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 2023, the states with the highest death rates due to heart disease were Oklahoma, Mississippi, and Alabama. That year, there were around 251 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 162 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 22 percent of all deaths in 2023. That year, cancer was the second leading cause of death, followed by unintentional injuries and cerebrovascular diseases. 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 sixth-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.

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 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

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

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.

In 2020, deaths due to cardiovascular disease (CVD) in the United Kingdom were highest among those over 75 years old, accounting for approximately ** thousand female deaths and ** thousand male deaths. The most common CVDs are heart attack, stroke, heart failure, arrhythmia, and heart valve complications. Symptoms of CVDs include chest pain, breathlessness, fatigue, swollen limbs, and irregular heartbeat.

In the United Kingdom (UK), the death rate due to cardiovascular diseases has decreased dramatically from its peak in 2002 with ***** deaths per 100,000 population to ***** deaths per 100,000 in 2020. The most common CVDs are heart attack, stroke, heart failure, arrhythmia, and heart valve complications. Symptoms of CVDs include chest pain, breathlessness, fatigue, swollen limbs, and irregular heartbeat.

According to Cognitive Market Research, the Global Heart Attack Diagnostics Market Size was USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031.

The Heart Attack Diagnostics market will expand significantly by XX% CAGR between 2024 and 2031.
The non-invasive heart attack diagnostics type accounts for the largest market share and is anticipated to a healthy growth over the approaching years.
The Electrocardiogram Heart Attack Diagnostics holds the largest market share compared to others.
The usage of Heart Attack Diagnostics by hospitals & clinics as end-users holds the largest market share compared to others.
North-America region dominated the market and accounted for the highest revenue of XX% in 2022 and it is projected that it will grow at a CAGR of XX% in the future.

Factors Affecting the Heart Attack Diagnostics Market

The unhealthy lifestyles among people are raising heart attacks and related diseases.

The common sedentary lifestyle and poor nutrition are the main causes of heart attacks. It is revealed that youths who lead modern lifestyles are more likely to suffer from heart attacks. Poor food choices like fast food, processed foods, and sugary drinks are all increasing the risk of obesity, cholesterol, and other cardiovascular diseases. Researchers have revealed that being overweight or obese increases a person’s risk of coronary heart disease by up to 28%. (Source: https://www.imperial.ac.uk/news/181111/fat-increased-risk-heart-disease/#:~:text=Researchers%20have%20found%20that%20being,blood%20sugar%20and%20cholesterol%20levels.)

A lack of physical activity, loss of body workout, high tobacco consumption and smoking, high-stress levels, and inadequate healthcare can also lead to an increased risk of such disease. For instance, a new brief by World Health Organizations, the World Heart Federation, and the University of Newcastle Australia revealed that every year, nearly 1.9 million die from tobacco-induced heart disease. (Source:https://www.who.int/news/item/22-09-2020-tobacco-responsible-for-20-of-deaths-from-coronary-heart-disease#:~:text=Every%20year%2C%201.9%20million%20people,Day%2C%20marked%20on%2029%20September.)

This increased prevalence of heart-related cases is pushing people to consult healthcare services leading to the market growth of heart attack diagnostics.

The skill shortage in healthcare services can restrict the growth of the market.

Healthcare organizations face numerous challenges in the recruiting process compared to other industries. The process of attracting and selecting candidates with specific clinical, medical, and administrative skills is crucial and an ongoing battle, especially for positions like physicians, nurses, and specialist practitioners.

Furthermore, the rising portion of the healthcare workforce attaining retirement age, an older population seeking more healthcare services, and new technology shifting, altogether are leading to the shortage of skilled professions along with the rise in the need and demand for the same. The situation got worse when the global pandemic hit and stretched resources to the breaking point creating immense challenges for the service providers.

The Bureau of Labor Statistics (BLS) estimates that the U.S. will face a shortage of 195,400 nurses by 2031. The various reasons from people able to live longer to the unhealthy lifestyles of people leading to the rise in chronic disease, are increasing the need for medical professionals; however, the talent supply is unable to keep up with the demand limiting the growth of the market. (Source: https://www.peoplescout.com/insights/managing-skills-shortage-health-care/)

The increasing Research & Development projects to develop digital technology for improving heart health.

The need for technology-based solutions to enhance heart health is evident yet many people are still hesitant to accept and embrace these solutions due to issues like trust concerns, relevance, and ease of use. This gap creates an opportunity for the research community to present, validate, and create scalable, and engaging health-tech solutions to pursue them. To make it possible, various healthcare service providers, key players, and the government are continuously getting involved in investments and research to find...

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

In 2020, over *** thousand women and *** thousand men aged over 75 years in Germany died from a cardiovascular disease. While between 55 and 74 years of age, CVD was responsible for the death of **** thousand men and **** thousand women. The most common CVDs are heart attack, stroke, heart failure, arrhythmia, and heart valve complications. Symptoms of CVDs include chest pain, breathlessness, fatigue, swollen limbs, and irregular heartbeat.

Table contains count and age-adjusted rate of heart disease deaths among county residents. Data are summarized at county, city, zip code and census tract level. Data are masked when the number of events is 1 to 10. Data are presented for zip codes (ZCTAs) fully within the county. Source: Santa Clara County Public Health Department, Vital Records Business Intelligence System, 2011-2020. Data as of 7/1/2021METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographygeoname (String): Geography IDyear (String): Year of deathcount (Numeric): Number of heart disease deathsAArate (Numeric): Age-adjusted rate of heart disease deaths

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.

The cardiovascular disease profiles have been updated by Public Health England (PHE).

The profiles provide an overview of data on cardiovascular and cardiovascular related conditions of heart disease, stroke, diabetes and kidney disease. They are intended to help commissioners and health professionals assess the impact of cardiovascular disease (CVD) on their local population, make decisions about services and improve outcomes for patients.

The cardiovascular disease profiles are one of a range of data and analysis resources produced by PHE’s National Cardiovascular Intelligence Network (NCVIN).

NVCIN has released a range of cardiovascular disease guidance, data and intelligence resources.

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”)

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.

During the months December 2020, January 2021, and February 2021, COVID-19 was the leading cause of death in the United States based on the average number of daily deaths. Heart disease and cancer are usually the number one and number two leading causes of death, respectively. This statistic shows the average number of daily deaths in the United States among the leading causes of death from March 2020 to September 2022.

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. Legacy unique identifier: P01730

This dataset tracks the updates made on the dataset "Heart Disease Mortality Data Among US Adults (35+) by State/Territory and County – 2018-2020" as a repository for previous versions of the data and metadata.

BackgroundCOVID-19 has led to significant global mortality, with Peru being among the countries most affected. While pre-existing comorbidities have been linked to most cases, the exact distribution of fatalities within the country remains unclear. We aimed to assess deaths attributed to cardiovascular diseases (CVD) before and during the COVID-19 pandemic across various regions and provinces in Peru.MethodsAn observational georeferencing study was designed. Peru faced four waves of COVID-19 over three years, with variable impacts across its three regions (Coast, Highlands, and Jungle). Deaths related to cardiovascular diseases, such as heart failure (HF), arrhythmia, acute myocardial infarction (AMI), strokes, and acute coronary syndrome, were examined as primary variables. The study period spanned pre-pandemic years (2017–2019) and pandemic years (2020–2021), utilizing death data from the National Death Information System (SINADEF). The georeferencing analysis was conducted using ArcGIS v10.3.ResultsA total of 28,197 deaths were recorded during the study period, with significant increases during the pandemic (2020–2021). Cardiovascular deaths were disproportionately higher during the pandemic, totaling 19,376 compared to 8,821 in the pre-pandemic period (p