Spatial variation in mortality risk for XXXX near a petrochemical refinery: A population-based case-control study.

@entity1 @entity1583
INTRODUCTION: The study investigated the geographic variation of mortality risk for @entity1583 ( @entity1583 ) in order to identify potential high-risk areas near an Italian petrochemical refinery. MATERIAL AND METHODS: A population-based case-control study was conducted and residential histories for 171 cases and 338 sex- and age-matched controls were collected. Confounding factors were obtained from interviews with consenting relatives for 109 HM deaths and 267 controls. To produce risk mortality maps, two different approaches were applied and compared. We mapped (1) adaptive kernel density relative risk estimation for case-control studies which estimates a spatial relative risk function using the ratio between cases and controls' densities, and (2) estimated odds ratios for case-control study data using Generalized Additive Models (GAMs) to smooth the effect of location, a proxy for exposure, while adjusting for confounding variables. RESULTS: No high-risk areas for HM mortality were identified among all subjects ( @entity1 and @entity1 combined), by applying both approaches. Using the adaptive KDE approach, we found a significant increase in death risk only among @entity1 in a large area 2-6km southeast of the refinery and the application of GAMs also identified a similarly-located significant high-risk area among @entity1 only (global p-value<0.025). Potential confounding risk factors we considered in the GAM did not alter the results. CONCLUSION: Both approaches identified a high-risk area close to the refinery among @entity1 only. Those spatial methods are useful tools for public policy management to determine priority areas for intervention. Our findings suggest several directions for further research in order to identify other potential environmental exposures that may be assessed in forthcoming studies based on detailed exposure modeling.
