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      Cooking outdoors or with cleaner fuels does not increase malarial risk in children under 5 years: a cross-sectional study of 17 sub-Saharan African countries

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          Abstract

          Background

          Smoke from solid biomass cooking is often stated to reduce household mosquito levels and, therefore, malarial transmission. However, household air pollution (HAP) from solid biomass cooking is estimated to be responsible for 1.67 times more deaths in children aged under 5 years compared to malaria globally. This cross-sectional study investigates the association between malaria and (i) cleaner fuel usage; (ii) wood compared to charcoal fuel; and, (iii) household cooking location, among children aged under 5 years in sub-Saharan Africa (SSA).

          Methods

          Population-based data was obtained from Demographic and Health Surveys (DHS) for 85,263 children within 17 malaria-endemic sub-Saharan countries who were who were tested for malaria with a malarial rapid diagnostic test (RDT) or microscopy. To assess the independent association between malarial diagnosis (positive, negative), fuel type and cooking location (outdoor, indoor, attached to house), multivariable logistic regression was used, controlling for individual, household and contextual confounding factors.

          Results

          Household use of solid biomass fuels and kerosene cooking fuels was associated with a 57% increase in the odds ratio of malarial infection after adjusting for confounding factors (RDT adjusted odds ratio (AOR):1.57 [1.30–1.91]; Microscopy AOR: 1.58 [1.23–2.04]) compared to cooking with cleaner fuels. A similar effect was observed when comparing wood to charcoal among solid biomass fuel users (RDT AOR: 1.77 [1.54–2.04]; Microscopy AOR: 1.21 [1.08–1.37]). Cooking in a separate building was associated with a 26% reduction in the odds of malarial infection (RDT AOR: 0.74 [0.66–0.83]; Microscopy AOR: 0.75 [0.67–0.84]) compared to indoor cooking; however no association was observed with outdoor cooking. Similar effects were observed within a sub-analysis of malarial mesoendemic areas only.

          Conclusion

          Cleaner fuels and outdoor cooking practices associated with reduced smoke exposure were not observed to have an adverse effect upon malarial infection among children under 5 years in SSA. Further mixed-methods research will be required to further strengthen the evidence base concerning this risk paradigm and to support appropriate public health messaging in this context.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12936-022-04152-3.

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          Most cited references40

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          mice: Multivariate Imputation by Chained Equations inR

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            Multiple imputation using chained equations: Issues and guidance for practice

            Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments. 2010 John Wiley & Sons, Ltd.
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              When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts

              Background Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the missingness. Therefore, the analysis of trial data with missing values requires careful planning and attention. Methods The authors had several meetings and discussions considering optimal ways of handling missing data to minimise the bias potential. We also searched PubMed (key words: missing data; randomi*; statistical analysis) and reference lists of known studies for papers (theoretical papers; empirical studies; simulation studies; etc.) on how to deal with missing data when analysing randomised clinical trials. Results Handling missing data is an important, yet difficult and complex task when analysing results of randomised clinical trials. We consider how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommend analytical approaches which may prevent bias caused by unavoidable missing data. We consider the strengths and limitations of using of best-worst and worst-best sensitivity analyses, multiple imputation, and full information maximum likelihood. We also present practical flowcharts on how to deal with missing data and an overview of the steps that always need to be considered during the analysis stage of a trial. Conclusions We present a practical guide and flowcharts describing when and how multiple imputation should be used to handle missing data in randomised clinical. Electronic supplementary material The online version of this article (10.1186/s12874-017-0442-1) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                s.bartington@bham.ac.uk
                Journal
                Malar J
                Malar J
                Malaria Journal
                BioMed Central (London )
                1475-2875
                27 April 2022
                27 April 2022
                2022
                : 21
                : 133
                Affiliations
                [1 ]GRID grid.6572.6, ISNI 0000 0004 1936 7486, Institute of Applied Health Research, , University of Birmingham, ; Edgbaston, Birmingham, UK
                [2 ]GRID grid.6572.6, ISNI 0000 0004 1936 7486, School of Geography, Earth and Environmental Sciences, , University of Birmingham, ; Edgbaston, Birmingham, UK
                [3 ]GRID grid.8991.9, ISNI 0000 0004 0425 469X, Department of Disease Control, , London School of Hygiene and Tropical Medicine, ; London, UK
                [4 ]GRID grid.8991.9, ISNI 0000 0004 0425 469X, Centre on Climate Change and Planetary Health, , London School of Hygiene & Tropical Medicine, ; London, UK
                [5 ]GRID grid.412563.7, ISNI 0000 0004 0376 6589, NIHR Birmingham Biomedical Research Centre, , University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, ; Birmingham, UK
                Author information
                http://orcid.org/0000-0003-3743-9925
                http://orcid.org/0000-0002-8179-7618
                http://orcid.org/0000-0001-6583-8347
                https://orcid.org/0000-0002-8796-4114
                http://orcid.org/0000-0002-6897-8625
                http://orcid.org/0000-0002-7352-3027
                http://orcid.org/0000-0002-2777-1847
                Article
                4152
                10.1186/s12936-022-04152-3
                9044678
                35477567
                36edcf62-b606-4845-bfcc-fc9a5711b7d8
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 21 October 2021
                : 6 April 2022
                Funding
                Funded by: University of Birmingham Global Challenges Scholarship
                Funded by: NIHR Birmingham Biomedical Research Centre
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Infectious disease & Microbiology
                malaria,household air pollution,children under 5 years,low and middle-income country,sub-saharan africa,biomass

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