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      Mutual Associations of Exposure to Ambient Air Pollutants in the First 1000 Days of Life With Asthma/Wheezing in Children: Prospective Cohort Study in Guangzhou, China

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          Abstract

          Background

          The first 1000 days of life, encompassing pregnancy and the first 2 years after birth, represent a critical period for human health development. Despite this significance, there has been limited research into the associations between mixed exposure to air pollutants during this period and the development of asthma/wheezing in children. Furthermore, the finer sensitivity window of exposure during this crucial developmental phase remains unclear.

          Objective

          This study aims to assess the relationships between prenatal and postnatal exposures to various ambient air pollutants (particulate matter 2.5 [PM 2.5], carbon monoxide [CO], sulfur dioxide [SO 2], nitrogen dioxide [NO 2], and ozone [O 3]) and the incidence of childhood asthma/wheezing. In addition, we aimed to pinpoint the potential sensitivity window during which air pollution exerts its effects.

          Methods

          We conducted a prospective birth cohort study wherein pregnant women were recruited during early pregnancy and followed up along with their children. Information regarding maternal and child characteristics was collected through questionnaires during each round of investigation. Diagnosis of asthma/wheezing was obtained from children’s medical records. In addition, maternal and child exposures to air pollutants (PM 2.5 CO, SO 2, NO 2, and O 3) were evaluated using a spatiotemporal land use regression model. To estimate the mutual associations of exposure to mixed air pollutants with the risk of asthma/wheezing in children, we used the quantile g-computation model.

          Results

          In our study cohort of 3725 children, 392 (10.52%) were diagnosed with asthma/wheezing. After the follow-up period, the mean age of the children was 3.2 (SD 0.8) years, and a total of 14,982 person-years were successfully followed up for all study participants. We found that each quartile increase in exposure to mixed air pollutants (PM 2.5, CO, SO 2, NO 2, and O 3) during the second trimester of pregnancy was associated with an adjusted hazard ratio (HR) of 1.24 (95% CI 1.04-1.47). Notably, CO made the largest positive contribution (64.28%) to the mutual effect. After categorizing the exposure according to the embryonic respiratory development stages, we observed that each additional quartile of mixed exposure to air pollutants during the pseudoglandular and canalicular stages was associated with HRs of 1.24 (95% CI 1.03-1.51) and 1.23 (95% CI 1.01-1.51), respectively. Moreover, for the first year and first 2 years after birth, each quartile increment of exposure to mixed air pollutants was associated with HRs of 1.65 (95% CI 1.30-2.10) and 2.53 (95% CI 2.16-2.97), respectively. Notably, SO 2 made the largest positive contribution in both phases, accounting for 50.30% and 74.70% of the association, respectively.

          Conclusions

          Exposure to elevated levels of mixed air pollutants during the first 1000 days of life appears to elevate the risk of childhood asthma/wheezing. Specifically, the second trimester, especially during the pseudoglandular and canalicular stages, and the initial 2 years after birth emerge as crucial susceptibility windows.

          Trial Registration

          Chinese Clinical Trial Registry ChiCTR-ROC-17013496; https://tinyurl.com/2ctufw8n

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          Most cited references47

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          Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

          Summary Background Previous attempts to characterise the burden of chronic respiratory diseases have focused only on specific disease conditions, such as chronic obstructive pulmonary disease (COPD) or asthma. In this study, we aimed to characterise the burden of chronic respiratory diseases globally, providing a comprehensive and up-to-date analysis on geographical and time trends from 1990 to 2017. Methods Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, we estimated the prevalence, morbidity, and mortality attributable to chronic respiratory diseases through an analysis of deaths, disability-adjusted life-years (DALYs), and years of life lost (YLL) by GBD super-region, from 1990 to 2017, stratified by age and sex. Specific diseases analysed included asthma, COPD, interstitial lung disease and pulmonary sarcoidosis, pneumoconiosis, and other chronic respiratory diseases. We also assessed the contribution of risk factors (smoking, second-hand smoke, ambient particulate matter and ozone pollution, household air pollution from solid fuels, and occupational risks) to chronic respiratory disease-attributable DALYs. Findings In 2017, 544·9 million people (95% uncertainty interval [UI] 506·9–584·8) worldwide had a chronic respiratory disease, representing an increase of 39·8% compared with 1990. Chronic respiratory disease prevalence showed wide variability across GBD super-regions, with the highest prevalence among both males and females in high-income regions, and the lowest prevalence in sub-Saharan Africa and south Asia. The age-sex-specific prevalence of each chronic respiratory disease in 2017 was also highly variable geographically. Chronic respiratory diseases were the third leading cause of death in 2017 (7·0% [95% UI 6·8–7·2] of all deaths), behind cardiovascular diseases and neoplasms. Deaths due to chronic respiratory diseases numbered 3 914 196 (95% UI 3 790 578–4 044 819) in 2017, an increase of 18·0% since 1990, while total DALYs increased by 13·3%. However, when accounting for ageing and population growth, declines were observed in age-standardised prevalence (14·3% decrease), age-standardised death rates (42·6%), and age-standardised DALY rates (38·2%). In males and females, most chronic respiratory disease-attributable deaths and DALYs were due to COPD. In regional analyses, mortality rates from chronic respiratory diseases were greatest in south Asia and lowest in sub-Saharan Africa, also across both sexes. Notably, although absolute prevalence was lower in south Asia than in most other super-regions, YLLs due to chronic respiratory diseases across the subcontinent were the highest in the world. Death rates due to interstitial lung disease and pulmonary sarcoidosis were greater than those due to pneumoconiosis in all super-regions. Smoking was the leading risk factor for chronic respiratory disease-related disability across all regions for men. Among women, household air pollution from solid fuels was the predominant risk factor for chronic respiratory diseases in south Asia and sub-Saharan Africa, while ambient particulate matter represented the leading risk factor in southeast Asia, east Asia, and Oceania, and in the Middle East and north Africa super-region. Interpretation Our study shows that chronic respiratory diseases remain a leading cause of death and disability worldwide, with growth in absolute numbers but sharp declines in several age-standardised estimators since 1990. Premature mortality from chronic respiratory diseases seems to be highest in regions with less-resourced health systems on a per-capita basis. Funding Bill & Melinda Gates Foundation.
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            Outdoor air pollution and asthma

            The Lancet, 383(9928), 1581-1592
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              A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures

              Background: Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiology. Various strategies have arisen to answer questions about exposure mixtures, including methods such as weighted quantile sum (WQS) regression that estimate a joint effect of the mixture components. Objectives: We demonstrate a new approach to estimating the joint effects of a mixture: quantile g-computation. This approach combines the inferential simplicity of WQS regression with the flexibility of g-computation, a method of causal effect estimation. We use simulations to examine whether quantile g-computation and WQS regression can accurately and precisely estimate the effects of mixtures in a variety of common scenarios. Methods: We examine the bias, confidence interval (CI) coverage, and bias–variance tradeoff of quantile g-computation and WQS regression and how these quantities are impacted by the presence of noncausal exposures, exposure correlation, unmeasured confounding, and nonlinearity of exposure effects. Results: Quantile g-computation, unlike WQS regression, allows inference on mixture effects that is unbiased with appropriate CI coverage at sample sizes typically encountered in epidemiologic studies and when the assumptions of WQS regression are not met. Further, WQS regression can magnify bias from unmeasured confounding that might occur if important components of the mixture are omitted from the analysis. Discussion: Unlike inferential approaches that examine the effects of individual exposures while holding other exposures constant, methods like quantile g-computation that can estimate the effect of a mixture are essential for understanding the effects of potential public health actions that act on exposure sources. Our approach may serve to help bridge gaps between epidemiologic analysis and interventions such as regulations on industrial emissions or mining processes, dietary changes, or consumer behavioral changes that act on multiple exposures simultaneously. https://doi.org/10.1289/EHP5838
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                Author and article information

                Contributors
                Journal
                JMIR Public Health Surveill
                JMIR Public Health Surveill
                JPH
                JMIR Public Health and Surveillance
                JMIR Publications (Toronto, Canada )
                2369-2960
                2024
                17 April 2024
                : 10
                : e52456
                Affiliations
                [1 ] Department of Public Health and Preventive Medicine School of Medicine, Jinan University Guangzhou China
                [2 ] China Greater Bay Area Research Center of Environmental Health School of Medicine, Jinan University Guangzhou China
                [3 ] Department of Neonatology, The Third Affiliated Hospital of Guangzhou Medical University Guangzhou China
                [4 ] Guangzhou Panyu Central Hospital Guangzhou China
                [5 ] Department of Prevention and Health Care, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University Shenzhen China
                Author notes
                Corresponding Author: Tao Liu gztt_2002@ 123456163.com
                Author information
                https://orcid.org/0000-0002-8006-1959
                https://orcid.org/0000-0002-7929-3445
                https://orcid.org/0000-0002-9843-475X
                https://orcid.org/0009-0004-1268-1576
                https://orcid.org/0000-0003-4152-6132
                https://orcid.org/0009-0009-7052-2596
                https://orcid.org/0009-0007-6036-1333
                https://orcid.org/0009-0002-7908-2012
                https://orcid.org/0009-0005-4013-4702
                https://orcid.org/0000-0002-6426-7156
                https://orcid.org/0009-0002-4732-6924
                https://orcid.org/0000-0002-9266-4802
                https://orcid.org/0000-0002-3832-038X
                https://orcid.org/0000-0003-1451-5251
                Article
                v10i1e52456
                10.2196/52456
                11063886
                38631029
                6bdeac43-0953-4118-a8d3-0f42a622cf9e
                ©Fenglin Tian, Xinqi Zhong, Yufeng Ye, Xiaohan Liu, Guanhao He, Cuiling Wu, Zhiqing Chen, Qijiong Zhu, Siwen Yu, Jingjie Fan, Huan Yao, Wenjun Ma, Xiaomei Dong, Tao Liu. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 17.04.2024.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

                History
                : 6 September 2023
                : 1 December 2023
                : 21 December 2023
                : 5 March 2024
                Categories
                Original Paper
                Original Paper

                pregnancy,air pollution,asthma,wheezing,birth cohort,children
                pregnancy, air pollution, asthma, wheezing, birth cohort, children

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