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

被引:48
|
作者
Rachele, Jerome N. [1 ,2 ]
Kavanagh, Anne M. [3 ]
Badland, Hannah [4 ]
Giles-Corti, Billie [4 ]
Washington, Simon [5 ,6 ]
Turrell, Gavin [1 ,2 ]
机构
[1] Queensland Univ Technol, Sch Publ Hlth & Social Work, Brisbane, Qld 4059, Australia
[2] Queensland Univ Technol, Inst Hlth & Biomed Innovat, Brisbane, Qld 4059, Australia
[3] Univ Melbourne, Melbourne Sch Populat & Global Hlth, Ctr Hlth Equ, Gender & Womens Hlth Unit, Carlton, Vic 3053, Australia
[4] Univ Melbourne, Melbourne Sch Populat & Global Hlth, McCaughey VicHlth Community Wellbeing Unit, Carlton, Vic 3053, Australia
[5] Queensland Univ Technol, Civil Engn & Built Environm Sch, Fac Sci & Engn, Ctr Accid Res & Rd Safety,Hlth Fac, Brisbane, Qld 4001, Australia
[6] Queensland Univ Technol, Inst Hlth & Biomed Innovat, Brisbane, Qld 4001, Australia
基金
澳大利亚国家健康与医学研究理事会; 英国医学研究理事会;
关键词
TIME PHYSICAL-ACTIVITY; LEISURE-TIME; PUBLIC TRANSPORT; ACTIVE-TRANSPORT; FINNISH MEN; INEQUALITIES; BEHAVIOR; METAANALYSIS; MORTALITY; EDUCATION;
D O I
10.1136/jech-2015-205620
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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.
引用
收藏
页码:1217 / 1223
页数:7
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