Nkemdilim, Jennifer, Loh, Venurs ORCID: https://orcid.org/0000-0003-1821-1087, Reid, Rebecca ORCID: https://orcid.org/0000-0002-8911-5861 and Rachele, Jerome ORCID: https://orcid.org/0000-0002-5101-4010 (2025) Associations between neighbourhood socioeconomic disadvantage and body mass index: longitudinal evidence from the Household, Income and Labour Dynamics in Australia Survey (2006–2021). Journal of Epidemiology and Community Health, 79 (10). pp. 746-752. ISSN 0143-005X

Abstract

Background Obesity prevalence differs by neighbourhood. One such characteristic of these neighbourhoods is the level of socioeconomic disadvantage. Understanding the nature of neighbourhood socioeconomic inequalities is important for shaping targeted interventions and policies to promote equitable access to resources and opportunities that support healthy living. The aim of this study was to examine associations between neighbourhood socioeconomic disadvantage and body mass index (BMI) over a 16-year period among a population-representative Australian sample. Methods This study used data from 208 309 observations collected between 2006 and 2021 from the Household, Income and Labour Dynamics in Australia Survey. Neighbourhood disadvantage was measured via a census-derived index, and participants self-reported height and weight, which was computed to BMI. Data were analysed using multilevel and fixed effects regression to examine overall associations, trends over time and changes in neighbourhoods with changes in BMI. Results There was an overall association between neighbourhood socioeconomic disadvantage and BMI. BMI was higher among those in the most disadvantaged neighbourhoods compared with the least disadvantaged neighbourhoods (β=1.31, 95% CI 1.15 to 1.46). BMI trends over time were widening with greater increases in BMI among those in the most disadvantaged neighbourhoods (Q1: β=0.04, 95% CI 0.02 to 0.06 and Q2: β=0.05, 95% CI 0.03 to 0.06). Changes in the level of neighbourhood socioeconomic disadvantage were positively associated with changes in BMI, with the strongest association among those transitioning to more disadvantaged neighbourhoods (Q1: β=0.10, 95% CI 0.02 to 0.18 and Q2: β=0.08, 95% CI 0.02 to 0.15). Conclusions Using methodologically rigorous epidemiological approaches along with longitudinal, national data, this study found strong evidence of neighbourhood socioeconomic inequalities in BMI. Understanding the neighbourhood-level mechanisms likely to exacerbate these inequalities remains a future research priority.

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