Modeling historic environmental pollutant exposures and non-Hodgkin lymphoma risk

• Published in: Environmental research 2023-05, Vol.224, p.115506-115506, Article 115506
• Main Authors: Boyle, Joseph, Ward, Mary H., Cerhan, James R., Rothman, Nathaniel, Wheeler, David C.
• Format: Article
• Language: English
• Subjects: 6): exposome; Bayes Theorem; Carcinogens; Case-Control Studies; Dust - analysis; Environmental exposure; Environmental Exposure - statistics & numerical data; Environmental Pollutants; Humans; Lymphoma, Non-Hodgkin - epidemiology; Mixture analysis; Models, Statistical; Non-Hodgkin lymphoma; Spatial analysis
• ISSN: 0013-9351; 1096-0953; 1096-0953
• DOI: 10.1016/j.envres.2023.115506

Environmental exposures to chemicals are suspected risk factors for non-Hodgkin lymphoma (NHL), but few studies have assessed historic environmental risk factors. In this study, we estimated the associations between NHL and 1) historic environmental pollutant emissions from the Risk Screening Environmental Indicators (RSEI) model, which uses a database from the Environmental Protection Agency of toxic release emissions to air, water, and land, and 2) chemical mixtures measured in house dust (groups of PCBs, PAHs, and two mixtures of pesticides) for study participants enrolled in the NCI-SEER population-based case-control study (1998–2000) at four SEER centers – Detroit, Iowa, Los Angeles County, and Seattle. We assigned 11 years of annual temporally-varying historic environmental exposure scores by intersecting residential locations from participants' residential histories with a fine grid from the RSEI model and by performing inverse distance weighting between facilities releasing specific carcinogenic chemicals and residential locations for spatially-precise exposure assignments. We used Bayesian index low-rank kriging multiple membership models to identify important lag times for RSEI scores, cumulative effects of RSEI scores, and specific carcinogenic chemical releases into the environment. We found a significant positive association between RSEI scores and NHL at the maximum time lag of 11 years (OR = 1.17, 95% CI (1.06, 1.32)) and a significant cumulative RSEI score effect (OR = 1.30, 95% CI (1.02, 1.84)) for long-term residents in Detroit, where benzene and trichloroethylene were the most important chemicals driving this association. Additionally, we identified significant inverse associations for two study centers and time lags that did not persist in cumulative exposure models. Large weights for dichloromethane and pentachlorophenol in models of cumulative exposure also support evidence for their association with NHL risk. These results underscore the importance of considering historic and cumulative environmental exposures and using residential histories for diseases with long latency periods such as NHL. •We assigned 11 years of historic environmental exposure scores based on residence.•Used Bayesian index low-rank kriging multiple membership models to identify lags.•Found a significant positive association between exposure scores and NHL.•Benzene and trichloroethylene were the most important chemicals.•Results show importance of considering his