Childhood Obesity Prevention - A Promising New Method for Tackling the Epidemic

As we begin again to consider health care reform in this nation, the issue of childhood obesity should not be ignored. Childhood obesity is a public health epidemic with serious ramifications for health, productivity, and long-term health care system costs. As the epidemic grows, there is an increasingly urgent need for accurate population-based data to understand obesity prevalence, trends, and disparities among youth. Thousands of obesity intervention programs are under way across the country, yet very few datasets are available to evaluate their long-term effectiveness. Indeed, lack of data even constrains the ability of funders to confidently allocate resources to where efforts are most needed.

One means of addressing this critical lack of data is to use our existing public health infrastructure to build a system to capture de-identified (meaning no personal information) body mass index (BMI) data on children to monitor rates of obesity at the community level. Such a system could begin to provide critical data needed to evaluate the effectiveness of many childhood obesity intervention efforts already under way. The value of a BMI-tracking system built on infrastructure like that provided by immunization registries is that it provides the aggregate data that we need to evaluate the epidemic and the many interventions under way, but it keeps personal information about individual children exactly where it belongs: in the hands of their doctors and their families.

Built with proven identity-protecting measures, we could see a truly national impact from a system that provides quality local data on rates of childhood obesity matched against information on community interventions and clinical practices. For the government sector, the information would provide a crucial tool for resource allocation, program planning, and evaluation for programs like WIC (Women, Infants, and Children) or Medicaid. The clinical sector could also benefit by having an innovative and cost-effective method of assessing the quality and effectiveness of clinical care. For example, health plans could assess the extent to which providers are following professional guidelines to screen children annually using BMI and providing nutrition and physical activity guidance. Local communities would have access to aggregate data to understand the association between environmental factors such as access to healthy foods and recreation areas and obesity. Because surveillance systems of this type have large amounts of data, it is possible to look at disparities by geographic area, age, gender, ethnicity, and other traits. Such analysis is crucial for planning intervention programs appropriately targeted to different populations.

Despite the obvious strategic value of BMI-tracking systems, few are in place, and even fewer meet the key characteristics of an optimal system. These characteristics include having data collected by health professionals in clinical settings; making data available in real time; and ensuring that data collected can be analyzed at the local level to identify ethnic, gender, or age group disparities within districts, census tracts, ZIP codes, and other divisions.

There are reasons for these minimum characteristics. For one, clinicians are best-prepared to collect data, because they are trained and equipped to conduct accurate assessments. They are also best-positioned to provide appropriate follow-up and treatment in a confidential setting for kids found to be with or at risk of obesity. A second reason is that real-time data and analysis is important for allowing BMI-tracking data to be linked with other health data systems, and de-identified data can be aggregated for use by multiple stakeholder groups.

Most states now conducting BMI assessments are doing so in school settings. Arkansas has led the nation’s thinking in childhood obesity surveillance in schools, demonstrating both the benefits and the drawbacks of a school-based system (Arkansas Advocates for Children and Families, 2008; Arkansas Center for Health Improvement, 2008). We owe much to colleagues in Arkansas, who have done a great deal to raise awareness and demonstrate the feasibility of statewide surveillance. At the same time, the challenges of instituting a clinical function in a school setting (University of Arkansas Office for Education Policy, 2008) can be not only costly but fraught with complications.

Schools often lack trained staff and equipment to obtain the necessary measurements for an accurate and reliable BMI assessment. In addition, there are concerns that conveying obesity information to children and families in the school setting might stigmatize and embarrass youth. Data privacy laws for schools are another issue, since they are interpreted very stringently and defined broadly, placing legal pressure on the data collection effort that need not be applied. Perhaps worst of all, mechanisms to refer overweight and obese youth and families to appropriate treatment services are lacking or completely absent in most school settings, a problem not found with the clinically based system that we are advocating.

What is needed is a more robust mechanism for monitoring childhood obesity that leverages our existing public health infrastructure and can avoid or manage the criteria and privacy concerns discussed above while avoiding the complications that arise in a school-based system. Recent efforts in Michigan may serve as a model that other states throughout the nation could emulate.

Over the past 18 months, the leaders and partners of Michigan’s Department of Community Health have been working to add BMI-tracking capacities to the “MCIR,” the Michigan Care Improvement Registry ( MCIR was created in 1998 to collect reliable immunization information and make it accessible to authorized users online. Today, the MCIR contains records on 57 million immunizations given to 4.7 million children and is widely used and accepted by clinicians across the state (Hoyle, 2006, 2008). About 13,000 users log into the system daily, and nearly all medical providers in the state participate in MCIR (Enger, 2007; “MCIR Update,” 2008).

With the very simple addition of just two new data fields needed for BMI – height and weight – the MCIR will become a powerful new tool for preventing and managing obesity in children. Adding BMI data to the MCIR will enable public health officials to track the epidemic of obesity in the state, understand how it is changing over time, and determine which demographic and geographic subgroups are in greatest need of services and resources. In addition, these data can enable analysts in Michigan to study whether policies and programs are making a difference. Making use of an existing and tested registry such as MCIR also ensures that data are secure and kept private.

Adding BMI to MCIR will also foster improved compliance with recommendations of medical and public health professionals that all children have their BMI screened annually. Health plans can assess compliance with BMI reporting recommendations and offer financial incentives to encourage providers to conduct the assessments. Actual calculation of the BMI from height and weight and comparing the data with standard populations to determine an individual child’s weight status is a time-consuming process fraught with error. The MCIR can perform the calculation and provide the BMI and weight status for that child automatically and in real time – a valuable service, especially for the majority of Michigan providers, who do not have electronic medical records.

Finally, the MCIR will help to improve clinical obesity prevention and treatment efforts in the state. Clinicians will be able to print out BMI percentile growth charts on children simply by entering their height and weight measurements. If the system is programmed to flag children with elevated BMIs, a printed growth chart indicating that a child is overweight can help providers initiate awkward conversations with parents who frequently are unaware that their children are overweight or even obese (Barlow, Bobra, Elliott, Brownson, & Haire-Joshu, 2007; Flower, Perrin, Viadro, & Ammerman, 2006; O’Brien, Holubkov, & Reis, 2004; Rattay, Ramakrishnan, Atkinson, Gilson, & Drayton, 2009). The system could provide clinicians with easy-to-follow tools based on clinical guidelines to provide indicated prevention counseling, screening for co-morbidities, and referrals for more intensive services while keeping this information within the confines of the doctor-patient relationship.

Michigan is one of the first states to begin to use its public health infrastructure to tackle childhood obesity and could serve as a model for other states. More than 30 states and many cities and counties already maintain population-based health information registries and disease surveillance systems integrated into clinical settings that, like the Michigan registry, could be used to track BMI (American Immunization Registry Association, 2009).

A nationwide effort to explore the range of options for state-level BMI surveillance is urgently needed. We hope that the national discourse on health system reform includes plans for a BMI-tracking system for children in every state. With money already provided for health IT in the stimulus bill, funding is available. Indeed, unlike with many recent health IT proposals, there is good reason to believe that BMI-tracking systems will prove to be cost-effective and deliver benefits quickly.

While not a cure for childhood obesity, leveraging our existing health infrastructure to tackle childhood obesity as Michigan is doing could help arrest one of our nation’s leading causes of health care problems and expenditures. We should act now, especially while the need to reform the health care system has captured the nation’s and our policymakers’ attention.


- American Immunization Registry Association (2009). Registry Profiles. Retrieved January 20, 2009, from
- Arkansas Advocates for Children and Families (2008). Fit Not Fat: Helping Arkansas Children Eat Healthy and Move More (p. 36). Retrieved September 16, 2008, from
- Arkansas Center for Health Improvement (2008). Assessment of Childhood and Adolescent Obesity in Arkansas: Year Five (Fall 2007-Spring 2008). Retrieved November 24, 2008, from
- Barlow, S. E., Bobra, S. R., Elliott, M. B., Brownson, R. C., & Haire-Joshu, D. (2007). Recognition of childhood overweight during health supervision visits: Does BMI help pediatricians? Obesity, 15(1), 225-232. DOI: 10.1038/oby.2007.535.
- Enger, K. (2007). Geographic Analysis of Immunization Patterns Using the Michigan Care Improvement Registry (MCIR). Retrieved January 27, 2009, from
- Flower, K.B., Perrin, E.M., Viadro, C.I., & Ammerman, A.S. (2006). Using body mass index to identify overweight children: barriers and facilitators in primary care. Ambulatory Pediatrics: The Official Journal of the Ambulatory Pediatric Association, 7(1), 38-44. DOI: 10.1016/j.ambp.2006.09.008.
- Hoyle, T. (2006, March). Collaboration + Innovation = Immunization Information Systems Success. Atlanta, GA. Retrieved October 24, 2008, from
- Hoyle, T. (2008, September 15). Public Health Population-Based Systems: A New Approach: State Surveillance of  Children’s BMI. Retrieved January 20, 2009, from
- MCIR Update (2008). Retrieved January 27, 2009, from
- O’Brien, S.H., Holubkov, R., & Reis, E.C. (2004). Identification, evaluation, and management of obesity in an academic primary care center. Pediatrics, 114(2), e154-159.
- Rattay, K.T., Ramakrishnan, M., Atkinson, A., Gilson, M., & Drayton, V. (2009). Use of an electronic medical record system to support primary care recommendations to prevent, identify, and manage childhood obesity. Pediatrics, 123 (Supplement_2), S100-S107.
- University of Arkansas Office for Education Policy (2008). Rethinking the Body Mass Index Initiative. Policy Brief.


All postings to the Health Policy Forum (whether from employees or those outside the Institute) represent the views of the individual authors and/or organizations and do not necessarily represent the position, interests, strategy, or opinions of Altarum Institute.


Collecting and using de-identified BMI data as a gross proxy for the prevalence of and trends in childhood obesity in disparate communities is a sound idea and a doable and necessary first step. But of course the real objective is to move past this overall “outcome” measure and make demonstrable impact on the problem. And this will require that a causal connection be made between childhood obesity management program design features and components, and their results.

Health Plans and health management organizations of varying types and locations charged with designing and implementing childhood obesity management programs have experienced widely divergent levels of success in reaching and engaging their “members” (parents as well as their children) to modify behaviors so as to achieve measurable improvements.

What is needed is a system to collect, organize, and analyze this real world experience data to determine what works both clinically AND from an intervention, outreach, and engagement standpoint. Arguably we are quite knowledgeable about what behaviors are most likely to achieve healthy weight for most people. But our track record at consistently successful intervention to positively influence those behaviors is somewhere between unknown and marginally effective at best.

Any experienced healthcare program delivery executive knows that what comprises effective tactics and approaches to outreach, communication, education, engagement, behavior modification, steerage to providers, follow-up, and measurement vary widely by a full range of demographic, cultural, and geographic dimensions. These effectiveness determining dimensions include at least ethnicity, socio-economic status, family culture, education, overall health status, local provider accessibility and quality, as well as the normal age, gender, and location variables.

Ultimately it is necessary to connect individual (still de-identified) weight health (BMI) data with healthcare program components applied to that same individual. Only then can we properly conduct proper performance assurance analysis to learn what works. Healthcare organizations across the country are designing and implementing programs every day in an unconnected set of experiments to see what approaches are most effective. Who will collect, organize, analyze and make this program performance data actionable and available on an ongoing basis for everyone?

I trace my family history so I will know who to blame.

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