRI assessments: the first before starting with the study medication (baseline session), the second during treatment with MPH or placebo (week 8) and the final assessment after trial end following a 1-week washout period (week 17).

ePOD-SSRI: A 16 week RCT with FLX in 80 medication naïve patients suffering from MDD and anxiety disorders (AD). It involves two seperate NPA and MRI assessments: before starting with the study medication (baseline session) and after treatment with FLX following a 3-week washout period (week 19).

ePOD-Pharmo: A cohort study based on medical prescription data. One hundred and fifty subjects will be recruited through a database containing prescription data on MPH or FLX (and other antidepressants) Subjects in this cohort based study will receive the same assessments as in the RCTs but only once.

Randomized controlled trials: design and study samples

The two RCTs consist of 16-week multicenter randomized, double blind, placebo-controlled trials with a washout period of one week (MPH) or three weeks (FLX). Subjects are stratified into two age categories: MPH: boys aged 10-12 years, and adults aged 23-40 years. FLX: girls aged 12-14 years and adults aged 23-40 years. These two age groups are randomly assigned to either placebo or active treatment. MRI and NPA assessments will take place before treatment (baseline), during treatment (only in the MPH trial) and following the washout period (see Figure for the timeline for ePOD-SSRI RCT). Baseline measurements will be compared with the results obtained at trial end, and for the ePOD-MPH RCT also during the trial. Differences in outcome measures will be compared between the two age categories (children vs. adult), in addition to healthy controls (separate study). In view of our hypothesis that the active treatment results in long lasting or even permanent changes in the developing brain, we expect no or a small change in change scores between baseline-, and post-treatment assessments, whereas in children we expect to find larger changes, as enduring changes will have taken place in the developing brain, but only transient accommodation in the developed brain. Washout periods were chosen based on chemical properties (rate of elimination based on five half-live times) and ethical considerations (time without treatment).

A total of 50 children (10-12 years of age) and 50 adult (23-40 years of age) male outpatients diagnosed with ADHD (all subtypes) and in need of pharmacological therapy will be included in ePOD-MPH RCT. A total of 40 adolescent (12-14 years of age) and 40 adult (23-40 years of age) female outpatients with moderate to severe MDD or an anxiety disorder in need of pharmacological treatment will be included in ePOD-SSRI RCT. Patients that have used medications or drugs that influence the monoamine systems before age 23 are not eligible.

We chose to include only male patients in ePOD-MPH to limit subject variation and because ADHD is most prevalent in males []. Thus, to keep our sample as homogenously as possible and prevent inclusion problems, only male subjects are included in the ePOD-MPH study. The cut-off point of 10-12 years of age was chosen because peak prevalence of ADHD is 10 years of age [] and also because several MRI parameters greatly change until 8-10 years of age [], whereas the rate of increase of neuronal growth and pruning reduces after 10 years of age. The cut off point of 23 years for matured brain in the adults is chosen in line with previous studies involving a comparison between matured versus immature brain [].

Only female subjects are included in ePOD-SSRI based on the higher prevalence of MDD and AD in this population []. Thus, to keep our sample as homogenously as possible and prevent inclusion problems, only female subjects are included in the ePOD-SSRI study. For the adolescent group we chose a cut-off point of 12-14 years of age because the risk of MDD and AD onset increases approximately 8 fold at this age compared to children younger than 10 years of age [, ].

Cohort based study: design and study sample

In the ePOD-Pharmo study, we investigate the long-term effects of age following SSRI or MPH treatment on our main outcome parameter (phMRI). Exposed subjects are stratified into two age groups: one group that has been prescribed early in life with these medications, and another group late in life. Subjects are recruited through a medical prescription database from the Pharmo Institute (Utrecht, the Netherlands). This out-patient pharmacy database is a database that contains drug dispensing data since 1986 from over 3 million residents in the Netherlands, corresponding to approximately 20% of the Dutch population. The dispensing date, prescriber, prescribed dosage regimen, and duration are known. Subjects participate in a single assessment day (cross-sectional design) with similar NPA and MRI investigations as in the ePOD RCTs, mentioned above. Subjects eligible for study participation are 23-40 years of age and presumably diagnosed with ADHD or MDD/anxiety disorder. The early exposed group contains subjects with a history of MPH (male subjects) or SSRI treatment (female subjects) before the age of 16 (thus at least 7 years ago). The late exposed group contains subjects treated between 23 and 40 years of age. The early-, and late exposed groups will be compared to an age-, and gender matched unexposed control group, consisting of medication naive subjects suffering from ADHD or MDD/anxiety disorder. Every group (six in total) will contain 25 subjects.

Assessments

Imaging parameters

Imaging parameters are directed towards the DAergic and 5-HTergic system. DAergic and 5-HTergic brain activity will be assessed using phMRI, which is the primary outcome measure of the ePOD project. In addition, DA connectivity will be assessed using rs-fMRI and DTI, and functional brain activity using DA-related (motor inhibition) or 5-HT-related (emotional processing) fMRI tasks. Due to time restrictions of the ePOD-SSRI scan protocol, 5-HT connectivity will only be assessed using DTI.

rs-fMRI

A relatively new fMRI approach (i.e., resting-state fMRI (rs-fMRI)) allows assessment of changes in organization of whole functional networks, including DAergic and 5-HTergic networks. Rs-fMRI detects baseline brain activity related to ongoing neuronal signaling at "rest" and is performed by low-pass filtering of spontaneous blood oxygenation level-dependent (BOLD) fMRI signals []. A decreased functional connectivity between anterior cingulated cortex and precuneus has been found using this technique in adult ADHD patients []. There are a number of studies that have investigated the effects of MPH or SSRIs on this parameter [, -], which found that these drugs normalize brain activation and functional connectivity abnormalities in patients suffering from ADHD or MDD. In accordance with this literature, we expect to find age-dependent normalization of functional connectivity abnormalities.

fMRI

We have selected two task-related fMRI scans either based upon their involvement of the DA system and/or the 5-HT system and the known interaction with MPH or FLX and treatment response in anxiety and depressive disorders. In view of our hypothesis we expect to find a normalized pattern of activation on these tasks in children during treatment, which will persist after the end of the trial. In contrast, the activation pattern in adult subjects will normalize during the trial and fall back to pre-treatment (hypoactivation) values after the end of the trial. The fMRI tasks consist of the following:

A motor inhibition task (only MPH trial and ePOD-Pharmo study): Frontal-striatal function and its modulation by MPH will be assessed using a motor inhibition task: the go/no-go task []. MPH has been shown to normalize striatal hypoactivation in ADHD subjects []. Specifically, fronto-striatal activation during response inhibition will be measured on two versions of a go/no-go task, each with and without administration of MPH. The effects of MPH on frontal and striatal activation during response inhibition will be compared within and between groups.

Neuropsychological tests

A neuropsychological test battery (Standard Reaction Time Task, Rey Auditory Verbal Learning Task [], Sustained Attention to Response Task (SART) [], N-back (working memory task) [], Maudsley Index of Delay Aversion (MIDA) []) will be administered, addressing reaction time, verbal memory, sustained attention, working memory and delay aversion in particular. This information can be linked to results from imaging in order to determine any links between behavioral and fMRI data and changes in the monoamine systems. We will look for correlations between altered cognitive responses and fMRI responses, phMRI responses, DTI measures and rs-fMRI response.

Cortisol measurements

In the ePOD-SSRI study salivary cortisol levels will be determined in salivary samples taken at home on a 'normal' weekday in the week before baseline and washout assessment days in order to determine the cortical awakenings response (CAR) and the diurnal cortisol cycle. Samples will be collected at 5 different moments: 1) directly after waking up, 2) 30 minutes after waking up, 3) 4 hours after waking up, 4) 8 hours after waking up, and 5) and 12 hours after waking up. To determine the peak after a 5-HT challenge, one salivary sample will be collected before the MRI scan session (baseline measure) and a second sample 30 minutes after the 5-HT challenge (directly after the MRI scan) on the day of both the MRI scan sessions.

Potential confounders

The study is designed to limit several important possible confounding parameters, such as gender effects (only women are included in the FLX trial and only men in the MPH trial) and aging effect (small age range, only young adults included). A within subject approach (pre- and post-treatment measurement in every subject) is used to rule out most between subject differences in the RCTs. Because of the design of the study, we have limited power and can correct for a maximum of 2 or 3 confounders. Therefore, age (in months) and ratings of symptom severity will be taken into account as covariates. In addition in the ePOD-SSRI study, the 5-HTTLPR polymorphism will be determined. The long allele of this SERT polymorphism in the promoter region (5-HTTLPR) has an activity twice that of the short allele [], resulting in higher densities of SERT. It is expected to be an important confounder to take into account when measuring SERT functioning. Also, significant associations between the long variant and a favorable treatment response have been repeatedly reported [].

Power analysis

Since these trials are the first to examine 5-HT and DA functioning following FLX and MPH treatment in children and young adults using MR imaging, there is only limited and indirect data available to perform a sample size calculation. The goal of our research is to detect differences in the age-dependency effect of FLX and MPH on the outgrowth of the DA-ergic and 5-HT-ergic system if these differences are in the magnitude of a standardized effect size of 1.25. From pilot experiments in rats and studies in humans with known alterations of 5-HT and/or DA (e.g., MDMA users or d-amphetamine users) we presume that the expected differences with our methods will lead to standardized effect sizes of at least 1.25. Both current trials will have the benefit of having before and after treatment measurements data from each patient. This paired data will reduce the between subject variability. This will increase the power of our trial to detect differences between groups. A sample size of 15 patients in each treatment-by-age group (4 groups) will be sufficient to detect standardized effect size of 1.25 with a two-sided significance level of 5% and a power of 90% to demonstrate age-dependency of the effects of MPH and FLX. To account for an expected drop-out of 25%, we will include 20 patients in each group for the FLX trial. Because the expected drop-out in the MPH trial is probably higher, due to motion artifacts in MRI scanning, we will include 25 patients in each treatment-by-age group. Because of slightly higher subject variability in the ePOD-Pharmo study (age and duration of treatment) again a sample size of 25 was chosen.

Statistical analysis

To evaluate the age-dependency of the effect of MPH and FLX on the outgrowth of the DA-ergic and 5-HT-ergic system, the change in our primary outcome measures (CBF) from baseline to post-treatment will be determined for each patient (∆i). These individual changes (∆i) will be used to estimate the treatment effect in adolescents (mean ∆ in treated patients minus mean ∆ in placebo treated patients) and in adults, which will be compared, as shown also in Figure . All analysis will initially be conducted using the intension-to-treat principle, but for the imaging outcomes a per-protocol analysis will also be performed.

The central analysis examines whether this treatment effect is different in adolescents compared to adults (effect modification or interaction by age). This hypothesis will be formally examined using ANOVA. The model includes treatment group (2 categories), age group (2 categories), and the interaction between treatment and age to examine whether the impact of MPH and FLX treatment differs by age. Depending on the imaging modality we will use a whole brain voxel based analysis or an ROI analysis. The same approach can be used for explorative analysis on the age-dependency of the effects on secondary outcome measures such as behavioral outcome (fMRI, neuropsychological assessment) and behavioral measures, and cortisol response for the FLX trial and sleep-log actigraph for the MPH trial.

Ethical considerations

Evidently, there are important ethical considerations that need to be taken into account with medication studies in children. In our case, the most important restriction is the duration of the clinical trial, or the time that a child would not receive adequate treatment (placebo condition). The duration of the RCT could not be longer than the time a child would otherwise also not receive adequate treatment, due to (relatively) long waiting lists in the Netherlands: typically 4 months at the time these studies were being evaluated by the Central Committee on Human Research in the Netherlands (CCMO). In the MPH study we overcome the treatment delay by including patients from the waiting list and offering psycho-education when necessary. In addition, in the ePOD-SSRI trial we give at least 18 sessions Cognitive Behavioral Therapy (CBT) to all adolescent participants. Therapy will be in accordance with the 'Doepressie' protocol, a psychotherapeutic program which is a Dutch translation of the internationally well-used program 'Coping with Depression Course for Adolescents' []. CBT is not part of standard clinical practice in the adult MDD population and will therefore not be provided to the adult patients. Adult MDD patients, who already receive some form of behavioral therapy at the start of the study, may continue this if they wish, but adult MDD patients cannot start a new therapy. Moreover, studies with SSRI's, especially in children have shown a placebo response up to 40% making treatment with placebo more ethically acceptable.

The RCTs have been approved by the Central Committee on Human Research in the Netherlands (CCMO), the Pharmo cohort study has been approved by the local medical ethics committee (METC) of the Academic Medical Center Amsterdam (AMC). All subjects participate on a voluntary base and receive a small financial compensation (50 euro and travel expenses). Written and informed consent from both patients and legal caregivers will be obtained in all cases.

In the ePOD project we propose a set of neuroimaging studies and neuropsychological assessments in which we examine the neural circuitry in adolescents with depression or anxiety and ADHD before and after treatment. As pointed out recently in an editorial from the American Journal of Psychiatry [], this type of research is greatly needed in a field in which most imaging studies have been conducted in adults. Because of ongoing brain development during adolescence, the neuropathophysiology, let alone the treatment, that underlie these disorders could be distinct. Slowly emerging evidence suggests that the long-term effects of drug exposure are delayed and expressed once the vulnerable system reaches maturation (i.e., typically during adulthood). This phenomenon, known as neuronal imprinting, occurs when the effects of drug exposure outlast the drug itself []. Thus, understanding the persistent effects critically depends on the window of observation. Therefore, ePOD is a unique clinical study in children and adults which will exactly grab this window of opportunity to measure age related effects of psychotropic drugs with sophisticated neuroimaging techniques. Embracing this concept should influence how we conduct preclinical assessments of developmental drug exposure, and ultimately how we conduct clinical assessments of drug efficacy, effectiveness, and safety for the treatment of childhood psychiatric disorders [].

As the safety of antidepressants to children still is a subject of concern, particularly since FLX is now licensed for the treatment of MDD in children of 8 years and older, information about the safety of FLX in treating childhood depression is needed. Especially the potential for an increased suicide risk in association with SSRIs in general has led to much debate [], as has also been pointed out by the Medicines Evaluation Board of the Netherlands [] and several comments in the Lancet in response to an article by Ebmeier and colleagues [].

The neurotransmitter 5-HT plays a crucial role in axonal outgrowth of 5-HT projections during brain development []. Earlier animal work demonstrated that postnatal 5-HT pharmacological manipulations can lead to abnormal outgrowth of the 5-HT system [-]. As an SSRI, FLX increases extracellular 5-HT concentrations by blocking SERT. Recently, studies in non-human primates have shown that FLX persistently upregulates SERT, but not 5-HT1A receptors, in the neocortex and the hippocampus of non-human primates []. These findings are in line with pilot experiments of our group and findings of Wegerer et al. and Bock et al., in rats, which also indicated that this effect persists into adulthood, long after discontinuation of treatment with SSRIs [, ]. Also, we showed with phMRI that juvenile-treated rats respond more strongly to a 5-HT challenge than same-age untreated rats, while adult-treated rats show a diminished response after previous chronic treatment []. This study showed that the phMRI technique is very well suited to address the primary objective of the ePOD-MPH studies: investigating whether the effect(s) of FLX on serotonin depend upon age. As may be expected, on a behavioural level, results are less consistent, although age-dependent responses to SSRIs on depression-like behaviour are described in both rats and mice [, , ]. All these findings most likely reflect the earlier described neuronal imprinting effects.

MPH is being prescribed to increasingly younger children [, ]. A meta-analysis has shown that in the USA and Australia up to 18 - 66% of those treated with stimulants do not meet the criteria for ADHD []. The increased prescription rates and concerns about proper diagnostic protocols have led to much public debate on the safety of MPH in the treatment of children. Indeed, a meta-analysis has shown that non-compliance is estimated at 20-65% and is attributed in part to apprehension about the safety of psychostimulants []. Recent work on the effects of MPH has shown that it may indeed normalize rates of cortical thinning, especially that of the prefrontal cortex []. In addition, in adult ADHD several reports on grey matter reductions were not able to distinguish between ADHD and psychostimulant effects [, ]. However, reports on greater rates of depression and anxiety in the treated groups of the MTA study sample and in several studies involving rats indicate that effects of MPH treatment may have mixed positive and negative effects [, , ]. Our main outcome parameter phMRI may be able to shed more light on the effects of MPH on the development of the DAergic system. This will increase our understanding of the safety and working mechanisms of MPH in a vulnerable population. In addition, we will gain insight into basal neurocognitive and neuroadaptive processes in the developing brain, as well as increasing our knowledge on the pathophysiology of ADHD.

However, there are also some limitations of the present study designs that need to be mentioned. One limitation is that the treatment provided to adolescents and adults is not the same in the ePOD-SSRI RCT. Adolescent subjects will receive CBT, whereas adult patients will not. As mentioned previously, CBT is not part of standard clinical practice in the adult MDD population and will therefore not be provided to the adult patients. From a methodological point of view it would have been ideal to isolate the effect of FLX and add no other treatment than this one in both age groups. However, this is not ethical as CBT is always part of standard clinical practice in the Netherlands in adolescent patients suffering from MDD. However, since change scores from baseline to post-treatment will be determined for each patient (∆i), the potential effect of (the lack) of CBT will be minimized. Another limitation is that no conclusions from the ePOD-SSRI and ePOD-MPH RCTs can be made on the long-term effects of these medications on brain development. The RCTs last for 'only' 4 months, and the washout period is 3 weeks maximum. For that reason, we designed the ePOD-Pharmo study, in which subjects are screened at least 7 years later following early FLX or MPH exposure. In addition, all participants in the RCTs are asked if they are willing to participate in a follow up study, scheduled in 3-5 years, and most are willing to participate. Thus, by combining the RCTs in which we investigate the causality of the age-dependency of FLX and MPH, together with the ePOD-Pharmo study which is directed towards the long-term effects of these medicines, will ultimately provide missing knowledge.

As recently indicated by Tao and colleagues, studies are needed that use the same methodology simultaneously in both adolescents and adults, to overcome methodological differences, and correct interpretation of the age-dependency of results []. Sample differences in age and illness status or differences in the image acquisition/analysis approach may obscure the age-dependency of the findings. These issues are overcome by the current study design. Since this study employs randomized controlled trials and has the benefit of having before and after treatment measurements from each patient, we will be able to reduce subject variability. This increases the ability of our trial to detect differences between groups. Moreover, this study employs novel non-invasive MRI techniques in children and adolescents, which provide new insights into the effects of psychotropic drugs on the developing brain. The use of phMRI in assessing DAergic and 5-HTergic functionality may have important prognostic factors, for instance in predicting responsiveness to psychostimulants or antidepressant medication in the near future.

So far, most imaging studies have been conducted in adults. Ongoing brain development during adolescence may distinct the neural mechanisms that underlie psychiatric disorders like depression, anxiety and ADHD. Examination of these mechanisms during early phases of the disorder provides the opportunity to avoid confounds due to complex treatment histories or potential scarring from years of disease. A better understanding of adolescent-specific mechanisms will be "a critical foundation for the advancement of early treatment interventions, which could significantly affect public health" [].

In the ePOD studies we propose a set of neuroimaging studies and neuropsychological assessments in which we examine the neural circuitry in adolescents with depression or anxiety and adolescents with ADHD before and after treatment. The combination of prospective studies with a cross-sectional cohort study, using the same outcome measures, will increase our understanding not only of the working mechanisms of both FLX and MPH in children and adolescents, but also provide more information about the safety of these substances in the maturing brain.

Marieke LJ Schouw, Anne Klomp contributed equally to this work.

Acknowledgements

This research project was part of a project supported by the Netherlands Organization for Research (NWO) (Veni no.916.86.125) and an AMC Fellowship awarded to dr. L. Reneman. The funder had no role in the study design, data collection and analysis, decision to publish, or the preparation of the manuscript.

Authors' original submitted files for images

Competing interests

Authors' contributions

Conception of the study was conducted by LR. All authors participated in design of the study. MB, MS, AK, HT, AS, and CB acquire the data. MB, MS, AK, HT, AS, CB, MdR and HR will analyse the data. MB, MS, AK, HT, AS, CB, MdR, RR, HR, HG and LR will participate in data interpretation. MB, MS, AK and LR drafted the manuscript. All authors revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.

The authors declare that they have no competing interests.

Below are the links to the authors' original submitted files for images.

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The effects of Psychotropic drugs On Developing brain (ePOD) study: methods and design

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Animal studies have shown that methylphenidate (MPH) and fluoxetine (FLX) have different effects on dopaminergic and serotonergic system in the developing brain compared to the developed brain. The effects of Psychotropic drugs On the Developing brain (ePOD) study is a combination of different approaches to determine whether there are related findings in humans.

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Marilyn Wedge is a family therapist who strongly condemns the prescription and use of psychotropic drugs in her book Suffer The Children. Wedge’s concern sparks from the rising number of clients she’s received who are troubled by the prospect of an array of psychiatric disorders. She believes the recent increase in pronounced mentally ill children is invalid. Wedge blames society for relying on drugs and psychiatrically labeling what she calls normal childhood behavior and reactions. In replacement to medication, Wedge suggests alternative methods of family therapy to rule out psychiatrical diagnoses and i don’t know what because i didn’t actually read that part! but … OBVIOUSLY she believes in therapy bc she’s a freaking therapist. PLUS she uses like one freaking fact the entire time. ugh im over itttt

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whoaaa look it's the same lady... Marilyn Wedge

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The ADHD Fallacy: It's Time To Stop Treating Childhood as a Disease

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How did our image of childhood evolve so that behaviors once considered normal are now considered a disorder?

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some nice numbers

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The following information is the most accurate and documented data available on psychiatric drug usage in the United States. The information is from IQVia (formerly IMS Health), a company that provides information, services and technology for the healthcare industry. It is the largest vendor of U.S. physician prescribing data. The following data was taken from the IQVia Total Patient Tracker Database for Year 2017, extracted April 2018. To see drug regulatory agency warnings, studies and documented side effects of the psychiatric drugs prescribed to children, click hyperlinks below in blue (i.e., ADHD Drugs, Antidepressants, Antipsychotics, etc).

Breakdown:
0-1 Years 125,361
2-3 Years 202,319
4-5 Years 306,079

Grand Total 0-17 Years 7,213,599 kids on psychiatric drugs

Breakdown:
0-1 Years 328
2-3 Years 1,919
4-5 Years 77,396

Grand Total 0-17 Years 3,655,472 kids on ADHD Drugs

Breakdown:
0-1 Years 6,687
2-3 Years 10,957
4-5 Years 21,299

Grand Total 0-17 Years 2,100,315 kids on antidepressants

Breakdown:
0-1 Years 3,913
2-3 Years 27,001
4-5 Years 53,750

Grand Total 0-17 Years 1,194,805 kids on antipsychotics

Breakdown:
0-1 Years 102,960
2-3 Years 148,894
4-5 Years 143,692

Grand Total 0-17 Years 1,445,509 kids on anti-anxiety drugs

SOURCE: IQVia, Total Patient Tracker (TPT) Database, Year 2017, Extracted April 2018.

For more information including non-drug, non-harmful medical alternatives visit, Parents: Get the Facts-Know Your Rights

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Number of Children & Adolescents Taking Psychiatric Drugs in the U.S. | CCHR International

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The following information is the most accurate and documented data available on psychiatric drug usage in the United States. The information is from IQVia (formerly IMS Health), a company that provides information, services and technology for the healthcare industry. It is the largest vendor of U.S. physician prescribing data.

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Importance The increased prescribing of antipsychotics for children and youth has heightened concerns that this practice increases the risk of type 2 diabetes mellitus.

Objective To compare the risk of type 2 diabetes in children and youth 6 to 24 years of age for recent initiators of antipsychotic drugs vs propensity score-matched controls who had recently initiated another psychotropic medication.

Design, Setting, and Participants Retrospective cohort study of the Tennessee Medicaid program with 28 858 recent initiators of antipsychotic drugs and 14 429 matched controls. The cohort excluded patients who previously received a diagnosis of diabetes, schizophrenia, or some other condition for which antipsychotics are the only generally recognized therapy.

Main Outcomes and Measures Newly diagnosed diabetes during follow-up, as identified from diagnoses and diabetes medication prescriptions.

Users of antipsychotics had a 3-fold increased risk for type 2 diabetes (HR = 3.03 [95% CI = 1.73-5.32]), which was apparent within the first year of follow-up (HR = 2.49 [95% CI = 1.27-4.88]). The risk increased with cumulative dose during follow-up, with HRs of 2.13 (95% CI = 1.06-4.27), 3.42 (95% CI = 1.88-6.24), and 5.43 (95% CI = 2.34-12.61) for respective cumulative doses (gram equivalents of chlorpromazine) of more than 5 g, 5 to 99 g, and 100 g or more ( Results P < .04). The risk remained elevated for up to 1 year following discontinuation of antipsychotic use (HR = 2.57 [95% CI = 1.34-4.91]). When the cohort was restricted to children 6 to 17 years of age, antipsychotic users had more than a 3-fold increased risk of type 2 diabetes (HR = 3.14 [95% CI = 1.50-6.56]), and the risk increased significantly with increasing cumulative dose ( P  < .03). The risk was increased for use restricted to atypical antipsychotics (HR = 2.89 [95% CI = 1.64-5.10]) or to risperidone (HR = 2.20 [95% CI = 1.14-4.26]).

Conclusions and Relevance Children and youth prescribed antipsychotics had an increased risk of type 2 diabetes that increased with cumulative dose.

Increasing antipsychotic use among children and youth raises the concern that this practice increases the risk of type 2 diabetes mellitus in this vulnerable population. For adults, there is considerable evidence linking antipsychotic use to increased risk of type 2 diabetes. Several antipsychotics have metabolic effects, such as weight gain, increased glucose level, and insulin resistance, that are thought to be precursors to diabetes. Epidemiologic studies have confirmed an increased risk for type 2 diabetes for individuals using some types of antipsychotics, particularly the atypical antipsychotic drugs. However, the evidence for children and youth is less extensive. Although metabolic studies of children suggest that antipsychotic use might increase the risk of type 2 diabetes, epidemiologic data are more limited. Obstacles to studies of this population include the lower incidence of type 2 diabetes, the need to distinguish between type 1 and type 2 diabetes, and the identification of appropriate comparison groups.

Prior to the introduction of the atypical antipsychotic drugs, the primary indications for antipsychotics in pediatric or adolescent populations were schizophrenia and other psychotic disorders. Subsequently, use expanded to include bipolar disorders, affective disorders, and symptoms related to behavior and conduct, which now account for the majority of prescriptions. There are other well-recognized alternative medications for each of these conditions; indeed, antipsychotics are often a secondary or off-label therapeutic choice. Thus, an increased risk of diabetes conferred by antipsychotic medications would be an important component of risk-benefit evaluation.

To better define the relationship between antipsychotic use and newly diagnosed type 2 diabetes among children and youth, we conducted a retrospective cohort study of the Tennessee Medicaid program. The cohort included recent initiators of antipsychotics who had received no diagnosis for which antipsychotics were generally the only recognized pharmacotherapy and a propensity score-matched control group of recent initiators of other psychotropic medications.

Study data were obtained from the computerized files of the Tennessee Medicaid program, augmented with linkage to a statewide hospital discharge database and computerized birth certificates. Study files (enrollment, pharmacy, hospital, outpatient, nursing home, and linked death certificates) allowed for the identification of the study cohort, the classification of baseline comorbidity, the tracking of medication use, and the ascertainment of diabetes.

Antipsychotics and other study medications were identified from Medicaid pharmacy files. These included the date that the prescription was dispensed, drug name, quantity, dose, and days of supply. Computerized pharmacy records are an excellent source of medication data because they are not subject to information bias and have a high level of concordance with patient self-reports of medication use. Residual misclassification should be limited and, if nondifferential, should bias toward the null.

The study population included children and youth 6 to 24 years of age enrolled in Medicaid for at least 1 year between January 1, 1996, and December 31, 2007. The lower age limit is the youngest age for which there are material numbers of case reports of type 2 diabetes; the upper age limit corresponds to the World Health Organization's definition of youth. Cohort eligibility (eAppendix and eTables 1 and 2 in Supplement) required that, during the past year, there was adequate enrollment and health care utilization to ensure availability of data for study variables, no evidence of life-threatening illness or institutional residence, no evidence of diabetes, and no evidence of pregnancy (gestational diabetes might be misdiagnosed) or polycystic ovarian syndrome (treated with oral hypoglycemics). Cohort members could not have been in the hospital in the past month because changes in the medication regimen cannot be identified until up to 30 days following hospital discharge.

Cohort antipsychotic users had recently initiated antipsychotic therapy by filling an antipsychotic prescription on a day of cohort eligibility. The first such prescription during the study period was the qualifying prescription. They could have nonqualifying use of antipsychotics in the 90 days preceding the qualifying prescription (allowed inclusion of patients starting an antipsychotic shortly before meeting cohort eligibility criteria) but had to have a prior period of 365 days free of antipsychotic use. The cohort was restricted to recent users to include cases of diabetes that occurred early in therapy and to ensure that baseline covariates were unaffected by chronic antipsychotic effects.

We excluded patients with diagnosed conditions for which antipsychotics generally are the only recommended treatment. These included schizophrenia or related psychoses, organic psychoses, autism, mental retardation, Tourette syndrome, or other tic disorders. We also excluded patients prescribed clozapine or long-acting injectable preparations-usually indicators of schizophrenia or related psychoses-as well as those with parenterally administered drugs, typically given for transient agitation.

Potential controls were recent initiators of other psychotropic drugs, defined as for antipsychotics, with no antipsychotic use in the 365 days preceding the qualifying prescription. Control drugs included mood stabilizers (lithium or anticonvulsant mood stabilizers [absent evidence of a neurologic indication]), antidepressants with a psychiatric diagnosis, psychostimulants, α-agonists with diagnosed attention-deficit/hyperactivity disorder (ADHD) or other problems of behavior/conduct, and benzodiazepines with a psychiatric diagnosis (eAppendix and eTable 3 in Supplement).

From the pool of potential controls, we calculated the propensity scores, the conditional probability of being an antipsychotic user, given the study covariates. These were factors that might be directly or indirectly related to both antipsychotic use and the development of type 2 diabetes (eAppendix and eTable 4 in Supplement). The 115 covariates included demographic characteristics, psychiatric diagnoses and medications, metabolic disorders and related conditions (eg, diagnosed obesity), obstetric-gynecologic conditions (eg, absent/irregular menstruation), cardiovascular disease (eg, hypertension), respiratory disorders (eg, sleep apnea), musculoskeletal symptoms (eg, joint pain), other somatic conditions, and intensity of health care utilization (medical surveillance) for both psychiatric and somatic comorbidity. The estimation of the propensity scores was stratified according to the presence of a bipolar disorder (diagnosis or mood stabilizer prescription) because the propensity score coefficients differed for patients with this disorder.

The final control group included 1 control for every 2 antipsychotic users, frequency-matched with the antipsychotic users according to propensity score to ensure baseline comparability with regard to study covariates. Because we sought a control group highly comparable to the antipsychotic users, we required that the controls be matched within centiles (1%) of the antipsychotic propensity score distribution. The 1:2 matching ratio was established by a preliminary analysis of the potential control pool, indicating that there were too few controls to permit such close 1 to 1 matching (eAppendix and eTable 5 in Supplement).

Follow-up began on t ₀, the day following the filling of the qualifying prescription for the antipsychotic or control drug, which permitted exclusion of participants based on medical care on the prescription fill date (eg, schizophrenia diagnosis). Follow-up ceased (eAppendix and eTable 6 in Supplement) with the end of the study, owing to a failure of the participant to meet study inclusion/exclusion criteria, a diagnosis of diabetes, or the death of the participant or 365 days following the last day of antipsychotic/control drug use. Follow-up for controls also ended with an antipsychotic prescription. Antipsychotic users or controls who left the cohort could reenter if they subsequently met the study eligibility criteria, unless follow-up terminated because of diabetes.

Antipsychotic Exposure Variables

The primary exposure variable was antipsychotic use status (user or control) on t ₀, the first day of follow-up. Because baseline exposure status did not change during follow-up, the analysis provided a conservative assessment of antipsychotic effects. At baseline, antipsychotic users were also classified according to daily dose on t ₀, expressed as chlorpromazine equivalents (eAppendix and eTable 7 in Supplement).

We defined time-dependent antipsychotic exposure variables in order to study antipsychotic cumulative dose and cessation of use. Cumulative dose on a given follow-up day was the sum of all previously dispensed antipsychotic doses (chlorpromazine equivalents). A follow-up day was considered "recent use" if the prescription days of supply indicated use on that day or within the preceding 90 days. The "former use" of antipsychotics was defined as more than 90 days without use of an antipsychotic.

Newly diagnosed cases of diabetes during study follow-up were identified from medical care encounters using an algorithm that was validated in a sample of the study cohort. A primary discharge diagnosis of diabetes met the case definition. Otherwise, we required both a diagnosis of diabetes and a prescription for an antidiabetic medication within a 120-day period. We required confirmation because single outpatient diabetes-related medical care encounters often were false positives. Outpatient diagnoses of diabetes in the absence of an antidiabetic medication prescription frequently indicated subthreshold hyperglycemia, whereas prescriptions for oral hypoglycemics in the absence of a diagnosis of diabetes were often considered the treatment of choice for polycystic ovarian syndrome. The presumed date of the diagnosis was that of the earliest diabetes-related medical encounter. Type 1 diabetes was indicated by exclusive treatment with insulin; other cases were considered type 2 diabetes.

In the validation study, 84% of cases identified by our computer algorithm as having type 2 diabetes were confirmed as being true cases, 10% had subthreshold hyperglycemia, 3% had type 1 diabetes, and 3% had polycystic ovarian syndrome. The positive predictive value for type 2 diabetes did not differ materially between antipsychotic users (82%) and controls (85%).

The statistical analysis compared the adjusted incidences of diabetes according to antipsychotic exposure status. Relative risk was estimated with the hazard ratio (HR), calculated from Cox regression models with a robust sandwich estimation of variance to account for persons reentering the cohort. There was no evidence of interaction between time and antipsychotic use status (HR for interaction, 1.00; P  < .30), indicating that the proportional hazards assumption was satisfied. Regression models included the baseline propensity score (deciles), to adjust for residual confounding, as well as age and calendar year during follow-up. Other time-dependent covariates were not included in the primary analysis because these might be on the causal pathway for development of diabetes (eg, new diagnosis of obesity).

For the analyses of antipsychotic dose, the propensity score may not control for confounding, given that the distribution of study covariates could vary according to dose. Thus, models for these analyses included a disease risk score (the probability of type 2 diabetes, conditional on no antipsychotic use), expressed as deciles. Tests for the dose-response relationship used a single degree of freedom orthogonal polynomial contrast for linear trend.

An a priori subgroup analysis was performed for children 6 to 17 years of age, with follow-up ending on the day before their 18th birthdays. Subgroup analyses also were performed according to sex, the presence of a bipolar disorder (the most common labeled indication for antipsychotics in the cohort), use of psychostimulants (which can possibly limit weight gain), and a diagnosis of ADHD or conduct disorder.

Given that since 2004 guidelines have recommended routine glucose monitoring for antipsychotic users, we performed several analyses to assess the potential effect of differential screening during follow-up. Thus, follow-up time was classified according to the presence of a metabolic panel with glucose or other screening test in the past year. The screening variable was lagged 90 days to exclude the tests associated with the diagnosis of diabetes in the cases. We also performed an analysis that excluded person-time subsequent to 2004, which should be little affected by screening recommendations.

Additional analyses modeled possible clustering effects introduced by the frequency matching, restricted the cohort to new users of antipsychotics and control medications, did not allow antipsychotic users to reenter the cohort, and utilized an alternative definition of type 2 diabetes. All analyses were performed with SAS version 9.2 (SAS Institute Inc). All P values are 2-sided. The institutional review board at Vanderbilt University and the Tennessee Bureau of TennCare and the Department of Health approved our study, which was funded by grants from federal agencies with no role in study conduct or reporting.

The cohort included 28 858 children and youth who had recently initiated antipsychotic therapy (eAppendix and eTable 1 in Supplement). There were 14 429 propensity score-matched controls who had recently initiated a control psychotropic drug and who were selected from 122 738 potential controls. By virtue of the matching, the baseline characteristics of the controls were entirely comparable to those of the antipsychotic users ( Table 1). Both antipsychotic users and controls had initiated use of the study psychotropic drug within a mean of fewer than 6 days prior to cohort entry.

Cohort members had a mean age of 14.5 years, and 56% were male participants ( Table 1). The most frequently recorded psychiatric diagnoses were mood disorders (including bipolar disorder), ADHD, and conduct disorder. Metabolic disorders and other factors potentially associated with diabetes were relatively infrequent, although 23% of cohort members had had a diagnostic test that included glucose measurement administered in the year preceding t ₀.

The median starting dose for cohort antipsychotic users was 67 mg (interquartile range, 33-100 mg) of chlorpromazine equivalents. Of antipsychotic users, 87% were prescribed an atypical agent (eAppendix and eTable 8 in Supplement); use of typical antipsychotics was largely restricted to the earlier study years. The most frequently prescribed individual antipsychotic was risperidone (n = 10 718; 37% of antipsychotic users), followed by quetiapine fumarate (n = 5807; 20% of antipsychotic users) and olanzapine (n = 5671; 20% of antipsychotic users). Risperidone users were younger, more likely to be male, had greater prevalence of diagnosed ADHD, and were started at lower doses than were users of other atypical antipsychotics.

The cohort had 55 984 person-years of follow-up, during which there were 21 cases of type 1 diabetes (3.8 cases per 10 000 person-years). These cases consisted of persons who had a mean age of 13 years, 62% were male, and 29% had Medicaid enrollment related to disability ( Table 2). Antipsychotic users had no significantly increased risk for type 1 diabetes (HR = 1.13 [95% CI = 0.43-3.00]).

There were 106 incident cases of type 2 diabetes (18.9 cases per 10 000 person-years) during cohort follow-up. The mean age of the persons was 16.7 years, 37% were male, and 34% had Medicaid enrollment related to disability ( Table 2).

Antipsychotic users had a 3-fold increased risk for type 2 diabetes ( Figure 1 and Table 3; HR = 3.03 [95% CI = 1.73-5.32]). The increased risk was apparent within the first year of follow-up (HR = 2.49 [95% CI = 1.27-4.88]). Risk did not vary significantly according to baseline dose but did increase with cumulative dose during follow-up ( Table 2). The HR for cohort members with a cumulative dose of 100 g or greater of chlorpromazine equivalents was 5.43 [95% CI = 2.34-12.61], whereas the HR for those with a cumulative dose of less than 5 g of chlorpromazine equivalents was 2.13 [95% CI = 1.06-4.27] ( P  = .04). The risk remained elevated for up to 1 year following the discontinuation of antipsychotic use (HR = 2.57 [95% CI = 1.34-4.91]). Although this was less than that for cohort members who continued to use antipsychotics (HR = 3.19 [95% CI = 1.77-5.74]), the difference was not statistically significant.

When the cohort was restricted to children 6 to 17 years of age, antipsychotic users had more than a 3-fold increased risk of type 2 diabetes ( Figure 2; HR = 3.14 [95% CI = 1.50-6.56]). For these children, the incidence increased with increasing cumulative dose ( Figure 2), from an HR of 2.00 (95% CI = 0.76-5.30) for cumulative doses of less than 5 g to an HR of 7.05 (95% CI = 2.63-18.89) for cumulative doses of 100 g or more ( P  = .03).

We examined the risk for atypical antipsychotics, which were used by 87% of cohort users, and for individual atypical drugs. The risk for type 2 diabetes increased with the cumulative dose of all atypical antipsychotics, including risperidone, which drug was used by approximately 40% of the cohort antipsychotic users (eAppendix and eTable 9 in Supplement). Significantly increased HRs were present for other atypical antipsychotics; the difference between HRs for risperidone and HRs for aripiprazole was statistically significant ( P  < .001).

We examined several subgroups defined by baseline cohort characteristics ( Figure 3), including age, sex, presence of a bipolar disorder, psychostimulant use, and a diagnosis of either ADHD or conduct disorder. For each of these subgroups, the risk of type 2 diabetes was significantly increased for antipsychotic users, and the estimates for the subgroups defined by the individual factors did not differ statistically.

We also assessed the risk of type 2 diabetes according to screening for elevated glucose levels in either the year preceding t ₀ or during follow-up ( Figure 3). For the controls, 28% of the follow-up person-time had such screening, as did 33% of the person-time for the antipsychotic users. Both exposure groups had increased HRs for type 2 diabetes, and these did not differ significantly according to screening status. Similar results were present in an analysis according to the presence of a screening test during follow-up (there was a mean number of 0.35 tests during follow-up for the controls and of 0.51 tests for the antipsychotic users). An analysis that excluded person-time subsequent to 2004 (the year of the first publication of guidelines recommending screening of antipsychotic users) also demonstrated increased risk for antipsychotic users (HR = 2.73 [95% CI = 1.35-5.53]).

The increased risk of type 2 diabetes among antipsychotic users persisted in several sensitivity analyses that assessed study assumptions (eAppendix and eTable 10 in Supplement). These analyses included control for possible clustering induced by the frequency matching (HR = 3.07 [95% CI = 1.74-5.39]), restriction of the cohort to new users of antipsychotics and control psychotropic medications (HR = 3.05 [95% CI = 1.70-5.46]), not permitting antipsychotic users who left the cohort to reenter (HR = 2.86 [95% CI = 1.55-5.26]), and use of an alternative definition for type 2 diabetes that required a prescription to confirm a diagnosis, except in the case of a primary inpatient diagnosis (HR = 3.11 [95% CI = 1.74-5.57]). In each of these analyses, the risk for type 2 diabetes increased with cumulative antipsychotic dose (eAppendix and eTable 10 in Supplement).

In this cohort of children and youth who had recently initiated use of an antipsychotic or a control psychotropic drug, antipsychotic users had a risk of newly diagnosed type 2 diabetes 3 times greater than that for propensity score-matched controls. The excess risk occurred within the first year of antipsychotic use, increased with cumulative antipsychotic dose, and was present for children 6 to 17 years of age. The increased risk persisted for up to 1 year following cessation of antipsychotic use.

Study cases of type 2 diabetes were identified from diagnoses from clinical practitioners and prescriptions for antidiabetic drugs. Thus, there was the potential for false positives. However, a validation study conducted in a cohort sample found that the case definition had a positive predictive value of 84% and that this did not differ materially according to antipsychotic use status. These data suggest that the errors made by clinical practitioners in the diagnosis of diabetes are unlikely to explain the study findings.

Another type of misclassification that may have affected our findings was the incomplete identification of type 2 diabetes in the cohort. In routine practice, many children and youth may not undergo the testing necessary for this diagnosis. This could introduce bias if the diagnostic scrutiny of the antipsychotic group was greater than that for controls. To minimize this potential bias, we sought to ensure comparability of medical surveillance for both groups. Thus, antipsychotic users and controls had recently initiated psychotropic drug therapy and were closely matched at baseline according to glucose or diabetes screening tests, as well as recent medical care utilization, which is an indicator of diagnostic scrutiny.

However, guidelines published in 2004 recommending routine glucose monitoring for antipsychotic users could have led to differential surveillance during follow-up. Thus, we performed several analyses to assess this possibility. When data were analyzed according to the presence of a screening test, either at any time or only during follow-up, the increased risk was present for both those who were and those were not screened. This analysis should be conservative, given that screening could be triggered by antipsychotic-related weight gain. Furthermore, in an analysis that excluded person-time subsequent to the year of guideline publication, the magnitude of the increased risk for antipsychotic users was little changed.

We could not directly control for obesity, which is closely linked to the development of type 2 diabetes. However, a material difference in body mass index between antipsychotic users and controls seems unlikely, given that controls were very closely matched according to 115 study covariates, many of which either are plausibly associated with increased body mass or might mediate an association between obesity and antipsychotic use. These included diagnosed obesity, disorders or diagnostic testing linked to excess weight, psychiatric diagnoses, psychotropic drug use, and demographic characteristics. The observed dose-response relationship is further evidence that the study findings were not due to confounding by obesity.

For adults, the risk of type 2 diabetes conferred by antipsychotics is most pronounced for atypical antipsychotics and may vary according to specific drug. In the study cohort, 87% of antipsychotic users received atypical drugs. Study findings were largely unchanged when the cohort was restricted to this group or to risperidone, the most frequently prescribed individual drug. Olanzapine, quetiapine, ziprasidone, and aripiprazole were used less frequently, but each had a significantly elevated HR. However, this post hoc finding must be interpreted cautiously. There was marked variation in the baseline characteristics of users of specific antipsychotics. Our study was not designed to study the comparative risk of individual drugs, given that sample size did not permit a propensity score match for specific antipsychotics. Furthermore, it is possible that high-risk patients may have been recommended a drug perceived to have greater metabolic safety.

Both the pathophysiology and the epidemiology of type 2 diabetes indicate that its development is a chronic process. Although the risk of type 2 diabetes did increase with cumulative dose of antipsychotics, which is consistent with a chronic process, we also found that a significantly increased risk was present during the first year of therapy. Cases of early-onset antipsychotic-associated diabetes have been reported for adults. In one series, the majority of cases occurred within 6 months of drug initiation. Although there are fewer case reports in the literature for children, early-onset cases also have been described. Further study of the pathophysiology of antipsychotic-associated diabetes is needed.

In the study cohort, there were an estimated 15.8 additional cases of type 2 diabetes per 10 000 person-years of antipsychotic exposure, or a number needed to harm of 633. However, this number should be applied cautiously in clinical practice because the baseline risk for a child or youth will vary substantially according to age and body mass index. Furthermore, the study cohort consisted of Tennessee Medicaid enrollees (approximately 40% of the state's children), which also limits the generalizability of study findings, given that the incidence of type 2 diabetes in children covered by Medicaid may be elevated owing to economic and social factors, as well as to a greater prevalence of behavioral risk factors, disability, and chronic illness.

In conclusion, in the study cohort of children and youth between 6 and 24 years of age, those recently initiating an antipsychotic medication had a 3-fold greater risk of newly diagnosed type 2 diabetes than did propensity score-matched controls. Risk was elevated during the first year of antipsychotic use, increased with increasing cumulative dose, and was present for children younger than 18 years of age.

Corresponding Author: Wayne A. Ray, PhD, Division of Pharmacoepidemiology, Department of Preventive Medicine, Vanderbilt University School of Medicine, 1500 21st Ave S, Ste 2600, Nashville, TN 37212 ( wayne.ray@vanderbilt.edu).

Submitted for Publication: September 11, 2012; final revision received December 2, 2012; accepted January 21, 2013.

Published Online: August 21, 2013. doi:10.1001/jamapsychiatry.2013.2053.

Author Contributions: All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: All authors.

Acquisition of data: Bobo, Cooper, Daugherty, Ray.

Analysis and interpretation of data: Bobo, Cooper, Stein, Olfson, Graham, Ray.

Drafting of the manuscript: Bobo, Ray.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Graham, Ray.

Obtained funding: Ray.

Administrative, technical, or material support: Cooper, Daugherty.

Study supervision: Cooper, Ray.

Conflict of Interest Disclosures: Dr Bobo has received research support from Cephalon and served on speaker bureaus for Janssen Pharmaceutica and Pfizer. Dr Olfson has received support from a research grant to Columbia University from Eli Lilly (end date June 20, 2010) and one from Bristol-Myers Squibb (end date January 31, 2010). No other disclosures are reported.

Funding/Support: This study was supported by the Agency for Healthcare Research and Quality, Centers for Education and Research on Therapeutics cooperative agreement (grant HS1-16974).

Disclaimer: The views expressed are solely the responsibility of the authors and do not necessarily represent the official views of the US Food and Drug Administration.

Additional Information: The first draft of the article was written by Drs Bobo and Ray, who vouch for the data and the analysis.

Additional Contributions: We gratefully acknowledge the Tennessee Bureau of Medicaid and the Department of Health, which provided study data.

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Antipsychotics and the Risk of Type 2 Diabetes

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In a retrospective cohort study, Bobo and coauthors compare the risk of type 2 diabetes in children and youth 6 to 24 years of age for recent initiators of antipsychotic drugs vs propensity score-matched controls who had recently another psychotropic medication.

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International Journal of Pediatric Endocrinology

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There is increasing use of antipsychotic drugs in pediatric and psychiatry practice for a wide range of behavioral and affective disorders. These drugs have prominent side effects of interest to pediatric endocrinologists, including weight gain and associated metabolic risk factors and hyperprolactinemia. The drugs block dopamine action, thus disinhibiting prolactin secretion. Hyperprolactinemia is especially prominent with first-generation antipsychotics such as haloperidol and the second-generation drugs, most commonly risperidone, with some patients developing gynecomastia or galactorrhea or, as a result of prolactin inhibition of gonadotropin releasing hormone from the hypothalamus, amenorrhea. With concern about the long-term effects of antipsychotics on bone mass and pituitary tumor formation, it is prudent to monitor serum prolactin levels in antipsychotic drug-treated pediatric patients and consider treatment with an agent less likely to induce hyperprolactinemia.

Bone Mineral Density

Prolactin

Clozapine

Risperidone

Olanzapine

The practice of psychiatry and the shift to noninstitutional care of severe psychiatric disorders are the result of antipsychotic medications, beginning with chlorpromazine in the early 1950s. Approximately 10 other drugs, known as first-generation or typical antipsychotics, followed over the subsequent 30 years. These drugs were effective in treating positive symptoms of psychosis such as hallucination and delusion but did not alleviate the negative symptoms of withdrawal, apathy, cognitive impairment, or loss of affect. Furthermore, they were associated with frequent extrapyramidal symptoms, including acute dystonia, akinesia, akathisia, tardive dyskinesia, and parkinsonism. A series of newer drugs began emerging in 1989, referred to as second generation or atypical antipsychotics, thought to be more effective than the older agents in alleviating the negative, cognitive, and affective symptoms, with fewer extrapyramidal adverse effects [ 1].

The antipsychotic drugs differ in their side effect profiles, as they affect different neuroreceptors (histamine, α-adrenergic, muscarinic, dopamine, or serotonin). The principal concern for endocrinologists with the newer drugs has been the metabolic effects of weight gain, glucose intolerance, hyperlipidemia, and hypertension [ 2]. This is particularly important with the increasing use of these agents in pediatrics to treat bipolar disorder, schizophrenia, autism, oppositional and other behavior disturbances, Tourette disorder, and pervasive developmental disorder. In 2003-2004, 1% of all pediatric visits resulted in the prescription of atypical antipsychotic medication [ 3]. The importance and implications of the metabolic side effects of atypical antipsychotics for pediatric patients have been recently reviewed [ 1, 4].

This paper will examine the side effect of hyperprolactinemia in children and adolescents treated with antipsychotic drugs.

Prolactin, a 198-amino acid polypeptide, is secreted by the anterior pituitary lactotroph cells in a pulsatile manner with 13-14 peaks per day, the peak amplitude 60% above nadir [ 5]. There is also a marked circadian variation with maximum secretion 4 hours from sleep onset and minimum 6 hours after waking [ 6]. Thus, there can be as much as a fourfold variation in level depending on the time of day or night sampling is done; there are also transient mild increases related to meals, stress, and sexual activity [ 7]. Prolactin levels are higher during menstrual midcycle and the 2nd half of the cycle. During pregnancy, levels rise 10-20-fold, reaching 200  g/L at term and 300 g/L during nursing [ 8]. Normal upper limit range is 15-25  g/L [ 7].

Prolactin stimulates breast enlargement during pregnancy and milk production during lactation, while reducing libido and fertility, which may have evolutionary/survival significance.

Secretion of prolactin is inhibited predominantly by dopamine produced in the tuberoinfundibular neurons of the hypothalamus, released from nerve endings in the median eminence and carried through the portal hypophyseal circulation to the pituitary, there binding to dopamine receptors on lactotrophs, inhibiting prolactin gene transcription [ 9]. Antipsychotic drugs are thought to disinhibit prolactin secretion by receptor blockade. Serotonin stimulates prolactin secretion via serotonin and 5- receptors. Estrogens, binding to specific intracellular receptors in lactotrophs, enhance prolactin gene transcription and synthesis [ 6]. They also inhibit dopamine synthesis in the tuberoinfundibular neurons and reduce D ₂ receptor levels on lactotrophs in animal models [ 10, 11].

Gonadotropin releasing hormone (GnRH), released in a pulsatile manner from the hypothalamus, stimulates release of luteinizing hormone (LH) and follicle stimulating hormone (FSH) from the anterior pituitary. Prolactin inhibits the release of GnRH in the hypothalamus. The positive feedback of estradiol on LH secretion in women is also blocked. Consequently, estrogen levels in women and testosterone levels in men are suppressed, with marked individual variability in the prolactin level causing gonadal hypofunction.

In children and adolescents, hyperprolactinemia resulting from prolactinomas, which are rare, can result in galactorrhea, amenorrhea, gynecomastia, and maturational delay with growth failure [ 12]. Of concern is the potential effect of the induced hypogonadotropism state on the critical peak bone formation of adolescence and the maintenance of bone density through adulthood.

Psychiatric disorders may be associated with modest elevations in serum prolactin concentrations as a stress phenomenon [ 13]. Further prolactin elevation can be measured within minutes to hours after the start of treatment with first-generation antipsychotic drugs, with levels up to 10-fold after several weeks at therapeutic dosages. Levels typically fall to normal within 2 to 4 days of stopping the drugs but may take up to 3 weeks to return to normal [ 14].

In adult psychiatric patients clozapine and quetiapine did not raise plasma prolactin levels at any dosage. Olanzapine only did so at higher dosages, but risperidone and amisulpride caused marked, sustained increase in serum prolactin levels in a substantial number of patients [ 14]. Aripiprazole, a relatively new atypical antipsychotic, also does not appear to increase prolactin levels [ 15]. Ziprasidone caused only transient elevations in psychiatric patients and healthy volunteers [ 16].

A review published in 2004 of 14 reports of the effects of both first- and second-generation antipsychotic agents in children and adolescents included 276 patients of whom 49 had prolactin elevations [ 17]. A report of 35 patients aged 9-19 years found no prolactin elevation with clozapine, but in 9 of 10 with haloperidol and 7 of 10 with olanzapine [ 18]. In another study of 11 outpatients aged 4-17 years treated with risperidone, 9 developed hyperprolactinemia of whom one had amenorrhea, and one had gynecomastia [ 19]. A further study reported that prolactin levels were increased in all 34 patients aged 5-14 years treated with risperidone [ 20]. Hyperprolactinemia was also noted in pediatric patients using ziprasidone and olanzapine. As with adults, ziprasidone- associated prolactin elevation was mild and transient, but associated with mild gynecomastia [ 21] and in one case with galactorrhea that resolved after drug discontinuation [ 22]. Two studies of the effects of quetiapine led to slightly differing results: one showed no increase in prolactin levels in 10 12-15-year-old children while the other study of 15 13-17-year-olds found a slight increase of no clinical significance, from a mean of 11.3 to 14.4 ng/mL [ 17]. Clozapine was not associated with increased prolactin levels, and the single patient who developed galactorrhea with risperidone had resolution of the problem when switched to clozapine [ 23].

A retrospective study analyzed prolactin levels and hyperprolactinemia attributable side effects from 5 clinical trials involving 592 children and adolescents of subaverage intelligence with conduct or other disruptive behavior disorders aged 5 to 15 years treated with risperidone. There was a weak effect of risperidone on prolactin concentrations during short-term treatment and lesser effect with long-term treatment, with side effects of gynecomastia, amenorrhea, or galactorrhea in only 2.2% [ 24]. These relatively benign findings have been attributed to low drug dosage for behavioral rather than psychiatric disorders and decreasing compliance over time [ 25].

Quite different findings emerged from a small double-blind placebo-controlled study of the effect of relatively low dose risperidone on prolactinemia in 10 children and adolescents with mental retardation and pervasive developmental disorders. Prolactin levels approximately tripled, and this increase was sustained for a mean 33 weeks of treatment [ 26].

Three adolescents were reported with risperidone-induced hyperprolactinemia resulting in gynecomastia in one boy that cleared and did not recur with olanzapine, gynecomastia with galactorrhea in another boy with comparable prolactinemia that resolved when he was switched to clozapine, and amenorrhea and galactorrhea in the third patient that resolved when she was changed to quetiapine. Their prolactin levels were 2100, 1670, and 1990 mIU/L (58, 46, and 55  g/L) when they were hyperprolactinemic and reduced to 63, 90, and 191 mIU/L (2, 2.5, and 5.3 g/L) after resolution [ 27]. Among 10 psychotic adolescents treated with risperidone, Holzer and Eap had 3 males developing gynecomastia and 2 females developing galactorrhea, along with 3 others having hyperprolactinemia without symptoms [ 25].

Sixteen adolescents aged 13-17 years with subaverage intelligence and disruptive behavior disorders treated with olanzapine for 8 weeks had significant elevations in serum prolactin levels, from baseline to  (SD) g/L without any symptoms or signs of hyperprolactinemia [ 28].

A study from Italy compared short- and long-term effects on prolactin of risperidone and olanzapine in 42 children and adolescents treated for a year [ 29]. They found that after adjusting for dose and the greater potency of risperidone, the increase in prolactin levels during risperidone treatment was 10.7 times higher than that during olanzapine treatment. Only one subject had a symptom of hyperprolactinemia, transient mild galactorrhea with risperidone which resolved without a change in therapy. Similarly, a randomized comparison of quetiapine and risperidone in 22 15-18-year-old adolescents with new-onset psychosis found prolactin elevation in 91% of those treated with risperidone versus 9% of those treated with quetiapine [ 30].

Dutch investigators have recently conducted an extensive literature review of studies of antipsychotic medication effects on prolactin level and associated side effects in children and adolescents [ 31]. They found 29 publications with study durations longer than 3 weeks. Twenty of these were concerning risperidone, 7 olanzapine, 5 quetiapine, 4 haloperidol, 3 pimozide, 2 clozapine, and 1 ziprasidone. They found that all antipsychotics with the exception of clozapine, ziprasidone, and quetiapine increased the mean prolactin level from 8 to 25-28 ng/mL. The incidence of hyperprolactinemia was 90% with haloperidol, 80% with pimozide, 62% with risperidone, 31% with olanzapine, and 12% with quetiapine. Risperidone, olanzapine, and pimozide were seen to induce a persistent elevation in prolactin levels. Associated gynecomastia, galactorrhea, or irregular menses were reported in 4.8% of the children and adolescents. Data from this review and subsequent reports are summarized in Table 1. Interpretation of the numerous studies is confounded by variation in study design, diagnoses, dosages, and age distribution, varying use of concomitant medication, short duration of some studies, compliance uncertainty, lack of prolactin baseline values (one fourth of the studies analyzed by Roke et al. [ 31]), and other missing data as noted in the table. Furthermore, single measurements of prolactin are subject to diurnal variation and stress influence. There may also be publication bias, as all data on prolactin values from manufacturers' files have not been published [ 31]. The effects of hyperprolactinemia may be underestimated because they depend on self-report, and may be mistaken for common adolescent problems of gynecomastia and irregular menstrual cycles.

22 (male)

10.1 (2.8)

1.7 (1.2)

52

6.5 (1.9)

14.9 (8.6)

-

0

-

-

7 (male)

14 (1.9)

7.5 (2.5)

52

7.8 (2.4)

11.3 (4.9)

-

0

-

-

The degree of hyperprolactinemia induced by short-term risperidone treatment in children and youth is dose dependent [ 17, 33]. This dose dependency is linked to plasma concentrations of both risperidone and its active metabolite 9-hydroxyrisperidone [ 33]. CYP2D6 is primarily responsible for the conversion of risperidone to 9-hydroxyrisperidone. In an examination of the possible role of activity of this enzyme in risperidone-induced prolactin release in children, Troost et al. [ 34] found a positive correlation of the fourfold elevation in serum prolactin level at 8 and 24 weeks with dose per kilogram body weight ( , ), number of functional CYP2D6 genes, serum 9-hydroxyrisperidone concentration ( , ) and negative correlation with the risperidone/9-hydroxyrisperidone ratio ( , ) but not with risperidone concentration ( , ). Thus, more rapid CYP2D6 metabolism may be a risk factor for hyperprolactinemia with risperidone. Polymorphic variation in the dopamine D2 receptor may be another pharmacogenetic factor determining risk for risperidone-induced hyperprolactinemia in children and adolescents. Two variants were identified that were associated with higher prolactin concentration in a study of 107 patients treated for up to 3 years [ 32].

Because second-generation antipsychotics are being increasingly prescribed for children and adolescents with conditions that are not psychoses and that are also treated with stimulants, the potential mitigating effect of the stimulants on the side effects of the antipsychotics has been examined [ 35]. Stimulant drugs have opposite effects on the dopamine receptor than do the antipsychotics. Subjects were 153 4-19-year-olds treated with antipsychotics, 71 of whom were coprescribed stimulants. The antipsychotic drugs included risperidone (33%), aripiprazole (30%), quetiapine (18%), olanzapine (12%), and ziprasidone (6%). In addition to no effect of cotreatment with stimulants on the side effect of hyperprolactinemia, there was no effect on body composition, metabolic parameters, sedation, or overall efficacy of the antipsychotic agent.

Second-generation antipsychotics are being increasingly prescribed for children and adolescents with a wide range of behavioral disturbances in addition to psychoses, resulting in metabolic and hormonal changes of importance to the consulting pediatric endocrinologist. In addition to weight gain and associated comorbidities of insulin resistance, hyperprolactinemia is a common side effect resulting from the inhibition of dopamine action. First-generation antipsychotics, particularly haloperidol, and the second-generation antipsychotic drugs, most prominently risperidone, appear to be associated with the greatest risk for hyperprolactinemia; some treated individuals developing hyperprolactinemia will have galactorrhea, amenorrhea, or gynecomastia. Hyperprolactinemia may have a deleterious effect on peak bone mass attainment and increase long-term osteopenia risk, even in the absence of overt symptoms or signs of hyperprolactinemia. This adverse effect may be enhanced by the commonly associated treatment with SSRIs. The effect of psychotropic drugs on bone mass accrual needs further study. The suggestion of a greater risk for pituitary tumors related to drug affinity for D2 receptors also requires continued study. Thus, in addition to surveillance for signs and symptoms of hyperprolactinemia in children and adolescents taking antipsychotic medications, monitoring serum prolactin concentrations is warranted. In the presence of hyperprolactinemia, cessation of antipsychotic therapy or changing to a formulation less likely to raise prolactin levels should be considered.

The author is a consultant to a law firm pursuing litigation with manufacturers of second-generation antipsychotic drugs.

An erratum to this article is available at http://dx.doi.org/10.1186/1687-9856-2013-13 .

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Hyperprolactinemia with Antipsychotic Drugs in Children and Adolescents

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There is increasing use of antipsychotic drugs in pediatric and psychiatry practice for a wide range of behavioral and affective disorders. These drugs have prominent side effects of interest to pediatric endocrinologists, including weight gain and associated metabolic risk factors and hyperprolactinemia. The drugs block dopamine action, thus disinhibiting prolactin secretion.

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Discusses the concerns of drugs used on toddlers as there isn't sufficient research. Often times, a child's home/family situation is overlooked before prescribing drugs.

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Susanna N. Visser presented a report on Friday at the Georgia Mental Health Forum in Atlanta on the diagnosis and medication for A.D.H.D. in young children. Credit Credit Dustin Chambers for The New York Times

ATLANTA - More than 10,000 American toddlers 2 or 3 years old are being medicated for attention deficit hyperactivity disorder outside established pediatric guidelines, according to data presented on Friday by an official at the Centers for Disease Control and Prevention.

The report, which found that toddlers covered by Medicaid are particularly prone to be put on medication such as Ritalin and Adderall, is among the first efforts to gauge the diagnosis of A.D.H.D. in children below age 4. Doctors at the Georgia Mental Health Forum at the Carter Center in Atlanta, where the data was presented, as well as several outside experts strongly criticized the use of medication in so many children that young.

The American Academy of Pediatrics standard practice guidelines for A.D.H.D. do not even address the diagnosis in children 3 and younger - let alone the use of such stimulant medications, because their safety and effectiveness have barely been explored in that age group. "It's absolutely shocking, and it shouldn't be happening," said Anita Zervigon-Hakes, a children's mental health consultant to the Carter Center. "People are just feeling around in the dark. We obviously don't have our act together for little children."

Dr. Lawrence H. Diller, a behavioral pediatrician in Walnut Creek, Calif., said in a telephone interview: "People prescribing to 2-year-olds are just winging it. It is outside the standard of care, and they should be subject to malpractice if something goes wrong with a kid."

Friday's report was the latest to raise concerns about A.D.H.D. diagnoses and medications for American children beyond what many experts consider medically justified. Last year, a nationwide C.D.C. survey found that 11 percent of children ages 4 to 17 have received a diagnosis of the disorder, and that about one in five boys will get one during childhood.

A vast majority are put on medications such as methylphenidate (commonly known as Ritalin) or amphetamines like Adderall, which often calm a child's hyperactivity and impulsivity but also carry risks for growth suppression, insomnia and hallucinations.

Only Adderall is approved by the Food and Drug Administration for children below age 6. However, because off-label use of methylphenidate in preschool children had produced some encouraging results, the most recent American Academy of Pediatrics guidelines authorized it in 4- and 5-year-olds - but only after formal training for parents and teachers to improve the child's environment were unsuccessful.

Children below age 4 are not covered in those guidelines because hyperactivity and impulsivity are developmentally appropriate for toddlers, several experts said, and more time is needed to see if a disorder is truly present.

Susanna N. Visser, who oversees the C.D.C.'s research on the disorder, compiled Friday's report through two sources: Medicaid claims in Georgia and claims by privately insured families nationwide kept by MarketScan, a research firm. Her report did not directly present a total number of toddlers 2 and 3 years old nationwide being medicated for the disorder, however her data suggested a number of at least 10,000 and perhaps many more.

Dr. Visser's analysis of Georgia Medicaid claims found about one in 225 toddlers being medicated for A.D.H.D., or 760 cases in that state alone. Dr. Visser said that nationwide Medicaid data were not yet available, but Georgia's rates of the disorder are very typical of the United States as a whole.

"If we applied Georgia's rate to the number of toddlers on Medicaid nationwide, we would expect at least 10,000 of those to be on A.D.H.D. medication," Dr. Visser said in an interview. She added that MarketScan data suggested that an additional 4,000 toddlers covered by private insurance were being medicated for the disorder.

Dr. Visser said that effective nonpharmacological treatments, such as teaching parents and day care workers to provide more structured environments for such children, were often ignored. "Families of toddlers with behavioral problems are coming to the doctor's office for help, and the help they're getting too often is a prescription for a Class II controlled substance, which has not been established as safe for that young of a child," Dr. Visser said. "It puts these children and their developing minds at risk, and their health is at risk."

Very few scientific studies have examined the use of stimulant medications in young children. A prominent 2006 study found that methylphenidate could mollify A.D.H.D.-like symptoms in preschoolers, but only about a dozen 3-year-olds were included in the study, and no 2-year-olds. Most researchers on that study, sponsored by the National Institute of Mental Health, had significant financial ties to pharmaceutical companies that made A.D.H.D. medications.

Some doctors said in interviews on Friday that they understood the use of stimulant medication in 2- and 3-year-olds under rare circumstances.

Keith Conners, a psychologist and professor emeritus at Duke University who since the 1960s has been one of A.D.H.D.'s most prominent figures, said that he had occasionally recommended it when nothing else would calm a toddler who was a harm to himself or others.

Dr. Doris Greenberg, a behavioral pediatrician in Savannah, Ga., who attended Dr. Visser's presentation, said that methylphenidate can be a last resort for situations that have become so stressful that the family could be destroyed. She cautioned, however, that there should not be 10,000 such cases in the United States a year.

"Some of these kids are having really legitimate problems," Dr. Greenberg said. "But you also have overwhelmed parents who can't cope and the doctor prescribes as a knee-jerk reaction. You have children with depression or anxiety who can present the same way, and these medications can just make those problems worse."

Dr. Visser said she could offer no firm explanation for why she found toddlers covered by Medicaid to be medicated for the disorder far more often than those covered by private insurance.

Dr. Nancy Rappaport, a child psychiatrist and director of school-based programs at Cambridge Health Alliance outside Boston who specializes in underprivileged youth, said that some home environments can lead to behavior often mistaken for A.D.H.D., particularly in the youngest children.

"In acting out and being hard to control, they're signaling the chaos in their environment," Dr. Rappaport said. "Of course only some homes are like this - but if you have a family with domestic violence, drug or alcohol abuse, or a parent neglecting a 2-year-old, the kid might look impulsive or aggressive. And the parent might just want a quick fix, and the easiest thing to do is medicate. It's a travesty."

A version of this article appears in print on , on Page A11 of the New York edition with the headline: Thousands of Toddlers Are Medicated for A.D.H.D., Report Finds, Raising Worries. Order Reprints | Today's Paper | Subscribe

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Thousands of Toddlers Are Medicated for A.D.H.D., Report Finds, Raising Worries

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ATLANTA - More than 10,000 American toddlers 2 or 3 years old are being medicated for attention deficit hyperactivity disorder outside established pediatric guidelines, according to data presented on Friday by an official at the Centers for Disease Control and Prevention.

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JoAnne Solchany PhD, ARNP, Board Certified Psychiatric Nurse Practitioner and Therapist (from Seattle 😌) advocates treating mental disorders developed in children separated from their families with psychotropic drugs.

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Children who enter foster care face many changes and challenges that can lead to mental health disorders treated with psychotropic medications. Many have experienced abuse and neglect. Some have witnessed violence and trauma. Others have parents who suffer profound mental health issues. Regardless of what led to their involvement in the child welfare system, all face separation, broken relationships, and confusion. Increasing their vulnerabilities are risk factors such as poverty, neighborhood violence, exposure to parental mental illness, racial discrimination, lack of food, and homelessness.

The paths to a mental health diagnosis are numerous and complex. Genetically some of these children are already prone to disruptions in their mental health. Mental health disorders such as attention deficit disorder or bipolar disorder, even borderline personality disorder, are thought to have genetic components that place children at risk for developing the same disorders as their parents. Environmental experiences are also linked to mental health issues such as conduct disorder, depression, and anxiety disorder. Exposure to violence is directly linked to post traumatic stress disorder and depression.

For children and teens in foster care who struggle with mental health disorders, the goal is to help them heal and function optimally. Among their needs are to:

understand what is happening to them, why they are not living with their parents, and their options.

be able to feel their emotions and work through them, but know how to manage them in age-appropriate ways so they do not interfere with their success, growth, and development.

be able to communicate with those who advocate for them and make decisions on their behalf.

feel stable or organized in their thinking so they can reclaim age-appropriate power and take charge of their lives.

Medications can help children and teens in foster care, but they can also further impair them, derail them, and sabotage them. Without a clear understanding of their mental health issues, misdiagnoses can be made and incorrect medications can be prescribed.

If there is no reliable caregiver who can describe the child's struggles, information collected can be biased and incomplete. If emotional trauma underlies the presenting symptoms and is not addressed, medications can have no effect or increase problems. If medications are prescribed but other therapies are not provided and supervision of the medication is inadequate, healing and stabilization supporting healthy growth will not occur. Finally, if caregivers are not adequately trained and educated in caring for a child with significant emotional and psychological needs, medications can often be given to the child to "manage their behaviors" rather than to truly treat the child's illness.

Children and teens also need to be safe. Depression or suicidal thinking must be addressed. Self-abusive behaviors must be contained and risk-taking behaviors reduced. Medications can be part of a successful intervention and treatment plan.

To adequately and successfully represent and speak for a child or teen in foster care, the child's advocate must be able to communicate with the child and discuss the child's experiences. Does the child manage his or her acting-out behaviors and emotions, use positive social skills, think clearly, and track the ongoing events in their lives? Working with children and teens in foster care requires a solid understanding of the positive and negative aspects of medication use in this population.

Medications make life easier for many children and adults everyday. Medications save lives, relieve pain, manage troublesome symptoms, and help people improve their ability to function in the world. These medications specifically target symptoms and issues associated with mental illness and mental health.

Thought disorders, such as schizophrenia, can often be helped by medications such as resperidone (Risperdal), which promotes the ability to think more clearly and function at a higher level.

Mood disorders, such as depression, can be helped with medications such as sertraline (Zoloft), which help regulate emotions and address basic issues like sleep and concentration.

Behavior disorders, such as conduct disorder, can often be helped with anti-anxiety medications such as atenolol (Tenormin), to help manage aggressive outbursts.

Attention disorders, such as attention deficit disorder, can be managed with medications such as amphetamines (Adderal) to improve focus, increase concentration ability, and decrease vulnerability to distraction.

In children and adolescents, medications can sometimes be used to help diagnose mental health conditions. For example, when a child has a difficult time paying attention in class, is falling behind with school work, and is having severe tantrums involving aggression, diagnoses such as attention deficit hyperactivity disorder, post traumatic stress disorder, depression, or even bipolar disorder might be considered. A stimulant typically used to treat ADHD may be tried to see if the child responds well to the treatment and the symptoms come under control. The successful use of the medication helps to secure the ADHD diagnosis. If, however, a child is thought to be bipolar and is placed on aripiprazole (Abilify) or lithium with no effects or worsening of symptoms, the diagnosis of bipolar disorder may be ruled out and alternative diagnoses will be considered.

No medication comes without risk of side effects or other interferences in the body. Some antidepressants can reduce the ability to experience emotions, even pleasurable emotions, and can eliminate interest in sex. Behavior management medications often cause drowsiness and withdrawn behavior. Attention disorder medications can interfere with appetite or sleep and create additional problems for a child. Some psychotropic medications cause tics, nightmares, and even some of the same symptoms they aim to help, such as hearing voices. Some medications used to treat thought disorders can cause lifelong side effects that do not go away even when the medication is stopped.

Antipsychotic medications such as resperidone (Risperdal) or olanzapine (Zyprexa) as well as some of the mood stabilizing agents and antidepressants can cause weight gain to varying degrees. A few pounds can profoundly impact the developing child's self-esteem, performance, and relationships. Some medications are linked to obesity, even when used for only a few months. Not only does weight gain impact self confidence but it also places children and teens at risk for diabetes, heart disease, eating disorders, and a lack of compliance with treatment recommendations. Rapid weight gain also decreases the motivation to exercise, the desire to socialize, and the ability to engage in typical physical activities. Weight gain with psychotropic medications is a serious issue that needs to be taken into account.

Managing mental health issues and the symptoms experienced by children and adolescents involves many modalities:

Medication treatment, or psychopharmacology, can alleviate or lessen the symptoms that accompany many mental health disorders. For example, medication may decrease the impulse to tantrum, help a child regulate physiologic responses to emotions, or eliminate auditory hallucinations. In addition, proper medication support can provide behavioral stability and support with emotional regulation that a child or teen may need to readily engage in other forms of therapy. For example, a very depressed teen who cannot control her crying when she needs to be able to talk about her abuse and history can feel more in control emotionally with the right medication, allowing her to discuss the important issues and aid in her healing.

Behavioral therapy, for example using reward charts, can help increase positive behaviors and decrease negative acting out.

Cognitive behavioral therapy can help correct a pattern of negative thoughts that interfere with the ability to relate to others.

Play therapy can help heal past trauma and facilitate a child's return to normal functioning.

Child-parent psychotherapy involves working directly with the parent and child to address issues within their relationship and help the child increase healthy ways of interacting and functioning.
Parents are helped to become more reflective, develop a deeper understanding of their child and their role in their child's life. They also learn how to interact with their child in ways that promote a healthy and secure attachment and to support a healthy growth and development trajectory. Parent coaching can also be an element of this modality.

Dialectical behavioral therapy (DBT) can provide important skills, such as distress tolerance and emotional regulation, in struggling adolescents and help them integrate them into their daily interactions.

All of these treatments are valid and can help manage symptoms, facilitate healing, and return children to optimal functioning.

Medication can be important to successfully treat a child or teen dealing with a mental illness. Medication can be helpful when a child is disturbed or overwhelmed by his or her own behavior and has not been able to learn to manage the symptoms through other means. A medication trial might help control the interfering symptoms and increase self-esteem and functional ability.

Children who are self-abusive, impulsive, or aggressive to others may need medication to help them manage their impulses and aggression. Children and teens who hear voices or have visual hallucinations may require medication to alleviate these symptoms. Medications can also support physiologic regulation that often gets out of sync with mood disorders and trauma; in other words, medication can help stabilize sleep patterns, appetite, and concentration.

Medication use can be helpful and is often instrumental in treating mental health issues in children and teens. The concerns lie in what medications are being used, if they are being well monitored, and if they are the right ones for the child based on symptoms, age, functional ability, and improvement.

The American Academy of Child & Adolescent Psychiatry makes the following recommendations for the use of psychotropic medication with children and teens:

1. Before initiating pharmacotherapy, a psychiatric evaluation is completed. Understand the child as fully as possible. A psychiatric evaluation should include a family background, therapies already tried, current medications, the child's ability to function in multiple places, i.e., home, school, social circles, and mental health status. This information provides a baseline for starting any kind of treatment.

2. Before starting pharmacotherapy, a medical history is obtained, and a medical evaluation is considered when appropriate.
A medical history is critical to understanding any health issues that could relate to the child's symptoms. For example, a history of a head injury might relate to the development of aggressive behavior, or a seizure disorder history might relate to a decreased ability to focus. A medical evaluation would help establish baselines on such issues as growth (height and weight), cardiac function, and elimination patterns (particularly important in diagnosing encopresis and enuresis).

3. The prescriber is advised to communicate with other professionals involved with the child to obtain collateral history and set the stage for monitoring outcome and side effects during the medication trial.
Children act differently depending on their environments; their behaviors and emotional states can vary with different caregivers or other people. For example, a child can function well at school and thrive in a structured environment, but act out at home due to the presence of domestic violence. Gathering baseline information will help understand the impact of the medication over time.

4. The prescriber develops a psychosocial and psychopharmacological treatment plan based on the best available evidence.
A treatment plan is important to think through all necessary potential treatments as well as medication options. The evidence supporting these choices should be addressed as well.

5. The prescriber develops a plan to monitor the patient, short and long term. Short-term monitoring helps to assess for any developments or increases in suicidal ideation as well as initial side effects, such as stomachaches or drowsiness. Long-term monitoring helps assess the continued impact and potential changes over time.

6. Prescribers should be cautious when implementing a treatment plan that cannot be appropriately monitored. Treatment plans should be followed and evaluated over time. Treatment modalities or requirements that cannot be monitored should be reconsidered.

7. Prescriber provides feedback about the diagnosis and educates the patient and family about the child's disorder, and the treatment and monitoring plan. The patient and caregivers should be kept updated and educated about the diagnosis, what it means, the treatment of the disorder, and the monitoring of the treatment plans.

8. Complete and document the assent of the child and the consent of the caregivers before initiating medication treatment and at important points during treatment. Assent and consent needs to be given before treatment starts. Both children and caregivers should be given easy-to-understand information and have the risks and benefits of medication explained to them.

9. The assent and consent discussion focuses on the risks and benefits of the proposed and alternative treatments. The risks and benefits of medication and of alternative treatments, such as psychotherapy, should be discussed.

10. Implement medication trials using an adequate dose and for an adequate treatment period. If medications are tried, the dosage needs to reach therapeutic levels and the medication needs to be tried for an adequate period to reach a therapeutic stage. Some medications work quickly, and leave the system quickly. Other medications need to reach certain levels to work well, and then those levels need to remain stable. For example, Zoloft is often used to treat obsessive-compulsive disorder with doses as high as 200 mg./day. With depression, the basic dose is 50 mg./day. Lithium is a medication that needs to reach a therapeutic level and be kept stable to be effective.

11. The prescriber reassesses the patient if the child does not respond to the initial medication trial as expected. Finding the right or best medication might take time; some children do better on one medication than another, just like adults. At times, several medications might be tried before the best medication for that individual is found. For example, certain stimulant medications can have no effect on some children but work extremely well in others. The same phenomena can happen with antidepressants. The underlying reasons for specific symptoms may differ from person to person and the medications used to treat those symptoms can differ as well.

12. The prescriber needs a clear rationale for using medication combinations. Combining medications increases the risk to the child; understanding what is needed and what medications compliment one another is key. For example, combining an antidepressant for depression and a stimulant for ADHD might be warranted. Placing a child on two antidepressants at the same time or two antipsychotics does not make sense in most cases.

13. Discontinuing medication in children requires a specific plan.
Most medications should not be stopped abruptly. They should be tapered off. Discontinuing medication should be part of a plan within the overall treatment goals

Judges and attorneys should consider the following questions when considering the best interests of or advocating for a child or teen in care. Children and teens have little, if any, power over their lives when they enter care. They generally lack the knowledge to understand what they need medically, regardless of the type of treatment needed. Asking the following questions will help identify their needs and determine which recommended treatments are in their best interests.

What is this medication needed for?

What kind of symptoms is this child experiencing? Are these symptoms interfering with the child's ability to function? Are these problems an issue in multiple environments?

Were you able to obtain an accurate medical, behavioral, and psychological history from parents and past providers?

Children in foster care do not always have a consistent caregiver who can be a reliable historian for what a child has experienced or what kinds of symptoms they are dealing with. Parents who are in conflict with their child may exaggerate symptoms or blame the child when they are really at the root of the presenting issues. Other parents may not have been around their children enough to provide accurate information. Parents and other caregivers can also become so frustrated by a child's behaviors that they exaggerate the child's symptoms to gain added support and sympathy. It is important to explore the source of the information about the child.

What else has been tried?

Has counseling been provided? Has it been consistent? Has the child had a psychiatric evaluation? Has the child had a medical examination?

What other modes of treatment or intervention will also be provided?

Medications should never be the sole mode of treatment for mental health disorders. Counseling should be provided to help children learn to manage or minimize their symptoms. Children often need to learn new skills, such as anger management or problem solving, to help them interact with others more successfully. Some children need to talk about their trauma or their grief to make sense of and resolve it; medication will not do this for them. Additional types of treatment may include Play Therapy, Social Skills Group, Parent-Child Interactive Therapy (PCIT), Dialectic Behavioral Therapy (DBT), Cognitive Behavioral Therapy (CBT), Child-Parent Psychotherapy, Parent Coaching, and Anger Management Groups.

Who will monitor the ongoing use of this medication? How often will this child be seen?

Successful medication management includes regular follow-ups. Especially when first started, medications often need to be adjusted for proper dosage or better timing. The development of side effects needs to be monitored. In children, medications often need to be slowly introduced over several weeks; the incremental adjustments will need to be monitored. Medication changes and ending a medication often require tapering as well. About 20% of people have some type of difficulty with the first psychotropic medication they are prescribed and will need to work with their mental health provider to find a better treatment option. It is important to consider who will take the child to appointments on a regular basis so a consistent adult is also well informed of the medications being used.

What are the possible side effects of this medication and how will they be handled?

Some medications carry transient side effects, such as stomach upset or initial drowsiness. These often disappear over the first few days on the medication or they can be minimized by taking the medication at night rather than in the morning. Other side effects, such as vomiting, confusion, or inability to sleep, may mean this medication will not work with this child or that the child needs additional medication to balance the effects. Some side effects are seen weeks or even months after a medication is introduced. Some antipsychotics lead to rapid weight gain while some stimulants used to treat attention disorders lead to significant weight loss-these issues can impact overall health and can add to self esteem and other mental well-being problems.

What evidence supports the use of this medication with children?

What do we know about how this medication works in children? Are there safety warnings that go along with this medication? What evidence do we have that it will not harm the child? Is this medication well tolerated in children?

Will this child be able to comply with the prescribed medication?

Is there someone available who can assure the child has regular access to the medication and that it is being given as directed? Is this medication easy to use? For example, is it a once-a-day dose versus a four-times-a-day dose? Is the type of medication right for this child? For example, is there a liquid form available for a child who cannot swallow pills? Will additional lab tests be needed to start or sustain use of this medication? For example, will the child need a baseline EKG to assess for cardiac functioning or will the child require regular blood tests to assess medication levels. Can the patient afford the drug? If a patient cannot afford a medication, he or she will not be able to take it. Is it covered under Medicaid? Medicaid often has rules for what kinds of medications it will cover; alternative medications can often be prescribed, but sometimes a very specific drug may be needed for certain symptoms.

Does the child agree with taking this medication?

Despite the age of consent, how does this child feel about being on this medication? Has it been discussed with the child? Has the child been told what to expect? Is someone talking regularly with the child about how it feels while on this medication?

Who has given permission to begin this child on medication?

Who should be giving permission? The parent? The foster parent? The prescriber? The child's advocate? The child? The social worker? Do the people involved in this child's life know of this medication and understand the risks and benefits? Have they been taught how to properly administer and monitor this medication?

What other medications is this child on? Can this medication be safely combined with the current medication?

Is the child already on medications for other things such as asthma or acid reflux? Can this new medication be safely used with the current medications? Who has assessed this? Does the prescriber of the psychotropic medication know what the child is already on? What over-the-counter medications, vitamins, or naturopathic medications is the child taking?

How will this medication help improve this child's functioning?

What challenges is the child struggling with that should change with this medication? Will this make life easier for this child?

What are the risks versus benefits of using this medication? What are the risks versus benefits of not using the medication?

It is critical to understand the risks of any medication and of any other intervention or therapy. Equally important is understanding the benefits of using the medication or other therapies. The benefits need to outweigh the risks. Both the patient and the caregivers need to fully understand the risks and benefits as well.

Is a second opinion warranted in this case?

Cases involving children on multiple medications, young children under six, and the use of atypical medications should always be reviewed by other practitioners. Children who have been difficult to treat or who have tried various medications previously may require a second opinion.

Psychotropic medication use in children and adolescents has increased over the past decade. Many medications used today are safer, have fewer side effects, and are more effective than medications used 15 years ago. However, little research has studied the long-term effects of these medications or their effects on children and adolescents. Despite this lack of knowledge, psychotropic medications are used to treat and manage behavioral, emotional, and psychological symptoms experienced by children and teens.

Children in foster care or in other state care appear especially vulnerable to medication use. Concerns continue to be raised over adequate monitoring, second opinions, use of multiple medications at once, consent for the use of medications with children in care, and providing other necessary treatments such as counseling. Evidence shows individuals experience greater improvement when medication is combined with counseling than without.

The risks and benefits of treating a child with and without medication need to be examined with each medication considered. Children should be on the least-potent medication and the lowest possible dose, and for the shortest amount of time. Their developmental progress across all domains should be considered and protected. Psychotropic medications should be supportive and helpful and never place a child at risk of harm.

JoAnne Solchany, PhD, ARNP, is a Board Certified Psychiatric Nurse Practitioner and Therapist with infants, children, and families in Seattle. Her focus is on children and families in the child welfare system, child-parent relationships, and infant mental health.

This article was adapted from Psychotropic Medication and Children in Foster Care: Tips for Advocates and Judges, a new practice and policy brief developed by the ABA's Improving Understanding of Maternal and Child Health Project, a project of the ABA Center on Children and the Law.

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Psychotropic Medication and Children in Foster Care: Tips for Advocates and Judges

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Children who enter foster care face many changes and challenges that can lead to mental health disorders treated with psychotropic medications. Many have experienced abuse and neglect. Some have witnessed violence and trauma. Others have parents who suffer profound mental health issues.

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Although some research suggests the use of psychotropics on children is dangerous to their mental health, there is not enough hard evidence to prove anything for sure. Psychologists are working to deepen research so drugs can be administered properly.

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Should our children be taking psychotropics?

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An APA group is reviewing children's use of psychoactive medications and will examine psychologists' current and future roles in the area.

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Although some research suggests the use of psychotropics on children is dangerous to their mental health, there is not enough hard evidence to prove anything for sure. Psychologists are working to deepen research so drugs can be administered properly.

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Should our children be taking psychotropics?

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An APA group is reviewing children's use of psychoactive medications and will examine psychologists' current and future roles in the area.

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Dosed: The Medication Generation Grows Up

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Dosed: The Medication Generation Grows Up

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In the portion i read, Kaitlyn Bell Barnett meets with adults who were on psychotropics as teenagers. Together they discuss the side effects of the drugs and how that affected their daily lives. The subjects discuss and array of complications like how certain medications can increase risk of disease, lead to the need more more medication, cause other mental issues, and deplete sex life. Additionally, the subjects talk about how normal teenage struggles can complicate the diagnosis and treatment of disorders.

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Dr. Peter Breggin MD disapproves of the use of psychotropic drugs on children because he doesn't think it's in their best interest.

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Electroconvulsive therapy (ECT) is a medical treatment most commonly used in patients with severe major depression or bipolar disorder that has not responded to other treatments.

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a reference of classes of psychotropic drugs in case of confusion.

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DFPS - Classes Of Psychotropic Medications - Psychotropic Medication

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The mission of the Texas Department of Family and Protective Services (DFPS) is to protect the unprotected -- children, elderly, and people with disabilities -- from abuse, neglect, and exploitation.

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some nice charts

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Language: English | French

Abstract

Objective:

Despite limited information related to efficacy in children, psychotropic medications are commonly prescribed as a first-line treatment for a range of psychiatric diagnoses in children in a variety of clinical settings. Usage has increased over the past three decades. Although psychotropic medications are often effective at treating psychiatric symptoms, the risk of adverse effects (AE) in children is unclear. The current research seeks to identify the mental health characteristics of those children at highest risk of experiencing potential AE from psychotropic medications.

Methods:

Psychotropic medication monitoring checklists were used to record possible AE for 99 pediatric clients in a tertiary mental health residential treatment centre for the duration of one to eight weeks. Client characteristics, including the number of diagnoses and behavioural variables, were explored for predictive value of potential AE observed.

Results:

Results showed that the total number of potential AE was positively predicted by the number of DSM-IV categories diagnosed, as well as behavioural symptoms of impulsiveness and uncooperativeness.

Conclusions:

The findings of this study indicate that the number of potential AE from psychotropic medications may be predictable based on client characteristics. Predicting this likelihood during initial assessment can be useful in directing and monitoring treatment, as well as preventing serious events related to medication use.

Keywords: children, adverse effects, psychotropic medication monitoring checklist, residential care

Résumé

Objectif:

Malgré l'information limitée sur leur efficacité chez les enfants, les médicaments psychotropes sont communément prescrits comme traitement de première ligne pour une variété de diagnostics psychiatriques chez les enfants, dans divers milieux cliniques. L'usage a augmenté dans les 30 dernières années. Bien que les médicaments psychotropes soient souvent efficaces pour traiter les symptômes psychiatriques, le risque d'effets indésirables (EI) chez les enfants n'est pas déterminé. La recherche actuelle vise à identifier les caractéristiques de la santé mentale des enfants les plus à risque d'éprouver des EI potentiels des médicaments psychotropes.

Méthodes:

Les listes de surveillance des médicaments psychotropes ont été utilisées pour repérer des EI possibles chez 99 clients pédiatriques dans un centre tertiaire de traitement résidentiel de santé mentale pour une durée d'une à huit semaines. Les caractéristiques des clients, notamment le nombre de diagnostics et de variables comportementales, ont été explorées pour la valeur prédictive des EI potentiels observés.

Résultats:

Les résultats ont indiqué que le nombre total d'EI potentiels était positivement prédit par le nombre de catégories du DSM-IV diagnostiquées, et par les symptômes comportementaux d'impulsivité et de non-coopération.

Conclusions:

Les résultats de cette étude indiquent que le nombre d'EI potentiels des médicaments psychotropes peut être prédictible d'après les caractéristiques des clients. Prédire cette probabilité durant l'évaluation initiale peut être utile pour orienter et surveiller le traitement, et prévenir des incidents sérieux liés à l'utilisation de médicaments.

Mots-clés : enfants, effets indésirables, liste de surveillance des médicaments psychotropes, soins résidentiels

There is much unknown about the use of psychotropic medications and their effects on children. Knowledge of the efficacy, specificity and adverse effects (AE) of psychotropic medications in pediatric populations lags behind what is known in adults ( Wolraich, 2003). Despite this, physicians may generalize adult prescribing patterns to children ( American Psychiatric Association, 2000). While the benefits of symptom relief often outweigh the risk of AE, determining the risks associated with psychotropic medications is an important endeavour ( Bridge et al., 2007).

Children can be more sensitive to AE than adults. In a study of antipsychotic medications, it was found that extrapyramidal AE occur more commonly in young people than adults ( Sikich, Hamer, Bashford, Sheitman, & Lieberman, 2004). While second-generation atypical antipsychotics lead to fewer extrapyramidal symptoms, AE associated with these medications include significant weight gain and increased risk of diabetes mellitus and dyslipidemia ( Gentile, 2006). AE severity may cause clients or their parents to refuse the continuation of pharmacologic therapy, thereby increasing the risk of re-experiencing their original symptoms ( Charach, Volpe, Boydell, & Gearing, 2008).

Despite limited information related to efficacy with children, psychotropic medications are a commonly prescribed first-line treatment for a range of psychiatric diagnoses in children in a variety of clinical settings. Approximately 85% of children diagnosed with attention deficit hyperactivity disorder (ADHD) are prescribed stimulant medications, 60% with bipolar disorder are prescribed mood stabilizers, and 57% of depressed outpatient pediatric clients are treated with antidepressant medications ( Moreno et al., 2007; Olfson, Gameroff, Marcus, & Jensen, 2003; Olfson, Gameroff, Marcus, & Waslick, 2003).

The prescribing rate of psychotropic medications is increasing in children, as is the number of medication types prescribed per child ( Comer, Olfson, & Mojtabai, 2010; Olfson, Marcus, Weissman, & Jensen, 2002). Over a 12-year period, multiclass psychotropic prescriptions rose in children from 14.3% to 20.2% ( Comer et al., 2010). The reasons for these changes may be the increasing level of empirical evidence supporting the usage of psychotropic medications for conditions like ADHD, obsessive-compulsive disorder, major depressive disorder, and childhood anxiety disorders, and a more thorough understanding of the biological basis of these conditions ( McClellan & Werry, 2003). Increasing usage of psychotropic medications may also be in response to recent emphasis on rapid symptom reduction by clients, parents, or physicians ( Stahl, 2008).

There is further uncertainty as to how children react to being treated with multiclass psychotropic regimens ( Safer, Zito, & dosReis, 2003). The risks of AE (including those that are fatal) are potentially compounded by the simultaneous usage of multiple psychotropic medications ( Safer et al., 2003), as many potential AE are shared across multiple medication types. A lack of evidence regarding concomitant psychotropic medication administration and its safety has been cited in the past as a concern for children in foster care ( Zito et al., 2008). The concern surrounding excessive usage of psychotropic medications has led to the creation of physician training programs and quality improvement initiatives to reduce such prescribing patterns ( Patrick, Schleifer, Nurenberg, & Gill, 2006).

Psychotherapy, such as cognitive behavioural therapy (CBT), is also considered a first-line treatment for conditions such as obsessive-compulsive disorder and generalized anxiety disorder ( James, James, Cowdrey, Soler, & Choke, 2013). Psychosocial interventions avoid the potential AE of psychotropic medications and are actually preferred by some adolescents over pharmacotherapy ( Bradley, McGrath, Brannen, & Bagnell, 2010). However, the costs of psychotherapy in private settings and the lack of such resources in many communities make pharmacotherapy a more readily available option for many ( James, Soler, & Weatherall, 2005).

Increasing our knowledge about the efficacy of psychotropic medications and AE in children is vital to ensure client safety. The present study attempts to determine the relationship between the number and commonality/severity of potential AE experienced by children and the number of disorders diagnosed according to criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) and standardized criteria measuring the complexity of psychiatric presentation.

Methods

Ethics approval was obtained from the Western University Research Ethics Board. Consent to use client data for research purposes was obtained from parents/guardians and youth.

Participants

The primary participants were 99 clients (78% boys; = 11.97 years; SD = 2.35 years) on five residential units at a tertiary mental health care facility for children with complex psychiatric concerns: a short-term stabilization unit for children (ages 6-12 years), a unit for adolescent boys (13-18 years), a unit for adolescent girls (10-18 years), a unit for boys and girls with developmental delays (6-12 years), and a unit for adolescents (13-18 years) with developmental delays. Data collection took place over a four-month time period. These 99 patients altogether provided 565 weeks of data. A secondary pool of 627 clients (77% boys; = 12.0 years; SD = 2.63 years) from the same residential units over a five-year timespan provided additional data on three standardized instruments at intake to enable scores to be condensed into manageable factors to inform analyses in the current study (see details below).

Materials

The Psychotropic Medication Monitoring Checklists (PMMC; Ninan et al., 2014) were developed over a one-year period by a four-person physician-pharmacist team. The team first identified common psychotropic medications for children, based on the information by the United States Food and Drug Administration (FDA), Health Canada, and the team's consensus opinions. The researchers then proceeded to conduct a comprehensive review regarding potential AE. These sources included product monographs, website resources such as Medscape and the Clinical Handbook of Psychotropic Drugs for Children and Adolescents ( Bezchlibnyk-Butler & Virani, 2007). Independent AE checklists were prepared for seven classes of prescribed drugs: alpha agonists, anticonvulsants, antipsychotics, atomoxetine, lithium, selective serotonin reuptake inhibitors (SSRIs), and stimulants. Each checklist divides the AE into three categories (common, infrequent, and rare but serious), based on both their likelihood of occurrence and the severity. Observed symptoms are considered "potential" AE rather than "actual" until confirmed by a physician as being directly related to the medication. This step was not included in the design of the current study. An example of the PMMC is provided in Appendix A. Ninan and colleagues (2014) recently demonstrated that the PMMC are useful educational tools that improve AE monitoring by residential staff (i.e., child and youth workers).

The Brief Child and Family Phone Interview (BCFPI; Cunningham, Pettingill, & Boyle, 2004) measures the type and the severity of a child's problems. It includes five mental health subscales (regulating attention, impulsivity, and activity level; cooperativeness; conduct; separating from parents; self-harm), one global functioning scale and one global family situation scale. Its classification reliability has been verified when compared to more extensive diagnostic interviews such as the Diagnostic Interview Schedule for Children- Version IV ( Boyle et al., 2008).

The Child and Adolescent Functional Assessment Scale (CAFAS; Hodges, 1994) measures eight problem areas, including school/work, home, community, behaviour towards others, moods/emotions, self-harmful behaviour, substance use, and thinking. Its predictive value for juvenile recidivism, contact with the law, and poor school attendance has been verified ( Quist & Matshazi, 2000; Hodges & Kim, 2000). The discriminant validity of CAFAS has also been verified to differentiate between psychiatric inpatient clients and children in alternative care, and it was found to be useful in predicting the utilization of psychiatric services ( Hodges, Wong, & Latessa, 1998).

Child Behaviour Checklist (CBCL; Achenbach, 1992) measures child behavioural and emotional problems. These problems include anxious/depressed, withdrawn/depressed, somatic complaints, social problems, thought problems, attention problems, rule-breaking behaviour, and aggressive behaviour. The CBCL has good psychometric properties and is regarded as the "Gold Standard" for evaluating child behavioural and socio-emotional problems ( Achenbach & Rescorla, 2001). Its reliability and validity, both convergent and discriminant, have been well documented ( Dutra, Campbell, & Westen, 2004).

Procedure

At the time of referral, the BCFPI was conducted with the child's primary caregiver. Prior to admission, the primary caregiver also completed a battery of standardized psychological health assessments, including the CBCL. The CAFAS was completed by the client's primary clinician at admission and follow-up time points. Prior to discharge, clients were assessed by a psychologist or psychiatrist for mental health disorders using the DSM-IV. The PMMC were completed daily by residential staff (i.e., child and youth workers and nurses) for each client for one to eight weeks during their residential stay. Potential AE were recorded for each day based on staff observations and communication between staff and with clients. Clients were not asked directly if they were experiencing each AE, but any expressed by clients were noted on the checklists. The responsible physician reviewed the PMMC regularly. In the case of weekend or holiday leaves of absence, medication monitoring was temporarily suspended. Therefore, the average number of AE per day for the current analysis was calculated using only the information on the monitored days. provides a brief description of client characteristics. shows the total number of AE reported during the study, and the percent of weeks with at least one AE reported.

Table 1.

Table 2.

Hierarchical linear modeling (HLM), a complex form of logistic regression was used for the data analyses. HLM was chosen in order to model a nested structure where weeks of monitoring are nested within clients ( Woltman, Feldstain, MacKay, & Rocchi, 2012). Weeks of PMMC completion were chosen as a random effect since this number varied among clients for several reasons (e.g., medication prescription changes, admission after the study began, discharge prior to study completion). The dependent variables in the analyses were the average number of AE a client experienced daily within a week and the commonality/severity of such symptoms (common, infrequent, rare but serious). The independent variables were the number of DSM-IV diagnoses and standardized intake scores. Because the variables are highly correlated with each other, the problem of multicollinearity will adversely affect the HLM analyses. Therefore, predictors were placed in the HLM analysis one at a time.

Because each of the standardized intake measures (BCFPI, CAFAS, CBCL) included multiple sub-scores, and because sub-scores across instruments were correlated with each other, factor analyses using a pool of data collected previously from a larger number of clients (N=627) were first conducted to condense these scores into fewer manageable factors. Factor analyses using principal component analyses with direct oblimin were conducted ( Yong & Pearce, 2013). Seven factors were extracted because their corresponding eigenvalues were greater than one ( Kaiser, 1960). Results are shown in . The first factor represented problems in impulsiveness, social, thought and attention problems (abbreviated as impulsiveness). The second factor represented conduct problems and substance abuse (abbreviated as conduct). The third factor represented problems in school, work, home, and community setting (abbreviated as setting). The fourth factor represented withdrawn/depressed and somatic complaints (abbreviated as withdrawn). The fifth factor represented issues with cooperativeness, and social functioning (abbreviated as uncooperativeness). The sixth factor corresponded to separation anxiety and anxious/depression problems (abbreviated as anxiety). The final factor represented problems with thinking and self-harm (abbreviated as thinking). All numbers were coded such that a higher score represents more severe problems in the corresponding area. The subscales of the primary participants were transformed into z scores and then amalgamated into the seven factors by taking the mean of the contributing subscales for use in the HLM analyses described below.

Table 3.

Results

Results of the HLM analyses are summarized in . The first research question was whether the average number of potential AE is predicted by the number of DSM-IV diagnoses. Results showed that the regression coefficient ( B) relating the number of DSM-IV diagnoses and number of potential AE was positive and statistically significant ( B = 0.17, SE = 0.08, t = 2.02, p = .047). Next, potential AE were divided into common, infrequent, and rare but serious categories. Similar analyses as above were then conducted. Only for common AE, the regression coefficient continued to show that the number was positively and significantly predicted by the number of DSM-IV diagnoses ( B = 0.09, SE = 0.05, t = 2.01, p = .048). One may question whether there is a relationship between the number of DSM-IV diagnoses and the number of medication types prescribed (i.e., the number of checklists completed per client within a week). Therefore, another HLM was conducted with the number of DSM-IV diagnoses predicting the number of medication types, but the result was non-significant ( B = −0.11, SE = 0.17, t = −0.66, p = .52).

Table 4.

An additional area of inquiry was whether the factors derived from the standardized instruments at intake predicted the number of potential AE that clients experienced (i.e., impulsiveness, conduct, setting, withdrawn, uncooperativeness, anxiety, thinking). Again, the problem of multicollinearity among the seven derived factors will affect the HLM analyses and thus only one predicting factor was entered at a time to predict the outcome variable - the number of potential AE. Two out of the seven factors significantly predicted the outcome: impulsiveness ( B = 1.83, SE = 0.88, t = 2.09, p = .048) and uncooperativeness ( B = 1.38, SE = 0.54, t = 2.53, p = .018).

Factors that predict the commonality/severity of different AE were then examined. Although there were no significant predicting factors for the number of common AE, uncooperativeness significantly predicted the number of infrequent AE ( B = 0.88, SE = 0.27, t = 3.27, p = .003). Due to the fact that the reports of rare but serious AE are too few to allow for reliable results, some of the analysis solutions do not converge and thus were not reported in .

Discussion

The identification of children at highest risk of AE is crucial in guiding future treatment choices and prescribing patterns. There is a need for individualized risk-benefit analysis to minimize AE ( Ipser & Stein, 2007). The American Academy of Child and Adolescent Psychiatry ( AACAP, 2009) advocates for prescribers to determine the best medication trial for each client, and to educate the client and family about potential AE. We sought to identify individual risk factors that may predict whether a child will experience potential AE during psychopharmacological treatment.

Children diagnosed with a higher number of DSM-IV disorders were more likely to experience a greater number of potential AE, with the number of diagnoses and number of prescribed medication types (i.e., number of PMMC) found to be unrelated. Additionally, two of the seven behavioural factors from standardized intake information (CBCL, CAFAS, BCFPI) significantly predicted the number of potential AE experienced: impulsiveness and uncooperativeness. Uncooperativeness further significantly predicted infrequent AE. These factors included signs of decreased willingness to cooperate and reduced social functioning, and may possibly influence adherence to medication treatment thereby affecting AE experiences. Alternatively, impulsiveness and uncooperativeness could lead to multiclass prescribing given the typical challenges involved with managing these clinical symptoms. Others have advised against concomitant administration of multiple medication types (e.g., AACAP, 2009; Safer et al., 2003). Additional caution during client assessment and the formation of pharmacologic treatment plans may thus be warranted. Best practice indicates that psychiatric evaluations must be comprehensive enough to determine psychosocial factors that may prevent a safe medication trial ( AACAP, 2009). Our results indicate that there may be ways to predict such complications before they occur. Psychoeducation of impulsive and uncooperative clients and their families may need to emphasize the potential risks of psychotropic medications. As previously mentioned, this may be related to factors such as medication non-adherence or multiclass prescribing given the clinical challenges, which could influence the experience of AE.

Study Limitations

Our study was completed in one tertiary mental health facility serving clients with complex presentations, therefore the generalizability of the findings are limited to facilities that similarly serve children with complex needs. It may be viewed as a limitation that clients were not asked directly about specific AE, and reporting depended on staff observations and spontaneous client report. While training in how to use the PMMC does allow for asking the client how he or she feels, we caution against listing potential AE to clients to avoid false reporting or inducing the potential AE through suggestion. Another limitation of the study was the small number of rare but serious potential AE in our sample. The potential AE were not confirmed as actual AE, which may also limit the generalizability of the findings. Confirmation of AE required the prescribing physician's assessment of the potential AE as a separate task outside of this study's design. It should also be noted that uncooperativeness and impulsive behaviours are often difficult to control using psychotropic medications. Furthermore, these symptoms, commonly found in oppositional defiant disorder and conduct disorder, are often exhibited with co-morbid conditions such as ADHD, yielding more complex DSM-IV diagnoses ( Humphreys, Aguirre, & Lee, 2012).

Conclusion

The findings of this study indicate that there may be ways to predict whether children are more likely to experience AE from psychotropic medications. If we are able to predict this likelihood during an initial assessment, it can be used to direct and monitor pharmacologic treatment. According to established practice parameters, prescribers are expected to identify which symptoms are best addressed pharmacologically and which are best addressed with psychosocial treatment ( AACAP, 2009). Any factors that may impact client safety must be considered. Our findings indicate that uncooperative and impulsive symptoms may be classified as risk factors and may impact prescribing habits if further elucidated.

Clinical Significance

Knowledge of the likelihood of potential AE may impact current clinical practice as it can be of use in the direction and monitoring of treatment, as well as in the education of clients and their families. Greater emphasis should be placed on the risks of pharmacotherapy, and non-pharmacologic options should be fully explored to minimize the risk to these clients. Medication prescribing patterns should be considered with added caution in children who are found to have impulsive or uncooperative behaviours.

Acknowledgements/Conflicts of Interest

We thank the children, parents and service providers who participated in this research. This work was supported by the AHSC AFP Innovation Fund at Western University. There are no conflicts of interest to disclose for all authors of this paper.

Appendix A.

References

Achenbach TM. Manual for Child Behavior Checklist/2-3 and 1992 Profile. Burlington, VT: University of Vermont, Department of Psychiatry; 1992.

Achenbach TM, Rescorla LA. Manual for ASEBA Preschool Forms & Profiles. Burlington, VT: University of Vermont, Research Center for Children, Youth, & Families; 2001.

Bezchlibnyk-Butler KZ, Virani AS. Clinical handbook of psychotropic drugs for children and adolescents. 2nd ed. Cambridge, MA: Hogrefe & Huber Publishers; 2007.

Cunningham C, Pettingill P, Boyle M. The Brief Child and Family Phone Interview Version 3: Interviewer's Manual. Hamilton, Ontario: BCFPI Works; 2004.

Hodges K. Child and Adolescent Functional Assessment Scale (CAFAS) Revised ed. Ypsilanti, MI: Eastern Michigan University, Department of Psychology; 1994.

Kaiser HF. The application of electronic computers to factor analysis. Educational and Psychological Measurement. 1960;20:141-151.

Stahl SM. Stahl's essential pschopharmacology: Neuroscientific basis and practical applications. UK: Cambridge University Press; 2008.

Woltman H, Feldstain A, MacKay JC, Rocchi M. An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology. 2012;8(1):52-69.

Yong AG, Pearce S. A beginner's guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology. 2013;9(2):79-94.

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Adverse Effects of Psychotropic Medications in Children: Predictive Factors

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Language: English | French Despite limited information related to efficacy in children, psychotropic medications are commonly prescribed as a first-line treatment for a range of psychiatric diagnoses in children in a variety of clinical settings. Usage has increased over the past three decades.

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The Journal of Psychiatry and Neuroscience 

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The Journal of Psychiatry and Neuroscience 

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This source actually confuses me as I don't know which side of the argument it falls on. It discusses both the pros and cons and concludes that further research is needed to know anything for certain. Overall, it seems to lean more so against the idea, but doesn't totally oppose the administration of drugs to adolescents.

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In recent years there has been an increase in the number of drugs approved for use in children and adolescents to treat emerging mental illness. These drugs were approved on the basis of short-term efficacy studies, and little is known about their long-term effects.

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An anti-psychiatry movement has argued for years that children are over-medicated, that psychiatric drugs are dangerous, that diet and exercise can cure mental health problems, and even that mental illness isn't real. Parents who live with children who have mental health issues know otherwise. Suicide threats, self-harm, and disruptive behavior can throw the lives of children and their family into ceaseless chaos.

So for many families, psychiatric medications offer stunning relief from a life that has begun to feel unmanageable. Yet the decision may still be a conflicted one. Parents may worry that opponents of psychiatry are right, or that the side effects of medication will be extensive. The truth is that as long as you are working with a skilled psychiatrist, psychiatric medications are safe and effective. Here are five things you need to know:
There Is Not an Over-medication Crisis
Opponents of psychiatric medication point to increases in the diagnosis of conditions like ADHD over the past 20 years. Most children with psychiatric conditions actually receive no treatment at all.

So what about scary numbers suggesting that toddlers are taking stimulants and that psychiatric drugs are used to "treat" normal childhood behaviors? In some cases, children with serious emotional problems receive treatment from pediatricians or family physicians, not from licensed psychiatrists. Though many family doctors offer quality treatment, they don't have the training to diagnose complex mental health issues. They may also be unaware of other strategies-such as lifestyle management and psychotherapy -that are equally important to the successful treatment of childhood behavioral and mental health issues.

The problem, then, is that skepticism about psychiatry has caused some parents to avoid psychiatrists-even though psychiatrists are the people best equipped to treat their children's issues. A licensed psychiatrist will work with you to determine the lowest effective dose, the right combination of treatment strategies, and the best way to get your family back on track.
Lifestyle Remedies Work-But Often Aren't Enough
Lifestyle remedies can help with a range of mental health conditions. For example, reducing caffeine and sugar can help with ADHD. Some research points to the power of exercise to alleviate symptoms of depression and anxiety. That doesn't mean that lifestyle remedies are interchangeable with medication.

Instead, the best treatment protocol is one that incorporates both lifestyle changes and medication. Anything less is giving your child substandard treatment. Mental illnesses are real illnesses, so treating them with diet alone is akin to trying to treat an ear infection with dietary changes. It just won't work. And it might make things a lot worse.
Untreated Psychiatric Conditions Are Far More Dangerous Than Medication
Every medication has side effects, and rarely, those side effects are serious. Working with a skilled psychiatrist can help reduce the potential side effects of medication because your psychiatrist will assess your child's overall health, explore risk factors, and carefully monitor the dosage. Even when there are some minimal risks or side effects associated with taking medication, the right psychiatric drug is still a good bet.

Psychiatric conditions ruin lives. They interfere with the ability to perform at school, to make friends, to form relationships, and to get a job. Mental illness is a factor in 90 percent of suicides, which means that untreated mental illness can be fatal.

Psychiatric conditions also increase the risk of physical health conditions. They can make it difficult to make good health decisions, and may directly affect physical health. One recent study found that people with untreated severe mental illnesses die, on average, 25 years younger than those in the general population. If you want your child to have a long and full life, treating her mental illness is one of the best choices you can make.
A Good Doctor Will Monitor for Side Effects
Psychiatrists are skilled at assessing which children are most vulnerable to side effects. A skilled physician will monitor your child for side effects, start with a low dosage, and offer suggestions for reducing side effects when they appear. So while parents are understandably concerned about side effects, those who choose a skilled doctor have much less to worry about. Be open and honest with your child's doctor to get the best possible recommendations.
Medication Might Not Be Permanent
Mental illness still carries a stigma. That means that some parents have a difficult time accepting that their child might need to take medication-particularly if they'll need to take medication forever. But there's good news: Your child might not need to take medication forever.

For many kids, medication is a stop-gap measure. It helps them feel and behave better while a team works to address underlying problems and find effective lifestyle remedies. So while you should be open to the idea of your child taking medication if they need to, medication might not have to be the permanent solution you worry it will become.

# data[].attributes.attachment_link.title
When Your Child Needs Psychiatric Medication

# data[].attributes.attachment_link.description
An anti-psychiatry movement has argued for years that children are over-medicated, that psychiatric drugs are dangerous, that diet and exercise can cure mental health problems, and even that mental illness isn't real. Parents who live with children who have mental health issues know otherwise.

# data[].attributes.attachment_link.readable_text
An anti-psychiatry movement has argued for years that children are over-medicated, that psychiatric drugs are dangerous, that diet and exercise can cure mental health problems, and even that mental illness isn't real. Parents who live with children who have mental health issues know otherwise. Suicide threats, self-harm, and disruptive behavior can throw the lives of children and their family into ceaseless chaos.

So for many families, psychiatric medications offer stunning relief from a life that has begun to feel unmanageable. Yet the decision may still be a conflicted one. Parents may worry that opponents of psychiatry are right, or that the side effects of medication will be extensive. The truth is that as long as you are working with a skilled psychiatrist, psychiatric medications are safe and effective. Here are five things you need to know:
There Is Not an Over-medication Crisis
Opponents of psychiatric medication point to increases in the diagnosis of conditions like ADHD over the past 20 years. Most children with psychiatric conditions actually receive no treatment at all.

So what about scary numbers suggesting that toddlers are taking stimulants and that psychiatric drugs are used to "treat" normal childhood behaviors? In some cases, children with serious emotional problems receive treatment from pediatricians or family physicians, not from licensed psychiatrists. Though many family doctors offer quality treatment, they don't have the training to diagnose complex mental health issues. They may also be unaware of other strategies-such as lifestyle management and psychotherapy -that are equally important to the successful treatment of childhood behavioral and mental health issues.

The problem, then, is that skepticism about psychiatry has caused some parents to avoid psychiatrists-even though psychiatrists are the people best equipped to treat their children's issues. A licensed psychiatrist will work with you to determine the lowest effective dose, the right combination of treatment strategies, and the best way to get your family back on track.
Lifestyle Remedies Work-But Often Aren't Enough
Lifestyle remedies can help with a range of mental health conditions. For example, reducing caffeine and sugar can help with ADHD. Some research points to the power of exercise to alleviate symptoms of depression and anxiety. That doesn't mean that lifestyle remedies are interchangeable with medication.

Instead, the best treatment protocol is one that incorporates both lifestyle changes and medication. Anything less is giving your child substandard treatment. Mental illnesses are real illnesses, so treating them with diet alone is akin to trying to treat an ear infection with dietary changes. It just won't work. And it might make things a lot worse.
Untreated Psychiatric Conditions Are Far More Dangerous Than Medication
Every medication has side effects, and rarely, those side effects are serious. Working with a skilled psychiatrist can help reduce the potential side effects of medication because your psychiatrist will assess your child's overall health, explore risk factors, and carefully monitor the dosage. Even when there are some minimal risks or side effects associated with taking medication, the right psychiatric drug is still a good bet.

Psychiatric conditions ruin lives. They interfere with the ability to perform at school, to make friends, to form relationships, and to get a job. Mental illness is a factor in 90 percent of suicides, which means that untreated mental illness can be fatal.

Psychiatric conditions also increase the risk of physical health conditions. They can make it difficult to make good health decisions, and may directly affect physical health. One recent study found that people with untreated severe mental illnesses die, on average, 25 years younger than those in the general population. If you want your child to have a long and full life, treating her mental illness is one of the best choices you can make.
A Good Doctor Will Monitor for Side Effects
Psychiatrists are skilled at assessing which children are most vulnerable to side effects. A skilled physician will monitor your child for side effects, start with a low dosage, and offer suggestions for reducing side effects when they appear. So while parents are understandably concerned about side effects, those who choose a skilled doctor have much less to worry about. Be open and honest with your child's doctor to get the best possible recommendations.
Medication Might Not Be Permanent
Mental illness still carries a stigma. That means that some parents have a difficult time accepting that their child might need to take medication-particularly if they'll need to take medication forever. But there's good news: Your child might not need to take medication forever.

For many kids, medication is a stop-gap measure. It helps them feel and behave better while a team works to address underlying problems and find effective lifestyle remedies. So while you should be open to the idea of your child taking medication if they need to, medication might not have to be the permanent solution you worry it will become.

# data[].attributes.attachment_link.title
When Your Child Needs Psychiatric Medication

# data[].attributes.attachment_link.description
An anti-psychiatry movement has argued for years that children are over-medicated, that psychiatric drugs are dangerous, that diet and exercise can cure mental health problems, and even that mental illness isn't real. Parents who live with children who have mental health issues know otherwise.

# data[].attributes.attachment_link.readable_text
Language: English | French

Abstract

Objective:

Despite limited information related to efficacy in children, psychotropic medications are commonly prescribed as a first-line treatment for a range of psychiatric diagnoses in children in a variety of clinical settings. Usage has increased over the past three decades. Although psychotropic medications are often effective at treating psychiatric symptoms, the risk of adverse effects (AE) in children is unclear. The current research seeks to identify the mental health characteristics of those children at highest risk of experiencing potential AE from psychotropic medications.

Methods:

Psychotropic medication monitoring checklists were used to record possible AE for 99 pediatric clients in a tertiary mental health residential treatment centre for the duration of one to eight weeks. Client characteristics, including the number of diagnoses and behavioural variables, were explored for predictive value of potential AE observed.

Results:

Results showed that the total number of potential AE was positively predicted by the number of DSM-IV categories diagnosed, as well as behavioural symptoms of impulsiveness and uncooperativeness.

Conclusions:

The findings of this study indicate that the number of potential AE from psychotropic medications may be predictable based on client characteristics. Predicting this likelihood during initial assessment can be useful in directing and monitoring treatment, as well as preventing serious events related to medication use.

Keywords: children, adverse effects, psychotropic medication monitoring checklist, residential care

Résumé

Objectif:

Malgré l'information limitée sur leur efficacité chez les enfants, les médicaments psychotropes sont communément prescrits comme traitement de première ligne pour une variété de diagnostics psychiatriques chez les enfants, dans divers milieux cliniques. L'usage a augmenté dans les 30 dernières années. Bien que les médicaments psychotropes soient souvent efficaces pour traiter les symptômes psychiatriques, le risque d'effets indésirables (EI) chez les enfants n'est pas déterminé. La recherche actuelle vise à identifier les caractéristiques de la santé mentale des enfants les plus à risque d'éprouver des EI potentiels des médicaments psychotropes.

Méthodes:

Les listes de surveillance des médicaments psychotropes ont été utilisées pour repérer des EI possibles chez 99 clients pédiatriques dans un centre tertiaire de traitement résidentiel de santé mentale pour une durée d'une à huit semaines. Les caractéristiques des clients, notamment le nombre de diagnostics et de variables comportementales, ont été explorées pour la valeur prédictive des EI potentiels observés.

Résultats:

Les résultats ont indiqué que le nombre total d'EI potentiels était positivement prédit par le nombre de catégories du DSM-IV diagnostiquées, et par les symptômes comportementaux d'impulsivité et de non-coopération.

Conclusions:

Les résultats de cette étude indiquent que le nombre d'EI potentiels des médicaments psychotropes peut être prédictible d'après les caractéristiques des clients. Prédire cette probabilité durant l'évaluation initiale peut être utile pour orienter et surveiller le traitement, et prévenir des incidents sérieux liés à l'utilisation de médicaments.

Mots-clés : enfants, effets indésirables, liste de surveillance des médicaments psychotropes, soins résidentiels

There is much unknown about the use of psychotropic medications and their effects on children. Knowledge of the efficacy, specificity and adverse effects (AE) of psychotropic medications in pediatric populations lags behind what is known in adults ( Wolraich, 2003). Despite this, physicians may generalize adult prescribing patterns to children ( American Psychiatric Association, 2000). While the benefits of symptom relief often outweigh the risk of AE, determining the risks associated with psychotropic medications is an important endeavour ( Bridge et al., 2007).

Children can be more sensitive to AE than adults. In a study of antipsychotic medications, it was found that extrapyramidal AE occur more commonly in young people than adults ( Sikich, Hamer, Bashford, Sheitman, & Lieberman, 2004). While second-generation atypical antipsychotics lead to fewer extrapyramidal symptoms, AE associated with these medications include significant weight gain and increased risk of diabetes mellitus and dyslipidemia ( Gentile, 2006). AE severity may cause clients or their parents to refuse the continuation of pharmacologic therapy, thereby increasing the risk of re-experiencing their original symptoms ( Charach, Volpe, Boydell, & Gearing, 2008).

Despite limited information related to efficacy with children, psychotropic medications are a commonly prescribed first-line treatment for a range of psychiatric diagnoses in children in a variety of clinical settings. Approximately 85% of children diagnosed with attention deficit hyperactivity disorder (ADHD) are prescribed stimulant medications, 60% with bipolar disorder are prescribed mood stabilizers, and 57% of depressed outpatient pediatric clients are treated with antidepressant medications ( Moreno et al., 2007; Olfson, Gameroff, Marcus, & Jensen, 2003; Olfson, Gameroff, Marcus, & Waslick, 2003).

The prescribing rate of psychotropic medications is increasing in children, as is the number of medication types prescribed per child ( Comer, Olfson, & Mojtabai, 2010; Olfson, Marcus, Weissman, & Jensen, 2002). Over a 12-year period, multiclass psychotropic prescriptions rose in children from 14.3% to 20.2% ( Comer et al., 2010). The reasons for these changes may be the increasing level of empirical evidence supporting the usage of psychotropic medications for conditions like ADHD, obsessive-compulsive disorder, major depressive disorder, and childhood anxiety disorders, and a more thorough understanding of the biological basis of these conditions ( McClellan & Werry, 2003). Increasing usage of psychotropic medications may also be in response to recent emphasis on rapid symptom reduction by clients, parents, or physicians ( Stahl, 2008).

There is further uncertainty as to how children react to being treated with multiclass psychotropic regimens ( Safer, Zito, & dosReis, 2003). The risks of AE (including those that are fatal) are potentially compounded by the simultaneous usage of multiple psychotropic medications ( Safer et al., 2003), as many potential AE are shared across multiple medication types. A lack of evidence regarding concomitant psychotropic medication administration and its safety has been cited in the past as a concern for children in foster care ( Zito et al., 2008). The concern surrounding excessive usage of psychotropic medications has led to the creation of physician training programs and quality improvement initiatives to reduce such prescribing patterns ( Patrick, Schleifer, Nurenberg, & Gill, 2006).

Psychotherapy, such as cognitive behavioural therapy (CBT), is also considered a first-line treatment for conditions such as obsessive-compulsive disorder and generalized anxiety disorder ( James, James, Cowdrey, Soler, & Choke, 2013). Psychosocial interventions avoid the potential AE of psychotropic medications and are actually preferred by some adolescents over pharmacotherapy ( Bradley, McGrath, Brannen, & Bagnell, 2010). However, the costs of psychotherapy in private settings and the lack of such resources in many communities make pharmacotherapy a more readily available option for many ( James, Soler, & Weatherall, 2005).

Increasing our knowledge about the efficacy of psychotropic medications and AE in children is vital to ensure client safety. The present study attempts to determine the relationship between the number and commonality/severity of potential AE experienced by children and the number of disorders diagnosed according to criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) and standardized criteria measuring the complexity of psychiatric presentation.

Methods

Ethics approval was obtained from the Western University Research Ethics Board. Consent to use client data for research purposes was obtained from parents/guardians and youth.

Participants

The primary participants were 99 clients (78% boys; = 11.97 years; SD = 2.35 years) on five residential units at a tertiary mental health care facility for children with complex psychiatric concerns: a short-term stabilization unit for children (ages 6-12 years), a unit for adolescent boys (13-18 years), a unit for adolescent girls (10-18 years), a unit for boys and girls with developmental delays (6-12 years), and a unit for adolescents (13-18 years) with developmental delays. Data collection took place over a four-month time period. These 99 patients altogether provided 565 weeks of data. A secondary pool of 627 clients (77% boys; = 12.0 years; SD = 2.63 years) from the same residential units over a five-year timespan provided additional data on three standardized instruments at intake to enable scores to be condensed into manageable factors to inform analyses in the current study (see details below).

Materials

The Psychotropic Medication Monitoring Checklists (PMMC; Ninan et al., 2014) were developed over a one-year period by a four-person physician-pharmacist team. The team first identified common psychotropic medications for children, based on the information by the United States Food and Drug Administration (FDA), Health Canada, and the team's consensus opinions. The researchers then proceeded to conduct a comprehensive review regarding potential AE. These sources included product monographs, website resources such as Medscape and the Clinical Handbook of Psychotropic Drugs for Children and Adolescents ( Bezchlibnyk-Butler & Virani, 2007). Independent AE checklists were prepared for seven classes of prescribed drugs: alpha agonists, anticonvulsants, antipsychotics, atomoxetine, lithium, selective serotonin reuptake inhibitors (SSRIs), and stimulants. Each checklist divides the AE into three categories (common, infrequent, and rare but serious), based on both their likelihood of occurrence and the severity. Observed symptoms are considered "potential" AE rather than "actual" until confirmed by a physician as being directly related to the medication. This step was not included in the design of the current study. An example of the PMMC is provided in Appendix A. Ninan and colleagues (2014) recently demonstrated that the PMMC are useful educational tools that improve AE monitoring by residential staff (i.e., child and youth workers).

The Brief Child and Family Phone Interview (BCFPI; Cunningham, Pettingill, & Boyle, 2004) measures the type and the severity of a child's problems. It includes five mental health subscales (regulating attention, impulsivity, and activity level; cooperativeness; conduct; separating from parents; self-harm), one global functioning scale and one global family situation scale. Its classification reliability has been verified when compared to more extensive diagnostic interviews such as the Diagnostic Interview Schedule for Children- Version IV ( Boyle et al., 2008).

The Child and Adolescent Functional Assessment Scale (CAFAS; Hodges, 1994) measures eight problem areas, including school/work, home, community, behaviour towards others, moods/emotions, self-harmful behaviour, substance use, and thinking. Its predictive value for juvenile recidivism, contact with the law, and poor school attendance has been verified ( Quist & Matshazi, 2000; Hodges & Kim, 2000). The discriminant validity of CAFAS has also been verified to differentiate between psychiatric inpatient clients and children in alternative care, and it was found to be useful in predicting the utilization of psychiatric services ( Hodges, Wong, & Latessa, 1998).

Child Behaviour Checklist (CBCL; Achenbach, 1992) measures child behavioural and emotional problems. These problems include anxious/depressed, withdrawn/depressed, somatic complaints, social problems, thought problems, attention problems, rule-breaking behaviour, and aggressive behaviour. The CBCL has good psychometric properties and is regarded as the "Gold Standard" for evaluating child behavioural and socio-emotional problems ( Achenbach & Rescorla, 2001). Its reliability and validity, both convergent and discriminant, have been well documented ( Dutra, Campbell, & Westen, 2004).

Procedure

At the time of referral, the BCFPI was conducted with the child's primary caregiver. Prior to admission, the primary caregiver also completed a battery of standardized psychological health assessments, including the CBCL. The CAFAS was completed by the client's primary clinician at admission and follow-up time points. Prior to discharge, clients were assessed by a psychologist or psychiatrist for mental health disorders using the DSM-IV. The PMMC were completed daily by residential staff (i.e., child and youth workers and nurses) for each client for one to eight weeks during their residential stay. Potential AE were recorded for each day based on staff observations and communication between staff and with clients. Clients were not asked directly if they were experiencing each AE, but any expressed by clients were noted on the checklists. The responsible physician reviewed the PMMC regularly. In the case of weekend or holiday leaves of absence, medication monitoring was temporarily suspended. Therefore, the average number of AE per day for the current analysis was calculated using only the information on the monitored days. provides a brief description of client characteristics. shows the total number of AE reported during the study, and the percent of weeks with at least one AE reported.

Table 1.

Table 2.

Hierarchical linear modeling (HLM), a complex form of logistic regression was used for the data analyses. HLM was chosen in order to model a nested structure where weeks of monitoring are nested within clients ( Woltman, Feldstain, MacKay, & Rocchi, 2012). Weeks of PMMC completion were chosen as a random effect since this number varied among clients for several reasons (e.g., medication prescription changes, admission after the study began, discharge prior to study completion). The dependent variables in the analyses were the average number of AE a client experienced daily within a week and the commonality/severity of such symptoms (common, infrequent, rare but serious). The independent variables were the number of DSM-IV diagnoses and standardized intake scores. Because the variables are highly correlated with each other, the problem of multicollinearity will adversely affect the HLM analyses. Therefore, predictors were placed in the HLM analysis one at a time.

Because each of the standardized intake measures (BCFPI, CAFAS, CBCL) included multiple sub-scores, and because sub-scores across instruments were correlated with each other, factor analyses using a pool of data collected previously from a larger number of clients (N=627) were first conducted to condense these scores into fewer manageable factors. Factor analyses using principal component analyses with direct oblimin were conducted ( Yong & Pearce, 2013). Seven factors were extracted because their corresponding eigenvalues were greater than one ( Kaiser, 1960). Results are shown in . The first factor represented problems in impulsiveness, social, thought and attention problems (abbreviated as impulsiveness). The second factor represented conduct problems and substance abuse (abbreviated as conduct). The third factor represented problems in school, work, home, and community setting (abbreviated as setting). The fourth factor represented withdrawn/depressed and somatic complaints (abbreviated as withdrawn). The fifth factor represented issues with cooperativeness, and social functioning (abbreviated as uncooperativeness). The sixth factor corresponded to separation anxiety and anxious/depression problems (abbreviated as anxiety). The final factor represented problems with thinking and self-harm (abbreviated as thinking). All numbers were coded such that a higher score represents more severe problems in the corresponding area. The subscales of the primary participants were transformed into z scores and then amalgamated into the seven factors by taking the mean of the contributing subscales for use in the HLM analyses described below.

Table 3.

Results

Results of the HLM analyses are summarized in . The first research question was whether the average number of potential AE is predicted by the number of DSM-IV diagnoses. Results showed that the regression coefficient ( B) relating the number of DSM-IV diagnoses and number of potential AE was positive and statistically significant ( B = 0.17, SE = 0.08, t = 2.02, p = .047). Next, potential AE were divided into common, infrequent, and rare but serious categories. Similar analyses as above were then conducted. Only for common AE, the regression coefficient continued to show that the number was positively and significantly predicted by the number of DSM-IV diagnoses ( B = 0.09, SE = 0.05, t = 2.01, p = .048). One may question whether there is a relationship between the number of DSM-IV diagnoses and the number of medication types prescribed (i.e., the number of checklists completed per client within a week). Therefore, another HLM was conducted with the number of DSM-IV diagnoses predicting the number of medication types, but the result was non-significant ( B = −0.11, SE = 0.17, t = −0.66, p = .52).

Table 4.

An additional area of inquiry was whether the factors derived from the standardized instruments at intake predicted the number of potential AE that clients experienced (i.e., impulsiveness, conduct, setting, withdrawn, uncooperativeness, anxiety, thinking). Again, the problem of multicollinearity among the seven derived factors will affect the HLM analyses and thus only one predicting factor was entered at a time to predict the outcome variable - the number of potential AE. Two out of the seven factors significantly predicted the outcome: impulsiveness ( B = 1.83, SE = 0.88, t = 2.09, p = .048) and uncooperativeness ( B = 1.38, SE = 0.54, t = 2.53, p = .018).

Factors that predict the commonality/severity of different AE were then examined. Although there were no significant predicting factors for the number of common AE, uncooperativeness significantly predicted the number of infrequent AE ( B = 0.88, SE = 0.27, t = 3.27, p = .003). Due to the fact that the reports of rare but serious AE are too few to allow for reliable results, some of the analysis solutions do not converge and thus were not reported in .

Discussion

The identification of children at highest risk of AE is crucial in guiding future treatment choices and prescribing patterns. There is a need for individualized risk-benefit analysis to minimize AE ( Ipser & Stein, 2007). The American Academy of Child and Adolescent Psychiatry ( AACAP, 2009) advocates for prescribers to determine the best medication trial for each client, and to educate the client and family about potential AE. We sought to identify individual risk factors that may predict whether a child will experience potential AE during psychopharmacological treatment.

Children diagnosed with a higher number of DSM-IV disorders were more likely to experience a greater number of potential AE, with the number of diagnoses and number of prescribed medication types (i.e., number of PMMC) found to be unrelated. Additionally, two of the seven behavioural factors from standardized intake information (CBCL, CAFAS, BCFPI) significantly predicted the number of potential AE experienced: impulsiveness and uncooperativeness. Uncooperativeness further significantly predicted infrequent AE. These factors included signs of decreased willingness to cooperate and reduced social functioning, and may possibly influence adherence to medication treatment thereby affecting AE experiences. Alternatively, impulsiveness and uncooperativeness could lead to multiclass prescribing given the typical challenges involved with managing these clinical symptoms. Others have advised against concomitant administration of multiple medication types (e.g., AACAP, 2009; Safer et al., 2003). Additional caution during client assessment and the formation of pharmacologic treatment plans may thus be warranted. Best practice indicates that psychiatric evaluations must be comprehensive enough to determine psychosocial factors that may prevent a safe medication trial ( AACAP, 2009). Our results indicate that there may be ways to predict such complications before they occur. Psychoeducation of impulsive and uncooperative clients and their families may need to emphasize the potential risks of psychotropic medications. As previously mentioned, this may be related to factors such as medication non-adherence or multiclass prescribing given the clinical challenges, which could influence the experience of AE.

Study Limitations

Our study was completed in one tertiary mental health facility serving clients with complex presentations, therefore the generalizability of the findings are limited to facilities that similarly serve children with complex needs. It may be viewed as a limitation that clients were not asked directly about specific AE, and reporting depended on staff observations and spontaneous client report. While training in how to use the PMMC does allow for asking the client how he or she feels, we caution against listing potential AE to clients to avoid false reporting or inducing the potential AE through suggestion. Another limitation of the study was the small number of rare but serious potential AE in our sample. The potential AE were not confirmed as actual AE, which may also limit the generalizability of the findings. Confirmation of AE required the prescribing physician's assessment of the potential AE as a separate task outside of this study's design. It should also be noted that uncooperativeness and impulsive behaviours are often difficult to control using psychotropic medications. Furthermore, these symptoms, commonly found in oppositional defiant disorder and conduct disorder, are often exhibited with co-morbid conditions such as ADHD, yielding more complex DSM-IV diagnoses ( Humphreys, Aguirre, & Lee, 2012).

Conclusion

The findings of this study indicate that there may be ways to predict whether children are more likely to experience AE from psychotropic medications. If we are able to predict this likelihood during an initial assessment, it can be used to direct and monitor pharmacologic treatment. According to established practice parameters, prescribers are expected to identify which symptoms are best addressed pharmacologically and which are best addressed with psychosocial treatment ( AACAP, 2009). Any factors that may impact client safety must be considered. Our findings indicate that uncooperative and impulsive symptoms may be classified as risk factors and may impact prescribing habits if further elucidated.

Clinical Significance

Knowledge of the likelihood of potential AE may impact current clinical practice as it can be of use in the direction and monitoring of treatment, as well as in the education of clients and their families. Greater emphasis should be placed on the risks of pharmacotherapy, and non-pharmacologic options should be fully explored to minimize the risk to these clients. Medication prescribing patterns should be considered with added caution in children who are found to have impulsive or uncooperative behaviours.

Acknowledgements/Conflicts of Interest

We thank the children, parents and service providers who participated in this research. This work was supported by the AHSC AFP Innovation Fund at Western University. There are no conflicts of interest to disclose for all authors of this paper.

Appendix A.

References

Achenbach TM. Manual for Child Behavior Checklist/2-3 and 1992 Profile. Burlington, VT: University of Vermont, Department of Psychiatry; 1992.

Achenbach TM, Rescorla LA. Manual for ASEBA Preschool Forms & Profiles. Burlington, VT: University of Vermont, Research Center for Children, Youth, & Families; 2001.

Bezchlibnyk-Butler KZ, Virani AS. Clinical handbook of psychotropic drugs for children and adolescents. 2nd ed. Cambridge, MA: Hogrefe & Huber Publishers; 2007.

Cunningham C, Pettingill P, Boyle M. The Brief Child and Family Phone Interview Version 3: Interviewer's Manual. Hamilton, Ontario: BCFPI Works; 2004.

Hodges K. Child and Adolescent Functional Assessment Scale (CAFAS) Revised ed. Ypsilanti, MI: Eastern Michigan University, Department of Psychology; 1994.

Kaiser HF. The application of electronic computers to factor analysis. Educational and Psychological Measurement. 1960;20:141-151.

Stahl SM. Stahl's essential pschopharmacology: Neuroscientific basis and practical applications. UK: Cambridge University Press; 2008.

Woltman H, Feldstain A, MacKay JC, Rocchi M. An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology. 2012;8(1):52-69.

Yong AG, Pearce S. A beginner's guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology. 2013;9(2):79-94.

# data[].attributes.attachment_link.title
Adverse Effects of Psychotropic Medications in Children: Predictive Factors

# data[].attributes.attachment_link.description
Language: English | French Despite limited information related to efficacy in children, psychotropic medications are commonly prescribed as a first-line treatment for a range of psychiatric diagnoses in children in a variety of clinical settings. Usage has increased over the past three decades.

# data[].attributes.attachment_link.readable_text
Abstract

In this article, I examine children's reported experiences with stimulant drug treatments for attention deficit hyperactivity disorder in light of bioethical arguments about the potential threats of psychotropic drugs to authenticity and moral agency. Drawing on a study that involved over 150 families in the USA and the UK, I show that children are able to report threats to authenticity, but that the majority of children are not concerned with such threats. On balance, children report that stimulants improve their capacity for moral agency, and they associate this capacity with an ability to meet normative expectations. I argue that although under certain conditions stimulant drug treatment may increase the risk of a threat to authenticity, there are ways to minimise this risk and to maximise the benefits of stimulant drug treatment. Medical professionals in particular should help children to flourish with stimulant drug treatments, in good and in bad conditions.

Introduction

It is the attempted standardization of a human being and of a notion of achievement that is limiting, prescriptive and bullying ... Ritalin and other forms of enforcement and psychological policing are the contemporary equivalent of the old practice of tying up children's hands in bed, so they won't touch their genitals. The parent stupefies the child for the parent's good.1 Hanif Kureishi

Few topics generate such heated debate as the use of stimulant drugs in children. Stimulants, such as Ritalin or Adderall, are most frequently used as a treatment for attention deficit hyperactivity disorder (ADHD), a controversial condition characterised by symptoms of hyperactivity, impulsiveness and inattention. In some areas, diagnostic prevalence of ADHD is as high as 27% of school-age children; 2 a more sober, but still controversial, estimate of worldwide prevalence of ADHD among children is 5%. 3 Stimulant use for ADHD varies across and within countries 4 and is growing most rapidly (at over 20% annually) in the developing world. 5

As popular author Hanif Kureishi's passionate protest against Ritalin illustrates, stimulant drug use in children is a vital node around which public concerns with children's liberties and childrearing practices are voiced. This protest is not new. Kureishi's article, published in the New York Times in February 2012, 1 echoes a sustained series of critical analyses of stimulant drug use in children, which began soon after Ritalin first came on the market in 1955. Writing a decade ago, the philosopher Frances Fukuyama agreed that Ritalin is a means of 'socially controlling children' and suggested that its use had significant implications for 'conventional understanding of identity and moral behaviour'. 6 In 2003, the US President's Council on Bioethics raised similar concerns: If the development of character depends on the effort to choose and act appropriately, often in face of resisting desires and impulses, then the more direct pharmacological approach bypasses a crucial element... By treating the restlessness of youth as a medical, rather than a moral, challenge, those resorting to behavior-modifying drugs might deprive that child of an essential part of this education. They might also encourage him to change his self-understanding, by coming to look upon himself as governed largely by chemical impulses and not by moral decisions grounded in some sense of what is right and appropriate (105-6).7

Such bioethical arguments about moral agency and authenticity overlap to some degree with a longstanding sociological critique of Ritalin. This critique has focused primarily on the medicalisation of undesirable social behaviours in children as part of an oppressive biopolitics, and the role of institutions (clinic, school, family, pharmaceutical industry) in creating a need and desire for pharmaceutical control of children's behaviour. 8 , 9 An important common concern in different disciplinary analyses is that stimulants might suppress children's capacity to protest bad conditions, thereby allowing those conditions to prevail.

It is appropriate that the behavioural manipulation of young children by invasive means such as drugs continues to motivate public and intellectual debate. Arguments about children's liberties are particularly resonant at a time when scientific knowledge and tools are used ever more pervasively to shape behavioural, cognitive and emotional capacities across early developmental stages. 10 , 11 It is certainly plausible that such tools could undermine children's developing sense of self and their capacity for moral agency. Nevertheless, stimulant drug use continues to rise exponentially around the world, with little heed to passionate and persistent protests by public intellectuals and many others. We therefore need a balanced account of why, in spite of all the acknowledged concerns, stimulant drugs remain a compelling resource for children and families. To achieve such an account, we need to know what stimulant drugs are doing in the lives of children and families, and whether, and if so in what way, the ethical concerns about developing an authentic self and moral agency fit with children's daily experiences on the drugs. If we can better understand this fit, it should be possible to refine some of the relevant concepts, improve the arguments and ultimately move the debate along from where it has become stuck. It should also be possible to address an important practical concern: If there are broader cultural agendas at work in child development, how should we go about making good judgments about what is best for individual children?

In this article I use an empirical approach to evaluate the claim that stimulant drugs pose a threat to authenticity and to children's capacity for moral agency. Drawing on a study involving over 150 families in two countries, I show that children are able to report threats to authenticity related to stimulant drug treatments, but that the majority of children are not concerned with such threats. On balance, children report that stimulant drugs improve their capacity for moral agency, and they associate this capacity with an ability to meet normative expectations. I argue that although under certain conditions stimulant drug treatment may increase the risk of a threat to authenticity, there are ways to minimise this risk and to maximise the benefits of stimulant drug treatment. Medical professionals in particular should help children to flourish with stimulant drug treatments, in good and in bad conditions.

Methods

This article draws on data collected from the VOICES study (Voices On Identity, Childhood, Ethics and Stimulants). The study used a mixed-methods approach to examine the social and ethical implications of ADHD diagnosis and stimulant drug treatments, with particular attention to issues of moral agency, authenticity, stigma and personal responsibility. As part of the study, 151 children aged 9-14 were interviewed in the USA and the UK between 2008 and 2010. There were three groups in each country: children diagnosed with ADHD and taking stimulant medication; children diagnosed with ADHD and not medicated; and children without a psychiatric diagnosis. Diagnosed children were recruited from National Health Service (NHS) sites in the UK, and from university and community clinics in the USA. All child participants gave verbal and written assent and all parents gave verbal and written consent to participation. The study received NHS ethics approval in the UK, and Institutional Review Board (IRB) approvals in the USA. Consent was recorded on forms approved by the ethics committees. In all articles on the VOICES study, pseudonyms are used, and identifying details are sometimes changed to assure anonymity and confidentiality of participants and their families.

Parents filled out standardised measures as well as an open questionnaire. Memos were used to document conversations held with parents during the consent procedures and after children's interviews. Parent questionnaires were used to compare the severity of children's symptoms, as well as the quality and substance of the original clinical evaluation for ADHD. Overall, no significant differences were found among groups of diagnosed children; however, UK children scored higher than US children on the oppositional-defiant subscale of the Conners' Child Behavior Checklist.

Efforts were made to match children in both countries by broad demographic characteristics (age, gender, socio-economic status, as measured by the Hollingshead Four-Factor Index). The average child in the study is an 11-year-old white boy from a lower middle-class socio-economic background. We interviewed significantly more girls and ethnic minorities in the US than in the UK.

Interviews were first analysed thematically, using a coding frame developed in a coding team. Specific interview sections were subsequently reanalysed using methods appropriate to particular research questions, including discourse analysis and quantitative content analysis. Analytical comparisons rely on the concept of the 'ecological niche' rather than on national origin. 18 The niche concept views the child in the context of individual, familial, social and national factors. These factors make up a network of proximal and distal, multi-directional influences in, on and from children. In the VOICES study, we identified two modal niches that children inhabited: a 'conduct niche' and a 'performance niche'. These modal niches are further described in the discussion below. 19

Authenticity and stimulants

Accounts of experiences with stimulant drugs are an important contribution to bioethical thinking not least because of the unique dosing practices around these drugs. Sustained-release versions (Ritalin XR, Concerta and Adderall are the most prominent commercial brands) have an effect for a maximum of 8 h; short-acting formulations are effective for 3-4 h. Between doses, children return to baseline functioning. Children may forget to take their medication on a given day; or, often with support of their parents, they skip doses, particularly at weekends and school holidays. The on-off rhythm of stimulant drug effects and the need to repeatedly justify stimulant drug doses support a degree of reflexive awareness in caregivers and in children themselves about the association between individual behaviour and drug effects. 20 This reflexivity offers a unique opportunity for research on the perceived impacts of stimulant drugs.

In interviews, children were asked directly to discuss their views on whether or not the use of stimulants posed a threat to authenticity. Through verbal questioning, drawing and a vignette, children were repeatedly prompted to engage with two main questions on authenticity in relation to stimulant drugs (stated here in shorthand form): 'Do you feel like you are a different person when you are on/off medication?' and 'Do you feel like the 'you' on/off medication is more/less the real you?' Throughout the interviews, children were encouraged to refuse, contradict or revise the interviewer's formulation of their experiences. Other questions addressed how a child's own views of himself or herself on and off medication correlated with other people's views (peers, teachers, parents).

The majority of children interviewed report that medication has an instrumental impact on behaviour, but does not make them feel that they are a different person. INT: Would you say that you feel like you're a different person when you take your tablets? I'm the same person either way [on or off medication]. Both of them are me. Medication slows my brain down and makes good ideas stay longer. (Joe, US, age 10)INT: Some people think taking medication might turn you into a different person. What do you think about that? With medication it's not that you're a different person; you're still the same person, but you just act a little better. Medication will help you control yourself. (Angie, US, age 11)INT: Would you say that you're the same person when you're taking your medication, or are you a different person - or is that just a silly question? I'm a bit, I'm a bit of both. Like I am a bit the same person I always are [on medication]. It's just, I act a bit different, but I'm, I actually are always, I am always the same person. (Laurence, UK, age 10)

Angie and Laurence make a distinction between the effects of medication on the person, and the effects of medication on behaviour. This distinction is frequently reflected in parents' remarks: I tell him [child] that even if I'm unhappy with his behaviour, I still love him. Or: I'm glad to know it's a problem with his behaviour and that he's not a naughty child. Children do not simply parrot their parents' beliefs about this person-behaviour distinction. They also defuse an assumption, contained in many critical analyses, about the power of stimulant medications: Children claim that while stimulants are helpful, they are not sufficiently powerful to engender radical change in the person or in behaviour.The medication doesn't really change who I am completely. I still have problems even when I'm on the medication. (Flora, US, age 12)... I'm always hyper even when I'm on my tablets ... [On medication] I'm mostly good, but I still have moods... I'm still am what I am. (Don, UK, age 11)When I'm on my medication I'm still [myself]. It's just that I'm a bit more and a bit less of some of the things ... The medication can't just take you over and then control you and what you do ... It can't make you a Ben or a Tom or someone else with a 3-letter name. (Davy, UK, age 14)

Similarly, when asked directly whether the self on or off medication is experienced as a more real self, children frequently resist the binary option presented by the question, in favour of articulating a continuous sense of self: INT: Who would you say is the real Justin? The Justin on medication, or the Justin off medication. Or, would you say actually, you know, I don't agree that I'm real one way, or not real the other way? Yeah, I don't agree with that. I'm just ... there's only a slight change in my personality, but I'm still the same person. (Justin, US, age 14)

This experience of variations within the self is not associated with distress in most children we interviewed; it is not reported as an experience of self-alienation. Even when children consider that their real self may be more the self off medication, they do not see this as a reason to stop taking medication: The tablets are making me into, like, a person who has like, two heads. Have you ever seen, um ... the Nutty Professor. The first one where he keeps changing into Buddy and the Professor. He wants to be thin and slim ... he wants to be perfect ... So I feel like him; I feel like, I feel like I'm the Nutty Professor and when I'm on my tablets, I feel like Buddy - not perfect, but you know ...INT: So, how does it feel to be these two people? Well, I've got used to it actually, so I know what to expect and what to do ...INT: So it doesn't feel bad to you, to have to deal with both of these people inside you? No. (Zach, UK, age 13)

If the authentic self is represented by an inner voice, then both Buddy and the Nutty Professor whisper in Zach's ear. 12 Zach experiences no obvious authenticity-conflict in relation to these voices; they are different parts of him that coexist within the whole. Alexandre Erler makes the point that proponents of the inner voice authenticity argument do not define what the authentic voice is when there are 'multiple yet incompatible candidates'. 21 Zach suggests that this definitional effort may need to account for the possibility that authenticity can include multiplicity and incompatibility.

This textured account of the self is even more interesting when considered in the context of accounts that parents gave verbally, with their children present, and in writing. When reporting on the impact of medication, parents tend to emphasise both binary change in a child and self-discovery, with statements like: He's like a different child; or, Finally he can know himself as a successful child; or, She's so much happier with herself now. In this study, six children consistently echoed such accounts. All six children were 9 years old (the youngest children we interviewed), representing 40% of 9-year-olds with experience of stimulants in the sample. Their common age suggests that characteristics of the developmental stage, for example, concrete reasoning and a desire to please adults, may increase the risk that stimulant drug treatment (at least rhetorically) 'bypasses' moral struggle. When asked directly about differences in themselves on and off medication, this group of children delivers binary self-evaluations:[With the medicine] I ain't being naughty no more... I'm not doing anything to other people now. [The medicine] makes me good again. [If I didn't take it] I'd probably get into lots of trouble. [Tablets] have changed my life ... like, stopped making me bad ... and pushing people over ... and starting fights. (Cecil, UK, age 9)

Cecil's mother tells us that Cecil has been on medication since age 4, and that medication has made all the difference.

The majority of children in this study express a more subtle idea: stimulant drugs put them in the position of being able to choose between versions of themselves depending on what the situation calls for. Their accounts suggest that they value a version of authenticity that is less about self-discovery and more about the potential for self-actualisation via the development of self-control: I know when [the medication] is working because I'm more quieter. I can sit still longer. And I don't have to fiddle about so much. I prefer myself like, in the middle. Like if you've got ADHD, you would prefer yourself in the middle, because you'll know when you're about to go into, when you're going to be hyper and when you can control yourself a lot more. And that's why I think I should be more in the middle a lot. (Timothy, UK, age 11)[With medication] I'm in the middle ... it's kind of like me looking to the angel and me looking to the hell. Angel, hell; angel, hell ... (Tristan, UK, age 9)

It is notable that Timothy and Tristan speak to the very concerns that Fukuyama 6 and other bioethicists have raised: the importance of genuine moral decision-making to the development of moral agency. However, in these children's reports, stimulant drugs enable, rather than threaten, moral reasoning in face of conflicting impulses. In Tristan's words, he can choose 'the angel' or he can choose 'hell'. Children value this position of choice. Arguably, it is the repeated experience of sitting 'in the middle' and choosing the right action that supports the development of moral responsibility and understanding of oneself as a moral agent. Moreover, as Glenn points out below, it is the child who makes the decision about how to act, not the medication:If you're like driving in a car, and like, there's two different ways, and you usually always go this way ... and then one day you want to go the other way, but ... the ADHD acts as like a blocker, so you can't ... It [the medicine] opens the blocker so that you can go [the right] way. But you still have the choice of going the wrong way ... It's harder [without medication], that's what's the truth. But it's not like [on medication] you're a robot ... (Glenn, US, age 10)

Such accounts suggest that the view represented by Kureishi 1 and others, that children are 'stupefied' by Ritalin, or turned into mindless, obedient zombies, unfairly characterises a majority of children's capacities for self-reflection and moral awareness while taking medication and disregards the high value children place on their capacity for moral agency, which many experience as being enabled by medication.

Conclusions

We have seen that children generally have positive responses to stimulant drug treatments. They primarily see stimulants as supporting their capacity for moral agency. Niche conditions influence how children express their experiences with stimulant drug treatments, and how they conceive the value and work of the drugs. Most children in this study did not experience stimulant drug treatment as a threat to authenticity, but such a threat is a potential risk to children. It is possible to identify concrete steps to minimise the risks and to maximise the benefits of stimulant drug treatment: I have argued that medical professionals should take a lead in building on children's evident capacity for critical reflection on niche conditions, moral behaviour and the norming work of stimulant drugs, as part of a commitment to children's flourishing.

How do the perspectives of children move the Ritalin debates on? Let us say that after reading this article, a sceptic is not convinced that stimulant drugs can support children's flourishing. Plausible justifications for such a position certainly remain. However, the sceptic will now need to acknowledge that the debate cannot accurately be framed as one between absolute goods and absolute harms; for example, children's liberties versus Ritalin. Children's perspectives show us, at the least, that it is possible to make important claims for stimulant drugs as a good. This acknowledgement changes the nature of the debate: if the sceptic wants to maintain his or her position, he or she ought to do so with respect for the aspirational human agents involved, and for the complexity of the problem, and not in wilful ignorance. Perhaps the sceptic will be more inclined to have empathy with those who might suffer as a consequence of his or her position, as indeed, many children and families who are subjected to the debate in its traditional divisive form, suffer.

I am hopeful that if we can change the nature of the debate, by framing the case of stimulant drugs as a problem of tensions among contending goods, we will get better and farther in our arguments, generate more insightful public discourse, and ultimately enable more relevant and more reasonable evaluations of the impact of stimulant drug treatments on children's flourishing. More generally, as the arsenal of interventions into children's cognition and behaviour grows, balanced, deliberative and empirically grounded analyses should enable more accurate descriptions of bad conditions, in which children's vulnerability is increased by interventions. This accuracy should translate into identification of relevant means by which the social and moral development of children in general-but particularly those who are at risk-is promoted.

Acknowledgments

Thanks to the VOICES study research assistants, our clinical collaborators and the UK MHRN. Huge gratitude to the children, parents, relatives and doctors who participated in the study. Special thanks to Hanna Pickard and the OCN reading group for insightful comments and careful reading of drafts. Thanks also to Brocher meeting participants for helpful discussions on authenticity. VOICES is funded by a Wellcome Trust university award no. 08029.

View Abstract

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Not robots: children's perspectives on authenticity, moral agency and stimulant drug treatments

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In this article, I examine children's reported experiences with stimulant drug treatments for attention deficit hyperactivity disorder in light of bioethical arguments about the potential threats of psychotropic drugs to authenticity and moral agency.