The number of deaths caused by heart disease has decreased in the United States from ***** per 100,000 population in 1990 to ***** 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 ***** 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 **** billion U.S. dollars of revenue in 2024. 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 United States, some ** 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.

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

It was estimated that in the fiscal year 2022-2023, there were 132,940 deaths in Canada from ischemic heart disease among those aged 20 years and older. Furthermore, there were 51,365 deaths from acute myocardial infarction, commonly known as a heart attack. This statistic shows the number of deaths in Canada attributable to ischemic heart disease and acute myocardial infarction from 2000-2023.

Legacy unique identifier: P00255

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: P00320

2016 to 2018, 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

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

2014 to 2016, 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

The Framingham Township Heart Institute offers a 10-year data set on coronary heart disease

From 2017 to 2020, around *** percent of males and *** percent of females in the United States aged 40 to 59 years had coronary heart disease. This statistic shows the percentage of adults in the U.S. who had coronary heart disease in the period from 2017 to 2020, by age and gender.

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.

A dataset providing GP recorded coronary heart disease.

Coronary heart disease (CHD) is the leading cause of death both in the UK and worldwide.

It's responsible for more than 73,000 deaths in the UK each year. About 1 in 6 men and 1 in 10 women die from CHD.

In the UK, there are an estimated 2.3 million people living with CHD and around 2 million people affected by http://www.nhs.uk/conditions/Angina/Pages/Introduction.aspx">angina (the most common symptom of coronary heart disease).

CHD generally affects more men than women, although from the age of 50 the chances of developing the condition are similar for both sexes.

As well as angina (chest pain), the main symptoms of CHD are http://www.nhs.uk/conditions/heart-attack/Pages/Introduction.aspx">heart attacks and http://www.nhs.uk/conditions/Heart-failure/Pages/Introduction.aspx">heart failure. However, not everyone has the same symptoms and some people may not have any before CHD is diagnosed.

CHD is sometimes called ischaemic heart disease.

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.

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.

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.

The sample dataset was selected from Isfahan Cardiovascular Research Center (ICRC). It was de-identified due to the restrictions on individual-level data (Personal Health Information Protection Act) based on [1,2]. They could be used to identify 10-year CVD incidence risk based on the model proposed in the corresponding publication [3]. The representative age category could be used in the model instead of the exact age that had been removed since it was potentially identifying as the recruitment period was short and was fully described [1]. Since the Hazard Ratio of age was 1.038, the estimated risks are comparable to what was obtained with the exact age. The categories of Systolic Blood Pressure (SBP) and Total Cholesterol (Tch) were used in the model, and thus here provided. High waist-to-hip ratio (WHR) was defined as WHR ≥ 0.80 in women and ≥ 0.95 in men. 1. Hrynaszkiewicz I, Norton ML, Vickers AJ, Altman DG. Preparing raw clinical data for publication: guidance for journal editors, authors, and peer reviewers. BMJ. 2010;340, http://www.bmj.com/content/340/bmj.c181.long .2. National Heart, Lung and Blood Institute. Guidelines for Preparing Clinical Study Data Sets for Submission to the NHLBI Data Repository. https://www.nhlbi.nih.gov/research/funding/human-subjects/set-preparation-guidelines . 3.
Nizal Sarrafzadegan, Razieh Hassannejad, Hamid Reza Marateb, Mohammad Talaei, Masoumeh Sadeghi, Hamid Reza Roohafza, Farzad Masoudkabir, Shahram OveisGharan, Marjan Mansourian, Mohammad Reza Mohebian, Miquel Angel Mañanas, "PARS Risk Charts: A 10-year Study of Risk Assessment for Cardiovascular Diseases in Eastern Mediterranean Region", submitted to PLOS One.

Decrease heart disease deaths from 9,703 in 2013 to 8,403 by 2019.

The heart attack datasets were collected at Zheen hospital in Erbil, Iraq, from January 2019 to May 2019. The attributes of this dataset are: age, gender, heart rate, systolic blood pressure, diastolic blood pressure, blood sugar, ck-mb and troponin with negative or positive output. According to the provided information, the medical dataset classifies either heart attack or none. The gender column in the data is normalized: the male is set to 1 and the female to 0. The glucose column is set to 1 if it is > 120; otherwise, 0. As for the output, positive is set to 1 and negative to 0.