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Yu Y, Tang Z, Huang Y, Zhang J, Wang Y, Zhang Y, Wang Q. Assessing long-term effects of gaseous air pollution exposure on mortality in the United States using a variant of difference-in-differences analysis. Sci Rep 2024; 14:16220. [PMID: 39003417 PMCID: PMC11246484 DOI: 10.1038/s41598-024-66951-9] [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: 03/09/2024] [Accepted: 07/05/2024] [Indexed: 07/15/2024] Open
Abstract
Long-term mortality effects of particulate air pollution have been investigated in a causal analytic frame, while causal evidence for associations with gaseous air pollutants remains extensively lacking, especially for carbon monoxide (CO) and sulfur dioxide (SO2). In this study, we estimated the causal relationship of long-term exposure to nitrogen dioxide (NO2), CO, SO2, and ozone (O3) with mortality. Utilizing the data from National Morbidity, Mortality, and Air Pollution Study, we applied a variant of difference-in-differences (DID) method with conditional Poisson regression and generalized weighted quantile sum regression (gWQS) to investigate the independent and joint effects. Independent exposures to NO2, CO, and SO2 were causally associated with increased risks of total, nonaccidental, and cardiovascular mortality, while no evident associations with O3 were identified in the entire population. In gWQS analyses, an interquartile range-equivalent increase in mixture exposure was associated with a relative risk of 1.067 (95% confidence interval: 1.010-1.126) for total mortality, 1.067 (1.009-1.128) for nonaccidental mortality, and 1.125 (1.060-1.193) for cardiovascular mortality, where NO2 was identified as the most significant contributor to the overall effect. This nationwide DID analysis provided causal evidence for independent and combined effects of NO2, CO, SO2, and O3 on increased mortality risks among the US general population.
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Affiliation(s)
- Yong Yu
- Center of Health Administration and Development Studies, School of Public Health, Hubei University of Medicine, Shiyan, 442000, China
| | - Ziqing Tang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Yuqian Huang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Jingjing Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Yixiang Wang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Yunquan Zhang
- Center of Health Administration and Development Studies, School of Public Health, Hubei University of Medicine, Shiyan, 442000, China.
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China.
| | - Qun Wang
- Center of Health Administration and Development Studies, School of Public Health, Hubei University of Medicine, Shiyan, 442000, China.
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Wang L, Wen L, Shen J, Wang Y, Wei Q, He W, Liu X, Chen P, Jin Y, Yue D, Zhai Y, Mai H, Zeng X, Hu Q, Lin W. The association between PM 2.5 components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models. ENVIRONMENTAL RESEARCH 2024; 252:118827. [PMID: 38580006 DOI: 10.1016/j.envres.2024.118827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND PM2.5 is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develop an analytical methodology combining traditional and novel machine learning models to evaluate PM2.5's combined effects on blood pressure (BP) and identify the most toxic components. METHODS We measured late-pregnancy BP of 1138 women from the Heshan cohort while simultaneously analyzing 31 PM2.5 components. We utilized multiple linear regression modeling to establish the relationship between PM2.5 components and late-pregnancy BP and applied Random Forest (RF) and generalized Weighted Quantile Sum (gWQS) regression to identify the most toxic components contributing to elevated BP and to quantitatively evaluate the cumulative effect of the PM2.5 component mixtures. RESULTS The results revealed that 16 PM2.5 components, such as EC, OC, Ti, Fe, Mn, Cu, Cd, Mg, K, Pb, Se, Na+, K+, Cl-, NO3-, and F-, contributed to elevated systolic blood pressure (SBP), while 26 components, including two carbon components (EC, OC), fourteen metallics (Ti, Fe, Mn, Cr, Mo, Co, Cu, Zn, Cd, Na, Mg, Al, K, Pb), one metalloid (Se), and nine water-soluble ions (Na+, K+, Mg2+, Ca2+, NH4+, Cl-, NO3-, SO42-, F-), contributed to elevated diastolic blood pressure (DBP). Mn and Cr were the most toxic components for elevated SBP and DBP, respectively, as analyzed by RF and gWQS models and verified against each other. Exposure to PM2.5 component mixtures increased SBP by 1.04 mmHg (95% CI: 0.33-1.76) and DBP by 1.13 mmHg (95% CI: 0.47-1.78). CONCLUSIONS Our study highlights the effectiveness of combining traditional and novel models as an analytical strategy to quantify the health effects of PM2.5 constituent mixtures.
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Affiliation(s)
- Lijie Wang
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Li Wen
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jianling Shen
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Yi Wang
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Qiannan Wei
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Wenjie He
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Xueting Liu
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Peiyao Chen
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Yan Jin
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Dingli Yue
- Guangdong Ecological and Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangzhou, 510308, China
| | - Yuhong Zhai
- Guangdong Ecological and Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangzhou, 510308, China
| | - Huiying Mai
- Department of Obstetrics and Gynecology, Heshan Maternal and Child Health Hospital, Jiangmen, 529700, China
| | - Xiaoling Zeng
- Department of Obstetrics and Gynecology, Heshan Maternal and Child Health Hospital, Jiangmen, 529700, China
| | - Qiansheng Hu
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
| | - Weiwei Lin
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
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Qi Q, Yu F, Nair AA, Lau SSS, Luo G, Mithu I, Zhang W, Li S, Lin S. Hidden danger: The long-term effect of ultrafine particles on mortality and its sociodemographic disparities in New York State. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134317. [PMID: 38636229 DOI: 10.1016/j.jhazmat.2024.134317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/09/2024] [Accepted: 04/14/2024] [Indexed: 04/20/2024]
Abstract
Although previous studies have shown increased health risks of particulate matters, few have evaluated the long-term health impacts of ultrafine particles (UFPs or PM0.1, ≤ 0.1 µm in diameter). This study assessed the association between long-term exposure to UFPs and mortality in New York State (NYS), including total non-accidental and cause-specific mortalities, sociodemographic disparities and seasonal trends. Collecting data from a comprehensive chemical transport model and NYS Vital Records, we used the interquartile range (IQR) and high-level UFPs (≥75 % percentile) as indicators to link with mortalities. Our modified difference-in-difference model controlled for other pollutants, meteorological factors, spatial and temporal confounders. The findings indicate that long-term UFPs exposure significantly increases the risk of non-accidental mortality (RR=1.10, 95 % CI: 1.05, 1.17), cardiovascular mortality (RR=1.11, 95 % CI: 1.05, 1.18) particularly for cerebrovascular (RR=1.21, 95 % CI: 1.10, 1.35) and pulmonary heart diseases (RR=1.33, 95 % CI: 1.13, 1.57), and respiratory mortality (borderline significance, RR=1.09, 95 % CI: 1.00, 1.18). Hispanics (RR=1.13, 95 % CI: 1.00, 1.29) and non-Hispanic Blacks (RR=1.40, 95 % CI: 1.16, 1.68) experienced significantly higher mortality risk after exposure to UFPs, compared to non-Hispanic Whites. Children under five, older adults, non-NYC residents, and winter seasons are more susceptible to UFPs' effects.
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Affiliation(s)
- Quan Qi
- Department of Economics, University at Albany, State University of New York, Albany, NY, USA
| | - Fangqun Yu
- Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA
| | - Arshad A Nair
- Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA
| | - Sam S S Lau
- Research Centre for Environment and Human Health & College of International Education, School of Continuing Education, Hong Kong Baptist University, Hong Kong, China; Institute of Bioresource and Agriculture, Hong Kong Baptist University, Hong Kong, China
| | - Gan Luo
- Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA
| | - Imran Mithu
- Community, Environment and Policy Division, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Sean Li
- Rausser College of Natural Resources, University of California, Berkeley, CA, USA
| | - Shao Lin
- Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, Rensselaer, NY, USA; Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY, USA.
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Li D, Shi T, Meng L, Zhang X, Li R, Wang T, Zhao X, Zheng H, Ren X. An association between PM 2.5 components and respiratory infectious diseases: A China's mainland-based study. Acta Trop 2024; 254:107193. [PMID: 38604327 DOI: 10.1016/j.actatropica.2024.107193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/13/2024]
Abstract
The particulate matter with diameter of less than 2.5 µm (PM2.5) is an important risk factor for respiratory infectious diseases, such as scarlet fever, tuberculosis, and similar diseases. However, it is not clear which component of PM2.5 is more important for respiratory infectious diseases. Based on data from 31 provinces in mainland China obtained between 2013 and 2019, this study investigated the effects of different PM2.5 components, i.e., sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and organic matter (OM), and black carbon (BC), on respiratory infectious diseases incidence [pulmonary tuberculosis (PTB), scarlet fever (SF), influenza, hand, foot, and mouth disease (HFMD), and mumps]. Geographical probes and the Bayesian kernel machine regression (BKMR) model were used to investigate correlations, single-component effects, joint effects, and interactions between components, and subgroup analysis was used to assess regional and temporal heterogeneity. The results of geographical probes showed that the chemical components of PM2.5 were associated with the incidence of respiratory infectious diseases. BKMR results showed that the five components of PM2.5 were the main factors affecting the incidence of respiratory infectious diseases (PIP>0.5). The joint effect of influenza and mumps by co-exposure to the components showed a significant positive correlation, and the exposure-response curve for a single component was approximately linear. And single-component modelling revealed that OM and BC may be the most important factors influencing the incidence of respiratory infections. Moreover, respiratory infectious diseases in southern and southwestern China may be less affected by the PM2.5 component. This study is the first to explore the relationship between different components of PM2.5 and the incidence of five common respiratory infectious diseases in 31 provinces of mainland China, which provides a certain theoretical basis for future research.
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Affiliation(s)
- Donghua Li
- School of Public Health, Lanzhou University, Chengguan District, Lanzhou City, Gansu Province 730000, China
| | - Tianshan Shi
- School of Public Health, Lanzhou University, Chengguan District, Lanzhou City, Gansu Province 730000, China
| | - Lei Meng
- Gansu Provincial Center for Disease Control and Prevention, Chengguan District, Lanzhou City, Gansu Province 730000, China
| | - Xiaoshu Zhang
- Gansu Provincial Center for Disease Control and Prevention, Chengguan District, Lanzhou City, Gansu Province 730000, China
| | - Rui Li
- School of Public Health, Lanzhou University, Chengguan District, Lanzhou City, Gansu Province 730000, China
| | - Tingrong Wang
- School of Public Health, Lanzhou University, Chengguan District, Lanzhou City, Gansu Province 730000, China
| | - Xin Zhao
- School of Public Health, Lanzhou University, Chengguan District, Lanzhou City, Gansu Province 730000, China
| | - Hongmiao Zheng
- School of Public Health, Lanzhou University, Chengguan District, Lanzhou City, Gansu Province 730000, China
| | - Xiaowei Ren
- School of Public Health, Lanzhou University, Chengguan District, Lanzhou City, Gansu Province 730000, China.
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Luo D, Wang L, Zhang M, Martinez L, Chen S, Zhang Y, Wang W, Wu Q, Wu Y, Liu K, Xie B, Chen B. Spatial spillover effect of environmental factors on the tuberculosis occurrence among the elderly: a surveillance analysis for nearly a dozen years in eastern China. BMC Public Health 2024; 24:209. [PMID: 38233763 PMCID: PMC10795419 DOI: 10.1186/s12889-024-17644-5] [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: 08/17/2023] [Accepted: 01/02/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND In many areas of China, over 30% of tuberculosis cases occur among the elderly. We aimed to investigate the spatial distribution and environmental factors that predicted the occurence of tuberculosis in this group. METHODS Data were collected on notified pulmonary tuberculosis (PTB) cases aged ≥ 65 years in Zhejiang Province from 2010 to 2021. We performed spatial autocorrelation and spatial-temporal scan statistics to determine the clusters of epidemics. Spatial Durbin Model (SDM) analysis was used to identify significant environmental factors and their spatial spillover effects. RESULTS 77,405 cases of PTB among the elderly were notified, showing a decreasing trend in the notification rate. Spatial-temporal analysis showed clustering of epidemics in the western area of Zhejiang Province. The results of the SDM indicated that a one-unit increase in PM2.5 led to a 0.396% increase in the local notification rate. The annual mean temperature and precipitation had direct effects and spatial spillover effects on the rate, while complexity of the shape of the greenspace (SHAPE_AM) and SO2 had negative spatial spillover effects. CONCLUSION Targeted interventions among the elderly in Western Zhejiang may be more efficient than broad, province-wide interventions. Low annual mean temperature and high annual mean precipitation in local and neighboring areas tend to have higher PTB onset among the elderly.
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Affiliation(s)
- Dan Luo
- Department of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Luyu Wang
- School of Urban Design, Wuhan University, Hubei, Wuhan, China
| | - Mengdie Zhang
- Zhejiang University School of Public Health, Hangzhou, Zhejiang, China
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA
| | - Songhua Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yu Zhang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Wei Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Qian Wu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yonghao Wu
- Department of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China, 310058
| | - Kui Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
- National Centre for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Bo Xie
- School of Urban Design, Wuhan University, Hubei, Wuhan, China.
| | - Bin Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
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Mao JJ, Chen HL, Li CH, Lu JW, Gu YY, Feng J, Zhang B, Ma JF, Qin G. Population impact of fine particulate matter on tuberculosis risk in China: a causal inference. BMC Public Health 2023; 23:2285. [PMID: 37980514 PMCID: PMC10657490 DOI: 10.1186/s12889-023-16934-8] [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: 08/01/2023] [Accepted: 10/08/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Previous studies have suggested the potential association between air pollution and tuberculosis incidence, but this association remains inconclusive and evidence to assess causality is particularly lacking. We aimed to draw causal inference between fine particulate matter less than 2.5 μm in diameter (PM2.5) and tuberculosis in China. METHODS Granger causality (GC) inference was performed within vector autoregressive models at levels and/or first-differences using annual national aggregated data during 1982-2019, annual provincial aggregated data during 1982-2019 and monthly provincial aggregated data during 2004-2018. Convergent cross-mapping (CCM) approach was used to determine the backbone nonlinear causal association based on the monthly provincial aggregated data during 2004-2018. Moreover, distributed lag nonlinear model (DLNM) was applied to quantify the causal effects. RESULTS GC tests identified PM2.5 driving tuberculosis dynamics at national and provincial levels in Granger sense. Empirical dynamic modeling provided the CCM causal intensity of PM2.5 effect on tuberculosis at provincial level and demonstrated that PM2.5 had a positive effect on tuberculosis incidence. Then, DLNM estimation demonstrated that the PM2.5 exposure driven tuberculosis risk was concentration- and time-dependent in a nonlinear manner. This result still held in the multi-pollutant model. CONCLUSIONS Causal inference showed that PM2.5 exposure driving tuberculosis, which showing a concentration gradient change. Air pollutant control may have potential public health benefit of decreasing tuberculosis burden.
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Affiliation(s)
- Jun-Jie Mao
- Joint Division of Clinical Epidemiology, Affiliated Hospital of Nantong University, School of Public Health of Nantong University, Nantong, China
- Jiangyin Center for Disease Control and Prevention, Wuxi, China
| | - Hong-Lin Chen
- Department of Epidemiology and Biostatistics, School of Public Health of Nantong University, Nantong, China
| | - Chun-Hu Li
- Department of Epidemiology and Biostatistics, School of Public Health of Nantong University, Nantong, China
| | - Jia-Wang Lu
- Department of Infectious Diseases, Affiliated Hospital of Nantong University, Nantong, China
| | - Yuan-Yuan Gu
- Centre for the Health Economy, Macquarie University, Sydney, NSW, Australia
| | - Jian Feng
- National Key Clinical Construction Specialty - Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong, China
| | - Bin Zhang
- Department of Infectious Diseases, Affiliated Hospital of Nantong University, Nantong, China.
| | - Jun-Feng Ma
- Nantong Center for Disease Control and Prevention, Nantong, China.
| | - Gang Qin
- Joint Division of Clinical Epidemiology, Affiliated Hospital of Nantong University, School of Public Health of Nantong University, Nantong, China.
- Department of Epidemiology and Biostatistics, School of Public Health of Nantong University, Nantong, China.
- National Key Clinical Construction Specialty - Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong, China.
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Zhu PP, Gao Y, Zhou GZ, Liu R, Li XB, Fu XX, Fu J, Lin F, Zhou YP, Li L. Short-term effects of high-resolution (1-km) ambient PM 2.5 and PM 10 on hospital admission for pulmonary tuberculosis: a case-crossover study in Hainan, China. Front Public Health 2023; 11:1252741. [PMID: 37736088 PMCID: PMC10509552 DOI: 10.3389/fpubh.2023.1252741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 08/16/2023] [Indexed: 09/23/2023] Open
Abstract
Introduction There is limited evidence regarding particulate matter (PM)'s short-term effects on pulmonary tuberculosis (PTB) hospital admission. Our study aimed to determine the short-term associations of the exposure to ambient PM with aerodynamic diameters <2.5 μm (PM2.5) and < 10 μm (PM10) with hospital admission for PTB in Hainan, a tropical province in China. Methods We collected individual data on patients hospitalized with PTB, PM2.5, PM10, and meteorological data from 2016 to 2019 in Hainan Province, China. Conditional logistic regression models with a time-stratified case-crossover design were used to assess the short-term effects of PM2.5 and PM10 on hospital admission for PTB at a spatial resolution of 1 km × 1 km. Stratified analyses were performed according to age at admission, sex, marital status, administrative division, and season of admission. Results Each interquartile range (IQR) increases in the concentrations of PM2.5 and PM10 were associated with 1.155 (95% confidence interval [CI]: 1.041-1.282) and 1.142 (95% CI: 1.033-1.263) hospital admission risks for PTB at lag 0-8 days, respectively. The stratified analyses showed that the effects of PM2.5 and PM10 were statistically significant for patients aged ≥65 years, males, married, and those residing in prefecture-level cities. Regarding seasonal differences, the associations between PM and hospital admission for PTB were statistically significant in the warm season but not in the cold season. The effect of PM2.5 was consistently stronger than that of PM10 in most subgroups. Conclusion Short-term exposure to PM increases the risk of hospital admission for PTB. The potential impact of PM with smaller aerodynamic diameter is more detrimental. Our findings highlight the importance of reducing ambient PM level to alleviate the burden of PTB.
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Affiliation(s)
- Pan-Pan Zhu
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Yi Gao
- Department of Infectious Disease and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- Department of Infectious Disease, Hainan General Hospital, Hainan Medical University, Haikou, Hainan, China
| | - Gui-Zhong Zhou
- Department of Infectious Disease, The Second Affiliated Hospital, Hainan Medical University, Haikou, Hainan, China
| | - Rui Liu
- Department of Infectious Disease, The Second Affiliated Hospital, Hainan Medical University, Haikou, Hainan, China
| | - Xiao-Bo Li
- Department of Neurosurgery, Haikou Municipal People’s Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou, Hainan, China
| | - Xian-Xian Fu
- Clinical Lab, Haikou Municipal People’s Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou, Hainan, China
| | - Jian Fu
- Department of Infectious Disease, Hainan General Hospital, Hainan Medical University, Haikou, Hainan, China
| | - Feng Lin
- Department of Infectious Disease, Hainan General Hospital, Hainan Medical University, Haikou, Hainan, China
| | - Yuan-Ping Zhou
- Department of Infectious Disease and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Li Li
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
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