Does income inequality modify the association between air pollution and health?
BACKGROUND
It has been hypothesized that socioeconomic status may act as an effect modifier of the association between air pollution and health. In this study, we investigated whether income inequality may modify the association between fine particulate pollution and self-reported health.


METHODS
We combined several different sources of data. Demographic and socio-economic data, at the individual level, were drawn from the 2001 US Behavioral Risk Factor Surveillance System (BRFSS). County-level particulate pollution data for the year 2001 were provided by the US Environmental Protection Agency. State-level income inequality was measured by the Gini index using US census data from the year 2000. We used a hierarchical logistic regression to model the association between general self-reported health and fine particulate pollution accounting for income inequality as an effect modifier and controlling for the usual confounders.


RESULTS
We found that when income inequality is low (10th percentile of the Gini distribution), the odds of reporting fair or poor health for a 10microg/m3 increase in particulate pollution is 1.34 (95% confidence interval 1.21-1.48). The analogous odds ratio for higher income inequality (60th percentile of the Gini distribution) is 1.11 (95% confidence interval 1.06-1.16).


CONCLUSIONS
Income inequality was found to be an effect modifier of the association between general self-reported health and particulate pollution. However, these findings challenged our hypothesis that people living in higher income inequality areas are more vulnerable to the impact of air pollution. We discuss the factors driving these results.
Charafeddine
Rana
R
Léa-Roback Research Centre on Social Inequalities in Health of Montreal, Montréal, Québec, Canada H2L 1M3. Rana.charafeddine@umontreal.ca
Boden
Leslie I
LI
D000328:Adult; D000397:Air Pollution / Q000032:analysis*; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D007182:Income / Q000145:classification* / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D052638:Particulate Matter / Q000032:analysis*; D012647:Self-Assessment; D014481:United States
D052638:Particulate Matter