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PLAGUE Inc/Business Insider:
https://www.businessinsider.com/wuhan-coronavirus-causing-interest-in-plague-game-reduce-anxiety-2020-1
CDC-Outbreak:
https://emergency.cdc.gov/recentincidents/
GHSA-CDC:
Global Health Security Agenda:
https://www.cdc.gov/globalhealth/security/ghsagenda.htm

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Online Risk Assessment from Northwell health. 

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Online Risk Assessment from Northwell health. 

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https://www.northwell.edu/support-and-resources/wellness-and-prevention/health-assessments

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This goes over Assessment , Policy Development and Assurance. These are the three core public health functions. It also goes over public health services.

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Core public health functions illustration with the public health services divided into the separate functions they correlate with. 

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Core public health functions illustration with the public health services divided into the separate functions they correlate with. 

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https://www.cdc.gov/healthliteracy/training/page1299.html

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In this paper, we explain public health as a professional discipline to clinical informaticians. Defining public health presents a challenge, for two different but key reasons. First, public health as a discipline can be difficult to fathom because of the diverse range of problems it addresses and, consequently, the broad scope of activities. Second, many clinicians and other caregivers already have an idea of what public health is about, but that idea is typically limited in scope and is sometimes quite mistaken. At a minimum, it reflects and is constrained by the perspective that is inherent in the nature of clinical work. Yet understanding the discipline of public health is critical for informaticians. Through an understanding of public health and the developing specialty of public health informatics, their training, talents, creativity, and experience can be applied in new ways to the fundamental mission of public health-to promote physical and mental health and prevent disease, injury, and disability to realize the vision of healthy people in healthy communities.

In this paper, we explain the unique perspectives of public health as a discipline, broadly outline and discuss the fundamental nature of public health, present a case study to illustrate and amplify that discussion, and articulate several "grand challenges" for the new and evolving field of public health informatics.

Definition of Public Health

Tip of the Iceberg

Although the public health sector has protected and kept U.S. residents healthy, the majority of Americans have had limited direct exposure to the ways in which public health is practiced. Exposure to the public health system is typically confined to limited activities undertaken by local or state health departments. These activities might include the vaccination of children and the provision of official copies of birth certificates, among others. For years, public health hospitals have been counted among the most visible manifestations of the public health system, serving as "clinical care provider of last resort." Indeed, many people equate public health solely with providing care to indigent populations. For others, public health involves routine restaurant inspections and investigations of food-borne epidemics in the community. For many clinicians, their primary (and generally one-way) interaction with public health involves submitting notifiable disease reports when they diagnose communicable diseases among their patients.

Certainly, public health agencies are involved in all of these activities. But to understand public health only in these terms is similar to understanding marine biology in terms of shells on the shore. It misses the broader, often invisible aspects of the discipline that have had and continue to have profound salutary effects on human health. For example, the majority of Americans take for granted certain aspects of daily life that exist because of the historical and continuing efforts of the public health sector, such as clean water, safe roads, a protected food supply, and proper disposal of solid and liquid waste. In addition, many of the fundamental processes of public health (e.g., ongoing disease surveillance, environmental monitoring, and prevention research) generally take place outside the public consciousness, although they are no less important for being invisible.

Shifting Priorities, Multiple Disciplines, Various Roles

Apart from limited exposure to the public health system, the discipline of public health can be difficult to grasp, in part because the focus of public health action has evolved over the past two centuries. This is because the activities of public health are primarily driven by its goal-to improve health and prevent disease, injury, and disability. Thus, as threats to human health vary with time and geography, so too do specific public health projects and efforts. For example, during the 18th and 19th centuries, yellow fever was a great scourge in America. In Philadelphia in 1793, for example, yellow fever killed 5,000 people in only three months-roughly 10 percent of the population. Today, this disease is essentially unknown in the United States, and it is not a current focus of public health action.

As progress was made against infectious diseases in the 20th century, the public health community expanded its focus to include other threats to human health (e.g., occupational illnesses, motor vehicle injuries, chronic diseases, exposures to toxic waste and other environmental hazards, interpersonal violence, and suicide). Of course, new or reemerging infectious disease threats (e.g., AIDS, hantavirus, and drug-resistant tuberculosis) continue to command the attention of public health officials.

Another aspect of public health that makes the concept hard to grasp derives from its varying interventions, which have involved diverse professional disciplines. Many activities-like road and building design, toxic waste disposal, water treatment, school safety, immigrant health, protection of the food supply, even municipal design to promote healthy lifestyles-have been key to promoting health and preventing disease and injury. In addition to nurses, physicians, epidemiologists, and statisticians, the public health system also relies on engineers, social workers, outreach workers, laboratory workers, health communication specialists, sanitarians, environmental specialists, nutritionists, lawyers, legislators, and others to apply efforts that promote health and prevent disease. The public health system comprises allied government, community, professional, voluntary, and academic institutions and organizations that might not be officially termed (or consider themselves) public health agencies. In this paper, we focus on governmental public health agencies.

Finally, public health as a discipline encompasses an amalgam of science, action, research, policy, advocacy, and government. Public health practitioners play each of these roles at different times, but as a professional discipline, public health is not adequately described by any one of them. If public health were simply a scientific enterprise, government function, or any one of the elements listed previously, the concept would be easier to grasp. But public health professionals do wear distinct hats according to the nature of the various threats to community health, the political and social context of the community, and the often incomplete scientific knowledge regarding available preventive interventions. This also makes understanding the extent and nature of public health as a discipline difficult.

Defining Principles and Core Functions of Public Health

Although the focus of public health action and the nature of public health interventions continue to evolve, the fundamental principles of public health remain clear and stable. We previously stated four principles that define, guide, and provide the context for the types of activities and challenges that are undertaken in furtherance of public health. These principles derive from the scope and nature of public health itself and tend to distinguish public health from medicine and health care. These four principles can be useful as a guide to those attempting to understand public health.

The primary focus of public health is to promote the health of populations and not the health of specific individuals.

The primary strategy of public health is prevention of disease and injury by altering the conditions or the environment that put populations at risk.

Public health professionals explore the potential for prevention at all vulnerable points in the causal chains leading to disease, injury, or disability; public health activities are not restricted to particular social, behavioral, or environmental contexts.

Public health interventions must reflect the governmental context in which public health is practiced.

With these four principles as guides, public health can be further defined and understood in terms of its core functions. In 1988, the Institute of Medicine (IOM) published The Future of Public Health, in which it articulated three activities that ideally comprise the core functions of an effective public health system-assessment, policy development, and assurance. The first core function refers to the assessment and monitoring of the health of communities and populations at risk, to identify health problems and priorities. Policy development involves formulating public policies, in collaboration with community and government leaders, that are designed to solve identified health problems. Assurance refers to the responsibility of the public health system to ensure that all populations have access to appropriate and cost-effective care, including health promotion and disease prevention services, and evaluation of the effectiveness of that care.

Information is central to each of these core functions. For example, the essence of community health assessment is the collection, analysis, interpretation, and communication of data and information. Timely and authoritative information is also central to the informed development of public health policy. Finally, the assurance function described in the IOM report moves public health away from "clinical care provider of last resort" toward the role of monitor and communicator of information about community access to critical health services. Thus, each of these core functions accentuates the importance of public health as information broker, directly underscoring the need for public health officials to be effective planners, developers, and users of health information systems.

People and Facilities

People

The governmental portion of the public health workforce embodies approximately 500,000 professionals, divided among federal, state, and local departments of health. As described previously, they practice many professions, including informatics. These public health practitioners operate within the political realities of government organizations and are constrained by limited budgets and inadequate information system resources. Despite these limitations, they are highly committed and dedicated, and their shared motivation is their belief in the power of prevention to ensure community health.

Public health professionals at the federal level frequently specialize or subspecialize in a narrow field, whereas those working on the front lines of public health (e.g., in local health departments) tend to be generalists with more diverse responsibilities. Depending on funding or need, state public health staff fall somewhere between. For example, an epidemiologist working at the Centers for Disease Control and Prevention (CDC) might focus on asthma or on monitoring the incidence and prevalence of asthma. A CDC health educator might develop training materials pertaining to influenza. In contrast, a state health department epidemiologist might focus on prevention of environmental illnesses, or a state health educator might concentrate on all diseases preventable by vaccine. In smaller cities or counties, a single local health department staff person may cover a wide range of programs; the same person might investigate infectious diseases, disseminate information about chronic diseases, manage the Women, Infants, and Children (WIC) and other direct-service programs, and communicate public health concerns to elected officials.

State and local public health staff and others in the health-care system rely on federal experts as a national resource, whereas federal and state public health departments produce much of the research, guidelines, and recommendations that local public health departments implement.

At all levels of public health, staff must build strong community collaboration, solicit and respond to the concerns of the public, and represent public health programs to elected officials.

Public health professionals adopt this challenging work despite liabilities-lack of academic preparation in public health, barriers to ongoing training, and low pay. As of 1997, 78 percent of local health department executives did not have graduate degrees in public health. Many state and federal public health executives are appointed by elected officials and are subject to political tides. Nationwide, a state's chief public health executive stays in the job an average of two years.

Ongoing training is essential in a field as broad and dynamic as public health, but the barriers to accessing needed training are formidable. For professionals practicing in a rural area, travel time to centralized training can be substantial. For instance, a public health professional from Kittson County in Minnesota, who attends a four-hour training session in Minneapolis, will spend approximately 12 hours commuting. Although public health personnel are taking increasing advantage of distance-learning technologies, for a professional to take the time away from daily work for training in the many areas for which highly technical expertise is required might be impossible.

Recruiting and retaining public health staff are also becoming increasingly difficult. The public health agency for Nobles-Rock counties in Minnesota offers a beginning registered nurse $12.54 per hour, while the regional hospital in the same town offers registered nurses $16.74 per hour as a starting wage. This disparity, more than $8,000 per nurse per year, is exacerbated for information technology professionals and is impossible for even the most fervent believer in the value of public health to ignore. Many agencies report positions that remain unfilled for months or years.

Facilities

Among the 59 U.S. state and territorial health departments and approximately 3,000 county and city health departments, facilities vary widely in size and resources, from new, state-of-the-art buildings to substandard portable trailers. Many outdated, crowded health department buildings are geographically separate from other government offices that have fast Internet connections and technical support.

Public health professionals are challenged to execute their broad responsibilities with limited electronic communication capacity, data systems, and other informatics tools. Examples include the following:

In 1999, an e-mail test message sent to local health departments showed that only 35 percent of the test e-mails were received.

Only 45 percent of the local agencies had the capacity to issue a broadcast facsimile.

Less than 50 percent had continuous high-speed (>56 KB) access to the Internet.

In March 2001, 41 percent of Minnesota's local public health agencies reported that all managers had desktop access to the Internet; 31 percent reported that all support staff had desktop access; and only 19 percent reported that all professional staff (e.g., nurses, epidemiologists, sanitarians, and health educators) had desktop access to the Internet.

Public health staff recognize that integrated, computerized information systems and the World Wide Web are critical tools, but these key components of public health infrastructure traditionally have not been funded by grants or new appropriations. Historically, the U. S. Congress has funded public health programs for disease prevention and control (e.g., cancer, tuberculosis, and sexually transmitted diseases), but such program-specific funding provides no incentives for developing integrated systems that would benefit multiple programs. Personnel at local and state health departments, therefore, are required to use distinct, incompatible applications to enter and analyze data; data cannot be easily exchanged, linked, or merged by different programs, or used to evaluate problems by person, across time or geographic area. (Additional discussion of public health informatics challenges appears below.)

Case Study: Neural Tube Defects

Epidemiology, the basic science of public health, is the study of the distribution and determinants of health-related states or events in specified populations and the application of this study to control health problems. An analogy heard in a medical school epidemiology course is that a clinician tries to decide what kind of disease a person has, whereas a public health practitioner tries to determine what kind of person has a disease or condition (i.e., what factors can be influenced to prevent that disease or condition). Through this case study, the clinical informatician will understand more about the practice of public health, where the patient is the population.

Public Health Approach to a Problem

The public health approach to a problem is illustrated in Figure 1. Public health surveillance is defined generally as the ongoing systematic collection, analysis, and interpretation of health-related data for use in planning, implementing, and evaluating public health practice. Surveillance is a key data-driven activity of public health and is crucial for the detection and description of problems. After a potential problem is recognized through surveillance, we identify risk factors to determine the cause of the problem. After risk factors have been characterized, we evaluate interventions to decide which ones work most effectively to prevent disease or illness. Subsequently, we implement programs that include such interventions. We then loop back to the beginning, to ongoing surveillance, to determine whether our programs have affected disease incidence.

Not all conditions of interest are under public health surveillance, partly because no single source of data or system contains information for all diseases or conditions of interest. Public health surveillance information systems, like other distributed information systems, are resource-intensive. They use data from various sources, some of which are collected from health care providers, laboratories, or individuals or directly from medical records and birth and death certificates explicitly for surveillance purposes. Decisions about what conditions to monitor are made not by CDC but rather by the state health departments or state legislatures, who might also manage or appropriate specified funds for conducting surveillance activities. Thus, the conditions that are under public health surveillance vary by state.

Factors that influence what conditions are under surveillance in each state include the frequency of disease (i.e., incidence, prevalence, and mortality), the severity (i.e., hospitalization rate, case-fatality rate, years of potential life lost, and disability- and quality-adjusted life-years), the cost of caring for those with the condition, its preventability, its communicability, and the public interest in the condition. These criteria for surveillance show why public health officials monitor the occurrence of neural tube defects (NTDs). Neural tube defects affect 1 in every 1,000 pregnancies in the United States, or approximately 4,000 fetuses annually. Anencephalic infants are stillborn or die shortly after birth, and although many infants with spina bifida survive, they often suffer from severe lifelong disabilities. The total monetary cost of spina bifida over a lifetime has been calculated as $294,000 per infant (in 1992 dollars). It is now known that approximately 50 percent of NTDs can be prevented.

Surveillance: What is the Problem?

A universally available source of data regarding the incidence of NTDs is the birth certificate, which is completed for all live births in the United States. A standardized format is used for birth certificates, the U.S. Standard Certificate of Birth (Figure 2), which includes information about the parents, the month prenatal care began, the gestational age and Apgar scores of the infant, the mother's relevant medical and reproductive history, details about the labor and delivery, and check-boxes to indicate whether the newborn has certain congenital anomalies. This latter revision, introduced in 1989, lists anencephaly and spina bifida first and second, respectively.

As with any other information system, however, birth certificate data have certain limitations. First, timeliness is an issue. A birth certificate generally must be completed, usually by a clerk at the hospital, and filed within five to ten days of birth. The local registrar forwards the information to the state department of health within one to four weeks and states send birth certificate data to the National Center for Health Statistics (NCHS) of CDC anytime, from daily to weekly to monthly. Most states send the data to NCHS within 60 days of birth. Finally, nationwide data are not usually available until a year after the calendar year in which birth occurred.

Second, although the checkboxes for anencephaly and spina bifida appear on the birth certificate, a few states have not consistently required that congenital anomalies be reported. Third, even if congenital anomalies are supposed to be reported, they might not be recorded on the birth certificate. The sensitivity of the birth certificate for anencephaly has been documented at approximately 60 percent and for spina bifida at only 40 percent. Fortunately, given the relative clarity and ease of the diagnosis at birth, the positive predictive value or accuracy of the birth certificate for NTDs approaches 100 percent.

Finally, however, a key limitation of using birth certificates for complete ascertainment or determination of NTDs is that they do not include fetuses that have been miscarried or terminated. Approximately 50 percent of fetuses with NTDs are diagnosed prenatally and aborted. Thus, in states where detection of all birth defects is a priority, additional resources are invested in gathering data from other sources in addition to the birth certificate.

Approximately 21 states or localities conduct active surveillance for birth defects. Active surveillance refers to the increased effort and resources invested by the health department to seek information on a regular basis from sources. Active surveillance, which is initiated primarily by a health department, differs from passive surveillance, which relies on providers to report to the health department.

These birth defects monitoring programs cover various fractions of their population and use various combinations of data sources, which include obstetric offices, prenatal diagnostic centers and laboratories, fetal and neonatal autopsy programs, outpatient perinatal centers, genetic service and other specialty clinics, hospital discharge data and medical records, vital records, ultrasonography records, and neonatal intensive care units; certain ones include passive reports from schools and community agencies. Data from each of these systems are useful for decision making and planning within a jurisdiction, providing information that facilitates understanding the incidence of a problem, the population affected, their rates of prenatal diagnosis and termination and, thus, the most useful points for and types of public health interventions.

Risk Factor Identification: What is the Cause?

Diverse scientific investigations might prove useful to public health officials attempting to identify the etiology or cause of a problem or, at minimum, risk factors that influence the occurrence of a condition. For NTDs, such studies include basic or molecular research into the genetic and biochemical bases of NTDs. They also include clinical research such as that conducted among family planning clinic or prenatal care patients (e.g., case-control or cohort studies to evaluate risk factors and clinical trials to study patient interventions). Naturally, population-based research regarding diet or risk behaviors (e.g., ecologic studies of cultures or nations, state- or communitywide surveys, or case-control studies using cases identified through surveillance) also play a role in the public health understanding of the problem.

Public health officials might also learn more about the cause of a problem during the course of outbreak or cluster investigations. When a cluster of anencephaly occurred along the Texas-Mexico border several years ago, public health officials conducted an investigation because of concerns that the outbreak was related to environmental contamination either from the industrial plants along the Mexican border or from pesticide use and that the number of cases was not a statistical anomaly. However, they were unable to identify an etiologic association for this cluster.

Folic Acid and Neural Tube Defects

In July 1991, the British Medical Research Council reported that daily consumption of dietary supplements containing 4 mg of folic acid produced a 72 percent reduction in the recurrence of NTDs. The results were so striking that the study was stopped early. This randomized trial supported earlier results by Smithells et al. that showed a similar effect with multivitamin supplements containing 0.36 mg of folic acid in a non-randomized intervention among women at high risk (i.e., women with a prior pregnancy resulting in an NTD).

Officials at CDC determined that this information could prevent NTDs and published a special report in August 1991 that included interim recommendations that women with a history of an infant or fetus with an NTD take 4 mg/day of folic acid, starting when they planned to become pregnant. This amount was 10 times the current recommended daily allowance of folic acid for pregnant women. Given the compelling data from these studies and a sense of urgency about informing the public, this recommendation did not involve CDC's other partner federal agencies, particularly the Food and Drug Administration (FDA). The FDA was apprehensive about safety, about the possibility that taking this amount of folic acid would mask the ability to diagnose vitamin B12 deficiency through blood smears.

Soon after, however, another randomized clinical trial was also stopped early in Hungary after a protective effect was shown for women with no prior history of a pregnancy resulting in an NTD. In the Hungarian study, the women took a multivitamin containing only 0.8mg of folic acid. At this point, several studies documented a protective effect of taking folic acid in amounts ranging from 0.1 to 5.0mg. This time, CDC-working with the Office of the Assistant Secretary for Health, FDA, the Health Resources and Services Administration (HRSA), and the National Institutes of Health-developed a statement regarding folic acid for all women.

Thus, in 1992, the U.S. Public Health Service (PHS) recommended that all women consume 0.4 mg folic acid daily. The recommended daily consumption was directed at all women, not just women planning to become pregnant, because development and closure of the neural tube takes place within 28 days after conception, before the majority of women know they are pregnant; in addition, approximately 50 per cent of pregnancies in the United States are unplanned. The recommendation did not specify how the population might obtain the appropriate amount of folic acid, although it did describe three potential approaches-improvement of dietary habits, fortification of the U.S. food supply, and use of dietary supplements. The report stated that FDA would explore the issue of fortification of the food supply, balancing the goal of increasing folic acid intake with concerns about safety. This process required FDA to participate in federal rule making, which involved obstetric and other medical professionals, the scientific community, consumers, and industry as well as other PHS agencies, underscoring the governmental context of public health actions.

Evaluate Intervention: What Works?

Although science indicated that folic acid was effective in preventing certain NTDs, the question of how to implement effective programs to produce this outcome remained. First was the issue of ensuring that health care providers and consumers were aware of the recommendation, especially the need of women to take folic acid before becoming pregnant. Second, dietary changes are widely acknowledged as difficult to implement, even for a motivated, knowledgeable persons. And above all, the fact that half of pregnancies in this country are unplanned augmented the public health challenge. Clearly, the approach to this problem would have to be multi-pronged.

Implement Program: How Do You Do It?

As in the 1992 recommendation, FDA pursued the option of fortification of the food supply. They published a proposed rule in the Federal Register in 1993 and a final rule in March 1996. At that time, fortification was optional; however, the rule required fortification of enriched grain products (e.g., flours and pastas) with 140μg folic acid/100g grain beginning Jan 1, 1998. This action was expected to add 0.1mg of folate to the average person's daily diet and was intended to result in 50 percent of women of reproductive age receiving 0.4mg folate from all sources. Questions regarding whether more birth defects could be prevented with a larger dose of folic acid remain, but FDA wanted to balance this theoretic benefit with concerns about safety and masking vitamin B12 deficiency.

Public health partners working together to disseminate information about folic acid included CDC, FDA, HRSA, and state health agencies, along with obstetric associations and other provider groups, health plans, maternal and child health advocacy groups, public school educators, and community organizations. A CDC publication, "Preventing Neural Tube Birth Defects: A Prevention Model and Resource Guide," outlines ways to design, develop, deliver, and evaluate an NTD prevention program in a community by using folic acid promotion as a model. It includes informational materials targeted at the media, physician's offices, clinics, schools, and even health clubs. In addition, in 1999, CDC, the March of Dimes Birth Defects Foundation, and the National Council on Folic Acid began a national education campaign with materials targeted to women who are thinking about pregnancy, the contemplators, and women who are able to get pregnant even if not planning to in the near future, the non-contemplators. The "Before You Know It" and "Ready, Not" brochures produced for these two groups, respectively, along with other materials, were developed after focus groups were conducted with women who were contemplators and non-contemplators, including Spanish-speaking women from various countries of origin. These materials are readily available in English and Spanish from the CDC Web site and the CDC facsimile information service.

Surveillance: How to Measure Impact?

As with any public health program, continual surveillance to determine whether these programs are having an effect on the incidence of NTDs is essential. However, with delays in the availability of birth certificate data and the utility of assessing more immediately the effects of these programs, evaluation also involves conducting surveillance for outcomes earlier in the causal pathway of disease (). Thus, by analyzing the levels of folate in fortified grains, we can measure the effects of the fortification rule on the amount of folic acid in the environment.

To estimate the effects of education campaigns, CDC partnered with the March of Dimes to survey women about their awareness of folic acid and its role in preventing birth defects. Folic acid awareness increased from 52 percent in 1995 to 75 percent in 2000, and the women surveyed also reported an increase in consumption of vitamins containing folic acid, from 28 percent in 1995 to 34 percent in 2000.

We might also enumerate sales of folic acid-containing vitamins to discern whether behavior has changed. Results from the National Health and Nutrition Examination Survey (NHANES), conducted periodically by NCHS, provide another gauge of the increase in folic acid intake among women. Analysis of blood samples from NHANES 1999 demonstrated a substantial increase in serum and red blood cell folate concentrations among women between 1 and 44 years of age after folic acid fortification (Figure 4). A similar increase in serum folate levels among clinical specimens from men and women supports the hypothesis that folic acid fortification and not dietary supplements might be contributing to this trend.

Folic acid fortification might be having its intended effect on the incidence of NTDs. An analysis of birth certificate data through 1999 shows that the birth prevalence of NTDs decreased from 37.8 per 100,000 live births before fortification to 30.5 per 100,000 live births after fortification, a 19 percent decline (Figure 5).Analysis of data regarding births to women who received only third-trimester or no prenatal care, and thus could not have terminated a pregnancy with an NTD, demonstrates a similar decline (Figure 6).Data from the Metropolitan Atlanta Congenital Defects Program, which includes prenatally diagnosed cases, also exhibit a decline (Figure 7). Factors other than folic acid fortification (e.g., increased vitamin supplementation) may also have contributed to this decline; public health officials will continue to monitor the occurrence of NTDs to further evaluate the effects of these public health interventions.

Grand Challenges of Public Health Informatics

As might be expected from the public health principles described above and illustrated by the case study, the nature of public health also defines a special set of informatics application challenges. For example, to assess the health and risk status of a a population, data must be obtained from multiple disparate sources (e.g., hospitals, social service agencies, police, departments of labor and industry, population surveys, and on-site inspections). Data about particular individuals from these sources must be accurately combined, then individual-level data must be compiled into usable, aggregate forms at the population level. This information must be presented in clear and compelling ways to legislators and other policymakers, scientists, advocacy groups, and the public while ensuring the confidentiality of the health information of specific individuals.

Although information science and technology can improve public health practice in various ways, three areas represent grand challenges for public health informatics-developing coherent, integrated national public health information systems, developing closer integration of public health and clinical care, and addressing pervasive concerns about the effects of information technology on confidentiality and privacy.

One goal of public health informatics is ensuring the capacity to assess community problems in a comprehensive manner through the development of integrated nationwide public health data systems. This will require a clear definition of public health data needs and the sources of these data, consensus on data and communication standards-to facilitate data quality, comparability, and exchange-with policies to support data sharing and mechanisms and tools for accessing and disseminating data and information in a useful manner. Because electronic reporting will increasingly form the basis for surveillance systems, developmental efforts must also address such concerns as unambiguously defining the specific medical conditions that trigger automated data transmissions, working with reporting organizations to ensure that they have appropriate software and electronic communication capabilities, and ensuring that adequate capacity exists for analysis of the increased volumes of public health data that are anticipated.

A second challenge for public health informatics is facilitating the improved exchange of information between public health and clinical care. Much of the data in public health information systems comes from forms that are filled out by hand and later computer-coded. Even where reporting is electronic, initial data entry is typically manual. This results in serious under-reporting of many reportable diseases and conditions.

Data should flow automatically to public health from clinical and laboratory information systems. When these data are appropriately compiled by public health information systems, they should allow rapid and accurate assessments and disease control responses, as well as the formulation of improved clinical guidelines and interventions. Conversely, automated presentation to clinicians of prevention guidelines has been shown to improve clinical care, and there are other ways in which the skills and activities of the public health community (e.g., community outreach) could benefit clinical care. Electronic information sharing and data exchange provide the means by which we can better integrate public health and clinical care activities, but creativity and hard work are needed to take full advantage of these opportunities.

Finally, privacy, confidentiality, and security are pervasive and persistent challenges to progress in public health informatics. Information systems are correctly perceived by the public as being a double-edged sword: Whatever is done to make integrated, comprehensive information more easily available for laudable and worthwhile purposes must of necessity create new opportunities for misuse. Public health often collects extremely sensitive personal medical information that has the potential for tremendous harm if improperly disclosed. Federal legislation that provides a fair and workable balance between individual privacy and the common good is needed to reassure the public and establish legal guidelines for handling sensitive information.

The Health Insurance Portability and Accountability Act (HIPAA) of 1996 will result in both privacy and security standards for all health plans (including Medicare and Medicaid), clearinghouses, and providers who use electronic data. Public health has had an excellent record of information protection in the past; the recently published HIPAA Privacy Rule continues to permit disclosure of protected health information to public health authorities for public health activities. Public health agencies should adopt and enforce confidentiality policies that incorporate fair information practices and use state-of-the-art security measures to implement those policies.

To these three specific challenges for public health informatics, we can add another challenge, potentially more important (if less concrete) than the rest- to apply information technology in unanticipated ways to reengineer public health and invent new ways to protect and promote community health. If, as we have said, the goal of public health is to promote health and prevent unnecessary disease, injury, and disability and the means are open-ended, we suggest that unexplored and unimagined ways to promote and protect community health using the power of modern information technology still exist. We have briefly outlined and illustrated the complex, multidimensional nature of public health as a discipline; we anticipate working with our clinical informatics colleagues as critical partners in addressing these major public health informatics challenges.

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Public health is a complex discipline that has contributed substantially to improving the health of the population. Public health action involves a variety of interventions and methods, many of which are now taken for granted by the general public. The ...

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1- How does the epi-x system help citizens against New (Novel) Virus Outbreaks?
2-Discuss how EHRs  can contribute to containing new outbreaks.
3-For the CDC, What is the difference in Passive and Active Surveillance.
4-Theorize on how AI and EHRs may help to isolate a new virus before an outbreak.

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https://www.youtube.com/watch?v=A8bSzVwnpp4

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The CDC shares the information, but EHRs Complile and Alert.

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The CDC shares the information, but EHRs Complile and Alert.

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Why this link is worth the read, "These data can be used to enhance case findings, populate fields in data entry screens, detect and calculate rates of certain events and detect clusters and outbreaks"

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By Linda R. Greene, RN, MPS, CIC

The last decade has brought unprecedented changes in healthcare delivery. Although there has been continued development of new devices, drugs and techniques, the major change for those in infection prevention and epidemiology is to identify, manage and use clinical data to provide important information leading to early recognition of disease including prevalence and risk factors for infections, in and across multiple healthcare settings. The potential for more rapid identification and dissemination of important information extends from public health to the individual organization level.(1)

Advances in health information technology (HIT) will continue to affect all areas of healthcare from administration to clinicians and front line staff. These advances have been spurred by the enactment of The American Recovery and Reinvestment Act of 2009. This act identified three main components of what is referred to as "meaningful use." These include the use of a certified electronic health record in a meaningful manner, the electronic exchange of healthcare information, and the use of technology to submit quality and other important information. Recommendations for stage 3 of this process include a focus on tools to measure and improve outcomes.(2)

From an infection prevention perspective, requirements for public reporting of healthcare-associated infections (HAIs), increased focus of infection prevention efforts across the continuum of care, and rapid advances in information technology have provided new opportunities for strengthening epidemiology and surveillance activities. As changes in surveillance definitions have evolved, so have the methods of surveillance, spurred by rapid advances in information technology. For example, the widespread adoption and improvements in the electronic health record (EHR) offers unparalleled opportunities to improve surveillance and epidemiology.

By definition, health informatics is a multidisciplinary field that uses HIT to improve health care by combining higher quality, higher efficiency and new opportunities. It deals with the resources, devices and methods required to optimize the acquisition, storage, retrieval and use of information in healthcare. Health informatics tools include computers, clinical guidelines, formal medical terminologies and information and communication systems.(3)

Surveillance is a core function of public health agencies that requires timely, accurate and complete data. Electronic records offer an opportunity to improve links between healthcare providers and public health departments, making surveillance more effective and timely. Advances in health information technologies, including the EHR systems, electronic laboratory reporting (ELR) and health information exchange (HIE), have provided public health departments with information systems and networks to automate disease reporting and surveillance. Historically, public health agencies have relied on physicians and infection preventionists (IPs) to manually report notifiable diseases following diagnosis, usually from a confirmatory laboratory test result. HIE is the electronic transfer of clinical and administrative information across diverse and often competing healthcare organizations Several studies in public health informatics have successfully demonstrated that less manual, more automated approaches like ELR, which utilize EHR systems and HIE networks, can improve the completeness and timeliness of notifiable disease surveillance efforts.(4)

The evolving field of surveillance informatics presents both challenges and opportunities. The challenges include finding efficient and effective ways of combining multiple sources of complex data and information into meaningful and actionable knowledge (e.g., for situational awareness). As these challenges are met, opportunities will arise for faster, better and lower cost surveillance and interpretation of health events and trends.

HAIs and antimicrobial resistance are major problems in U.S. healthcare institutions. Surveillance is a critical component of prevention efforts. At the organizational level, HAI surveillance will involve the capture and use of data in existing electronic databases. These data can be used to enhance case findings, populate fields in data entry screens, detect and calculate rates of certain events and detect clusters and outbreaks. EHRs and clinical decision support systems (CDSSs) have the potential to enhance antimicrobial stewardship. These systems enable clinicians and antimicrobial stewardship programs (ASPs) to more efficiently review pharmacy, microbiology and clinical data. With the increased emphasis on antimicrobial stewardship, the ability to link antimicrobial use patterns, microbiology reports and pharmacy records cannot be understated. In addition, CDSS tools can be utilized to perform prospective audit and feedback.(5)

As such electronic systems become more advanced and robust; there are limitless opportunities for infection prevention. Currently EHRs can provide important information regarding care processes and implementation of evidence based practices. These process measures enhance the ability to interpret findings from outcome-based surveillance and lead naturally to focused prevention efforts.

One logical next step is to leverage the electronic health record to identify key risk factors for the development of certain infections in individual patients, thereby identifying patients at risk and pre-emptively implementing preventive strategies. Infection surveillance will certainly undergo dramatic changes over the next few years as it transitions from a paper or partially automated system to a fully automated system. With these changes will come both opportunities and challenges. Each organization's infection control program is unique based upon the demographics of the patient population and scope of services provided, but the expectation is that each is based on sound epidemiologic principles.

Given the pace of change, it is essential that informatics principles be widely understood if IPs are to develop the capacity to manage and utilize information systems to address HAIs and other adverse events. As technology leads to changes in surveillance and data reporting, IPs can focus their efforts towards data analysis, education, collaboration and implementation of evidence based interventions. As the field of healthcare informatics continues to evolve, the IP should be involved in adoption efforts at local, regional, and national levels to leverage the opportunities created by current health IT initiatives.(6) This will ensure that IPs are engaged in leveraging technology and helping to create the roadmap for tomorrow.
Linda R. Greene, RN, MPS, CIC, is manager of infection prevention at Highland Hospital, an affiliate of the University of Rochester Medical Center.
References:
1. van Mourik MS et.al. Automated surveillance for healthcare-associated infections: opportunities for improvement Clin Infect Dis July 2013;57;1: 85-93.
2. http://www.hrsa.gov/healthit/meaningfuluse/stage1clinicalquality/whatis.html/. Retrieved Oct.16, 2015
3. http://www.cdc.gov/mmwr/preview/mmwrhtml/su6103a5.htm/. Retrieved Oct.14, 2015
4. http://www.cdc.gov/ehrmeaningfuluse/index.html/. Retrieved Oct.19, 2015
5. Forrest G, et.al. Use of Electronic Health Records and Clinical Decision Support Systems for Antimicrobial Stewardship CID 2014:59 (Supp3): S122- S133
6. Atreja A, et.al. Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention, and control AJIC; 36; 3: S37- S46

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The Surveillance, Informatics and Epidemiology Triangle

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The last decade has brought unprecedented changes in healthcare delivery. Although there has been continued development of new devices, drugs and techniques, the major change for those in infection prevention and epidemiology is to identify, manage and use clinical data to provide important information leading to early recognition of disease including prevalence and risk factors for infections, in and across multiple healthcare settings.

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Extremely relevant right now as we face the Coronavirus and leads us to the next post on Epi-X

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Section 2: Steps of an Outbreak Investigation

Once the decision to conduct a field investigation of an acute outbreak has been made, working quickly is essential - as is getting the right answer. In other words, epidemiologists cannot afford to conduct an investigation that is "quick and dirty." They must conduct investigations that are "quick and clean."() Under such circumstances, epidemiologists find it useful to have a systematic approach to follow, such as the sequence listed in Table 6.2. This approach ensures that the investigation proceeds without missing important steps along the way.

The steps listed in Table 6.2 are presented in conceptual order; in practice, however, several steps may be done at the same time, or the circumstances of the outbreak may dictate that a different order be followed. For example, the order of the first three listed steps is highly variable - a health department often verifies the diagnosis and establishes the existence of an outbreak before deciding that a field investigation is warranted. Conceptually, control measures come after hypotheses have been confirmed, but in practice control measures are usually implemented as soon as the source and mode of transmission are known, which may be early or late in any particular outbreak investigation.

Each of the steps is described below in more detail, based on the assumption that you are the health department staff member scheduled to conduct the next field investigation.

Step 1: Prepare for field work

The numbering scheme for this step is problematic, because preparing for field work often is not the first step. Only occasionally do public health officials decide to conduct a field investigation before confirming an increase in cases and verifying the diagnosis. More commonly, officials discover an increase in the number of cases of a particular disease and then decide that a field investigation is warranted. Sometimes investigators collect enough information to perform descriptive epidemiology without leaving their desks, and decide that a field investigation is necessary only if they cannot reach a convincing conclusion without one.

Regardless of when the decision to conduct a field investigation is made, you should be well prepared before leaving for the field. The preparations can be grouped into two broad categories: (a) scientific and investigative issues, and (b) management and operational issues. Good preparation in both categories is needed to facilitate a smooth field experience.

Scientific and investigative issues

As a field investigator, you must have the appropriate scientific knowledge, supplies, and equipment to carry out the investigation before departing for the field. Discuss the situation with someone knowledgeable about the disease and about field investigations, and review the applicable literature. In previous similar outbreaks, what have been the sources, modes of transmission, and risk factors for the disease? Assemble useful references such as journal articles and sample questionnaires.

Before leaving for a field investigation, consult laboratory staff to ensure that you take the proper laboratory material and know the proper collection, storage, and transportation techniques. By talking with the laboratory staff you are also informing them about the outbreak, and they can anticipate what type of laboratory resources will be needed.

You also need to know what supplies or equipment to bring to protect yourself. Some outbreak investigations require no special equipment while an investigation of SARS or Ebola hemorrhagic fever may require personal protective equipment such as masks, gowns, and gloves.

Finally, before departing, you should have a plan of action. What are the objectives of this investigation, i.e., what are you trying to accomplish? What will you do first, second, and third? Having a plan of action upon which everyone agrees will allow you to "hit the ground running" and avoid delays resulting from misunderstandings.

Management and operational issues

A good field investigator must be a good manager and collaborator as well as a good epidemiologist, because most investigations are conducted by a team rather than just one individual. The team members must be selected before departure and know their expected roles and responsibilities in the field. Does the team need a laboratorian, veterinarian, translator/interpreter, computer specialist, entomologist, or other specialist? What is the role of each? Who is in charge? If you have been invited to participate but do not work for the local health agency, are you expected to lead the investigation, provide consultation to the local staff who will conduct the investigation, or simply lend a hand to the local staff? And who are your local contacts?

Depending on the type of outbreak, the number of involved agencies may be quite large. The investigation of an outbreak from an animal source may include state and federal departments of agriculture and/or the Food and Drug Administration (FDA). If criminal or bioterrorist intent is suspected, law enforcement agencies and the Federal Bureau of Investigation (FBI) may be in charge, or at least involved. In a natural disaster (hurricane or flood), the Federal Emergency Management Agency (FEMA) may be the lead. Staff from different agencies have different perspectives, approaches, and priorities that must be reconciled. For example, whereas the public health investigation may focus on identifying a pathogen, source, and mode of transmission, a criminal investigation is likely to focus on finding the perpetrator. Sorting out roles and responsibilities in such multi-agency investigations is critical to accomplishing the disparate objectives of the different agencies.

A communications plan must be established. The need for communicating with the public health and clinical community has long been acknowledged, but the need for communicating quickly and effectively with elected officials and the public became obvious during the epidemics of West Nile Virus encephalitis, SARS, and anthrax. The plan should include how often and when to have conference calls with involved agencies, who will be the designated spokesperson, who will prepare health alerts and press releases, and the like. When a federal agency is involved in the survey of 10 or more individuals, the data collection instrument must first be cleared by the White House Office of Management and Budget (OMB).

In addition, operational and logistical details are important.
Arrange to bring a laptop computer, cell phone or phone card, camera, and other supplies. If you are arriving from outside the area, you should arrange in advance when and where you are to meet with local officials and contacts when you arrive in the field. You must arrange travel, lodging, and local transportation. Many agencies and organizations have strict approval processes and budgetary limits that you must follow. If you are traveling to another country, you will need a passport and often a visa. You should also take care of personal matters before you leave, especially if the investigation is likely to be lengthy.

Step 2: Establish the existence of an outbreak

An outbreak or an epidemic is the occurrence of more cases of disease than expected in a given area or among a specific group of people over a particular period of time. Usually, the cases are presumed to have a common cause or to be related to one another in some way. Many epidemiologists use the terms outbreak and epidemic interchangeably, but the public is more likely to think that epidemic implies a crisis situation. Some epidemiologists apply the term epidemic to situations involving larger numbers of people over a wide geographic area. Indeed, the Dictionary of Epidemiology defines outbreak as an epidemic limited to localized increase in the incidence of disease, e.g., village, town, or closed institution.()

In contrast to outbreak and epidemic, a cluster is an aggregation of cases in a given area over a particular period without regard to whether the number of cases is more than expected. This aggregation of cases seems to be unusual, but frequently the public (and sometimes the health agency) does not know the denominator. For example, the diagnosis in one neighborhood of four adults with cancer may be disturbing to residents but may well be within the expected level of cancer occurrence, depending on the size of the population, the types of cancer, and the prevalence of risk factors among the residents.

One of the first tasks of the field investigator is to verify that a cluster of cases is indeed an outbreak. Some clusters turn out to be true outbreaks with a common cause, some are sporadic and unrelated cases of the same disease, and others are unrelated cases of similar but unrelated diseases.

Even if the cases turn out to be the same disease, the number of cases may not exceed what the health department normally sees in a comparable time period. Here, as in other areas of epidemiology, the observed is compared with the expected. The expected number is usually the number from the previous few weeks or months, or from a comparable period during the previous few years. For a notifiable disease, the expected number is based on health department surveillance records. For other diseases and conditions, the expected number may be based on locally available data such as hospital discharge records, mortality statistics, or cancer or birth defect registries. When local data are not available, a health department may use rates from state or national data, or, alternatively, conduct a telephone survey of physicians to determine whether they are seeing more cases of the disease than usual. Finally, a survey of the community may be conducted to establish the background or historical level of disease.

Even if the current number of reported cases exceeds the expected number, the excess may not necessarily indicate an outbreak. Reporting may rise because of changes in local reporting procedures, changes in the case definition, increased interest because of local or national awareness, or improvements in diagnostic procedures. A new physician, infection control nurse, or healthcare facility may more consistently report cases, when in fact there has been no change in the actual occurrence of the disease. Some apparent increases are actually the result of misdiagnosis or laboratory error. Finally, particularly in areas with sudden changes in population size such as resort areas, college towns, and migrant farming areas, changes in the numerator (number of reported cases) may simply reflect changes in the denominator (size of the population).

Whether an apparent problem should be investigated further is not strictly tied to verifying the existence of an epidemic (more cases than expected). Sometimes, health agencies respond to small numbers of cases, or even a single case of disease, that may not exceed the expected or usual number of cases. As noted earlier, the severity of the illness, the potential for spread, availability of control measures, political considerations, public relations, available resources, and other factors all influence the decision to launch a field investigation.

Exercise 6.2

For the month of August, 12 new cases of tuberculosis and 12 new cases of West Nile virus infection were reported to a county health department. You are not sure if either group of cases is a cluster or an outbreak. What additional information might be helpful in making this determination?
Check your answer.
Step 3: Verify the diagnosis

The next step, verifying the diagnosis, is closely linked to verifying the existence of an outbreak. In fact, often these two steps are addressed at the same time. Verifying the diagnosis is important: (a) to ensure that the disease has been properly identified, since control measures are often disease-specific; and (b) to rule out laboratory error as the basis for the increase in reported cases.

First, review the clinical findings and laboratory results. If you have questions about the laboratory findings (for example, if the laboratory tests are inconsistent with the clinical and epidemiologic findings), ask a qualified laboratorian to review the laboratory techniques being used. If you need specialized laboratory work such as confirmation in a reference laboratory, DNA or other chemical or biological fingerprinting, or polymerase chain reaction, you must secure a sufficient number of appropriate specimens, isolates, and other laboratory material as soon as possible.

Second, many investigators - clinicians and non-clinicians - find it useful to visit one or more patients with the disease. If you do not have the clinical background to verify the diagnosis, bring a qualified clinician with you. Talking directly with some patients gives you a better understanding of the clinical features, and helps you to develop a mental image of the disease and the patients affected by it. In addition, conversations with patients are very useful in generating hypotheses about disease etiology and spread. They may be able to answer some critical questions: What were their exposures before becoming ill? What do they think caused their illness? Do they know anyone else with the disease? Do they have anything in common with others who have the disease?

Third, summarize the clinical features using frequency distributions. Are the clinical features consistent with the diagnosis? Frequency distributions of the clinical features are useful in characterizing the spectrum of illness, verifying the diagnosis, and developing case definitions. These clinical frequency distributions are considered so important in establishing the credibility of the diagnosis that they are frequently presented in the first table of an investigation's report or manuscript.

Step 4: Construct a working case definition

A case definition is a standard set of criteria for deciding whether an individual should be classified as having the health condition of interest. A case definition includes clinical criteria and - particularly in the setting of an outbreak investigation - restrictions by time, place, and person. The clinical criteria should be based on simple and objective measures such as "fever ≥ 40°C (101°F)," "three or more loose bowel movements per day," or "myalgias (muscle pain) severe enough to limit the patient's usual activities. " The case definition may be restricted by time (for example, to persons with onset of illness within the past 2 months), by place (for example, to residents of the nine-county area or to employees of a particular plant) and by person (for example, to persons with no previous history of a positive tuberculin skin test, or to premenopausal women). Whatever the criteria, they must be applied consistently to all persons under investigation.

The case definition must not include the exposure or risk factor you are interested in evaluating. This is a common mistake. For example, if one of the hypotheses under consideration is that persons who worked in the west wing were at greater risk of disease, do not define a case as "illness among persons who worked in the west wing with onset between..." Instead, define a case as "illness among persons who worked in the facility with onset between..." Then conduct the appropriate analysis to determine whether those who worked in the west wing were at greater risk than those who worked elsewhere.

Diagnoses may be uncertain, particularly early in an investigation. As a result, investigators often create different categories of a case definition, such as confirmed, probable, and possible or suspect, that allow for uncertainty. To be classified as confirmed, a case usually must have laboratory verification. A case classified as probable usually has typical clinical features of the disease without laboratory confirmation. A case classified as possible usually has fewer of the typical clinical features. For example, in the box on page 6-16, you can see the Pan American Health Organization (PAHO) recommended case definition for meningococcal disease.() Here you can see the different categories that PAHO uses for this diagnosis.

Meningococcal Disease - PAHO Case Definition

Clinical case definition

An illness with sudden onset of fever (>38.5°C rectal or >38.0°C axillary) and one or more of the following: neck stiffness, altered consciousness, other meningeal sign or petechial or puerperal rash.

Laboratory criteria for diagnosis

Positive cerebrospinal fluid (CSF) antigen detection or positive culture.

Case classification

Suspected: A case that meets the clinical case definition.

Probable: A suspected case as defined above and turbid CSF (with or without positive Gram stain) or ongoing epidemic and epidemiological link to a confirmed case.

Confirmed: A suspected or probable case with laboratory confirmation.

Source: Pan American Health Organization. Case Definitions Meningococcal Disease. Epidemiological Bulletin 2002; 22(4):14-5.

In the outbreak setting, the investigators would need to specify time and place to complete the outbreak case definition. For example, if investigating an epidemic of meningococcal meningitis in Bamako, the case definition might be the clinical features as described in the box with onset between January and April of this year among residents and visitors of Bamako.

Classifications such as confirmed-probable-possible are helpful because they provide flexibility to the investigators. A case might be temporarily classified as probable or possible while laboratory results are pending. Alternatively, a case may be permanently classified as probable or possible if the patient's physician decided not to order the confirmatory laboratory test because the test is expensive, difficult to obtain, or unnecessary. For example, while investigating an outbreak of diarrhea on a cruise ship, investigators usually try to identify the causative organism from stool samples from a few afflicted persons. If the tests confirm that all of those case-patients were infected with the same organism, for example norovirus, the other persons with compatible clinical illness are all presumed to be part of the same outbreak and to be infected with the same organism. Note that while this approach is typical in the United States, some countries prefer to acquire laboratory samples from every affected person, and only those with a positive laboratory test are counted as true cases.

A case definition is a tool for classifying someone as having or not having the disease of interest, but few case definitions are 100% accurate in their classifications. Some persons with mild illness may be missed, and some persons with a similar but not identical illness may be included. Generally, epidemiologists strive to ensure that a case definition includes most if not all of the actual cases, but very few or no false-positive cases. However, this ideal is not always met. For example, case definitions often miss infected people who have mild or no symptoms, because they have little reason to be tested.

More About Case Definitions

Early in an investigation, investigators may use a "loose" or sensitive case definition that includes confirmed, probable, and possible cases to characterize the extent of the problem, identify the populations affected, and develop hypotheses about possible causes. The strategy of being more inclusive early on is especially useful in investigations that require travel to different hospitals, homes, or other sites to gather information, because collecting extra data while you are there is more efficient than having to return a second time. This illustrates an important axiom of field epidemiology: Get it while you can. Later on, when hypotheses have come into sharper focus, the investigator may tighten the case definition by dropping the "possible" and sometimes the "probable" category. In analytic epidemiology, inclusion of false-positive cases can produce misleading results. Therefore, to test these hypotheses by using analytic epidemiology (see Step 8), specific or tight case definitions are recommended.

Other investigations, particularly those of a newly recognized disease or syndrome, begin with a relatively specific or narrow case definition. For example, acquired immunodeficiency syndrome (AIDS) and severe acute respiratory syndrome (SARS) both began with relatively specific case definitions. This ensures that persons whose illness meets the case definition truly have the disease in question. As a result, investigators could accurately characterize the typical clinical features of the illness, risk factors for illness, and cause of the illness. After the cause was known and diagnostic tests were developed, investigators could use the laboratory test to learn about the true spectrum of illness, and could broaden the case definition to include those with early infection or mild symptoms.

Exercise 6.3

In 1989, a worldwide epidemic of a previously unrecognized syndrome occurred. This condition was characterized by severe myalgias (muscle pains) and an elevated number of a particular type of white blood cell called an eosinophil. The illness was given the name eosinophilia-myalgia syndrome. Public health officials initially used the following case definition:()

Eosinophil count ≥2,000 cells/mm ³ in the absence of any other known cause of eosinophilia (in particular, parasitic or fungal infection, end-stage renal disease, leukemia, allergic disorder, or drug reaction)

Using the information in the line listing below, determine whether or not each should be classified as a case, according to the initial case definition above.

Table 6.3 Line Listing of 7 Persons with Suspected Eosinophilia-myalgia

Eventually, public health officials agreed on the following revised case definition:()

Reclassify each patient using the revised case definition.
Check your answer.
Exercise 6.4

In December 2003, an outbreak of gastroenteritis occurred among tenth-grade students who had participated in a city-wide field trip. Half of the students traveled from December 2 to December 7 (Tour A); the other half traveled from December 3 to December 8 (Tour B). The itineraries were similar. Although teachers and other adult chaperones accompanied the students on both tours, no adult reported illness. In addition, no illness was reported among students who did not go on the field trip, and no cases of E. coli O157 were reported in the community that week.

A line listing of 26 persons with symptoms of abdominal pain and/or diarrhea is presented below. Using the information in the line listing, develop a case definition that you might use for the outbreak investigation. [Note that persons infected with E. coli O157 typically experience severe abdominal cramps, bloody diarrhea, and low grade fever after a 1- to 8-day incubation period (usually 2-4 days). ]

Table 6.4 Line Listing of 26 Persons with Symptoms - School District A, December 2003
Check your answer.
Step 5: Find cases systematically and record information

As noted earlier, many outbreaks are brought to the attention of health authorities by concerned healthcare providers or citizens. However, the cases that prompt the concern are often only a small and unrepresentative fraction of the total number of cases. Public health workers must therefore look for additional cases to determine the true geographic extent of the problem and the populations affected by it.

Usually, the first effort to identify cases is directed at healthcare practitioners and facilities - physicians' clinics, hospitals, and laboratories - where a diagnosis is likely to be made. Investigators may conduct what is sometimes called stimulated or enhanced passive surveillance by sending a letter describing the situation and asking for reports of similar cases. Alternatively, they may conduct active surveillance by telephoning or visiting the facilities to collect information on any additional cases.

In some outbreaks, public health officials may decide to alert the public directly, usually through the local media. In other situations, the media may have already spread the word. For example, in an outbreak of listeriosis in 2002 caused by contaminated sliceable turkey deli meat, announcements in the media alerted the public to avoid the implicated product and instructed them to see a physician if they developed symptoms compatible with the disease in question.()

If an outbreak affects a restricted population such as persons on a cruise ship, in a school, or at a work site, and if many cases are mild or asymptomatic and therefore undetected, a survey of the entire population is sometimes conducted to determine the extent of infection. A questionnaire could be distributed to determine the true occurrence of clinical symptoms, or laboratory specimens could be collected to determine the number of asymptomatic cases.

Finally, investigators should ask case-patients if they know anyone else with the same condition. Frequently, one person with an illness knows or hears of others with the same illness.

In some investigations, investigators develop a data collection form tailored to the specific details of that outbreak. In others, investigators use a generic case report form. Regardless of which form is used, the data collection form should include the following types of information about each case.

Identifying information. A name, address, and telephone number is essential if investigators need to contact patients for additional questions and to notify them of laboratory results and the outcome of the investigation. Names also help in checking for duplicate records, while the addresses allow for mapping the geographic extent of the problem.

Demographic information. Age, sex, race, occupation, etc. provide the person characteristics of descriptive epidemiology needed to characterize the populations at risk.

Clinical information. Signs and symptoms allow investigators to verify that the case definition has been met. Date of onset is needed to chart the time course of the outbreak. Supplementary clinical information, such as duration of illness and whether hospitalization or death occurred, helps characterize the spectrum of illness.

Risk factor information. This information must be tailored to the specific disease in question. For example, since food and water are common vehicles for hepatitis A but not hepatitis B, exposure to food and water sources must be ascertained in an outbreak of the former but not the latter.

Reporter information. The case report must include the reporter or source of the report, usually a physician, clinic, hospital, or laboratory. Investigators will sometimes need to contact the reporter, either to seek additional clinical information or report back the results of the investigation.

Traditionally, the information described above is collected on a standard case report form, questionnaire, or data abstraction form. Examples of case report forms are shown in Figure 6.1 (in Exercise 6.5). Investigators then abstract selected critical items onto a form called a line listing (See Lesson 2 for more information on line listings. )

An example of the line listing from the 2001 anthrax investigation is shown in Table 6.5.() In a line listing, each column represents an important variable, such as name or identification number, age, sex, case classification, etc., while each row represents a different case. New cases are added to a line listing as they are identified. Thus, a line listing contains key information on every case and can be scanned and updated as necessary. Even in the era of computers, many epidemiologists still maintain a handwritten line listing of key data items, and turn to their computers for more complex manipulations and cross-tabulations.

Table 6.5 Line Listing of Demographic, Clinical, and Exposure Characteristics of 22 Cases of Bioterrorism-Related Anthrax-United States, 2001

NY, New York; FL, Florida; NJ, New Jersey; PA, Pennsylvania; VA, Virginia; DC, District of Columbia; MD, Maryland; CT, Connecticut; F, female; M, male; W, white; B, black; A, Asian; W,H, white with Hispanic ethnicity; NY, New York; NBC, National Broadcasting Company; AMI, American Media Inc.; USPS, United States Postal Service; CBS, Columbia Broadcasting System; PCR, polymerase chain reaction; IHC, immunohistochemical staining; + positive; IgG, immunoglobulin G.

Case status and anthrax presentation are described in the anthrax surveillance case definition in the Methods section.

Source: Jernigan DB, Raghunathan PL, Bell BP, Brechner R, Bresnitz EA, Butler JC, et al. Investigation of bioterrorism-related anthrax, United States, 2001: epidemiologic findings. Emerg Infect Dis 2002;8:1019-28.

Exercise 6.5

Review the six case report forms in Figure 6.1. Create a line listing based on this information.

Figure 6.1
Check your answer.
Step 6: Perform descriptive epidemiology

Conceptually, the next step after identifying and gathering basic information on the persons with the disease is to systematically describe some of the key characteristics of those persons. This process, in which the outbreak is characterized by time, place, and person, is called descriptive epidemiology. It may be repeated several times during the course of an investigation as additional cases are identified or as new information becomes available.

This step is critical for several reasons.

Summarizing data by key demographic variables provides a comprehensive characterization of the outbreak - trends over time, geographic distribution (place), and the populations (persons) affected by the disease.

From this characterization you can identify or infer the population at risk for the disease.

The characterization often provides clues about etiology, source, and modes of transmission that can be turned into testable hypotheses (see Step 7).

Descriptive epidemiology describes the where and whom of the disease, allowing you to begin intervention and prevention measures.

Early (and continuing) analysis of descriptive data helps you to become familiar with those data, enabling you to identify and correct errors and missing values.

Time

Traditionally, a special type of histogram is used to depict the time course of an epidemic. This graph, called an epidemic curve, or epi curve for short, provides a simple visual display of the outbreak's magnitude and time trend. The classic epidemic curve, such as the one shown in Figure 6.2a from an outbreak of Salmonella enterica serotype Enteritidis, graphs the number of cases by date or time of onset of illness.

Figure 6.2a Outbreak of Salmonella Enteritidis Gastroenteritis - Maryland, 2003 (Epidemic Curve by 12-Hour Intervals)

Source Castel AD, Blythe D, Edwards L, Totaro J, Shah D, Moore M. A large outbreak of Salmonella Enteritidis infections associated with crabcakes at a church fundraiser-Maryland, 2003. Presented at 53rd Annual Epidemic Intelligence Service Conference, April 19-23, 2004, Atlanta.

Epidemic curves are a basic investigative tool because they are so informative (see Lesson 6).

The epi curve shows the magnitude of the epidemic over time as a simple, easily understood visual. It permits the investigator to distinguish epidemic from endemic disease. Potentially correlated events can be noted on the graph.

The shape of the epidemic curve may provide clues about the pattern of spread in the population, e.g., point versus intermittent source versus propagated.

The curve shows where you are in the course of the epidemic - still on the upswing, on the down slope, or after the epidemic has ended. This information forms the basis for predicting whether more or fewer cases will occur in the near future.

The curve can be used for evaluation, answering questions like: How long did it take for the health department to identify a problem? Are intervention measures working?

Outliers - cases that don't fit into the body of the curve -may provide important clues.

If the disease and its incubation period are known, the epi curve can be used to deduce a probable time of exposure and help develop a questionnaire focused on that time period.

Drawing an epidemic curve. To draw an epidemic curve, you first must know the time of onset of illness for each case. For some diseases, date of onset is sufficient. For other diseases, particularly those with a relatively short incubation period, hour of onset may be more suitable (see Lesson 6).

Occasionally, you may be asked to draw an epidemic curve when you don't know either the disease or its incubation time. In that situation, it may be useful to draw several epidemic curves with different units on the x-axis to find one that best portrays the data. For example, the epidemic curves shown in Figures 6.2b and 6.2c display the same data as in Figure 6.2a; the x-axis is measured in units of 12 hours in Figure 6.2a, 6 hours in Figure 6.2b, and 24 hours (1 day) in 6.2c. Figure 6.2d shows the same data one more time, but with stacks of squares that each represent one case.

Figure 6.2b Outbreak of Salmonella Enteritidis Gastroenteritis - Maryland, 2003 (Epidemic Curve by 6-Hour Intervals)

Source: Castel AD, Blythe D, Edwards L, Totaro J, Shah D, Moore M. A large outbreak of Salmonella Enteritidis infections associated with crabcakes at a church fundraiser-Maryland, 2003. Presented at 53rd Annual Epidemic Intelligence Service Conference, April 19-23, 2004, Atlanta.

Figure 6.2c Outbreak of Salmonella Enteritidis Gastroenteritis - Maryland, 2003 (Epidemic Curve by One Day Intervals)

Source: Castel AD, Blythe D, Edwards L, Totaro J, Shah D, Moore M. A large outbreak of Salmonella Enteritidis infections associated with crabcakes at a church fundraiser-Maryland, 2003. Presented at 53rd Annual Epidemic Intelligence Service Conference, April 19-23, 2004, Atlanta.

Figure 6.2d Outbreak of Salmonella Enteritidis Gastroenteritis - Maryland, 2003 (Epidemic Curve by 6-Hour Intervals)

Source: Castel AD, Blythe D, Edwards L, Totaro J, Shah D, Moore M. A large outbreak of Salmonella Enteritidis infections associated with crabcakes at a church fundraiser-Maryland, 2003. Presented at 53rd Annual Epidemic Intelligence Service Conference, April 19-23, 2004, Atlanta.

Interpreting an epidemic curve. The first step in interpreting an epidemic curve is to consider its overall shape. The shape of the epidemic curve is determined by the epidemic pattern (for example, common source versus propagated), the period of time over which susceptible persons are exposed, and the minimum, average, and maximum incubation periods for the disease.

An epidemic curve that has a steep upslope and a more gradual down slope (a so-called log-normal curve) is characteristic of a point-source epidemic in which persons are exposed to the same source over a relative brief period. In fact, any sudden rise in the number of cases suggests sudden exposure to a common source one incubation period earlier (Figure 6.3).

In a point-source epidemic, all the cases occur within one incubation period. If the duration of exposure is prolonged, the epidemic is called a continuous common-source epidemic, and the epidemic curve has a plateau instead of a peak. An intermittent common-source epidemic (in which exposure to the causative agent is sporadic over time) usually produces an irregularly jagged epidemic curve reflecting the intermittence and duration of exposure and the number of persons exposed. In theory, a propagated epidemic - one spread from person-to-person with increasing numbers of cases in each generation - should have a series of progressively taller peaks one incubation period apart, but in reality few produce this classic pattern.

Figure 6.3 Typical Epi Curves for Different Types of Spread

Adapted from: European Programme for Intervention Epidemiology Training [Internet]. Solna, Sweden: Smittskyddsinstitutet [updated 2004 Sep 27; cited 2006 Sep 22].

Figure 6.4 Number of Cases of Acute Hemorrhagic Conjunctivitis, By Month and Week of Report - Puerto Rico, August 7-October 30, 2003

Adapted from: Acute hemorrhagic conjunctivitis outbreak caused by Coxsackievirus A24-Puerto Rico, 2003. MMWR 2004;53:632-4.

As noted above, the epidemic curve shows where you are in the natural course of the epidemic. Consider the epidemic curve of acute hemorrhagic conjunctivitis in Puerto Rico, shown in Figure 6.4. If you only had data through Week 35, that is, through point A, you might conclude that the outbreak is still on the upswing, with more cases to come. On the other hand, if you had data through point B, you might judge that the outbreak has peaked and may soon be over.

The cases that stand apart may be just as informative as the overall pattern. An early case may represent a background or unrelated case, a source of the epidemic, or a person who was exposed earlier than most of the cases (for example, the cook who tasted a dish hours before bringing it to the big picnic). Similarly, late cases may represent unrelated cases, cases with long incubation periods, secondary cases, or persons exposed later than most others (for example, someone eating leftovers). On the other hand, these outlying cases sometimes represent miscoded or erroneous data. All outliers are worth examining carefully because if they are part of the outbreak, they may have an easily identifiable exposure that may point directly to the source.

In a point-source epidemic of a known disease with a known incubation period, the epidemic curve can be used to identify a likely period of exposure. Knowing the likely period of exposure allows you to ask questions about the appropriate period of time so you can identify the source of the epidemic.

To identify the likely period of exposure from an epidemic curve of an apparent point source epidemic:

1. Look up the average and minimum incubation periods of the disease. This information can be found on disease fact sheets available on the Internet or in the Control of Communicable Diseases Manual.()

2. Identify the peak of the outbreak or the median case and count back on the x-axis one average incubation period. Note the date.

3. Start at the earliest case of the epidemic and count back the minimum incubation period, and note this date as well.

Ideally, the two dates will be similar, and represent the probable period of exposure. Since this technique is not precise, widen the probable period of exposure by, say, 20% to 50% on either side of these dates, and then ask about exposures during this widened period in an attempt to identify the source.

In a similar fashion, if the time of exposure and the times of onset of illness are known but the cause has not yet been identified, the incubation period can be estimated from the epidemic curve. Subtract the time of onset of the earliest cases from the time of exposure to estimate the minimum incubation period. Subtract the time of onset of the median case from the time of exposure to estimate the median incubation period. These incubation periods can be compared with a list of incubation periods of known diseases to narrow the possibilities.

EXAMPLE: Interpreting an Epidemic Curve

Consider, for example, the outbreak of hepatitis A illustrated by the epidemic curve in Figure 6.5. The incubation period for hepatitis A ranges from 15 to 50 days (roughly 2 to 7 weeks), with an average incubation period of 28-30 days (roughly one month). Because cases can occur from 15 to 50 days after exposure, all cases from a point source exposure should occur within a span of 50 - 15 = 35 days.
Image Description
Is this epidemic curve consistent with a point-source epidemic? (That is, do all of the cases occur with one incubation period?)

Yes. The date of onset of the first case was during the week of October 28. The date of onset of the last known case was during the week of November 18, less than one month later. All of the cases occur within the range of incubation periods expected for a point source exposure. Therefore, the epidemic curve can be used to identify the likely period of exposure.

What is the peak of the outbreak or the median date of onset?

Both the peak of the outbreak and the median case occurred during the week of November 4.

When is the likely date(s) of exposure, based on one average incubation period prior to the peak (median date) of the outbreak?

Since both the peak and the median of the outbreak occurred during the week of November 4, the most likely period of exposure was a month earlier, in early October.

When is the beginning of the outbreak?

The earliest case occurred during the week of October 28.

When is the likely dates of exposure, based on the minimum incubation period before the first case?

Subtracting 2 weeks from the week of October 28 points to the week of October 14.

Thus you would look for exposures during the weeks of October 7 and 14, plus or minus a few days. This turned out to be the exact period during which a restaurant employee, diagnosed with hepatitis A in mid-October, would have been shedding virus while still working. In summary, the graph reflects an outbreak (number of cases clearly in excess of usual) beginning during the week of October 28, peaking during the week of November 4, and ending during the week of November 18. Based on these data and knowledge of the incubation period of hepatitis A, the period of exposure was probably in early to mid-October.

Exercise 6.6

An outbreak of an acute respiratory disease, coccidioidomycosis, occurred among volunteers, group leaders, and archaeologists who began working at a Native American archaeological site in Utah on June 18.()
Check your answer.
Place

Assessment of an outbreak by place not only provides information on the geographic extent of a problem, but may also demonstrate clusters or patterns that provide important etiologic clues. A spot map is a simple and useful technique for illustrating where cases live, work, or may have been exposed.

Some spot maps indicate each patient's residence. If the map shows a cluster or other pattern (such as cases along a road), the investigator must consider possible explanations - perhaps water supplies, wind currents, or proximity to a restaurant or grocery. A spot map, like that used by John Snow in London in 1854 ( see Lesson 1, Figure 1.1), can give clues about mode of spread.() For example, clustering of cases in a wing of a nursing home is consistent with either a focal source or person-to-person spread, whereas scattering of cases throughout the facility is more consistent with a widely disseminated vehicle or a source common to the residents that is not associated with room assignment, such as a common dining hall or water supply. In an outbreak of pneumococcal pneumonia in a nursing home in New Jersey, cases were more common in the north wing than in the south wing (Figure 6.6). Nursing home staff did report that the 2 residents of the south wing who developed pneumonia did spend much of their time in the north wing.()

Figure 6.6 Cases of Pneumonia by Room, Nursing Home A - New Jersey, 2001

Adapted from: Tan C. A preventable outbreak of pneumococcal pneumonia among unvaccinated nursing home residents in New Jersey during 2001. Infect Control Hosp Epidemiol 2003;24:848-52.

Often, a spot map by site of presumed exposure is more informative than one by residence. Figure 6.7 shows the location of staff in two offices in the U.S. Senate's Hart Building who had nasal swabs positive for B. anthracis after an envelope containing anthrax spores was opened in their presence.()

To look for clustering in an outbreak of surgical wound infections in a hospital, cases may be plotted by operating room, recovery room, and ward room. In studying "sick-building syndrome" and other disorders related to air-flow patterns in buildings, cases should be plotted by work location. A spot map may even plot sites of recreational or other outdoor exposures.

Figure 6.7 Desk Locations of Persons with Nasal Swabs Positive for Bacillus anthracis, Hart Building - Washington, DC, 2001
Image Description
Adapted from: Lukacs SL, Hsu V, Harper S, Handzel T, Hayslett J, Khabbaz R,,et al. Anthrax outbreak averted: public health response to a contaminated envelope on Capital Hill-Washington, DC, 2001. Presented at 51st Annual Epidemic Intelligence Service Conference, April 22-26, 2004, Atlanta.

Spot maps are useful for demonstrating cases within a geographic area, but they do not take the size of the underlying population into account. To compare incidence between different areas with different population densities, an area map showing area-specific rates is preferable. Figure 6.8 shows the number of cases of human granulocytic ehrlichiosis by county in Wisconsin during 1996-1998.() The most cases occurred in Washburn (n=21) and Chippewa (n=17) Counties. By dividing the number of cases by the size of the population, county-specific rates of ehrlichiosis can be calculated (Figure 6.9). While Jackson (n=11) and Rusk (n=9) Counties had fewer cases than Chippewa, their populations are much smaller, and they turned out to have higher rates of disease.

Figure 6.8 Cases of Human Granulocytic Ehrlichiosis by County - Wisconsin, May 1996-December 1998
Image Description
Source Ramsey AH, Belongia EA, Gale CM, Davis JP. Outcomes of treated human granulocytic ehrlichiosis cases. Emerg Infect Dis 2002;8:398-401.

Figure 6.9 Rates of Human Granulocytic Ehrlichiosis by County - Wisconsin, May 1996-December 1998
Image Description
Source: Ramsey AH, Belongia EA, Gale CM, Davis JP. Outcomes of treated human granulocytic ehrlichiosis cases. Emerg Infect Dis 2002;8:398-401.

Person

Characterization of the outbreak by person provides a description of whom the case-patients are and who is at risk. Person characteristics that are usually described include both host characteristics (age, race, sex, and medical status) and possible exposures (occupation, leisure activities, and use of medications, tobacco, and drugs). Both of these influence susceptibility to disease and opportunities for exposure.

The two most commonly described host characteristics are age and sex because they are easily collected and because they are often related to exposure and to the risk of disease. Depending on the outbreak, occupation, race, or other personal characteristics specific to the disease under investigation and the setting of the outbreak may also be important. For example, investigators of an outbreak of hepatitis B might characterize the cases by intravenous drug use and sexual contacts, two of the high risk exposures for that disease. Investigators of a school-based gastroenteritis outbreak might describe occurrence by grade or classroom, and by student versus teacher or other staff.

Early in an investigation, investigators may restrict the descriptive epidemiology to numbers of cases. However, in many circumstances the investigators also calculate rates (number of cases divided by the population or number of people at risk). Numbers indicate the burden of disease and are useful for planning and service delivery. Rates are essential for identifying groups with elevated risk of disease.

Summarizing by time, place, and person

After characterizing an outbreak by time, place, and person, it is useful to summarize what you know. For example, during an investigation of an outbreak of Legionnaires' disease in Louisiana, members of the investigative team discussed what they knew based on the descriptive epidemiology.() Specifically, the epidemic curve indicated that the outbreak was basically over, because no new case had been reported in the previous two weeks. The affected population had a greater proportion of persons who were black, female, young, and less likely to smoke than persons in a typical Legionnaires' outbreak. There appeared to be no clustering by either residence or worksite, and no connection with exposure to the town's cooling towers. Thus, the investigators were forced to develop new hypotheses about a source of Legionnaires' disease to explain this outbreak.

"Round up the usual suspects."

Step 7: Develop hypotheses

Although the next conceptual step in an investigation is formulating hypotheses, in reality, investigators usually begin to generate hypotheses at the time of the initial telephone call. Depending on the outbreak, the hypotheses may address the source of the agent, the mode (and vehicle or vector) of transmission, and the exposures that caused the disease. The hypotheses should be testable, since evaluating hypotheses is the next step in the investigation.

In an outbreak context, hypotheses are generated in a variety of ways. First, consider what you know about the disease itself: What is the agent's usual reservoir? How is it usually transmitted? What vehicles are commonly implicated? What are the known risk factors? In other words, by being familiar with the disease, you can, at the very least, "round up the usual suspects."

Another useful way to generate hypotheses is to talk to a few of the case-patients, as discussed in Step 3. The conversations about possible exposures should be open-ended and wide-ranging, not necessarily confined to the known sources and vehicles. In some challenging investigations that yielded few clues, investigators have convened a meeting of several case-patients to search for common exposures. In addition, investigators have sometimes found it useful to visit the homes of case-patients and look through their refrigerators and shelves for clues to an apparent foodborne outbreak.

Just as case-patients may have important insights into causes, so too may the local health department staff. The local staff know the people in the community and their practices, and often have hypotheses based on their knowledge.

The descriptive epidemiology may provide useful clues that can be turned into hypotheses. If the epidemic curve points to a narrow period of exposure, what events occurred around that time? Why do the people living in one particular area have the highest attack rate? Why are some groups with particular age, sex, or other person characteristics at greater risk than other groups with different person characteristics? Such questions about the data may lead to hypotheses that can be tested by appropriate analytic techniques.

Given recent concerns about bioterrorism, investigators should consider intentional dissemination of an infectious or chemical agent when trying to determine the cause of an outbreak. An intentional act, one with either terrorist or criminal intent, should be considered under a variety of circumstances listed in Table 6.6. Investigators of an outbreak of salmonellosis in The Dalles, Oregon, were stumped when they were able to implicate salad bars in several local restaurants, but could not identify any common ingredients or distribution system.() A year later, a member of a local cult admitted that the cult had intentionally contaminated the salads bars with Salmonella organisms. The lesson learned is that when the epidemiology does not fit the usual or natural patterns of transmission, investigators should think about intentional modes of transmission.

Table 6.6 Epidemiologic Clues to Bioterrorism

1. Single case of disease caused by an uncommon agent (e.g., glanders, smallpox, viral hemorrhagic fever, inhalational or cutaneous anthrax) without adequate epidemiologic explanation

2. Unusual, atypical, genetically engineered, or antiquated strain of an agent (or antibiotic-resistance pattern)

3. Higher morbidity and mortality in association with a common disease or syndrome or failure of such patients to respond to usual therapy

4. Unusual disease presentation (e.g., inhalational anthrax or pneumonic plague)

5. Disease with an unusual geographic or seasonal distribution (e.g., tularemia in a non-endemic area, influenza in the summer)

6. Stable endemic disease with an unexplained increase in incidence (e.g., tularemia, plague)

7. Atypical disease transmission through aerosols, food, or water, in a mode suggesting deliberate sabotage (i.e., no other physical explanation)

8. No illness in persons who are not exposed to common ventilation systems (have separate closed ventilation systems) when illness is seen in persons in close proximity who have a common ventilation system

9. Several unusual or unexplained diseases coexisting in the same patient without any other explanation

10. Unusual illness that affects a large, disparate population (e.g., respiratory disease in a large population may suggest exposure to an inhalational pathogen or chemical agent)

11. Illness that is unusual (or atypical) for a given population or age group (e.g., outbreak of measles-like rash in adults)

12. Unusual pattern of death or illness among animals (which may be unexplained or attributed to an agent of bioterrorism) that precedes or accompanies illness or death in humans

13. Unusual pattern of death or illness among humans (which may be unexplained or attributed to an agent of bioterrorism) that precedes or accompanies illness or death in animals

14. Ill persons who seek treatment at about the same time (point source with compressed epidemic curve)

15. Similar genetic type among agents isolated from temporally or spatially distinct sources

16. Simultaneous clusters of similar illness in noncontiguous areas, domestic or foreign

17. Large number of cases of unexplained diseases or deaths

Source: Treadwell TA, Koo D, Kuker K, Khan AS. Epidemiologic clues to bioterrorism. Public Health Reports 2003; 118:92-8.

Outliers also can provide important clues. In an outbreak of thyrotoxicosis in 1985, most cases came from Luverne, Minnesota, and the surrounding areas. Only one case was identified in Sioux Falls, South Dakota, 60 miles away. Did this person ever go to Luverne? Yes. Was she a friend or acquaintance of any of the Luverne cases? Not really. What does she do when she goes to Luverne? Visit my father and buy the locally produced ground beef that he sells in his store. Aha! The hypothesis that the locally produced ground beef was the vehicle could easily be tested by asking case-patients and controls (persons without thyrotoxicosis or other thyroid disease) whether they ate ground beef from the same source. Case-patients did, controls did not.()

Step 8: Evaluate hypotheses epidemiologically

After a hypothesis that might explain an outbreak has been developed, the next step is to evaluate the plausibility of that hypothesis. Typically, hypotheses in a field investigation are evaluated using a combination of environmental evidence, laboratory science, and epidemiology. From an epidemiologic point of view, hypotheses are evaluated in one of two ways: either by comparing the hypotheses with the established facts or by using analytic epidemiology to quantify relationships and assess the role of chance.

The first method is likely to be used when the clinical, laboratory, environmental, and/or epidemiologic evidence so obviously supports the hypotheses that formal hypothesis testing is unnecessary. For example, in an outbreak of hypervitaminosis D that occurred in Massachusetts in 1991, investigators found that all of the case-patients drank milk delivered to their homes by a local dairy. Therefore, investigators hypothesized that the dairy was the source and the milk was the vehicle. When they visited the dairy, they quickly recognized that the dairy was inadvertently adding far more than the recommended dose of vitamin D to the milk. No analytic epidemiology was really necessary to evaluate the basic hypothesis in this setting or to implement appropriate control measures, although investigators did conduct additional studies to identify additional risk factors. 38, 39

In many other investigations, however, the circumstances are not as straightforward, and information from the series of cases is not sufficiently compelling or convincing. In such investigations, epidemiologists use analytic epidemiology to test their hypotheses. The key feature of analytic epidemiology is a comparison group. The comparison group allows epidemiologists to compare the observed pattern among case-patients or a group of exposed persons with the expected pattern among noncases or unexposed persons. By comparing the observed with expected patterns, epidemiologists can determine whether the observed pattern differs substantially from what should be expected and, if so, by what degree. In other words, epidemiologists can use analytic epidemiology with its hallmark comparison group to quantify relationships between exposures and disease, and to test hypotheses about causal relationships. The two most common types of analytic epidemiology studies used in field investigations are retrospective cohort studies and case-control studies, as described in the following sections.

Retrospective cohort studies

A retrospective cohort study is the study of choice for an outbreak in a small, well-defined population, such as an outbreak of gastroenteritis among wedding guests for which a complete list of guests is available. In a cohort study, the investigator contacts each member of the defined population (e.g., wedding guests), determines each person's exposure to possible sources and vehicles (e.g., what food and drinks each guest consumed), and notes whether the person later became ill with the disease in question (e.g., gastroenteritis).

After collecting similar information from each attendee, the investigator calculates an attack rate for those exposed to (e.g., who ate) a particular item and an attack rate for those who were not exposed. Generally, an exposure that has the following three characteristics or criteria is considered a strong suspect:

1. The attack rate is high among those exposed to the item.

2. The attack rate is low among those not exposed, so the difference or ratio between attack rates is high.

3. Most of the case-patients were exposed to the item, so that the exposure could "explain" or account for most, if not all, of the cases.

Relative and attributable risk. Commonly, the investigator compares the attack rate in the exposed group to the attack rate in the unexposed group to measure the association between the exposure (e.g., the food item) and disease. This is called the risk ratio or the relative risk. When the attack rate for the exposed group is the same as the attack rate for the unexposed group, the relative risk is equal to 1.0, and the exposure is said not to be associated with disease. The greater the difference in attack rates between the exposed and unexposed groups, the larger the relative risk, and the stronger the association between exposure and disease.

Method for calculating risk ratio:

Attack rate (risk)
in exposed group Attack rate (risk)
in unexposed group

Table 6.7 includes data from an investigation of an outbreak of Salmonella Typhimurium gastroenteritis following a company's holiday banquet in December 2003.() Approximately 135 persons attended the party, and of 116 who were interviewed, 57 (49%) met the case definition. Food-specific attack rates for those who did and did not eat each of 9 items served only at this banquet are presented.

Scan the column of attack rates among those who ate the specified items and consider the three criteria listed on the previous page. Which item shows the highest attack rate? Is the attack rate low among persons not exposed to that item? Were most of the 57 case-patients exposed to that food item?

Table 6.7 Attack Rates By Items Served at Company A's Holiday Banquet - Virginia, December 2003

Source: Jani AA, Barrett E, Murphy J, Norton D, Novak C, Painter J, Toney D. A steamship full of trouble: an outbreak of Salmonella Typhimurium DT 104 gastroenteritis at a holiday banquet-Virginia, 2003. Presented at 53rd Annual Epidemic Intelligence Service Conference, April 19-23, 2004, Atlanta.

Beef, which had the highest attack rate among those who ate it, the lowest attack rate among those who did not eat it, and could account for almost all (53 of 57) of the cases, was indeed the culprit. The data showing the relationship between an exposure and disease are often displayed in a two-by-two table. The following two-by-two table shows the data for beef and gastroenteritis.

Table 6.8 Risk of Gastroenteritis By Consumption of Beef - Virginia, December 2003

Risk ratio = 65.4 ⁄ 11.4 = 5.7

Proportion of cases exposed = 53 ⁄ 57 = 93.0%
Population attributable risk percent = (49.1 − 11.4) ⁄ 49.1 = 76.7%

Source: Jani AA, Barrett E, Murphy J, Norton D, Novak C, Painter J, Toney D. A steamship full of trouble: an outbreak of Salmonella Typhimurium DT 104 gastroenteritis at a holiday banquet -Virginia, 2003. Presented at 53rd Annual Epidemic Intelligence Service Conference, April 19-23, 2004, Atlanta.

The risk ratio is calculated as the ratio of the attack rates or risks, i.e., 65.4% divided by 11.4%, which equals 5.7. This risk ratio indicates that persons who ate the beef were 5.7 times more likely to become ill than those who did not eat the beef.

( AR P − AR U ) AR P

Considering the third criterion listed earlier, notice that almost all (53 out of 57) of the cases could be accounted for by the beef. Some investigators use a more quantitative approach and calculate a population attributable risk percent for each food. The population attributable risk percent describes the proportion of illness in the entire study population that could be attributable to a given exposure, assuming that those who became ill in the unexposed group and a similar proportion in the exposed group must be attributable to something else. The population attributable risk percent may actually be an underestimate in many outbreaks, since it does not take into account such common occurrences as cross-contamination of foods or sampling of a spouse's dish. The population attributable risk percent for beef was 76.7% (see Table 6.8), much higher than that for any other food.

Statistical significance testing. When an exposure is found to have a relative risk different from 1.0, many investigators calculate a chi-square or other test of statistical significance to determine the likelihood of finding an association as large or larger on the basis of chance alone. A detailed description of statistical testing is beyond the scope of this lesson, but the following text presents some key features and formulas.

To test an association for statistical significance, assume first that the exposure is not related to disease, i.e., the relative risk (RR) equals 1.0. This assumption is known as the null hypothesis. The alternative hypothesis, which will be adopted if the null hypothesis proves to be implausible, is that exposure is associated with disease. Next, compute a measure of association, such as a risk ratio or odds ratio. Then calculate a chi-square or other statistical test. This test indicates the probability of finding an association as strong as or stronger than the one you have observed if the null hypothesis were really true, that is, if in reality the exposure being tested was not related to the disease. This probability is called the p-value. A very small p-value means that the observed association occurs only rarely if the null hypothesis is true. If the p-value is smaller than some cutoff that has been specified in advance, commonly 0.05 or 5%, you discard or reject the null hypothesis in favor of the alternative hypothesis.

Table 6.9 shows the standard notation for a two-by-two table.

Table 6.9 Standard Notation of a Two-By-Two Table

The most common statistical test for data in a two-by-two table from an outbreak is the chi-square test. To apply this test, calculate the chi-square statistic, then look up its corresponding p-value in a table of chi-squares, such as Table 6.10. Since a two-by-two table has 1 degree of freedom, a chi-square larger than 3.84 corresponds to a p-value smaller than 0.05. This means that if you planned to reject the null hypothesis if the p-value is less than 0.05, you can do so if your value for chi-square is greater than 3.84. Recognize, however, that the chi-square and similar tests are guides to help you make a decision about a hypothesis. Whichever decision you make, you may be right or you may be wrong. You could calculate a p-value that is not less than 0.05 and consequently fail to reject the null hypothesis, which may turn out to be true. This often occurs when a study has relatively few people. The opposite can also occur - a p-value less than 0.05 can actually be a chance finding rather than the true explanation of the outbreak.

Table 6.10 Table of Chi-Squares

Consider the gastroenteritis and beef consumption data presented in Table 6.8. The relative risk is 5.7, which most epidemiologists would deem a "strong" association between exposure and disease. In addition, the p-value is exceedingly small, less than 0.001, and far less than the commonly used cutoff of 0.05. So the investigators rejected the null hypothesis (that beef was not associated with illness) and adopted the alternative hypothesis (that beef was indeed associated with illness). In this outbreak, the association between eating beef at the banquet and gastroenteritis was both strong (RR=5.7) and statistically significant (p < 0.001).

The chi-square test works well if the number of people in the study is greater than about 30. For smaller studies, a test called the Fisher Exact Test may be more appropriate. Because the Fisher Exact Test is tedious to calculate, let Epi Info or another computer program perform the calculations for you.

Confidence intervals. An alternative to calculating a p-value is calculating a confidence interval. A 95% confidence interval, the interval used most commonly by epidemiologists, corresponds to a p=0.05 cut-off. In non-technical terms, a confidence interval for a risk ratio is the range of values of the risk ratio consistent with the data in a study. A wide confidence interval indicates that the study is consistent with a wide range of values, i.e., the study is not very precise in describing the strength of the association (risk ratio) between exposure and disease. A narrow confidence interval indicates that the risk ratio is fairly precise. Consider again the gastroenteritis data in Table 6.8. The 95% confidence interval for the risk ratio of 5.7 ranged from 2.2 to 14.6. This confidence interval indicates that the study is consistent with risk ratios for the beef/gastroenteritis association in that range.

Because a confidence interval provides more information than a p-value does, many medical and epidemiologic journals now prefer confidence intervals to p-values. However, in the outbreak setting, the difference may be irrelevant. If the objective of an outbreak investigation is to identify the culprit such as a contaminated food, a relative risk and p-value may do just as well as a relative risk and confidence interval.

Case-control studies

A cohort study is feasible only when the population is well defined and can be followed over a period of time. However, in many outbreak settings, the population is not well defined and speed of investigation is important. In such settings, the case-control study becomes the study design of choice.

In a case-control study, the investigator asks both case-patients and a comparison group of persons without disease ("controls") about their exposures. Using the information about disease and exposure status, the investigator then calculates an odds ratio to quantify the relationship between exposure and disease. Finally, a p-value or confidence interval is calculated to assess statistical significance.

Choosing controls. When designing a case-control study, one of the most important decisions is deciding who the controls should be. The controls must not have the disease being studied, but should represent the population in which the cases occurred. In other words, they should be similar to the cases except that they don't have the disease. The controls provide the level of exposure you would expect to find among the case-patients if the null hypothesis were true. If exposure is much more common among the case-patients than among the controls, i.e., the observed exposure among case-patients is greater than expected exposure provided by the controls, then exposure is said to be associated with illness.

In practice, choosing who the most appropriate control group is may be quite difficult. In addition, investigators must consider logistical issues, such as how to contact potential controls, gain their cooperation, ensure that they are free of disease, and obtain appropriate exposure data from them. In a community outbreak, a random sample of the healthy population may, in theory, be the best control group. In practice, however, persons in a random sample may be difficult to contact and enroll. Nonetheless, many investigators attempt to enroll such "population-based" controls through dialing of random telephone numbers in the community or through a household survey.

Other common control groups consist of:

Neighbors of case-patients,

Patients from the same physician practice or hospital who do not have the disease in question,

Friends of case-patients.

While controls from these groups may be more likely to participate in the study than randomly identified population-based controls, they may not be as representative of the population. If the control group is systematically different from the case group in certain ways, a true association between exposure and disease may be missed or a spurious association may be observed between a non-causal exposure and disease. A systematic difference between cases and controls that results in a mistaken estimate of the association between exposure and disease is called a bias.

When designing a case-control study, you must consider a variety of other issues about controls, including how many to use. Sample size formulas are available to help you make this decision. In general, the more subjects (case-patients and controls) in a study, the easier it will be to find a statistically significant association.

Often, the number of case-patients that can be enrolled in a study is limited by the size of the outbreak. For example, in a hospital, four or five cases may constitute an outbreak. Fortunately, potential controls are usually plentiful. In an outbreak of 50 or more cases, one control per case will usually suffice. In smaller outbreaks, you might use two, three, or four controls per case. Including more than four controls per case is rarely worth the effort in terms of increasing the statistical power of your investigation.

As an example, consider again the outbreak of Legionnaires' disease that occurred in Louisiana described at the end of Step 6. Investigators enrolled 27 case-patients into a case-control study. They also enrolled two controls per case, a total of 54 controls. Using descriptive epidemiology, the investigators did not see any connection with the town's various cooling towers. Using analytic epidemiology, the investigators determined quantitatively that case-patients and controls were about equally exposed to cooling towers. However, case-patients were far more likely to shop at a particular grocery store, as shown in the following two-by-two table.35

Table 6.11 Exposure to Grocery Store A Among Cases and Controls, Legionellosis Outbreak - Louisiana, 1990

Data Source: Mahoney FJ, Hoge CW, Farley TA, Barbaree JM, Breiman RF, Benson RF, McFarland LM. Communitywide outbreak of Legionnaires' disease associated with a grocery store mist machine. J Infect Dis 1992;165:736-9.

Odds ratios. In most case-control studies, the population is not well defined, and the total number of people exposed (or unexposed) to a suspected vehicle or source is not known. Without a proper denominator, attack rates cannot be calculated. In the example above, since the investigators did not know how many community residents did or did not shop at Grocery Store A, they could not calculate attack rates or a risk ratio. For a case-control study, the measure of association of choice is the odds ratio. Fortunately, for a rare disease such as legionellosis and most other outbreak-associated diseases, the odds ratio from a case-control study approximates the relative risk that would have been found if a cohort study had been feasible.

The odds ratio for Grocery Store A is calculated as:

25 × 26 ⁄ 28 × 2 = 11.6

An odds ratio of 11 is quite large, indicating that shopping at Grocery Store A was strongly associated with developing legionellosis. These data would seem to indicate that persons exposed to Grocery Store A had 11.6 times the odds of developing legionellosis than persons not exposed to that store.

To test the statistical significance of this finding, a chi-square test can be computed using the formula shown earlier.

For Grocery Store A, the chi-square is:

= 28,579,716 ⁄ 2,163,672

= 13.02

Referring to Table 6.10, a chi-square of 13.02 corresponds to a p-value less than 0.001. A p-value this small indicates that the null hypothesis is highly improbable, and the investigators rejected the null hypothesis. The 95% confidence interval ranged from 2.3 to 78.7. Although this confidence interval is quite wide and includes a wide range of values compatible with the data in the study, it does not include the null hypothesis value of 1.0.

Exercise 6.7

You are called to help investigate a cluster of 17 persons who developed brain cancer in an area over the past couple of years. Most, perhaps all, used cell phones. Which study design would you choose to investigate a possible association between cell phone use and brain cancer?
Check your answer.
Exercise 6.8

Investigators conducted a case-control study of histoplasmosis among industrial plant workers in Nebraska.() The following table shows the number of case-patients and controls who worked in Building X, near a recently excavated site.

1. What is the appropriate measure of association?

2. Calculate this measure.

3. The chi-square is 9.41, and the 95% confidence interval is 1.6-25.1. How would you interpret your results?

Check your answer.
Exercise 6.9

Consider the following data from an outbreak of gastroenteritis among college football players.() At which meal do you think the critical exposure occurred?
Check your answer.
Step 9: Reconsider, refine, and re-evaluate hypotheses

Unfortunately, analytic studies sometimes are unrevealing. This is particularly true if the hypotheses were not well founded at the outset. It is an axiom of field epidemiology that if you cannot generate good hypotheses (for example, by talking to some case-patients or local staff and examining the descriptive epidemiology and outliers), then proceeding to analytic epidemiology, such as a case-control study, is likely to be a waste of time.

When analytic epidemiology is unrevealing, rethink your hypotheses. Consider convening a meeting of the case-patients to look for common links or visiting their homes to look at the products on their shelves. Consider new vehicles or modes of transmission.

An investigation of an outbreak of SalmonellaMuenchen in Ohio illustrates how a reexamination of hypotheses can be productive. In that investigation, a case-control study failed to implicate any plausible food source as a common vehicle. Interestingly, all case-households but only 41% of control households included persons aged 15-35 years. The investigators thus began to consider vehicles of transmission to which young adults were commonly exposed. By asking about drug use in a second case-control study, the investigators implicated marijuana as the likely vehicle. Laboratory analysts subsequently isolated the outbreak strain of S. Muenchen from several samples of marijuana provided by case-patients.()

Even when an analytic study identifies an association between an exposure and disease, the hypothesis may need to be honed. For example, in the investigation of Legionnaires' disease (Table 6.11), what about Grocery Store A linked it to disease? The investigators asked case-patients and controls how much time they spent in the store and where they went in the store. Using the epidemiologic data, the investigators were able to implicate the ultrasonic mist machine that sprayed the fruits and vegetables. This association was confirmed in the laboratory, where the outbreak subtype of the Legionnaires' disease bacillus was isolated from the water in the mist machine's reservoir.()

Sometimes a more specific control group is needed to test a more specific hypothesis. For example, in many hospital outbreaks, investigators use an initial study to narrow their focus. They then conduct a second study, with more closely matched controls, to identify a more specific exposure or vehicle. In a large community outbreak of botulism in Illinois, investigators used three sequential case-control studies to identify the vehicle. In the first study, investigators compared exposures of case-patients and controls from the general public to implicate a restaurant. In a second study they compared restaurant exposures of case-patients and healthy restaurant patrons to identify a specific menu item, a meat and cheese sandwich. In a third study, investigators used radio broadcast appeals to identify healthy restaurant patrons who had eaten the implicated sandwich. Compared to case-patients who had also eaten the sandwich, controls were more likely to have avoided the onions that came with the sandwich. Type A Clostridium botulinum was then identified from a pan of leftover sautéed onions used to make only that particular sandwich.()

Finally, recall that one reason to investigate outbreaks is research. An outbreak may provide an "experiment of nature" that would be unethical to set up deliberately but from which the scientific community can learn when it does happen to occur. For example, the outbreak of West Nile virus in Queens, New York, in 1999 was promptly investigated to determine the extent of the outbreak and risk factors for disease so appropriate control measures could be developed and implemented.() However, capitalizing on this unfortunate "experiment of nature," investigators continued to follow the patients to determine the persistence of IgM and the prognosis of patients up to two years after infection.(,) Thus, the investigations resulted not only in the development of appropriate control and prevention strategies, but also in increased knowledge about a health problem not previously seen or studied in the Western hemisphere.

When an outbreak occurs, whether it is routine or unusual, consider what questions remain unanswered about that particular disease and what kind of study you might do in this setting to answer some of those questions. The circumstances may allow you to learn more about the disease, its modes of transmission, the characteristics of the agent, host factors, and the like.

Step 10: Compare and reconcile with laboratory and environmental studies

While epidemiology can implicate vehicles and guide appropriate public health action, laboratory evidence can confirm the findings. The laboratory was essential in both the outbreak of salmonellosis linked to marijuana and in the Legionellosis outbreak traced to the grocery store mist machine. You may recall that the investigation of pneumonia among attendees of an American Legion conference in Philadelphia in 1976 that gave Legionnaires' disease its name was not considered complete until a new organism was isolated in the laboratory some six months later.()

Environmental studies are equally important in some settings. They are often helpful in explaining why an outbreak occurred. For example, in the investigation of the outbreak of E. coli O157:H7 among visitors to a county fair, the epidemiologists were able to identify one very strong risk factor - consumption of beverages with ice purchased from a vendor in zone 6. Environmental inspection of the fairgrounds identified lack of chlorination of the well supplying water to that zone. Furthermore, the well was found to be close to the manure pits and a septic tank for the worker's dormitory. Flourescein dye poured into the bathroom of the dorm found its way into the well water, revealing cross-contamination. Finally, laboratorians were able to culture E. coli from the well, the supply line, and the tap at zone 6.() Thus the epidemiologic, environmental, and laboratory arms of the investigation complemented one another, and led to an inescapable conclusion that the well had been contaminated and was the source of the outbreak.

While you may not be an expert in these other areas, you can help. Use a camera to photograph working or environmental conditions. Coordinate with the laboratory, and bring back physical evidence to be analyzed.

Step 11: Implement control and prevention measures

In most outbreak investigations, the primary goal is control of the outbreak and prevention of additional cases. Indeed, although implementing control and prevention measures is listed as Step 11 in the conceptual sequence, in practice control and prevention activities should be implemented as early as possible. The health department's first responsibility is to protect the public's health, so if appropriate control measures are known and available, they should be initiated even before an epidemiologic investigation is launched. For example, a child with measles in a community with other susceptible children may prompt a vaccination campaign before an investigation of how that child became infected.

Confidentiality is an important issue in implementing control measures. Healthcare workers need to be aware of the confidentiality issues relevant to collection, management and sharing of data. For example, in the treatment of tuberculosis (TB), the relationship between the patient and the healthcare worker is extremely important because of the serious consequences of treatment failure. If patient information is disclosed to unauthorized persons without the patient's permission, the patient may be stigmatized or experience rejection from family and friends, lose a job, or be evicted from housing. Moreover, the healthcare worker may lose the trust of the patient, which can affect adherence to TB treatment. Therefore, confidentiality - the responsibility to protect a patient's private information - is critical in TB control and many other situations.()

In general, control measures are usually directed against one or more segments in the chain of transmission (agent, source, mode of transmission, portal of entry, or host) that are susceptible to intervention. For some diseases, the most appropriate intervention may be directed at controlling or eliminating the agent at its source. A patient with a communicable disease such as tuberculosis, whether symptomatic or asymptomatic, may be treated with antibiotics both to clear the infection and to reduce the risk of transmission to others. For an environmental toxin or infectious agent that resides in soil, the soil may be decontaminated or covered to prevent escape of the agent.

Some interventions are aimed at blocking the mode of transmission. Interruption of direct transmission may be accomplished by isolation of someone with infection, or counseling persons to avoid the specific type of contact associated with transmission. Similarly, to control an outbreak of influenza-like illness in a nursing home, affected residents could be cohorted, that is, put together in a separate area to prevent transmission to others. Vehicle borne transmission may be interrupted by elimination or decontamination of the vehicle. For example, contaminated foods should be discarded, and surgical equipment is routinely sterilized to prevent transmission. Efforts to prevent fecal-oral transmission often focus on rearranging the environment to reduce the risk of contamination in the future and on changing behaviors, such as promoting hand washing. For airborne diseases, strategies may be directed at modifying ventilation or air pressure, and filtering or treating the air. To interrupt vector borne transmission, measures may be directed toward controlling the vector population, such as spraying to reduce the mosquito population that may carry West Nile virus.

Some simple and effective strategies protect portals of entry. For example, bed nets are used to protect sleeping persons from being bitten by mosquitoes that may transmit malaria. A dentist's mask and gloves are intended to protect the dentist from a patient's blood, secretions, and droplets, as well to protect the patient from the dentist. Wearing of long pants and sleeves and use of insect repellent are recommended to reduce the risk of Lyme disease and West Nile virus infection.

Some interventions aim to increase a host's defenses. Vaccinations promote development of specific antibodies that protect against infection. Similarly, prophylactic use of antimalarial drugs, recommended for visitors to malaria-endemic areas, does not prevent exposure through mosquito bites but does prevent infection from taking root.

Step 12: Initiate or maintain surveillance

Once control and prevention measures have been implemented, they must continue to be monitored. If surveillance has not been ongoing, now is the time to initiate active surveillance. If active surveillance was initiated as part of case finding efforts, it should be continued. The reasons for conducting active surveillance at this time are twofold. First, you must continue to monitor the situation and determine whether the prevention and control measures are working. Is the number of new cases slowing down or, better yet, stopping? Or are new cases continuing to occur? If so, where are the new cases? Are they occurring throughout the area, indicating that the interventions are generally ineffective, or are they occurring only in pockets, indicating that the interventions may be effective but that some areas were missed?

Second, you need to know whether the outbreak has spread outside its original area or the area where the interventions were targeted. If so, effective disease control and prevention measures must be implemented in these new areas.

Step 13: Communicate findings

As noted in Step 1, development of a communications plan and communicating with those who need to know during the investigation is critical. The final task is to summarize the investigation, its findings, and its outcome in a report, and to communicate this report in an effective manner. This communication usually takes two forms:

An oral briefing for local authorities. If the field investigator is responsible for the epidemiology but not disease control, then the oral briefing should be attended by the local health authorities and persons responsible for implementing control and prevention measures. Often these persons are not epidemiologists, so findings must be presented in clear and convincing fashion with appropriate and justifiable recommendations for action. This presentation is an opportunity for the investigators to describe what they did, what they found, and what they think should be done about it. They should present their findings in a scientifically objective fashion, and they should be able to defend their conclusions and recommendations.

A written report. Investigators should also prepare a written report that follows the usual scientific format of introduction, background, methods, results, discussion, and recommendations. By formally presenting recommendations, the report provides a blueprint for action. It also serves as a record of performance and a document for potential legal issues. It serves as a reference if the health department encounters a similar situation in the future. Finally, a report that finds its way into the public health literature serves the broader purpose of contributing to the knowledge base of epidemiology and public health.

In recent years, the public has become more aware of and interested in public health. In response, health departments have made great strides in attempting to keep the public informed. Many health departments strive to communicate directly with the public, usually through the media, both during an investigation and when the investigation is concluded.

References (This Section)

1. Palmer SR. Epidemiology in search of infectious diseases: methods in outbreak investigation. J Epidemiol Comm Health 1989;43:311-4.

2. Last JM. A dictionary of epidemiology, 4th ed. New York: Oxford U Press, 2001:129.

3. PAHO. Case definitions: meningococcal disease and viral meningitis. Epidemiol Bull 2001;22(4):14-6.

4. Centers for Disease Control and Prevention. Eosinophilia-myalgia syndrome - New Mexico. MMWR 1989;38:765-7.

5. Centers for Disease Control and Prevention. Eosinophilia-myalgia syndrome and L-tryptophan-containing products - New Mexico, Minnesota, Oregon, and New York, 1989. MMWR 1989;38:785-8.

6. Centers for Disease Control and Prevention. Public health dispatch: outbreak of listeriosis - northeastern United States, 2002. MMWR 2002;51:950-1.

7. Jernigan DB, Raghunathan PL, Bell BP, Brechner R, Bresnitz EA, Butler JC, et al. Investigation of bioterrorism-related anthrax, United States, 2001: epidemiologic findings. Emerg Infect Dis 2002;8:1019-28.

8. Heyman DL, ed. Control of communicable diseases manual, 18th ed. Washington, DC: American Public Health Association, 2004.

9. Peterson LR, Marshall SL, Barton-Dickson C, Hajjeh RA, Lindsley MD, Warnock DW, et al. Coccidioidomycosis among workers at an archaeologic site, northeast Utah. Emerg Infect Dis 2004;10:637-42.

10. Snow J. Snow on cholera. London: Humphrey Milford: Oxford U Press, 1936.

11. Tan C. A preventable outbreak of pneumococcal pneumonia among unvaccinated nursing home residents - New Jersey, 2001. Presented at Northeast Regional Epidemic Intelligence Service Conference, March 14, 2002, New York City.

12. Lukacs SL, Hsu V, Harper S, Handzel T, Hayslett J, Khabbaz R, et al. Anthrax outbreak averted: public health response to a contaminated envelope on Capital Hill-Washington, DC, 2001. Presented at 51st Annual Epidemic Intelligence Service Conference, April 22-26, 2004, Atlanta.

13. Ramsey AH, Belongia EA, Gale CM, Davis JP. Outcomes of treated human granulocytic ehrlichiosis cases. Emerg Infect Dis 2002;8:383-401.

14. Mahoney FJ, Hoge CW, Farley TA, Barbaree JM, Breiman RF, Benson RF, McFarland LM. Communitywide outbreak of Legionnaires' disease associated with a grocery store mist machine. J Infect Dis 1992;165: 736-9.

15. Torok TJ, Tauxe RV, Wise RP, Livengood JR, Sokolow R, Mauvais S, et al. A large community outbreak of salmonellosis caused by intentional contamination of restaurant salad bars. JAMA 1997;278:389-95.

16. Hedberg CW, Fishbein DB, Janssen RS, Meyers B, McMillen JM, MacDonald KL, et al. An outbreak of thyrotoxicosis caused by the consumption of bovine thyroid gland in ground beef. N Engl J Med 1987;316:993-8.

17. Jacobus CH, Holick MF, Shao Q, Chen TC, Holm IA, Kolodny JM, et al. Hypervitaminosis D associated with drinking milk. New Engl J Med 1992;326:1173-7.

18. Blank S, Scanlon KS, Sinks TH, Lett S, Falk H. An outbreak of hypervitaminosis D associated with the overfortication of milk from a home-delivery dairy. Am J Public Health 1995;85:656-9.

19. Jani AA, Barrett E, Murphy J, Norton D, Novak C, Painter J, Toney D. A steamship full of trouble: an outbreak of Salmonella Typhimurium DT 104 gastroenteritis at a holiday banquet - Virginia, 2003. Presented at the 53rd Annual Epidemic Intelligence Service Conference; 2004 Apr 19-23; Atlanta.

20. Centers for Disease Control and Prevention. Outbreak of histoplasmosis among industrial plant workers - Nebraska, 2004. MMWR 2004;53:1020-2.

21. Becker KM, Moe CL, Southwick KL, MacCormack JN. Transmission of Norwalk virus during a football game. N Engl J Med 2000;343;1223-7.

22. Taylor DN, Wachsmuth IK, Shangkuan YH, Schmidt EV, Barrett TJ, Schrader JS, et al. Salmonellosis associated with marijuana: a multistate outbreak traced by plasmid fingerprinting. New Engl J Med 1982;306:1249-53.

23. MacDonald KL, Spengler RF, Hatheway CL, Hargrett NT, Cohen ML. Type A botulism from sauteed onions. JAMA 1985;253:1275-8.

24. Nash D, Mostashari F, Fine A, Miller J, O'Leary D, Murray K, et al. The outbreak of West Nile virus infection in the New York City area in 1999. N Engl J Med 2001;344:1807-14.

25. Roehrig JT, Nash D, Maldin B, Labowitz A, Martin DA, Lanciotti RS, et al. Persistence of virus-reactive serum immunoglobulin M antibody in confirmed West Nile virus encephalitis cases. Emerg Infect Dis 2003;9:376-9.

26. Klee AL, Maldin B, Edwin B, IPoshni I, Mostashari F, Fine A, et al. Long-term prognosis for clinical West Nile Virus infection. Emerg Infect Dis 2004;10:1405-11.

27. Fraser DW, Tsai TF, Orenstein W, Parkin WE, Beecham HJ, Sharrar RG, et al. Legionnaires' disease: description of an epidemic of pneumonia. N Engl J Med 1977;297:1189-97.

28. Bopp DJ, Saunders BD, Waring AL, Waring AL, Ackelsberg J, Dumas N, et al. Detection, isolation, and molecular subtyping of Escherichia coli O157:H7 and Campylobacter jejuni associated with a large waterborne outbreak. J Clin Microbiol 2003;41:174-80.

29. Division of Tuberculosis Elimination [Internet]. Atlanta: CDC; [updated 1999 Oct; cited 2006 Sep 19]. Self study modules on Tuberculosis, Module 7: Confidentiality in Tuberculosis Control: Background. Available from: /nchstp/tb/pubs/ssmodules/module7/ss7background.htm.

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principles of epidemiology in public health practice

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Principles of Epidemiology: Lesson 6, Section 2|Self-Study Course SS1978|CDC

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This course covers basic epidemiology principles, concepts, and procedures useful in the surveillance and investigation of health-related states or events. It is designed for federal, state, and local government health professionals and private sector health professionals who are responsible for disease surveillance or investigation.

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CDC's Epidemic Information Exchange (Epi-X) is a secure, web-based network that serves as a powerful information exchange. It connects public health professionals involved in identifying, investigating, and responding to public health threats.

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When users log on to the website, they see brief summaries of reports of outbreaks, and through Forum can participate in discussions of ongoing health events or other subjects of interest. Users can describe a new health event in their jurisdiction by submitting a report. Any other user can then comment on posted reports or add to standing Epi-X Forum discussions when they have questions, identify related cases, or wish to contribute insights.

Public health officials across the United States have come to depend on Epi-X because of its secure website, discussion forums, user directory, and emergency notification capabilities. Eddy Bresnitz, MD, MS, former president of the Council of State and Territorial Epidemiologists (CSTE), recognized the contribution of Epi-X in his 2008 CSTE conference plenary address. He stated that he "counts Epi-X as a major accomplishment towards enhancing disease investigations."

The success of Epi-X depends largely on three guiding principles: building community, being prepared, and responding to user needs. Epi-X does this by:

Secure reporting of health events

Notification and alerting

Secure user contact information (Epi-X Directory)

Build a Community

Public health officials across many disciplines and in various agencies are invited to join Epi-X. To recruit users, Epi-X partners with:
AAPCC -
APHL -
ASTHO -
CSTE -
NACCHO -
NASPHV -

Persons with public health responsibilities in other federal agencies are also recruited, including those from Department of Defense, Department of State, Department of Homeland Security, Environmental Protection Agency, Food and Drug Administration, Health and Human Services, United States Department of Agriculture, and Federal Emergency Management Agency.

Representatives from partner organizations and agencies convene annually at the Epi-X editorial board meeting. At this meeting, they discuss what has worked in the past and possible courses to follow in upcoming years. In addition, participants discuss proposals to add user groups or new types of content.

One such proposal, to add more local users, was brought before the board in 2002. The board supported the proposal, but recommended that Epi-X be able to target report access and notification to subsets of users before adding any large groups of new users. (Until that time, every user could search for and view most reports posted on Epi-X). The targeting function was added in 2003 and local users from NACCHO were then actively recruited.

Fun fact: Epi-X has 6,038 users, approximately 35 percent are from state government, 35 percent are from local government, and 20 percent are from CDC.

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CDC Works For You 24/7 Saving Lives - Epi-X: Building a Network to Save Lives; The Epi-X network includes epidemiologists, laboratorians, veterinarians, health officers, environmental health specialists, statisticians, pharmacologists, and other key health officials from local, state, and federal agencies. Epi-X works continuously with these partners, providing the guidance and support needed to handle a variety of health threats.

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Centers for Disease Control and Prevention (CDC)

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Introduction to the Public Health Preparedness Capabilities (101)

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During disasters, chaos usually ensues along with panic. Having healthcare informatics can help organize the patients and carry out the safest way to keep them safe. It can help with breaking down which patients are most critical, to where all of the safety zones in the hospital are.  It also helps spread the word for such a disaster, and warning so facilities can be as prepared as possible.

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Hospitals practice drills for emergencies small and large scale. This video shows a hospitals preparation for a large scale disaster.

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by Jill Burrington-Brown, MS, RHIA

Disaster planning relies on imagining events that we hope and often believe will never happen. After the tragedies of September 11, however, imagining a large-scale disaster is no longer difficult and disaster planning has come to the fore. In this article, we look at several important lessons we can learn from healthcare professionals who have responded to recent disaster situations.

Disaster plans accompanied by serious practice drills are important. Most facilities have disaster plans and practice them, but don't expect to ever carry out the plan. However, it is important to know before a disaster strikes who is in charge in the facility during and after the incident, who in your area is in charge, and what responsibilities you and your staff will have. Even with planning and practice, most facilities are neither ready nor prepared for a large-scale disaster.

Remember that although orderly drills are helpful, the disaster itself will not be orderly. Control as much as you can ahead of time. Plan for more disaster victims than you think you'll ever receive. Plan that victims will arrive at all hospital entrances, and plan that collecting information will not be easy. Traditional admitting procedures will be impossible. Your facility should have a patient identification system that is simple and ready for use, enables tracking of the patients later by investigative authorities, and allows for the finding of the patients by relatives.

According to AHIMA's director of HIM products and services, Harry Rhodes, MBA, RHIA, after the Oklahoma City bombing in 1995, the disaster identification tags for patients were changed to include check boxes where appropriate (gender, hair and eye color, race, as well as child, adolescent, adult) to speed up the identification and check-in process. Also, pre-numbered stickers used to identify samples, records, and order sheets were attached to the back of the identification tags.

Internal and external communication will be complicated. Richard Westfal, MD, associate director of the Department of Emergency Medicine at St. Vincent's Catholic Medical Center in New York City, told Medscape that portable radios for communication were a big help during the aftermath of the September 11 disaster. Because telephones were tied up with calls from family members, doctors, volunteers, and others, having radios to communicate among all the key treatment areas and central supply was essential.

According to Sheryl McLain, MS, of the Oklahoma Hospital Association, the resources donated by wireless and local phone companies were essential for communications after the Oklahoma City bombing, and these companies must be included in community disaster planning. According to the American Hospital Association Web site, she also noted that working with the media will be a challenge, and that the facility should plan to have 24-hour coverage for the public relations department and determine ahead of time what the release of information policies will be during a disaster.

Plan for volunteers. People will come to your facility to volunteer during a disaster, and unless you are prepared for them, they can hinder your operations. Decide in advance if you will use non-employees and if so, designate a volunteer coordinator. Make plans for those who can help with administrative functions and volunteer clinicians who can treat patients. Further, determine if and how you will check credentials of healthcare practitioners. Have adhesive-backed name badges on hand to identify approved volunteers at a glance.

Plan for identification of and communication about victims. The rules for release of information are somewhat different in a disaster. The HIPAA regulations allow for disclosure of personal health information in a disaster for the purposes of notifying a family member of a patient's location, general condition, or death (Sec.164.510). Also, the state health department is allowed access to personal health information in a disaster.

Sharing patient information among hospitals is important, and a centralized computer linkage would save time and effort. Part of the communication plan should be how your facility will communicate with other facilities and centralize information about location of victims.

One of the issues in New York was managing identification of victims. Facilities received many requests for patient health information from friends and families without any kind of release. The New York City Medical Examiner's Office told AHIMA that it was obtaining dental records or DNA mapping from next of kin or relying on their public health authority to obtain this information. Facilities should maintain the same level of concern for proper release of information.

Plan for debriefing and post-traumatic stress disorder counseling for staff. According to McLain, this step is not optional. Crisis intervention after the disaster will be a necessity. Emotional healing takes time, so counseling, both group and individual, should be planned. Project Heartland, a mental health clearinghouse formed after the Oklahoma City bombing, provided services to 190,000 individuals. In New York, services have been provided to more than 100,000 people to date: survivors, people who lost a loved one, rescue workers and first-hand witnesses, as well as those who lost jobs, homes, or businesses. The staff taking care of the victims and their families will be profoundly affected.

The terrorist attacks of September 11 remind us that we must take disaster planning seriously. While a disaster is statistically unlikely, each healthcare facility should take time to prepare as much as possible and use the lessons learned from those who have been there ahead of us. u

References

"Caring For Our Own." Joint Commission Perspectives 21, no. 1 (2001).

Hersche, B. and O.C. Wenker. "Principles of Hospital Disaster Planning." The Internet Journal of Rescue and Disaster Medicine 1, no. 2 (2000). Available at www.icaap.org/iuicode?86.1.2.19.

McLain, Sheryl L. "The Oklahoma City Bombing: Lessons Learned by Hospitals." Available on the American Hospital Association Web site ( www.aha.org) under "Disaster Readiness."

Rhodes, Harry. "Disaster Plans: Lessons Learned." Journal of AHIMA 67, no. 4 (1996). Also available in the AHIMA Library in the Communities of Practice at www.ahima.org.

Saint-Jacques, Alfred J. "When Disaster Strikes: A Look at Emergency Response to the World Trade Center Disaster, An Interview with Richard Westfal, M.D." (2001). Available at www. medscape.com.

Jill Burrington-Brown (jill.burrington-brown@ahima.org) is an HIM practice manager at AHIMA.

Article citation:
Burrington-Brown, Jill. "Practical Disaster Planning for Healthcare Facilities." Journal of AHIMA 73, no.2 (2002): 56,58.

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Practical Disaster Planning for Healthcare Facilities

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by Jill Burrington-Brown, MS, RHIA Disaster planning relies on imagining events that we hope and often believe will never happen. After the tragedies of September 11, however, imagining a large-scale disaster is no longer difficult and disaster planning has come to the fore.

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EHRs and realtime updates to Isolate New Outbreaks.

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EHRs and realtime updates to Isolate New Outbreaks.

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EHR's, such as Epic, can provide massive new DATA Insight to contain new Virus Threats!

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Epic this week sent out an update to its healthcare customers, meant to help providers nationwide detect potential cases of the Wuhan novel coronavirus, or 2019-nCoV.

WHY IT MATTERS
Epic has standardized the new travel screening questionnaire in an effort to ensure clinicians and other front-line medical staff ask patients about recent international travel.

If patients say they have traveled from China, or show symptoms consistent with Wuhan novel coronavirus, providers are now advised to start isolation precautions to help contain potential infection.

Epic says this new update was developed in collaboration with biocontainment experts, infectious disease physicians and others with guidance from the U.S. Centers for Disease Control.

Early this week, the Epic Travel and Communicable Disease Advisory Board, which comprises infectious disease clinicians practicing at healthcare organizations using Epic, started discussing updates to the travel screening in Epic to detect possible cases of 2019-nCoV.

Members of the advisory board include experts at Providence St. Joseph Health in Washington, which, upon treating the first 2019-nCoV case in the United States, "quickly turned on an electronic prompt for doctors and nurses to ask travel screening questions," as reported by Politico.

The board determined that healthcare organizations will automatically get the new coronavirus alert within Epic's travel screening activity. In addition, Epic says its representatives are reaching out to its provider customers to verify that they have IT workflows that follow CDC guidelines.

THE LARGER TREND
In 2014, Epic found itself in the middle of a debate related to another public health crisis - the outbreak of the Ebola virus - and to what extent EHR workflows might be related to timely detection and effective response protocols.

That September, Thomas Eric Duncan was admitted to the emergency department at Texas Health Presbyterian Hospital in Dallas, complaining of symptoms consistent with Ebola. Although he told his nurse that he'd recently traveled to Africa, and it was noted in his EHR, that detail wasn't discerned by the physician, who discharged him from the ED. Duncan returned to the hospital four days later with worsening symptoms and only then was he diagnosed as the first Ebola patient in the U.S.

Initially, Texas Health Resources sought to explain why that key travel information was missed by noting that, "as designed, the travel history would not automatically appear in the physician's standard workflow."

Two days later, however, THR walked back that statement, clarifying that there was " no flaw in the EHR in the way the physician and nursing portions interacted related to this event" and that the patient's travel history "was available to the full care team."

At any rate, the case highlighted how technology, the health systems that configure it and the clinicians who use it are all key factors for fast and efficient diagnosis.

ON THE RECORD
"Rapid response to an outbreak is critical to contain it, get patients the treatment they need, and protect other patients and staff," said James Doyle, Epic's clinical informatics R&D lead, in a statement about the new coronavirus update.

He added that Epic is committed to helping ensure its healthcare clients will be "covered by the latest guidelines from the CDC in consultation with leading experts."

"Clinicians on our travel advisory board define the standard for the rapid translation of public health outbreak notifications into actionable warnings," added Dr. Chris Alban, a clinical informaticist at Epic. "Organizations using Epic automatically get advisories in their system as soon as they're created. Health systems can respond quickly with this configuration that's been vetted by the experts."
Twitter: @MikeMiliardHITN
Email the writer:mike.miliard@himssmedia.com Healthcare IT News is a publication of HIMSS Media.

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Epic pushes out software update to help spot coronavirus

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Epic this week sent out an update to its healthcare customers, meant to help providers nationwide detect potential cases of the Wuhan novel coronavirus, or 2019-nCoV. WHY IT MATTERS Epic has standardized the new travel screening questionnaire in an effort to ensure clinicians and other front-line medical staff ask patients about recent international travel.

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Multiple perspectives on planning for emergencies: An introduction to the special issue on planning and foresight for emergency preparedness and management

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Multiple perspectives on planning for emergencies: An introduction to the special issue on planning and foresight for emergency preparedness and management

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https://www.sciencedirect.com/science/article/pii/S0040162513001662

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observation by the chair of the Accident Investigation Board in Finland

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observation by the chair of the Accident Investigation Board in Finland

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"The situation in a disaster is never so bad that poor communication and flow of information cannot change things for the worse.”

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Important features of such tools are proposed, including: (1) Interactive tutorials to improve risk comprehension and prepare patients and clinicians for the decision making task; (2) choices between different presentation modes to meet patients’ individual reading levels and presentation preferences; (3) risk calculations that account for individual risk profiles; (4) performance of necessary calculations to reach the actual decision; (5) automatic updates of evidence; and (6) the use of different preference-elicitation techniques.

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Community Health Risk Assessments are evaluations of a community to find out what health risks they are prone to. They are used in public health programs (McGonigle& Mastrian 2018). Data can be collected about communities either from existing data, focus groups, statistics, health records, and making observations just from driving around the community.

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Goals of community health risk assessments. 

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Goals of community health risk assessments. 

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1. What are  needs or issues in the community?

2. Collaborations with multiple agencies in the community are vital.

3. get resources

4. get community to have increased awareness of the identified issues

5. take a proactive approach.

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Nursing and Community Health Risk Assessments 

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Nursing and Community Health Risk Assessments 

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Community Health Nursing - Community Assessment and Community Health Plan

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Role of nursing in relation to community health risk assessments. 

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Role of nursing in relation to community health risk assessments. 

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As nurses, we are always assessing our patients. We are constantly documenting risk assessments such fall and suicide risks. Nurses play a vital role in community health risk assessments also. Nurses can help identify certain needs of the community or risks that the community. At the very least, nursing documentation will help when data is being collected to identify risks and needs. When a plan is in place to provide education or share resources, nurses can help in implementing that plan. Nurses also can collaborate with other community agencies to provide input and feedback.

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Effective ways to identify community needs or risks. 

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Effective ways to identify community needs or risks. 

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Just driving around the community can help bring better understanding of needs.

demographics

close proximity to health facilities

grocery stores (how close and what kind)

transportation

violence

mental health

housing

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Action plan and implementation for Blue Island Community through collaboration between community and agencies in the community. 

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Action plan and implementation for Blue Island Community through collaboration between community and agencies in the community. 

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Centers for Disease Control and Prevention (CDC)

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Be Healthy Blue Island: A Roadmap to a Healthier Community

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Here is an article on population health and an action plan for nursing. 

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Here is an article on population health and an action plan for nursing. 

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Abstract

The passage of the Affordable Care Act shifted the focus of health care from individual, patient specific, episodic care, towards health management of groups of people with an emphasis on primary and preventive care. Population health management assists to attain and maintain health while improving quality and lowering costs. The recent Catalyst for Change report creates an urgent call for harnessing the power of nurses-in our communities, schools, businesses, homes and hospitals-to build capacity for population health. Informatics Nurse Specialists are prepared to bridge roles across practice, research, education, and policy to support this call. Each year, the AMIA Nursing Informatics Working Group convenes an expert panel to reflect on the "hot topics" of interest to nursing. Not surprisingly, the 2017 topic was on the current state and challenges of population health. The following summary reflects the panel's perspectives and recommendations for action.

INTRODUCTION

Population health is defined as the "health outcomes of a group of individuals, including the distribution of such outcomes within the group." The passage of the Affordable Care Act (ACA) shifted the focus of health care from individual, patient specific, episodic care, towards health management of groups of people with an emphasis on primary and preventive care. Population health management assists these groups to attain and maintain health with an increased focus on shared accountability for the upstream environment, social, and community factors that contribute to chronic disease and cost. With more than 3 million nurses in the United States, in all sectors of health care, nurses, because of their role, their education, and the respect they have earned, are well positioned to help shape and improve our nation's health status and care infrastructure. The recent Catalyst for Change report (RWJF, 2017) creates an urgent call for harnessing the power of nurses-in our communities, schools, businesses, homes, and hospitals-to build capacity for population health. Informatics Nurse Specialists (INSs) are prepared and well positioned to bridge roles across practice, research, education, and policy to support this call.

Each year, the AMIA Nursing Informatics Working Group (NIWG) convenes an expert panel to focus on a "hot topic" of interest to nursing. Not surprisingly, the 2017 topic was on the current state and challenges of population health. The following summary reflects the panel's perspectives and recommendations for action.

POPULATION HEALTH IN PRACTICE

Nursing practice is experiencing a paradigm shift from siloed health information technology (HIT) designed to collect data at points-of-care within organizations, to data collected along a patient's health trajectory, co-ordinated across where they live, work, and play. However, the health care data collected today is largely designed, maintained, and groomed for transactions between providers and payers and for managing point-of-care contacts with patients. The information, communication, and technology infrastructure must: integrate data, creating robust analytic environments designed to assemble and assess populations; identify and predict adverse outcomes; support programs and interventions to address inter-professional work flow needs across all care venues; and measure success.

Nurse leaders, nurses, and INSs are active and innovative in addressing the move from transactional systems to a robust data warehouse environment with an operational analytic infrastructure. Basic capabilities start with patient identity management (PIM) and patient/primary care provider attribution. Health Information Exchanges (HIE) deal with complex PIM issues and where no HIE exists, PIM may not be possible., Successful population health management requires primary care at its core; however; attribution is, at its best, a compromise among a complex set of methodological choices and, at its worst, fraught with errors in the data collection and workflow management process.

While organizations accountable for care can demonstrate analytic expertise using risk prediction and prescription models, these capabilities come at high cost when manually executed, lacking in scale and efficiency. These challenges make it near impossible for organizations to make informed, strategic decisions for managing the health outcomes of patients, risk contracting, and gaining overall competitive advantage.

Recommendations for practice include:

Assemble a workforce of INS's prepared to manage the complexities of population health.

Incorporate robust analytic tools that provide near real-time knowledge that bridge patient-centered care across the continuum.

Test translational models for moving population health methods and findings from research into practice.

Inform HIT policymakers on practice challenges and barriers to achieving population health knowledge.

POPULATION HEALTH IN EDUCATION

Population health is not a new concept for nursing education. As far back as Florence Nightingale, nurses have fostered a philosophy of care that focuses on the assessment and management of physical, biological, social, psychological, and environmental influences on patients and populations. This population focus, however, has diminished over time with a push towards education and competency development to prepare nurses for acute and critical care. Today, there is a resurgence of interest in population health-based nursing education, encouraging nurses to reclaim their history and take on key roles in a health care system responsive to the needs of populations.

Nurses must be competent in: near real-time assessment and identification of issues negatively affecting health and well-being; discernment of patterns from data and populations; creating linkages among community resources and services; developing population-focused interventions; and evaluating the impact of the efforts., Next generation INSs must elevate their support of population health efforts through: training and competency in data capture from disparate systems; validation and maintenance of data; transformation and analysis of data; inclusion of social determinants of health (SDOH) to address disparities; predictive and prescriptive analysis; risk stratification; and evaluation of education programs.

Accreditation and certification bodies as well as licensing boards for the graduates of programs must ensure that all nursing students from entry to practice to graduate level, including INS students are provided learning opportunities to attain these competencies and advance graduates who demonstrate these skills. As we move our emphasis from an acute care focus to population health model, it will require rethinking and a shift in focus for many nursing and informatics programs.

Recommendations for education:

Nursing education programs must prepare nurses to:

Tackle the impact of SDOH on improving health and reducing health inequalities

Consider community resources and provide learning opportunities to build engagement

Assess process and outcomes of community-based interventions

Nursing informatics graduate programs must prepare the INSs to:

Manage data capture from disparate systems, beyond the electronic health record (EHR)

Understand data validation, transformation, and maintenance

Develop skills in data science and analytics to assess the health status and risk levels of populations

Evaluate the use of risk stratification, as well as predictive and prescriptive analytics

POPULATION HEALTH IN RESEARCH

Population health research investigates various determinants of health, including health care services, individuals' genetics and health behaviors, physical and social environments. Funding agencies have increased their support for population health research in response to rapidly growing chronic illness populations and skyrocketing medical costs. For example, the strategic plan of the National Institute of Nursing Research (NINR) focuses on 4 scientific themes: symptom science, wellness, self-management of chronic conditions, and end-of-life and palliative care. The Agency for Healthcare Research and Quality (AHRQ) supports research initiatives to improve health care quality and make health care safer, more accessible, affordable, and efficient.

Health information technology (HIT) is a vital component that supports population health research. Some of examples HIT areas includes: meaningful use of EHRs, HIE, interoperability, Big Data, and health care analytics. Many studies have demonstrated the important roles of HIT components in knowledge discovery and the development and implementation of new health interventions while engaging patients in their care.

Informatics Nurse Specialists including both researchers and practitioners, have an in-depth understanding of the associations among the data, clinical practices and workflow, and track records of using HIT in health-related research. They can also significantly contribute to the advancement of population health research by using robust HIT tools that improve quality of care and patient outcomes and contributing to the development of efficient and interoperable health care systems.

Recommendations for research:

Enhance nurses' knowledge about big data and population health knowledge

Develop new systems and tools that engage and empower patients to improve quality of care, outcomes, and wellness activities.

Map data to standardized terminologies and protocols for interoperability and outcome measurement

Contribute to the science of the associations among the data, clinical practices, and workflow.

Research health information systems that can contribute to reducing health disparities.

POPULATION HEALTH IN POLICY

The AMIA NIWG panel concluded with an update on public policy and invited continued engagement in population health efforts supported by AMIA and The Alliance for Nursing Informatics (ANI). Multiple policy issues impact population health, with only a few highlighted during this event. For example, AMIA's Policy Invitational (API 2017) explored "redefining our picture of health," through the lens of how patients experience care, research, wellness, and community-rather than those of provider institutions, payers, and health IT vendors. Policy recommendations focused on the development of an integrated, socio-technical ecosystem that enables an individual (the "n-of-1") to improve the health of populations (the "n-of-many"), and vice versa.

Implementation of the 21st Century Cures Act and testimony to the Senate Health, Education, Labor & Pensions committee reports progress on: interoperability; secure and trusted bi-directional exchange; Trusted Exchange Framework and Common Agreement (TEFCA); clinician burden; and information blocking. The new Centers for Medicare and Medicaid Services "Meaningful Measures" Initiative was launched to reduce regulatory burden and promote innovation in the transition value-based payment.

The fields of public, population, and community health informatics offer overlapping perspectives to policy considerations, recognizing social, economic, and environmental risk factors as predictors of health disparities at the individual and community level. Nurse-led population health improvement efforts need to consider policies which address SDOH, health inequities, access to affordable care, and healthy behaviors. Although early in development, infrastructure is needed across nursing practice, education, and research settings to develop and test population-based care and payment accountability models. Alternate Payment Models (APM) and payment reforms that seek to deliver better care at lower cost achieved 29% in 2016, with expected progress toward the goal of 50% by 2018. Policy will continue to evolve to find ways to significantly improve affordability and value for both consumers and the health care system.

Policy recommendations:

Promote health policy that encourages person-centered, integrated health care, research, preventive care, and wellness in the context of a community ecosystem

Monitor progress on the Office of the National Coordinator's Shared Nationwide Interoperability Roadmap and the 21st Century Cures Act

Incentivize data transparency and bi-directional health data exchange bridging institution-centric and person-centric approaches

Advocate funding for population-focused nursing education and research, including support of the recommendations laid out in the Institute of Medicine's 2010 report, The Future of Nursing and the RWJF 2017 Catalyst for Change: Harnessing the Power of Nurses to Build Population Health for the 21st Century

CONCLUSION

In conclusion, the AMIA NIWG Population Health panel acclaims that INSs are in integral roles to actively support capacity building for nurses to build population health in the 21st century, aligned with the Catalyst for Change white paper. Informatics Nurse Specialists are critical partners to lead population health, care coordination across settings of care, and inter-professional and community stakeholder collaboration. Informatics Nurse Specialists are well positioned to influence practice, education, research, and health care policy. Preparing nurses for robust roles to improve population health will require clear articulation of nursing's value proposition to impact health, cost, and outcomes for both the individual and populations; and active engagement in redesigning health data infrastructures to address current gaps. The AMIA NIWG panel of opinion leaders have provided background, identified recommendations, and perspectives to meet the demands of an evolving health care system, including the application of new knowledge and skills.

FUNDING

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of interest statement. None declared.

CONTRIBUTORS

All authors contributed to overall intellectual content and sections of writing.

ACKNOWLEDGMENTS

The authors thank our colleagues in the AMIA Nursing Informatics Working Group (NIWG) who helped inspire and shape this work: Juliana Brixey, Sarah Collins, Kandace Kelly, Laura Heerman-Langford, Judy Murphy, Marisa Wilson, and Ron Piscotty.

References

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Population health: a nursing action plan

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The passage of the Affordable Care Act shifted the focus of health care from individual, patient specific, episodic care, towards health management of groups of people with an emphasis on primary and preventive care. Population health management assists to attain and maintain health while improving quality and lowering costs.

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And article on employee risk assessment with feedback. 

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And article on employee risk assessment with feedback. 

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The Guide to Community Preventive Services (The Community Guide)

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Welcome to The Community Guide! Let us know what you think of the website by completing this quick survey.

Tabs

Snapshot

Summary of CPSTF Finding

The Community Preventive Services Task Force (CPSTF) recommends the use of assessments of health risks with feedback when combined with health education programs, with or without additional interventions, on the basis of strong evidence of effectiveness in improving one or more health behaviors or conditions in populations of workers. Additionally, the CPSTF recommends the use of assessments of health risks with feedback when combined with health education programs to improve the following outcomes among participants:

Tobacco use (strong evidence of effectiveness)

Excessive alcohol use (sufficient evidence of effectiveness)

Seat belt use (sufficient evidence of effectiveness)

Dietary fat intake (strong evidence of effectiveness)

Blood pressure (strong evidence of effectiveness)

Cholesterol (strong evidence of effectiveness)

Number of days lost from work due to illness or disability (strong evidence of effectiveness)

Healthcare services use (sufficient evidence of effectiveness)

Summary health risk estimates (sufficient evidence of effectiveness)

The CPSTF finds insufficient evidence for

Body composition

Consumption of fruit and vegetables

Fitness

The CPSTF has related findings for assessments of health risks with feedback when implemented alone (insufficient evidence).

Intervention

This intervention includes:

An assessment of personal health habits and risk factors (that may be used in combination with biomedical measurements of physiologic health)

A quantitative estimation or qualitative assessment of future risk of death and other adverse health outcomes

Provision of feedback in the form of educational messages and counseling that describes how changing one or more behavioral risk factors might change the risk of disease or death

Worksite interventions may use an assessment of health risks with feedback (AHRF) alone or as part of a broader worksite health promotion program that includes health education and other health promotion components offered as follow-up to the assessment.

CPSTF Finding and Rationale Statement

Read the full CPSTF Finding and Rationale Statement [PDF - 144 kB] for details including implementation issues, possible added benefits, potential harms, and evidence gaps.

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What the CPSTF Found

About The Systematic Review

The CPSTF finding is based on evidence from a systematic review of 51 studies (search period through June 2005). The review was conducted on behalf of the CPSTF by a team of specialists in systematic review methods, and in research, practice, and policy related to worksite health promotion.

Context

There is no information for this section.

Summary of Results

Detailed results from the systematic review are available in the CPSTF Finding and Rationale Statement [PDF - 144 kB].

Fifty-one studies qualified for the review.

This review considered a range of outcome measures for each outcome category. Conclusions for each of these outcomes are based on a review of both quantified and qualitatively described results.

Health Behavior Outcomes

Dietary Behavior
Fourteen studies qualified for the review.

With the exception of one study that showed no change in intake of fruits and vegetables, changes in dietary behaviors were in favor of the intervention.

Intake of fruits and vegetables: median increase of 0.09 servings per day (6 studies)

Percent of employees with high risk fat intake: median relative decrease of 5.4% (interquartile interval: -21.9% to -1.8%; 13 studies)

Physical Activity
Eighteen studies qualified for the review.

The majority of results were in favor of the intervention.

Percent of employees who were physically active: median relative increase of 15.3% (interquartile interval: 8.3% to 37.2%; 16 study arms)

Tobacco Use
Twenty-nine studies qualified for the review.

All results were in favor of the intervention.

Prevalence rates (percent of employees who smoke): median relative decrease of 13.3% (interquartile interval: -24.0% to -3.3%; 27 study arms)

Cessation rates (percent of employees who quit):17.8% (interquartile interval: 12.0% to 22.6%; 21 study arms)

Physiologic Indicators

Blood Pressure
Thirty-one studies qualified for the review.

Results were in favor of the intervention.

Diastolic blood pressure: median decrease of 1.8 mm Hg (interquartile interval: -4.4 to -0.3 mm Hg; 22 study arms)

Systolic blood pressure: median decrease of 2.6 mm Hg (interquartile interval: -4.8 to -0.3 mm Hg; 24 study arms)

Change in prevalence rate of employees with high risk blood pressure reading: median decrease of 4.5 percentage points (interquartile interval: -8.7 to -0.4 percentage points; 16 study arms)

Body Composition (weight, body mass index [BMI] or percent body fat)
Twenty-seven studies qualified for the review.

Some of the results were in favor of the intervention and some were not.

Change in body weight: median decrease of 0.56 pounds (interquartile interval: -5.1 to +1.5 pounds; 17 study arms)

Change in BMI: median decrease of 0.50 points (interquartile interval: -1.1 to -0.3 points BMI)

Cholesterol
Twenty-seven studies qualified for the review

Results were in favor of the intervention.

Total cholesterol: median decrease of 4.8 mg/dL (interquartile interval: -10.4 to 0.0 mg/dL; 23 study arms)

HDL cholesterol: median increase of 0.94 mg/dL (interquartile interval: -0.9 to 2.3 mg/dL; 10 study arms)

Percent of employees with high risk readings: decrease of 6.6 percentage points (interquartile interval: -14.8 to -2.4 percentage points; 12 study arms)

Other Variables

Summary of Economic Evidence

Detailed results from the systematic review are available in the CPSTF Finding and Rationale Statement [PDF - 144 kB].

Nine studies qualified for the economic review. All monetary values are reported in 2005 U.S. dollars.

Program costs

Costs per participant per year ranged from $65 to $285 (7 studies).

Costs for per employee per year ranged from $40 to $234 (2 studies).

Program benefits

Benefits per participant per year ranged from $93 to $695 (7 studies).

Benefits per employee per year ranged from $160 to $272 (2 studies).

The benefit-to-cost ratio-defined as averted medical costs, productivity losses due to the program as both, divided by program costs-ranged from 1.4:1 to 4.6:1. This means that every dollar invested into the intervention yielded an annual gain between $1.40 and $4.60 (8 studies).

Applicability

Results from this review should be applicable to programs implemented in small, medium, or large companies in a range of settings.

Whites and African Americans were well represented among studies reporting information on race; however, there was not enough information available to determine if the intervention had differential effects for different racial or ethnic groups.

Evidence Gaps

Each Community Preventive Services Task Force (CPSTF) review identifies critical evidence gaps-areas where information is lacking. Evidence gaps can exist whether or not a recommendation is made. In cases when the CPSTF finds insufficient evidence to determine whether an intervention strategy works, evidence gaps encourage researchers and program evaluators to conduct more effectiveness studies. When the CPSTF recommends an intervention, evidence gaps highlight missing information that would help users determine if the intervention could meet their particular needs. For example, evidence may be needed to determine where the intervention will work, with which populations, how much it will cost to implement, whether it will provide adequate return on investment, or how users should structure or deliver the intervention to ensure effectiveness. Finally, evidence may be missing for outcomes different from those on which the CPSTF recommendation is based.
Identified Research Gaps The following outlines evidence gaps for assessment of health risks with feedback when used alone, and when combined with health education programs, with or without additional interventions.
This review of the use of assessments of health risks with feedback in worksite settings addressed important questions that earlier reviews were unable to address, such as:

Does AHRF, when used alone, lead to behavior change or change in health outcomes among employees?

Does this type of assessment, when used with other worksite-based intervention components result in change?

And finally, what types of behaviors or health outcomes are affected by these interventions?

The structure of this review, however, leaves two additional questions about worksite health promotion programs unanswered:

Are worksite health promotion programs with a health education component effective in the absence of AHRF and

Does AHRF add value to worksite health promotion programs with regards to behavior change and improvement in health outcomes?

The field will also likely be interested in addressing questions related to implementation of the intervention: what components are necessary and for whom are they most effective? How many times must AHRF occur and for how long must employees be exposed to additional intervention components? What qualifications of staff or health educators are needed? How long do the effects last? With regards to the assessment: Are there key assessment questions or aspects of the assessment (like biometric screening) that provide information resulting in a more effective intervention? Does the format of the questionnaire or the feedback make a difference? Is employee participation in creation of the program important and what role does organizational support play in participation rates and overall effectiveness?

Finally, questions regarding economic efficiency will be of interest to most in the field and should be addressed more systematically. A first step would be to clearly delineate the aspects of program costs and benefits that should be assessed in program evaluation. How many employees need to be reached for a positive ROI? What should the GRP (gross rating product) be for the ROI? Is there a "break even point" or a certain amount of time for which costs will outweigh benefits before there are actual savings from program implementation? Although the questions above stem from this review of assessments of health risk with feedback, many of them pertain to the broader field of worksite health promotion and can be used to inform future evaluation of these programs.

Study Characteristics

Of the 51 studies included in the AHRF Plus review:

Twenty studies included an untreated or lesser treated comparison group.

Five studies used a retrospective cohort.

Four studies were a time series study.

Twenty-two studies were included as before-after study designs.

Evaluated interventions were conducted in a variety of worksites including manufacturing plants, healthcare facilities, health insurance companies, government offices, field settings, banks, schools, and in an ambulance service workforce.

Most studies were conducted in companies or worksites with more than 500 employees and in urban or suburban settings.

Six studies were conducted in medium-sized companies (50 -499 employees) and one in a small company (50 employees).

Forty studies were conducted in the U.S., two in a group of European countries, two each in Australia and Finland, and one study was conducted in each of the following countries: Canada, Japan, the Netherlands, Sweden, and Switzerland.

Whites and African Americans were well represented among studies reporting information on race.

The average age of participants was 40 years, and a range of educational levels and job positions was represented.

Almost all of the evaluated interventions included health education lasting more than 1 hour or occurring at more than one time during the course of the intervention.

Health education was provided in group settings, one-on-one, or both.

Some of the evaluated interventions also provided enhanced access to physical activity (17 programs), nutrition (6 programs), and medical care (3 programs).

About one-third of the interventions offered some form of incentive or competition for participating or for meeting a program goal.

Publications

Supporting Materials

Analytic Framework

Analytic Framework [PDF - 324 kB] - see Figure 1 on page S241

Summary Evidence Table

Included Studies

The number of studies and publications do not always correspond (e.g., a publication may include several studies or one study may be explained in several publications).
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Aldana SG, Jacobson BH, Harris CJ, Kelley PL. Mobile work site health promotion programs can reduce selected employee health risks. Journal of Occupational Medicine 1993;35:922-8.

Aldana SG, Jacobson BH, Kelley PL, Quirk M. The effectiveness of a mobile worksite health promotion program in lowering employee health risk. American Journal of Health Promotion 1994;8:254-6.

Anderson J, Dusenbury L. Worksite cholesterol and nutrition: An intervention project in Colorado. American Association of Occupational Health Nurses Journal 1999;47: 99-106.

Anderson RC, Anderson KE. Positive changes and worksite health education. Psychological Reports 1994;74:607-10.

Baier CA, Grodzin CJ, Port JD, Leksas L, Tancredi DJ. Coronary risk factor behavior change in hospital personnel following a screening program. American Journal of Preventive Medicine 1992;8:115-22.

Bertera RL. Behavioral risk factor and illness day changes with workplace health promotion: two-year results. American Journal of Health Promotion 1993;7:365-73.

Blair SN, Piserchia PV, Wilbur CS, Crowder JH. A public health intervention model for work-site health promotion. Impact on exercise and physical fitness in a health promotion plan after 24 months. JAMA 1986;255:921-6.

Bly JL, Jones RC, Richardson JE, Bly JL, Jones RC, Richardson JE. Impact of worksite health promotion on health care costs and utilization. Evaluation of Johnson & Johnson's Live for Life program. JAMA 1986;256:3235-40.

Breslow L, Fielding J, Herrman AA, Wilbur CS. Worksite health promotion: its evolution and the Johnson & Johnson experience. Preventive Medicine 1990;19:13-21.

Brill PA, Kohl HW, Rogers T, Collingwood TR, Sterling CL, Blair SN. Relationship between sociodemographic characteristics and recruitment, retention, and health improvements in a worksite health promotion program. American Journal of Health Promotion 1991;5:215-21.

Donnelly IW. Using health promotion to improve workers' health. Managing Employee Health Benefits 1996:74-7.

Dunton S, Perkins DD, Zopf KJ. The impact of worksite-based health risk appraisal programs on observed safety belt use. Health Education Research 1990;5:207-16.

Edington M, Karjalainen T, Hirschland D, Edington D. The UAW-GM Health Promotion Program: Successful Outcomes. American Association of Occupational Health Nurses Journal 2002;50:26-31.

Edye BV, Mandryk JA, Frommer MS, Healey S, Ferguson DA. Evaluation of a worksite programme for the modification of cardiovascular risk factors. Medical Journal of Australia 1989;150:574-81.

Erfurt JC, Foote A, Heirich MA. Worksite wellness programs: incremental comparison of screening and referral alone, health education, follow-up counseling, and plant organization. American Journal of Health Promotion 1991;5:438-48.

Erfurt JC, Holtyn K. Health promotion in small business: What works and what doesn't work. Journal of Occupational Medicine 1991;33:66-73.

Fielding JE, Knight K, Mason T, Klesges RC, Pelletier KR. Evaluation of the IMPACT blood pressure program. Journal of Occupational Medicine 1994;36:743-6.

Fitzgerald ST, Gibbens S, Agnew J. Evaluation of referral completion after a workplace cholesterol screening program. American Journal of Preventive Medicine 1991;7:335-40.

Frommer MS, Mandryk JA, Edye BV, Healey S, Berry G, Ferguson DA. A randomised controlled trial of counseling in a workplace setting for coronary heart disease risk factor modification: effects on blood pressure. Asia Pacific Journal of Public Health 1990;4:25-33.

Goetzel RZ, Dunn RL, Ozminkowski RJ, Satin K, Whitehead DA, Cahill K. Differences between descriptive and multivariate estimates of the impact of Chevron Corporation's Health Quest program on medical expenditures. Journal of Occupational & Environmental Medicine 1998:40(6):538-45.

Goetzel RZ, Kahr TY, Aldana SG, Kenny GM. An evaluation of Duke University's live for life health promotion program and its impact on employee health. American Journal of Health Promotion 1996;10.

Goetzel RZ, Ozminkowski RJ, Bruno JA, RutterKR, Isaac F, Wang S. The long-term impact of Johnson & Johnson's Health & Wellness Program on employee health risks. Journal of Occupational & Environmental Medicine 2002;44:417-24.

Goetzel RZ, Sepulveda M, Knight K, Eisen M, Wade S, et al. Association of IBM's "A Plan for Life" health promotion program with changes in employees' health risk status. Journal of Occupational Medicine 1994;36:1005-9.

Gomel M, Oldenburg B, Simpson JM. Work-site cardiovascular risk reduction: a randomized trial of health risk assessment, education, counseling, and incentives. American Journal of Public Health 1993;83:1231-8.

Gomel MK, Oldenburg B, Simpson JM, Chilvers M, Owen N. Composite cardiovascular risk outcomes of a work-site intervention trial. American Journal of Public Health 1997;87:673-6.

Gregg W, Foote A, Erfurt JC, Heirich MA. Worksite follow-up and engagement strategies for initiating health risk behavior changes. Health Education Quarterly 1990;17:455-78.

Guico-Pabia CJ, Cioffi L, Shoner LG. The Lucent-Takes-Heart cardiovascular health management program. Successful workplace screening. AAOHN Journal 2002;50: 365-72.

Hartman TJ, Himes JH, McCarthy PR, Kushi LH. Effects of a low-fat, worksite intervention on blood lipids and lipoproteins. Journal of Occupational & Environmental Medicine 1995;37:690-6.

Hartman TJ, McCarthy PR, Himes JH. Use of eating-pattern messages to evaluate changes in eating behaviors in a worksite cholesterol education program. Journal of the American Dietary Association 1993;93:1119-23.

Heirich MA, Foote A, Erfurt JC, Konopka B. Work-site physical fitness programs. Comparing the impact of different program designs on cardiovascular risks. Journal of Occupational Medicine 1993;35:510-7.

Holt MC, McCauley M, Paul D. Health impacts of AT&T's Total Life Concept (TLC) program after five years. American Journal of Health Promotion 1995;9:421-5.

Knight KK, Goetzel RZ, Fielding JE, Eisen M, Jackson GW, et al. An evaluation of Duke University's LIVE FOR LIFE health promotion program on changes in worker absenteeism. Journal of Occupational Medicine 1994;36:533-6.

Kronenfeld JJ, Jackson K, Blair SN, Davis K, Gimarc JD, et al. Evaluating health promotion: a longitudinal quasi-experimental design. Health Education Quarterly 1987;14:123-39.

Maes S, Verhoeven C, Kittel F, Scholten H. Effects of a Dutch work-site wellness-health program: the Brabantia Project. American Journal of Public Health 1998;88: 1037-41.

McCarthy PR, Lansing D, Hartman TJ, Himes JH. What works best for worksite cholesterol education? Answers from targeted focus groups. Journal of the American Dietary Association 1992;92:978-81.

Merrill BE, Sleet DA. Safety belt use and related health variables in a worksite health promotion program. Health Education Quarterly 1984;11:171-9.

Musich S, McDonald T, Hirschland D, Edington D. Examination of risk status transitions among active employees in a comprehensive worksite health promotion program. Journal of Occupational & Environmental Medicine 2003;45:393-9.

Nilsson PM, Klasson EB, Nyberg P. Life-style intervention at the worksite - reduction of cardiovascular risk factors in a randomized study. Scandinavian Journal of Work, Environment & Health 2001;27:57-62.

Oldenburg B, Owen N, Parle M, Gomel M. An economic evaluation of four work site based cardiovascular risk factor interventions. Health Education Quarterly 1995;22: 9-19.

Ostwald SK. Changing employees' dietary and exercise practices: an experimental study in a small company. American Journal of Health Promotion 1989;4:141-2.

Ozminkowski RJ. Long-term impact of Johnson & Johnson's Health & Wellness Program on health care utilization and expenditures. Journal of Occupational & Environmental Medicine 2002;44:21-9.

Ozminkowski RJ, Goetzel RZ, Smith MW, Cantor RI, Shaughnessy A, Harrison M. The Impact of the Citibank, NA, Health Management Program on Changes in Employee Health Risks Over Time. Journal of Occupational & Environmental Medicine 2000;42: 502-11.

Ozminkowski RJ, Dunn RL, Goetzel RZ, Cantor RI, Murnane J, Harrison M. Return on investment evaluation of the Citibank, NA, Health Management Program. American Journal of Health Promotion 1999;14:31-43.

Pelletier B, Boles M, Lynch W. Change in health risks and work productivity over time. Journal of Occupational & Environmental Medicine 2004;46.

Pilon BA, Renfroe D. Evaluation of an employee health risk appraisal program. AAOHN Journal 1990;38:230-5.

Pilon BA, Renfroe D. Evaluation of an employee health risk appraisal program. American Association of Occupational Health Nurses Journal 1990;38:230-5.

Poole K, Kumpfer K, Pett M. The impact of an incentive-based worksite health promotion program on modifiable health risk factors. American Journal of Health Promotion 2001;16:21-6, ii.

Purath J, Miller AM, McCabe G, Wilbur J. A brief intervention to increase physical activity in sedentary working women. Canadian Journal of Nursing Research 2004;36:76-91.

Puska P, Niemensivu H, Puhakka P, Alhainen L, Koskela K, et al. Results of a one-year worksite and mass media based intervention on health behaviour and chronic disease risk factors. Scandinavian Journal of Work, Environment & Health 1988;16: 241-50.

Schultz AB, Lu C, Barnett TE, Yen LT, McDonald T, et al. Influence of participation in a worksite health-promotion program on disability days. Journal of Occupational & Environmental Medicine 2002;44:776-80.

Sepulveda M, Goetz A, Grana J. Measuring second-order selection bias in a work site health program. Journal of Occupational & Environmental Medicine 1994;36:326-33.

Serxner S, Gold D, Anderson D, Williams D. The impact of a worksite health promotion program on short-term disability usage. Journal of Occupational & Environmental Medicine 2001;43:25-9.

Serxner SA, Gold DB, Bultman KK. The impact of behavioral health risks on worker absenteeism. Journal of Occupational & Environmental Medicine 2001;43:347-54.

Shi L. Impact of increasing intensity of health promotion intervention on risk reduction. Evaluation & the Health Professions 1992;15:3-25.

Shi L. Health promotion, medical care use, and costs in a sample of worksite employees. Evaluation Review 1993;17:475-87.

Shimizu T, Nagashima S, Mizoue T, Higashi T, Nagata S. A psychosocial-approached health promotion program at a Japanese worksite. Journal of the University of Occupational & Environmental Health 2003;25:23-34.

Shipley RH, Orleans T, Wilbur CS, Piserchia PV, McFadden DW. Effect of the Johnson & Johnson Live for Life Program on Employee Smoking. Preventive Medicine 1988; 12:25-34.

Sloan RP, Gruman J. Participation in workplace health promotion programs: the contribution of health and organizational factors. Health Education Quarterly 1988;15:269-88.

Sorensen G, Stoddard A, Hunt MK, Hebert JR, Ockene JK, et al. The effects of a health promotion-health protection intervention on behavior change: the WellWorks Study. American Journal of Public Health 1998;88:1685-90.

Sorensen G, Stoddard AM, LaMontagne AD, Emmons K, Hunt MK, et al. A comprehensive worksite cancer prevention intervention: behavior change results from a randomized controlled trial (United States). Cancer Causes Control 2002;13: 493-502.

Sorensen G, Thompson B, Glanz K, Feng Z, Kinne S, et al. Work site-based cancer prevention: primary results from the Working Well Trial. American Journal of Public Health 1996;86:939-47.

Sorensen G, Himmelstein JS, Hunt MK, Youngstrom R, Hebert JR, et al. A model for worksite cancer prevention: integration of health protection and health promotion in the WellWorks Project. American Journal of Public Health 1995;10:55-62.

Spilman MA, Goetz A, Schultz J, Bellingham R, Johnson D. Effects of a corporate health promotion program. Journal of Occupational Medicine 1986;28:285-9.

Stevens MM, Paine-Andrews A, Francisco VT. Improving employee health and wellness: a pilot study of the employee-driven Perfect Health Program. American Journal of Health Promotion 1996;11:12-14.

Strychar IM, Champagne F, Ghadirian P, Bonin A, Jenicek M, Lasater TM. Impact of receiving blood cholesterol test results on dietary change. American Journal of Preventive Medicine 1998;14:103-10.

Tilley BC, Glanz K, Kristal AR, Hirst K, Li S, Vernon SW et al. (1999). Nutrition intervention for high-risk auto workers: Results of the Next Step Trial. Preventive Medicine, 28, 284-292.

Tilley BC, Vernon SW, Glanz K, Myers R, Sanders K, et al. Worksite cancer screening and nutrition intervention for high-risk auto workers: design and baseline findings of the Next Step Trial. Preventive Medicine 1997;26:227-35.

Tilley BC, Vernon SW, Myers R, Glanz K, Lu M, et al. The Next Step Trial: impact of a worksite colorectal cancer screening promotion program. Preventive Medicine 1999; 27:276-83.

Walton C, Timms J. Providing worksite health promotion through university-community partnerships: the South Carolina DOT project. American Association of Occupational Health Nurses Journal 1999;47:449-55.

Weinstein ND, Grubb PD, Vautier JS. Increasing automobile seat belt use: an intervention emphasizing risk susceptibility. Journal of Applied Psychology 1986;71: 285-90.

Williams A, Wold J, Dunkin J, Idleman L, Jackson C. CVD prevention strategies with urban and rural African American women. Applied Nursing Research 2004;17:187-94.

Wood EA. Lifestyle risk factors and absenteeism trends - a six-year corporate study. AWHP'S Worksite Health 1997;4:32-5.

Wood EA, Olmstead GW, Craig JL. An evaluation of lifestyle risk factors and absenteeism after two years in a worksite health promotion program. American Journal of Health Promotion 1989;4:128-33.

World Health Organisation European Collaborative Group. European collaborative trial of multifactorial prevention of coronary heart disease: final report on the 6-year results. Lancet 1986;1:869-72.

World Health Organization. An international controlled trial in the mulitfactorial prevention of coronary heart disease. International Journal of Epidemiology 1974;3: 219-24.

World Health Organization European Collaborative Group. Multifactorial trial in the prevention of coronary heart disease: 2. Risk factor changes at two and four years. European Heart Journal 1982;3:184-90.

World Health Organization European Collaborative Group. Multifactorial trial in the prevention of coronary heart disease: 3. Incidence and mortality results. European Heart Journal 1983;4:141-7.

Yen L, Edington MP, McDonald T, Hirschland D, Edington DW. Changes in health risks among the participants in the United Auto Workers - General Motors LifeSteps Health Promotion Program. American Journal of Health Promotion 2001;16:7-15.
Economic Review
Aldana SG, Jacobson BH, Harris CJ, Kelley PL. Mobile work site health promotion programs can reduce selected employee health risks. J Occup Med 1993;35(9):922-8.

Bertera RL. The effects of workplace health promotion on absenteeism and employment costs in a large industrial population. Am J Public Health 1990; 80(9):1101-5.

Burton W, Conti D. Disability management: corporate medical department management of employee health and productivity. J Occup Environ Med 2000;42:1006 -12.

Erfurt JC, Foote A, Heirich MA. The cost effectiveness of worksite wellness programs for hypertension control, weight loss, and smoking cessation. J Occup Med 1991;33(9):962-70.

Harvey MR, Whitmer RW, Hilyer JC, Brown KC. The impact of a comprehensive medical benefıt cost management program for the City of Birmingham: results at fıve years. Am J Health Promot 1993;7(4):296 -303.

Leutzinger J, Hawes C, Hunnicutt D, Richling D. Predicting the ratio of benefıt to cost in a cardiovascular disease-prevention program. Managing Employee Health Benefıts 1995;Fall:1-10.

Ozminkowski RJ, Dunn RL, Goetzel RZ, Cantor RI, Murnane J, Harrison M. Return on investment evaluation of the Citibank, N.A. Health Management Program. Am J Health Promot 1999;14(1):31- 43.

Schultz AB, Lu C, Barnett TE, et al. Influence of participation in a worksite health-promotion program on disability days. J Occup Environ Med 2002;44(8):776 -80.

Shi L. Health promotion, medical care use, and costs in a sample of worksite employees. Eval Rev 1993;17(5):475-87.

Search Strategies

The following outlines the search strategy used for reviews of assessment of health risks with feedback when used alone, and when combined with health education programs, with or without additional interventions.
The articles to be reviewed were obtained from systematic searches of multiple databases, reviews of bibliographic reference lists, and consultations with experts in the field. The following databases were searched: Medline, Employee Benefits, NTIS, Sports Information Resource Center, Cambridge Scientific Abstracts, Inc., Business Week, ABI Inform, Health Promotion and Education, Cumulative Index to Nursing & Allied Health Literature, Office of Smoking and Health, AIDSLine, PsycInfo, and Sociological Abstracts.

Database: Ovid MEDLINE(R) [1966 to June Week 4 2005]

1. exp health behavior/ or exp health education/ or health promotion/ or exp primary prevention/ (218287)

2. exp work/ or workplace/ or occupational health/ (22008)

3. risk assessment/ or risk factors/ or health status indicators/ or mass screening/ (357682)

4. (health risk assessment$ or health risk appraisal$ or health screening$ or health status assessment$ or health assessment$ or screening$).mp. [mp=title, original title, abstract, name of substance word, subject heading word] (189292)

5. (risk awareness or risk profile$ or lifestyle check$ or health quiz or health measur$ or health report$ or risk identification).mp. [mp=title, original title, abstract, name of substance word, subject heading word] (5429)

6. 3 or 4 or 5 (490335)

7. (health maintenance organization$ or health clinic$ or insurance compan$).mp. [mp=title, original title, abstract, name of substance word, subject heading word] (18831)

8. 2 or 7 (40777)

9. 1 and 6 and 8 (607)

10. limit 9 to english language (559)

Database: Employee Benefits_1986-2005/Jun (c) 2005

Sets selected:

Set Items Description

1. 831 HEALTH()BEHAVIOR OR HEALTH()EDUCATION OR HEALTH()PROMOTION OR PRIMARY()PREVENTION

2. 1291 HEALTH()RISK()ASSESSMENT? OR HEALTH()RISK()APPRAISAL?
OR HEALTH()SCREENING? OR HEALTH()STATUS()ASSESSMENT?
OR HEALTH()ASSESSMENT? OR SCREENING?

3. 554 RISK()AWARENESS OR RISK()PROFILE?
OR LIFESTYLE()CHECK? OR HEALTH()QUIZ
OR HEALTH()MEASUR? OR HEALTH()REPORT?
OR RISK()IDENTIFICATION

4. 1360 S2 OR S3

5. 310 S1 AND S4

Databases:BAMP_2005/Jul W2 (c) 2005 and PAIS Int._1976-2005/Jun (c) 2005

Sets selected:

Set Items Description

Database: TGG Management Contents(R)_86-2005/Jul W2 (c) 2005 and Business Week_1985-2005/Jul 21 (c) 2005

Sets selected:

Set Items Description

Database: ABI Inform(R)_1971-2005/Jul 22 (c)

Sets selected:

Set Items Description

1. 6388 HEALTH()BEHAVIOR OR HEALTH()PROMOTION OR HEALTH()EDUCATION OR PRIMARY()PREVENTION

2. 6342 (HEALTH()RISK()ASSESSMENT? OR HEALTH()RISK()APPRAISAL? OR HEALTH()SCREENING? OR HEALTH()STATUS()ASSESSMENT? OR HEALTH()ASSESSMENT? OR SCREENING?)/TI,AB

3. 1039 (RISK()AWARENESS OR RISK()PROFILE? OR LIFESTYLE()CHECK? OR HEALTH()QUIZ? OR HEALTH()MEASUR? OR HEALTH()REPORT? OR RISK()IDENTIFICATION)/TI,AB

4. 7371 S2 OR S3

5. 252 S1 AND S4

6. 517172 WORKSITE? OR WORKPLACE? OR EMPLOYER? OR EMPLOYEE? OR WORKER?

7. 184 S5 AND S6

8. 184 S9/ENG

Database: CDP File (Health Promotion and Education Database)

Descriptors: "WORKSITE" and "HEALTH RISK APPRAISAL" and
Form: "JOURNAL ARTICLE" OR
Descriptors: "worksite" not "health risk appraisal" and
Textwords: HEALTH ADJ1 RISK ADJ1 ASSESSMENT* OR HEALTH ADJ1 SCREEN* OR RISK ADJ1 ASSESSMENT* OR HEALTH ADJ1 ASSESSMENT* OR *RISK ADJ1 (AWARENESS* OR PROFILE* OR IDENTIFICATION) OR HEALTH ADJ1 (MEASURING OR REPORT* OR QUIZ*) OR LIFESTYLE ADJ1 CHECK* and Form: "JOURNAL ARTICLE"
TOTAL = 212

Database: CINHAL

Descriptors: "WORKSITE" and "HEALTH RISK APPRAISAL" and
Form: "JOURNAL ARTICLE" OR
Descriptors: "worksite" not "health risk appraisal" and
Textwords: HEALTH ADJ1 RISK ADJ1 ASSESSMENT* OR HEALTH ADJ1 SCREEN* OR RISK ADJ1 ASSESSMENT* OR HEALTH ADJ1 ASSESSMENT* OR *RISK ADJ1 (AWARENESS* OR PROFILE* OR IDENTIFICATION) OR HEALTH ADJ1 (MEASURING OR REPORT* OR QUIZ*) OR LIFESTYLE ADJ1 CHECK* and Form: "JOURNAL ARTICLE"
TOTAL = 212

Database: NTIS, Intl Cpyrght All Rights Res

Sets selected:

Set Items Description

1. 9285 HEALTH()BEHAVIOR OR HEALTH()EDUCATION OR HEALTH()PROMOTION OR PRIMARY()PREVENTION

2. 4804 WORKSITE OR WORKPLACE OR WORK-SITE OR OCCUPATIONAL()HEALTH

3. 289 S1 AND S2

4. 17896 HEALTH()RISK()ASSESSMENT? OR HEALTH()RISK()APPRAISAL? OR HEALTH()SCREENING? OR HEALTH()STATUS()ASSESSMENT? OR HEALTH()ASSESSMENT? OR SCREENING?

5. 477 RISK()AWARENESS OR RISK()PROFILE? OR LIFESTYLE()CHECK? OR HEALTH()QUIZ OR HEALTH()MEASUR? OR HEALTH()REPORT? OR RISK()IDENTIFICATION

6. 18332 S4 OR S5

7. 47 S3 AND S6

Database: SportDiscuss

Sets selected:

Set Items Description

1. 9166 HEALTH()BEHAVIOR OR HEALTH()EDUCATION OR HEALTH()PROMOTION OR PRIMARY()PREVENTION

2. 1589 WORKSITE OR WORKPLACE OR WORK-SITE OR OCCUPATIONAL()HEALTH

3. 479 S1 AND S2

4. 2169 HEALTH()RISK()ASSESSMENT? OR HEALTH()RISK()APPRAISAL? OR HEALTH()SCREENING? OR HEALTH()STATUS()ASSESSMENT? OR HEALTH()ASSESSMENT? OR SCREENING?

5. 176 RISK()AWARENESS OR RISK()PROFILE? OR LIFESTYLE()CHECK? OR HEALTH()QUIZ OR HEALTH()MEASUR? OR HEALTH()REPORT? OR RISK()IDENTIFICATION

6. 2341 S4 OR S5

7. 22 S3 AND S6

8. 22 S7/ENG

Database: OSH (Office of Smoking and Health)

Descriptors: ("WORKERS" OR "WORKPLACE*" OR "WORKSITE CESSATION PROGRAMS") AND ("HEALTH EDUCATION*" OR "HEALTH PROMOTION*" OR
"PREVENTION*" OR "INTERVENTION*") and
Textwords: RISK* OR SCREEN* OR ASSESSMENT* OR LIFESTYLE ADJ1 CHECK OR HEALTH ADJ1 QUIZ* OR HEALTH ADJ1 MEASUR* OR HEALTH ADJ1 REPORT*
Total - 20

Database: Sociological Abstracts [1963 to April 2005]

Database: PsycINFO [1967 to June Week 4 2005]

Database: AIDSLINE [1980 to December 2000] CLOSED FILE

1. exp health behavior/ or exp health education/ or health promotion/ or exp primary prevention/ (14069)

2. exp work/ or workplace/ or occupational health/ (347)

3. risk assessment/ or risk factors/ or health status indicators/ or mass screening/ (18933)

4. (health risk assessment$ or health risk appraisal$ or health screening$ or health status assessment$ or health assessment$ or screening$).mp. [mp=title, abstract, mesh subject heading] (6055)

5. (risk awareness or risk profile$ or lifestyle check$ or health quiz or health measur$ or health report$ or risk identification).mp. [mp=title, abstract, mesh subject heading] (239)

6. 3 or 4 or 5 (23204)

7. (health maintenance organization$ or health clinic$ or insurance compan$).mp. [mp=title, abstract, mesh subject heading] (488)

8. 2 or 7 (831)

9. 1 and 6 and 8 (36)

10. limit 9 to english language (36)

11. limit 10 to nonmedline (23)

12. from 11 keep 1-23 (23)

Review References

There is no information for this section.

Considerations for Implementation

Considerations for Implementation

The following considerations are drawn from studies included in the evidence review, the broader literature, and expert opinion.

Employers may be reluctant to implement interventions involving AHRF due to employee concerns over breach of confıdentiality of health records to other employees or to health insurance providers. Programs that offer incentives for completion of HRA tools or require that employees meet specifıc health standards (for example, they must fall within a particular BMI range) may exacerbate concerns.

Engaging employees can be challenging. Those who think or know that they have important health risks may be least likely to participate.

Even if there is broad participation in AHRF, there may be low participation in intervention components offered in addition to AHRF. In such cases, employers may be inclined to reduce the scope of or cancel these components.

While not evaluated in the included studies, interventions may lead to increased employee satisfaction.

The review team noted potential harms, including increased anxiety for workers during the feedback portion; false positives; and overestimates of risk status.

Crosswalks

Healthy People 2020

Healthy People 2020 includes the following objectives related to this CPSTF recommendation.

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Worksite: Assessment of Health Risks with Feedback (AHRF) to Change Employees' Health - AHRF Plus Health Education With or Without Other Interventions

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Welcome to The Community Guide! Let us know what you think of the website by completing this quick survey. Snapshot The Community Preventive Services Task Force (CPSTF) recommends the use of assessments of health risks with feedback when combined with health education programs, with or without additional interventions, on the basis of strong evidence of effectiveness in improving one or more health behaviors or conditions in populations of workers.

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Data mining from internet searches related to illness and even social media posts from the population can be extremely helpful in predicting an outbreak or emergency situation at real time. As McGonigle & Mastrian (2018) state, great care and discernment must be used because not internet searches or social media is accurate.

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Importance of Population Health data in Regional HIE and Future National HIN.

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Importance of Population Health data in Regional HIE and Future National HIN.

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Seamless shared data from not only health data, but also all hospitals, pharmacies, laboratories, clinics, poison control, and environmental agencies are imperative to effectively and swiftly identify and implement action plans when assessing needs and emergencies. For this to happen, it has to start at the regional health information exchanges and eventually national health information network (McGonigle & Mastrian 2018).

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CDC tools for Patient Health Information Network Vocabulary Access and Distribution System (PHIN VADS) is a directory of standard vocabulary use to better share data. This would help greatly in providing quick feedback. 

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CDC tools for Patient Health Information Network Vocabulary Access and Distribution System (PHIN VADS) is a directory of standard vocabulary use to better share data. This would help greatly in providing quick feedback. 

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PHIN VADS provides standard vocabularies to CDC and its public health partners in one place. PHIN VADS is a web-based enterprise vocabulary system for accessing, searching, and distributing vocabularies used in public health and clinical care practice. It promotes the use of standards-based vocabulary to support the exchange of consistent information among public health partners. Currently, there are 206 code systems, 1,506 value sets, 77 views and more than 5 million concepts in PHIN VADS based on the code system/domain recommendations and value set recommendations from Health Information Technology Standards Panel (HITSP) C80 specificationexternal icon . Users can access and view vocabularies in the context of public health with file download options for Value Sets, Value Set Concepts, Views and Groups available in a tab-delimited text format and also in Microsoft Excel format (additional download file formats are scheduled for a future release of PHIN VADS). The main purpose of PHIN VADS is to distribute the value sets associated with HL7 message implementation guides.

To access PHIN VADS, go to: http://phinvads.cdc.gov.

Release notes detailing changes of content and/or application updates in the current PHIN VADS release can be found in the PHIN VADS application via the 'Release Notes' link in the left navigation pane.

PHIN VADS Content and Resources

PHIN VADS provides all the vocabulary metadata needed for HL7 Messaging or Clinical Document Architecture (CDA). Users can navigate and download value sets based on HL7 Implementation Guides or PHIN Vocabulary Groups (Domains).

Frequently Used PHIN VADS Content (Hot Topics)

PHIN VADS provides quick access to commonly and frequently used value sets and messaging guide vocabulary views in the home page ( http://phinvads.cdc.gov) under "Hot Topics." Some of the hot topics are listed below:

1. RCMT - Reportable Condition Mapping Table (RCMT) provides mapping between conditions and its associated lab test and results.

2. Public Health Meaningful Use vocabulary (Electronic Laboratory Reporting [ELR], Immunization and Syndromic Surveillance)

1. ELR HL7 2.5.1 - Value sets associated with Electronic Laboratory Reporting HL7 2.5.1 Implementation Guide.

2. Immunization HL7 2.5.1 - Value sets associated with Immunization HL7 2.5.1 implementation guide.

3. Syndromic Surveillance for Emergency Department (ED) and Urgent Care (UC) - Value sets associated with Syndromic Surveillance PHIN messaging guide.

4. Reporting to Central Cancer Registries - Value sets associated with Implementation Guide for Healthcare Provider Reporting to Central Cancer Registries (Release 1.0).

3. NHSN Healthcare Associated Infection (HAI) - All the value sets associated with National Healthcare Safety Network (NHSN) - Healthcare Associated Infection (HAI) - Release 5.

4. PHLIP Flu Lab Report Vocabulary - All the value sets associated with PHLIP Flu lab report HL7 message implementation guide.

PHIN VADS Web Service

The PHIN VADS web site is powered by an open web service that can be directly consumed by third party applications. These web services are public and built upon open standards such as HL7 Common Terminology Services (CTS) and IHE Sharing Value Set (SVS).

The VADS Web Service consists of an interface that defines the web methods that are available, the domain objects that represent vocabulary information, and data transfer objects (DTOs) to transfer data to and from the web service. Custom programs, applications, or data extraction scripts can be written to directly use PHIN VADS Web Services.

A sample web service code and a detailed application program interface (API) are available in the PHIN VADS Developer's Toolkit at the following PHIN VADS site: http://phinvads.cdc.gov/vads/developersGuide.action.

Contacting Us

To report issues or questions contact the VADS team at phinvs@cdc.gov. Enhancement Requests/Defect Reports can also be submitted to the PHIN VADS team using this same email address with the subject header: Enhancement or Defect.

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PHIN VADS provides standard vocabularies to CDC and its public health partners in one place. PHIN VADS is a web-based enterprise vocabulary system for accessing, searching, and distributing vocabularies used in public health and clinical care practice. It promotes the use of standards-based vocabulary to support the exchange of consistent information among public health partners.

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Goal of HIE to promote readiness in the medical community

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Goal of HIE to promote readiness in the medical community

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How interoperability & data sharing can improve patient safety

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CalRedi collects data rapidly to look out for disease outbreaks. They collect data from state and local levels in California. 

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CalRedi collects data rapidly to look out for disease outbreaks. They collect data from state and local levels in California. 

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Centers for Disease Control and Prevention (CDC)

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Modernizing disease surveillance in California

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Ariosto, et. al. (2018). Population Health: a nursing action plan.  Oxford Academic,  volume 1 issue 1, pages 7-10. 

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Ariosto, et. al. (2018). Population Health: a nursing action plan.  Oxford Academic,  volume 1 issue 1, pages 7-10. 

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https://academic.oup.com/jamiaopen/article/1/1/7/4996946

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https://www.healthcatalyst.com/population-health/?_bt=342704128220&_bk=+population +health&_bm=b&_bn=g&_bg=74595061808&gclid=Cj0KCQiAmsrxBRDaARIsANyiD1ofU5zAc3MnXxZrwil9C4NeT8zMVdzjk1Yhn0EPW37inHAPxb2CW14aAmD-EALw_wcB

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IDENTIFY ONE NEED OR AREA OF RISK IN YOUR LOCAL COMMUNITY.  WHAT ARE SOME THINGS YOU CAN DO TO PROMOTE HEALTH IN YOUR COMMJNITY?

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WHAT IS ONE NEW THING YOU LEARNED ABOUT USING INFORMATICS TO PROMOTE COMMJNITY HEALTH?

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Role of Technology and Informatics in Patient Safe Communication

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short and to the point explanation to population health management

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NCQA - National Committee for Quality Assurance

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Silis is Working to Improve Communication Among Healthcare Providers

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Silis is Working to Improve Communication Among Healthcare Providers

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https://www.youtube.com/watch?v=O0WjZHet9js

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https://www.youtube.com/watch?v=MXUdYeZemj4

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The Centers for Disease Control and Prevention (CDC) is closely monitoring an outbreak of respiratory illness caused by a novel (new) coronavirus first identified in Wuhan, Hubei Province, China.

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The latest numbers: At least 170 people are dead and more than 7,000 cases have been confirmed in mainland China, as the Wuhan coronavirus spreads across Asia and the rest of the world.

China on lockdown: Nearly 60 million people are under partial or full lockdowns in Chinese cities.

Global spread: There are 91 confirmed cases outside of China, including at least five in the US.

Our live coverage has ended. Go here to read more about the Wuhan coronavirus.

At least three of the Japanese citizens evacuated from Wuhan on Wednesday have been diagnosed with coronavirus, according to Japan's health ministry.

Only one of the patients have a fever, while the other two are not showing any symptoms. They are currently quarantined in Tokyo.

Two flights have repatriated over 400 Japanese citizens from Wuhan on Wednesday and Thursday mornings local time.

At least 7,711 cases of Wuhan coronavirus have been confirmed in China, including 170 deaths as of the end of Wednesday, according to China's National Health Commission.

This number reflects an increase of 1,737 cases and 38 deaths from Tuesday.

Tibet has now confirmed its first case of the coronavirus. All 31 provinces in mainland China have now reported cases of the Wuhan coronavirus.

A student at Arizona State University has started a petition to cancel classes after someone from the university was confirmed to be infected with the coronavirus.

ASU freshman Taskina Bhuiyan, who is majoring in microbiology, started the petition on Change.org, and it had more than 21,000 signatures as of Wednesday evening.

It is unclear whether the infected person is a student or staff member. The Arizona Department of Health Services (ADHS) identified the person only as a "member of the Arizona State University community who does not live in university housing."

The person recently returned from Wuhan, China, where the virus is believed to have originated.

"As part of our case investigation, individuals who may have been exposed to the patient will be notified by public health and provided with proper follow-up," ADHS communications director Chris Minnick told CNN.

The ADHS said the patient is not severely ill. The patient is being kept in isolation to prevent the illness from spreading, according to the health department.
Keep reading.

Malaysia arrested four people on Wednesday on suspicion of spreading fake news about the Wuhan coronavirus outbreak on social media, according to the Malaysian Communications and Multimedia Commission.

The four include a 49-year-old part-time tutor, two 25-year-old and 30-year-old pharmacy assistants and a 24-year-old student.

They have been accused of sharing false information about the novel coronavirus on Facebook and Twitter from Jan. 25 to 27.

If prosecuted, they are subject to a maximum fine of $12,232 each and imprisonment for up to a year.

Singapore's Ministry of Health has confirmed three more cases of Wuhan coronavirus Thursday morning, bringing the country's total to 10.

All three cases are Chinese nationals who traveled from Wuhan, according to a statement from Singapore's Health Ministry.

Among the new cases are a married couple - a 56-year-old man and 56-year-old woman- and another 56-year-old man, who are in isolation rooms at the National Center of Infectious Diseases.

All three new patients were asymptomatic during the flight to Singapore.

The South Korean Foreign Affairs Ministry announced early Thursday morning (local time) that two planned charter flights to evacuate citizens from Wuhan had been delayed.

The flights had been scheduled to depart from Wuhan at 3 p.m. and 5 p.m. local time. Now, the South Korean government is working with the Chinese government to organize flights as early as tonight, according to a Korean official.

A planned flight to bring British citizens back from Wuhan today has also been delayed.

President Trump announced Wednesday that he received a briefing on the coronavirus.

"Just received a briefing on the Coronavirus in China from all of our GREAT agencies, who are also working closely with China," the President tweeted. "We will continue to monitor the ongoing developments. We have the best experts anywhere in the world, and they are on top of it 24/7!"

He included pictures that appear to show a meeting in the White House Situation Room.

Trump suggested Wednesday that he'd recently spoken to Chinese President Xi Jinping amid concerns over containing the Wuhan coronavirus, but a White House official tells CNN that the last time the two world leaders spoke was in December.
Read his tweet:

The US Centers for Disease Control and Prevention on Wednesday said that 195 passengers who were evacuated from Wuhan to an airbase in California have been screened, monitored and evaluated every step of the way.

None of the passengers show signs of sickness, CDC officials said during a news conference Wednesday afternoon, but they will remain under a voluntary quarantine at the air base.

The evacuees are not required to stay at the base. The health agency said the travelers agreed to a request to remain there for three days of testing, monitoring and rest.

"The good news here is that despite an aggressive public health investigation to find new cases, we have not," said Dr. Nancy Messonnier, the director of the CDC's National Center for Immunization and Respiratory Diseases.

"The situation in China is concerning," Messonnier said. "However we are looking hard here in the United States. We will continue to be proactive. I expect that we will find additional cases."

So far the agency has confirmed five cases of coronavirus in the US in four states and 165 suspected cases.

The latest numbers: At least 170 people are dead and more than 7,000 cases have been confirmed in mainland China, as the Wuhan coronavirus spreads across Asia and the rest of the world.

China on lockdown: Nearly 60 million people are under partial or full lockdowns in Chinese cities.

Global spread: There are 91 confirmed cases outside of China, including at least five in the US.

Our live coverage has ended. to read more about the Wuhan coronavirus.

At least three of the Japanese citizens evacuated from Wuhan on Wednesday have been diagnosed with coronavirus, according to Japan's health ministry.

Only one of the patients have a fever, while the other two are not showing any symptoms. They are currently quarantined in Tokyo.

Two flights have repatriated over 400 Japanese citizens from Wuhan on Wednesday and Thursday mornings local time.

At least 7,711 cases of Wuhan coronavirus have been confirmed in China, including 170 deaths as of the end of Wednesday, according to China's National Health Commission.

This number reflects an increase of 1,737 cases and 38 deaths from Tuesday.

Tibet has now confirmed its first case of the coronavirus. All 31 provinces in mainland China have now reported cases of the Wuhan coronavirus.

A student at Arizona State University has started a petition to cancel classes after someone from the university was confirmed to be infected with the coronavirus.

ASU freshman Taskina Bhuiyan, who is majoring in microbiology, started the petition on Change.org, and it had more than 21,000 signatures as of Wednesday evening.

It is unclear whether the infected person is a student or staff member. The Arizona Department of Health Services (ADHS) identified the person only as a "member of the Arizona State University community who does not live in university housing."

The person recently returned from Wuhan, China, where the virus is believed to have originated.

"As part of our case investigation, individuals who may have been exposed to the patient will be notified by public health and provided with proper follow-up," ADHS communications director Chris Minnick told CNN.

The ADHS said the patient is not severely ill. The patient is being kept in isolation to prevent the illness from spreading, according to the health department.
Keep reading.

Malaysia arrested four people on Wednesday on suspicion of spreading fake news about the Wuhan coronavirus outbreak on social media, according to the Malaysian Communications and Multimedia Commission.

The four include a 49-year-old part-time tutor, two 25-year-old and 30-year-old pharmacy assistants and a 24-year-old student.

They have been accused of sharing false information about the novel coronavirus on Facebook and Twitter from Jan. 25 to 27.

If prosecuted, they are subject to a maximum fine of $12,232 each and imprisonment for up to a year.

Singapore's Ministry of Health has confirmed three more cases of Wuhan coronavirus Thursday morning, bringing the country's total to 10.

All three cases are Chinese nationals who traveled from Wuhan, according to a statement from Singapore's Health Ministry.

Among the new cases are a married couple - a 56-year-old man and 56-year-old woman- and another 56-year-old man, who are in isolation rooms at the National Center of Infectious Diseases.

All three new patients were asymptomatic during the flight to Singapore.

The South Korean Foreign Affairs Ministry announced early Thursday morning (local time) that two planned charter flights to evacuate citizens from Wuhan had been delayed.

The flights had been scheduled to depart from Wuhan at 3 p.m. and 5 p.m. local time. Now, the South Korean government is working with the Chinese government to organize flights as early as tonight, according to a Korean official.

A planned flight to bring British citizens back from Wuhan today has also been delayed.

President Trump announced Wednesday that he received a briefing on the coronavirus.

"Just received a briefing on the Coronavirus in China from all of our GREAT agencies, who are also working closely with China," the President tweeted. "We will continue to monitor the ongoing developments. We have the best experts anywhere in the world, and they are on top of it 24/7!"

He included pictures that appear to show a meeting in the White House Situation Room.

Trump suggested Wednesday that he'd recently spoken to Chinese President Xi Jinping amid concerns over containing the Wuhan coronavirus, but a White House official tells CNN that the last time the two world leaders spoke was in December.
Read his tweet:

The US Centers for Disease Control and Prevention on Wednesday said that 195 passengers who were evacuated from Wuhan to an airbase in California have been screened, monitored and evaluated every step of the way.

None of the passengers show signs of sickness, CDC officials said during a news conference Wednesday afternoon, but they will remain under a voluntary quarantine at the air base.

The evacuees are not required to stay at the base. The health agency said the travelers agreed to a request to remain there for three days of testing, monitoring and rest.

"The good news here is that despite an aggressive public health investigation to find new cases, we have not," said Dr. Nancy Messonnier, the director of the CDC's National Center for Immunization and Respiratory Diseases.

"The situation in China is concerning," Messonnier said. "However we are looking hard here in the United States. We will continue to be proactive. I expect that we will find additional cases."

So far the agency has confirmed five cases of coronavirus in the US in four states and 165 suspected cases.

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coronavirus news and live updates: virus spreads globally with more than 7

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By Helen Regan, Jessie Yeung, Steve George and Amy Woodyatt, CNN

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Coronavirus news and live updates: Virus spreads globally with more than 7,000 cases - CNN

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The latest numbers: At least 170 people are dead and more than 7,000 cases have been confirmed in mainland China, as the Wuhan coronavirus spreads across Asia and the rest of the world. China on lockdown: Nearly 60 million people are under partial or full lockdowns in Chinese cities.

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When the Coronavirus became an international headline, a popular online game was considered by many to be a legititmate resource for information, and they had to make an immediate PSA

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Amid the fear and intrigue surrounding the coronavirus, people are downloading a simulation video game in which players use real-time strategy to spread a deadly outbreak around the world.

The video game's developers warn people not to take the game too seriously and to seek advice about how epidemics travel from authoritative sources.

The coronavirus has sickened more than 4,500 people and killed more than 100. The illness originated in China before moving to other parts of the globe, i ncluding the USA.

Plague Inc. is an 8-year-old app and PC game in which users play the role of a disease intent on infecting the world with a pathogen.

U.K.-based game developer Ndemic Creations said the game saw a surge in users as news about the coronavirus spread. The video game maker issued a reminder to players that Plague Inc. is not based on science.
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"Whenever there is an outbreak of disease, we see an increase in players, as people seek to find out more about how diseases spread and to understand the complexities of viral outbreaks," Ndemic Creations said in a statement on its website. "We specifically designed the game to be realistic and informative, while not (sensationalizing) serious real-world issues."
Follow Dalvin Brown on Twitter: @Dalvin_Brown.
The company included a link to information from the World Health Organization about coronavirus. The influx of players caused the app developer's website to shut down last week, Ndemic Creations tweeted.

PC Mag reported that the game became the most-downloaded iPhone app in China and the USA over the past week.

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Amid coronavirus fears, people download epidemic-simulating video game Plague Inc.

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Amid the fear and intrigue surrounding the coronavirus, people are downloading a simulation video game in which players use real-time strategy to spread a deadly outbreak around the world. The video game's developers warn people not to take the game too seriously and to seek advice about how epidemics travel from authoritative sources.