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Racial Disparities in Obesity Prevalence in Mississippi: Role of Socio-Demographic Characteristics and Physical Activity

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  • Mina Qobadi

    (Center of Excellence in Minority Health and Health Disparities, School of Public Health, Jackson State University, Jackson, MS 39213, USA)

  • Marinelle Payton

    (Center of Excellence in Minority Health and Health Disparities, School of Public Health, Jackson State University, Jackson, MS 39213, USA)

Abstract

Although the etiology of obesity is complex, social disparities are gaining attention for their contribution to obesity. The aim of this study was to estimate prevalence of obesity and to explore the associations between socio-demographic characteristics and obesity by race in Mississippi. Data from the 2014 Mississippi Behavior Risk Factors Surveillance System (BRFSS) were used in this study ( n = 3794). Descriptive statistics, Chi-square tests and logistic regressions were conducted using SAS Proc. Survey procedures to account for BRFSS’s multistage complex survey design and sample weights. The overall prevalence of self-reported obesity was 37%. Multiple logistic regression model showed gender was the only variable associated with increased risk of obesity among blacks. Black females were more likely to be obese (Adjusted OR [aOR] = 2.0, 95% CI: 1.4–2.7, ref = male) after controlling for confounders. Among white adults, obesity was significantly associated with physical activity, gender, age and education levels. Those aged 25–44 years (aOR = 1.7, 95% CI: 1.1–2.6, ref ≥ 64 years), those were physically inactivity (aOR = 1.8, 95% CI: 1.4–2.5, ref = physically active) or had high school education (OR = 1.6, 95% CI: 1.2–2.3, ref = college graduate) or some college (aOR = 1.5, 95% CI: 1.2–2.3, ref = college graduate) were more likely to be obese; females (aOR = 0.8; 95% CI: 0.6–0.9, ref = male) and those aged 18–24 years (aOR = 0.50, 95% CI: 0.21–0.9, ref ≥ 64 years) were less likely to be obese.

Suggested Citation

  • Mina Qobadi & Marinelle Payton, 2017. "Racial Disparities in Obesity Prevalence in Mississippi: Role of Socio-Demographic Characteristics and Physical Activity," IJERPH, MDPI, vol. 14(3), pages 1-10, March.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:3:p:258-:d:92080
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    References listed on IDEAS

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    1. Grier, S.A. & Kumanyika, S.K., 2008. "The context for choice: Health implications of targeted food and beverage marketing to African Americans," American Journal of Public Health, American Public Health Association, vol. 98(9), pages 1616-1629.
    2. Jackson, J.S. & Knight, K.M. & Rafferty, J.A., 2010. "Race and unhealthy behaviors: Chronic stress, the HPA Axis, and physical and mental health disparities over the life course," American Journal of Public Health, American Public Health Association, vol. 100(5), pages 933-939.
    3. Andreyeva, Tatiana & Kelly, Inas Rashad & Harris, Jennifer L., 2011. "Exposure to food advertising on television: Associations with children's fast food and soft drink consumption and obesity," Economics & Human Biology, Elsevier, vol. 9(3), pages 221-233, July.
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