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Public code and data for Water Treatment and Child Mortality: A Meta-analysis and Cost-effectiveness Analysis by Kremer, Luby, Maertens, Tan, and Więcek.

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Water Meta-Analysis

Code for Water Treatment and Child Mortality: A Meta-analysis and Cost-effectiveness Analysis by Kremer, Luby, Maertens, Tan, and Więcek.

Computational requirements

All code runs in R (last run on R version 4.2.3 and RStudio version 2023.09). renv.lock lists all necessary packages and their respective versions. main.R last run on a 10-core Intel-based laptop with Windows 10 and 16.0 GB of RAM. Approximate runtime was 95 minutes.

Description of programs/code

To recreate the exhibits in the paper,

  1. Open water-ma.RProj
  2. To install all the necessary packages, run renv::restore() (this step requires the package renv)
  3. Run main.R

The code used to create the exhibits in the paper is in code/. main.R tracks all the inputs and outputs for each script. Below is an overview of what they do.

  • Scripts in code/functions create functions that are used across multiple scripts. The are sourced by .Rprofile when water-ma.RProj is launched.
  • Scripts in code/wrangling process the raw data and create the final data sets used across different analysis scripts. The first section of main.R runs the scripts in this directory. Running this section is optional, since it will only recreate the data sets in data/final.
  • Scripts in code/cea prepare the data used for cost-effectiveness analysis and include auxiliary code used in the cost-effectiveness analysis. The second section of main.R runs the scripts in this directory. Running this section is also optional. It will recreate data/final/mortality_rate.rds and the under-five mortality rates in data/transformed/u5-per-hh.csv and data/transformed/weighted_u5_mr.csv.
  • Scripts in code/ma_models fit meta-analysis models.
    • fit_ma_bayes.R uses rstan model in logit_model.stan. It takes a moment to run and will save outputs to output/stan.
    • load_bayes_ma.R is called from other scripts to load the outputs of fit_ma_bayes.R.
    • fit_ma_frequentist.R is called from other scripts as fits frequentist meta-analysis models.
  • Scripts in code/generate_outputs recreate the results in the paper.
    • generate_figures.R recreates all the figures.
    • code/generate_outputs/generate_tables.R recreates the meta-analysis tables in the paper.
    • code/generate_outputs/generate_cea_results.R recreates the cost-effectiveness results.
    • code/generate_outputs/generate_summary_of_individual_studies.R recreates the table with summary of individual studies. The data required to run this script cannot be publicly shared. - generate_text.R for printing all values that are cited inline in main text and supplement - generate_incexcl_analysis.R has values for supplement section 1 ("comparison with other RCTs")
  • Scripts in code/publication_bias perform the tests mentioned in supplement section 5.

Controlled Randomness

License for code

Code is licensed under License: Unlicense, with the exception of code/publication_bias/RobustVariance.R and code/publication_bias/metastudiesfunctions.R by Maximilian Kasy, which are licensed under License: CC BY 4.0.

List of paper exhibits and programs

Paper File Created by
Fig 2A output/figures/freq-forest.pdf generate_figures.R
Fig 2B output/figures/bayes-forest.pdf generate_figures.R
Tab 2 output/figures/table-cea-estimates generate_cea_results.R
Fig S1 output/figures/ma-week-plot.pdf generate_figures.R
Fig S2 output/figures/fig-compliance-diarr-hist.pdf generate_figures.R
Fig S3 output/figures/funnel.pdf generate_figures.R
Fig S4 output/figures/diarr-pub-bias-funnel.pdf generate_figures.R
Fig S5 output/figures/bubble-plot-year.pdf generate_figures.R
Fig S6 output/figures/mortality-vs-baseline.pdf generate_figures.R
Fig S7 output/figures/dist-diarrhea-prevalence.pdf generate_figures.R
Tab S3 output/tables/mortality_all_summary.csv generate_tables.R
Tab S4 output/tables/table-loo-study.csv generate_tables.R
Tab S5 output/tables/additional-sa-results.csv generate_tables.R
Tab S6 output/tables/cea-globalbenefits.csv generate_cea_results.R
Tab S7 output/tables/table-cea-estimates.csv generate_cea_results.R

Data Availability and Provenance Statements

Some data used in this study cannot be made publicly available. The raw data files used by the code are listed below.

Raw data

Data File Provided Citation License Notes
List of diarrhea studies data/raw/diarrhea_studies.xlsx Yes License: CC BY 3.0 IGO Contains data provided in the supplementary material Wolf et al., 2018. The original dataset extracted from the publication is available here. The data was provided for 80 studies included in the meta-analysis. We added the relevant data for studies included in the meta-analysis but not included in the Wolf et al., 2018 study (Peletz et al., 2012, Null et al., 2018, Luby et al., 2018, Humphrey et al., 2019, Kirby et al., 2019, Haushofer et al., 2020, Dupas et al., 2021, and ucation vs control) Quick et al., 1999, Conroy et al. 1999, Morris et al. 2018). In addition to the existing data, we added the following information for each of the studies: (1) Compliance rate, and (2) How is compliance defined. Wolf et al., 2018 collects estimates for under-5 diarrhea morbidity in studies with any WaSH intervention.
Studies summary data/raw/summary_data.csv Yes Summary data on each RCT, number of cases in treatment, number of cases in control etc.
UN World Population Prospects, 2019 revision data/raw/weighted_mr/WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.xlsx Yes United Nations, Department of Economic and Social Affairs, Population Division ([2019]). World Population Prospects [2019], archive. Copyright © 1992-2022 by United Nations License: CC BY 3.0 IGO Downloaded from https://population.un.org/ on on Apr 5, 2023
UNdata M49 Country Codes data/raw/u5-per-hh/UNSD.csv Yes Terms of use available at https://data.un.org/ Copied from https://unstats.un.org/ on on Apr 5, 2023
Studies microdata Files in data/raw/mortality_counts No This data was acquired directly from the authors of each paper included in the study.
DHS data data/raw/u5-per-hh/idhs_00001 No Elizabeth Heger Boyle, Miriam King and Matthew Sobek. IPUMS-Demographic and Health Surveys: Version 9 [dataset]. IPUMS and ICF, 2022. https://doi.org/10.18128/D080.V9 Accessed through [https://dhsprogram.com/data/Access-Instructions.cfm] on Apr 5, 2023
IHME Diarrhea Prevalence data/raw/IHME_GLOBAL_DIARRHEA_2000_2019_PREV_A1_S3_ADMIN_1_Y2020M08D31.CSV No Institute for Health Metrics and Evaluation (IHME). Global Under-5 Diarrhea Incidence, Prevalence, and Mortality Geospatial Estimates 2000-2019. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME), 2020. Terms of use can be found here Downloaded from https://ghdx.healthdata.org/) on on Jul 28, 2023
World Bank income groups data/raw/weighted_mr/OGHIST.xlsx Yes World Bank: World Bank Country and Lending Groups. Downloaded on August 13, 2024. License: CC BY 4.0 IGO Detailed terms of use for data is available here Downloaded from https://datahelpdesk.worldbank.org on Aug 13, 2024
World Bank regions data/raw/weighted_mr/CLASS.xlsx Yes World Bank: World Bank Country and Lending Groups. Downloaded on April 5, 2023. License: CC BY 4.0 IGO Detailed terms of use for data is available here Downloaded from https://datahelpdesk.worldbank.org on Apr 5, 2023
data/raw/weighted_mr/all_data.csv
WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) - Household data data/raw/weighted_mr/JMP_2021_WLD.xlsx Yes Progress on household drinking water, sanitation and hygiene 2000–2022: special focus on gender. New York: United Nations Children’s Fund (UNICEF) and World Health Organization (WHO), 2023. License: CC BY 4.0 IGO Downloaded from https://washdata.org on on Apr 5, 2023
WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) - Access to drinking water data/raw/weighted_mr/washdash-download.csv Yes Progress on household drinking water, sanitation and hygiene 2000–2022: special focus on gender. New York: United Nations Children’s Fund (UNICEF) and World Health Organization (WHO), 2023. License: CC BY 4.0 IGO Downloaded from https://washdata.org/ on Apr 5, 2023

Intermediate data

Intermediate data sets created during data processing are stored in data/transformed. The final data sets used for analysis or to create exhibits in the paper are stored in data/final and listed below

File Created by Provided Citation
data/final/diarrhea_studies.rds code/wrangling/clean_studies_list.R Yes
data/final/ma_datasets.Rdata code/wrangling/prep_adjusted_data.R Yes
data/final/mortality_rate.rds code/cea/weighted-mr.R Yes
data/final/individual_data_anonymised.rds code/individual_data_anonymised/pre_individual_data.R Yes

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Public code and data for Water Treatment and Child Mortality: A Meta-analysis and Cost-effectiveness Analysis by Kremer, Luby, Maertens, Tan, and Więcek.

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