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标题：Small-Area Estimation and Prioritizing Communities for Obesity Control in Massachusetts
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作者：Wenjun Li ; Jennifer L. Kelsey ; Zi Zhang 等
期刊名称：American journal of public health
印刷版ISSN：0090-0036
出版年度：2009
卷号：99
期号：3
页码：511-519
DOI：10.2105/AJPH.2008.137364
语种：English
出版社：American Public Health Association
摘要：Objectives. We developed a method to evaluate geographic and temporal variations in community-level obesity prevalence and used that method to identify communities in Massachusetts that should be considered high priority communities for obesity control. Methods. We developed small-area estimation models to estimate community-level obesity prevalence among community-living adults 18 years or older. Individual-level data from the Behavioral Risk Factors Surveillance System from 1999 to 2005 were integrated with community-level data from the 2000 US Census. Small-area estimation models assessed the associations of obesity (body mass index ≥ 30 kg/m²) with individual- and community-level characteristics. A classification system based on level and precision of obesity prevalence estimates was then used to identify high-priority communities. Results. Estimates of the prevalence of community-level obesity ranged from 9% to 38% in 2005 and increased in all communities from 1999 to 2005. Fewer than 7% of communities met the Healthy People 2010 objective of prevalence rates below 15%. The highest prevalence rates occurred in communities characterized by lower income, less education, and more blue-collar workers. Conclusions. Similar to the rest of the nation, Massachusetts faces a great challenge in reaching the national obesity control objective. Targeting high-priority communities identified by small-area estimation may maximize use of limited resources. Obesity, a major health concern in the United States,¹ results in an estimated 240 000 premature deaths^{2 – 4} and medical costs of over $90 billion^{5 , 6} annually. An objective of Healthy People 2010 is to reduce the prevalence of adult obesity (body mass index [BMI] ≥ 30 kg/m²) to less than 15% nationwide.⁷ Numerous studies^{8 – 10} confirm that rates of obesity are rapidly rising in almost all sociodemographic groups. Recent national data^{11 , 12} suggest that obesity has reached an historical high, affecting 32% of the adult population.¹¹ Obesity control is a complex process that requires approaches at all levels—federal, state, and community, as well as organizational, interpersonal, and individual.^{13 , 14} Control programs that address the specific needs of communities are likely to be more effective than are nonspecific programs planned at higher geopolitical levels such as county or state.^{15 , 16} Similar to the rest of the nation, Massachusetts encounters shortages of resources for obesity control. For effective use of limited resources, estimates of community-specific prevalence are needed to identify communities with the greatest needs. In addition, city and town public officials and community-based organizations increasingly ask the Massachusetts Department of Public Health for obesity prevalence data that are timely and community specific. In Massachusetts, the Behavioral Risk Factor Surveillance System (BRFSS)¹⁷ is the only source of population-based data on the prevalence of obesity among residents 18 years or older. However, the current BRFSS does not provide prevalence data at the community level because most communities do not have adequate sample sizes for directly calculating prevalence with reasonable precision. Small-area estimation models can be adapted to overcome this limitation and provide community-level prevalence estimates. Using the BRFSS data and small-area estimation models, we estimated and analyzed geographic variations and temporal trends in obesity prevalence in 398 communities (including 339 towns and small cities and 59 subdivisions of the 12 largest cities) in Massachusetts. To assist in the planning of statewide obesity control efforts, we developed a method for classifying communities into priority groups based on adult obesity prevalence estimates and the precision of these estimates.