OBJECTIVE: This study used spatial statistical methods to test the hypotheses that county-level adult obesity prevalence in the United States is (1) regionally concentrated at significant levels, and (2) linked to local-level factors, after controlling for state-level effects. METHODS: Data were obtained from the Centers for Disease Control and Prevention and other secondary sources. The units of analysis were counties. The dependent variable was the age-adjusted percentage of adults who were obese in 2009 (body mass index >30 kg/m2). RESULTS: The prevalence of county-level obesity varied from 13.5% to 47.9% with a mean of 30.3%. Obesity prevalence across counties was not spatially random: 15.8% belonged to high-obesity regions and 13.5% belonged to low-obesity regions. Obesity was positively associated with unemployment, outpatient healthcare visits, physical inactivity, female-headed families, black populations, and less education. Obesity was negatively correlated with physician numbers, natural amenities, percent ≥65 years, Hispanic populations, and larger population size. A number of variables were notable for not reaching significance after controlling for other factors, including poverty and food environment measures. CONCLUSIONS: The findings demonstrate the importance of local-level factors in explaining geographic variation in obesity prevalence, and thus hold implications for geographically targeted interventions to combat the obesity epidemic.
OBJECTIVE: This study used spatial statistical methods to test the hypotheses that county-level adult obesity prevalence in the United States is (1) regionally concentrated at significant levels, and (2) linked to local-level factors, after controlling for state-level effects. METHODS: Data were obtained from the Centers for Disease Control and Prevention and other secondary sources. The units of analysis were counties. The dependent variable was the age-adjusted percentage of adults who were obese in 2009 (body mass index >30 kg/m2). RESULTS: The prevalence of county-level obesity varied from 13.5% to 47.9% with a mean of 30.3%. Obesity prevalence across counties was not spatially random: 15.8% belonged to high-obesity regions and 13.5% belonged to low-obesity regions. Obesity was positively associated with unemployment, outpatient healthcare visits, physical inactivity, female-headed families, black populations, and less education. Obesity was negatively correlated with physician numbers, natural amenities, percent ≥65 years, Hispanic populations, and larger population size. A number of variables were notable for not reaching significance after controlling for other factors, including poverty and food environment measures. CONCLUSIONS: The findings demonstrate the importance of local-level factors in explaining geographic variation in obesity prevalence, and thus hold implications for geographically targeted interventions to combat the obesity epidemic.
Authors: Peter T Katzmarzyk; Corby K Martin; Robert L Newton; John W Apolzan; Connie L Arnold; Terry C Davis; Kara D Denstel; Emily F Mire; Tina K Thethi; Phillip J Brantley; William D Johnson; Vivian Fonseca; Jonathan Gugel; Kathleen B Kennedy; Carl J Lavie; Eboni G Price-Haywood; Daniel F Sarpong; Benjamin Springgate Journal: Contemp Clin Trials Date: 2018-02-08 Impact factor: 2.226
Authors: Elise L Rice; Minal Patel; Katrina J Serrano; Chan L Thai; Kelly D Blake; Robin C Vanderpool Journal: Public Health Rep Date: 2018-05-23 Impact factor: 2.792
Authors: Kathryn E Smith; Tyler B Mason; Ross D Crosby; Scott G Engel; Scott J Crow; Stephen A Wonderlich; Carol B Peterson Journal: Appetite Date: 2017-09-21 Impact factor: 3.868
Authors: Candice A Myers; Tim Slack; Stephanie T Broyles; Steven B Heymsfield; Timothy S Church; Corby K Martin Journal: Obesity (Silver Spring) Date: 2016-12-23 Impact factor: 5.002
Authors: Andrew P. Loehrer; David C. Chang; John W. Scott; Matthew M. Hutter; Virendra I. Patel; Jeffrey E. Lee; Benjamin D. Sommers Journal: JAMA Surg Date: 2018-03-01 Impact factor: 14.766