Elsevier

Preventive Medicine

Volume 105, December 2017, Pages 271-274
Preventive Medicine

Short Communication
Neighborhood socioeconomic disadvantage and body mass index among residentially stable mid-older aged adults: Findings from the HABITAT multilevel longitudinal study

https://doi.org/10.1016/j.ypmed.2017.09.017Get rights and content

Highlights

  • Body Mass Index (BMI) increased significantly over time for mid-older age adults.

  • Residents of more disadvantaged neighborhoods had a higher BMI.

  • BMI increased at the same rate regardless of level of neighborhood disadvantage.

Abstract

Despite a body of evidence on the relationship between neighborhood socioeconomic disadvantage and body mass index (BMI), few studies have examined this relationship over time among ageing populations. This study examined associations between level of neighborhood socioeconomic disadvantage and the rate of change in BMI over time. The sample included 11,035 participants aged between 40 and 65 years at baseline from the HABITAT study, residing in 200 neighborhoods in Brisbane, Australia. Data were collected biennially over four waves from 2007 to 2013. Self-reported height and weight were used to calculate BMI, while neighborhood disadvantage was measured using a census-based composite index. All models were adjusted for age, education, occupation, and household income. Analyses were conducted using multilevel linear regression models. BMI increased over time at a rate of 0.08 kg/m2 (95% CI 0.02, 0.13) and 0.17 kg/m2 (95% CI 0.11, 0.29) per wave for men and women respectively. Both men and women residing in the most disadvantaged neighborhoods had a higher average BMI than their counterparts living in the least disadvantaged neighborhoods. There were no evident differences in the rate of BMI change over time by level of neighborhood disadvantage. The findings suggest that by mid-older age, the influence of neighborhood socioeconomic conditions over time on BMI may have already played out. Future research should endeavor to identify the genesis of neighborhood socioeconomic inequalities in BMI, the determinants of these inequalities, and then suitable approaches to intervening.

Introduction

Neighborhood social and economic environments have been shown to contribute to poor health behaviors and outcomes (Badland et al., 2017, Ghani et al., 2016, Loh et al., 2016, Marmot et al., 2008, Rachele et al., 2016a, Rachele et al., 2016b, Rachele et al., 2015, Rachele and Turrell, 2016), and understanding how this relationship plays out over time has become a research priority (Glass and McAtee, 2006). The effect of exposure to social conditions appears to be cumulative: a dose-response association has been consistently observed between higher levels of exposure to social and economic disadvantage and increased disease risk (Hallqvist et al., 2004). Late life also appears to be a period of increasing vulnerability to the influence of disadvantage (Lantz et al., 2001). In this light, a number of cross-sectional studies have shown that adult residents of disadvantaged neighborhoods were more likely to be overweight or obese, even after adjusting for their individual socioeconomic position (King et al., 2006). The prevalence of obesity worldwide almost doubled between 1980 and 2014 (World Health Organization, 2015), with approximately 38% of men and 40% of women classified as overweight (BMI  25 kg/m2), and 11% of men and 15% of women as obese (BMI  30 kg/m2) in 2014 (World Health Organization, 2015). In Australia in 2014–15, 63.4% of adults were overweight or obese, up from 56.3% in 1995 (Australia Bureau of Statistics, 2015). Overweight and obesity are strongly linked to poor health and all-cause mortality (Di Angelantonio, 2016). Having a high body mass index (BMI) means that an individual is more likely to present with non-communicable diseases, including type 2 diabetes, coronary heart disease and stroke (World Health Organization, 2015). High BMI can also have adverse social impacts including discrimination, social exclusion, reduced earning and unemployment (World Health Organization, 2015).

Longitudinal studies examining the rate of change in BMI over time provide mixed findings. For example, among a study of 48,359 African-American women from the United States who participated in the Black Women's Health Study, Coogan et al. (2010) found that lower neighborhood socioeconomic background was significantly associated with weight gain and incidence of obesity at 10 year follow-up. Among participants in the Whitehall II study in the United Kingdom, Stafford et al. (2010) found a significant association between living in a socioeconomically deprived neighborhood and weight gain among women (n = 2501) living in the most deprived neighborhood over 10 years, but no association among men (n = 5650). However, no association was found between weight gain and neighborhood disadvantage after nine year follow-up of 13,167 participants in the Atherosclerosis Risk in Communities Study (Mujahid et al., 2005), or after 16 year follow-up of 1487 women in the United States (Ruel et al., 2010). Feng and Wilson (2015) examined neighborhood disadvantage and BMI between 2006 and 2012 (seven waves) among participants aged 15 to 75 + years using the Household, Income and Labour Dynamics in Australia (HILDA) survey and found that neighborhood-level inequalities in BMI were already evident in the 15–24 year old age group. While neighborhood socioeconomic differences remained constant among men through the age groups, the gap became wider among women over time. From the age of 75 and older, neighborhood socioeconomic differences in BMI narrowed for both genders.

Against a back-drop of weight-gain as people age (Feng and Wilson, 2015), and evidence that demonstrates a relationship over time between exposure to social contexts and health (Glass and McAtee, 2006, Hallqvist et al., 2004), building the evidence base is an important step in understanding the influence of neighborhood socioeconomic disadvantage on rate of weight gain. Specifically, it will provide policy-makers and intervention researchers with evidence about what age to intervene, in order to prevent inequalities in BMI widening between socioeconomic groups. Hence, this study examines whether the relationship between time and BMI differs depending on the level of neighborhood socioeconomic disadvantage, using data from the How Areas in Brisbane Influence healTh And acTivity (HABITAT) project. HABITAT is a multilevel longitudinal (2007–2018) study of mid-aged adults (40–65 years in 2007) living in Brisbane, Australia. Brisbane is the capital city of the state of Queensland, and the third largest city in Australia with a population of approximately 2.3 million and a median age of 35 in 2014 (Australian Bureau of Statistics, 2015). Rates of overweight and obesity among adults across greater metropolitan Brisbane vary from 58 to 62% (Australian Institute of Health and Welfare, 2016).

Section snippets

Methods

The primary aim of HABITAT is to examine patterns of change in physical activity, sedentary behavior and health over the period 2007–2018 and to assess the relative contributions of environmental, social, psychological and socio-demographic factors to these changes. Details about HABITAT's sampling design have been published elsewhere (Burton et al., 2009). Briefly, a multi-stage probability sampling design was used to select a stratified random sample. Overall, 1625 Census Collector's

Results

The socio-demographic characteristics and mean (95% confidence interval) BMI for waves 1 and 4 are presented in Table 1. Men living in the least disadvantaged neighborhoods (Q1) had the lowest mean BMI at both baseline and wave 4; while men in Q4 and Q5 had the highest mean BMI at baseline and wave 4 respectively. Women living in the least disadvantaged neighborhoods had the lowest BMI, and those living in the most disadvantaged neighborhoods had the highest BMI at both baseline and wave 4.

The

Discussion

This study examined the rate of change in BMI over time, and whether the relationship between time and BMI differed by level of neighborhood disadvantage. Although BMI increased over time for both men and women, there were no differences in the rate of BMI change by level of neighborhood disadvantage for either gender. Feng and Wilson (2015) found that neighborhood socioeconomic inequalities in BMI already existed among participants in the youngest age category (15–24 years), suggesting that the

Acknowledgments

The HABITAT study is funded by the National Health and Medical Research Council (NHMRC) (ID 497236, 339718, 1047453). JNR is supported by the NHMRC Centre for Research Excellence in Healthy Livable Communities (ID 1061404).

Conflicts of interest

None declared.

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