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Associations between individual socioeconomic position, neighbourhood disadvantage and transport mode: baseline results from the HABITAT multilevel study
  1. Jerome N Rachele1,
  2. Anne M Kavanagh2,
  3. Hannah Badland3,
  4. Billie Giles-Corti3,
  5. Simon Washington4,
  6. Gavin Turrell1
  1. 1School of Public Health and Social Work and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
  2. 2Gender and Women's Health Unit, Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
  3. 3McCaughey VicHealth Community Wellbeing Unit, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
  4. 4Civil Engineering and Built Environment School, Science and Engineering Faculty, Centre for Accident Research and Road Safety, Health Faculty and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
  1. Correspondence to Dr Jerome N Rachele, School of Public Health and Social Work and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4059, Australia; j.rachele{at}qut.edu.au

Abstract

Background Understanding how different socioeconomic indicators are associated with transport modes provide insight into which interventions might contribute to reducing socioeconomic inequalities in health. The purpose of this study was to examine associations between neighbourhood-level socioeconomic disadvantage, individual-level socioeconomic position (SEP), and usual transport mode.

Methods This investigation included 11 036 residents from 200 neighbourhoods in Brisbane, Australia. Respondents self-reported their usual transport mode (car or motorbike, public transport, walking or cycling). Indicators for individual-level SEP were education, occupation and household income; and neighbourhood disadvantage was measured using a census-derived index. Data were analysed using multilevel multinomial logistic regression. High SEP respondents and residents of the most advantaged neighbourhoods who used a private motor vehicle as their usual form of transport was the reference category.

Results Compared with driving a motor vehicle, the odds of using public transport were higher for white collar employees (OR 1.68, 95% CrI 1.41–2.01), members of lower income households (OR 1.71 95% CrI 1.25–2.30) and residents of more disadvantaged neighbourhoods (OR 1.93, 95% CrI 1.46–2.54); and lower for respondents with a certificate-level education (OR 0.60, 95% CrI 0.49–0.74) and blue collar workers (OR 0.63, 95% CrI 0.50–0.81). The odds of walking for transport were higher for the least educated (OR 1.58, 95% CrI 1.18–2.11), those not in the labour force (OR 1.94, 95% CrI 1.38–2.72), members of lower income households (OR 2.10, 95% CrI 1.23–3.64) and residents of more disadvantaged neighbourhoods (OR 2.73, 95% CrI 1.46–5.24). The odds of cycling were lower among less educated groups (OR 0.31, 95% CrI 0.19–0.48).

Conclusions The relationships between socioeconomic characteristics and transport modes are complex, and provide challenges for those attempting to encourage active forms of transportation. Further work is required exploring the individual-level and neighbourhood-level mechanisms behind choice of transport mode, and what factors might influence individuals from different socioeconomic backgrounds to change to more active transport modes.

  • Health inequalities
  • MULTILEVEL MODELLING
  • Neighborhood/place
  • SOCIAL EPIDEMIOLOGY
  • PHYSICAL ACTIVITY

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