Use of Small-Area Estimates to Describe County-Level Geographic Variation in Prevalence of Extreme Obesity Among US Adults

Abstract

IMPORTANCEThe prevalence of extreme obesity continues to increase among adults in the US, yet there is an absence of subnational estimates and geographic description of extreme obesity. This shortcoming prevents a thorough understanding of the geographic distribution of extreme obesity, which in turn limits the ability of public health agencies and policy makers to target areas with a known higher prevalence.OBJECTIVES To use small-area estimation to create county-level estimates of extreme obesity in the US and apply spatial methods to identify clusters of high and low prevalence. DESIGN, SETTING, AND PARTICIPANTSA cross-sectional analysis was conducted using multilevel regression and poststratification with data from the 2012 Behavioral Risk Factor Surveillance System and the US Census Bureau to create prevalence estimates of county-level extreme obesity (body mass index Ն40 [calculated as weight in kilograms divided by height in meters squared]). Data were included on adults (aged Ն18 years) living in the contiguous US. Analysis was performed from June 4 to December 28, 2018. MAIN OUTCOMES AND MEASURES Multilevel logistic regression models estimated the probability of extreme obesity based on individual-level and area-level characteristics. Census counts were multiplied by these probabilities and summed by county to create county-level prevalence estimates. Moran index values were calculated to assess spatial autocorrelation and identify spatial clusters of hot and cold spots. Estimates of moderate obesity were obtained for comparison.RESULTS Overall, the weighted prevalence of extreme obesity was 4.0% (95% CI, 3.9%-4.1%) and the prevalence of moderate obesity was 23.7% (95% CI, 23.4%-23.9%). County-level prevalence of extreme obesity ranged from 1.3% (95% CI, 1.3%-1.3%) to 15.7% (95% CI, 15.3%-16.0%). The Pearson correlation coefficient comparing model-predicted estimates with direct estimates was 0.81 (P < .001). The Moran index I score was 0.35 (P < .001), indicating spatial clustering. Significant clusters of high and low prevalence were identified. Hot spots indicating clustering of high prevalence of extreme obesity in several regions, including the Mississippi Delta region and the Southeast, were identified, as well as clusters of low prevalence in the Rocky Mountain region and the Northeast.CONCLUSIONS AND RELEVANCE Substantial geographic variation was identified in the prevalence of extreme obesity; there was considerable county-level variation even in states generally known as having high or low prevalence of obesity. The results suggest that extreme obesity prevalence demonstrates spatial dependence and clustering and may support the need for substate analysis and benefit of disaggregation of obesity by group. Findings from this study can inform local and national policies seeking to identify populations most at risk from very high body mass index.

Novel Estimates Reveal Subnational Heterogeneities in Disease-Relevant Contact Patterns in the United States

“…We expect the importance of mobility data will be highly context-specific, and is not a prerequisite for this approach. Finally, we echo past advice in the small-area literature: model at the finest level possible and aggregate to the desired geography and time-scale (37,38).…”

Plasma Fatty Acids, Not Dietary Fatty Acids, Associated with Obesity in Four Ethnic Minority Groups Unique to Southwest China: A Case-Control Study

“…This correlation disappeared in the Hani people. The Hani people had the lowest DFAs intake [6,35] . This also supported our speculation.…”

“…The Naxi people live in higher altitudes and colder areas (compared to the other 3 ethnic minorities, the altitude is about 1200m higher and the temperature is about 3℃ lower). Low temperature could directly increase the proportion of unsaturated fatty acids in total PFAs [35,36] . The Bulang and Wa people share a similar environment, dietary habits (DFAs intake and PFA levels), and prevalence of obesity, but there were differences in the correlation between DFAs and PFAs.…”

Geographical disparities in adult obesity prevalence: small-area analysis of the Chilean National Health Surveys

“…For example, emerging evidence reported substantial geographical disparities in childhood mortality, cardiovascular and suicide rates (3)(4)(5), infectious diseases (6)(7)(8), and in healthcare costs (9,10) at subnational levels, highlighting the importance of geographic variation and the fast-growing demand for reliable small-area estimates. Concerning obesity, some studies based on NHS and surveillance records found a substantial difference in the small-area pattern of obesity rates among adults in high-income countries (11)(12)(13)(14)(15)(16) and in children (17)(18)(19). Since these studies cross-sectionally estimate obesity rates in smaller areas (20,21), there is a gap of knowledge of the increase of obesity rate among adults, especially in a setting where medium-low income households persist.…”