Associations between individual socioeconomic position, neighbourhood disadvantage and transport mode: baseline results from the HABITAT multilevel study

Rachele, Jerome N.; Kavanagh, Anne; Badland, Hannah; Billie Giles‐Corti; Washington, Simon; Turrell, Gavin · 2015 · OpenAlex-citations

DOI: 10.1136/jech-2015-205620

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Summary

This study investigates the complex associations between individual socioeconomic position (SEP), neighbourhood disadvantage, and usual transport mode, aiming to identify interventions that could reduce socioeconomic health inequalities. The research was motivated by mixed findings in previous literature regarding how SEP influences travel choices and the common practice of combining walking and cycling into a single "active transport" category, which may obscure distinct patterns. Understanding these relationships is critical for designing policies that encourage active travel and public transport use, thereby increasing physical activity and reducing non-communicable diseases. The analysis utilized baseline data from the HABITAT multilevel study, comprising 11,036 residents aged 40–65 from 200 neighbourhoods in Brisbane, Australia. Respondents self-reported their usual weekday transport mode (car/motorbike, public transport, walking, or cycling). Individual SEP was measured via education, occupation, and household income, while neighbourhood disadvantage was assessed using the Australian Bureau of Statistics’ Index of Relative Socioeconomic Disadvantage. Multilevel multinomial logistic regression was employed to analyze the data, adjusting for confounders such as age, sex, disability, and living arrangements. The reference category for comparisons was high-SEP individuals in advantaged neighbourhoods who used private motor vehicles. The results revealed distinct and sometimes contradictory associations depending on the socioeconomic indicator and transport mode. Compared to driving, the odds of using public transport were higher for white-collar employees, lower-income households, and residents of disadvantaged neighbourhoods, but lower for those with certificate-level education or blue-collar jobs. Walking for transport was more likely among the least educated, those not in the labour force, lower-income households, and residents of disadvantaged neighbourhoods. Conversely, cycling odds were significantly lower among less educated groups and showed no significant association with income, occupation, or neighbourhood disadvantage. These findings indicate that education, occupation, and income capture different dimensions of socioeconomic status that influence transport choices through discrete pathways. The study concludes that a "one-size-fits-all" approach to promoting active transport is inappropriate due to the complex, varying relationships between socioeconomic factors and travel modes. The authors argue that combining walking and cycling into a single metric attenuates associations to the null, as education negatively correlates with cycling but positively with walking. Policymakers must consider specific socioeconomic profiles and local infrastructure when designing interventions. For instance, while disadvantaged neighbourhoods show higher rates of walking and public transport use, they do not exhibit higher cycling rates, suggesting different barriers or mechanisms at play. Future research should explore the underlying mechanisms and built environment factors that drive these choices to better target interventions for reducing health inequities.

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• demographic disparities