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Sun Y, Bhuyan R, Jiao A, Avila CC, Chiu VY, Slezak JM, Sacks DA, Molitor J, Benmarhnia T, Chen JC, Getahun D, Wu J. Association between particulate air pollution and hypertensive disorders in pregnancy: A retrospective cohort study. PLoS Med 2024; 21:e1004395. [PMID: 38669277 PMCID: PMC11087068 DOI: 10.1371/journal.pmed.1004395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 05/10/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Epidemiological findings regarding the association of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP) are inconsistent; evidence for HDP risk related to PM2.5 components, mixture effects, and windows of susceptibility is limited. We aimed to investigate the relationships between HDP and exposure to PM2.5 during pregnancy. METHODS AND FINDINGS A large retrospective cohort study was conducted among mothers with singleton pregnancies in Kaiser Permanente Southern California from 2008 to 2017. HDP were defined by International Classification of Diseases-9/10 (ICD-9/10) diagnostic codes and were classified into 2 subcategories based on the severity of HDP: gestational hypertension (GH) and preeclampsia and eclampsia (PE-E). Monthly averages of PM2.5 total mass and its constituents (i.e., sulfate, nitrate, ammonium, organic matter, and black carbon) were estimated using outputs from a fine-resolution geoscience-derived model. Multilevel Cox proportional hazard models were used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of PM2.5 constituents. The distributed lag model was applied to estimate the association between monthly PM2.5 exposure and HDP risk. This study included 386,361 participants (30.3 ± 6.1 years) with 4.8% (17,977/373,905) GH and 5.0% (19,381/386,361) PE-E cases, respectively. In single-pollutant models, we observed increased relative risks for PE-E associated with exposures to PM2.5 total mass [adjusted hazard ratio (HR) per interquartile range: 1.07, 95% confidence interval (CI) [1.04, 1.10] p < 0.001], black carbon [HR = 1.12 (95% CI [1.08, 1.16] p < 0.001)] and organic matter [HR = 1.06 (95% CI [1.03, 1.09] p < 0.001)], but not for GH. The population attributable fraction for PE-E corresponding to the standards of the US Environmental Protection Agency (9 μg/m3) was 6.37%. In multi-pollutant models, the PM2.5 mixture was associated with an increased relative risk of PE-E ([HR = 1.05 (95% CI [1.03, 1.07] p < 0.001)], simultaneous increase in PM2.5 constituents of interest by a quartile) and PM2.5 black carbon gave the greatest contribution of the overall mixture effects (71%) among all individual constituents. The susceptible window is the late first trimester and second trimester. Furthermore, the risks of PE-E associated with PM2.5 exposure were significantly higher among Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods (p < 0.05 for Cochran's Q test). Study limitations include potential exposure misclassification solely based on residential outdoor air pollution, misclassification of disease status defined by ICD codes, the date of diagnosis not reflecting the actual time of onset, and lack of information on potential covariates and unmeasured factors for HDP. CONCLUSIONS Our findings add to the literature on associations between air pollution exposure and HDP. To our knowledge, this is the first study reporting that specific air pollution components, mixture effects, and susceptible windows of PM2.5 may affect GH and PE-E differently.
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Affiliation(s)
- Yi Sun
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, California, United States of America
| | - Rashmi Bhuyan
- Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, California, United States of America
- Occupational and Environmental Medicine Residency Program, University of California, Irvine, California, United States of America
- Department of Occupational Medicine, Kaiser Permanente Northern California, Antioch, California, United States of America
| | - Anqi Jiao
- Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, California, United States of America
| | - Chantal C. Avila
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America
| | - Vicki Y. Chiu
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America
| | - Jeff M. Slezak
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America
| | - David A. Sacks
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America
- Department of Obstetrics and Gynecology, University of Southern California, Keck School of Medicine, Los Angeles, California, United States of America
| | - John Molitor
- College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon, United States of America
| | - Tarik Benmarhnia
- Scripps Institution of Oceanography, University of California, San Diego, California, United States of America
| | - Jiu-Chiuan Chen
- Departments of Population & Public Health Sciences and Neurology, University of Southern California, Keck School of Medicine, Los Angeles, California, United States of America
| | - Darios Getahun
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California, United States of America
| | - Jun Wu
- Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, California, United States of America
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Chen C, Chen H, Kaufman JS, Benmarhnia T. Differential Participation, a Potential Cause of Spurious Associations in Observational Cohorts in Environmental Epidemiology. Epidemiology 2024; 35:174-184. [PMID: 38290140 PMCID: PMC10826917 DOI: 10.1097/ede.0000000000001711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 12/18/2023] [Indexed: 02/01/2024]
Abstract
Differential participation in observational cohorts may lead to biased or even reversed estimates. In this article, we describe the potential for differential participation in cohorts studying the etiologic effects of long-term environmental exposures. Such cohorts are prone to differential participation because only those who survived until the start of follow-up and were healthy enough before enrollment will participate, and many environmental exposures are prevalent in the target population and connected to participation via factors such as geography or frailty. The relatively modest effect sizes of most environmental exposures also make any bias induced by differential participation particularly important to understand and account for. We discuss key points to consider for evaluating differential participation and use causal graphs to describe two example mechanisms through which differential participation can occur in health studies of long-term environmental exposures. We use a real-life example, the Canadian Community Health Survey cohort, to illustrate the non-negligible bias due to differential participation. We also demonstrate that implementing a simple washout period may reduce the bias and recover more valid results if the effect of interest is constant over time. Furthermore, we implement simulation scenarios to confirm the plausibility of the two mechanisms causing bias and the utility of the washout method. Since the existence of differential participation can be difficult to diagnose with traditional analytical approaches that calculate a summary effect estimate, we encourage researchers to systematically investigate the presence of time-varying effect estimates and potential spurious patterns (especially in initial periods in the setting of differential participation).
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Affiliation(s)
- Chen Chen
- From the Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA
| | - Hong Chen
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada
- Public Health Ontario, Toronto, Ontario, Canada
- ICES, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Jay S. Kaufman
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Tarik Benmarhnia
- From the Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA
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