Bias in the estimated association between all-cause mortality and long-term exposure to a specific chemical component of fine particulate matter: The example of black carbon
Introduction: Long-term exposure to fine particulate matter (PM2.5) has been linked to many adverse health outcomes, which can vary significantly depending on the chemical profile of the PM2.5. However, many meta-analyses of the health effects of a specific component of PM2.5 have ignored the effect...
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Elsevier
2025-09-01
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| Series: | Ecotoxicology and Environmental Safety |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651325012047 |
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| author | Jianyu Deng Ning Kang Xueqiu Ni Hong Lu Meng Wang Mingkun Tong Pengfei Li Mingjin Tang Tao Xue Mei Zheng Tong Zhu |
| author_facet | Jianyu Deng Ning Kang Xueqiu Ni Hong Lu Meng Wang Mingkun Tong Pengfei Li Mingjin Tang Tao Xue Mei Zheng Tong Zhu |
| author_sort | Jianyu Deng |
| collection | DOAJ |
| description | Introduction: Long-term exposure to fine particulate matter (PM2.5) has been linked to many adverse health outcomes, which can vary significantly depending on the chemical profile of the PM2.5. However, many meta-analyses of the health effects of a specific component of PM2.5 have ignored the effects of other components, leading to omitted variable bias (OVB). This study developed a new method to address this problem and conducted a simulation using black carbon (BC) as an example. Method: We used data from two published meta-analyses as input for our model, with supplementary information obtained from a reanalysis product of PM2.5 components. Based on the classical OVB formula, we developed a post hoc adjusted model and verified its performance via a simulation study. We obtained pooled estimates of the effect of BC on all-cause mortality, with adjustment for the effect of non-black carbon (NBC) components. Finally, based on the estimated effects of BC and NBC, we investigated global patterns in PM2.5 toxicity (i.e., the per-unit effect of PM2.5) and the degree of OVB associated with ignoring the differential effects of BC and NBC. Results: The post hoc adjusted model included 46 individual estimates of the effects of BC or NBC on all-cause mortality. Results from the model indicate that a 10 μg/m³ increase in BC and NBC was associated with a 49 % (95 % confidence interval [CI]: 26 – 76 %) and 6 % (95 % CI: 3 – 10 %) increase in mortality risk, respectively. Based on global average total PM2.5 mass composition values (6.1 % and 93.9 % for BC and NBC, respectively), we estimated that the relative risk of all-cause mortality increased by 1.09 (95 % CI: 1.06 – 1.12) per 10 μg/m3 increment in long-term PM2.5 exposure. Estimation of the effects of BC on mortality based on observations obtained within one city yielded a median OVB of 147 % (95 % CI: −151 – 700) when using a single-pollutant model. Conclusion: In meta-analyses on the health impacts of PM2.5 components, ignoring the differential effects of BC and NBC causes significant biases in estimating associations with all-cause mortality. Our study presents a novel method to adjust for OVB in meta-analyses, and we find that BC more harmful than NBC components of PM2.5 by using the novel method. |
| format | Article |
| id | doaj-art-0bb1de6557ce42b58db0cdc60b0cf5e4 |
| institution | Kabale University |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
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| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-0bb1de6557ce42b58db0cdc60b0cf5e42025-08-20T03:46:45ZengElsevierEcotoxicology and Environmental Safety0147-65132025-09-0130311885910.1016/j.ecoenv.2025.118859Bias in the estimated association between all-cause mortality and long-term exposure to a specific chemical component of fine particulate matter: The example of black carbonJianyu Deng0Ning Kang1Xueqiu Ni2Hong Lu3Meng Wang4Mingkun Tong5Pengfei Li6Mingjin Tang7Tao Xue8Mei Zheng9Tong Zhu10Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health / Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing 100191, ChinaInstitute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health / Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing 100191, ChinaInstitute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health / Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing 100191, ChinaInstitute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health / Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing 100191, ChinaDepartment of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY 14214, USAInstitute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health / Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing 100191, ChinaInstitute of Medical Technology, Peking University Health Science Centre, Beijing 100191, China; Advanced Institute of Information Technology, Peking University, Hangzhou 311215, ChinaState Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, ChinaInstitute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health / Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing 100191, China; Advanced Institute of Information Technology, Peking University, Hangzhou 311215, China; State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing 100871, China; Corresponding author at: Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health / Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing 100191, China.State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing 100871, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, ChinaState Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing 100871, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, ChinaIntroduction: Long-term exposure to fine particulate matter (PM2.5) has been linked to many adverse health outcomes, which can vary significantly depending on the chemical profile of the PM2.5. However, many meta-analyses of the health effects of a specific component of PM2.5 have ignored the effects of other components, leading to omitted variable bias (OVB). This study developed a new method to address this problem and conducted a simulation using black carbon (BC) as an example. Method: We used data from two published meta-analyses as input for our model, with supplementary information obtained from a reanalysis product of PM2.5 components. Based on the classical OVB formula, we developed a post hoc adjusted model and verified its performance via a simulation study. We obtained pooled estimates of the effect of BC on all-cause mortality, with adjustment for the effect of non-black carbon (NBC) components. Finally, based on the estimated effects of BC and NBC, we investigated global patterns in PM2.5 toxicity (i.e., the per-unit effect of PM2.5) and the degree of OVB associated with ignoring the differential effects of BC and NBC. Results: The post hoc adjusted model included 46 individual estimates of the effects of BC or NBC on all-cause mortality. Results from the model indicate that a 10 μg/m³ increase in BC and NBC was associated with a 49 % (95 % confidence interval [CI]: 26 – 76 %) and 6 % (95 % CI: 3 – 10 %) increase in mortality risk, respectively. Based on global average total PM2.5 mass composition values (6.1 % and 93.9 % for BC and NBC, respectively), we estimated that the relative risk of all-cause mortality increased by 1.09 (95 % CI: 1.06 – 1.12) per 10 μg/m3 increment in long-term PM2.5 exposure. Estimation of the effects of BC on mortality based on observations obtained within one city yielded a median OVB of 147 % (95 % CI: −151 – 700) when using a single-pollutant model. Conclusion: In meta-analyses on the health impacts of PM2.5 components, ignoring the differential effects of BC and NBC causes significant biases in estimating associations with all-cause mortality. Our study presents a novel method to adjust for OVB in meta-analyses, and we find that BC more harmful than NBC components of PM2.5 by using the novel method.http://www.sciencedirect.com/science/article/pii/S0147651325012047Omitted variable biasPM2.5Black carbonLong-term exposureAll-cause mortality |
| spellingShingle | Jianyu Deng Ning Kang Xueqiu Ni Hong Lu Meng Wang Mingkun Tong Pengfei Li Mingjin Tang Tao Xue Mei Zheng Tong Zhu Bias in the estimated association between all-cause mortality and long-term exposure to a specific chemical component of fine particulate matter: The example of black carbon Ecotoxicology and Environmental Safety Omitted variable bias PM2.5 Black carbon Long-term exposure All-cause mortality |
| title | Bias in the estimated association between all-cause mortality and long-term exposure to a specific chemical component of fine particulate matter: The example of black carbon |
| title_full | Bias in the estimated association between all-cause mortality and long-term exposure to a specific chemical component of fine particulate matter: The example of black carbon |
| title_fullStr | Bias in the estimated association between all-cause mortality and long-term exposure to a specific chemical component of fine particulate matter: The example of black carbon |
| title_full_unstemmed | Bias in the estimated association between all-cause mortality and long-term exposure to a specific chemical component of fine particulate matter: The example of black carbon |
| title_short | Bias in the estimated association between all-cause mortality and long-term exposure to a specific chemical component of fine particulate matter: The example of black carbon |
| title_sort | bias in the estimated association between all cause mortality and long term exposure to a specific chemical component of fine particulate matter the example of black carbon |
| topic | Omitted variable bias PM2.5 Black carbon Long-term exposure All-cause mortality |
| url | http://www.sciencedirect.com/science/article/pii/S0147651325012047 |
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