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

What is heart disease?

The term “heart disease” refers to several types of heart conditions. The most common type of heart disease in the United States is coronary artery disease (CAD), which affects the blood flow to the heart. Decreased blood flow can cause a heart attack. What are the symptoms of heart disease?

Sometimes heart disease may be “silent” and not diagnosed until a person experiences signs or symptoms of a heart attack, heart failure, or an arrhythmia. When these events happen, symptoms may include1

Heart attack: Chest pain or discomfort, upper back or neck pain, indigestion, heartburn, nausea or vomiting, extreme fatigue, upper body discomfort, dizziness, and shortness of breath.
Arrhythmia: Fluttering feelings in the chest (palpitations).
Heart failure: Shortness of breath, fatigue, or swelling of the feet, ankles, legs, abdomen, or neck veins.

Learn the Facts About Heart Disease

About 697,000 people in the United States died from heart disease in 2020—that’s 1 in every 5 deaths.1,2

Learn more facts. What are the risk factors for heart disease?

High blood pressure, high blood cholesterol, and smoking are key risk factors for heart disease. About half of people in the United States (47%) have at least one of these three risk factors.2 Several other medical conditions and lifestyle choices can also put people at a higher risk for heart disease, including

Diabetes
Overweight and obesity
Unhealthy diet
Physical inactivity
Excessive alcohol use

Learn about how heart disease and mental health disorders are related.

Learn more about heart disease, heart attack, and related conditions:

Coronary Artery Disease
Heart Attack
Men and Heart Disease
Women and Heart Disease
Other Related Conditions

What is cardiac rehabilitation?

Cardiac rehabilitation (rehab) is an important program for anyone recovering from a heart attack, heart failure, or some types of heart surgery. Cardiac rehab is a supervised program that includes

Physical activity
Education about healthy living, including healthy eating, taking medicine as prescribed, and ways to help you quit smoking
Counseling to find ways to relieve stress and improve mental health

A team of people may help you through cardiac rehab, including your health care team, exercise and nutrition specialists, physical therapists, and counselors or mental health professionals. Heart Disease Quiz

Test your knowledge of heart disease!

CDC’s Public Health Efforts Related to Heart Disease

State Public Health Actions to Prevent and Control Chronic Diseases
Million Hearts®
WISEWOMAN

More Information

American Heart Association
National Heart, Lung, and Blood Institute

References

Centers for Disease Control and Prevention, National Center for Health Statistics. About Multiple Cause of Death, 1999–2020. CDC WONDER Online Database website. Atlanta, GA: Centers for Disease Control and Prevention; 2022. Accessed February 21, 2022.
Tsao CW, Aday AW, Almarzooq ZI, Beaton AZ, Bittencourt MS, Boehme AK, et al. Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association. Circulation. 2022;145(8):e153–e639.
Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart disease and stroke statistics—2021 update: a report from the American Heart Association. Circulation. 2021;143:e254–e743.

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.

2018 2020, county-level U.S. heart disease death rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. heart disease 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: I00-I09, I11, I13, I20-I51; 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

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of coronary heart disease (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 coronary heart disease (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) with coronary heart disease 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 with coronary heart disease 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 with coronary heart disease, 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 coronary heart diseaseB) the NUMBER of people within that MSOA who are estimated to have coronary heart diseaseAn 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 coronary heart disease, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from coronary heart disease, and where those people make up a large percentage of the population, indicating there is a real issue with coronary heart disease 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 coronary heart disease, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of coronary heart disease.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.

Cardiovascular diseases (CVDs) are the leading cause of death in India, with heart attacks (myocardial infarctions) accounting for a significant portion. India has a higher heart disease burden than many other nations, with cases occurring at younger ages compared to Western countries. This dataset incorporates key medical and lifestyle risk factors such as diabetes, hypertension, obesity, smoking, air pollution exposure, and healthcare access.

With a diverse representation across India's states, the dataset reflects the urban-rural disparity in healthcare, lifestyle patterns, and emergency response times. It can be used for predictive modeling, machine learning applications, epidemiological research, and policy analysis to improve early detection and intervention strategies for heart disease.

Quick Start 🚀: If you're not up for reading all of this, head straight to the file section. There, you'll find detailed explanations of the files and all the variables you need.

This dataset contains the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile has 13 clinical features.

Dataset Characteristics: Multivariate

Subject Area: Health and Medicine

Associated Tasks: Classification, Regression, Clustering

Feature Type: Integer, Real

Instances: 299

Features: 12

Dataset Information

A detailed description of the dataset can be found in the Dataset section of the following paper:

Title: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Authors:

Davide Chicco Giuseppe Jurman Source: BMC Medical Informatics and Decision Making 20, 16 (2020)

DOI: https://doi.org/10.1186/s12911-020-1023-5

Dataset Details

Statistical quantitative description of the category features

number of patients. %: percentage of patients. Full sample: 299 individuals. Dead patients: 96 individuals. Survived patients: 203 individuals.

Number of deaths caused by diseases of the circulatory system, by age group and sex, 2000 to most recent year.

Update 2 March 2023: Following the merger of NHS Digital and NHS England on 1st February 2023 we are reviewing the future presentation of the NHS Outcomes Framework indicators. As part of this review, the annual publication which was due to be released in March 2023 has been delayed. Further announcements about this dataset will be made on this page in due course. Directly standardised mortality rate from cardiovascular disease for people aged under 75, per 100,000 population. To ensure that the NHS is held to account for doing all that it can to prevent deaths from cardiovascular disease in people under 75. Some different patterns have been observed in the 2020 mortality data which are likely to have been impacted by the coronavirus (COVID-19) pandemic. Statistics from this period should also be interpreted with care. Legacy unique identifier: P01730

This Dataset is for HME Workshop in Oct 3, 2020

Introduction

World Health Organization has estimated 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the United States and other developed countries are due to cardio vascular diseases. The early prognosis of cardiovascular diseases can aid in making decisions on lifestyle changes in high risk patients and in turn reduce the complications. This research intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using logistic regression Data Preparation

Task

The task is to predict whether patient have 10 year risk of coronary heart disease CHD or not. Additionally, participants also asked to create some data visualization about the data to gained actionable insight about the topic.

Source

The dataset is publically available on the Kaggle website, and it is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD).The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes. Variables Each attribute is a potential risk factor. There are both demographic, behavioral and medical risk factors.

Data Description

Demographic: • Sex: male or female("M" or "F") • Age: Age of the patient;(Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous) Behavioral • is_smoking: whether or not the patient is a current smoker ("YES" or "NO") • Cigs Per Day: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.) Medical( history) • BP Meds: whether or not the patient was on blood pressure medication (Nominal) • Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal) • Prevalent Hyp: whether or not the patient was hypertensive (Nominal) • Diabetes: whether or not the patient had diabetes (Nominal) Medical(current) • Tot Chol: total cholesterol level (Continuous) • Sys BP: systolic blood pressure (Continuous) • Dia BP: diastolic blood pressure (Continuous) • BMI: Body Mass Index (Continuous) • Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.) • Glucose: glucose level (Continuous) Predict variable (desired target) • 10 year risk of coronary heart disease CHD(binary: “1”, means “Yes”, “0” means “No”)

2018 - 2020, 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/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

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

No. of Deaths: Caused by: All Other Forms of Heart Disease data was reported at 1,259.000 Person in Sep 2024. This records a decrease from the previous number of 1,378.000 Person for Jun 2024. No. of Deaths: Caused by: All Other Forms of Heart Disease data is updated quarterly, averaging 1,141.500 Person from Mar 2017 (Median) to Sep 2024, with 30 observations. The data reached an all-time high of 1,378.000 Person in Jun 2024 and a record low of 918.000 Person in Jun 2020. No. of Deaths: Caused by: All Other Forms of Heart Disease data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G012: Number of Deaths: Cause of Death.

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.

Context

Cardiovascular disease (CVD) is the most common cause of morbidity and mortality among men and women globally. An estimated 18 million deaths are reported from CVDs annually, representing nearly a third of all global deaths. Most of these deaths (85%) are due to heart attack and stroke. Every three in four CVD deaths happen in low and middle-income countries.

[World Health Organization](https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) defines CVDs as a group of disorders of the heart and blood vascular system including but not limited to:

* Coronary heart disease
* Cerebrovascular disease 
* Peripheral heart disease
* Rheumatic heart disease
* Congential heart disease
* Deep vein thrombosis and pulmonary embolism 

Dataset information

Age: age in years. Sex: sex (1=male; 0=female). Cp: chest pain type (0 = typical angina; 1 = atypical angina; 2 = non-anginal pain; 3: asymptomatic). Trestbps: resting blood pressure in mm Hg on admission to the hospital. Chol: serum cholesterol in mg/dl. fbs: fasting blood sugar > 120 mg/dl (1=true; 0=false). Restecg: resting electrocardiographic results ( 0=normal; 1=having ST-T wave abnormality; 2=probable or definite left ventricular hypertrophy). Thalach: maximum heart rate achieved. Exang: exercise-induced angina (1=yes; 0=no). Oldpeak: ST depression induced by exercise relative to rest. Slope: the slope of the peak exercise ST segment (0=upsloping; 1=flat; 2=downsloping). Ca: number of major vessels (0–3) colored by fluorosopy. Thal: thalassemia (3=normal; 6=fixed defect; 7=reversable defect). Target: heart disease (1=no, 2=yes).

Acknowledgements

I would like to thank my fellow Kagglers @rahulgulia and @taylormartin94 for inspiration in conducting the analysis. Also much thanks to Analytics Vidhya for providing amazing resources for conducting the analysis.

**Inspiration**

With the advancement in data science and machine learning - we should be able to employ the newest and most robust methods to predict the future risk of cardiovascular disease. Further web development will allow tailoring research findings to policymakers and the public.

This dataset includes count and age-adjusted rate per 100,000 population of mortality (death) in Virginia for 9 chronic conditions by year and by demographic groups (i.e., age, race/ethnicity, and sex). Age group values include 0 to 17 years, 18 to 44 years, 45 to 54 years, 55 to 64 years, 65 to 74 years, and 75+ years. Race/ethnicity values include American Indian or Alaska Native, Asian or Pacific Islander, Black or African American, Hispanic or Latino, and White. Sex values include female and male. Data set includes mortality data from 2016 to the most current year for Virginia residents.

The 9 chronic conditions include: Alzheimer’s Disease, Cardiovascular disease, Chronic Kidney Disease, Chronic Obstructive Pulmonary Disease, Asthma, Diabetes, Stroke, Heart Disease, and Hypertension. The International Classification of Diseases, Tenth Revision (ICD-10) codes are used to identify chronic disease mortality indicators. Definitions are based on Underlying Cause of Death on the death certificate outlined in the “Underlying Cause-of-Death List for Tabulating Mortality Statistics” instruction manual developed by the National Center for Health Statistics at the Centers for Disease Control and Prevention (CDC) found here OCR Document (cdc.gov).

This dataset includes count and age-adjusted rate per 100,000 population of mortality (death) for 9 chronic conditions by year and by geography (i.e., the state and 35 health districts). Data set includes mortality data from 2016 to the most current year for Virginia residents.

The 9 chronic conditions include: Alzheimer’s Disease, Cardiovascular disease, Chronic Kidney Disease, Chronic Obstructive Pulmonary Disease, Asthma, Diabetes, Stroke, Heart Disease, and Hypertension. The International Classification of Diseases, Tenth Revision (ICD-10) codes are used to identify chronic disease mortality indicators. Definitions are based on Underlying Cause of Death on the death certificate outlined in the “Underlying Cause-of-Death List for Tabulating Mortality Statistics” instruction manual developed by the National Center for Health Statistics at the Centers for Disease Control and Prevention (CDC) found on OCR Document (cdc.gov).

Series Name: Number of deaths attributed to non-communicable diseases by type of disease and sex (number)Series Code: SH_DTH_RNCOMRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory diseaseTarget 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-beingGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

This dataset presents annual statistics on the number of deaths in the State of Qatar, categorized by major causes such as road traffic accidents, cardiovascular diseases, neoplasms (cancers), diabetes, and respiratory conditions. The data is structured by year and type of cause, supporting analysis of public health trends, disease burden, and road safety indicators. It is a valuable source for policy development, healthcare planning, and monitoring national health strategies.

US Age-Standardized Stroke Mortality Rates (2013-15) by State/County/Gender/Race

Investigating Variations in Rates

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

About this dataset

This dataset contains the age-standardized stroke mortality rate in the United States from 2013 to 2015, by state/territory, county, gender and race/ethnicity. The data source is the highly respected National Vital Statistics System. The rates are reported as a 3-year average and have been age-standardized. Moreover, county rates are spatially smoothed for further accuracy. The interactive map of heart disease and stroke produced by this dataset provides invaluable information about the geographic disparities in stroke mortality across America at different scales - county, state/territory and national. By using the adjustable filter settings provided in this interactive map, you can quickly explore demographic details such as gender (Male/Female) or race/ethnicity (e.g Non-Hispanic White). Conquer your fear of unknown with evidence! Investigate these locations now to inform meaningful action plans for greater public health resilience in America and find out if strokes remain a threat to our millions of citizens every day! Updated regularly since 2020-02-26, so check it out now!

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How to use the dataset

The US Age-Standardized Stroke Mortality Rates (2013-2015) by State/County/Gender/Race dataset provides valuable insights into stroke mortality rates among adults ages 35 and over in the USA between 2013 and 2015. This dataset contains age-standardized data from the National Vital Statistics System at the state, county, gender, and race level. Use this guide to learn how best use this dataset for your purposes!

Understand the Data

This dataset provides information about stroke mortality rates among adult Americans aged 35+. The data is collected from 2013 to 2015 in three year averages. Even though it is possible to view county level data, spatial smoothing techniques have been applied here. The following columns of data are provided: - Year – The year of the data collection - LocationAbbr – The abbreviation of location where the data was collected
- LocationDesc – A description of this location
- GeographicLevel – Geographic level of granularity where these numbers are recorded * DataSource - source of these statistics * Class - class or group into which these stats fall * Topic - overall topic on which we have stats * Data_Value - age standardized value associated with each row * Data_Value_Unit - units associated with each value * Stratification1– First stratification defined for a given row * Stratification2– Second stratification defined for a given row

Additionally, several other footnotes fields such as ‘Data_value_Type’; ‘Data_Value_Footnote _Symbol’; ‘StratificationCategory1’ & ‘StratificatoinCategory2’ etc may be present accordingly .  

## Exploring Correlations

Now that you understand what individual columns mean it should take no time to analyze correlations within different categories using standard statistical methods like linear regressions or boxplots etc. If you want to compare different regions , then you can use LocationAbbr column with locations reduced geographical levels such as State or Region. Alternatively if one wants comparisons across genders then they can refer column labelled Stratifacation1 alongwith their desired values within this

Research Ideas

• Creating a visualization to show the relationship between stroke mortality and specific variations in race/ethnicity, gender, and geography.
• Comparing two or more states based on their average stroke mortality rate over time.
• Building a predictive model that disregards temporal biases to anticipate further changes in stroke mortality for certain communities or entire states across the US

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 | |:--...