The number of deaths caused by heart disease has decreased in the United States from 321.8 per 100,000 population in 1990 to 161.5 deaths per 100,000 population in 2019. Nevertheless, heart disease is still the leading cause of death in the country, followed closely by cancer, which has a mortality rate of 146.2 per 100,000 people.

Heart disease in the U.S.

Diseases of the heart and blood vessels are often associated with atherosclerosis which occurs when plaque builds up along arterial walls. This can limit the flow of blood and can lead to blood clots, a common cause of stroke or heart attacks. Other types of heart disease include arrhythmia (abnormal heart rhythms) and heart valve problems. Many of these diseases can be treated with medication, although many complications will still remain. One of the leading cholesterol lowering drugs in the United States, Crestor, generated around 1.2 billion U.S. dollars of revenue in 2021.

Risk Factors for heart disease There are many risk factors associated with the development of heart disease including family history, ethnicity, and age. However, there are other factors that can be modified through lifestyle changes such as physical inactivity, smoking, and unhealthy diets. Obesity has also been commonly associated with risk factors like hypertension and diabetes type II. In the U.S., some 30 percent of white adults are currently obese.

In 2022, the states with the highest death rates due to heart disease were Oklahoma, Mississippi, and Alabama. That year, there were around 257 deaths due to heart disease per 100,000 population in the state of Oklahoma. In comparison, the overall death rate from heart disease in the United States was 167 per 100,000 population. The leading cause of death in the United States Heart disease is the leading cause of death in the United States, accounting for 21 percent of all deaths in 2022. That year, cancer was the second leading cause of death, followed by unintentional injuries and COVID-19. In the United States, a person has a one in six chance of dying from heart disease. Death rates for heart disease are higher among men than women, but both have seen steady decreases in heart disease death rates since the 1950s. What are risk factors for heart disease? Although heart disease is the leading cause of death in the United States, the risk of heart disease can be decreased by avoiding known risk factors. Some of the leading preventable risk factors for heart disease include smoking, heavy alcohol use, physical inactivity, an unhealthy diet, and being overweight or obese. It is no surprise that the states with the highest rates of death from heart disease are also the states with the highest rates of heart disease risk factors. For example, Oklahoma, the state with the highest heart disease death rate, is also the state with the third-highest rate of obesity. Furthermore, Mississippi is the state with the highest levels of physical inactivity, and it has the second-highest heart disease death rate in the United States.

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.

In 2019, almost 205 men died from heart diseases per 100,000 population, a significant decrease from the rate of 699 recorded for the year 1950. This statistic shows the death rate for heart diseases among men in the U.S. from 1950 to 2019, per 100,000 population.

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

Years of life lost due to mortality from coronary heart disease (ICD-10 I20-I25). Years of life lost (YLL) is a measure of premature mortality. Its primary purpose is to compare the relative importance of different causes of premature death within a particular population and it can therefore be used by health planners to define priorities for the prevention of such deaths. It can also be used to compare the premature mortality experience of different populations for a particular cause of death. The concept of years of life lost is to estimate the length of time a person would have lived had they not died prematurely. By inherently including the age at which the death occurs, rather than just the fact of its occurrence, the calculation is an attempt to better quantify the burden, or impact, on society from the specified cause of mortality. Legacy unique identifier: P00319

These data represent the predicted (modeled) prevalence of Coronary Heart Disease among adults (Age 18+) for each census tract in Colorado. Coronary Heart Disease is defined as ever being diagnosed by a doctor, nurse, or other health professional with either Angina or Coronary Heart Disease.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

In 2022, it was estimated that around 15.3 percent of those aged 65 years and older in the United States had been diagnosed with coronary heart disease. This statistic shows the percentage of U.S. adults aged 65 years and older who had ever been told by a doctor or other health professional they had coronary heart disease from 2019 to 2022.

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.

Rate: Age-adjusted rate of deaths due to coronary heart disease per 100,000 population.

Definition: deaths with coronary heart disease as the underlying cause (ICD-10 codes: I11, I20-I25).

Data Sources:

(1) Death Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health

(2) Population Estimates, State Data Center, New Jersey Department of Labor and Workforce Development.

BackgroundIschemic heart disease (IHD) is a leading cause of death and disability, particularly affecting the older adult population. Extreme temperatures, especially very low and very high temperatures, are known to exacerbate cardiovascular disease burden. With the ongoing global climate change, understanding the impact of non-optimal temperatures on IHD burden becomes increasingly important, especially in vulnerable populations such as the older adult.MethodsThis study used data from the Global Burden of Disease Study 2021 (GBD 2021) to analyze the spatiotemporal trends of low and high temperatures on IHD burden in the older adult population (aged 60 and above) from 1990 to 2021. We used age-standardized rates (ASR), annual percentage change (EAPC), and the Bayesian age-period-cohort (BAPC) model to forecast 2050. Additionally, the geographic differences in IHD burden were analyzed using World Bank regions.ResultsFrom 1990 to 2021, the IHD burden in the older adult population was mainly attributed to low temperatures. However, it has increased the burden of IHD due to high temperatures, especially in tropical and low-income regions. The analysis of gender difference revealed that men are usually more affected by high temperatures, though generally, women are more sensitive to low temperatures. Forecasts are that in the future, the burden of IHD due to high temperatures will continue to rise, especially in areas with limited adaptive capacity.ConclusionAlthough low temperature remains the most important contributor to IHD burden among the older adult, the burden attributable to high temperature is on the rise, which increases the need to address the extreme temperature fluctuation. That is more so in poor-income and tropical regions where the most vulnerable populations bear a higher risk for health. Thus, there is an urgent need to develop adaptive public health measures against the dual health risks from extreme temperatures. The findings emphasize that targeted interventions are necessary, with adjustments in regional differences and gender-specific risks to effectively address the growing health threats from climate change.

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.

This dataset presents information on age-standardized incidence rates of ischemic heart disease (IHD) for Alberta, Alberta Health Services (AHS) continuum zones, former health regions, and peer groups expressed as per 100,000 population.

IntroductionTo assess the prevailing trends in the incidence of ischemic heart disease (IHD) across 204 countries and territories from 1990 to 2019, and to elucidate their correlations with age, period, and birth cohort, a comprehensive analysis was conducted.MethodsFrom 1990 to 2019, we employed the Global Burden of Disease Study (GBD) Results Tool in conjunction with an age-period-cohort model. This approach facilitated the estimation of annual percentage changes in incidence, referred to as net drifts, encompassing the overall population. Additionally, we calculated annual percentage changes spanning ages 15 - 19 to 95 + years, denoted as local drifts. Furthermore, our analysis involved determining period and cohort relative risks, elucidating the effects associated with distinct periods and birth cohorts.ResultsGlobally, 21,203,479 [95% uncertainty interval (UI): 18,799,322 − 23,704,124] cases of IHD occurred in 2019. There were 33 countries with at least 100000 cases. Between 1990 and 2019, the net drift of IHD incidence exhibited a range from −1.7% per year [95% confidence interval (CI): −1.79, −1.61] in countries with a high socio-demographic index (SDI) to 0.08% per year (95% CI: 0.05, 0.11) in countries with a low SDI. Age effects across all countries and genders demonstrated an increasing trend over time, indicating age as a significant risk factor for IHD. Moreover, period and cohort effects in higher SDI countries exhibited a more rapid decline in both genders compared to lower SDI countries. The findings indicated that nations with a higher SDI manifested overall favorable trends in the relative risk of IHD incidence, both across time and in successive younger birth cohorts.DiscussionThe incidence of IHD serves as a valuable and accessible indicator for assessing trends in IHD provision, spanning from early youth through later life. Enhancements in IHD prevention have the potential to mitigate risks for successively younger cohorts and, over time, redistribute the risk across all age groups. Despite global declines in IHD incidence over the last three decades, decreasing trends in incidence have slowed and, in some countries, flattened. Many countries have experienced unfavorable period and cohort effects.

In 2019, about 126 women died from heart diseases per 100,000 population, a significant decrease from the rate of 486.6 recorded for the year 1950. This statistic shows the death rate for heart diseases among women in the U.S. from 1950 to 2019, per 100,000 population.

Adult respondents ages 18+ who were ever diagnosed with heart disease by a doctor. Years covered are 2011 to 2012 by zip code. Data taken from the California Health Interview Survey Neighborhood Edition (AskCHIS NE) (http://askchisne.ucla.edu/), downloaded January 2016."Field" = "Definition""ZIPCODE" = postal zip code in LA County "Zip_code" = postal zip code in LA County "PAdHrtDis" = fraction of projected 18 and older population with disease conditions residing in Zip Code"PAdHrtDis2" = percentage of projected 18 and older population with Heart disease conditions residing in Zip Code"NAdHrtDis" = number of projected 18 and older population with Heart disease conditions residing in Zip Code"Pop_18olde" = projected 18 and older population total residing in Zip CodeHealth estimates available in AskCHIS NE (Neighborhood Edition) are model-based small area estimates (SAEs).SAEs are not direct estimates (estimates produced directly from survey data, such as those provided through AskCHIS).CHIS data and analytic results are used extensively in California in policy development, service planning and research, and is recognized and valued nationally as a model population-based health surveyFAQ: 1. Which cycle of CHIS does AskCHIS Neighborhood Edition provide estimates for?All health estimates in this version of AskCHIS Neighborhood Edition are based on data from the 2011- 2012 California Health Interview Survey. Socio-demographic indicators come from the 2008-2012 American Community Survey (ACS) 5-year summary tables. 2. Why do your population estimates differ from other sources like ACS? The population estimates in AskCHIS NE represent the CHIS 2011-2012 population sample, which excludes Californians living in group quarters (such as prisons, nursing homes, and dormitories). 3. Why isn't there data available for all ZIP codes / cities in Los Angeles?While AskCHIS NE has data on all ZCTAs (Zip Code Tabulation Areas), two factors may influence our ability to display the estimates:A small population (under 15,000): currently, the application only shows estimates for geographic entities with populations above 15,000. If your ZCTA has a population below this threshold, the easiest way to obtain data is to combine it with a neighboring ZCTA and obtain a pooled estimate. A high coefficient of variation: high coefficients of variation denote statistical instability.

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 presents information on age-standardized prevalence of ischemic heart disease (IHD) for Alberta, for select geographies , expressed as a percentage.

This dataset documents rates and trends in local hypertension-related cardiovascular disease (CVD) death rates. Specifically, this report presents county (or county equivalent) estimates of hypertension-related CVD 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 (female, male). 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.

For current version see: https://data.sandiegocounty.gov/Health/2021-Non-Communicable-Chronic-Diseases/v7dt-rwpx

Basic Metadata *Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native.

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Code Source: ICD-9CM - AHRQ HCUP CCS v2015. ICD-10CM - AHRQ HCUP CCS v2018. ICD-10 Mortality - California Department of Public Health, Group Cause of Death Codes 2013; NHCS ICD-10 2e-v1 2017.

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx