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      A hierarchical model for estimating the exposure-response curve by combining multiple studies of acute lower respiratory infections in children and household fine particulate matter air pollution

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          Abstract

          Supplemental Digital Content is available in the text.

          Background:

          Adverse health effects of household air pollution, including acute lower respiratory infections (ALRIs), pose a major health burden around the world, particularly in settings where indoor combustion stoves are used for cooking. Individual studies have limited exposure ranges and sample sizes, while pooling studies together can improve statistical power.

          Methods:

          We present hierarchical models for estimating long-term exposure concentrations and estimating a common exposure-response curve. The exposure concentration model combines temporally sparse, clustered longitudinal observations to estimate household-specific long-term average concentrations. The exposure-response model provides a flexible, semiparametric estimate of the exposure-response relationship while accommodating heterogeneous clustered data from multiple studies. We apply these models to three studies of fine particulate matter (PM 2.5) and ALRIs in children in Nepal: a case-control study in Bhaktapur, a stepped-wedge trial in Sarlahi, and a parallel trial in Sarlahi. For each study, we estimate household-level long-term PM 2.5 concentrations. We apply the exposure-response model separately to each study and jointly to the pooled data.

          Results:

          The estimated long-term PM 2.5 concentrations were lower for households using electric and gas fuel sources compared with households using biomass fuel. The exposure-response curve shows an estimated ALRI odds ratio of 3.39 (95% credible interval = 1.89, 6.10) comparing PM 2.5 concentrations of 50 and 150 μg/m 3 and a flattening of the curve for higher concentrations.

          Conclusions:

          These flexible models can accommodate additional studies and be applied to other exposures and outcomes. The studies from Nepal provides evidence of a nonlinear exposure-response curve that flattens at higher concentrations.

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          Most cited references32

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          Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter

          Richard Burnett, Hong Chen, Mieczysław Szyszkowicz … (2018)
          Significance Exposure to outdoor concentrations of fine particulate matter is considered a leading global health concern, largely based on estimates of excess deaths using information integrating exposure and risk from several particle sources (outdoor and indoor air pollution and passive/active smoking). Such integration requires strong assumptions about equal toxicity per total inhaled dose. We relax these assumptions to build risk models examining exposure and risk information restricted to cohort studies of outdoor air pollution, now covering much of the global concentration range. Our estimates are severalfold larger than previous calculations, suggesting that outdoor particulate air pollution is an even more important population health risk factor than previously thought.
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            Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

            Christopher Troeger, Brigette F Blacker, Ibrahim A Khalil … (2018)
            Summary Background Lower respiratory infections are a leading cause of morbidity and mortality around the world. The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, provides an up-to-date analysis of the burden of lower respiratory infections in 195 countries. This study assesses cases, deaths, and aetiologies spanning the past 26 years and shows how the burden of lower respiratory infection has changed in people of all ages. Methods We used three separate modelling strategies for lower respiratory infections in GBD 2016: a Bayesian hierarchical ensemble modelling platform (Cause of Death Ensemble model), which uses vital registration, verbal autopsy data, and surveillance system data to predict mortality due to lower respiratory infections; a compartmental meta-regression tool (DisMod-MR), which uses scientific literature, population representative surveys, and health-care data to predict incidence, prevalence, and mortality; and modelling of counterfactual estimates of the population attributable fraction of lower respiratory infection episodes due to Streptococcus pneumoniae, Haemophilus influenzae type b, influenza, and respiratory syncytial virus. We calculated each modelled estimate for each age, sex, year, and location. We modelled the exposure level in a population for a given risk factor using DisMod-MR and a spatio-temporal Gaussian process regression, and assessed the effectiveness of targeted interventions for each risk factor in children younger than 5 years. We also did a decomposition analysis of the change in LRI deaths from 2000–16 using the risk factors associated with LRI in GBD 2016. Findings In 2016, lower respiratory infections caused 652 572 deaths (95% uncertainty interval [UI] 586 475–720 612) in children younger than 5 years (under-5s), 1 080 958 deaths (943 749–1 170 638) in adults older than 70 years, and 2 377 697 deaths (2 145 584–2 512 809) in people of all ages, worldwide. Streptococcus pneumoniae was the leading cause of lower respiratory infection morbidity and mortality globally, contributing to more deaths than all other aetiologies combined in 2016 (1 189 937 deaths, 95% UI 690 445–1 770 660). Childhood wasting remains the leading risk factor for lower respiratory infection mortality among children younger than 5 years, responsible for 61·4% of lower respiratory infection deaths in 2016 (95% UI 45·7–69·6). Interventions to improve wasting, household air pollution, ambient particulate matter pollution, and expanded antibiotic use could avert one under-5 death due to lower respiratory infection for every 4000 children treated in the countries with the highest lower respiratory infection burden. Interpretation Our findings show substantial progress in the reduction of lower respiratory infection burden, but this progress has not been equal across locations, has been driven by decreases in several primary risk factors, and might require more effort among elderly adults. By highlighting regions and populations with the highest burden, and the risk factors that could have the greatest effect, funders, policy makers, and programme implementers can more effectively reduce lower respiratory infections among the world's most susceptible populations. Funding Bill & Melinda Gates Foundation.
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              Respiratory risks from household air pollution in low and middle income countries.

              Stephen Gordon, Nigel G. Bruce, Jonathan Grigg … (2014)
              A third of the world's population uses solid fuel derived from plant material (biomass) or coal for cooking, heating, or lighting. These fuels are smoky, often used in an open fire or simple stove with incomplete combustion, and result in a large amount of household air pollution when smoke is poorly vented. Air pollution is the biggest environmental cause of death worldwide, with household air pollution accounting for about 3·5-4 million deaths every year. Women and children living in severe poverty have the greatest exposures to household air pollution. In this Commission, we review evidence for the association between household air pollution and respiratory infections, respiratory tract cancers, and chronic lung diseases. Respiratory infections (comprising both upper and lower respiratory tract infections with viruses, bacteria, and mycobacteria) have all been associated with exposure to household air pollution. Respiratory tract cancers, including both nasopharyngeal cancer and lung cancer, are strongly associated with pollution from coal burning and further data are needed about other solid fuels. Chronic lung diseases, including chronic obstructive pulmonary disease and bronchiectasis in women, are associated with solid fuel use for cooking, and the damaging effects of exposure to household air pollution in early life on lung development are yet to be fully described. We also review appropriate ways to measure exposure to household air pollution, as well as study design issues and potential effective interventions to prevent these disease burdens. Measurement of household air pollution needs individual, rather than fixed in place, monitoring because exposure varies by age, gender, location, and household role. Women and children are particularly susceptible to the toxic effects of pollution and are exposed to the highest concentrations. Interventions should target these high-risk groups and be of sufficient quality to make the air clean. To make clean energy available to all people is the long-term goal, with an intermediate solution being to make available energy that is clean enough to have a health impact.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Environ Epidemiol
                Journal ID (iso-abbrev): Environ Epidemiol
                Journal ID (publisher-id): EE9
                Title: Environmental Epidemiology
                Publisher: Lippincott Williams & Wilkins (Hagerstown, MD )
                ISSN (Electronic): 2474-7882
                Publication date Collection: December 2020
                Publication date (Electronic): 18 November 2020
                Volume: 4
                Issue: 6
                Electronic Location Identifier: e119
                Affiliations
                [a ]Department of Statistics, Colorado State University, Fort Collins, Colorado
                [b ]Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
                [c ]Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California
                [d ]Division of Epidemiology, School of Public Health, University of California, Berkeley, California
                [e ]Department of Global Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia
                [f ]Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
                Author notes
                [* ]Corresponding Author. Address: Department of Statistics, Colorado State University, 1877 Campus Delivery, Fort Collins, CO 80523. E-mail: joshua.keller@ 123456colostate.edu (J.P. Keller).
                Article
                Accession ID: 00005
                DOI: 10.1097/EE9.0000000000000119
                PMC ID: 7941787
                PubMed ID: 33778354
                SO-VID: 349c5dd2-ea54-48a0-acd5-8b91ab7cae3e
                Copyright © Copyright © 2020 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

                History
                Date received : 24 July 2020
                Date accepted : 15 September 2020
                Categories
                Subject: Original Research Article
                Custom metadata
                OPEN-ACCESS TRUE
                SDC T

                Keywords: bayesian,exposure-response curve,hierarchical models,household air pollution,measurement error,particulate matter
                Data availability:
                Keywords: bayesian, exposure-response curve, hierarchical models, household air pollution, measurement error, particulate matter

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