Original and derived data products referenced in the original manuscript are provided in the data package.

Description of the data and file structure

Original data:

Table_1_source_papers.csv: Papers that met review criteria and which are summarized in Table 1 of the manuscript.

1. ID: The paper identification number
2. Topic: The broad topic (i.e., each row of Table 1)
3. Authors: The names of the authors of the paper
4. Article Title: The title of the paper
5. Source Title: The name of the journal in which the paper was published
6. Abstract: The paper's abstract, retrieved from the Web of Science search
7. study_type: Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.
8. pathogen_broad: Broad classification of the type of pathogen discussed in the paper.
9. transmission_type: Categorization of indirect, direct, sexual, vector, or other transmission modes.
10. pathogen_type: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.
11. country: Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).

Derived data:

change_livestock_country.csv: A dataframe containing values used to generate Figure 4a in the manuscript.

1. County Name: The name of the county in Kenya
2. Sheep and goats 1980: The estimated number of sheep and goats in 1980
3. Sheep and goats 2016: The estimated number of sheep and goats in 2016
4. pct_change_shoat: The percent change in sheep and goat numbers from 1980 to 2016
5. Cattle 1980: The estimated number of cattle in 1980
6. Cattle 2016: The estimated number of cattle in 2016
7. pct_change_cattle: The percent change in cattle numbers from 1980 to 2016
8. Camel 1980: The estimated number of camels in 1980
9. Camel 2016: The estimated number of camels in 2016
10. pct_change_camel: The percent change in camel numbers from 1980 to 2016
11. human_pop 1980: The estimated human population in the county in 1980
12. human_pop 2016: The estimated human population in the county in 1980
13. pct_change_human: The percent change in the human population from 1980 to 2016
14. area_sq_km: The land area of the county
15. change_ind_per_sq_km_shoat: Absolute change in number of sheep and goats from 1980 to 2016
16. change_ind_per_sq_km_cattle: Absolute change in number of cattle from 1980 to 2016
17. change_ind_per_sq_km_camel: Absolute change in number of camels from 1980 to 2016

country_avg_schist_wormy_world.csv: A dataframe containing values used to generate Figure 3 in the manuscript.

• Country: The country in which the schistosome prevalence studies were performed.
• Latitude: The latitute in decimal degrees
• Longitude: The longitute in decimal degrees
• Maximum.prevalence: The mean maximum schistosomiasis prevalence of studies conducted within each country.

kenya_precip_change_1951_2020.csv: A dataframe containing values used to generate Figure 4b in the manuscript.

• Precipitation (mm): Binned annual precipitation values
• 1951-1980: The density of observations for each annual precipitation value for the 1951-1980 period
• 1971-2000: The density of observations for each annual precipitation value for the 1971-2000 period
• 1991-2020: The density of observations for each annual precipitation value for the 1991-2020 period

Sharing/Access information

Data were derived from the following sources:

• Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PloS ONE, 11(9), e0163249. https://doi.org/10.1371/journal.pone.0163249

• London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global Atlas of Helminth Infections: STH and Schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0

• World Bank Group. (2023). Climate Data & Projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

This dataset was collected as part of a research project focused on detecting leaf diseases in pea plants using deep learning and computer vision techniques. It contains labeled images of healthy and diseased pea plant leaves collected under real-world conditions between May 2023 and August 2025 in Urmar Payan, near Peshawar, Khyber Pakhtunkhwa, Pakistan.The goal of this dataset is to support AI-based solutions in agriculture, including disease classification, yield improvement, and sustainable crop monitoring. The images are suitable for training and testing machine learning models, particularly convolutional neural networks (CNNs). This dataset was used in the author's final year undergraduate project in AI and plant health monitoring.

Standardized Multi-Channel Dataset for Glaucoma (SMDG-19), a standardization of 19 public glaucoma datasets for AI applications.

Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public datasets, comprised of full-fundus glaucoma images, associated image metadata like, optic disc segmentation, optic cup segmentation, blood vessel segmentation, and any provided per-instance text metadata like sex and age. This dataset is designed to be exploratory and open-ended with multiple use cases and no established training/validation/test cases. This dataset is the largest public repository of fundus images with glaucoma.

Citation

Please cite at least the first work in academic publications: 1. Kiefer, Riley, et al. "A Catalog of Public Glaucoma Datasets for Machine Learning Applications: A detailed description and analysis of public glaucoma datasets available to machine learning engineers tackling glaucoma-related problems using retinal fundus images and OCT images." Proceedings of the 2023 7th International Conference on Information System and Data Mining. 2023. 2. R. Kiefer, M. Abid, M. R. Ardali, J. Steen and E. Amjadian, "Automated Fundus Image Standardization Using a Dynamic Global Foreground Threshold Algorithm," 2023 8th International Conference on Image, Vision and Computing (ICIVC), Dalian, China, 2023, pp. 460-465, doi: 10.1109/ICIVC58118.2023.10270429. 3. Kiefer, Riley, et al. "A Catalog of Public Glaucoma Datasets for Machine Learning Applications: A detailed description and analysis of public glaucoma datasets available to machine learning engineers tackling glaucoma-related problems using retinal fundus images and OCT images." Proceedings of the 2023 7th International Conference on Information System and Data Mining. 2023. 4. R. Kiefer, J. Steen, M. Abid, M. R. Ardali and E. Amjadian, "A Survey of Glaucoma Detection Algorithms using Fundus and OCT Images," 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2022, pp. 0191-0196, doi: 10.1109/IEMCON56893.2022.9946629.

Please also see the following optometry abstract publications: 1. A Comprehensive Survey of Publicly Available Glaucoma Datasets for Automated Glaucoma Detection; AAO 2022; https://aaopt.org/past-meeting-abstract-archives/?SortBy=ArticleYear&ArticleType=&ArticleYear=2022&Title=&Abstract=&Authors=&Affiliation=&PROGRAMNUMBER=225129 2. Standardized and Open-Access Glaucoma Dataset for Artificial Intelligence Applications; ARVO 2023; https://iovs.arvojournals.org/article.aspx?articleid=2790420 3. Ground truth validation of publicly available datasets utilized in artificial intelligence models for glaucoma detection; ARVO 2023; https://iovs.arvojournals.org/article.aspx?articleid=2791017

Please also see the DOI citations for this and related datasets: 1. SMDG; @dataset{smdg, title={SMDG, A Standardized Fundus Glaucoma Dataset}, url={https://www.kaggle.com/ds/2329670}, DOI={10.34740/KAGGLE/DS/2329670}, publisher={Kaggle}, author={Riley Kiefer}, year={2023} } 2. EyePACS-light-v1 @dataset{eyepacs-light-v1, title={Glaucoma Dataset: EyePACS AIROGS - Light}, url={https://www.kaggle.com/ds/3222646}, DOI={10.34740/KAGGLE/DS/3222646}, publisher={Kaggle}, author={Riley Kiefer}, year={2023} } 3. EyePACS-light-v2 @dataset{eyepacs-light-v2, title={Glaucoma Dataset: EyePACS-AIROGS-light-V2}, url={https://www.kaggle.com/dsv/7300206}, DOI={10.34740/KAGGLE/DSV/7300206}, publisher={Kaggle}, author={Riley Kiefer}, year={2023} }

Dataset Objective

The objective of this dataset is a machine learning-ready dataset for glaucoma-related applications. Using the help of the community, new open-source glaucoma datasets will be reviewed for standardization and inclusion in this dataset.

Data Standardization

• Full fundus images (and corresponding segmentation maps) are standardized using a novel algorithm (Citation 1) by cropping the background, centering the fundus image, padding missing information, and resizing to 512x512 pixels. This standardization ensures that the most amount of foreground information is prevalent during the resizing process for machine-learning-ready image processing.
• Each available metadata text is standardized by provided each fundus image as a row and each fundus attribute as a column in a CSV file

Dataset Instance	Original Fundus	Standardized Fundus Image
sjchoi86-HRF	https://user-images.githubusercontent.com/65875562/204170005-2d4dd051-0032-40c8-ba0b-390b6080bb69.png">	https://user-images.githubusercontent.com/65875562/204170011-51b7d001-4d43-4f0d-835e-984d45116b18.png">
BEH	https://user-images.githubusercontent.com/65875562/211052753-93f8a3aa-cc65-4790-8da6-229f512a6afb.PNG">	<img src="htt...

Data from: Priority setting for global WASH challenges in the age of wastewater-based epidemiological surveillance

https://doi.org/10.5061/dryad.fj6q5742f

A brief summary of dataset contents

Dataset #1: Estimates of mortality due to inadequate water, sanitation, and hygiene (WASH) during the COVID-19 Global Health Emergency

VARIABLES Region = The name for country groupings used by WHO
Age category = All observations have either the value 1 (<5 years) or 5 (all ages) Deaths 2019 due to unsafe WASH point estimate = The point estimate for the number of deaths due to unsafe WASH in 2019, by WHO region, by age category Deaths 2019 due to unsafe WASH upper estimate = The upper bound estimate for the number of deaths due to unsafe WASH in 2019, by WHO region, by age category Deaths 2019 due to unsafe WASH lower estimate = The lower bound estimate for the number of deaths due to unsafe WASH in 2019, by WHO region, by age category Estimated number Jan 3 2020-May 5 20...

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138. Johnson AG, Linde L, Ali AR, et al. COVID-19 Incidence and Mortality Among Unvaccinated and Vaccinated Persons Aged ≥12 Years by Receipt of Bivalent Booster Doses and Time Since Vaccination — 24 U.S. Jurisdictions, October 3, 2021–December 24, 2022. MMWR Morb Mortal Wkly Rep 2023;72:145–152. Johnson AG, Linde L, Payne AB, et al. Notes from the Field: Comparison of COVID-19 Mortality Rates Among Adults Aged ≥65 Years Who Were Unvaccinated and Those Who Received a Bivalent Booster Dose Within the Preceding 6 Months — 20 U.S. Jurisdictions, September 18, 2022–April 1, 2023. MMWR Morb Mortal Wkly Rep 2023;72:667–669.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

08/16/2023: the Index P maps have been updated to Version 2.0*06/08/2023: the Index P maps are currently being updated and will be re-uploaded shortly.This file contains transmission suitability maps for dengue virus (DENV) transmitted by Aedes aegypti mosquitoes from 1979 to 2022 for 186 countries or territories. These maps were generated using a mosquito-borne viral suitability measure referred to as Index P. Each file contains the monthly, yearly and typical year summary statistics of the time series of DENV transmission potential of Aedes aegypti at the spatial pixel level (~28km2) for a given country. An excel document that describes the file contents for each country is also provided. Full details are available in an article published at Scientific Data: "Nakase, T., Giovanetti, M., Obolski, U. et al. Global transmission suitability maps for dengue virus transmitted by Aedes aegypti from 1981 to 2019. Sci Data 10, 275 (2023). https://doi.org/10.1038/s41597-023-02170-7"This dataset can be used to understand past and current climate-driven transmission suitability of DENV for its primary vector Aedes aegypti in an area of interest.*We have added several years to the time series (1979-1980, 2020-2022) and assumed more informative priors for the scaling coefficients to minimize extreme values of Index P noted in the previous version.

Non-communicable diseases (NCDs) remain the biggest contributor to global mortality. An important way to control NCDs is to focus on reducing the prevalence of the common NCD risk factors for better NCD prevention planning. Uganda conducted its first nationally representative NCD risk factor survey in 2014, and a second in 2023. We analyzed the prevalence of the common NCD risk factors to assess changes in these between 2014 and 2023. Both surveys drew countrywide samples, and the World Health Organization’s STEPS tool was used to collect the data. We calculated weighted prevalence of the following NCD risk factors: high blood pressure, high blood glucose, overweight and obesity, current alcohol consumption, current tobacco use, inadequate consumption of fruits and vegetables, inadequate physical activity, and sedentariness. The 2014 survey enrolled 3987 participants, whereas the 2023 survey enrolled 3694. The risk factor prevalences that increased significantly were: high blood glucose from 1.5% in 2014 to 3.3% in 2023 (p< 0.001); overweight and obesity from 19.3% in 2014 to 24.1% in 2023 (p< 0.001); current alcohol consumption from 28.5% in 2014 to 31.1% in 2023 (p=0.013); and sedentariness from 26.6% in 2014 to 31.9% in 2023 (p< 0.001). The risk factor prevalences that decreased significantly were: inadequate physical activity from 5.0% in 2014 to 3.6% in 2023 (p=0.003), and current smoke tobacco use from 9.6% in 2014, to 8.3% in 2023 (p= 0.046). No significant changes were observed in the prevalence of high blood pressure from 24.6% in 2014 to 25.4% in 2023 (p= 0.418), and inadequate consumption of fruits and vegetables from 87.8% in 2014 to 86.4% in 2023 (p=0.067). There is an urgent need for various stakeholders in Uganda to implement interventions targeting reduction in the prevalence of NCD risk factors to prevent the increasing burden of NCDs and associated mortality.

Complete data extracted from 147 full text articles on 138 eligible studies.

Azerbaijan Prevalence of Stunting: Height for Age: % of Children Under 5 data was reported at 6.600 % in 2023. This records a decrease from the previous number of 17.800 % for 2013. Azerbaijan Prevalence of Stunting: Height for Age: % of Children Under 5 data is updated yearly, averaging 18.000 % from Dec 1996 (Median) to 2023, with 7 observations. The data reached an all-time high of 28.000 % in 1996 and a record low of 6.600 % in 2023. Azerbaijan Prevalence of Stunting: Height for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Azerbaijan – Table AZ.World Bank.WDI: Social: Health Statistics. Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;See SH.STA.STNT.ME.ZS for aggregation;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.