Geographic variations in risk: Adjusting for unmeasured confounders through joint modeling of multiple diseases

Nicky Best, Anna Louise Hansell

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

Abstract

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an important cause of mortality with marked geographic variations in Great Britain. Additional factors beyond cigarette smoking are likely to influence these variations, but direct information on smoking by area is not readily available. We compared methods of jointly modeling the spatial distribution of mortality from COPD and lung cancer, using the latter as a proxy for smoking, to identify areas in which risk factors other than smoking may be important. METHODS: We obtained district-level mortality and population data for men aged 45 years or older in 1981-1999 in Great Britain. Three models were compared: Bayesian ecological regression using observed (model 1) or spatially smoothed (model 2) lung cancer standardized mortality ratio (SMR) as a smoking proxy, and bivariate regression (model 3) treating smoking as a spatial latent variable common to both diseases. RESULTS: Model selection criteria favored models 2 and 3 over model 1. Between 9% (model 3) and 25% (model 2) of spatial variation in COPD mortality was estimated to be unrelated to smoking. After adjustment for lung cancer as a proxy for smoking, both models showed similar geographic patterns of higher COPD mortality in conurbation and mining areas, historically associated with heavy industry and higher air pollution levels. CONCLUSIONS: Joint modeling of multiple diseases can be used to investigate geographic variations in risk. These models reveal patterns that are adjusted for the effects of shared area-level risk factors for which no direct data are available.

Original languageEnglish
Pages (from-to)400-410
Number of pages11
JournalEpidemiology
Volume20
Issue number3
DOIs
Publication statusPublished - May 2009
Externally publishedYes

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