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Background: A variety of public health interventions have been undertaken in low- and middle-income countries (LMICs) to prevent morbidity and mortality associated with household air pollution (HAP) due to cooking, heating and lighting with solid biomass fuels. Pregnant women and children under five are particularly vulnerable to the effects of HAP, due to biological susceptibility and typically higher exposure levels. However, the relative health benefits of interventions to reduce HAP exposure among these groups remain unclear. This systematic review aims to assess, among pregnant women, infants and children (under 5 years) in LMIC settings, the effectiveness of interventions which aim to reduce household air pollutant emissions due to household solid biomass fuel combustion, compared to usual cooking practices, in terms of health outcomes associated with HAP exposure. Methods: This protocol follows standard systematic review processes and abides by the PRISMA-P reporting guidelines. Searches will be undertaken in MEDLINE, EMBASE, CENTRAL, WHO International Clinical Trials Registry Platform (ICTRP), The Global Index Medicus (GIM), ClinicalTrials.gov and Greenfile, combining terms for pregnant women and children with interventions or policy approaches to reduce HAP from biomass fuels or HAP terms and LMIC countries. Included studies will be those reporting (i) pregnant women and children under 5 years; (ii) fuel transition, structural, educational or policy interventions; and (iii) health events associated with HAP exposure which occur among pregnant women or among children within the perinatal period, infancy and up to 5 years of age. A narrative synthesis will be undertaken for each population-intervention-outcome triad stratified by study design. Clinical and methodological homogeneity within each triad will be used to determine the feasibility for undertaking meta-analyses to give a summary estimate of the effect for each outcome. Discussion: This systematic review will identify the effectiveness of existing HAP intervention measures in LMIC contexts, with discussion on the context of implementation and adoption, and summarise current literature of relevance to maternal and child health. This assessment reflects the need for HAP interventions which achieve measurable health benefits, which would need to be supported by policies that are socially and economically acceptable in LMIC settings worldwide. Systematic review registration: PROSPERO CRD42020164998
Established systematic review methods will be used. This protocol has been registered on the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42020164998) [23] and is presented in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA-P) guidelines [24]. The following Population-Intervention-Comparator-Outcome-Study design (PICOS) criteria will be used to determining primary study inclusion. Pregnant women (no limitation to trimester or number of previous pregnancies), children in infancy and children under the age of 5 years who are exposed to HAP originating from biomass solid fuel sources, used for cooking, heating and lighting within LMIC settings (World Bank definition 2020) [25]. HAP exposure can be determined through direct objective measurement (e.g. personal, kitchen area) of pollutant concentration (e.g. PM, CO) or use of a proxy measure (e.g. self-reported biomass fuel use, classification of ‘cleaner’ and ‘dirty’ fuels by household survey). Any intervention implemented which aims to reduce household air pollution emissions arising from indoor cooking or heating using solid biomass fuel. This includes interventions such as those which seek to improve access and take-up to cleaner fuels (e.g. refined biomass, ethanol, LPG, solar, electricity); structural interventions such as improved cookstoves (ICS), inbuilt stoves (e.g. plancha), ventilation and chimney hood; fuel policy; and behavioural/educational interventions (e.g. moving cooking outside, reducing time spent in the kitchen, removing children from the cooking area during cooking, altering fuel or food preparation). There will be no limitation to the length of duration of interventions or timing of deployment of intervention (e.g. anytime during pregnancy through to the fifth year of a child’s life). Alternative HAP intervention (e.g. any other intervention within inclusion criteria) or no intervention (e.g. exposure to standard HAP through using the current method of cooking, heating or lighting). Health outcomes relating to pregnancy and perinatal period (e.g. IUGR, birth weight, preterm birth, pre-eclampsia, pregnancy-induced hypertension, maternal mortality, perinatal/infant mortality, stillbirth and miscarriage) and early life (e.g. upper and lower respiratory tract infections, pneumonia, asthma, respiratory distress syndrome, otitis media, impaired neurodevelopment, mortality and burns) which have been previously associated with HAP exposure. There will be no limits to the follow-up duration of outcome measures. Eligible study designs are randomised control trials (RCTs), non-randomised control trials and quasi-experimental or natural experimental studies (before-after studies, interrupted time-series studies). Time-series or before-and-after studies will need to compare the same health outcomes in the same population pre- and post-intervention. It is recognised that before-and-after studies assessing pregnancy outcomes are unlikely to exist due to the difficulties in assessing changes in pregnancy outcomes within subsequent pregnancies, but will not be excluded if present. Any study that did not meet the inclusion criteria in all five areas (population, intervention, comparator, outcomes and study design) will be excluded. The following databases will be used to search for published, in progress and grey literature: MEDLINE (in process and 1947–date), EMBASE (1947–present), The Cochrane Central Register of Controlled Trials (CENTRAL), WHO International Clinical Trials Registry Platform (ICTRP) [26], ClinicalTrials.gov, The Global Index Medicus (GIM) [27] and Greenfile [28]. Furthermore, the use of manual searches of all reference lists in the included studies and previous systematic reviews related to the topics will ensure capture of all available literature. The systematic reviews will be identified whilst screening the search results for included studies and additionally searching Epistemonkios [29]. The search strategy, where the database platform allows, will include free-text terms and index terms that are contained within the following structure: “Population” AND (“Intervention” (“Household Air pollution” AND “LMICs”)) (Appendix), with population being defined as pregnant women and children under 5 and interventions being any intervention that aims to reduce the level of household air pollution. There will be no restrictions in place for the date of publication, language of publication, type of publication (e.g. conference abstracts) or type of study design. Two reviewers (KEW, EDC) will independently conduct article selection using the eligibility criteria, within Mendeley, after removal of duplicates. Relevant articles will be determined initially by title and abstracts, followed by retrieval and full paper assessment for selection of papers as per the inclusion criteria, with reasons for exclusion noted at each stage (including the screening stage). Authors will be contacted for clarification if required. Any difference in selected articles between reviewers will be discussed using a third independent reviewer (SEB) to adjudicate any remaining disagreements. The selection process will be graphically illustrated using a PRISMA flow diagram [24]. Data will be independently extracted from included studies by two reviewers (KEW, EDC) using an adapted (to type of study design) Cochrane Public Health Group data extraction form [30], in a Microsoft Excel spreadsheet (Microsoft Cooperation). The data extraction form will include critically appraisal of paper quality within the assessment process. Extracted data will include, but not be limited to: Given the likely variability between studies included in the review, in terms of design, population, intervention, comparator, outcomes and data type, the data extraction process will be piloted and then modified if required. Any differences between reviewers in data extracted will be discussed and using a third independent reviewer (SEB) to adjudicate any remaining disagreements. Risk of bias will be assessed using the Effective Public Health Practice Project (EPHPP) quality assessment tool for quantitative studies [31] by two reviewers independently (KEW, EDC), assigning low, medium and high risk of bias for each individual study. For trials where a parallel control group is used, it is accepted that random allocation and the blinding of participants and outcome assessor may not be always possible, due to the nature of the interventions and settings. A narrative synthesis will be undertaken for each population-intervention-outcome triad (as indicated in Fig. Fig.1)1) stratified by study design. Data collected will be tabulated reporting study characteristics, intervention, HAP exposure measurements (if any) and outcome details. It is likely that data may be reported in a mixture of formats for the same outcome (e.g. continuous data mean, proportion meeting a fixed change, risks/relative risks, odds ratios). In addition, there will be a range of health outcomes reported, as well as a mixture of type of interventions, geographical regions and social contexts reported, which are likely to not be directly comparable. Flow diagram of study grouping (population-intervention-outcome) for synthesis. IUGR intrauterine growth retardation Following on from the narrative analysis, meta-analysis will be considered within each triad, for each outcome measure, stratified by study design and the type of data available for the outcome. Clinical and methodological homogeneity within each triad will be used to determine the feasibility for meta-analysis where two or more studies in the same grouping report data in the same format at the same/similar time points. Any meta-analysis will be undertaken using a random effects model, due to an assumption that the studies represent a distribution of true effects. Determination of the level of between-study variation not attributable to chance will be calculated and displayed as an I2 value with 95% confidence interval. It is not anticipated that there will be more than a few studies in each meta-analysis, if even such an analysis is possible. The potential for additional sub-group analysis, sensitivity analysis or the assessment for the existence of small study effects using a funnel plot, will likely not exist. Risk of bias information will be used descriptively to contextualise the findings for each outcome whether a meta-analysis is undertaken or not. Recommendation for the improved conduct of studies in the field will be made.
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