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

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

This statistic shows the number of people having died from a stroke in the U.S. from 1960 to 2005, per 100,000 inhabitants. In 2000, there were 43.2 stroke deaths in the U.S. per 100,000 of the population.

Strokes, also referred to as Cerebrovascular Disease, was the cause of 51 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.

This dataset documents cardiovascular disease (CVD) death rates, relative and absolute excess death rates, and trends. Specifically, this report presents county (or county equivalent) estimates of CVD death rates in 2000-2020, trends during 2010-2019, and relative and absolute excess death rates in 2020 by age group (ages 35–64 years, ages 65 years and older). All estimates were generated using a Bayesian spatiotemporal model and a smoothed over space, time, and 10-year age groups. Rates are age-standardized in 10-year age groups using the 2010 US population. Data source: National Vital Statistics System.

From 2020 to 2022, 4.3 percent of Black or African Americans reported having a stroke. During this time, American Indians or Alaska Natives had the highest stroke rate, with 5.3 percent reporting a stroke. This statistic displays the percentage of adults in the U.S. who were told by a doctor they had a stroke from 2020 to 2022, by ethnicity.

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

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

From 2017 to 2020, around 14 percent of males and females in the United States aged 80 years and older suffered from a stroke. This statistic shows the prevalence of stroke among U.S. adults from 2017 to 2020, by age and gender.

From 2019 to 2021, Mississippi had the highest rate of death due to stroke of any U.S. state, with around 55 deaths per 100,000 population. This statistic shows the death rate for stroke in the United States for the period from 2019 to 2021, by state.

2000–2020. NVSS is a secure, web-based data management system that collects and disseminates the Nation's official vital statistics. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This was one of the datasets provided by the National Cardiovascular Disease Surveillance System and presented on DHDSP’s Data, Trends, and Maps online tool. This tool was retired in April of 2024 and this dataset will not be updated. Contact dhdsprequests@cdc.gov if you need assistance with data previously included in this dataset. The data are organized by location (national and state) and indicator; NVSS mortality data include CVDs (e.g., heart failure). The data can be viewed by temporal trends and stratified by age group, sex, and race/ethnicity.

The dataset contains risk-adjusted mortality rates, quality ratings, 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 5 procedures performed (Abdominal Aortic Aneurysm Repair, Unruptured/Open, Abdominal Aortic Aneurysm Repair, Unruptured/Endovascular, Carotid Endarterectomy, Pancreatic Resection, Percutaneous Coronary Intervention) in California hospitals. The 2022 IMIs were generated using AHRQ Version 2023, while previous years' IMIs were generated with older versions of AHRQ software (2021 IMIs by Version 2022, 2020 IMIs by Version 2021, 2019 IMIs by Version 2020, 2016-2018 IMIs by Version 2019, 2014 and 2015 IMIs by Version 5.0, and 2012 and 2013 IMIs by 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 statewide table for California overall rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings/resource/af88090e-b6f5-4f65-a7ea-d613e6569d96

Between 2020 and 2022, around 7.7 percent of individuals aged 65 years and older reported that they had a stroke, compared to just 0.9 percent of those aged 18 to 44 years. This statistic displays the percentage of adults in the U.S. who were told by a doctor they had a stroke between 2020 and 2022, by age.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of stroke and transient ischaemic attack (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to stroke and transient ischaemic attack (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) to have suffered a stroke or transient ischaemic attack was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population to have suffered a stroke or transient ischaemic attack was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA who have suffered a stroke or transient ischaemic attack, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have had a stroke or transient ischaemic attackB) the NUMBER of people within that MSOA who are estimated to have had a stroke or transient ischaemic attackAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have had a stroke or transient ischaemic attack, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from stroke and transient ischaemic attack, and where those people make up a large percentage of the population, indicating there is a real issue with stroke and transient ischaemic attack within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of stroke and transient ischaemic attack, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of stroke and transient ischaemic attack.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

We provide the raw data used for the following article:

Scrutinio D, Lanzillo B, Guida P, Passantino A, Spaccavento S, Battista P. Association Between Malnutrition and Outcomes in Patients With Severe Ischemic Stroke Undergoing Rehabilitation "Arch Phys Med Rehabil." 2020 May;101(5):852-860. doi: 10.1016/j.apmr.2019.11.012. Epub 2019 Dec 28. PMID: 31891712.

Abstract Objective: To investigate the incremental prognostic significance of malnutrition in patients with severe poststroke disability. Design: Retrospective cohort study. The patients were recruited from 3 specialized inpatient rehabilitation facilities. Nutritional status was assessed using the Prognostic Nutritional Index (PNI), which is calculated from serum albumin and total lymphocyte count. Scores >38 points reflect normal nutrition status, scores of 35-38 indicate moderate malnutrition, and scores <35 indicate severe malnutrition. The association of PNI categories with outcomes was assessed using multivariable regression analyses. Setting: Inpatient rehabilitation facility. Participants: Patients (NZ668) with ischemic stroke admitted to inpatient rehabilitation within 90 days from stroke occurrence and classified as Case-Mix Groups 0108, 0109, and 0110 of the current Medicare case-mix classification system. Interventions: Not applicable. Main Outcome Measures: Three outcomes were examined: (1) the combined outcome of transfer to acute care and death within 90 days from admission to rehabilitation; (2) 2-year mortality; and (3) FIM motor effectiveness, calculated as (FIM motor change/maximum FIM motoradmission FIM motor score)100. Results: Overall, the median time to rehabilitation admission was 18 days (range, 12-26 days). The prevalence of moderate and severe malnutrition was 12.7% and 11.5%, respectively. Ninety-one patients (13.6%) experienced the combined outcome. After adjusting for independent predictors including sex, atrial fibrillation, dysphagia, FIM cognitive score, and hemoglobin levels, neither moderate (PZ.280) nor severe malnutrition (PZ.482) were associated with the combined outcome. Similar results were observed when looking at 2-year mortality. Overall, FIM motor effectiveness was 30%24%. After adjusting for independent predictors, severe malnutrition (b coefficient 0.4580.216; PZ.034) was associated with FIM motor effectiveness. Conclusions: Approximately 1 in every 9 patients presented severe malnutrition. On top of the independent predictors, severe malnutrition did not provide additional prognostic information concerning risk of the combined outcome or 2-year mortality. Conversely, severe malnutrition was associated with poorer functional outcome as expressed by FIM motor effectiveness.

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.