Advancing Health Equity Metrics: Estimating the Burden of Lung Cancer Attributed to Known Carcinogens by Socio-economic Position

Abstract

Attributable burden of disease estimates reported population-wide do not reflect social disparities in exposures and outcomes. This makes one of the influential scientific tools in public health decision-making insensitive to the distribution of health impacts between socioeconomic groups. Our aim was to use the often-overlooked distributive property of the population attributable fraction (PAF) to quantitatively partition the population burden attributed to know risk factors into subgroups defined by their socioeconomic position (SEP). To illustrate our approach, we focus on lung cancer risk in relation to smoking and exposure to three occupational carcinogens: asbestos, silica dust and diesel motor exhaust. We directly estimate PAFs from a large population-based case-control study using multiple unconditional logistic regression, mutually adjusting for available known risk factors. We partition the PAFs of occupational exposures and smoking according to different SEP indicators: occupational class, prestige and trajectory, and education. Our results show that workplace exposures, smoking and their population health impacts concentrate among lower-SEP groups, a long-known reality that had never been measured through a PAF approach. While attempting to quantify the avoidable burden of diseases, it is useful to partition population-wide into SEP-specific metrics, as the modifiable exposures (behavioural, work-related, environmental) are socially stratified.