<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN" "JATS-archivearticle1-mathml3.dtd"> <article xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.2"><front><journal-meta><journal-id journal-id-type="nlm-ta">elife</journal-id><journal-id journal-id-type="publisher-id">eLife</journal-id><journal-title-group><journal-title>eLife</journal-title></journal-title-group><issn pub-type="epub" publication-format="electronic">2050-084X</issn><publisher><publisher-name>eLife Sciences Publications, Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">69298</article-id><article-id pub-id-type="doi">10.7554/eLife.69298</article-id><article-categories><subj-group subj-group-type="display-channel"><subject>Research Article</subject></subj-group><subj-group subj-group-type="heading"><subject>Epidemiology and Global Health</subject></subj-group></article-categories><title-group><article-title>Exposure to landscape fire smoke reduced birthweight in low- and middle-income countries: findings from a siblings-matched case-control study</article-title></title-group><contrib-group><contrib contrib-type="author" equal-contrib="yes" id="author-236324"><name><surname>Li</surname><given-names>Jiajianghui</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="equal-contrib1">†</xref><xref ref-type="fn" rid="con1"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" equal-contrib="yes" id="author-132257"><name><surname>Guan</surname><given-names>Tianjia</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0002-7820-2898</contrib-id><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="fn" rid="equal-contrib1">†</xref><xref ref-type="other" rid="fund3"/><xref ref-type="fn" rid="con2"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" equal-contrib="yes" id="author-236325"><name><surname>Guo</surname><given-names>Qian</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0003-3343-2849</contrib-id><xref ref-type="aff" rid="aff3">3</xref><xref ref-type="fn" rid="equal-contrib1">†</xref><xref ref-type="fn" rid="con3"/><xref ref-type="fn" rid="conf2"/></contrib><contrib contrib-type="author" id="author-236326"><name><surname>Geng</surname><given-names>Guannan</given-names></name><xref ref-type="aff" rid="aff4">4</xref><xref ref-type="fn" rid="con4"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" id="author-236327"><name><surname>Wang</surname><given-names>Huiyu</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0002-0841-0288</contrib-id><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con5"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" id="author-236328"><name><surname>Guo</surname><given-names>Fuyu</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con6"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" id="author-236329"><name><surname>Li</surname><given-names>Jiwei</given-names></name><xref ref-type="aff" rid="aff5">5</xref><xref ref-type="fn" rid="con7"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" corresp="yes" id="author-133703"><name><surname>Xue</surname><given-names>Tao</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0002-7045-2307</contrib-id><email>txue@hsc.pku.edu.cn</email><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="other" rid="fund1"/><xref ref-type="other" rid="fund2"/><xref ref-type="other" rid="fund4"/><xref ref-type="other" rid="fund5"/><xref ref-type="fn" rid="con8"/><xref ref-type="fn" rid="conf1"/></contrib><aff id="aff1"><label>1</label><institution>Institute of Reproductive and Child Health / Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre</institution><addr-line><named-content content-type="city">Beijing</named-content></addr-line><country>China</country></aff><aff id="aff2"><label>2</label><institution>Department of Health Policy, School of Health Policy and Management, Chinese Academy of Medical Sciences and Peking Union Medical College</institution><addr-line><named-content content-type="city">Beijing</named-content></addr-line><country>China</country></aff><aff id="aff3"><label>3</label><institution>School of Energy and Environmental Engineering, University of Science and Technology</institution><addr-line><named-content content-type="city">Beijing</named-content></addr-line><country>China</country></aff><aff id="aff4"><label>4</label><institution>School of Environment, Tsinghua University</institution><addr-line><named-content content-type="city">Beijing</named-content></addr-line><country>China</country></aff><aff id="aff5"><label>5</label><institution>College of Computer Science and Technology, Zhejiang University</institution><addr-line><named-content content-type="city">Hangzhou</named-content></addr-line><country>China</country></aff></contrib-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>Franco</surname><given-names>Eduardo</given-names></name><role>Reviewing Editor</role><aff><institution>McGill University</institution><country>Canada</country></aff></contrib><contrib contrib-type="senior_editor"><name><surname>Franco</surname><given-names>Eduardo</given-names></name><role>Senior Editor</role><aff><institution>McGill University</institution><country>Canada</country></aff></contrib></contrib-group><author-notes><fn fn-type="con" id="equal-contrib1"><label>†</label><p>These authors contributed equally to this work</p></fn></author-notes><pub-date date-type="publication" publication-format="electronic"><day>29</day><month>09</month><year>2021</year></pub-date><pub-date pub-type="collection"><year>2021</year></pub-date><volume>10</volume><elocation-id>e69298</elocation-id><history><date date-type="received" iso-8601-date="2021-04-10"><day>10</day><month>04</month><year>2021</year></date><date date-type="accepted" iso-8601-date="2021-09-26"><day>26</day><month>09</month><year>2021</year></date></history><pub-history><event><event-desc>This manuscript was published as a preprint at bioRxiv.</event-desc><date date-type="preprint" iso-8601-date="2021-04-30"><day>30</day><month>04</month><year>2021</year></date><self-uri content-type="preprint" xlink:href="https://doi.org/10.1101/2021.04.27.21256225"/></event></pub-history><permissions><copyright-statement>© 2021, Li et al</copyright-statement><copyright-year>2021</copyright-year><copyright-holder>Li et al</copyright-holder><ali:free_to_read/><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><ali:license_ref>http://creativecommons.org/licenses/by/4.0/</ali:license_ref><license-p>This article is distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use and redistribution provided that the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="elife-69298-v2.pdf"/><self-uri content-type="figures-pdf" xlink:href="elife-69298-figures-v2.pdf"/><related-article ext-link-type="doi" id="ra1" related-article-type="commentary" xlink:href="10.7554/eLife.74331"/><abstract><sec id="abs1"><title>Background:</title><p>Landscape fire smoke (LFS) has been associated with reduced birthweight, but evidence from low- and middle-income countries (LMICs) is rare.</p></sec><sec id="abs2"><title>Methods:</title><p>Here, we present a sibling-matched case–control study of 227,948 newborns to identify an association between fire-sourced fine particulate matter (PM<sub>2.5</sub>) and birthweight in 54 LMICs from 2000 to 2014. We selected mothers from the geocoded Demographic and Health Survey with at least two children and valid birthweight records. Newborns affiliated with the same mother were defined as a family group. Gestational exposure to LFS was assessed in each newborn using the concentration of fire-sourced PM<sub>2.5</sub>. We determined the associations of the within-group variations in LFS exposure with birthweight differences between matched siblings using a fixed-effects regression model. Additionally, we analyzed the binary outcomes of low birthweight (LBW) or very low birthweight (VLBW).</p></sec><sec id="abs3"><title>Results:</title><p>According to fully adjusted models, a 1 µg/m<sup>3</sup> increase in the concentration of fire-sourced PM<sub>2.5</sub> was significantly associated with a 2.17 g (95% confidence interval [CI] 0.56–3.77) reduction in birthweight, a 2.80% (95% CI 0.97–4.66) increase in LBW risk, and an 11.68% (95% CI 3.59–20.40) increase in VLBW risk.</p></sec><sec id="abs4"><title>Conclusions:</title><p>Our findings indicate that gestational exposure to LFS harms fetal health.</p></sec><sec id="abs5"><title>Funding:</title><p>PKU-Baidu Fund, National Natural Science Foundation of China, Peking University Health Science Centre, and CAMS Innovation Fund for Medical Sciences.</p></sec></abstract><kwd-group kwd-group-type="author-keywords"><kwd>child health</kwd><kwd>wildfire</kwd><kwd>climate change</kwd><kwd>maternal health</kwd></kwd-group><kwd-group kwd-group-type="research-organism"><title>Research organism</title><kwd>Human</kwd></kwd-group><funding-group><award-group id="fund1"><funding-source><institution-wrap><institution>PKU-Baidu Fund</institution></institution-wrap></funding-source><award-id>2020BD031</award-id><principal-award-recipient><name><surname>Xue</surname><given-names>Tao</given-names></name></principal-award-recipient></award-group><award-group id="fund2"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100012226</institution-id><institution>Fundamental Research Funds for the Central Universities</institution></institution-wrap></funding-source><award-id>BMU2021YJ042</award-id><principal-award-recipient><name><surname>Xue</surname><given-names>Tao</given-names></name></principal-award-recipient></award-group><award-group id="fund3"><funding-source><institution-wrap><institution>CAMS Innovation Fund for Medical Sciences</institution></institution-wrap></funding-source><award-id>2017-I2M-1-004</award-id><principal-award-recipient><name><surname>Guan</surname><given-names>Tianjia</given-names></name></principal-award-recipient></award-group><award-group id="fund4"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100005710</institution-id><institution>Energy Foundation</institution></institution-wrap></funding-source><award-id>G-2107-33169</award-id><principal-award-recipient><name><surname>Xue</surname><given-names>Tao</given-names></name></principal-award-recipient></award-group><award-group id="fund5"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100001809</institution-id><institution>National Natural Science Foundation of China</institution></institution-wrap></funding-source><award-id>4217050142</award-id><principal-award-recipient><name><surname>Xue</surname><given-names>Tao</given-names></name></principal-award-recipient></award-group><funding-statement>The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.</funding-statement></funding-group><custom-meta-group><custom-meta specific-use="meta-only"><meta-name>Author impact statement</meta-name><meta-value>Gestational exposure to landscape fire smoke harms maternal health, especially in low and middle-income countries.</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="s1" sec-type="intro"><title>Introduction</title><p>The natural cycle of landscape fires (e.g., wildfires, tropical deforestation fires, and agricultural biomass burning) plays an important role in maintaining the terrestrial ecosystem. Yet, landscape fire smoke (LFS) triggers a costly and growing global public health problem (<xref ref-type="bibr" rid="bib33">van der Werf et al., 2010</xref>). The frequency and intensity of landscape fire events have tended to increase (<xref ref-type="bibr" rid="bib7">Cascio, 2018</xref>; <xref ref-type="bibr" rid="bib32">Turetsky et al., 2010</xref>), driven by interactions between human activities (e.g., slash and slave agriculture) and climate change (<xref ref-type="bibr" rid="bib11">Hantson et al., 2015</xref>). Most emissions originate from fires located in tropical rainforests and savannas, where they cause recurrent episodes of severe ambient pollution that affect mainly low- and middle-income countries (LMICs) (<xref ref-type="bibr" rid="bib33">van der Werf et al., 2010</xref>). Worldwide concern regarding large-scale natural forest burning has sparked increasing interest in the harmful effects of LFS exposure on human health.</p><p>LFS is composed of hundreds of <ext-link ext-link-type="uri" xlink:href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/combustion-product">combustion products</ext-link>, such as carbon monoxide, nitrogen oxides, and polycyclic aromatic hydrocarbons (<xref ref-type="bibr" rid="bib22">Matz et al., 2020</xref>). Particulate matter with an aerodynamic diameter ≤2.5 μm (PM<sub>2.5</sub>) is one of the most widely studied pollutants derived from LFS. The 24 hr average PM<sub>2.5</sub> concentrations on burning days can be many times higher than those on normal days, and the peak level of ambient exposure can persist for several weeks (<xref ref-type="bibr" rid="bib2">Barn et al., 2016</xref>). Several epidemiological studies have shown adverse effects of LFS exposure on human health. The majority of the study outcomes involved morbidity and mortality caused by acute respiratory and cardiovascular diseases, particularly among vulnerable individuals, such as children and the elderly (<xref ref-type="bibr" rid="bib10">Delfino et al., 2009</xref>; <xref ref-type="bibr" rid="bib16">Johnston et al., 2012</xref>; <xref ref-type="bibr" rid="bib23">Morgan et al., 2010</xref>). However, the health impacts of LFS on susceptible pregnant women, another highly vulnerable group due to gestation-related physiological changes, such as an increased breathing rate during pregnancy (<xref ref-type="bibr" rid="bib17">Kolarzyk et al., 2005</xref>), are not thoroughly discussed. Previous studies have shown that gestational exposure to air pollutants, including those derived from LFS and other emissions, is associated with abnormal placental vascular function and, consequently, low birthweight (LBW) (<xref ref-type="bibr" rid="bib27">Pedersen et al., 2013</xref>), preterm birth (<xref ref-type="bibr" rid="bib26">Padula et al., 2019</xref>), and small for gestational age (<xref ref-type="bibr" rid="bib4">Bijnens et al., 2016</xref>). Exposure to air pollution induces subclinical disorders, such as inflammation, oxidative stress, increased blood viscosity, mitochondrial methylation, and hypoxia (<xref ref-type="bibr" rid="bib15">Janssen et al., 2015</xref>; <xref ref-type="bibr" rid="bib18">Lee et al., 2004</xref>), which renders the association between LFS and adverse maternal outcomes biologically plausible.</p><p>Birth weight comprehensively reflects the intrauterine environment quality, fetal growth, and maternal nutritional status. Therefore, LBW is a global public health problem. LBW neonates are at higher risk for a range of diseases in later life (e.g., early growth retardation and adulthood cardiorespiratory diseases) compared with normal newborns (<xref ref-type="bibr" rid="bib14">Hviid and Melbye, 2007</xref>; <xref ref-type="bibr" rid="bib29">Samaras et al., 2003</xref>). The estimated worldwide prevalence of LBW was 14.6% (95% confidence interval [CI] 12.4–17.1) in 2015, compared with 17.5% (95% CI 14.1–21.3) in 2000 (<xref ref-type="bibr" rid="bib5">Blencowe et al., 2019</xref>). Although some progress has been made in reducing LBW globally, the average annual reduction rate is insufficient to achieve the 2025 WHO global target (<xref ref-type="bibr" rid="bib35">World Health organization, 2017</xref>). Additionally, the relevant burden of LBW is unequally distributed in the world as 91% of LBW infants are from LMICs, particularly from Southern Asia and Sub-Saharan Africa (<xref ref-type="bibr" rid="bib5">Blencowe et al., 2019</xref>).</p><p>All risk factors for adverse birth outcomes should be screened thoroughly to protect maternal and infant health. In addition to conventional factors, such as nutrition and maternal condition, environmental factors, including ambient PM<sub>2.5</sub> exposure, also contribute to a considerable global burden of adverse birth outcomes (<xref ref-type="bibr" rid="bib21">Malley et al., 2017</xref>). Although a growing number of studies have suggested the deleterious effects of urban air pollution on LBW (<xref ref-type="bibr" rid="bib20">Li et al., 2020</xref>; <xref ref-type="bibr" rid="bib19">Li et al., 2017</xref>), the exposure–response function specifically for fire-sourced PM<sub>2.5</sub> has been insufficiently studied. Furthermore, studies concerning the association between LFS and birthweight have focused primarily on high-income countries, and their conclusions have been inconsistent (<xref ref-type="bibr" rid="bib1">Abdo et al., 2019</xref>; <xref ref-type="bibr" rid="bib6">Cândido da Silva et al., 2014</xref>; <xref ref-type="bibr" rid="bib13">Holstius et al., 2012</xref>; <xref ref-type="bibr" rid="bib25">O’Donnell and Behie, 2015</xref>). As most landscape fires occur in LMICs (<xref ref-type="fig" rid="fig1">Figure 1</xref>), studies to assess the relevant health impacts of LFS among residents of such countries are warranted. Hence, we explored the association between birthweight and fire-sourced PM<sub>2.5</sub> in 54 LMICs using a sibling-matched case–control study from 2000 to 2014, which was based on a series of global surveys.</p><fig id="fig1" position="float"><label>Figure 1.</label><caption><title>Distribution of the fire-sourced PM<sub>2.5</sub> concentrations (average for 2000–2014) and the locations of the analyzed livebirths (gray dots).</title><p>The circles represent the rate of very low birthweight births among the analyzed livebirths by country.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-69298.xml.media/fig1.jpg"/></fig></sec><sec id="s2" sec-type="methods"><title>Methods</title><sec id="s2-1"><title>Study population</title><p>The birthweight data and relevant individual variables were extracted from 113 individual Demographic and Health Surveys (DHSs) representing 54 LMICs from 2000 to 2014 (<xref ref-type="fig" rid="fig1">Figure 1</xref>). The DHSs were conducted by the US Agency for International Development to characterize the demography and health of more than 90 developing countries. DHSs are usually conducted every 5 years in each country using a two-stage sampling design. Information on demographic factors, socioeconomic factors, and health status was collected from a nationally representative probability sample of households. Well-trained fieldworkers conducted the surveys using uniformly designed questionnaires in the native language. The primary sampling units (e.g., villages or blocks) have begun to be geocoded in recent waves using global positioning systems (GPSs). The GPS-enhanced DHSs were matched with environmental exposure and thus included in our analyses. Please refer to the open-access website for detailed information on the DHS database: <ext-link ext-link-type="uri" xlink:href="https://www.dhsprogram.com/">https://www.dhsprogram.com/</ext-link>.</p><p>All females aged 15–49 years within each selected household were eligible respondents of the survey. The health outcome analyzed in this study was birthweight. The DHSs asked the screened mothers to recall the birthweight of all children born during the 5 years leading up to the survey date. To characterize different levels of reduced birthweight, we examined two binary outcomes: LBW (birthweight <2500 g) and, other covariates, such as maternal age and birth time (in months), were extracted from the relevant questionnaires.</p></sec><sec id="s2-2"><title>Exposure assessment</title><p>We assessed ambient exposure to LFS as the PM<sub>2.5</sub> concentration attributable to landscape fires (hereafter, defined as fire-sourced PM<sub>2.5</sub>), referring to a previous study (<xref ref-type="bibr" rid="bib31">Stowell et al., 2019</xref>). The fire-sourced PM<sub>2.5</sub> concentrations were estimated using the GEOS-Chem . The detailed settings of the GEOS-Chem are provided in the previous publication (<xref ref-type="bibr" rid="bib39">Xue et al., 2021b</xref>) and briefly summarized here. We used the 11-01 version of GEOS-Chem model (<ext-link ext-link-type="uri" xlink:href="https://geos-chem.seas.harvard.edu/">https://geos-chem.seas.harvard.edu/</ext-link>). The meteorological fields to drive the model were directly obtained from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), which is freely distributed by the Goddard Earth Sciences Data and Information Services Center (<ext-link ext-link-type="uri" xlink:href="https://disc.gsfc.nasa.gov/">https://disc.gsfc.nasa.gov/</ext-link>). Anthropogenic emissions inputted into the model were obtained from the global inventory of Community Emissions Data System (CEDS) (<xref ref-type="bibr" rid="bib12">Hoesly et al., 2018</xref>), from 2000 to 2014; the fire emissions during the same period were collected from the Global Fire Emission Database (GFED4s, <ext-link ext-link-type="uri" xlink:href="https://www.globalfiredata.org/">https://www.globalfiredata.org/</ext-link>). The GEOS-Chem finally simulated the ground-surface concentrations of PM<sub>2.5</sub> in a regular grid of 2° × 2.5° across the world.</p><p>The fire-sourced PM<sub>2.5</sub> was derived from a comparison between two GEOS-Chem model runs. The two models were identical except for switching on and off fire emissions. We quantified the fraction (<italic>ρ<sub>m</sub></italic>) of fire-attributed PM<sub>2.5</sub> using the following equation:<disp-formula id="equ1"><label>(1)</label><mml:math id="m1"><mml:mrow><mml:msub><mml:mi>ρ</mml:mi><mml:mrow><mml:msub><mml:mi/><mml:mrow><mml:mi>m</mml:mi><mml:mi>y</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi mathvariant="normal">P</mml:mi><mml:msubsup><mml:mi mathvariant="normal">M</mml:mi><mml:mrow><mml:mn>2.5</mml:mn><mml:mo>,</mml:mo><mml:mi mathvariant="normal">m</mml:mi><mml:mi mathvariant="normal">y</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">n</mml:mi></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:mi mathvariant="normal">P</mml:mi><mml:msubsup><mml:mi mathvariant="normal">M</mml:mi><mml:mrow><mml:mn>2.5</mml:mn><mml:mo>,</mml:mo><mml:mi mathvariant="normal">m</mml:mi><mml:mi mathvariant="normal">y</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">f</mml:mi><mml:mi mathvariant="normal">f</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy="false">)</mml:mo><mml:mrow><mml:mo>/</mml:mo></mml:mrow><mml:mi mathvariant="normal">P</mml:mi><mml:msubsup><mml:mi mathvariant="normal">M</mml:mi><mml:mrow><mml:mn>2.5</mml:mn><mml:mo>,</mml:mo><mml:mi mathvariant="normal">m</mml:mi><mml:mi mathvariant="normal">y</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:msubsup></mml:mrow></mml:math></disp-formula></p><p>where the superscripts <italic>on</italic> and <italic>off</italic> denote the two simulations switching on and off fire emissions, respectively; and the subscripts <italic>m</italic> and <italic>y</italic> are month and year indicators, respectively.</p><p>Additionally, considering the potential errors in the CTM procedure (<xref ref-type="bibr" rid="bib30">Solazzo et al., 2012</xref>), the bias correction approach has been widely applied to improve CTM performance in exposure assessments using ground-surface observational data (<xref ref-type="bibr" rid="bib42">Zhang et al., 2020</xref>). We utilized a well-developed and widely utilized product of satellite-based PM<sub>2.5</sub> estimates (<xref ref-type="bibr" rid="bib34">van Donkelaar et al., 2016</xref>) (PM<sub>2.5, <italic>y</italic></sub><italic><sup>satellite</sup></italic>) in the bias correction. This product has a spatial resolution of 0.05° × 0.05° and was publicly available from 1998 to 2018 as the gridded annual mean (<ext-link ext-link-type="uri" xlink:href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.GL.03">https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.GL.03</ext-link>). However, since a key input into the product, satellite measurements of aerosol from the Moderate Resolution Imaging Spectroradiometer are only available after February 2000, we believe that the estimates after the date have a better quality than those before. Therefore, we utilized the product to calibrate the monthly GEOS-Chem simulations from February 2000 to December 2014. We prepared the GEOS-Chem simulations (<italic>ρ<sub>m,y</sub></italic> and PM<sub>2.5, <italic>m,y</italic></sub><italic><sup>on</sup></italic>) in the same 0.05° × 0.05° grid using an inversed distance-weighted downscaling approach before bias correction. The bias correction rate (<italic>η<sub>y</sub></italic>) was calculated as<disp-formula id="equ2">.<label>(2)</label><mml:math id="m2"><mml:mrow><mml:mi>η</mml:mi><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mi mathvariant="normal">P</mml:mi><mml:mi mathvariant="normal">M</mml:mi></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:msubsup><mml:mo>÷</mml:mo><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mrow><mml:mo>/</mml:mo></mml:mrow><mml:mn>12</mml:mn><mml:mo>∗</mml:mo><mml:mo>∑</mml:mo><mml:mrow><mml:msub><mml:mi/><mml:mi>m</mml:mi></mml:msub></mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant="normal">P</mml:mi><mml:mi mathvariant="normal">M</mml:mi></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>The monthly exposure indicator of fire-sourced or non-fire-sourced PM<sub>2.5</sub> was finally derived as<disp-formula id="equ3"><label>(3)</label><mml:math id="m3"><mml:mrow><mml:mtable columnalign="left left" columnspacing="1em" rowspacing="4pt"><mml:mtr><mml:mtd><mml:mo stretchy="false">[</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant="normal">F</mml:mi><mml:mi mathvariant="normal">i</mml:mi><mml:mi mathvariant="normal">r</mml:mi><mml:mi mathvariant="normal">e</mml:mi><mml:mo>−</mml:mo><mml:mi mathvariant="normal">s</mml:mi><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">u</mml:mi><mml:mi mathvariant="normal">r</mml:mi><mml:mi mathvariant="normal">c</mml:mi><mml:mi mathvariant="normal">e</mml:mi><mml:mi mathvariant="normal">d</mml:mi><mml:mspace width="thinmathspace"/><mml:mspace width="thinmathspace"/><mml:mi mathvariant="normal">P</mml:mi><mml:mi mathvariant="normal">M</mml:mi></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mo stretchy="false">]</mml:mo><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>η</mml:mi><mml:mrow><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mi>ρ</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:mo>×</mml:mo><mml:msubsup><mml:mrow><mml:mi mathvariant="normal">P</mml:mi><mml:mi mathvariant="normal">M</mml:mi></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mo stretchy="false">[</mml:mo><mml:mrow><mml:mi mathvariant="normal">N</mml:mi><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">n</mml:mi><mml:mo>−</mml:mo><mml:mi mathvariant="normal">f</mml:mi><mml:mi mathvariant="normal">i</mml:mi><mml:mi mathvariant="normal">r</mml:mi><mml:mi mathvariant="normal">e</mml:mi><mml:mo>−</mml:mo><mml:mi mathvariant="normal">s</mml:mi><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">u</mml:mi><mml:mi mathvariant="normal">r</mml:mi><mml:mi mathvariant="normal">c</mml:mi><mml:mi mathvariant="normal">e</mml:mi><mml:mi mathvariant="normal">d</mml:mi><mml:mspace width="thinmathspace"/><mml:mspace width="thinmathspace"/><mml:mi mathvariant="normal">P</mml:mi><mml:msub><mml:mi mathvariant="normal">M</mml:mi><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:msub><mml:mo stretchy="false">]</mml:mo><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>η</mml:mi><mml:mrow><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:mo>×</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>ρ</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">)</mml:mo><mml:mo>×</mml:mo><mml:msubsup><mml:mrow><mml:mi mathvariant="normal">P</mml:mi><mml:mi mathvariant="normal">M</mml:mi></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></disp-formula></p><p>We utilized the non-fire-sourced PM<sub>2.5</sub> as a potential confounder in the subsequent epidemiological analysis. We also collected other environmental variables, including ambient temperature and humidity data, from an assimilated dataset called Modern-Era Retrospective analysis for Research and Applications, version 2 (please refer to the supplemental text for more details). The variables were downloaded as monthly gridded values and prepared in the 0.05° × 0.05° grid in the same way as we processed the GEOS-Chem outputs.</p><p>Since fire-sourced PM<sub>2.5</sub> is mixed with particles from other sources, it cannot be easily measured. Although the CTM-based approach has been widely utilized in a few large-scale studies (<xref ref-type="bibr" rid="bib38">Xue et al., 2021a</xref>; <xref ref-type="bibr" rid="bib41">Ye et al., 2021</xref>) to assess the LFS exposure, accuracy of its results cannot be evaluated directly. Therefore, in this study, we assessed the overall performance of the GEOS-Chem model by comparing the simulated PM<sub>2.5</sub> concentrations with the satellite-based estimates (<xref ref-type="bibr" rid="bib34">van Donkelaar et al., 2016</xref>). At the surveyed locations (i.e., the gray dots in <xref ref-type="fig" rid="fig1">Figure 1</xref>), we found that the two estimators were in good agreement with each other (Pearson correlation coefficient, R<sup>2</sup> = 0.76; more details on the comparison are shown in <xref ref-type="supplementary-material" rid="supp1">Supplementary file 1</xref>).</p><p>To understand the associations between birthweight and environmental variables, we focused on exposure during gestation. As the DHSs did not record the specific duration of gestation, we utilized the 9-month average preceding the birth month as exposure time during pregnancy.</p></sec><sec id="s2-3"><title>Study design</title><p>To evaluate the association between fire-sourced PM<sub>2.5</sub> and birthweight, we applied a sibling-matched case–control study, similar to our previous work (<xref ref-type="bibr" rid="bib39">Xue et al., 2021b</xref>; <xref ref-type="bibr" rid="bib40">Xue et al., 2021c</xref>; <xref ref-type="bibr" rid="bib40">Xue et al., 2021c</xref>; <xref ref-type="bibr" rid="bib37">Xue et al., 2019</xref>). We defined the descendants of the same mother as a family group of matched siblings and compared birthweights within each group. The family-level baseline birthweight (defined as the mean birthweight within each group) can be affected by many complex factors, including genetics, socioeconomic position, and quality of local medical services, which can be difficult to measure. We matched the mothers to control for those unmeasured confounders in the study design. A sibling-matched case–control design is a simple, robust, and cost-efficient method, particularly for large population data with potential heterogeneity (<xref ref-type="bibr" rid="bib8">Colley et al., 2019</xref>; <xref ref-type="bibr" rid="bib9">Curtis, 1997</xref>).</p><p>We extracted the females with at as least two valid records of newborns from all available individual DHSs. Each valid record was defined using the following inclusion criteria: (1) a valid birthweight, (2) a record of the GPS coordinates, (3) a valid birthdate, and (4) valid environmental exposure values (some of the satellite-based PM<sub>2.5</sub> estimates were not available for some locations, such as small islands). Finally, our dataset assembled 113 individual surveys from 54 countries (<xref ref-type="fig" rid="fig1">Figure 1</xref>) and included 227,948 newborns classified into 109,137 groups according to their mothers.</p></sec><sec id="s2-4"><title>Statistical analyses</title><p>According to the sibling-matched method, we used a fixed-effects regression model to evaluate the associations between fire-sourced PM<sub>2.5</sub> and LBW or VLBW. The model was specified as<disp-formula id="equ4"><label>(4)</label><mml:math id="m4"><mml:mrow><mml:msub><mml:mi>y</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>∼</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mi>β</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>z</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi mathvariant="normal">Θ</mml:mi><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></disp-formula></p><p>where the subscripts <italic>i</italic> and <italic>j</italic> denote the indices of the mother-affiliated group and child, respectively; <italic>x<sub>i,j</sub></italic> denotes the concentration of fire-sourced PM<sub>2.5</sub>; <bold><italic>z</italic></bold><italic><sub>i,j</sub></italic> denotes the adjusted covariates; <italic>y<sub>i,j</sub></italic> denotes the outcome variable; and <italic>θ<sub>i</sub></italic> is a nuisance parameter for the fixed effect to control for group-specific unmeasured confounders. The model was specified as Gaussian regression when setting <italic>y<sub>i,j</sub></italic> as the birthweight and as logit regression when setting <italic>y<sub>i,j</sub></italic> as the logit-transformed probability of LBW or VLBW. The adjusted covariates included maternal age; child sex; multiple births; non-fire-sourced PM<sub>2.5</sub>; spline terms of birth order (with 5 degrees of freedom [DF]), of temperature (3 DF), of humidity (3 DF), of the calendar year (5 DF), and of the monthly index (4 DF); and a spatiotemporal effect parameterized by the interaction between country and calendar year. The nonlinear effects of month and year controlled for the seasonal periodic variation and long-term trend in child health, respectively. The spatiotemporal effect captured country-specific trends. The association between birthweight and LFS was evaluated by the regression coefficient <italic>β</italic> for 1 μg/m<sup>3</sup> increases in fire-sourced PM<sub>2.5</sub>. The association with LBW or VLBW was evaluated by excess risk [ = (1 – exp(<italic>β</italic>)) × 100%] for a 1 μg/m<sup>3</sup> increase in fire-sourced PM<sub>2.5</sub>. All environmental exposures were quantified using the same time window. The major time window was the 9 months before birth. We also calculated the alternative exposure indicator as an average during the 3 or 6 months preceding birth.</p><p>In sensitivity analyses, we first derived the lag-distributed model to explore how the association varied during the exposure time window. As the specific duration of gestation was not recorded, before actual data analysis, we utilized the 9-month average preceding the birth month as the major exposure time window. After conducting the major analysis, we re-estimated the effects of fire-sourced PM<sub>2.5</sub> on birthweight change, LBW, or VLBW, by different time windows using the lag-distributed model. Next, subpopulation-specified associations were estimated using interaction analyses to detect potential heterogeneity. Third, we developed nonlinear associations by replacing the linear term of fire-sourced PM<sub>2.5</sub> using a set of thin-plate spline functions. Fourth, to examine potential recall bias, we estimated the associations by strata of the durations from birth to survey time (the duration is defined as recall period in this study). We assumed a shorter recall period indicated for a lower likelihood of outcome misclassification. Fifth, to test whether the estimated associations were attributable to LFS exposure or other direct damage related to landscape fires (e.g., destroying human habitats), we derived an indicator for the transported fire-sourced PM<sub>2.5</sub>, according to a satellite image of the burned area (MCD64A1, <ext-link ext-link-type="uri" xlink:href="https://lpdaacsvc.cr.usgs.gov/appeears/">https://lpdaacsvc.cr.usgs.gov/appeears/</ext-link>). The fire-sourced PM<sub>2.5</sub> at the pixels with the zero-burned area during gestation were defined as smoke transported from other locations. The associations with transported fire-sourced PM<sub>2.5</sub> could be indicative of the effect of LFS exposure via the inhalation pathway.</p><p>Family-level baseline birthweight and the correlated gestation length can be determined by factors such as genetics and socioeconomic level, which may also affect the association between the within-group change in birthweight and LFS exposure. The fixed-effects regression models actually infer an association by examining the coherence between derivation of individual-level birthweight from the family-level mean (i.e., the expected birthweight given a specific family) and the corresponding exposure. To show that, we defined the family-level baseline birthweight as <italic>y<sub>i</sub></italic>. Equation (4) was equivalently transformed as Δ<italic>y<sub>i,j</sub></italic> = (<italic>y<sub>i,j</sub> − y<sub>i</sub></italic>) ~ <italic>x<sub>i,j</sub> β+ <bold>z</bold><sub>i,j</sub><bold>γ</bold></italic> + (<italic>θ<sub>i</sub> − y<sub>i</sub></italic>), where (<italic>θ<sub>i</sub> − y<sub>i</sub></italic>) is viewed as a new nuisance parameter <italic>θ<sub>i</sub></italic><sup>*</sup>. The nature of fixed-effects model made it possible to explore how the effect of fire-sourced PM<sub>2.5</sub> varied according to family-level mean birthweight. Accordingly, we derived a baseline-varying association model as follows:<disp-formula id="equ5"><label>(5)</label><mml:math id="m5"><mml:mrow><mml:mi mathvariant="normal">△</mml:mi><mml:msub><mml:mi>y</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>∼</mml:mo><mml:mi>f</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">)</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>z</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mi>y</mml:mi><mml:mo>+</mml:mo><mml:msubsup><mml:mi mathvariant="normal">Θ</mml:mi><mml:mrow><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mo>∗</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math></disp-formula></p><p>All analyses were performed in R (version 4.0.2; The R Foundation for Statistical Computing, Vienna, Austria). Inference for the fixed-effects models was performed using the <italic>fixest</italic> package. The lag-distributed effect was parameterized using the <italic>dlnm</italic> package, and the thin-plate splines for the nonlinear association and baseline-varying effect were parameterized using the <italic>mgcv</italic> package. Source data for figures and R codes for the epidemiological models are documented in Source code 1.</p></sec></sec><sec id="s3" sec-type="results"><title>Results</title><sec id="s3-1"><title>Descriptive summary</title><p>Among the 227,948 livebirths analyzed from 54 LMICs, there were 109,137 groups of siblings who were matched to their mothers. Each group had an average of 2.13 (standard deviation [SD] 0.36) livebirths. Approximately half of the livebirths were from Sub-Saharan Africa and one-quarter from South Asia. The mean birthweight was 3,082 (SD 724) g. There were 31,854 LBW births and 2,912 very low birthweight (VLBW) births. More statistics on the analyzed livebirths are shown in <xref ref-type="supplementary-material" rid="supp1">Supplementary file 1b</xref>.</p><p>The spatial distribution of the long-term average fire-sourced PM<sub>2.5</sub> concentrations is displayed in <xref ref-type="fig" rid="fig1">Figure 1</xref>. Our modeling results indicate that the hotspots of LFS include regions of the Congo rainforest basin, Amazon rainforest basin, Indonesian forests, Siberia forests, and other forest areas. Our analyzed samples included newborns from the first three regions. Among our samples, the average concentration of full gestational exposure to fire-sourced PM<sub>2.5</sub> was 4.29 (SD: 5.53) µg/m<sup>3</sup>. A detailed summary of the environmental exposures is presented in <xref ref-type="supplementary-material" rid="supp1">Supplementary file 1b</xref>.</p><p>Gestational exposure to fire-sourced PM<sub>2.5</sub> varied widely between family groups; it was higher in those living closer to hotspot regions (e.g., forests). Approximately 92% of the variation in fire-sourced PM<sub>2.5</sub> exposure was between family groups and 8% was within family groups. For the total variation in birthweight, 71.7% was between groups and 28.3% within groups (<xref ref-type="table" rid="table1">Table 1</xref>). The within- or between-group variations reflected temporal or spatial changes in those variables, respectively. Birthweight and fire-sourced PM<sub>2.5</sub> were negatively correlated in the temporal dimension but positively correlated in the spatial dimension. As the spatial patterns in birthweight and fire-sourced PM<sub>2.5</sub> were determined by geographic, socioeconomic, and cultural factors, the between-group correlations may have been confounded by their common determiners, such as urbanization level. The complex correlations between birthweight and LFS suggest that statistical inference of their associations should be cautiously determined based on well-designed epidemiological analysis.</p><table-wrap id="table1" position="float"><label>Table 1.</label><caption><title>Between- or within-group variations in birthweight and gestational exposure to fire-sourced PM<sub>2.5</sub> and their correlations.</title></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="2" valign="bottom"/><th align="left" colspan="2" valign="bottom">Standard deviation (% of total variance)</th><th align="left" rowspan="2" valign="bottom">Correlation (R)</th></tr><tr><th align="left" valign="bottom">Birthweight(g)</th><th align="left" valign="bottom">Fire-sourced PM<sub>2.5</sub> g/m<sup>3</sup></th></tr></thead><tbody><tr><td align="left" valign="bottom">Total</td><td align="char" char="." valign="bottom">724 (100%)</td><td align="char" char="." valign="bottom">5.53 (100%)</td><td align="char" char="." valign="bottom">0.1594</td></tr><tr><td align="left" valign="bottom">Between groups of matched siblings</td><td align="char" char="." valign="bottom">613 (71.7%)</td><td align="char" char="." valign="bottom">5.30 (91.7%)</td><td align="char" char="." valign="bottom">0.1973</td></tr><tr><td align="left" valign="bottom">Within groups of matched siblings</td><td align="char" char="." valign="bottom">386 (28.3%)</td><td align="char" char="." valign="bottom">1.59 (8.3%)</td><td align="char" char="." valign="bottom">−0.0035</td></tr></tbody></table></table-wrap></sec><sec id="s3-2"><title>Association between LFS and birthweight</title><p>Although the fully adjusted model indicated that a 1 µg/m<sup>3</sup> increase in exposure to fire-sourced PM<sub>2.5</sub> during the 9 months before birth was significantly associated with a 2.17 g (95% CI 0.56–3.77) birthweight reduction, the association was sensitive to differences in the adjusted covariates and exposure time window (<xref ref-type="fig" rid="fig2">Figure 2</xref>). For example, the unadjusted model reported the estimated effect as a birthweight reduction of 0.84 g (95% CI −0.53 to 2.22). The lag-specific associations did not display a clear time-varying pattern (<xref ref-type="fig" rid="fig2s1">Figure 2—figure supplement 1</xref>), which suggests a linear increase in strength in the association with increased length of the exposure time window (<xref ref-type="fig" rid="fig2">Figure 2</xref>, <xref ref-type="fig" rid="fig2s2">Figure 2—figure supplement 2</xref>). The subpopulation-specific results suggest that the association was potentially heterogeneous (<xref ref-type="fig" rid="fig2s3">Figure 2—figure supplement 3</xref>). The association was stronger for female infants, newborns of nulliparous mothers, or newborns of unemployed mothers compared with the corresponding references (i.e., male infants, newborns of multiparous mothers, or newborns of employed mothers, respectively). The nonlinear analysis confirmed the adverse effect of LFS on birthweight (<xref ref-type="fig" rid="fig3">Figure 3</xref>). The sublinear association suggests a saturated effect of an ~60 g birthweight reduction for exposure to extreme fires, which contributed to a PM<sub>2.5</sub> concentration >~20 µg/m<sup>3</sup>.</p><fig-group><fig id="fig2" position="float"><label>Figure 2.</label><caption><title>Effects of fire-sourced PM<sub>2.5</sub>, estimated by linear models.</title><p>The dots with error bars show the estimated associations between gestational exposure to fire-sourced PM<sub>2.5</sub> and birthweight change, low birthweight, or very low birthweight. The dots represent the point estimates, and bars represent the corresponding 95% confidence intervals.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-69298.xml.media/fig2.jpg"/></fig><fig id="fig2s1" position="float" specific-use="child-fig"><label>Figure 2—figure supplement 1.</label><caption><title>The estimated associations between fire-sourced PM<sub>2.5</sub> and birthweight change, low birthweight or very low birthweight, by different lags.</title><p>The nth lagged exposure is defined as the concentration of fire-sourced PM<sub>2.5</sub> during the nth month before birth. The results are estimated from the lag-distributed models.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-69298.xml.media/fig2-figsupp1.jpg"/></fig><fig id="fig2s2" position="float" specific-use="child-fig"><label>Figure 2—figure supplement 2.</label><caption><title>The cumulated birthweight change associated with an average of fire-sourced PM<sub>2.5</sub> concentrations from a lagged month to birth.</title></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-69298.xml.media/fig2-figsupp2.jpg"/></fig><fig id="fig2s3" position="float" specific-use="child-fig"><label>Figure 2—figure supplement 3.</label><caption><title>The subpopulation-specific associations between gestational exposure to fire-sourced PM<sub>2.5</sub> and birthweight change, low birthweight and very low birthweight.</title></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-69298.xml.media/fig2-figsupp3.jpg"/></fig></fig-group><fig id="fig3" position="float"><label>Figure 3.</label><caption><title>The nonlinear association between gestational exposure to fire-sourced PM<sub>2.5</sub> and change in birthweight.</title><p>The solid line represents the point estimates, the dashed line represents the 95% confidence intervals, the boxplots represent the distributions of different exposure levels by regions, and the red dots represent the mean exposure level.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-69298.xml.media/fig3.jpg"/></fig></sec><sec id="s3-3"><title>Association between LFS and VLBW</title><p>Our fully adjusted models suggested that LFS exposure during the 9 months before birth was significantly associated with an increased risk of LBW or VLBW. According to the estimates, the risks of LBW and VLBW increased by 2.80% (95% CI 0.97–4.66) and 11.68% (95% CI 3.59–20.40), respectively, for each 1 µg/m<sup>3</sup> increase in exposure to fire-sourced PM<sub>2.5</sub>. Compared to LBW, VLBW was more strongly associated with fire-sourced PM<sub>2.5</sub>. The estimated association for LBW was sensitive to different covariate adjustments (<xref ref-type="fig" rid="fig2">Figure 2</xref>) and was potentially heterogeneous among subpopulations (<xref ref-type="fig" rid="fig2s3">Figure 2—figure supplement 3</xref>). The association for VLBW was robust, given the adjustments for different covariates (<xref ref-type="fig" rid="fig2">Figure 2</xref>), and was less heterogeneous (<xref ref-type="fig" rid="fig2s3">Figure 2—figure supplement 3</xref>). Among all effect modifiers, only the type of cooking energy, which was indicated for indoor sources of particulate matter, significantly changed the association between VLBW and fire-sourced PM<sub>2.5</sub> (p-value = 0.02, <xref ref-type="fig" rid="fig2s3">Figure 2—figure supplement 3</xref>). The use of unclear cooking energy, as a competing risk factor for fire-sourced PM<sub>2.5</sub>, significantly weakened the association between VLBW and fire-sourced PM<sub>2.5</sub>.</p><p>To further explore why VLBW was more strongly associated with fire-sourced PM<sub>2.5</sub> than was LBW, we developed a model to estimate the baseline-varying association between birthweight and fire-sourced PM<sub>2.5</sub>. As a result, the absolute change in birthweight for per unit exposure to fire-sourced PM<sub>2.5</sub> varied with the baseline of family-level mean birthweight (<xref ref-type="fig" rid="fig4s1">Figure 4—figure supplement 1</xref>). Livebirths with an extremely low or high baseline birthweight were more susceptible to fire-sourced PM<sub>2.5</sub> than were those with a moderate baseline birthweight in terms of the absolute birthweight change (<xref ref-type="fig" rid="fig4s1">Figure 4—figure supplement 1</xref>). As a reduction in birthweight and a family-level baseline co-determined the increased incidence of LBW or VLBW, the relative change in birthweight reflected the relevant risk. Newborns from families with a lower baseline were more susceptible to fire-sourced PM<sub>2.5</sub> in terms of a relative birthweight reduction (<xref ref-type="fig" rid="fig4">Figure 4</xref>). This finding partially explains why gestational exposure to fire-sourced PM<sub>2.5</sub> was robustly and strongly associated with VLBW. According to our estimates, a 1 µg/m<sup>3</sup> increase in fire-sourced PM<sub>2.5</sub> was associated with a relative birthweight reduction of 1.47% (95% CI 0.06–2.88), 0.54% (95% CI 0.03–1.05), 0.16% (95% CI 0.01–0.31), or 0.03% (95% CI −0.04–0.10) for livebirths with a baseline birthweight of 1500, 2000, 2500, or 3000 g, respectively.</p><fig-group><fig id="fig4" position="float"><label>Figure 4.</label><caption><title>Baseline-varying effect of fire-sourced PM<sub>2.5</sub> on birthweight.</title><p>The lines show the estimated association (y-axis) between gestational exposure to fire-sourced PM<sub>2.5</sub> and relative change in birthweight, given different baseline birthweights (x-axis). The solid line represents the point estimates, the dashed line represents the 95% confidence intervals, and the boxplots represent the distributions of different birthweights by region.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-69298.xml.media/fig4.jpg"/></fig><fig id="fig4s1" position="float" specific-use="child-fig"><label>Figure 4—figure supplement 1.</label><caption><title>The baseline-varying association between gestational exposure to fire-sourced PM<sub>2.5</sub> and absolute birthweight change.</title><p>The solid line presents the point estimates, the dashed line presents the 95% confidence intervals, and the boxplots present the distributions of different birthweight levels by regions.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-69298.xml.media/fig4-figsupp1.jpg"/></fig></fig-group></sec><sec id="s3-4"><title>Sensitivity analyses</title><p>First, we explored potential recall bias using sensitivity analyses, and the estimated associations were not significantly different according to recall period (<xref ref-type="fig" rid="fig2s3">Figure 2—figure supplement 3</xref>). Second, we examined the spatial variations in the estimated associations. Although analysis of differences in region-specific associations reported nonsignificant results (<xref ref-type="fig" rid="fig2s3">Figure 2—figure supplement 3</xref>), we could not completely rule out the possibility of spatial heterogeneity in the estimated results. As most of our analyzed livebirths were from Sub-Saharan Africa, only those estimates were significant, and the remaining region-specific results had wide-ranging 95% CIs (<xref ref-type="fig" rid="fig2s3">Figure 2—figure supplement 3</xref>). Third, we explored whether the estimated adverse effects were attributable to ambient LFS exposure or other impacts (e.g., social stress) relevant to landscape fires. The estimated effects of exposure to fire-sourced PM<sub>2.5</sub> were not significantly different between locations without burning and those with burning (<xref ref-type="fig" rid="fig2s3">Figure 2—figure supplement 3</xref>). These results confirm that ambient exposure was the major exposure pathway for our analyzed outcomes.</p></sec></sec><sec id="s4" sec-type="discussion"><title>Discussion</title><p>This study revealed an association between increased risk of VLBW and exposure to LFS during pregnancy in 54 LIMCs. We found that gestational exposure to fire-sourced PM<sub>2.5</sub> potentially reduced birthweight, and the infants with a low family-level mean of birthweight were susceptible to the effect of LFS. We present the first sibling-matched study on the association between birthweight and fire-sourced PM<sub>2.5</sub> from multiple LIMCs.</p><p>LFS contains numerous hazardous pollutants and exerts both short- and long-term effects on human health (<xref ref-type="bibr" rid="bib36">Xu et al., 2020</xref>). An adverse birth outcome is one of the consequences. <xref ref-type="bibr" rid="bib13">Holstius et al., 2012</xref> estimated that the mean birthweights of fire-exposed infants are 7.0 g (95% CI 11.8–2.2), 9.7 g (95% CI 14.5–4.8), and 3.3 g (95% CI −0.6–7.2) lower than those of unexposed infants when wildfires occurred during the first, second, and third trimesters, respectively. In a retrospective cohort study in Brazil, fire-exposed mothers (defined as the highest PM<sub>2.5</sub> concentration quartile) reported a higher risk of LBW than did unexposed mothers. The adjusted odds ratios for exposure during the second and third trimesters were 1.51 (95% CI 1.04–2.17) and 1.50 (95% CI 1.06–2.15), respectively (<xref ref-type="bibr" rid="bib6">Cândido da Silva et al., 2014</xref>). <xref ref-type="bibr" rid="bib1">Abdo et al., 2019</xref> showed that a 1 μg/m<sup>3</sup> increase in the wildfire-sourced PM<sub>2.5</sub> concentration during the first trimester was associated with a 5.7 g (95% CI 0.4–11.1) reduction in birthweight in the United States. In contrast, an Australian cross-sectional study reported that the average birthweight of male infants exposed to the 2003 Canberra wildfires was significantly higher than that of unexposed peers (<xref ref-type="bibr" rid="bib25">O’Donnell and Behie, 2015</xref>). That study attributed the positive association between wildfire smoke and birthweight change to the potentially increased levels of maternal blood glucose after exposure (<xref ref-type="bibr" rid="bib25">O’Donnell and Behie, 2015</xref>). The mixed results might be due to differences among studies in terms of epidemiological design, population characterization, exposure time window, and competing risk factors. Our results are comparable with most previous findings. Additionally, although there are few studies on fire-sourced PM<sub>2.5</sub> similar to ours, a large number of studies have focused on the effect of gestational exposure to urban ambient PM<sub>2.5</sub> on birthweight. A recent meta-analysis, which included more than 2.7 million participants, reported that the pooled risk ratio of LBW for a 10 μg/m<sup>3</sup> increase in PM<sub>2.5</sub> exposure was 1.081 (95% CI 1.043–1.120; <xref ref-type="bibr" rid="bib20">Li et al., 2020</xref>).</p><p>The family-level mean birthweight varied widely between households (<xref ref-type="table" rid="table1">Table 1</xref>). These variations may have been caused by many factors, including genetics, socioeconomic position (e.g., nutrition), and indoor household pollution. As birthweight is positively correlated with gestation length, a poor family-level baseline birthweight comprehensively indicates a combined risk of short gestation and LBW. Because the cumulative effect of fire-sourced PM<sub>2.5</sub> was observed to increase with exposure duration (<xref ref-type="fig" rid="fig2s2">Figure 2—figure supplement 2</xref>), a larger birthweight at the family level, as indicated for a longer gestation, increased the absolute effect of the per-unit LFS exposure (<xref ref-type="fig" rid="fig4s1">Figure 4—figure supplement 1</xref>).</p><p>As our models did not control for gestation length due to the lack of relevant data, the estimated associations reflected the joint outcome of LFS exposure by reducing gestation length or birthweight. The fetus grows in a sublinear temporal pattern (<xref ref-type="bibr" rid="bib28">Salomon et al., 2007</xref>) during the late gestational stage, which suggests its weight increases to an approximately stable level before birth. Considering this sublinear relationship and given the different baseline birthweights, the same reduction in gestation length led to different reductions in birthweight. The marginal effect of a shorter gestation period on the reduction in birthweight is larger for an infant with a lower baseline birthweight. For instance, if an infant is expected to reach the stable level of fetal weight, a marginal reduction in its gestational age will not considerably change the birthweight. As exposure to LFS has been reported to cause a short gestation (<xref ref-type="bibr" rid="bib1">Abdo et al., 2019</xref>), its indirect effect on reduced birthweight can be enhanced by poor baseline maternal health.</p><p>Reducing the risk of LBW is one of the 2025 WHO global targets, and it contributes to achieving the Sustainable Development Goals. Although major interventions to reduce LBW have focused on improving maternal nutrition, environmental toxic pollutants, also identified as risk factors, can additionally contribute to the relevant disease burden. For instance, exposure to air pollution was linked to a global burden of 476,000 infant deaths in 2019 during the first month of life via increased risks of LBW and premature birth (<xref ref-type="bibr" rid="bib24">Murray et al., 2020</xref>). LFS induces peak concentrations of PM<sub>2.5</sub> lasting for a few weeks or months. Its spatial distribution is also different from that of urban PM<sub>2.5</sub>. It is highly clustered in forestry areas (<xref ref-type="fig" rid="fig1">Figure 1</xref>). Compared to urban PM<sub>2.5</sub>, fire-sourced PM<sub>2.5</sub> is distributed more skewedly. Therefore, the individual-level impacts on exposed mothers are not negligible. Particularly, LFS exposure frequently occurred in LMICs (<xref ref-type="fig" rid="fig1">Figure 1</xref>), where the fire-sourced PM<sub>2.5</sub> adversely affected infants with other risk factors, such as malnutrition. However, due to the lower specificity of our model evaluating birthweight without adjusting for gestation length, we could not evaluate the clinical outcomes or disease burden of the LFS-attributed birthweight reductions. As there is an increasing probability of landscape fire under global climate change, assessing the adverse impacts of LFS exposure is of public health importance to protect maternal health in LMICs. Future studies should be conducted to answer this question.</p><p>This study was associated with the following limitations. First, the lack of data on specific gestation lengths in the DHSs limited our exploration. Without this information, we could not distinguish the exposure time window, which introduced potential exposure misclassifications into the epidemiological analyses. Particularly, for the newborns with lower birthweight, their gestations could be shorter, which could lead to a larger likelihood of exposure misclassification. This can explain why the exposures at the 4–6 months before birth were significantly linked to all examined outcomes (<xref ref-type="fig" rid="fig2s1">Figure 2—figure supplement 1</xref>). Compared to exposures during the time window, those at longer lags (e.g., lag-8 or -9 month) tend to be pre-pregnancy rather than gestational exposure. Also, we could not determine the clinical importance of the analyzed birthweight without normalizing it to gestation length. Second, characterizing LFS exposure is always challenging. Previous studies measured exposure using surrogates, such as the temporal duration of landscape fires or satellite remote sensing of fire points. To increase interpretability, we utilized fire-sourced PM<sub>2.5</sub> as a direct indicator of ambient exposure to LFS. However, as the exposure assessments were based on a CTM, the uncertainties introduced by the modeling procedure could not be avoided completely. The potential errors enhanced the exposure misclassifications in this study and thus may have resulted in a biased association. For CTM simulations, their accuracy depends on quality of the inputted emission inventories. Since the large fires can be easily captured by satellite images, which have been considered in the inventory utilized in this study, we believe the effect estimated from a high-exposure region is less biased than that from a low-exposure region. According to the region-specific estimates (<xref ref-type="fig" rid="fig2s3">Figure 2—figure supplement 3</xref>), we find that the estimated effects are significant in the highest-exposure region, Sub-Sahara Africa (<xref ref-type="fig" rid="fig3">Figure 3</xref>), but not in the rest. The result suggests that the exposure misclassification caused and introduced by CTM simulations may lead to underestimated associations. Third, since the analyzed samples were living newborns, our estimates were potentially subjected to survival bias. For instance, fire-sourced PM<sub>2.5</sub> was associated with extremely reduced birthweight, which increases the probability of stillbirth or neonatal death. In other words, our sample may have included fewer embryos more susceptible to fire-sourced PM<sub>2.5</sub> than expected. Therefore, survival bias could have led to an underestimated association by ignoring those susceptible embryos. Fourth, although there have been some previous studies analyzing the birthweight outcome using DHS data (<xref ref-type="bibr" rid="bib3">Bellizzi and Padrini, 2020</xref>), the self-reported records may be inaccurate due to potential recall bias and lead to potential outcome misclassification. The effect of LFS should be confirmed in future by high-quality data on clinical diagnosis of LBW or VLBW. Finally, a sibling-matched study requires a selected sample, which may have lowered the representativeness of this study. The inclusion of infants surveyed by the DHS in this study was determined by many factors, such as family size and planning, willingness to bear a child, socioeconomic position, and contraceptive behavior. However, some of those factors could not be easily measured, which impeded the development of a set of sampling weights to increase the representativeness of our estimates.</p><p>In conclusion, we used a global-scale sibling-matched case–control study to identify an association between gestational exposure to fire-sourced PM<sub>2.5</sub> and reduced birthweight in LMICs. The association was sensitive to regression model settings and potential heterogeneity among different subpopulations. Newborns from families with a low baseline level of birthweight were susceptible to LFS-associated birthweight reduction, which was consistent with our finding of a strong and robust association between fire-sourced PM<sub>2.5</sub> and VLBW. This study adds to the epidemiological evidence on the adverse effects of LFS on birthweight. Relevant interventions regarding frequently occurring landscape fires, such as climate change mitigations, should be taken to protect maternal and infant health.</p></sec></body><back><sec id="s5" sec-type="additional-information"><title>Additional information</title><fn-group content-type="competing-interest"><title>Competing interests</title><fn fn-type="COI-statement" id="conf1"><p>No competing interests declared</p></fn><fn fn-type="COI-statement" id="conf2"><p>No competing interests declared</p></fn></fn-group><fn-group content-type="author-contribution"><title>Author contributions</title><fn fn-type="con" id="con1"><p>Data curation, Formal analysis, Methodology, Investigation</p></fn><fn fn-type="con" id="con2"><p>Writing - review and editing, Supervision, Investigation</p></fn><fn fn-type="con" id="con3"><p>Formal analysis, Writing – original draft, Investigation</p></fn><fn fn-type="con" id="con4"><p>Data curation, Writing – original draft, Conceptualization</p></fn><fn fn-type="con" id="con5"><p>Formal analysis, Writing – original draft, Conceptualization</p></fn><fn fn-type="con" id="con6"><p>Writing – original draft, Conceptualization</p></fn><fn fn-type="con" id="con7"><p>Methodology, Conceptualization</p></fn><fn fn-type="con" id="con8"><p>Writing - review and editing, Data curation, Supervision, Funding acquisition, Conceptualization</p></fn></fn-group><fn-group content-type="ethics-information"><title>Ethics</title><fn fn-type="other"><p>Human subjects: Procedures and questionnaires for standard DHS surveys have been reviewed and approved by ICF Institutional Review Board. All analyses are based on the open-accessed DHS data. The research plan has been approved by DHS, and all analyses adhere the guideline of data usage from DHS.</p></fn></fn-group></sec><sec id="s6" sec-type="supplementary-material"><title>Additional files</title><supplementary-material id="supp1"><label>Supplementary file 1.</label><caption><title>Summary of analyzed variables.</title><p>(a) Comparison between the PM<sub>2.5</sub> concentrations simulated by GEOS-Chem model and those estimated from satellite measurements and other inputs. In the comparison, the satellite-based estimates are utilized as the gold-standard referent values and the GEOS-Chem simulations as predictions. (b) Population characteristics.</p></caption><media mime-subtype="docx" mimetype="application" xlink:href="elife-69298-supp1-v2.docx"/></supplementary-material><supplementary-material id="transrepform"><label>Transparent reporting form</label><media mime-subtype="docx" mimetype="application" xlink:href="elife-69298-transrepform1-v2.docx"/></supplementary-material><supplementary-material id="scode1"><label>Source code 1.</label><caption><title>The R codes and data to reproduce <xref ref-type="fig" rid="fig1">Figures 1</xref>—<xref ref-type="fig" rid="fig4">4</xref>.</title></caption><media mime-subtype="zip" mimetype="application" xlink:href="elife-69298-supp2-v2.zip"/></supplementary-material><supplementary-material id="repstand1"><label>Reporting standard 1.</label><caption><title>Checklist of STROBE items.</title></caption><media mime-subtype="doc" mimetype="application" xlink:href="elife-69298-repstand1-v2.doc"/></supplementary-material></sec><sec id="s7" sec-type="data-availability"><title>Data availability</title><p>All data analysed during this study is included in the manuscript and Supporting Information files. Data is all from publicly available sources. Specifically, the health data can be directly accessed from the Demographic and Health Surveys website, <ext-link ext-link-type="uri" xlink:href="https://www.dhsprogram.com/">https://www.dhsprogram.com/</ext-link>, after a free registration.</p></sec><ack id="ack"><title>Acknowledgements</title><p>This work was supported by National Natural Science Foundation of China (4217050142), PKU-Baidu Fund (2020BD031), the Fundamental Research Funds for the Central Universities (BMU2021YJ042), and the Energy Foundation (G-2107-33169) for TX; and CAMS Innovation Fund for Medical Sciences (2017-I2M-1-004) for TG.</p></ack><ref-list><title>References</title><ref id="bib1"><element-citation publication-type="journal"><person-group 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J Journal of Geophysical Research</source><volume>125</volume><elocation-id>e2019JD032293</elocation-id></element-citation></ref></ref-list></back><sub-article article-type="decision-letter" id="sa1"><front-stub><article-id pub-id-type="doi">10.7554/eLife.69298.sa1</article-id><title-group><article-title>Decision letter</article-title></title-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>Franco</surname><given-names>Eduardo</given-names></name><role>Reviewing Editor</role><aff><institution>McGill University</institution><country>Canada</country></aff></contrib></contrib-group><contrib-group><contrib contrib-type="reviewer"><name><surname>Balmes</surname><given-names>John</given-names></name><role>Reviewer</role><aff><institution>UC Berkeley</institution><country>United States</country></aff></contrib></contrib-group></front-stub><body><boxed-text id="box1"><p>Our editorial process produces two outputs: i) <ext-link ext-link-type="uri" xlink:href="https://sciety.org/articles/activity/10.1101/2021.04.27.21256225">public reviews</ext-link> designed to be posted alongside <ext-link ext-link-type="uri" xlink:href="https://www.medrxiv.org/content/10.1101/2021.04.27.21256225v1">the preprint</ext-link> for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.</p></boxed-text><p><bold>Acceptance summary:</bold></p><p>This paper reports the results of an analysis of the association between maternal exposure to landscape fire smoke during pregnancy and low birthweight of the offspring. Given the increasing number, intensity, and duration of landscape fires across the globe as well as the impact of low birthweight on public health, the manuscript will be of interest to both scientists and policymakers. The size of the study population drawn from 54 low and middle-income countries makes the paper an important contribution to the literature on the adverse health effects of biomass fire smoke.</p><p><bold>Decision letter after peer review:</bold></p><p>Thank you for submitting your article "Exposure to landscape fire smoke extremely reduced birthweight in low- and middle-income countries: findings from a siblings-matched case-control study" for consideration by <italic>eLife</italic>. Your article has been reviewed by 1 peer reviewer, and the evaluation has been overseen by Eduardo Franco as the Senior and Reviewing Editor. The following individual involved in review of your submission has agreed to reveal their identity: John Balmes (Reviewer #1).</p><p>You will find below the concerns and suggestions that you would have to address to have your submission considered for publication in <italic>eLife</italic>. Please submit a revised version that addresses these concerns directly. Although we expect that you will address these comments in your response letter, we also need to see the corresponding revision clearly marked in the text of the manuscript. Some of the comments may seem to be simple queries or challenges that do not prompt revisions to the text. Please keep in mind, however, that readers may have the same perspective as the reviewers. Therefore, it is essential that you attempt to amend or expand the text to clarify the narrative accordingly.</p><p>General Comments:</p><p>Li and colleagues used data from 2000 to 2014 in 54 low and middle-income countries (LMICs) to study the association between exposure to landscape fire smoke PM2.5 and birthweight, including very low birthweight. While there is a relatively robust epidemiological literature that supports an association between non-biomass fire smoke PM2.5 and low birthweight, there are relatively few studies that are specific to biomass smoke PM2.5 and birthweight. The authors of this paper conducted their study to specifically address this data gap. They took advantage of satellite data which provide estimates of PM2.5 levels that are now available for most locations in the world at a high geographic resolution (0.5 x 0.5 km). They enhanced the satellite exposure data using a chemical transport model to distinguish fire-sourced PM2.5 from non-fire PM2.5. The exposure modeling approach is sophisticated as is the statistical analysis of the association between the fire-sourced PM2.5 exposure estimates and birthweight outcomes.</p><p>The study has multiple strengths, including the first study of the association between fire-sourced PM2.5 and birthweight to use a sibling-matched case-control design, a large sample size (227,948 births born to 109,137 mothers), the focus on LMICs, the exposure modeling, a careful statistical analytic approach with alternate non-linear regression and sensitivity analyses, and the outcome of very low birthweight that is one of the World Health Organization targets to reduce the global burden of disease. Limitations notwithstanding, this is an impactful study. The results of the authors' analyses provide strong support for the concept that exposure to biomass smoke -- whether from a landscape fire set by farmers, a wildfire, or cooking with solid fuels -- can lead to low birthweight. This concept is especially important for LMICs that have large portions of their populations engaging in slash and burn agriculture and/or cooking with solid fuels. Given that reducing the incidence of low birthweight is a necessary to meet the 2025 United Nations Sustainable Development Goals, it is incumbent that policies to reduce landscape fires and household air pollution from cooking with solid fuels be considered by governments of LMICs. Such policies would also have a climate change mitigation benefit through reduction of greenhouse gases and aerosols.</p><p>Future research efforts to actually measure landscape fire smoke PM2.5 in different locations to provide ground-truthing for the chemical transport model exposure estimates used by the authors would be useful as would a study that could obtain gestation duration data.</p><p>Essential revisions:</p><p>Despite these strengths there several weaknesses of the study. Perhaps the most important is potential misclassification of exposure to landscape fire PM2.5 because levels of this pollutant were not measured, but rather estimated using a chemical transport model.</p><p>Another important weakness is the lack of gestation duration data that limited the primary analysis to a typical 9-month exposure period which also adds to exposure misclassification because some low-birthweight babies have shorter gestation periods.</p><p>A third weakness is that birthweight was not measured but rather provided by maternal recall which may not always be reliable, especially in rural areas of low-resourced countries when children are born at home. The authors acknowledge the potential for misclassification of exposure to fire-sourced PM2.5, but not for the birthweight outcome.</p><p>Title I suggest changing the title to "Exposure to landscape fire smoke extremely and reduced birthweight in low- and middle-income countries".</p><p>Line 46 I suggest the concluding statement of the Abstract be revised as follows: "Our findings indicate that gestational exposure to LFS harms fetal health."</p><p>Line 68 "Rarely discussed" is too strong. As the authors note in the Discussion, there are other published studies that have addressed the effect of biomass smoke exposure on birthweight.</p><p>Line 69 Should be "LFS" rather than LPS.</p><p>Line 85 Should be "adverse birth outcomes" rather than birth defects.</p><p>Line 90 Again, "little is known about fire-sourced PM2.5" is too strong given the published literature.</p><p>Line 151 Shouldn't this be the 9-month average preceding the "birthdate" rather than survey month?</p><p>Line 225 Here and elsewhere in the manuscript, I suggest "live births" rather than samples.</p><p>Line 358 It is not clear to this reviewer that "the association of birthweight with fire-sourced PM2.5 should be stronger than that with urban PM2.5." While this may turn out to be true with future research findings, without any data, this is purely speculative.</p><p>Line 386 Exposures to urban PM2.5 are also differentially distributed with low-income minority populations suffering higher exposures.</p></body></sub-article><sub-article article-type="reply" id="sa2"><front-stub><article-id pub-id-type="doi">10.7554/eLife.69298.sa2</article-id><title-group><article-title>Author response</article-title></title-group></front-stub><body><disp-quote content-type="editor-comment"><p>Essential revisions:</p><p>Despite these strengths there several weaknesses of the study. Perhaps the most important is potential misclassification of exposure to landscape fire PM2.5 because levels of this pollutant were not measured, but rather estimated using a chemical transport model.</p></disp-quote><p>We agree with the reviewer’s opinion that using a chemical transport mode to calculate landscape fire PM<sub>2.5</sub> may introduce potential misclassification. According to this comment, we make the following two revisions.</p><p>1. We enhance our statements on evaluation of the CTM model by added a new paragraph, which reads “Since fire-sourced PM<sub>2.5</sub> is mixed with particles from other sources, it cannot be easily measured. Although the CTM-based approach has been widely utilized in a few large-scale studies (Xue, Geng, Li, et al., 2021; Ye et al., 2021) to assess the LFS exposure, accuracy of its results cannot be evaluated directly. Therefore, in this study, we assessed the overall performance of the GEOS-Chem model by comparing the simulated PM<sub>2.5</sub> concentrations with the satellite-based estimates (Van Donkelaar et al., 2016). At the surveyed locations (i.e., the grey dots in Figure 1), we found the two estimators were in good agreement with each other (Pearson correlation coefficient, R<sup>2</sup> = 0.76; more details on the comparison are shown in Supplementary file 1b).”. Compared to emission inventories inputted into the GEOS-Chem model, the one on landscape fires can be in good quality, because the fire emission inventory is constrained by satellite image of burned areas. Our evaluation shows the GEOS-Chem model can well mimic the chemical and physical processes in PM<sub>2.5</sub> from all sources. If this also holds for fire-sourced PM<sub>2.5</sub>, it is reasonable to believe the model can estimate the fire-related exposure well, given the high-quality of inputted emission inventory. Therefore, this approach has been widely used in recent studies (e.g., Ye et al., Chen et al., Xue et al., the Lancet Planetary Health 2021).</p><p>2. We enhance the discussions on the limitations relevant to the exposure misclassifications. The modified sentences now read as “Second, characterizing LFS exposure is always challenging. Previous studies measured exposure using surrogates, such as the temporal duration of landscape fires or satellite remote sensing of fire points. To increase interpretability, we utilized fire-sourced PM<sub>2.5</sub> as a direct indicator of ambient exposure to LFS. However, as the exposure assessments were based on a CTM, the uncertainties introduced by the modeling procedure could not be avoided completely. The potential errors enhanced the exposure misclassifications in this study and thus may have resulted in a biased association. For CTM simulations, their accuracy depends on quality of the inputted emission inventories. Since the large fires can be easily captured by satellite images, which have been considered in the inventory utilized in this study, we believe the effect estimated from a high-exposure region is less biased than that from a low-exposure region. According to the region-specific estimates (Figure 2—figure supplement 3), we find the estimated effects are significant in the highest-exposure region, Sub-Sahara Africa (Figure 3), but not in the rest. The result suggests the exposure misclassification caused introduced by CTM simulations may lead to underestimated associations.”.</p><disp-quote content-type="editor-comment"><p>Another important weakness is the lack of gestation duration data that limited the primary analysis to a typical 9-month exposure period which also adds to exposure misclassification because some low-birthweight babies have shorter gestation periods.</p></disp-quote><p>Thank you for the reminder. We agree that using a typical 9-month exposure may add some misclassification, particularly for the low-birthweight samples. According to the comment, we make the following revisions.</p><p>1. We enhance discussions on the relevant limitation. The modified sentences now read as “First, the lack of data on specific gestation lengths in the DHSs limited our exploration. Without this information, we could not distinguish the exposure time window, which introduced potential exposure misclassifications into the epidemiological analyses. Particularly, for the newborns with lower birthweight, their gestations could be shorter, which could lead to a larger likelihood of exposure misclassification. This can explain why the exposures at the 4-6 months before birth were significantly linked to all examined outcomes (Figure 2—figure supplement 1). Compared to exposures during the time-window, those at longer lags (e.g., lag-8 or -9 month) tend to be pre-pregnancy rather than gestational exposure. Also, we could not determine the clinical importance of the analyzed birthweight without normalizing it to gestation length.”.</p><p>2. We enhance our statement on the sensitivity analysis of different exposure time-windows. The modified sentences not read as “In sensitivity analyses, we first derived the lag-distributed model to explore how the association varied during the exposure time window. As the specific duration of gestation was not recorded, before actual data analysis, we utilized the 9-month average preceding the birth month as the major exposure time-window. After conducting the major analysis, we re-estimated the effects of fire-sourced PM<sub>2.5</sub> on birthweight change, low birthweight or very low birthweight, by different time-windows using the lag-distributed model.”. The relevant results have been discussed in our Results section. The key results are presented in Figure 2—figure supplement 1.</p><disp-quote content-type="editor-comment"><p>A third weakness is that birthweight was not measured but rather provided by maternal recall which may not always be reliable, especially in rural areas of low-resourced countries when children are born at home. The authors acknowledge the potential for misclassification of exposure to fire-sourced PM2.5, but not for the birthweight outcome.</p></disp-quote><p>Suggestion accepted. According to the comments, we make the following revisions.</p><p>1. We enhance our statements on the sensitivity analysis on recall bias. To illustrate that, in method section, we add a few words, which read as “Fourth, to examine potential recall bias, we estimated the associations by strata of the durations from birth to survey time (the duration is defined as recall period in this study). We assumed a shorter recall period indicated for a lower likelihood of outcome misclassification.”. The relevant result is illustrated using the following words, which read as “First, we explored potential recall bias using sensitivity analyses, and the estimated associations were not significantly different according to recall period (Figure 2—figure supplement 3)”. Actually, we find the weakest association for either low birthweight or very low birthweight is observed for the subgroup of newborns closest to the survey date. This is may be caused by the small size of subgroup samples. It is possible, because mothers just after childbirth may not like to participate the household survey due to non-optimal physiological and psychological conditions.</p><p>We add the recall bias and the relevant outcome misclassification as a new limitation. To state that, in Discussion section, we add a few words, which read as “Fourth, although there have been some previous studies analyzing the birthweight outcome using DHS data (Bellizzi and Padrini, 2020), the self-reported records may be inaccurate due to potential recall bias and lead to potential outcome misclassification. The effect of LFS should be confirmed in future by high-quality data on clinical diagnosis of LBW or VLBW.”.</p><disp-quote content-type="editor-comment"><p>Title I suggest changing the title to "Exposure to landscape fire smoke extremely and reduced birthweight in low- and middle-income countries".</p></disp-quote><p>The reviewer’s comment reminds us that the “extremely” leads to ambiguity. In previous title, we aim to highlight the association between landscape fire smoke and very low birthweight, by using the word. To avoid the ambiguity, we revise the title as “Exposure to landscape fire smoke reduced birthweight in low- and middle-income countries: findings from a siblings-matched case-control study”. If the reviewer has further suggestion on the title, we would like to make more corrections.</p><disp-quote content-type="editor-comment"><p>Line 46 I suggest the concluding statement of the Abstract be revised as follows: "Our findings indicate that gestational exposure to LFS harms fetal health."</p></disp-quote><p>Suggestion accepted. We modified our abstract as “Our findings indicate that gestational exposure to LFS harms fetal health.”</p><disp-quote content-type="editor-comment"><p>Line 68 "Rarely discussed" is too strong. As the authors note in the Discussion, there are other published studies that have addressed the effect of biomass smoke exposure on birthweight.</p></disp-quote><p>Suggestion accepted. We modified our manuscript as:</p><p>“However, the health impacts of LFS on susceptible pregnant women, another highly vulnerable group due to gestation-related physiological changes, such as an increased breathing rate during pregnancy, are not thoroughly discussed.”</p><disp-quote content-type="editor-comment"><p>Line 69 Should be "LFS" rather than LPS.</p></disp-quote><p>We apologize for making a spelling mistake. We have modified the corresponding part as follows:</p><p>“Previous studies have shown that gestational exposure to air pollutants, including those derived from LFS and other emissions”</p><disp-quote content-type="editor-comment"><p>Line 85 Should be "adverse birth outcomes" rather than birth defects.</p></disp-quote><p>Suggestion accepted. We have modified our manuscript as:</p><p>“All risk factors for adverse birth outcomes should be screened thoroughly to protect maternal and infant health.”</p><disp-quote content-type="editor-comment"><p>Line 90 Again, "little is known about fire-sourced PM2.5" is too strong given the published literature.</p></disp-quote><p>Suggestion accepted. We have modified the sentence as:</p><p>“Although a growing number of studies have suggested the deleterious effects of urban air pollution on LBW (C. Li et al., 2020; X. Y. Li et al., 2017), the exposure-response function specifically for fire-sourced PM<sub>2.5</sub> has been insufficiently studied.”.</p><disp-quote content-type="editor-comment"><p>Line 151 Shouldn't this be the 9-month average preceding the "birthdate" rather than survey month?</p></disp-quote><p>We apologize for making a mistake and not expressing our meaning clearly. We have modified the manuscript as:</p><p>“As the DHSs did not record the specific duration of gestation, we utilized the 9-month average preceding the birth month as exposure time during pregnancy.”</p><disp-quote content-type="editor-comment"><p>Line 225 Here and elsewhere in the manuscript, I suggest "live births" rather than samples.</p></disp-quote><p>Suggestion accepted. We have replaced all the “samples” with “livebirths” in our manuscript.</p><disp-quote content-type="editor-comment"><p>Line 358 It is not clear to this reviewer that "the association of birthweight with fire-sourced PM2.5 should be stronger than that with urban PM2.5." While this may turn out to be true with future research findings, without any data, this is purely speculative.</p></disp-quote><p>Suggestion accepted. We remove this sentence in the revised manuscript.</p><disp-quote content-type="editor-comment"><p>Line 386 Exposures to urban PM2.5 are also differentially distributed with low-income minority populations suffering higher exposures.</p></disp-quote><p>We agree with the reviewer, and revise the relevant sentences correspondingly. We don’t mean that the urban PM<sub>2.5</sub> exposure is evenly distributed. Accordingly, now the relevant sentences are revised as:</p><p>“LFS induces peak concentrations of PM<sub>2.5</sub> lasting for a few weeks or months. Its spatial distribution is also different from that of urban PM<sub>2.5</sub>. It is highly clustered in forestry areas (Figure 1). Compared to urban PM<sub>2.5</sub>, fire-sourced PM<sub>2.5</sub> is distributed more skewedly.”</p></body></sub-article></article>