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BMC Public Health
Published in: BMC Public Health 1/2017

Open Access 01-12-2017 | Research article

Neighborhood educational disparities in active commuting among women: the effect of distance between the place of residence and the place of work/study (an ACTI-Cités study)

Authors: Camille Perchoux, Julie-Anne Nazare, Tarik Benmarhnia, Paul Salze, Thierry Feuillet, Serge Hercberg, Franck Hess, Mehdi Menai, Christiane Weber, Hélène Charreire, Christophe Enaux, Jean-Michel Oppert, Chantal Simon

Published in: BMC Public Health | Issue 1/2017

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Abstract

Background

Active transportation has been associated with favorable health outcomes. Previous research highlighted the influence of neighborhood educational level on active transportation. However, little is known regarding the effect of commuting distance on social disparities in active commuting. In this regard, women have been poorly studied. The objective of this paper was to evaluate the relationship between neighborhood educational level and active commuting, and to assess whether the commuting distance modifies this relationship in adult women.

Methods

This cross-sectional study is based on a subsample of women from the Nutrinet-Santé web-cohort (N = 1169). Binomial, log-binomial and negative binomial regressions were used to assess the associations between neighborhood education level and (i) the likelihood of reporting any active commuting time, and (ii) the share of commuting time made by active transportation modes. Potential effect measure modification of distance to work on the previous associations was assessed both on the additive and the multiplicative scales.

Results

Neighborhood education level was positively associated with the probability of reporting any active commuting time (relative risk = 1.774; p < 0.05) and the share of commuting time spent active (relative risk = 1.423; p < 0.05). The impact of neighborhood education was greater at long distances to work for both outcomes.

Conclusions

Our results suggest that neighborhood educational disparities in active commuting tend to increase with commuting distance among women. Further research is needed to provide geographically driven guidance for health promotion intervention aiming at reducing disparities in active transportation among socioeconomic groups.
Appendix
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Literature
1.
Sugiyama T, Ding D, Owen N. Commuting by car: weight gain among physically active adults. Am J Prev Med. 2013;44(2):169–73.CrossRefPubMed
2.
3.
Laverty AA, et al. Active travel to work and cardiovascular risk factors in the United Kingdom. Am J Prev Med. 2013;45(3):282–8.CrossRefPubMed
4.
Hamer M, Chida Y. Active commuting and cardiovascular risk: a meta-analytic review. Prev Med. 2008;46(1):9–13.CrossRefPubMed
5.
Oja P, Vuori I, Paronen O. Daily walking and cycling to work: their utility as health-enhancing physical activity. Patient Educ Couns. 1998;33(Supplement 1(0)):S87–94.CrossRefPubMed
6.
Wanner M, et al. Active transport, physical activity, and body weight in adults: a systematic review. Am J Prev Med. 2012;42(5):493–502.CrossRefPubMed
7.
Yang L, et al. Associations between active commuting and physical activity in working adults: cross-sectional results from the commuting and health in Cambridge study. Prev Med. 2012;55(5):453–7.CrossRefPubMedPubMedCentral
8.
Cooper AR, et al. Commuting to school: are children who walk more physically active? Am J Prev Med. 2003;25(4):273–6.CrossRefPubMed
9.
Bauman AE, et al. Correlates of physical activity: why are some people physically active and others not? Lancet. 2012;380(9838):258–71.CrossRefPubMed
10.
Ball K, et al. Personal, social and environmental determinants of educational inequalities in walking: a multilevel study. J Epidemiol Community Health. 2007;61(2):108–14.CrossRefPubMedPubMedCentral
11.
Cerin E, Leslie E, Owen N. Explaining socio-economic status differences in walking for transport: an ecological analysis of individual, social and environmental factors. Soc Sci Med. 2009;68(6):1013–20.CrossRefPubMed
12.
Rind E, et al. Are income-related differences in active travel associated with physical environmental characteristics? A multi-level ecological approach. Int J Behav Nutr Phys Act. 2015;12(1):73.CrossRefPubMedPubMedCentral
13.
14.
Juneau C, et al. Socioeconomic position during childhood and physical activity during adulthood: a systematic review. Int J Public Health. 2015:60(7):799-813.
15.
Ross CE. Walking, exercising, and smoking: does neighborhood matter? Soc Sci Med. 2000;51(2):265–74.CrossRefPubMed
16.
Perchoux, C., et al.. Accounting for the daily locations visited in the study of the built environment correlates of recreational walking (the RECORD Cohort Study). Prev Med. 2015;81:142-9
17.
Karusisi N, et al. Environmental conditions around itineraries to destinations as correlates of walking for transportation among adults: the RECORD cohort study. PLoS One. 2014;9(5):e88929.CrossRefPubMedPubMedCentral
18.
Chaix B, et al. The environmental correlates of overall and neighborhood based recreational walking (a cross-sectional analysis of the RECORD study). Int J Behav Nutr Phys Act. 2014;11(1):20.CrossRefPubMedPubMedCentral
19.
Turner MA, et al. Helping poor families gain and sustain access to high-opportunity neighborhoods. Washington: The Urban Institute; 2011.
20.
Galea S, et al. Education inequality and use of cigarettes, alcohol, and marijuana. Drug Alcohol Depend. 2007;90:S4–S15.CrossRefPubMed
21.
22.
Mowafi M, et al. Are neighborhood education levels associated with BMI among adults in Cairo, Egypt? Soc Sci Med. 2011;72(8):1274–83.CrossRefPubMed
23.
Leal C, et al. Are associations between neighborhood socioeconomic characteristics and body mass index or waist circumference based on model extrapolations? Epidemiology. 2011;22(5):694–703.CrossRefPubMed
24.
Chaix B, et al. Associations of supermarket characteristics with weight status and body fat: a multilevel analysis of individuals within supermarkets (RECORD study). PLoS One. 2012;7(4):e32908.CrossRefPubMedPubMedCentral
25.
Lewin A, et al. Residential neighborhood, geographic work environment, and work economic sector: associations with body fat measured by bioelectrical impedance in the RECORD Study. Ann Epidemiol. 2014;24(3):180–6.CrossRefPubMed
26.
Wang X, et al. Estimating mixed-mode urban trail traffic using negative binomial regression models. J Urban Plann Dev. 2013;140(1):04013006.CrossRef
27.
Hankey S, et al. Estimating use of non-motorized infrastructure: Models of bicycle and pedestrian traffic in Minneapolis, MN. Landsc Urban Plan. 2012;107(3):307–16.CrossRef
28.
Brand T, et al. What works in community-based interventions promoting physical activity and healthy eating? A review of reviews. Int J Environ Res Public Health. 2014;11(6):5866–88.CrossRefPubMedPubMedCentral
29.
Cleland V, et al. Effectiveness of interventions to promote physical activity among socioeconomically disadvantaged women: a systematic review and meta-analysis. Obes Rev. 2013;14(3):197–212.CrossRefPubMed
30.
Badland HM, Schofield GM, Schluter PJ. Objectively measured commute distance: associations with actual travel modes and perceptions to place of work or study in Auckland, New Zealand. J Phys Act Health. 2007;4(1):80–6.CrossRefPubMed
31.
World Health Organization Transport, Environment and Health. Vol. 89. Austria: Regional Office for Europe of the World Health Organization: 2004.
32.
Badland HM, Schofield GM, Garrett N. Travel behavior and objectively measured urban design variables: associations for adults traveling to work. Health & Place. 2008;14(1):85–95.CrossRef
33.
Dalton AM, et al. Neighbourhood, route and workplace-related environmental characteristics predict adults’ mode of travel to work. PLoS One. 2013;8(6):e67575.CrossRefPubMedPubMedCentral
34.
Hoehner, C.M., et al., Understanding the independent and joint associations of the home and workplace built environments on cardiorespiratory fitness and body mass index. Am J Epidemiol. 2013.
35.
36.
Panter JR, Jones A. Attitudes and the environment as determinants of active travel in adults: what do and don’t we know. J Phys Act Health. 2010;7(4):551–61.CrossRefPubMed
37.
Yeung J, Wearing S, Hills AP. Child transport practices and perceived barriers in active commuting to school. Transp Res A Policy Pract. 2008;42(6):895–900.CrossRef
38.
Larsen K, et al. The influence of the physical environment and sociodemographic characteristics on children's mode of travel to and from school. Am J Public Health. 2009;99(3):520.CrossRefPubMedPubMedCentral
39.
Panter JR, Jones AP, van Sluijs EM. Environmental determinants of active travel in youth: A review and framework for future research. Int J Behav Nutr Phys Act. 2008;5(1):34.CrossRefPubMedPubMedCentral
40.
Panter J, et al. The influence of distance to school on the associations between active commuting and physical activity. Pediatr Exerc Sci. 2011;23(1):72.CrossRefPubMed
41.
Panter JR, et al. Neighborhood, route, and school environments and children's active commuting. Am J Prev Med. 2010;38(3):268–78.CrossRefPubMed
42.
Panter JR, et al. Attitudes, social support and environmental perceptions as predictors of active commuting behaviour in school children. J Epidemiol Community Health. 2010;64(01):41–8.CrossRefPubMedPubMedCentral
43.
44.
Heinen E, et al. Changes in mode of travel to work: a natural experimental study of new transport infrastructure. Int J Behav Nutr Phys Act. 2015;12(1):81.CrossRefPubMedPubMedCentral
45.
Smith RD, Petticrew M. Public health evaluation in the twenty-first century: time to see the wood as well as the trees. J Public Health. 2010;32(1):2–7.CrossRef
46.
Panter J, Desousa C, Ogilvie D. Incorporating walking or cycling into car journeys to and from work: the role of individual, workplace and environmental characteristics. Prev Med. 2013;56(3):211–7.CrossRefPubMedPubMedCentral
47.
Hercberg S, et al. The Nutrinet-Santé Study: a web-based prospective study on the relationship between nutrition and health and determinants of dietary patterns and nutritional status. BMC Public Health. 2010;10(1):1.CrossRef
48.
Menai M, et al. Walking and cycling for commuting, leisure and errands: relations with individual characteristics and leisure-time physical activity in a cross-sectional survey (the ACTI-Cités project). Int J Behav Nutr Phys Act. 2015;12(1):150.CrossRefPubMedPubMedCentral
49.
Le Jeannic, T., La mobilité des Français, panorama issu de l’enquête nationale transports et déplacements 2008. Paris: ministère de l’Écologie, du Développement durable, des Transports et du Logement, 2010.
50.
Feuillet T, et al. Spatial heterogeneity of the relationships between environmental characteristics and active commuting: towards a locally varying social ecological model. Int J Health Geogr. 2015;14(1):12.CrossRefPubMedPubMedCentral
51.
Jones C, Ogilvie D. Motivations for active commuting: a qualitative investigation of the period of home or work relocation. Int J Behav Nutr Phys Act. 2012;9(1):109.CrossRefPubMedPubMedCentral
52.
Spiegelman D, Hertzmark E. Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol. 2005;162(3):199–200.CrossRefPubMed
53.
Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–6.CrossRefPubMed
54.
Greenland S, Pearce N. Statistical foundations for model-based adjustments. Annu Rev Public Health. 2015;36:89–108.CrossRefPubMed
55.
VanderWeele TJ, Knol MJ. A tutorial on interaction. Epidemiol Methods. 2014;3(1):33–72.
56.
Richards JL, et al. Does neighborhood deprivation modify the effect of preterm birth on children's first grade academic performance? Soc Sci Med. 2015;132:122–31.CrossRefPubMedPubMedCentral
57.
58.
Gascon, M.-O., et al., Calcul a posteriori des distances dans les enquêtes ménages déplacements. 2009.
59.
Lachapelle U, et al. Commuting by public transit and physical activity: where you live, where you work, and how you get thete. J Phys Act Health. 2011;8(1):S72.CrossRefPubMed
60.
Chaix B, et al. Active transportation and public transportation use to achieve physical activity recommendations? A combined GPS, accelerometer, and mobility survey study. Int J Behav Nutr Phys Act. 2014;11(1):1.CrossRef
61.
Higgins JP, et al. Measuring inconsistency in meta-analyses. BMJ Br Med J. 2003;327(7414):557.CrossRef
62.
63.
Wuerzer T, Mason SG. Cycling willingness: investigating distance as a dependent variable in cycling behavior among college students. Appl Geogr. 2015;60:95–106.CrossRef
64.
Mullan E. Swapping the lycra for the suit: Determinants of cycling for transport among leisure cyclists in Ireland. Int J Health Promot Educ. 2012;50(5):229–37.CrossRef
65.
McCormack GR, Shiell A. In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. Int J Behav Nutr Phys Act. 2011;8(125):10.1186.
66.
Geyer S, et al. Education, income, and occupational class cannot be used interchangeably in social epidemiology. Empirical evidence against a common practice. J Epidemiol Community Health. 2006;60(9):804–10.CrossRefPubMedPubMedCentral
67.
Andreeva, VA, et al. Comparison of the sociodemographic characteristics of the large NutriNet-Santé e-cohort with French Census data: the issue of volunteer bias revisited. J Epidemiol Community Health. 2015: p. 10.​1136/​jech-2014-205263.
68.
Salze P, et al. Estimating spatial accessibility to facilities on the regional scale: an extended commuting-based interaction potential model. Int J Health Geogr. 2011;10(1):1.CrossRef
69.
McCormack, GR, et al. Objective versus perceived walking distances to destinations: correspondence and predictive validity. Environ Behav. 2007.
70.
Perchoux C, et al. Conceptualization and measurement of environmental exposure in epidemiology: accounting for activity space related to daily mobility. Health Place, 2013. 21(0): p. 86-93.
71.
Schlossberg M, et al. School trips: effects of urban form and distance on travel mode. J Am Plan Assoc. 2006;72(3):337–46.CrossRef
Metadata
Title
Neighborhood educational disparities in active commuting among women: the effect of distance between the place of residence and the place of work/study (an ACTI-Cités study)
Authors
Camille Perchoux
Julie-Anne Nazare
Tarik Benmarhnia
Paul Salze
Thierry Feuillet
Serge Hercberg
Franck Hess
Mehdi Menai
Christiane Weber
Hélène Charreire
Christophe Enaux
Jean-Michel Oppert
Chantal Simon
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2017
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-017-4464-8

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