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

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 65years 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 0.08kg/m (95% CI 0.02, 0.13) and 0.17kg/m (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.

Neighbourhood disadvantage, geographic remoteness and body mass index among immigrants to Australia: A national cohort study 2006-2014

“…In Australian studies with the general population, cross-sectional associations between neighbourhood socioeconomic disadvantage and higher BMI have been demonstrated among men and women living in more deprived areas compared with those living in less deprived areas [ 34 , 35 ]. The two known longitudinal studies [ 36 , 37 ] confirm and extend these findings demonstrating that neighbourhood inequalities in BMI are maintained over time (BMI increasing at a similar rate across all groups)[ 36 ] and that neighbourhood inequalities are maintained through age groups in men and widen with age in women [ 37 ]. Geographic remoteness is also important, because immigrants living in rural Australia may have poorer general well-being compared with immigrants living in urban areas [ 38 ].…”

“…Comparable longitudinal research with the general Australian population is limited to two known studies. One study of a mid-older aged cohort living in Brisbane (the third largest city in Australia) found that, in both men and women, living in more disadvantaged neighbourhoods was associated with higher BMI and neighbourhood inequalities were maintained over time with all groups increasing in BMI at a similar rate [ 36 ]. The other study took a life course approach examining neighbourhood inequalities in BMI across different age groups in adulthood and found that neighbourhood inequalities were evident from 15–24 years and were maintained across age groups for men and widened for women [ 37 ].…”

Neighborhood socioeconomic characteristics, healthcare spatial access, and emergency department visits for ambulatory care sensitive conditions for elderly

“…An increasing body of literature aims to understand how neighborhood characteristics (for example, income and ethnicity) influence people's health status. Studies found the neighborhood disadvantage were associated with transportation availability for health seeking ( Rachele et al, 2017a ), body mass indices ( Rachele et al, 2017b ), and ACSC ED visits ( Fishman et al, 2018 ; Lugo-Palacios and Cairns, 2015 ).…”

Neighbourhood effects on obesity: scoping review of time-varying outcomes and exposures in longitudinal designs

“…A flowchart of the selection process is presented in figure 1. From the 12 757 identified studies, after screening for relevant titles, abstracts and full manuscripts, 66 articles that fitted the eligibility criteria were selected 30–95. Summary characteristics are shown in table 2 and complete characteristics of the studies are shown in online supplementary file 2.…”

“…For example, some food environment indicators focused on assessing healthy food environments, such as grocery store and supermarket densities,30 63 and others focused on fast-food restaurant and convenience store densities 66 91. For composite indexes, authors applied an array of indexing methods, from pre-existing indexes51 78 85 to summing different indicators43 71 81 or using principal component analyses 46 55 75 76 90 96…”