To reduce deaths from stroke.

As of 2021, there were **** deaths per 100 hospital admissions for stroke among those aged 45 years and older in Latvia. The statistic shows the thirty-day mortality after admission to hospital for ischaemic stroke in selected OECD countries as of 2021, per 100 admissions among adults aged 45 years and older.

Strokes, also referred to as Cerebrovascular Disease, was the cause of ** deaths per 100,000 population in the United Kingdom in 2023. Since the beginning of the provided time interval, the year 2000, the mortality rate from strokes has more than halved in the UK.

To reduce deaths from stroke.

Strokes, also referred to as Cerebrovascular Disease, was the cause of ** deaths per 100,000 population in the United Kingdom in 2023. Scotland had the highest rate of mortality across the UK, with ** deaths from strokes per 100,000.

This dataset contains risk-adjusted 30-day mortality and 30-day readmission rates, quality ratings, and number of deaths / readmissions and cases for ischemic stroke treated in California hospitals. This dataset does not include ischemic stroke treated in outpatient settings.

This dataset documents rates and trends in heart disease and stroke mortality. Specifically, this report presents county (or county equivalent) estimates of heart disease and stroke death rates in 2000-2019 and trends during two intervals (2000-2010, 2010-2019) by age group (ages 35–64 years, ages 65 years and older), race/ethnicity (non-Hispanic American Indian/Alaska Native, non-Hispanic Asian/Pacific Islander, non-Hispanic Black, Hispanic, non-Hispanic White), and sex (women, men). The rates and trends were estimated using a Bayesian spatiotemporal model and a smoothed over space, time, and demographic group. Rates are age-standardized in 10-year age groups using the 2010 US population. Data source: National Vital Statistics System.

2021 to 2023, 3-year average. Rates are age-standardized. County rates are spatially smoothed. The data can be viewed by sex and racial/ethnic group. Data source: National Vital Statistics System. Additional data, maps, and methodology can be viewed on the Interactive Atlas of Heart Disease and Stroke

2019 to 2021, 3-year average. Rates are age-standardized. County rates are spatially smoothed. The data can be viewed by sex and race/ethnicity. Data source: National Vital Statistics System. Additional data, maps, and methodology can be viewed on the Interactive Atlas of Heart Disease and Stroke https://www.cdc.gov/heart-disease-stroke-atlas/about/index.html

In 2022, Delaware had the highest rate of death due to stroke of any U.S. state, with around 57 deaths per 100,000 population. This statistic shows the death rate for stroke in the United States in 2022, by state.

To reduce deaths from stroke.

This statistic displays the number of deaths from stroke in England and Wales in 2022, by gender and age. In this year, over 3.8 thousand women aged 85 years and over died of stroke in England and Wales, compared to two thousand men of the same age.

In 2017, the mortality rate from all types of stroke worldwide was **** deaths per 100,000 population. This statistic shows the global age-standardized stroke mortality rate in 1990 and 2017, by stroke type.

Dataset from Ministry of Health. For more information, visit https://data.gov.sg/datasets/d_e3f0c53137e5ee11b6df121ec4987bb7/view

Create maps of U.S. stroke death rates by county. Data can be stratified by age, race/ethnicity, and sex. Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I60-I69; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.' Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)   RRR: 3 digits represent race/ethnicity     All - Overall     AIA - American Indian and Alaska Native, non-Hispanic     API - Asian and Pacific Islander, non-Hispanic     BLK - Black, non-Hispanic     HIS - Hispanic     WHT - White, non-Hispanic   S: 1 digit represents sex     A - All    F - Female     M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound.  Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria: At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

Stroke Mortality Rates in the US (Age-Standardized) 2012-2014

State/Territory and County Data

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains primary stroke mortality data from 2012 to 2014 among US adults aged 35+ across all states/territories and counties. Data is age-standardized and county rates are spatially smoothed to provide a better and more accurate view of the prevalence of mortality due to stroke. The data evaluation can be further divided by gender, race/ethnicity, stratification category 1, stratification 1, stratification category 2, or stratification 2. All data is sourced from the National Vital Statistics System (NVSS) ensuring it's accuracy and reliability. For even more information regarding heart disease related deaths as well as methodology employed in mapping such occurrences visit the Interactive Atlas of Heart Disease and Stroke. Looking deeper into these numbers may reveal hidden trends that could lead us closer towards reducing stroke related mortality in adults across our nation!

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How to use the dataset

The U.S. Stroke Mortality Rates (Age-Standardized) 2012-2014 dataset provides stroke mortality rates for adults aged 35 and over living in the United States from 2012 to 2014. This dataset is an ideal resource for examining the impact of stroke at a local or national level.

This guide will provide an introduction to understanding and using this data correctly, as well as highlighting some potential areas of investigation it may be used for:

• Understanding the Context: The first step towards understanding this data is to take a close look at its features and categories. These include year, location, geography level, data source, class, topic, value type/unit/ footnote symbol and stratification category/stratification which allow you to view data through multiple ways (e.g., by age group or by race).

  You can also filter your results with these attributes including specific years or different locations in order explore particular conditions within a certain area or year range (e.g., how many stroke related deaths occurred among blacks in California between 2012 – 2014?). It’s important to note that all county age-standardized rates are spatially smoothed — meaning each county rate is adjusted taking into account nearby counties — so the results you get might reflect wider regional trends more than actual localized patterns associated with individual counties.)

• Accessing & Previewing Data: Once you have familiarised yourself with the library concept behind this dataset it’s time access it's contents directly! To download your desired subset inside Kaggle platform just open up csv file titled 'csv- 1'. Alternatively ,you can use other open source tools such as Exasol Analytic Database technology (available on built-in 'notebook' feature) if you want work on even larger datasets with more processing power come into play ! Inside visualization tab users will be able view chart graphs( pie charts histograms etc ) from their query results .And once completed feel free export their respective visuals SVG PNG PDF formats too .

• Finding Answers: With all these processes complete ,you now should have plenty of datasets ready go in advance - great start but what does story tell us ? Well break things down compare different groups slices look at correlations trends deviations across various demographic filters questions about causal effects become much easier answer ! Leave creative freedom your side let those numbers feel ! So try pose some interesting interesting hypothesis determine how above factors could change across different states spend hours going through wealth

Research Ideas

• Utilizing location-specific stroke mortality data to pinpoint areas that need targeted public health interventions and outreach.
• Analyzing the correlation between age-standardized stroke mortality rates and demographic data, such as gender, race/ethnicity or socioeconomic status.
• Creating strategies focused on reducing stroke mortality in high risk demographic groups based on findings from the datasets geographical and sociological analysis tools

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description ...

As stroke mortality rates according to race were not known in Brazil, data on mortality for the year 2010 was collected from the Mortality Information System of the Brazilian Ministry of Health. Cerebrovascular mortality rates adjusted for age (per 100,000) were calculated with a confidence interval of 95% (95%CI) by sex and race/skin color. The differences between races were significant for men with rates of 44.4 (43.5;45.3), 48.2 (47.1;49.3) and 63.3 (60.6;66.6) for white, brown and black, respectively; and for women, with rates of 29.0 (28.3;29.7), 33.7 (32.8;34.6) and 51.0 (48.6;53.4) for white, brown and black, respectively. The burden of stroke mortality is higher among blacks compared to brown and white.

This dataset presents the under-75 mortality rate from stroke, a key indicator within the cardiovascular health domain. It captures the rate of deaths attributed to stroke among individuals aged under 75, using data classified under ICD-10 codes I60 to I69. The dataset is structured to support public health monitoring and policy development by providing age-standardised mortality rates per 100,000 population.

Rationale Reducing premature mortality from stroke is a public health priority. Monitoring this indicator helps assess the effectiveness of prevention strategies, healthcare interventions, and broader determinants of health. It supports efforts to reduce health inequalities and improve outcomes for cardiovascular conditions.

Numerator The numerator is the number of deaths from stroke (ICD-10 codes I60 to I69) registered in the respective calendar years.

Denominator For single-year rates, the denominator is the population of individuals aged under 75, aggregated into quinary age bands. For three-year rolling averages, it is the population-years (combined populations over three years) for the same age range and structure. Population estimates are based on the 2021 Census.

Caveats Data may not align precisely with figures published by the Office for National Statistics (ONS) due to differences in postcode lookup versions and the application of comparability ratios in the Office for Health Improvement and Disparities (OHID) data. Users should consider these factors when interpreting the results.

External references Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

This dataset tracks the updates made on the dataset "Stroke Mortality Data Among US Adults (35+) by State/Territory and County – 2021-2023" as a repository for previous versions of the data and metadata.

In 2021, it was estimated that the Solomon Islands had the highest death rate from stroke worldwide, with around 233 deaths per 100,000 population. In 2021, stroke was one of the leading causes of death worldwide, resulting in almost seven million deaths.