This layer represents the percentage of total Disability-Adjusted Life Year attributable to paratyphoid fever for 5-14 year-old females in 2015. One DALY can be thought of as one lost year of "healthy" life. The sum of DALYs across a population help to quantify the burden of disease, and to evaluate the gap between current health status and an ideal health situation. Data for other age ranges are also available in the table.Estimates and additional related resources can be found in the Global Burden of Study here: http://ghdx.healthdata.org/gbd-2015 For more information, visit the Institute for Health Metrics and Evaluation website: http://www.healthdata.org/gbdNote : Value -99 indicates that no data is available.A detailed description of the methodology and additional resources related to this topic can be found here: http://ghdx.healthdata.org/gbd-2015 For more information, visit the IHME website: http://www.healthdata.org/gbd

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:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ff695c5f66d6851cf80797b7057ade08b%2Fgraph1.jpg?generation=1715898858448928&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F906461bf5b2ebc0f6bc6c806c9a1654e%2Fgraph2.jpg?generation=1715898864457964&alt=media" alt="">

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 layer represents the percentage of total Disability-Adjusted Life Year attributable to hepatitis A for 15-49 year-old males in 2015. One DALY can be thought of as one lost year of "healthy" life. The sum of DALYs across a population help to quantify the burden of disease, and to evaluate the gap between current health status and an ideal health situation. Data for other age ranges are also available in the table.Estimates and additional related resources can be found in the Global Burden of Study here: http://ghdx.healthdata.org/gbd-2015 For more information, visit the Institute for Health Metrics and Evaluation website: http://www.healthdata.org/gbd

Compiled dataset with information available from the Global Burden of Disease Collaborative Network data sets, http://ghdx.healthdata.org/gbd-results-tool, the World Bank website https://tcdata360.worldbank.org/topics, the Center for Systemic Peace website https://www.systemicpeace.org/ and the International Disaster Database https://www.emdat.be/.

Any use of the dataset provided must adhere to the guidelines adopted by the aforementioned sources

Context

IHME United States Mortality Rates by County 1980-2014: National - All. (Deaths per 100,000 population)

To quickly get started creating maps, like the one below, see the Quick Start R kernel.

https://storage.googleapis.com/montco-stats/kaggleNeoplasms.png" alt="NeoplasmsMap">

How the Dataset was Created

This Dataset was created from the Excel Spreadsheet, which can be found in the download. Or, you can view the source here. If you take a look at the row for United States, for the column Mortality Rate, 1980*, you'll see the set of numbers 1.52 (1.44, 1.61). Numbers in parentheses are 95% uncertainty. The 1.52 is an age-standardized mortality rate for both sexes combined (deaths per 100,000 population).

In this Dataset 1.44 will be placed in the named column Mortality Rage, 1989 (Min)* and 1.61 is in column named Mortality Rate, 1980 (Max)* . For information on how these Age-standardized mortality rates were calculated, see the December JAMA 2016 article, which you can download for free.

https://storage.googleapis.com/montco-stats/kaggleUSMort.png" alt="Spreadsheet">

Reference

JAMA Full Article

Video Describing this Study (Short and this is worth viewing)

Data Resources

How Americans Die May Depend On Where They Live, by Anna Maria Barry-Jester (FiveThirtyEight)

Interactive Map from healthdata.org

IHME Data

Acknowledgements

This Dataset was provided by IHME

Institute for Health Metrics and Evaluation 2301 Fifth Ave., Suite 600, Seattle, WA 98121, USA Tel: +1.206.897.2800 Fax: +1.206.897.2899 © 2016 University of Washington

This layer represents the percentage of total Disability-Adjusted Life Year attributable to unsafe water sources for 15-49 year-old females in 2015. One DALY can be thought of as one lost year of "healthy" life. The sum of DALYs across a population help to quantify the burden of disease, and to evaluate the gap between current health status and an ideal health situation. Data for other age ranges are also available in the table. Estimates and additional related resources can be found in the Global Burden of Study here: http://ghdx.healthdata.org/gbd-2015 For more information, visit the Institute for Health Metrics and Evaluation website: http://www.healthdata.org/gbd

We obtained the analyzed data from the public repository of the Global Burden of Disease (GBD) study (https://vizhub.healthdata.org/sdg/#0 and http://ghdx.healthdata.org/record/ihme-data/gbd-2017-health-related-sdgs-1990-2030). However, under the request of the International Journal for Equity in Health in order to contribute to transparency and replicability of research, the authors of the study entitled “Human resources for health and maternal mortality in Latin America and the Caribbean over the last three decades: a systemic-perspective reflections”, made the data available. Any other use than exploring or replicating the results of the above-mentioned paper is not authorized and shall not be used without the previous authorization of the investigators. If you are interested in analyzing this database for original research purposes please contact Edson Serván Mori (eservan@insp.mx).

Top Covid19 Countries and Health Demographic Trend

Context

This is a time-series trend data collection with a series of json files primarily focused on countries most impacted by Covid-19. The tree formatted time series data should be able to enable various different kinds of analysis to answer questions about what may make a country's health system vulnerable to Covid-19 and what health demographics may help reducing the impact.

Demographic metrics

Healthcare GDP Expenditure 
Healthcare Employment
Hospital Bed Capacity
Air Pollution and Death Rate
Chronic illnesses and DALYs(Disability-Adjusted Life Years)
Body Weight 
Elderly(Aged 65+) Population
CT Scanner Density
Tobacco Consumption(Smoker population %)

More metrics can be added upon request.

Data Normalization

The raw CSV includes many different types of measurements such as number, percentage and per 1 million population. This data normalizes the time_series data by selecting data that is more about density, and number per capita data rather than absolute numbers. This could help doing comparison among nations since they may vary significantly on population.

Content

Most of the JSON files contain time_series data. For people who want to use the data as country metadata, the most-recent data attribute is collected in top_countries_latest_fact_summary.json

The JSON data focuses on the above mentioned demographic areas in a simple tree schema { Country_name: { metric_name:[ List of {year, value, unit} ] } }

Data source & License

The data is sourced from OECD(https://stats.oecd.org/) and GDHX(http://ghdx.healthdata.org/). The json files with prefix "gbd_" are from GDHX

Following citation is needed for using GDHX data:

GBD Results tool: Use the following to cite data included in this download: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018. Available from http://ghdx.healthdata.org/gbd-results-tool.

Inspiration

• Where does US rank in term of Healthcare/Preventive spending in GDP, hospital bed/ICU bed/physician density and long-term illness? In which areas can US do more to prevent future Cov-19 crisis?

• Is there correlation in a nation's medical preparedness and the rate of growth in confirmation, death rate and recovery rate? From GBD data graphs, it seems that Dalys(DALYs (Disability-Adjusted Life Years), rate per 100k) can divided nations into different camps.

• How does death rate from Cov-19 correlate with Death rate related to Cardiovascular diseases and Chronic respiratory diseases?

• What trends can we discover in various nation's health demographics over time? Are some areas getting better while others getting worse?

• With time span from 2010 to 2018, this dataset can also correlate with data related to recent outbreaks such as seasonal flus, Avian influenza, etc.

Example Notebook

With some quick analysis, it shows that the US actually ranks higher than China for DALYs(Disability-adjusted life years) caused by Chronic Respiratory conditions, which could be due to seasonal allergies. It seems counter-intuitive that this may suggest that countries with cleaner air may have higher burden of people with Chronic Respiratory conditions that may have made them more vulnerable in the Covid-19 crisis.

Example Kernel: https://www.kaggle.com/timxia/bar-chart-comparison-of-countries https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2F2fce05195108856422b437316f34e837%2FTobacco.png?generation=1585936274243838&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fe8db14764a47a8bce48fa79bdfdfb0f1%2FChronicDisease.png?generation=1585936274372639&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fc534d40af042b9a503325f41c49b83cb%2FAirPollution.png?generation=1585936274337626&alt=media" alt="">

We obtained the analyzed data from the public repository of the Global Burden of Disease (GBD) study (http://ghdx.healthdata.org). However, under the request of The Lancet Regional Health – Americas and in order to contribute to transparency and replicability of research, the authors of the study entitled “Persistent inequities in maternal mortality in Latin America and the Caribbean, 1990-2019”, made the data available. Any other use than exploring or replicating the results of the above-mentioned paper is not authorized and shall not be used without the previous authorization of the investigators. If you are interested in analyzing this database for original research purposes please contact Edson Serván Mori (eservan@insp.mx).

Summary

IHME research produced estimates for age-standardized mortality rates by county from chronic respiratory diseases. The estimates were generated using de-identified death records from the National Center for Health Statistics (NCHS); population counts from the U.S. Census Bureau, NCHS, and the Human Mortality Database; the cause list from the Global Burden of Disease Study (GBD); and the application of small area estimation models. This dataset provides estimates for age-standardized mortality rates by disease type and sex at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014, as well as the changes in rates for each location during this period. Also included are data on the 10 counties with the highest and lowest mortality rates for each disease type in 2014. Study results were published in JAMA in September 2017 in "Trends and patterns of differences in chronic respiratory disease mortality among US counties, 1980–2014."

Data Granularity

• Geography: US, county level
• Time period: 1980-2014

Data Sources

Data provider: Institute for Health Metrics and Evaluation (IHME) Link: http://ghdx.healthdata.org/record/ihme-data/united-states-chronic-respiratory-disease-mortality-rates-county-1980-2014

Prevalent cases, deaths, and DALYs for ACM in 2021, and percentage change in ASRs per 100 000, by GBD region, from 1990 to 2021 (generated from data available at https://ghdx.healthdata.org/gbd-results-tool).

This layer represents the percentage of Disability-Adjusted Life Year attributable to unsafe sanitation in 2015, for 5 to 14 year-old females. Data for other age ranges are also available in the table. One DALY can be thought of as one lost year of "healthy" life. The sum of DALYs across a population help to quantify the burden of disease, and to evaluate the gap between current health status and an ideal health situation. Estimates and additional related resources can be found in the Global Burden of Study here: http://ghdx.healthdata.org/gbd-2015 For more information, visit the Institute for Health Metrics and Evaluation website: http://www.healthdata.org/gbd

Additional file 1: Table S1. Prevalence of tension-type headache in 1990 and 2019 for both sexes and the percentage change in the age-standardised rates (ASRs) per 100000 in the North Africa and the Middle East region (Generated from data available from http://ghdx.healthdata.org/gbd-results-tool ). Table S2. Incidence of tension-type headache in 1990 and 2019 for both sexes and the percentage change in the age-standardised rates (ASRs) per 100000 in the Middle East and North Africa region (Generated from data available from http://ghdx.healthdata.org/gbd-results-tool ). Table S3. YLDs due to tension-type headache in 1990 and 2019 for both sexes and the percentage change in the age-standardised rates (ASRs) per 100000 in the Middle East and North Africa region (Generated from data available from http://ghdx.healthdata.org/gbd-results-tool ). Figure S1. The percentage change in the age-standardised point prevalence of tension-type headache in the Middle East and North Africa region from 1990 to 2019, by sex and country. (Generated from data available from http://ghdx.healthdata.org/gbd-results-tool ). Figure S2. The percentage change in the age-standardised incidence of tension-type headache in the Middle East and North Africa region from 1990 to 2019, by sex and country. (Generated from data available from http://ghdx.healthdata.org/gbd-results-tool ). Figure S3. The percentage change in the age-standardised YLDs of tension-type headache in the Middle East and North Africa region from 1990 to 2019, by sex and country. YLD= years lived with disability. (Generated from data available from http://ghdx.healthdata.org/gbd-results-tool ).

This layer represents the percentage of total Disability-Adjusted Life Years (DALYs) attributable to the lack of access to handwashing facility in 2015, for 5 to 14 year-old males. One DALY can be thought of as one lost year of "healthy" life. The sum of DALYs across a population help to quantify the burden of disease, and to evaluate the gap between current health status and an ideal health situation. Data for other age ranges are also available in the table. Estimates and additional related resources can be found in the Global Burden of Study here: http://ghdx.healthdata.org/gbd-2015 For more information, visit the Institute for Health Metrics and Evaluation website: http://www.healthdata.org/gbd

This layer represents the percentage of total Disability-Adjusted Life Year attributable to typhoid fever for 15-49 year-old females in 2015. One DALY can be thought of as one lost year of "healthy" life. The sum of DALYs across a population help to quantify the burden of disease, and to evaluate the gap between current health status and an ideal health situation. Data for other age ranges are also available in the table. Estimates and additional related resources can be found in the Global Burden of Study here: http://ghdx.healthdata.org/gbd-2015 For more information, visit the Institute for Health Metrics and Evaluation website: http://www.healthdata.org/gbd

Context

This is the accident record data by country from 1990-2019. Following a discussion on Kaggle started by @rafaelriveravelez led me to think about this problem statement and whats better than getting more engineering done on this data and getting some useful insights.

Content

The total number of deaths from road traffic incidents, including vehicle drivers or passengers, motorcyclists, cyclists and pedestrians.

The Columns consists of:- | Column Name | Description | | --- | --- | | Entity | Name of the Country | | Code| ISO Country Code | | Year | Year for which the data is taken | | Deaths | Number of deaths due to Road injuries - Sex: Both - Age: All Ages (Number) | | Sidedness | The side where vehicle is driven. If 0 then Right if 1 then left |

Acknowledgements

DEATHS - ROAD INJURIES - SEX: BOTH - AGE: ALL AGES (NUMBER) Variable time span 1990 – 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. Data publisher's source Institute for Health Metrics and Evaluation Link http://ghdx.healthdata.org/gbd-results-tool Retrieved 2021-09-22

Inspiration

Question asked by @rafaelriveravelez, led me to get this dataset for his/her ease

License (as per From Our World Data)

All work produced by Our World in Data is free for you to take and use All the charts, maps, data, and text produced by Our World in Data are free for you to take and use — no permission required. You just need to provide credit to Our World in Data and our underlying sources (more details below). Our work is licensed under a very permissive ‘Creative Commons’ (CC) license: the CC-BY license (the BY stands for ‘by attribution’).

How can you tell what is produced by Our World in Data? Charts and maps produced by Our World in Data will have our logo on them. Data we produced will say in the sources section “Our World in Data based on…” or “Official data collated by Our World in Data.”

Note: In early 2019 we changed our Creative Commons license from “By Attribution-Share Alike” (CC-BY-SA) to “By Attribution” (CC-BY). Some of our static charts still have the CC-BY-SA mark in the bottom right corner. You can disregard this, and consider all our work as licensed under CC-BY.f

This layer represents the percentage of total Disability-Adjusted Life Year attributable to unsafe water sources for 15-49 year-old males in 2015. One DALY can be thought of as one lost year of "healthy" life. The sum of DALYs across a population help to quantify the burden of disease, and to evaluate the gap between current health status and an ideal health situation. Data for other age ranges are also available in the table.Estimates and additional related resources can be found in the Global Burden of Study here: http://ghdx.healthdata.org/gbd-2015 For more information, visit the Institute for Health Metrics and Evaluation website: http://www.healthdata.org/gbd

The Mortality and Welfare Costs from Exposure to Environmental Risks database provides data on health impacts and welfare costs from exposure to environmentally-related risks. This dataset integrates data on mortality and Disability-Adjusted Life Years (DALYs) from the Global Burden of Disease Study 2019 (GBD 2019), and quantifies welfare costs using a methodology adapted from the OECD (2017), The Rising Cost of Ambient Air Pollution thus far in the 21st Century: Results from the BRIICS and the OECD Countries. The database covers a wide range of environmental risks, including air pollution, climate-related threats, exposure to hazardous substances such as lead and radon, unsafe water and sanitation, environment-related occupational risks, and environment-related behavioral risks. The data are disaggregated by gender and age groups, providing a granular view of how these risks impact dimensions of the population.

Data source(s):

GBD (2019), Global Burden of Disease Study 2019 Results, Institute for Health Metrics and Evaluation, Seattle, United States. http://ghdx.healthdata.org/gbd-results-tool

GBD 2019 Risk Factor Collaborators (2020), Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019, The Lancet, Volume 396, Issue 10258, Pages 1223-1249. https://doi.org/10.1016/S0140-6736(20)30752-2. Online Appendix 1: https://ars.els-cdn.com/content/image/1-s2.0-S0140673620307522-mmc1.pdf

OECD (2017), The Rising Cost of Ambient Air Pollution thus far in the 21st Century: Results from the BRIICS and the OECD Countries, OECD Publishing, Paris. http://dx.doi.org/10.1787/d1b2b844-en

Contact: env.stat@oecd.org

Dataset release date: December 2020

For further details on the dataset, consult the Dataset documentation.

BackgroundThe burden of chronic respiratory diseases has changed over the three decades. This study aims to describe the spatiotemporal trends of prevalence, mortality, and disability-adjusted life years (DALY) due to chronic respiratory diseases (CRDs) worldwide during 1990–2019 using data from the Global Burden of Disease Study 2019 (GBD 2019).MethodsThe prevalence, mortality, and DALY attributable to CRDs and risk factors from 1990 to 2019 were estimated. We also assessed the driving factors and potentiality for improvement with decomposition and frontier analyses, respectively.ResultsIn 2019, 454.56 [95% uncertainty interval (UI): 417.35–499.14] million individuals worldwide had a CRD, showing a 39·8% increase compared with 1990. Deaths due to CRDs were 3.97 (95%UI: 3.58–4.30) million, and DALY in 2019 was 103.53 (95%UI: 94.79–112.27) million. Declines by average annual percent change (AAPC) were observed in age-standardized prevalence rates (ASPR) (0.64% decrease), age-standardized mortality rates (ASMR) (1.92%), and age-standardized DALY rates (ASDR) (1.72%) globally and in 5 socio-demographic index (SDI) regions. Decomposition analyses represented that the increase in overall CRDs DALY was driven by aging and population growth. However, chronic obstructive pulmonary disease (COPD) was the leading driver of increased DALY worldwide. Frontier analyses witnessed significant improvement opportunities at all levels of the development spectrum. Smoking remained a leading risk factor (RF) for mortality and DALY, although it showed a downward trend. Air pollution, a growing factor especially in relatively low SDI regions, deserves our attention.ConclusionOur study clarified that CRDs remain the leading causes of prevalence, mortality, and DALY worldwide, with growth in absolute numbers but declines in several age-standardized estimators since 1990. The estimated contribution of risk factors to mortality and DALY demands the need for urgent measures to improve them.Systematic review registrationhttp://ghdx.healthdata.org/gbd-results-tool.

This layer represents the percentage of total Disability-Adjusted Life Year attributable to unsafe water, sanitation and handwashing for 15-49 year-old males in 2015. One DALY can be thought of as one lost year of "healthy" life. The sum of DALYs across a population help to quantify the burden of disease, and to evaluate the gap between current health status and an ideal health situation. Data for other age ranges are also available in the table. Estimates and additional related resources can be found in the Global Burden of Study here: http://ghdx.healthdata.org/gbd-2015 For more information, visit the Institute for Health Metrics and Evaluation website: http://www.healthdata.org/gbd