Dataset

This dataset was created by Joey Wang

Contents

Data collated from VizHub ( Global Burden of Disease 2017)Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Reference Life Table. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.

BACKGROUND Comprehensive analyses of statistical data on breast cancer incidence, mortality, and associated risk factors are of great value for decision-making related to reducing the disease burden of breast cancer. METHODS: Based on data from the Annual Report of China Tumour Registry and the Global Burden of Disease (GBD), we conducted summary and trend analyses of incidence and mortality rates of breast cancer in Chinese women from 2014 to 2018 for urban and rural areas in the whole, eastern, central, and western parts of the country, and projected the incidence and mortality rates of breast cancer for 2019 in comparison with the GBD 2019 estimates. And the comparative risk assessment framework estimated risk factors contributing to breast cancer deaths and disability-adjusted life years (DALYs) from GBD. RESULTS: The Annual Report of the Chinese Tumour Registry showed that showed that the mortality rate of breast cancer declined and the incidence rate remained largely unchanged from 2014 to 2018. There was a significant increasing trend in incidence rates among urban and rural women in eastern China and rural women in central China, whereas there was a significant decreasing trend in mortality rates among rural women in China. The two data sources have some differences in their predictions of breast cancer in China in 2019. The GBD data estimated the age-standard DALYs rates of high body-mass index, high fasting plasma glucose and diet high in red meat, which are the top three risk factors attributable to breast cancer in Chinese women, to be 29.99/100,000, 13.66/100,000 and 13.44/100,000, respectively. Conclusion: The trend of breast cancer incidence and mortality rates shown in the Annual Report of China Tumour Registry indicates that China has achieved remarkable results in reducing the burden of breast cancer, but there is still a need to further improve breast cancer screening and early diagnosis and treatment, and to improve the system of primary prevention. The GBD database provides risk factors for breast cancer in the world, Asia, and China, and lays the foundation for research on effective measures to reduce the burden of breast cancer.

Mortality data at the state level for Brasil. The data includes mortality rate, number of deaths and proportion of deaths. Additional information is included for sex and age breakdowns. The data was obtained from the Global Burden of Disease Study 2016 (GBD 2016) Results of the Institute for Health Metrics and Evaluation (IHME).

Accurate and comprehensive data on the incidence, mortality, and burden of ischemic heart disease (IHD), as well as the associated risk factors, are essential for informed decision-making in healthcare policy. This study aimed to estimate IHD incidence, deaths, and disability-adjusted life years (DALYs) stratified by country, gender, age group, and sociodemographic status from 1990 to 2019. Through statistical analysis of the global population and IHD data from the GBD database from 1990 to 2019, we analyzed the region, sex, age characteristics, and temporal development of IHD. At the same time, we also included the relevant influencing factors of the sociodemographic index (SDI) and analyzed the incidence of IHD in regions with different SDI levels. Furthermore, we examined the primary risk factors for IHD during the same period and employed statistical models to predict the global IHD incidence through 2044.

BackgroundAlzheimer’s disease and related dementias (ADRD) significant global public health challenges, leading to severe disability in patients and placing a heavy burden on caregivers. However, epidemiological studies focusing on ADRD in specific regions remain limited. This study aims to comprehensively analyze and describe the current status and changing trends of ADRD in Non-High-income East Asia (NHIEA), Non-High-income Southeast Asia (NHISEA), and High-income Asia Pacific (HIAP), providing more detailed real-world data to inform policymaking.MethodsThe data for ADRD used in this study were extracted from the 2021 Global Burden of Disease (GBD) database. We employed three major indicators of disease burden—prevalence, incidence, and years lived with disability (YLD)—and explored associated risk factors, further analyzing trends by age and sex. The results are presented as mean values with 95% uncertainty intervals (UIs). Additionally, we explored the differences between NHIEA, NHISEA, HIAP and other regions, as well as the potential associations between the disease burden of Alzheimer’s and other dementias and socioeconomic factors.ResultsThe findings indicate that the burden of dementia is rising in East and Southeast Asia, with women showing a higher burden across all indicators. Notably, in NHIEA, particularly in China, the burden of dementia has increased with the rising Social Demographic Index (SDI). China experienced a 27.3% increase in Alzheimer’s disease and other dementia ASYRs from 1990 to 2021, with a sharp 7.6% annual surge in 2021 alone, outpacing regional averages. Gender analysis revealed that women bear a disproportionate burden of Alzheimer’s disease and related dementias, especially after menopause, when the risk increases significantly. The study also identified smoking, high blood sugar, and high body mass index as important risk factors affecting the disease burden. The contribution of these risk factors varies across regions, genders, and age groups.ConclusionThe health burden of ADRD remains substantial, with distinct patterns observed across NHIEA, NHISEA, and HIAP, including regional variations in gender, age, and risk factors. These findings highlight the need for tailored approaches to allocate healthcare resources and implement appropriate control measures based on the specific conditions of each region to address this growing public health challenge. Future research should prioritize comparative analyses across continents and within regions to inform the development of more region-specific prevention strategies for ADRD.

BackgroundPancreatitis represents a significant global public health challenge, yet there is a lack of comprehensive analyses focusing on the burden of pancreatitis and its long-term trends among young individuals aged 15–39 years.MethodsThis study utilized data from the Global Burden of Disease (GBD 2021) database to analyze the prevalence, incidence, and disability-adjusted life years (DALYs) associated with pancreatitis in the 15–39 age group from 1990 to 2021. Temporal trends in disease burden were assessed by calculating the estimated annual percentage change (EAPC), with point estimates and their 95% uncertainty intervals (UIs) reported.ResultsBetween 1990 and 2021, the global number of cases related to pancreatitis—including prevalence, incidence, DALYs, and deaths—substantially increased in the 15–39 age group. However, age-standardized rates for prevalence, incidence, DALYs, and mortality showed a declining trend. Gender-specific analysis revealed that females had lower prevalence and incidence rates compared to males. Socio-demographic Index (SDI)-based subgroup analysis indicated that low-SDI regions experienced the largest increases in DALYs and deaths, while high-SDI regions showed the most significant declines in age-standardized DALYs and mortality rates. Geographically, East Asia demonstrated the largest decrease in the burden of pancreatitis, whereas Western Sub-Saharan Africa exhibited the highest increases in case numbers and deaths. Age-stratified analysis showed that individuals aged 35–39 years had the greatest increases in case numbers and disease burden, despite experiencing the most notable decline in incidence rates. Conversely, the 15–19 age group exhibited reductions in disease burden and mortality rates.ConclusionThis study highlights that, while the global number of pancreatitis cases among young individuals aged 15–39 has risen from 1990 to 2021, the overall disease burden has declined, particularly in high-income regions. In contrast, the disease burden in low-income regions continues to rise.

BackgroundSystematic collection of mortality/morbidity data over time is crucial for monitoring trends in population health, developing health policies, assessing the impact of health programs. In Poland, a comprehensive analysis describing trends in disease burden for major conditions has never been published. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides data on the burden of over 300 diseases in 195 countries since 1990. We used the GBD database to undertake an assessment of disease burden in Poland, evaluate changes in population health between 1990–2017, and compare Poland with other Central European (CE) countries.MethodsThe results of GBD 2017 for 1990 and 2017 for Poland and CE were used to assess rates and trends in years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life years (DALYs). Data came from cause-of-death registration systems, population health surveys, disease registries, hospitalization databases, and the scientific literature. Analytical approaches have been used to adjust for missing data, errors in cause-of-death certification, and differences in data collection methodology. Main estimation strategies were ensemble modelling for mortality and Bayesian meta-regression for disability.ResultsBetween 1990–2017, age-standardized YLL rates for all causes declined in Poland by 46.0% (95% UI: 43.7–48.2), YLD rates declined by 4.0% (4.2–4.9), DALY rates by 31.7% (29.2–34.4). For both YLLs and YLDs, greater relative declines were observed for females. There was a large decrease in communicable, maternal, neonatal, and nutritional disease DALYs (48.2%; 46.3–50.4). DALYs due to non-communicable diseases (NCDs) decreased slightly (2.0%; 0.1–4.6). In 2017, Poland performed better than CE as a whole (ranked fourth for YLLs, sixth for YLDs, and fifth for DALYs) and achieved greater reductions in YLLs and DALYs than most CE countries. In 2017 and 1990, the leading cause of YLLs and DALYs in Poland and CE was ischaemic heart disease (IHD), and the leading cause of YLDs was low back pain. In 2017, the top 20 causes of YLLs and YLDs in Poland and CE were the same, although in different order. In Poland, age-standardized DALYs from neonatal causes, other cardiovascular and circulatory diseases, and road injuries declined substantially between 1990–2017, while alcohol use disorders and chronic liver diseases increased. The highest observed-to-expected ratios were seen for alcohol use disorders for YLLs, neonatal sepsis for YLDs, and falls for DALYs (3.21, 2.65, and 2.03, respectively).ConclusionsThere was relatively little geographical variation in premature death and disability in CE in 2017, although some between-country differences existed. Health in Poland has been improving since 1990; in 2017 Poland outperformed CE as a whole for YLLs, YLDs, and DALYs. While the health gap between Poland and Western Europe has diminished, it remains substantial. The shift to NCDs and chronic disability, together with marked between-gender health inequalities, poses a challenge for the Polish health-care system. IHD is still the leading cause of disease burden in Poland, but DALYs from IHD are declining. To further reduce disease burden, an integrated response focused on NCDs and population groups with disproportionally high burden is needed.

About

GDB-11 enumerates small organic molecules up to 11 atoms of C, N, O and F following simple chemical stability and synthetic feasibility rules.
GDB-13 enumerates small organic molecules up to 13 atoms of C, N, O, S and Cl following simple chemical stability and synthetic feasibility rules. With 977 468 314 structures, GDB-13 is the largest publicly available small organic molecule database to date.

How to cite

To cite GDB-11, please reference:

Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physico-chemical properties, compound classes and drug discovery. Fink, T.; Reymond, J.-L. J. Chem. Inf. Model. 2007, 47, 342-353.

Virtual Exploration of the Small Molecule Chemical Universe below 160 Daltons. Fink, T.; Bruggesser, H.; Reymond, J.-L. Angew. Chem. Int. Ed. 2005, 44, 1504-1508.

To cite GDB-13, please reference:

970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13. Blum L. C.; Reymond J.-L. J. Am. Chem. Soc., 2009, 131, 8732-8733.

To cite GDB-17, please reference:

Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. Ruddigkeit Lars, van Deursen Ruud, Blum L. C.; Reymond J.-L. J. Chem. Inf. Model., 2012, 52, 2864-2875.

Download

You can download the databases and subsets of it using the links provided. All the molecules are stored in dearomatized, canonized SMILES format and compressed as tar/gz archive (for Windows users: Download 7-zip to open archives).

GDB-17
GDB-17-Set (50 million) GDB17.50000000.smi.gz 314 MB
Lead-like Set (100-350 MW & 1-3 clogP)(11 million) GDB17.50000000LL.smi.gz 75 MB
Lead-like Set (100-350 MW & 1-3 clogP) without small rings (3-4 ring atoms)(0.8 million) GDB17.50000000LLnoSR.smi.gz 55 MB

GDB-13
Entire GDB-13 (including all C/N/O/Cl/S molecules) gdb13.tgz 2.6 GB
GDB-13 Subsets (The sum of all the subsets below correspond to the entire GDB-13 above)
Graph subset (saturated hydrocarbons) gdb13.g.tgz 1.1 MB
Skeleton subset (unsaturated hydrocarbons) gdb13.sk.tgz 14 MB
Only carbon & nitrogen containing molecules gdb13.cn.tgz 443 MB
Only carbon & oxygen containing molecules gdb13.co.tgz 299 MB
Only carbon & nitrogen & oxygen containing molecules gdb13.cno.tgz 1.8 GB
Chlorine & sulphur containing molecules gdb13.cls.tgz 189 MB

GDB-13 Subsets (For details please refer to the Table 2 in J Comput Aided Mol Des 2011 25:637 to 647)
GDB-13 Subset AB (~635 Millions) AB.smi.gz 2.4 GB
GDB-13 Subset ABC (~441 Millions) ABC.smi.gz 1.7 GB
GDB-13 Subset ABCD (~277 Millions) ABCD.smi.gz 1.1 GB
GDB-13 Subset ABCDE (~140 Millions) ABCDE.smi.gz 565 MB
GDB-13 Subset ABCDEF (~43 Millions) ABCDEF.smi.gz 171 MB
GDB-13 Subset ABCDEFG (~13 Millions) ABCDEFG.smi.gz 50 MB
GDB-13 Subset ABCDEFGH (~1.4 Millions) ABCDEFGH.smi.gz 6.2 MB
GDB-13 Random Sample. Annotated with frequency and log-likelihood (Please refer to Exploring the GDB-13 chemical space using deep generative models)
GDB-13 Random Sample (1 Million) gdb13.1M.freq.ll.smi.gz 14.8 MB

FDB-17
FDB-17 FDB-17-fragmentset.smi.gz 62.2 MB

GDB4c
GDB4c (SMILES) GDB4c.smi.gz 6.2 MB
GDB4c3D (SMILES) GDB4c3D.smi.gz 161 MB
GDB4c3D (SDF) GDB4c3D.sdf.tar.gz 2 GB

Other
GDBMedChem (SMILES) GDBMedChem.smi 276 MB
GDBChEMBL (SMILES) GDBChEMBL.smi 353.6 MB
GDB-13 random selection (1 million) gdb13.rand1M.smi.gz 7.2 MB
Fragment-like subset (Rule of three) gdb13.frl.tgz 1.2 GB
Dark matter universe up to 9 heavy atoms dmu9.tgz 87 MB

GDB-11
Entire GDB-11 (including all C/N/O/F molecules) gdb11.tgz 122 MB
Fragrance Like Subsets: For details please refer to Ruddigkeit et al. Journal of Cheminformatics 2014, 6:27
FragranceDB (SuperScent + Flavornet) FragranceDB.smi 56 KB
TasteDB (SuperSweet + BitterDB) TasteDB.smi 44 KB
FragranceDB.FL (Fragrance-like subset of FragranceDB) FragranceDB.FL.smi 32 KB
ChEMBL.FL (Fragrance-like subset of ChEMBL) ChEMBL.FL.smi 452 KB
PubChem.FL Fragrance-like subset of PubChem PubChem.FL.smi 20 MB
ZINC.FL (Fragrance-like subset of ZINC) ZINC.FL.smi 1.3 MB
GDB-13.FL (Fragrance-like subset of GDB-13) GDB-13.FL.smi.gz 165 MB

Terms and conditions: The GDB databases may be downloaded free of charge. In published research involving GDB, cite the appropriate references mentioned above. GDB must not be used as part of or in patents. GDB and large portions thereof must not be redistributed without the express written permission of Jean-Louis Reymond.

This dataset contains processed data used in the study “Global, regional, and national burdens of HIV and syphilis in children aged 0–14 years from 1990 to 2021: a systematic analysis from the Global Burden of Disease Study 2021.” Data were extracted from the publicly available Global Burden of Disease (GBD) 2021 database (https://vizhub.healthdata.org/gbd-results/

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fb75a86186a0014480c981c5182acc9ff%2Fgraph3.png?generation=1715898880551749&alt=media" alt="">this graph was created in Loocker studio,PowerBi,Tableau:

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Dementia patients show worsening cognitive function over time, beyond what might be expected from typical aging.

Dementia affects memory, thinking, orientation, comprehension, calculation, learning capacity, language, and judgment. This is commonly accompanied by changes in mood, emotional control, behavior, or motivation.

Deaths - Alzheimer's disease and other dementias - Sex: Both - Age: Age-standardized (Rate) Source Institute for Health Metrics and Evaluation, Global Burden of Disease (2019) – processed by Our World in Data Date range 1990–2019 Unit deaths per 100,000 people Links http://ghdx.healthdata.org/gbd-results-tool The data of this indicator is based on the following sources: Institute for Health Metrics and Evaluation, Global Burden of Disease (2019) Data published by Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2021.

Retrieved on September 22, 2021 Retrieved from http://ghdx.healthdata.org/gbd-results-tool How we process data at Our World in Data: All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline How to cite this data: In-line citation If you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Institute for Health Metrics and Evaluation, Global Burden of Disease (2019) – processed by Our World in Data

Full citation

Institute for Health Metrics and Evaluation, Global Burden of Disease (2019) – processed by Our World in Data. “Deaths - Alzheimer's disease and other dementias - Sex: Both - Age: Age-standardized (Rate)” [dataset]. Institute for Health Metrics and Evaluation, Global Burden of Disease (2019) [original data].

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

This study estimates the economic losses (GDP), particularly the impact of COVID-19 deaths on non-health components of GDP in West Bengal state. The NHGDP losses were evaluated using cost-of-illness approach. Future NHGDP losses were discounted at 3%. Excess death estimates by the World Health Organisation (WHO) and Global Burden of Disease (GBD) were used. Sensitivity analysis was carried out by varying discount rates and Average Age of Death (AAD). 21,532 deaths in West Bengal since 17th March 2020 till 31st December 2022 decreased the future NHGDP by $0.92 billion. Nearly 90% of loss was due to deaths occurring in above 30 years age-group. The majority of the loss was borne among the 46–60 years age-group. The NHGDP loss/death was $42,646, however, the average loss/death declined with a rise in age. The loss increased to $9.38 billion and $9.42 billion respectively based on GBD and WHO excess death estimates. The loss increased to $1.3 billion by considering the lower age of the interval as AAD. At 5% and 10% discount rates, the losses reduced to $0.769 billion and $0.549 billion respectively. Results from the study suggest that COVID-19 contributed to major economic loss in West Bengal. The mortality and morbidity caused by COVID-19, the substantial economic costs at individual and population levels in West Bengal, and probably across India and other countries, is another argument for better infection control strategies across the globe to end the impact of this epidemic. Methods Various open domains were used to gather data on COVID-19 deaths in West Bengal and the aforementioned estimates. Economic losses in terms of Non-Health Gross Domestic Product (NHGDP)among six age-group brackets viz. 0–15, 16–30, 31–45, 46–60, 61–75 and 75 and above were estimated to facilitate comparisons and to initiate advocacy for an increase in health investments against COVID-19. This study used midpoint age as the age of death for all the age brackets. The legal minimum age for working i.e., 15 years. A sensitivity analysis was conducted to determine the effect of age on the overall total NHGDP loss estimate. The model was re-estimated assuming an average age at death to be the starting age of each age-group bracket. Based on existing literature discounted rate of interest to measure the value of life is taken as 2.9%. As a sensitivity analysis, NHGDP loss has also been computed using 5% and 10% of discounted rates of interest.

BackgroundThe 2021 Global Burden of Disease (GBD) study shows a continuous increase in the burden of chronic kidney disease due to diabetes mellitus type 2 (CKD-T2DM) from 1990 to 2021. This study examines the influence of dietary risk factors across various populations and socioeconomic groups.MethodsUtilizing the 2021 GBD data, we analyzed age-standardized CKD-T2DM metrics—including mortality, disability-adjusted life years (DALY), and age-standardized rates (ASR)—stratified by age, gender, and region. The study employs estimated annual percentage changes (EAPC) to monitor temporal trends and project future trends from 2022 to 2035 using bayesian age-period-cohort (BAPC) analysis.ResultsThe results indicate that, in 2021, 20.55% of CKD-T2DM mortality and 23.21% of CKD-T2DM DALY were attributed to poor diets, especially those low in fruits and high in red and high processed meat. Throughout this period, both mortality and DALY rates associated with dietary risks increased significantly, with the most rapid increase in diet high in sugar-sweetened beverages, highlighting the considerable impact of dietary factors on the global CKD-T2DM landscape. Geographic disparities in T2DM trends are evident, with the most significant increases in age-standardized mortality rates (ASMR) and age-standardized DALY rates (ASDR) observed in regions such as high-income North America and Central Latin America. Socio-demographic index (SDI) is negatively correlated with the CKD-T2DM burden attributable to dietary risk factors.ConclusionPublic health interventions that target dietary changes can significantly reduce the global burden of CKD-T2DM.

Overview
This dataset contains the modeled gridded fractional source contribution results for the 2021 Global Burden of Disease - Major Air Pollution Sources (GBD-MAPS) - Global study. Fractional results aggregated to the regional, national, and sub-national scales are available as Supplementary Datasets in McDuffie et al., Nature Communications, 2021.

This GBD-MAPS-Global methodology and results are described in the following article:
McDuffie, E. E., Martin, R. V., Spadaro, J. V., Burnett, R., Smith, S. J., O'Rourke, P., Hammer, M., van Donkelaar, A., Bindle, L., Shah, V., Jaegle, L., Luo, G., Yu, F., Adeniran, J., Lin, J., Brauer, M. Source Sector and Fuel Contributions to Ambient PM_2.5 Attributable Mortality Across Multiple Spatial Scales, Nature Communications

Notes:
All results provide the modeled fractional contribution of each designated source category to total surface PM_2.5 mass and attributable disease burden. Fractional source contribution results are derived from emission sensitivity simulations using the GEOS-Chem 3D global chemical transport model (DOI: 10.5281/zenodo.4718622) and emissions from the Community Emissions Data System (GBD-MAPS update) (DOI: 10.5281/zenodo.3754964), unless otherwise noted. Results are provided at the 0.01°x0.01° (~1 km x 1 km) spatial resolution and are based on original simulations conducted at 2°x2.5° (global) and 0.5°x0.625° (Europe, North America, and Asia regions) degree resolutions. All units are in fractional percentages (e.g., 0.0245 is 2.45%).

To calculate gridded absolute contributions of each source to PM_2.5 mass (used in the GBD-MAPS analysis):
1) Multiply the 0.01°x0.01° fractional source contributions by downscaled 0.01°x0.01° resolution PM_2.5 mass estimates, available in the DownscaledPM.zip folder as part of the GBD-MAPS-Global: Analysis Input Dataset (DOI: 10.5281/zenodo.4642700)

Data File Descriptions:
LatLon - The latitude and longitude values that correspond to each data point in the source-specific files (0.01°x0.01° resolution)

Sector Files (sum to 100%):

AFCID - Anthropogenic Fugitive, Combustion, and Industrial Dust
AGR - Agriculture - includes manure management, soil fertilizer emissions, rice cultivation, enteric fermentation, and other agriculture
ENEcoal - Energy Production (coal combustion only) - Includes electricity and heat production, fuel production and transformation, oil and gas fugitive/flaring, and fossil fuel fires
ENEother - Energy Production (all non-coal combustion) - Includes electricity and heat production, fuel production and transformation, oil and gas fugitive/flaring, and fossil fuel fires
GFEDagburn - Agricultural Waste Burning - Includes solid waste disposal, waste incineration, waste-water handling, and other waste handling (from the GFED fires inventory)
GFEDoburn - Other Open Fires - Includes deforestation, boreal forest, peat, savannah, and temperate forest fires (from the GFED fires inventory)
INDcoal - Industry (coal combustion only) - Includes Industrial combustion (iron and steel, non-ferrous metals, chemicals, pulp and paper, food and tobacco, non-metallic minerals, construction, transportation equipment, machinery, mining and quarrying, wood products, textile and leather, and other industry combustion) and non-combustion industrial processes and product use (cement production, lime production, other minerals, chemical industry, metal production, food, beverage, wood, pulp, and paper, and other non-combustion industrial emissions)
INDother - Industry (all non-coal combustion) - Includes Industrial combustion (iron and steel, non-ferrous metals, chemicals, pulp and paper, food and tobacco, non-metallic minerals, construction, transportation equipment, machinery, mining and quarrying, wood products, textile and leather, and other industry combustion) and non-combustion industrial processes and product use (cement production, lime production, other minerals, chemical industry, metal production, food, beverage, wood, pulp, and paper, and other non-combustion industrial emissions)
NRTR - non-road/ off-road transportation - Includes Rail, Domestic navigation, Other transportation
OTHER - all remaining sources, including: volcanic SO2, lightning NOx, biogenic soil NO, ocean emissions, biogenic emissions, very short lived iodine and bromine species, decaying plants (misc. inventories)
RCOC - Commercial Combustion - Includes commercial and institutional combustion
RCOO - Other Combustion - Includes combustion from agriculture, forestry, and fishing
RCORbiofuel - Residential combustion (solid biofuel combustion only) - includes residential heating and cooking
RCORcoal - Residential combustion (coal combustion only) - includes residential heating and cooking
RCORother - Residential Combustion (all non-coal and non-solid biofuel) - includes residential heating and cooking
ROAD - Road Transportation - includes cars, motorcycles, heavy and light duty trucks and buses
SHP - International Shipping - Includes international shipping and tanker loading
SLV - Solvents - Includes solvents production and application (degreasing and cleaning, paint application, chemical products manufacturing and processing, and other product use)
WDUST - Windblown Dust - (from the DEAD dust model)
WST - Waste - Includes solid waste disposal, waste incineration, waste-water handling, and other waste handling

Fuel Categories (do not sum to 100% for each grid cell as these only include combustion sources of PM_2.5):

BIOFUEL - Solid Biofuel (or biomass) Combustion- Includes solid biofuel
COAL - Total Coal Combustion - Includes hard coal, brown coal, coal coke
OILGAS- Liquid Oil and Natural Gas Combustion - Includes light and heavy oil, diesel oil, and natural gas

The Rhode Island Division of Statewide Planning, in partnership with 13 other Rhode Island state agencies, developed a platform to better align social equity policies, decisions, and outcomes into our planning process. The platform is intended to increase social equity data transparency and to overlay the unique justice issues communities across the state face.Visit RI Division of Statewide Planning for more details.The RI Social Equity Data Platform pulls together more than 37 spatial data indicators on public health, environmental justice, socioeconomics, and transportation into one easy to use, publicly accessible platform. The Platform is intended to help with the initial stage of incorporating equity into policies, plans, and practices – identifying how certain quantifiable indicators are distributed across population groups across the state. The Platform does not designate any specific areas as “equity areas,” but rather displays the extent of individual indicators for every census tract in the state.Access the RI Social Equity Data Platform

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BackgroundThe role of surgical care in promoting global health is the subject of much debate. The Global Burden of Disease 2010 study (GBD 2010) offers a new opportunity to consider where surgery fits amongst global health priorities. The GBD 2010 reinforces the DALY as the preferred methodology for determining the relative contribution of disease categories to overall global burden of disease without reference to the likelihood of each category requiring surgery. As such, we hypothesize that the GBD framework underestimates the role of surgery in addressing the global burden of disease.Methods and FindingsWe compiled International Classification of Diseases, Version 9, codes from the United States Nationwide Inpatient Sample from 2010. Using the primary diagnosis code for each hospital admission, we aggregated admissions into GBD 2010 disease sub-categories. We queried each hospitalization for a major operation to determine the frequency of admitted patients whose care required surgery. Major operation was defined according to the Agency for Healthcare Research and Quality (AHRQ). In 2010, 10 million major inpatient operations were performed in the United States, associated with 28.6% of all admissions. Major operations were performed in every GBD disease subcategory (range 0.2%–84.0%). The highest frequencies of operation were in the subcategories of Musculoskeletal (84.0%), Neoplasm (61.4%), and Transport Injuries (43.2%). There was no disease subcategory that always required an operation; nor was there any disease subcategory that never required an operation.ConclusionsSurgical care cuts across the entire spectrum of GBD disease categories, challenging dichotomous traditional classifications of ‘surgical’ versus ‘nonsurgical’ diseases. Current methods of measuring global burden of disease do not reflect the fundamental role operative intervention plays in the delivery of healthcare services. Novel methodologies should be aimed at understanding the integration of surgical services into health systems to address the global burden of disease.

IntroductionPediatric pulmonary arterial hypertension (PAH) is a rare and severe disorder characterized by obstructive vascular changes that can lead to right heart failure. The clinical presentation and underlying causes of pediatric PAH differ significantly from those in adults, often involving congenital heart disease and developmental lung disorders, such as bronchopulmonary dysplasia (BPD). Despite advances in treatment, pediatric PAH remains underrecognized globally.MethodsThis study analyzed global, regional, and national trends in pediatric PAH from 1990 to 2021 using data from the Global Burden of Disease (GBD) database.ResultsThe findings indicate a stable prevalence rate globally, with a slight increase in the absolute number of cases. Significantly, reductions were observed in both mortality and disability-adjusted life years (DALYs) associated with pediatric PAH, with mortality decreasing by 57.66% and DALYs by 63.59% over the study period, indicating progress in mitigating the disease burden. Substantial regional disparities were identified, with low-income regions, particularly Low Socio-Demographic Index (SDI) areas, experiencing the highest mortality and DALY rates. In contrast, high-middle SDI regions showed the greatest reductions in disease burden. The highest prevalence and burden were observed in South Asia, the Caribbean, and parts of Sub-Saharan Africa, with China, India, and Haiti bearing the greatest national burdens.DiscussionThese findings highlight the necessity for targeted health interventions, especially in low-resource settings, to improve early diagnosis, intervention, and treatment.

Updated: Apr 29, 2021