DOI: 10.5061/dryad.9zw3r22jz
title: Data from: Health trade-offs of boiling drinking water with solid fuels: A modeling study

description: 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.
description: 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.
description: 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.
description: # Data from: Health trade-offs of boiling drinking water with solid fuels:
 A modeling study 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 1. 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.
 2. Stove Number: Average number of stoves needed to produce the energy
 needed daily 3. 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: 1. Charcoal 2. Clean 3.
 Gasifier 4. Improved 5. LPG Within each folder for each fuel there are the
 following folders: 1. Cooking 2. Water Heating 3. 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: 1. Good 2.
 Ineffective 3. Lab 4. Loweffective 5. Moderate 6. Negative 7. NoBoiling 8.
 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 1.
 Air_WASH_Master_Script.R main code to run the entire code for the model
 for the Monte Carlo output 2. Air_WASH_Master_Uncertainty.R main code to
 run the uncertainty code 3. Air_water_code_person.R runs the parameters
 for the household, including air and water 4.
 Air_water_code_person_log_removal_wateronly.R Calculates the DALYs for
 each log removal value 5. Air_water_code_uncertainty_code.R Code to run
 uncertainty analysis 6. AirWASHPlots_Indicatedcountry_adult_and_child.R
 Creates output plots showing results 7.
 AirWASHPlots_Indicatedcountry_adult_and_child_manuscriptPlots_only.R
 Creates plots for the manuscript 8. CountryPopulation.R Creates a random
 generation of the country's population using the demographics of the
 country 9. countryspecificvariables.R Reads in country specific variables
 10. Emptyvariables.R creates empty variables to populate when running the
 code 11. IndoorAirPollution.R runs the indoor air pollution code to
 calculate the indoor air pollution concentration 12. infectionfunctions.R
 Calculates the QMRA (quantitative microbial risk assessment) for the water
 risk levels 13. Libraries.R runs the necessary libraries for the code 14.
 Master_Script_Plotting.R runs the main script to create the plots 15.
 Packages.R Required packages for the code 16. plot_calculations.R Runs
 calculations needed to create the plots 17. Plot_calculations_diff_total.R
 Calculates the difference in DALYs to be used in the plots 18.
 Plot_calculations_Total.R Calculates totals for different DALYs to be used
 in the plots 19. Plot_read_variables.R reads in variables for the plot 20.
 Plot_SD_calculations.R Calculates Standard Deviation for the plot 21.
 Plotting_Generate_Totals.R Calculates totals to be used in the plots 22.
 RelativeRisk.R Calculates the relative risk for different exposure levels
 of air pollution. 23. RelativeRisk_uncertainty.R Calculates the
 uncertainty for each level of air pollution exposure. 24.
 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](https://github.com/Emilyyyymarie/Air-WASH) Link on zenodo: [https://doi.org/10.5281/zenodo.13900946](https://doi.org/10.5281/zenodo.13900946) Github Release: [https://github.com/Emilyyyymarie/Air-WASH/releases/tag/v1.0.0](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](https://doi.org/10.1101/2024.03.22.24304348) Will add publication link here once it is published.