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      Exposure–response relationships for personal exposure to fine particulate matter (PM 2·5), carbon monoxide, and black carbon and birthweight: an observational analysis of the multicountry Household Air Pollution Intervention Network (HAPIN) trial

      research-article
      , Prof, PhD a , * , , Prof, PhD b , , Prof, PhD b , , Prof, PhD c , , PhD g , , PhD h , , PhD h , , Prof, PhD i , , PhD j , , Prof, PhD i , , PhD f , , PhD k , , PhD l , , PhD a , , Prof, PhD a , , Prof, PhD a , , PhD a , , MSc a , , PhD a , , BS m , , PhD n , , PhD o , , PhD i , , MPH i , , BSEH i , , PhD c , , MD c , , MS c , , MSPH c , , PhD p , , PhD g , , Prof, MD q , , Prof, PhD d , , PhD e , , Prof, MD n , , Prof, PhD r , HAPIN Investigators *
      The Lancet. Planetary Health
      Elsevier B.V

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          Summary

          Background

          Household air pollution (HAP) from solid fuel use is associated with adverse birth outcomes, but data for exposure–response relationships are scarce. We examined associations between HAP exposures and birthweight in rural Guatemala, India, Peru, and Rwanda during the Household Air Pollution Intervention Network (HAPIN) trial.

          Methods

          The HAPIN trial recruited pregnant women (9–<20 weeks of gestation) in rural Guatemala, India, Peru, and Rwanda and randomly allocated them to receive a liquefied petroleum gas stove or not (ie, and continue to use biomass fuel). The primary outcomes were birthweight, length-for-age, severe pneumonia, and maternal systolic blood pressure. In this exposure–response subanalysis, we measured 24-h personal exposures to PM 2·5, carbon monoxide, and black carbon once pre-intervention (baseline) and twice post-intervention (at 24–28 weeks and 32–36 weeks of gestation), as well as birthweight within 24 h of birth. We examined the relationship between the average prenatal exposure and birthweight or weight-for-gestational age Z scores using multivariate-regression models, controlling for the mother's age, nulliparity, diet diversity, food insecurity, BMI, the mother's education, neonate sex, haemoglobin, second-hand smoke, and geographical indicator for randomisation strata.

          Findings

          Between March, 2018, and February, 2020, 3200 pregnant women were recruited. An interquartile increase in the average prenatal exposure to PM 2·5 (74·5 μg/m 3) was associated with a reduction in birthweight and gestational age Z scores (birthweight: –14·8 g [95% CI –28·7 to –0·8]; gestational age Z scores: –0·03 [–0·06 to 0·00]), as was an interquartile increase in black carbon (7·3 μg/m 3; –21·9 g [–37·7 to –6·1]; –0·05 [–0·08 to –0·01]). Carbon monoxide exposure was not associated with these outcomes (1·7; –3·1 [–12·1 to 5·8]; –0·003 [–0·023 to 0·017]).

          Interpretation

          Continuing efforts are needed to reduce HAP exposure alongside other drivers of low birthweight in low-income and middle-income countries.

          Funding

          US National Institutes of Health (1UM1HL134590) and the Bill & Melinda Gates Foundation (OPP1131279).

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          Most cited references34

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis

            Summary Background Low birthweight (LBW) of less than 2500 g is an important marker of maternal and fetal health, predicting mortality, stunting, and adult-onset chronic conditions. Global nutrition targets set at the World Health Assembly in 2012 include an ambitious 30% reduction in LBW prevalence between 2012 and 2025. Estimates to track progress towards this target are lacking; with this analysis, we aim to assist in setting a baseline against which to assess progress towards the achievement of the World Health Assembly targets. Methods We sought to identify all available LBW input data for livebirths for the years 2000–16. We considered population-based national or nationally representative datasets for inclusion if they contained information on birthweight or LBW prevalence for livebirths. A new method for survey adjustment was developed and used. For 57 countries with higher quality time-series data, we smoothed country-reported trends in birthweight data by use of B-spline regression. For all other countries, we estimated LBW prevalence and trends by use of a restricted maximum likelihood approach with country-level random effects. Uncertainty ranges were obtained through bootstrapping. Results were summed at the regional and worldwide level. Findings We collated 1447 country-years of birthweight data (281 million births) for 148 countries of 195 UN member states (47 countries had no data meeting inclusion criteria). The estimated worldwide LBW prevalence in 2015 was 14·6% (uncertainty range [UR] 12·4–17·1) compared with 17·5% (14·1–21·3) in 2000 (average annual reduction rate [AARR] 1·23%). In 2015, an estimated 20·5 million (UR 17·4–24·0 million) livebirths were LBW, 91% from low-and-middle income countries, mainly southern Asia (48%) and sub-Saharan Africa (24%). Interpretation Although these estimates suggest some progress in reducing LBW between 2000 and 2015, achieving the 2·74% AARR required between 2012 and 2025 to meet the global nutrition target will require more than doubling progress, involving both improved measurement and programme investments to address the causes of LBW throughout the lifecycle. Funding Bill & Melinda Gates Foundation, The Children's Investment Fund Foundation, United Nations Children's Fund (UNICEF), and WHO.
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              Regression models in clinical studies: determining relationships between predictors and response.

              Multiple regression models are increasingly being applied to clinical studies. Such models are powerful analytic tools that yield valid statistical inferences and make reliable predictions if various assumptions are satisfied. Two types of assumptions made by regression models concern the distribution of the response variable and the nature or shape of the relationship between the predictors and the response. This paper addresses the latter assumption by applying a direct and flexible approach, cubic spline functions, to two widely used models: the logistic regression model for binary responses and the Cox proportional hazards regression model for survival time data.
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                Author and article information

                Contributors
                Journal
                Lancet Planet Health
                Lancet Planet Health
                The Lancet. Planetary Health
                Elsevier B.V
                2542-5196
                08 May 2023
                May 2023
                08 May 2023
                : 7
                : 5
                : e387-e396
                Affiliations
                [a ]Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai, India
                [b ]Gangarosa Department of Environmental Health, Emory University, Atlanta, GA, USA
                [c ]Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
                [d ]Hubert Department of Global Health, Emory University, Atlanta, GA, USA
                [e ]Department of Epidemiology, Emory University, Atlanta, GA, USA
                [f ]Rollins School of Public Health and Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA
                [g ]Berkeley Air Monitoring Group, Berkeley, CA, USA
                [h ]Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA
                [i ]Department of Environmental Health Sciences, University of Georgia, Athens, GA, USA
                [j ]Center for Health Studies, Universidad del Valle de Guatemala, Guatemala City, Guatemala
                [k ]Department of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
                [l ]Department of Global Health and Population, Harvard T H Chan School of Public Health, Harvard University, Boston, MA, USA
                [m ]Eagle Research Centre, Kigali, Rwanda
                [n ]Division of Pulmonary and Critical Care, School of Medicine and Center for Global Non-Communicable Disease Research and Training, Johns Hopkins University, Baltimore, MD, USA
                [o ]Cardiovascular Division, Washington University School of Medicine, St Louis, MO, USA
                [p ]Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
                [q ]Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
                [r ]Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, USA
                Author notes
                [* ]Correspondence to: Prof Kalpana Balakrishnan, Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai 600 116, India kalpanasrmc@ 123456ehe.org.in
                [*]

                Members listed at the end of the Article

                Article
                S2542-5196(23)00052-9
                10.1016/S2542-5196(23)00052-9
                10186177
                37164515
                262ed948-9f9c-4554-8c17-5874dc5da328
                © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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