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      WASH interventions and child diarrhea at the interface of climate and socioeconomic position in Bangladesh

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          Abstract

          Many diarrhea-causing pathogens are climate-sensitive, and populations with the lowest socioeconomic position (SEP) are often most vulnerable to climate-related transmission. Household Water, Sanitation, and Handwashing (WASH) interventions constitute one potential effective strategy to reduce child diarrhea, especially among low-income households. Capitalizing on a cluster randomized trial population (360 clusters, 4941 children with 8440 measurements) in rural Bangladesh, one of the world’s most climate-sensitive regions, we show that improved WASH substantially reduces diarrhea risk with largest benefits among children with lowest SEP and during the monsoon season. We extrapolated trial results to rural Bangladesh regions using high-resolution geospatial layers to identify areas most likely to benefit. Scaling up a similar intervention could prevent an estimated 734 (95% CI 385, 1085) cases per 1000 children per month during the seasonal monsoon, with marked regional heterogeneities. Here, we show how to extend large-scale trials to inform WASH strategies among climate-sensitive and low-income populations.

          Abstract

          Household water, sanitation, and handwashing (WASH) interventions can reduce diarrhoea-related morbidity in young children. Here, the authors report findings from a pre-specified secondary analysis of a cluster-randomised trial assessing how WASH impacts vary by socioeconomic position and season.

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          Most cited references38

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          Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

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            TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015

            We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.
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              Principled missing data methods for researchers

              The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication.
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                Author and article information

                Contributors
                pearl.ante@ucsf.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                20 February 2024
                20 February 2024
                2024
                : 15
                : 1556
                Affiliations
                [1 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Francis I. Proctor Foundation and Department of Ophthalmology, , University of California, San Francisco, ; San Francisco, CA USA
                [2 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Scripps Institution of Oceanography, , University of California, San Diego, ; San Diego, CA USA
                [3 ]Department of Epidemiology and Population Health, Stanford University, ( https://ror.org/00f54p054) Stanford, CA USA
                [4 ]GRID grid.414142.6, ISNI 0000 0004 0600 7174, Environmental Health and WASH, Health System and Population Studies Division, icddr,b, ; Dhaka, 1212 Bangladesh
                [5 ]Child Health Research Centre, The University of Queensland, ( https://ror.org/00rqy9422) South Brisbane, QLD Australia
                [6 ]Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, ( https://ror.org/01cq23130) Memphis, TN USA
                [7 ]Division of Infectious Diseases and Geographic Medicine, Stanford University, ( https://ror.org/00f54p054) Stanford, CA USA
                [8 ]Chan Zuckerberg Biohub, ( https://ror.org/00knt4f32) San Francisco, CA 94158 USA
                Author information
                http://orcid.org/0000-0002-3416-0817
                http://orcid.org/0000-0003-0520-2683
                http://orcid.org/0000-0001-5385-899X
                http://orcid.org/0000-0003-3631-3132
                http://orcid.org/0000-0001-6105-7295
                Article
                45624
                10.1038/s41467-024-45624-1
                10879131
                38378704
                74b1cdba-adb8-4cc4-abb1-61c1f27978cd
                © The Author(s) 2024

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 August 2023
                : 30 January 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000060, U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID);
                Award ID: R01-AI166671
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000865, Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation);
                Award ID: OPPGD759 (WASH Benefits Bangladesh trial)
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                epidemiology,randomized controlled trials,gastrointestinal diseases
                Uncategorized
                epidemiology, randomized controlled trials, gastrointestinal diseases

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