Data from: Health trade-offs of boiling drinking water with solid fuels: A modeling study
Data files
Mar 25, 2024 version files 5 MB
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Air_WASH_Data_Output.zip
5 MB
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README.md
3.28 KB
Mar 21, 2025 version files 5.02 MB
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Air_WASH_Data_Output.zip
5.01 MB
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README.md
7 KB
Abstract
Background: Billions of the world’s poorest households are faced with the lack of access to both safe drinking water and clean cooking. One solution to microbiologically contaminated water is boiling, often promoted without acknowledging the additional risks incurred from indoor air degradation from using solid fuels.
Objectives: This modeling study explores the tradeoff of increased air pollution from boiling drinking water under multiple contamination and fuel use scenarios typical of low-income settings.
Methods: We calculated the total change in disability-adjusted life years (DALYs) from household air pollution (HAP) and diarrhea from fecal contamination of drinking water for scenarios of different source water quality, boiling effectiveness, and stove type. We used Uganda and Vietnam, two countries with a high prevalence of water boiling and solid fuel use, as case studies.
Results: Boiling drinking water reduced the diarrhea disease burden by a mean of 1100 DALYs and 367 DALYs per 10,000 people for those under and over 5 years of age in Uganda, respectively, for high-risk water quality and the most efficient (lab-level) boiling scenario, with smaller reductions for less contaminated water and ineffective boiling. Similar results were found in Vietnam, though with fewer avoided DALYs in children under 5 due to different demographics. In both countries, for households with high baseline HAP from existing solid fuel use, adding water boiling to cooking on a given stove was associated with a limited increase in HAP DALYs due to the log-linear exposure-response curves. Boiling, even at low effectiveness, was associated with net DALY reductions for medium- and high-risk water, even with unclean stoves/fuels. Use of clean stoves coupled with effective boiling significantly reduced total DALYs.
Discussion: Boiling water generally resulted in net decreases in DALYs. Future efforts should empirically measure health outcomes from HAP vs. diarrhea associated with boiling drinking water using field studies with different boiling methods and stove types.
Air and WASH health risk comparison code and output files
Access this dataset on Dryad (DOI: 10.5061/dryad.9zw3r22jz)
Introduction
We developed a code to calculate the health impacts of boiling drinking water with solid fuels This code in R is written to compare the health risks from drinking water and indoor air pollution when boiling drinking water with various types of fuels. It can be run for various countries. Right now, data to run for two focus countries, Uganda and Vietnam, is provided. Data for additional countries can be added.
Authors
Emily Floess, Ayse Ercumen, Angela Harris, Andy Grieshop, NC State University
Data Generation
Data was generated from June 2020 to October 2024 using R.
Description of the data and file structure
This dataset includes the csv output files and a folder with the R code.
Description of Output CSV Files: Air_WASH_Data_Output.zip
The output files are in the folder Air_WASH_Data_Output. This includes a folder for each country: Uganda and Vietnam
For each country, the following folders are provided:
Averages
Averages includes:
Air: DALYs and PM
- DALYs: The folder called DALYs includes average values and standard deviation for adults and children, and also differences from the baseline. PM contains files with the average 24 hour PM2.5 concentrations.
- Stove Number: Average number of stoves needed to produce the energy needed daily
- Water: Average and standard deviation of drinking water DALYs for different boiling scenarios
MC_Output
This folder contains the output from the Monte Carlo iterations
Cooking
The folder includes folders for the fuels used:
- Charcoal
- Clean
- Gasifier
- Improved
- LPG
Within each folder for each fuel there are the following folders:
- Cooking
- Water Heating
- Water Heating Cooking
This includes the Monte Carlo outputs for each energy use scenario for children and adults.
Drinking Water
The drinking water folder contains monte carlo for each of the boiling scenarios:
- Good
- Ineffective
- Lab
- Loweffective
- Moderate
- Negative
- NoBoiling
- Verygood
This includes the Monte Carlo outputs for children and adults for safe, low, medium, and high levels of E. coli contaminated water.
Instances which give NA are when the distribution inputs are outside the model range.
Description of Code Files: Air-WASH.zip
The main folder contains the code files, with the following folders:
Health
This folder includes the values for the Relative Risk model disease burden, including ALRI (Acute lower respiratory infection), COPD (Chronic obstructive pulmonary disease), IHD (Ischemic heart disease), LC (Lung Cancer), and Stroke.
Uganda
This folder includes the parameters for Uganda, including the Disability Adjusted Life Years (DALYs for different age groups), the household parameters, and the population demographics
Vietnam
This folder includes the parameters for Vietnam, including the Disability Adjusted Life Years (DALYs for different age groups), the household parameters, and the population demographics
Results
This includes uncertainty data for making the plots, concentration data, and outputs for the logremoval plot, and outputs for Uganda and Vietnam. For Uganda and Vietnam, this includes Monte Carlo outputs for each of the scenarios of cooking and water heating, and for different stoves, water risk levels, and boiling effectivenesses. Instances which give NA are when the distribution inputs are outside the model range.
Code files
- Air_WASH_Master_Script.R main code to run the entire code for the model for the Monte Carlo output
- Air_WASH_Master_Uncertainty.R main code to run the uncertainty code
- Air_water_code_person.R runs the parameters for the household, including air and water
- Air_water_code_person_log_removal_wateronly.R Calculates the DALYs for each log removal value
- Air_water_code_uncertainty_code.R Code to run uncertainty analysis
- AirWASHPlots_Indicatedcountry_adult_and_child.R Creates output plots showing results
- AirWASHPlots_Indicatedcountry_adult_and_child_manuscriptPlots_only.R Creates plots for the manuscript
- CountryPopulation.R Creates a random generation of the country’s population using the demographics of the country
- countryspecificvariables.R Reads in country specific variables
- Emptyvariables.R creates empty variables to populate when running the code
- IndoorAirPollution.R runs the indoor air pollution code to calculate the indoor air pollution concentration
- infectionfunctions.R Calculates the QMRA (quantitative microbial risk assessment) for the water risk levels
- Libraries.R runs the necessary libraries for the code
- Master_Script_Plotting.R runs the main script to create the plots
- Packages.R Required packages for the code
- plot_calculations.R Runs calculations needed to create the plots
- Plot_calculations_diff_total.R Calculates the difference in DALYs to be used in the plots
- Plot_calculations_Total.R Calculates totals for different DALYs to be used in the plots
- Plot_read_variables.R reads in variables for the plot
- Plot_SD_calculations.R Calculates Standard Deviation for the plot
- Plotting_Generate_Totals.R Calculates totals to be used in the plots
- RelativeRisk.R Calculates the relative risk for different exposure levels of air pollution.
- RelativeRisk_uncertainty.R Calculates the uncertainty for each level of air pollution exposure.
- UncertaintyAnalysis_distribution.R Calculates the distributions for the uncertainty analysis.
Sharing/Access information
Links to other publicly accessible locations of the data:
The R code is available on a public github page, which also includes a readme.
Link on github: https://github.com/Emilyyyymarie/Air-WASH
Link on zenodo: https://doi.org/10.5281/zenodo.13900946
Github Release: https://github.com/Emilyyyymarie/Air-WASH/releases/tag/v1.0.0
Sources of Input Data
Input data was from the literature, and details are provided in our paper preprint and in the Github.
Code/Software
The csv files can be open in excel.
The code was writen in R
R version 4.3.1 (2023-06-16 ucrt) – “Beagle Scouts”
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
Sharing/Access Information:
We place no restrictions on the use this data, however, please cite this repository, and the preprint/paper once published.
Links to publications that cite or use the data:
Pre-print: https://doi.org/10.1101/2024.03.22.24304348
Will add publication link here once it is published.
The goal of this study was to develop a framework to compare health risks. We focus on two countries, Uganda and Vietnam to show how the framework is used. We synthesized established modeling tools to build an analytical framework to compare health impacts from IAP and fecally-contaminated drinking water at the household level, using DALYs as the primary metric to compare multiple risks. Input variables were selected from the best available data in the literature. We used DALYs to quantify health burdens because they account for morbidity with differential disease severity and mortality. Quantitative Microbial Risk Assessment (QMRA) models are commonly used to determine the risk associated with consuming water from a particular water source (Havelaar & Melse, 2003). For IAP, the population attributable fraction based on a dose-response curve for individual diseases is used to calculate the burden of disease (Asikainen et al., 2016; Pillarisetti et al., 2016).
The first module is called the water risk module, which uses a QMRA to calculate the DALYs from drinking water contaminated by fecal matter before and after treatment by boiling. The second module is the air risk module. This uses an indoor box model to quantify the PM2.5 concentrations for different stoves and uses combined with the Household Air Pollution Intervention Tool (HAPIT) to quantity the DALYs associated with IAP under various scenarios. We designed the model to be used for any country. However, we selected Uganda and Vietnam as case study countries as they are in distinct regions, have different population demographics, and high prevalences of boiling among household water treatment users.
The R code was designed to run a Monte Carlo simulation for different scenarios and produce outputs of indoor air pollution concentrations, and drinking water and air pollution DALYs in csv files.
The code is written in R (R Core Team (2021). R: A language and environment for statistcial computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.The data files can be opened in excel.