At present, prevalence and trends in obesity are assessed using body mass index (BMI) as the chief form of measurement. Data collection methodologies typically focus on specific geographic regions and populations, and the data collected are often collapsed to present summary statistics. However, as previously discussed in this report, the level of granularity available in data that is needed to assess specific differences among and across population subgroups, particularly at local and state levels, is often
insufficient. Furthermore, the myriad of studies and surveys assessing obesity prevalence and trends too often function in isolation. Because of this, information about the magnitude, significance, and comparability of trends is often inconsistent. Variations in and across population groups as well as inconsistency in the analytical methods used to collect data from these groups make comparisons between, or within, populations challenging. This can create gaps in understanding and interpreting reports on obesity prevalence and trends. However, the limitations inherent in data collection and analysis offer several promising opportunities to provide more complete and reliable information, while also improving future methods for collecting data. 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. Within each of these domains, the committee considered approaches to leveraging existing data collection while recognizing opportunities for innovation in this space. In the following discussions, the committee describes opportunities for improving data collection and filling data gaps within these domains as a means of ultimately improving estimates used to assess and report prevalence and trends in obesity.
Domain 1: Demographics and Population Subgroups
Race and Ethnicity
A number of demographic indicators are associated with incidence of obesity that can be used to predict prevalence of obesity in some population subgroups. Race and ethnicity are among these factors. As described in Chapter 2, Box 2-5, the demographic landscape of the United States is transitioning away from a single majority group and toward a more racial and ethnically diverse population. These shifts have the potential to affect obesity prevalence and indicators of trends, particularly in light of racial and ethnic differences in obesity prevalence that currently exist.
Current approaches to classifying race and ethnicity use broad categories, such as American Indian or Alaska Native, Asian, black or African American, Native Hawaiian or Other Pacific Islander, white, and Hispanic. These categories may not adequately differentiate groups. Heterogeneity among Hispanic and Asian populations is well described in reports from the U.S. Census Bureau (Ennis et al., 2011; Hoeffel et al., 2012). The mul-
tiplicity of tribes of indigenous Americans, as well as the diverse nature of African American and white population groups also have been described in Census reports (Hixson et al., 2011; Norris et al., 2012; Rastogi et al., 2011). Another element of heterogeneity within populations is immigration and acculturation (Grieco et al., 2012; Perez-Escamilla, 2011). This type of heterogeneity is important because it may serve as an indicator that can help answer questions about the generalizability and comparability of reports from one population or sets of populations to other populations.
Opportunities for Filling Data Gaps The continuing changes in American demographics present an opportunity to re-examine data collection approaches and identify ways to capture within-group heterogeneity among racial and ethnic subgroups. This could include the concepts of country of origin or ancestry, years of residency in the United States, and acculturation, when appropriate.
Under-Represented Periods of Childhood
One under-represented population subgroup whose obesity status is not adequately documented is infants and very young children through 2 years of age. Obesity is difficult to measure and define in early childhood because of rapid and sometimes unpredictable physiologic changes that occur during this period in life. These changes, in turn, can have a short-term impact on body composition and fat mass. Furthermore, no commonly recognized definition of obesity in children birth to age 2 years has been developed. Measures or estimates of weight status in infancy and early childhood are not consistently included in national, state, and local surveillance systems.
Infants and children up to age 2 years represent the youngest population subgroup sampled, and trend data in this population set the stage for understanding the evolution of obesity in the other age cohorts. Thus, patterns of weight gain across life stages may affect interpretation of trend data depending on timing of data collection. Identifying a means for determining obesity status during early life stages could contribute to better understanding of obesity trends later in life.
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. This type of information will contribute to understanding whether obesity at a given developmental stage affects obesity trends later in life.
Children with Medical Conditions
Children with physical limitations often have higher prevalence of obesity (Bandini et al., 2005). Moreover, they may face challenges in being measured, especially in settings with equipment limitations (CDC, 2016). Some medical conditions, such as childhood cancer, can present an increased risk of later obesity; both genetic variants and medical treatments are being examined as possible factors (Wilson et al., 2015).
Opportunities for Filling Data Gaps Development of methodological approaches to monitoring these populations over time will contribute to reporting accuracy as well as representation of an overlooked population subgroup in obesity prevalence and trend reports.
Children with Severe Obesity
Severe obesity currently exists among U.S. children and adolescents, with some subpopulation groups appearing to be at higher risk (Claire Wang et al., 2011; Lo et al., 2014; Robbins et al., 2015; Skinner and Skelton, 2014). Children with severe obesity experience increased obesity-related comorbidities in childhood and are at high risk of adult obesity and related chronic disease (Kelly et al., 2013). As discussed in Chapter 2, the criteria used to classify severe obesity and the terminology used to describe severe obesity have been inconsistent, although a cut point of 120 percent of the 95th percentile on the 2000 CDC sex-specific BMI-for-age growth charts is now common in the literature.
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.
Domain 2: Data Infrastructure
Building on Existing Infrastructure
National immunization registries are an example of an informational database with the potential to be expanded to include height and weight data, as well as calculated BMI. Such information can be included in the
database along with recording immunization statistics. This opportunity has already been considered in some states (Longjohn et al., 2010; Sheon et al., 2011) and may afford an opportunity to enhance and standardize data collection and storage of large population samples across life stages.
Opportunities for Filling Data Gaps The ability to collect and access height and weight data on a population over time provides an opportunity to include longitudinal assessment to estimates of obesity trends from local and state to national levels.
School-based health assessments offer an opportunity to obtain consistent measures of height and weight and have the potential to facilitate longitudinal trend assessment in the school-age population. Standardized methodologies to sample a representative group of students in all schools across the United States offers an approach to build on existing infrastructure and take advantage of the experience of others. However, some potential barriers exist to the expansion of school-based assessments, including the Family Educational Rights and Privacy Act (FERPA; see Box 3-2), a federal law that protects the privacy of student records; the Health Insurance Portability and Accountability Act of 1996 (HIPAA) (Public Law 104-191, 110 Stat. 1936), a federal law that protects the privacy of a person’s medical and health information; and Institutional Review Boards (IRBs), which must approve proposed non-exempt research involving human participants. Guidance from reports, such as the Joint Guidance on the Application of the Family Educational Rights and Privacy Act (FERPA) and the Health Insurance Portability and Accountability Act of 1996 (HIPAA) to Student Health Records (HHS and DOE, 2008), may help school and health officials better understand legal and policy implications associated with accessing school health data. Legal technical assistance provided to schools and school districts by the National Policy and Legal Analysis Network to Prevent Childhood Obesity (NPLAN, 2016) 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.
Informing Emerging New Infrastructures
Big data techniques offer an opportunity to harmonize approaches used to measure and collect data by using large electronic databases and resources. The Precision Medicine Initiative (NIH, 2016), unveiled by the Obama administration in 2015, will build a national research participant group of a million individuals of all ages. This cohort will include participants from diverse populations living in diverse social and economic circumstances, and represent an array of health statuses.
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.
Electronic Health Records
Electronic health records (EHRs) provide an extensive resource for obtaining aggregate data on individuals’ height and weight within and across populations. Some federal agencies (e.g., Health Resources and Services Administration) have adopted quality-of-care measures related to measuring BMI in clinical settings (e.g., Healthcare Effectiveness Data and Information Set). Validation studies show that clinical prevalence assessments obtained through EHRs compare favorably with common population-based assessments (Arterburn et al., 2010). However, it is important to recognize that some groups are not represented in this type of data source.
Opportunities for Filling Data Gaps A future step to build on the EHR infrastructure is to move the performance metric from collecting BMI to reporting BMI or percent of obesity within the population.
Collaboration is an informal and intangible component of infrastructure, particularly within universities, state and local government agencies (e.g., health departments), and stakeholder groups, that provides a format for developing strong and mutually beneficial relationships. This type of infrastructure also offers the opportunity to combine and share responsibilities for surveillance analysis, fiscal support, and consumer engagement. An example of a mutually beneficial collaboration is the Worcester Academic
Health Department, a partnership between The City of Worcester and Clark University to combine scholarship and practice to improve public health.
The Patient Protection and Affordable Care Act, enacted in 2010, added new requirements for charitable 501(c)(3) hospital organizations that include conducting a community health needs assessment (CHNA) and adopting an implementation strategy at least once every 3 years (IRS, 2015).
Opportunities for Filling Data Gaps Standardizing the metrics used in CHNAs is an example of an opportunity to achieve comparability of data across communities. 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) as a “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices” (WHO, 2011). mHealth also is a term that incorporates applications (i.e., apps) as a software tool that can be made available on hand-held devices. The ubiquity of smartphones makes mHealth a potential tool to easily collect height and weight from individuals. An example of a mobile health application is the Health E-Heart Study that pre-enrolls participants using a Web-based enrollment (Olgin et al., 2016). The application captures biometrics, behavior patterns, and family and medical history. Individual participants are re-“surveyed” every 6 months. mHealth and other mobile technologies used for data collection still face challenges in development, implementation, and utility. Bietz et al. (2015) describes some of the major barriers, including data ownership, access, and privacy, as well as difficulties in ensuring data quality. In an ever-changing technological landscape, unpredictability in the field also presents a challenge. Although promising for the future of data collection, these barriers currently limit the effectiveness of mHealth for the purposes of collecting prevalence or trend data.
Opportunities for Filling Data Gaps By itself, mobile device use and response for health surveillance can produce variable response rates (WHO, 2011). However, the usefulness could be improved if the app is used as a
complement to existing resources and approaches for surveillance. Although the use of mHealth is rapidly expanding, evaluation of this tool will need to be implemented at the same time.
Applications and Tools to Facilitate Self-Report
A number of Web-based tools and apps are commercially available to facilitate self-reporting of anthropometric data. Bluetooth and Wi-Ficonnected scales for weight measurement are one example. This technology combines a measurement device and a computer that can be linked to a personal home network. Each user in the network is individually defined, and data are uploaded automatically and can be reviewed by the user at any time. Such opportunities also may exist for other measures of adiposity, although the accuracy and reliability of these tools, at present, is a consideration. Devices used to assess body composition (e.g., bioelectrical impedance analysis scales, handheld devices), for example, are available for individual use, although they have not been validated for data collection purposes (Bioelectrical impedance analysis in body composition measurement: National Institutes of Health technology assessment conference statement, 1996; Kyle et al., 2014).
Opportunities for Filling Data Gaps The use of mobile, Web-based, and other technologies is a rapidly emerging field with far-reaching implications for data collection and research. This technology could be useful as a complement to surveillance data to produce estimates to predict trends in obesity across and among population groups.
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