Projections of climate change-attributable diarrhea burden: a systematic review
Data files
Nov 04, 2025 version files 48.57 KB
Abstract
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.
Dataset DOI: 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 >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 citations.
Data was derived from the following sources:
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