DOI: 10.5061/dryad.gqnk98t00
title: Projections of climate change-attributable diarrhea burden: a systematic review

description: This dataset supports the systematic review titled “Projections of climate
 change-attributable diarrhea burden: a systematic review.” It compiles
 projections of diarrheal disease outcomes under future climate change
 scenarios from published studies, expressed as percent change from
 baseline. The dataset is structured to facilitate comparative
 visualization and analysis across different climate scenarios, time
 periods, pathogen types, and development trajectories. Key variables
 include study and projection metadata (years, time_period, location,
 region_type, climate_scenario, hazard), disease and demographic
 characteristics (outcome, pathogen, pathogen_type, age), and indicators of
 future development assumptions (adaptation, vulnerability, Exposure, and
 combined Adaptation_Vulnerability). Outcome data are recorded as
 baseline_cases, additional_cases, projected_cases, and the derived
 percent_change. When percent change was directly reported in the original
 publication, it was extracted. When only absolute numbers were provided,
 percent change was calculated between baseline and projected values. If
 projected values were not given, back-calculations were attempted using
 other study data. For studies missing baseline values, we used Global
 Burden of Disease (GBD) estimates for the relevant time and location to
 approximate percent change. Studies lacking sufficient data to calculate
 or estimate percent change (n = 8) were excluded from summary figures and
 combined analyses. All data were abstracted from publicly available
 published sources, and no personal or sensitive information is included.
 The dataset includes associated R code for replicating visualizations
 (e.g., Figure 2 of the manuscript) and may be reused for meta-analyses,
 modeling, or future projections of climate-related disease burden.
description: # Projections of climate change-attributable diarrhea burden systematic
 review box plot and associated data Dataset DOI:
 [10.5061/dryad.gqnk98t00](10.5061/dryad.gqnk98t00) ## Description of the
 data and file structure In this study we summarized climate
 change-attributable projections of diarrheal diseases, appraised study
 methods and results, and examined evidence for adaptation strategies. We
 searched Web of Science and PubMed databases for terms related to
 diarrhea, dysentery, and projected climate change through February 2024
 and included studies with future projections of climate-related diarrhea
 burden. We summarized range of projected percent change in future
 diarrheal disease relative to study baseline, stratified by economic
 context, pathogen, time period, and emission scenario. We assessed
 modeling approaches and quality of evidence for included studies. This
 dataset includes data from the studies we included in our models, as well
 as the code for generating box plots from the dataset. ### Files and
 variables #### File: Climate Attributable Projections of Diarrhea Percent
 Change Clean.xlsx **Description:** Dataset ##### Variable Descriptions
 ##### Climate Attributable Projections of Diarrhea Percent Change_ALL
 Data.csv * Study (study from which the data was derived) * Years (years
 model was projected out to / NA (Not applicable)) * Time period (Near term
 (between 2020-2040 / Mid term (between 2040-2060) / Long term (2060 or
 later) / NA (Not applicable)) * Location (location the model was built for
 / NA (Not applicable)) * Region type (Income classification of the
 location that model was build for: (Income classification of the location
 that model was build for: Upper Middle Income Country (UMIC) / Low Middle
 income country (LMIC) / High income country (HIC) / Global (Full globe) /
 NA (Not applicable)) * Climate scenario (High - high emissions scenario)
 (Medium - medium emissions scenario) (Low - low emissions scenario) (NA -
 Not applicable) * Hazard (climate hazard included in the study model:
 temperature / precipitation / temperature and precipitation / sea
 temperature / humidity / drought / NA (Not applicable)) * Outcome (primary
 outcome modeled by the study: cases / disability affected life years
 (DALYs) / deaths / pathogen prevalence / years lived with disability
 (YLDs) / NA (Not applicable)) * Pathogen (specific outcome pathogen or
 infection modeled by the study / NA (Not applicable)) * Pathogen type
 (type of outcome pathogen or infection modeled by the study: Bacteria /
 Protozoa / Virus / All-cause / NA (Not applicable)) * Age (age of people
 modeled in the primary outcome of the study: infants (ages 0-1 year) /
 children (ages 1-18 years) / adults (ages &gt;18 years) / all (no age
 restriction) / NA (Not applicable)) * Adaptation, vulnerability, exposure
 Adaptation, vulnerability, exposure (development factors considered by the
 study; adaptation is incorporation of specific assumptions around policies
 or programs that are targeted at reducing climate related health risks;
 vulnerability accounts for underlying population characteristics, such as
 age or economic status, that may increase the risk of health consequences
 following hazard events; exposure accounts for population size : Recent
 conditions (current development factors assumed) / Considered (future
 development factors considered) / NA (Not applicable)) * Exposure
 (accounts for assumptions about underlying population characteristics:
 recent (recent or current population size assumed) / considered
 (population growth considered) / NA (Not applicable)) * Baseline cases
 (baseline number of diarrheal disease cases as stated by the study when
 available or outside sources / NA (Not applicable)) * Additional cases
 (additional number of cases projected by the study / NA (Not applicable))
 * Projected cases (Total projected number of cases from the study / NA
 (Not applicable)) * Percent change (percent change in the number of cases
 comparing projection to baseline / NA (Not applicable)) * Notes #### File:
 Climate Attributable Projections of Diarrhea Percent Change Boxplots.R
 **Description:** Code for generating box plots ## Code/software Microsoft
 Excel can be used to open the dataset. R Studio can be used to run the R
 code to generate box plots. ## Access information Other publicly
 accessible locations of the data: * Data can be located from the below
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