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      Environmental heat stress on maternal physiology and fetal blood flow in pregnant subsistence farmers in The Gambia, west Africa: an observational cohort study

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          Summary

          Background

          Anthropogenic climate change has caused extreme temperatures worldwide, with data showing that sub-Saharan Africa is especially vulnerable to these changes. In sub-Saharan Africa, women comprise 50% of the agricultural workforce, often working throughout pregnancy despite heat exposure increasing the risk of adverse birth outcomes. In this study, we aimed to improve understanding of the pathophysiological mechanisms responsible for the adverse health outcomes resulting from environmental heat stress in pregnant subsistence farmers. We also aimed to provide data to establish whether environmental heat stress also has physiological effects on the fetus.

          Methods

          We conducted an observational cohort study in West Kiang, The Gambia, at the field station for the Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine (named the MRC Keneba field station). Pregnant women who were aged 16 years or older and who were at <36 weeks’ gestation of any gravida or parity were invited to participate in the study. Participants were eligible if they were involved in agricultural or related manual daily tasks of living. Participants were ineligible if they refused to provide consent, had multiple pregnancies (eg, if they had twins), were acutely unwell, or were diagnosed with pre-eclampsia or eclampsia. Heat stress was measured by wet bulb globe temperature (WBGT) and by using the universal thermal climate index (UTCI), and maternal heat strain was directly measured by modified physiological strain index calculated from heart rate and skin temperature. Outcome measures of fetal heart rate (FHR) and fetal strain (defined as a FHR >160 beats per min [bpm] or <115 bpm, or increase in umbilical artery resistance index) were measured at rest and during the working period. Multivariable repeated measure models (linear regression for FHR, and logistic regression for fetal strain) were used to evaluate the association of heat stress and heat strain with acute fetal strain.

          Findings

          Between Aug 26, 2019, and March 27, 2020, 92 eligible participants were recruited to the study. Extreme heat exposure was frequent, with average exposures of WBGT of 27·2°C (SD 3·6°C) and UTCI equivalent temperature of 34·0°C (SD 3·7°C). The total effect of UTCI on fetal strain resulted in an odds ratio (OR) of 1·17 (95% CI 1·09–1·29; p<0·0001), with an adjusted direct effect of OR of 1·12 (1·03–1·21; p=0·010) with each 1°C increase in UTCI. The adjusted OR of maternal heat strain on fetal strain was 1·20 (1·01–1·43; p=0·038), using the UTCI model, with each unit increase.

          Interpretation

          Data from our study show that decreasing maternal exposure to heat stress and heat strain is likely to reduce fetal strain, with the potential to reduce adverse birth outcomes. Further work that explores the association between heat stress and pregnancy outcomes in a variety of settings and populations is urgently needed to develop effective interventions.

          Funding

          The Wellcome Trust.

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          Most cited references41

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          The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises

          For the Chinese, French, German, and Spanish translations of the abstract see Supplementary Materials section.
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            Distributed Lag Linear and Non-Linear Models in R: The Package dlnm.

            Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associations showing potentially non-linear and delayed effects in time series data. This methodology rests on the definition of a crossbasis, a bi-dimensional functional space expressed by the combination of two sets of basis functions, which specify the relationships in the dimensions of predictor and lags, respectively. This framework is implemented in the R package dlnm, which provides functions to perform the broad range of models within the DLNM family and then to help interpret the results, with an emphasis on graphical representation. This paper offers an overview of the capabilities of the package, describing the conceptual and practical steps to specify and interpret DLNMs with an example of application to real data.
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              Hot weather and heat extremes: health risks

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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
                07 December 2022
                December 2022
                07 December 2022
                : 6
                : 12
                : e968-e976
                Affiliations
                [a ]Medical Research Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia
                [b ]Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
                [c ]MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
                [d ]Nuffield Department of Women's and Reproductive Health and the George Institute for Global Health, University of Oxford, Oxford, UK
                [e ]Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
                [f ]Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
                [g ]Environmental Extremes Laboratory, University of Brighton, Eastbourne, UK
                Author notes
                [* ]Correspondence to: Dr Ana Bonell, Medical Research Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara 273, The Gambia ana.bonell@ 123456lshtm.ac.uk
                Article
                S2542-5196(22)00242-X
                10.1016/S2542-5196(22)00242-X
                9756110
                36495891
                799499e1-8596-444f-a76e-c556c0afcf24
                © 2022 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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