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.

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.

Strokes, also referred to as Cerebrovascular Disease, was the cause of ** deaths per 100,000 population in the United Kingdom in 2022. Scotland had the highest rate of mortality across the UK, with ** deaths from strokes per 100,000.

2018 to 2020, 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

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

Strokes, also referred to as Cerebrovascular Disease, was the cause of ** deaths per 100,000 population in the United Kingdom in 2022. Since the beginning of the provided time interval, the year 2000, the mortality rate from strokes has more than halved in the UK.

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

2020 - 2022, county-level U.S. stroke death rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. stroke death rates by county. Data can be stratified by age, race/ethnicity, and sex.Visit the CDC 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 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    ASN - Asian, non-Hispanic    BLK - Black, non-Hispanic    HIS - Hispanic NHP – Native Hawaiian or Other Pacific Islander, non-Hispanic MOR – More than one race, non-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

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

2015 to 2017, 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. http://www.cdc.gov/dhdsp/maps/atlas

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.

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.

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

This dataset tracks the updates made on the dataset "Stroke Mortality Data Among US Adults (35+) by State/Territory and County – 2015-2017" as a repository for previous versions of the data and metadata.

The dataset contains risk-adjusted mortality rates, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 6 procedures performed (Abdominal Aortic Aneurysm Repair, Carotid Endarterectomy, Craniotomy, Esophageal Resection, Pancreatic Resection, Percutaneous Coronary Intervention) in California hospitals. The 2014 and 2015 IMIs were generated using AHRQ Version 5.0, while the 2012 and 2013 IMIs were generated using AHRQ Version 4.5. The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to hospital table for hospital rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings

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.

Despite recent advances in care, stroke remains a life-threatening disease. Little is known about current hospital mortality with stroke and how it varies by hospital in a national clinical setting in Japan. Using the Diagnosis Procedure Combination database (a national inpatient database in Japan), we identified patients aged ≥20 years who were admitted to the hospital with a primary diagnosis of stroke within 3 days of stroke onset from April 2012 to March 2013. We constructed a multivariable logistic regression model to predict in-hospital death for each patient with patient-level factors, including age, sex, type of stroke, Japan Coma Scale, and modified Rankin Scale. We defined risk-standardized mortality ratio as the ratio of the actual number of in-hospital deaths to the expected number of such deaths for each hospital. A hospital-level multivariable linear regression was modeled to analyze the association between risk-standardized mortality ratio and hospital-level factors. We performed a patient-level Cox regression analysis to examine the association of in-hospital death with both patient-level and hospital-level factors. Of 176,753 eligible patients from 894 hospitals, overall in-hospital mortality was 10.8%. The risk-standardized mortality ratio for stroke varied widely among the hospitals; the proportions of hospitals with risk-standardized mortality ratio categories of ≤0.50, 0.51–1.00, 1.01–1.50, 1.51–2.00, and >2.00 were 3.9%, 47.9%, 41.4%, 5.2%, and 1.5%, respectively. Academic status, presence of a stroke care unit, higher hospital volume and availability of endovascular therapy had a significantly lower risk-standardized mortality ratio; distance from the patient’s residence to the hospital was not associated with the risk-standardized mortality ratio. Our results suggest that stroke-ready hospitals play an important role in improving stroke mortality in Japan.