Joel Schwartz,Yijing Feng,Edgar Castro,Yaguang Wei
{"title":"Causal Concentration-Response Modeling with Continuous Curves and Exposure Error Correction: PM2.5 and Mortality in the Medicare Cohort.","authors":"Joel Schwartz,Yijing Feng,Edgar Castro,Yaguang Wei","doi":"10.1289/ehp15238","DOIUrl":null,"url":null,"abstract":"BACKGROUND\r\nMany studies have reported associations of PM2.5 with mortality, but fewer at low concentrations, and even fewer using causal modeling or correcting for exposure error bias. None have corrected for the non-representativeness of monitoring locations.\r\n\r\nOBJECTIVES\r\nWe examined the association of PM2.5 with all-cause mortality in the Medicare cohort using a combination of causal modeling, flexible concentration-response modeling, and bias correction for exposure error, while controlling for NO2 and O3 as well as standard confounders.\r\n\r\nMETHODS\r\nUsing monitors not used to fit our PM2.5 model we fitted 72 regression calibration models stratified by season, region, and elevation in the US. We fitted a B-spline with 4 degrees of freedom to the calibrated PM2.5 and fitted separate generalized propensity score models for each spline component using gradient boosting. We also used inverse probability weights to account for the non-representativeness of monitoring locations. Using the generalized propensity scores and the B-splines, we fitted quasi-Poisson models to counts of deaths in each ZIP code-year stratified by race, Medicaid status, and gender. Separate models were fit for participants identifying as Black and as White, and for ZIP codes with higher and lower poverty rates. We fit a model using the original exposure to estimate the extent of exposure error bias.\r\n\r\nRESULTS\r\nThe propensity score analysis achieved good balance for all covariates. Controlling for the propensity scores, we found a concentration-response curve with no evidence of a threshold, and whose confidence interval did not include the null from 4 μg/m3 and upward. There were 223,666,531 person-years of follow-up between the current US EPA standard of 9 μg/m3 and the WHO guideline of 5 μg/m3, and the rate ratio between them was 1.088 (95% CI 1.064, 1.113). Using the original exposure, the rate ratio was 1.076 (95% CI 1.070,1.083). Hence, effects continue below the EPA standard and calibrated estimates of effect were 16% higher. Effects were larger from 8 μg/m3 among participants identifying as Black.\r\n\r\nDISCUSSION\r\nThe concentration-response curve between air pollution and mortality remains after adjustment for exposure error and using causal models and continues to concentrations below current US EPA and EU standards, and even below WHO guidelines. Exposure error in the original exposure resulted in noticeable downward bias at low concentrations. Persons identifying as Black are more susceptible. https://doi.org/10.1289/EHP15238.","PeriodicalId":11862,"journal":{"name":"Environmental Health Perspectives","volume":"47 5 1","pages":""},"PeriodicalIF":10.1000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Health Perspectives","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1289/ehp15238","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
BACKGROUND
Many studies have reported associations of PM2.5 with mortality, but fewer at low concentrations, and even fewer using causal modeling or correcting for exposure error bias. None have corrected for the non-representativeness of monitoring locations.
OBJECTIVES
We examined the association of PM2.5 with all-cause mortality in the Medicare cohort using a combination of causal modeling, flexible concentration-response modeling, and bias correction for exposure error, while controlling for NO2 and O3 as well as standard confounders.
METHODS
Using monitors not used to fit our PM2.5 model we fitted 72 regression calibration models stratified by season, region, and elevation in the US. We fitted a B-spline with 4 degrees of freedom to the calibrated PM2.5 and fitted separate generalized propensity score models for each spline component using gradient boosting. We also used inverse probability weights to account for the non-representativeness of monitoring locations. Using the generalized propensity scores and the B-splines, we fitted quasi-Poisson models to counts of deaths in each ZIP code-year stratified by race, Medicaid status, and gender. Separate models were fit for participants identifying as Black and as White, and for ZIP codes with higher and lower poverty rates. We fit a model using the original exposure to estimate the extent of exposure error bias.
RESULTS
The propensity score analysis achieved good balance for all covariates. Controlling for the propensity scores, we found a concentration-response curve with no evidence of a threshold, and whose confidence interval did not include the null from 4 μg/m3 and upward. There were 223,666,531 person-years of follow-up between the current US EPA standard of 9 μg/m3 and the WHO guideline of 5 μg/m3, and the rate ratio between them was 1.088 (95% CI 1.064, 1.113). Using the original exposure, the rate ratio was 1.076 (95% CI 1.070,1.083). Hence, effects continue below the EPA standard and calibrated estimates of effect were 16% higher. Effects were larger from 8 μg/m3 among participants identifying as Black.
DISCUSSION
The concentration-response curve between air pollution and mortality remains after adjustment for exposure error and using causal models and continues to concentrations below current US EPA and EU standards, and even below WHO guidelines. Exposure error in the original exposure resulted in noticeable downward bias at low concentrations. Persons identifying as Black are more susceptible. https://doi.org/10.1289/EHP15238.
期刊介绍:
Environmental Health Perspectives (EHP) is a monthly peer-reviewed journal supported by the National Institute of Environmental Health Sciences, part of the National Institutes of Health under the U.S. Department of Health and Human Services. Its mission is to facilitate discussions on the connections between the environment and human health by publishing top-notch research and news. EHP ranks third in Public, Environmental, and Occupational Health, fourth in Toxicology, and fifth in Environmental Sciences.