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

Overview of attention for article published in Journal of Epidemiology and Community Health (1978), August 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

twitter
12 tweeters
facebook
1 Facebook page

Citations

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22 Dimensions

Readers on

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72 Mendeley
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Title
Associations between individual socioeconomic position, neighbourhood disadvantage and transport mode: baseline results from the HABITAT multilevel study
Published in
Journal of Epidemiology and Community Health (1978), August 2015
DOI 10.1136/jech-2015-205620
Pubmed ID
Authors

Jerome N Rachele, Anne M Kavanagh, Hannah Badland, Billie Giles-Corti, Simon Washington, Gavin Turrell

Abstract

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. 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. 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). 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.

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
United States 1 1%
Unknown 70 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 22%
Researcher 10 14%
Student > Ph. D. Student 8 11%
Student > Bachelor 7 10%
Professor > Associate Professor 5 7%
Other 17 24%
Unknown 9 13%
Readers by discipline Count As %
Social Sciences 20 28%
Medicine and Dentistry 5 7%
Environmental Science 5 7%
Nursing and Health Professions 3 4%
Agricultural and Biological Sciences 3 4%
Other 17 24%
Unknown 19 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 November 2015.
All research outputs
#1,867,794
of 12,364,552 outputs
Outputs from Journal of Epidemiology and Community Health (1978)
#1,040
of 3,239 outputs
Outputs of similar age
#43,350
of 237,483 outputs
Outputs of similar age from Journal of Epidemiology and Community Health (1978)
#34
of 94 outputs
Altmetric has tracked 12,364,552 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,239 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.4. This one has gotten more attention than average, scoring higher than 68% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 237,483 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 94 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.