Citation

Berrie L, Feng Z, Rice D, Clemens T, Williamson L, Dibben C. Int. J. Epidemiol. 2024; ePub(ePub): ePub.

Copyright

(Copyright © 2024, International Epidemiological Association, Publisher Oxford University Press)

DOI

10.1093/ije/dyad153

PMID

38219793

Abstract

BACKGROUND: Previous studies have linked cycling with improved mental wellbeing but these studies tend to use cross-sectional survey data that have small sample sizes and self-reported health measures, and are potentially susceptible to omitted-variable bias and reverse causation. We use an instrumental variable approach and an objective measure of mental ill-health taken from linked administrative data to ask: 'Does cycle commuting reduce the risk of mental ill-health?' METHODS: Our study links data on commuting in Edinburgh and Glasgow from the Scottish population census with mental health prescriptions from the National Health Service Prescribing Information System records. We use road distance from home to nearest cycle path as an instrumental variable for cycle commuting.

RESULTS: In total, 378 253 people aged 16-74 years living and working in the City of Edinburgh and Glasgow City council areas at the 2011 census were included in our study; 1.85% of commuters in Glasgow and 4.8% of commuters in Edinburgh cycled to work. Amongst cyclists, 9% had a prescription for mental health compared with 14% amongst non-cyclists. Using a bivariate probit model, we estimate a mean average reduction in prescriptions for antidepressants and/or anxiolytics in the 5 years following the census of -15.1% (95% CI: -15.3% to -15.0%) amongst cycle commuters compared with those who use any other mode to commute.

CONCLUSIONS: This work suggests that cycle commuting is causally related to reduced mental ill-health and provides further evidence in support of the promotion of active travel to encourage commuters travelling shorter distances to shift to cycle commutes.

Language: en

Keywords

mental health; Commuting; active travel; cycle commuting; cycling; instrumental variable analysis