School neighborhood disadvantage as a predictor of long-term sick leave among teachers: prospective cohort study

Am J Epidemiol. 2010 Apr 1;171(7):785-92. doi: 10.1093/aje/kwp459. Epub 2010 Feb 23.

Abstract

This ongoing prospective study examined characteristics of school neighborhood and neighborhood of residence as predictors of sick leave among school teachers. School neighborhood income data for 226 lower-level comprehensive schools in 10 towns in Finland were derived from Statistics Finland and were linked to register-based data on 3,063 teachers with no long-term sick leave at study entry. Outcome was medically certified (>9 days) sick leave spells during a mean follow-up of 4.3 years from data collection in 2000-2001. A multilevel, cross-classified Poisson regression model, adjusted for age, type of teaching job, length and type of job contract, school size, baseline health status, and income level of the teacher's residential area, showed a rate ratio of 1.30 (95% confidence interval: 1.03, 1.63) for sick leave among female teachers working in schools located in low-income neighborhoods compared with those working in high-income neighborhoods. A low income level of the teacher's residential area was also independently associated with sick leave among female teachers (rate ratio = 1.50, 95% confidence interval: 1.18, 1.91). Exposure to both low-income school neighborhoods and low-income residential neighborhoods was associated with the greatest risk of sick leave (rate ratio = 1.71, 95% confidence interval: 1.27, 2.30). This study indicates that working and living in a socioeconomically disadvantaged neighborhood is associated with increased risk of sick leave among female teachers.

Publication types

  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Child
  • Chronic Disease / epidemiology
  • Faculty* / statistics & numerical data
  • Female
  • Finland / epidemiology
  • Health Status Disparities*
  • Humans
  • Income
  • Male
  • Mental Disorders / epidemiology
  • Multivariate Analysis
  • Occupational Diseases / epidemiology*
  • Poverty Areas*
  • Prospective Studies
  • Regression Analysis
  • Residence Characteristics*
  • Risk Factors
  • Sex Distribution
  • Sick Leave / statistics & numerical data*