| Titre : | Small-Area Estimation and Prioritizing Communities for Obesity Control in Massachusetts. (2009) |
| Auteurs : | . WENJUN LI ; Cynthia BODDIE-WILLIS ; Jennifer-L KELSEY ; Stephenie-C LEMON ; Solomon MEZGEBU ; George-W REED ; . ZI ZHANG |
| Type de document : | Article |
| Dans : | American journal of public health (vol. 99, n° 3, 2009) |
| Pagination : | 511-519 |
| Langues: | Anglais |
| Mots-clés : | Obésité ; Estimation ; Communauté ; Surveillance ; Contrôle ; Homme ; Amérique ; Amérique du Nord |
| Résumé : | [BDSP. Notice produite par INIST-CNRS rJR0xttA. Diffusion soumise à autorisation]. 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. |
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