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Assessing Prevalence and Trends in Obesity Navigating the Evidence (2016) / Chapter Skim
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8 Future Directions for Filling Data Gaps
Pages 189-198

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From page 189...
... This includes racial and ethnic composition, the full spectrum of childhood from birth to age 18 years, and childhood populations at increased risk of obesity. • Opportunities exist to leverage existing infrastructure and enhance collaborative efforts that could contribute to filing data gaps, such as accurately and consistently measuring and reporting height and weight across different ages and popula tion groups.
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From page 190...
... New and emerging technologies in data collection, aggregation, and distribution offer alternative ways to fill these data gaps. NEW AND EMERGING OPPORTUNITIES FOR FILLING DATA GAPS In discussing future directions in obesity research, the committee considered three primary domains: demographics and population subgroups, infrastructure, and technological advances.
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From page 191...
... Opportunities for Filling Data Gaps  Capturing relevant data points in early growth patterns that can be used to predict later childhood obesity is both a challenge and an opportunity. Development and adoption of a standardized reporting format will facilitate documentation of correlations between body composition changes and childhood developmental stages.
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From page 192...
... Opportunities for Filling Data Gaps  Limitations in the published literature about the levels of extreme obesity and how this is changing over time are a research opportunity. Children with severe obesity represent a high-risk population, thus developing standard reporting formats that consider severe obesity classifications across national, state, and local datasets will allow for better understanding of the movement of individuals from lower to higher obesity categories.
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From page 193...
... can help school and health officials understand issues related to joint use agreements and may be a pathway to overcome barriers to using school health assessments as a data source for research. Opportunities for Filling Data Gaps  These programs and others like them may offer an alternative mechanism to use existing infrastructure as a mechanism for obtaining estimates to assess obesity prevalence and trends in school-age populations.
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From page 194...
... Opportunities for Filling Data Gaps  Big data initiatives could present an opportunity to include more demographic characteristics in the population as well as apply a standardized protocol for collecting measured height and weight, calculated BMI, and birth weight. To ensure the accuracy of data collection, quality control measures, such as standardized protocols, will have to be in place.
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From page 195...
... Consensus about standardized methodologies to collect and assess data needed for obesity prevalence estimates and trends mapping could leverage existing infrastructure and enhance collaborations to effectively achieve this strategy. Domain 3: Advances in Technology E-Health and Mobile Health Systems The Global Observatory for eHealth defines mobile health (mHealth)
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From page 196...
... , for example, are available for individual use, although they have not been validated for data collection purposes (Bioelectrical impedance analysis in body composition m ­ easurement: National Institutes of Health technology assessment conference statement, 1996; Kyle et al., 2014)
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From page 197...
... and the Health Insurance Portability and Accountability Act of 1996 (HIPAA) to student health records.
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From page 198...
... 2015. Prevalence, disparities, and trends in obesity and severe obesity among students in the school district of Philadelphia, Pennsylvania, 2006-2013.
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Key Terms

  • severe obesity
  • data gaps
  • obesity prevalence
  • future directions
  • body composition

  • health records
  • childhood obesity
  • impedance analysis
  • health insurance
  • trend data

  • obesity trends
  • family educational
  • existing infrastructure
  • population subgroups
  • bioelectrical impedance

  • life stages
  • educational rights
  • privacy act
  • insurance portability
  • accountability act

  • electronic health
  • big data
  • health officials
  • weight data
  • health technology

  • health assessments
  • school health
  • obesity status
  • mobile health
  • medical conditions


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