Joanna M N Guimarães, Ana Paula Vasconcelos, Marcelo Cunha, Eduardo Faerstein
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Linear mixed effects models using BMI measured in all four study waves were performed, accounting for gender, race, length of residence, education and time-dependent age, and per capita family income. After adjustments, both income and racial segregation were positively associated with BMI differences (but not BMI changes) over time, in a dose-response pattern. For income segregation, mean differences in BMI for participants living in high and medium vs. low segregated neighborhoods were 1.04 kg/m<sup>2</sup> (β = 1.04; 95% CI 0.47, 1.62) and 0.86 kg/m<sup>2</sup> (0.86; 0.33, 1.39), respectively. For racial segregation, mean differences in BMI for participants living in high and medium vs low segregated neighborhoods were 0.71 kg/m<sup>2</sup> (0.71; 0.14, 1.29) and 0.30 kg/m<sup>2</sup> (0.30; - 0.24, 0.83), respectively. We also showed a moderate to strong correlation between racial and income segregation at baseline. 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引用次数: 0
摘要
社区或居住环境具有物质和社会属性,这可能导致超重和肥胖流行病中的不平等现象。我们利用巴西里约热内卢市1821名公务员的地理编码数据(基线第1波:1999年,第2波:2001-2002年,第3波:2006-2007年,第4波:2012-2013年),研究了基线社区水平收入和种族居住隔离(使用Gi*统计:低、中、高)与体重指数(以kg/m2为单位的BMI)变化之间的纵向关联。使用在所有四个研究波中测量的BMI进行线性混合效应模型,考虑性别,种族,居住时间,教育和时间依赖年龄,以及人均家庭收入。调整后,收入和种族隔离都与BMI差异呈正相关(但不是BMI变化)随着时间的推移,呈剂量反应模式。对于收入隔离,生活在高、中隔离社区与低隔离社区的参与者的BMI平均差异为1.04 kg/m2 (β = 1.04;95% CI 0.47, 1.62)和0.86 kg/m2 (0.86;0.33, 1.39)。对于种族隔离,生活在高度和中度隔离社区与低隔离社区的参与者的BMI平均差异为0.71 kg/m2 (0.71;0.14、1.29和0.30 kg / m2(0.30, - 0.24, 0.83),分别。我们还显示,在基线上,种族和收入隔离之间存在中度到强烈的相关性。减少体重指数和肥胖相关的健康不平等的策略应包括针对隔离社区及其致肥环境的特别努力。
Is Income and Racial Residential Segregation Associated with 13-Year Changes in Body Mass Index? A Longitudinal Analysis in the Brazilian Pró-Saúde Cohort Study.
Neighborhoods or residential environments have physical and social attributes which may contribute to inequalities in the overweight and obesity pandemic. We examined the longitudinal associations of baseline neighborhood-level income and racial residential segregation (using the Gi* statistic: low, medium, high) with changes in body mass index (BMI in kg/m2), using geocoded data from 1821 civil servants in the municipality of Rio de Janeiro, Brazil, followed-up for approximately 13 years (baseline wave 1: 1999, wave 2: 2001-2002, wave 3: 2006-2007, wave 4: 2012-2013). Linear mixed effects models using BMI measured in all four study waves were performed, accounting for gender, race, length of residence, education and time-dependent age, and per capita family income. After adjustments, both income and racial segregation were positively associated with BMI differences (but not BMI changes) over time, in a dose-response pattern. For income segregation, mean differences in BMI for participants living in high and medium vs. low segregated neighborhoods were 1.04 kg/m2 (β = 1.04; 95% CI 0.47, 1.62) and 0.86 kg/m2 (0.86; 0.33, 1.39), respectively. For racial segregation, mean differences in BMI for participants living in high and medium vs low segregated neighborhoods were 0.71 kg/m2 (0.71; 0.14, 1.29) and 0.30 kg/m2 (0.30; - 0.24, 0.83), respectively. We also showed a moderate to strong correlation between racial and income segregation at baseline. Strategies to reduce BMI and obesity-related health inequalities should include special efforts aimed at segregated neighborhoods and its obesogenic environments.
期刊介绍:
The Journal of Urban Health is the premier and authoritative source of rigorous analyses to advance the health and well-being of people in cities. The Journal provides a platform for interdisciplinary exploration of the evidence base for the broader determinants of health and health inequities needed to strengthen policies, programs, and governance for urban health.
The Journal publishes original data, case studies, commentaries, book reviews, executive summaries of selected reports, and proceedings from important global meetings. It welcomes submissions presenting new analytic methods, including systems science approaches to urban problem solving. Finally, the Journal provides a forum linking scholars, practitioners, civil society, and policy makers from the multiple sectors that can influence the health of urban populations.