Associations between local labour market histories and individual health outcomes
Riva, M. & Curtis, S. (2011) American Journal of Epidemiology, 173(Suppl 11), S317-S331 [ONS LS]
Abstracts of the 3rd North American Congress of Epidemiology, June 21-24, 2011 Montreal, Canada
The wider determinants of health operate over long time periods, so the socioeconomic ’life-history’ of a place is likely to influence health variation, though few studies have examined whether this is so. Set in England, this paper examines local associations between contemporary health outcomes and the local labour market history. Time series data on local employment rates from 1981 through to the 2000’s were compiled from the Decennial Census and the Labour Force Survey. Local areas having followed similar trends in employment rates were classified in eight groups using latent class growth models. These data were linked to a sample of 207,700 individuals obtained from the ONS Longitudinal Study. Associations between trends in area conditions and individual health outcomes (risk of mortality before the age of 75 and reporting a limiting long-term illness (LLTI) in 2001) were measured using logistic regression with robust adjustment for standard errors clustered in local areas. Models were adjusted for selected individuals’ socio-demographic variables, migration between groups of areas and length of exposure to area conditions. Results show that the odds of premature mortality and of reporting a LLTI are higher for people living in areas characterised by decreasing employment rates (mortality: Odds Ratio [OR]: 1.13; 95% Confidence Interval [CI]: 1.02, 1.25; LLTI: OR:1.68; 95%CI: 1.56, 1.80) and where local labour markets are persistently depressed over time (mortality: OR: 1.20; 95% CI: 1.09, 1.33; LLTI: OR: 1.72; 95%CI: 1.56, 1.89). Ancillary analyses show that trends in local socioeconomic conditions discriminate health inequalities better than area conditions measured concurrently to health outcomes. Long run socioeconomic conditions are useful to understand local health inequalities.