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.

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, Arkansas had the highest rate of death due to coronary heart disease of any U.S. state, with a rate of around *** deaths per 100,000. This statistic shows death rates for coronary heart disease in the United States in 2022, by state.

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.

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

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

DQS Death rates for heart disease, by sex, race, Hispanic origin, and age: United States from CDC WONDER

  Description

Data on death rates for diseases of heart in the United States, by age, sex, race, and Hispanic origin. Data are from Health, United States. SOURCE: National Center for Health Statistics, National Vital Statistics System, Mortality File. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/dqs-death-rates-for-heart-disease-by-sex-race-hisp.

In 2023, there were approximately 750.5 deaths by all causes per 100,000 inhabitants in the United States. This statistic shows the death rate for all causes in the United States between 1950 and 2023. Causes of death in the U.S. Over the past decades, chronic conditions and non-communicable diseases have come to the forefront of health concerns and have contributed to major causes of death all over the globe. In 2022, the leading cause of death in the U.S. was heart disease, followed by cancer. However, the death rates for both heart disease and cancer have decreased in the U.S. over the past two decades. On the other hand, the number of deaths due to Alzheimer’s disease – which is strongly linked to cardiovascular disease- has increased by almost 141 percent between 2000 and 2021. Risk and lifestyle factors Lifestyle factors play a major role in cardiovascular health and the development of various diseases and conditions. Modifiable lifestyle factors that are known to reduce risk of both cancer and cardiovascular disease among people of all ages include smoking cessation, maintaining a healthy diet, and exercising regularly. An estimated two million new cases of cancer in the U.S. are expected in 2025.

Heart Disease is among the most prevalent chronic diseases in the United States, impacting millions of Americans each year and exerting a significant financial burden on the economy. In the United States alone, heart disease claims roughly 647,000 lives each year — making it the leading cause of death. The buildup of plaques inside larger coronary arteries, molecular changes associated with aging, chronic inflammation, high blood pressure, and diabetes are all causes of and risk factors for heart disease. While there are different types of coronary heart disease, the majority of individuals only learn they have the disease following symptoms such as chest pain, a heart attack, or sudden cardiac arrest. This fact highlights the importance of preventative measures and tests that can accurately predict heart disease in the population prior to negative outcomes like myocardial infarctions (heart attacks) taking place

Heart Disease death rates by county, all races (includes Hispanic/Latino), all sexes, all ages, 2019-2023. Death data were provided by the National Vital Statistics System. Death rates (deaths per 100,000 population per year) are age-adjusted to the 2000 US standard population (20 age groups: <1, 1-4, 5-9, ... , 80-84, 85-89, 90+). Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by the National Cancer Institute. The US Population Data File is used for mortality data. The Average Annual Percent Change is based onthe APCs calculated by the Joinpoint Regression Program (Version 4.9.0.0). Due to data availability issues, the time period used in the calculation of the joinpoint regression model may differ for selected counties. Counties with a (3) after their name may have their joinpoint regresssion model calculated using a different time period due to data availability issues.

2019 - 2021, 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.Data 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 excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall    AIA - American Indian and Alaska Native, non-Hispanic    ASN - Asian, non-Hispanic    BLK - Black, non-Hispanic    HIS - Hispanic NHP – Native Hawaiian or Other Pacific Islander, non-Hispanic MOR – More than one race, non-Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

Abstract The world changed in just a few months after the emergence of the novel coronavirus disease 2019 (COVID-19), caused by a beta coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. Brazil currently has the world’s second-highest COVID-19 death toll, second only to the USA. The COVID-19 pandemic is spreading fast in the world with more than 181 countries affected. This editorial refers to the article published in Arquivos Brasileiros de Cardiologia: “Increase in home deaths due to cardiorespiratory arrest in times of COVID-19 pandemic.”1 Their main results show a gradual increase in the rate of out-of-hospital cardiac arrest during the Coronavirus disease 2019 (COVID-19) pandemic in the city of Belo Horizonte, Minas Gerais, Brazil. Their data demonstrate a proportional increase of 33% of home deaths in March 2020 compared to previous periods. Their study is the first Brazilian paper to demonstrate the same trend observed in other countries.

Data on death rates for diseases of heart in the United States, by age, sex, race, and Hispanic origin. Data are from Health, United States. SOURCE: National Center for Health Statistics, National Vital Statistics System, Mortality File. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

Death rate has been age-adjusted to the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Coronary heart disease is a type of heart disease in which the arteries of the heart cannot deliver enough oxygen-rich blood to the heart muscles. Over time, this can weaken the heart muscle and may lead to heart attack or heart failure. It is the most common type of heart disease in the US and has been the leading cause of death in Los Angeles County for the last two decades. Poor diet, sedentary lifestyle, tobacco exposure, and chronic stress are all important risk factors for coronary heart disease. Cities and communities can mitigate these risks by improving local food environments and encouraging physical activity by making communities safer and more walkable.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

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.

This dataset provides spatiotemporally smoothed, age-standardized mortality rates for cardiovascular diseases (heart disease, stroke, CHD, CVD) across U.S. counties from 2000-2019. It includes granular stratification by:

Demographics: Age groups (35-64, 65+), race/ethnicity (5 groups), and sex

Geography: County-level granularity with state abbreviations and FIPS codes

Methodology: Bayesian-smoothed estimates to address small-area variability

Developed by the CDC using National Vital Statistics System (NVSS) data, it supports research on health disparities, policy evaluation, and mortality trend analysis.

Key Dataset Details Coverage: 1,500+ U.S. counties | 20 years (2000-2019) | 5 cardiovascular conditions

Total Records: ~10 million rows (1.2 GB)

Unique Features: Confidence intervals, suppression flags, spatiotemporal smoothing metadata

Columns Overview Core Identifiers

LocationID: County FIPS code (e.g., 01001 = Autauga, AL)

Year: 2000-2019

GeographicLevel: County/State/National

Stratification

StratificationCategory[1-3]: Dimension type (Age group/Race/Sex)

Stratification[1-3]: Specific category (e.g., "Ages 35-64", "American Indian")

Metrics

Data_Value: Age-standardized rate per 100,000 (NA = suppressed)*

Confidence_limit_Low/High: 95% uncertainty bounds

Data_Value_Footnote: Reason for suppression (e.g., unreliable estimate)

Class/Topic: Disease classification (e.g., "Cardiovascular Diseases > All stroke")

Potential Uses ✅ Public health disparity research ✅ County-level healthcare resource allocation analysis ✅ Longitudinal mortality trend modeling ✅ Race/ethnicity- and sex-specific outcome studies

Limitations ⚠️ Suppressed Values: Small sample sizes trigger data suppression (~ symbol) ⚠️ Model-Dependent: Rates are Bayesian estimates, not raw counts ⚠️ Age Restriction: Only covers adults aged 35+

Acknowledgments Source: https://catalog.data.gov/dataset/rates-and-trends-in-heart-disease-and-stroke-mortality-among-us-adults-35-by-county-a-2000-45659 License: ODC-By 1.0 (Attribution required) Size: 1.2 GB (CSV)

According to Cognitive Market Research, The Global Cardiopulmonary Resuscitation Machine Market size is USD 215.9 million in 2023 and will expand at a 13.80% compound annual growth rate (CAGR) from 2023 to 2030.

The global cardiopulmonary resuscitation machines market will expand at a significant rate of 13.80% CAGR between 2023 and 2030.
The primary element driving the market expansion is the increase in chronic diseases and ailments that require pain management solutions daily.
Demand for piston CPR devices remains higher in the cardiopulmonary resuscitation machines market.
The EMT Rescue Units held the highest cardiopulmonary resuscitation machines market revenue share in 2023.
North America will continue to lead, whereas the Asia Pacific cardiopulmonary resuscitation machines market will experience the strongest growth until 2030.

Rising Prevalence of Cardiac Arrest to Provide Viable Market Output

The increased rate of cardiac arrest can also be connected to the rising prevalence of obesity, high cholesterol, and high blood pressure brought on by sedentary lifestyles and poor eating habits. Additionally, the global increase in cardiac disease and out-of-emergency clinic heart failure is projected to fuel the market for cardiopulmonary resuscitation devices.

In the United States, out-of-hospital cardiac arrests (OHCA) account for around 90% of all yearly fatalities. An estimated 356,461 people, or about 1,000 people per day, are anticipated to experience non-traumatic OHCA, as determined by EMS.

(Source:www.ahajournals.org/doi/10.1161/CIR.0000000000001052)

Several powerful groups support research and development to provide more trustworthy CPR technology. The rising incidence of various respiratory disorders is the main development driver for the global market. A rising senior population is also predicted to support the industry expansion.

Market Dynamics of Cardiopulmonary Resuscitation Machine

High Cost of Machine to Limit Market Growth

The expensive expense of this technology is one of the key things preventing the global market for cardiopulmonary resuscitation machines from expanding. The average price of cardiopulmonary resuscitation machines is $10,000 USD. Additional expenses are required for "life bands" and other disposable or replacement parts of cardiopulmonary resuscitation equipment. Therefore, in the worldwide cardiopulmonary resuscitation machines market, the high price of CPR devices, their weight, the requirement for careful planning, and the inconsistent findings of observational studies are major obstacles.

Impact of COVID-19 on the Cardiopulmonary Resuscitation Devices Market

Like many other medical equipment sectors, the cardiopulmonary resuscitation machines industry had unfavorable effects from the COVID-19 epidemic. The medical device market as a whole was substantially hit by COVID-19, with various industrial areas suffering extensive consequences. It impacted the medical device segment's volume reduction and supply chain difficulties with atypical PPE supply and demand patterns. The need for precautions to ensure that staff can perform safely on-site and remotely resulted in varied budgetary implications on industrial operations. Other risks have also increased due to the COVID-19 pandemic, such as those brought on by supplier relationships, overseas operations, regulatory and licensing changes, changes to the U.S. healthcare system, cyber security, and access to the capital market. Introduction of Cardiopulmonary Resuscitation Machine

Chest compressions and artificial ventilation (AV) were used in cardiopulmonary resuscitation (CPR), a treatment that could save a person's life in the event of asystole. Adult patients experiencing abrupt cardiac arrest can get external cardiac compressions using cardiopulmonary resuscitation equipment. CPR, commonly known as cardiopulmonary resuscitation, is a set of procedures that includes chest compressions. A growing number of variables, including the occurrence of illnesses like heart disease and respiratory sickness, the prevalence of lifestyle diseases, the aging of the population, and greater access to medical care, are boosting the expansion of the global market for cardiopulmonary resuscitation equipment.

With 17.9 million deaths yearly, cardiovascular diseases (CVDs) are the leading cause of death worldwide.

(Source:www.who.int/health-topi...

This statistic shows the death rate from diseases of the heart in the U.S. in 2015 and 2016, by age. In the age group between 35 and 44 years, there were around **** deaths per 100,000 inhabitants in the United States in 2016.

Key Indicators of Heart Disease

2022 annual CDC survey data of 400k+ adults related to their health status

What subject does the dataset cover?

According to the CDC, heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect "patterns" in the data that can predict a patient's condition.

Where did the data set come from and what treatments has it undergone?

The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the CDC: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.

What can you do with this data set?

As described above, the original dataset of nearly 300 variables was reduced to 40variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable "HadHeartAttack" as binary ("Yes" - respondent had heart disease; "No" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?

What steps did you use to convert the dataset?

Check out this notebook in my GitHub repository: https://github.com/kamilpytlak/data-science-projects/blob/main/heart-disease-prediction/2022/notebooks/data_processing.ipynb

Purpose:This dataset contains the Age-Adjusted Heart Disease Mortality Rate Per 100,000 Persons by Colorado Census Tract; Underlying Cause of Death Heart Disease (2019-2023). Numerator data are calculated from the 2019-2023 Colorado Department of Public Health and Environment Colorado Death Statistics. Population estimates for the denominator are calculated from the 2019-2023 U.S. Census American Community Survey.Update Schedule and URL: This dataset is updated annually (September) and is provided using death data directly assembled from the Colorado Department of Public Health and Environment Colorado Death Statistics. For inquiries about vital statistics or for data requests contact cdphe.healthstatistics@state.co.us, or use the data request system. Fields Description:GEOID: 11-digit Census Tract FIPS Identifier COUNTY: County NameNAME: Census Tract NameHD_ADJRATE: Age-Adjusted Heart Disease Mortality Rate Per 100,000 Persons (2019-2023, Colorado Death Statistics and U.S. Census American Community Survey)HD_L95CI: Heart Disease Mortality Rate Lower 95% Confidence IntervalHD_U95CI: Heart Disease Mortality Rate Upper 95% Confidence IntervalHD_STATEADJRATE: Statewide Age-Adjusted Heart Disease Mortality Rate Per 100,000 Persons (2019-2023, Colorado Death Statistics and U.S. Census American Community Survey)HD_SL95CI: Statewide Heart Disease Mortality Rate Lower 95% Confidence IntervalHD_SU95CI: Statewide Heart Disease Mortality Rate Upper 95% Confidence IntervalHD_DISPLAY: Heart Disease Mortality Rate Census Tract Map Display Designation (Estimate is Higher Than State Average Confidence Interval, Lower Than State Average Confidence Interval, Not Different Than State Average Confidence Interval, No Events or Data Suppressed)