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.

Chronic Disease Risk Factors

Modifiable Risk Factors for Chronic Diseases and Other Leading Causes of Death

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

About this dataset

This dataset contains data on behaviors that are risk factors for cardiovascular diseases (CVDs) from 2011 to present. It is one of many datasets included in the National Cardiovascular Disease Surveillance System, which offers comprehensive insights into the public health burden of CVDs in America. This dataset collects information on modifiable risk factors for chronic diseases and leading causes of death from the Behavioral Risk Factor Surveillance System (BRFSS). This includes indicators from CDC's Division for Heart Disease and Stroke Prevention, organized by location and indicator with stratifications based on age, sex and race/ethnicity. In providing a detailed picture of these conditions, the system can be effectively used to reduce preventative disease through education, policy changes and other interventions

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How to use the dataset

Research Ideas

• Identifying CVD risk factors by demographic, such as age, gender, and ethnicity
• Comparing differences in modifiable risk factors (diet, physical activity) between states or regions
• Analyzing trends in CVD deaths over time to inform preventive strategies

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: csv-1.csv | Column name | Description | |:-------------------------------|:-----------------------------------------------------| | Year | Year of the data. (Integer) | | LocationAbbr | Abbreviation of the location. (String) | | LocationDesc | Description of the location. (String) | | Datasource | Source of the data. (String) | | PriorityArea1 | Priority area 1 of the data. (String) | | PriorityArea2 | Priority area 2 of the data. (String) | | PriorityArea3 | Priority area 3 of the data. (String) | | PriorityArea4 | Priority area 4 of the data. (String) | | Category | Category of the data. (String) | | Topic | Topic of the data. (String) | | Indicator | Indicator if available. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Unit | Unit of each value field. (String) | | Data_Value_Alt | Alternative value for respective each field. (Float) | | Data_Value_Footnote_Symbol | Footnote symbol for the data value. (String) | | Break_Out_Category | Break out category for the data. (String) | | Break_out | Break out description for the data. (String) | | GeoLocation | Geographical location of the data. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit US Open Data Portal, data.gov.

Analysis of ‘Cardiovascular Study Dataset ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/christofel04/cardiovascular-study-dataset-predict-heart-disea on 30 September 2021.

--- Dataset description provided by original source is as follows ---

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”)

--- Original source retains full ownership of the source dataset ---

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 17.500 NA in 2016. This records an increase from the previous number of 17.200 NA for 2015. US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 17.500 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 21.600 NA in 2000 and a record low of 17.200 NA in 2015. US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

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

Age-Adjusted Mortality Rate From Heart Disease - This indicator shows the age-adjusted mortality rate from heart disease (per 100,000 population). Heart disease is the leading cause of death in Maryland accounting for 25% of all deaths. Between 2012-2014, over 30,000 people died of heart disease in Maryland.

Age-adjusted mortality rates for the contiguous United States in 2000–2005 were obtained from the Wide-ranging Online Data for Epidemiologic Research system of the U.S. Centers for Disease Control and Prevention (CDC) (2015). Age-adjusted mortality rates were weighted averages of the age-specific death rates, and they were used to account for different age structures among populations (Curtin and Klein 1995). The mortality rates for counties with < 10 deaths were suppressed by the CDC to protect privacy and to ensure data reliability; only counties with ≥ 10 deaths were included in the analyses. The underlying cause of mortality was specified using the World Health Organization’s International Statistical Classification of Diseases and Related Health Problems (10th revision; ICD-10). In this study, we focused on the all-cause mortality rate (A00-R99) and on mortality rates from the three leading causes: heart disease (I00-I09, I11, I13, and I20-I51), cancer (C00-C97), and stroke (I60- I69) (Heron 2013). We excluded mortality due to external causes for all-cause mortality, as has been done in many previous studies (e.g., Pearce et al. 2010, 2011; Zanobetti and Schwartz 2009), because external causes of mortality are less likely to be related to environmental quality. We also focused on the contiguous United States because the numbers of counties with available cause-specific mortality rates were small in Hawaii and Alaska. County-level rates were available for 3,101 of the 3,109 counties in the contiguous United States (99.7%) for all-cause mortality; for 3,067 (98.6%) counties for heart disease mortality; for 3,057 (98.3%) counties for cancer mortality; and for 2,847 (91.6%) counties for stroke mortality. The EQI includes variables representing five environmental domains: air, water, land, built, and sociodemographic (2). The domain-specific indices include both beneficial and detrimental environmental factors. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Messer, J. Jagai, K. Rappazzo, C. Gray, S. Grabich, and D. Lobdell. Associations between environmental quality and mortality in the contiguous United States 2000-2005. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 125(3): 355-362, (2017).

IntroductionPatients with mental disorders are at increased risk of cardiovascular events. We aimed to assess the cardiovascular mortality trends over the last two decades among patients with mental and behavioral co-morbidities in the US.MethodsWe performed a retrospective, observational study using the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) Multiple Cause of Death dataset. We determined national trends in age-standardized mortality rates attributed to cardiovascular diseases in patients with and without mental and behavioral disorders, from 1999 to 2020, stratified by mental and behavioral disorders subtype [ICD10 codes F], age, gender, race, and place of residence.ResultsAmong more than 18.7 million cardiovascular deaths in the United States (US), 13.5% [2.53 million] were patients with a concomitant mental and behavioral disorder. During the study period, among patients with mental and behavioral disorders, the age-adjusted mortality rate increased by 113.9% Vs a 44.8% decline in patients with no mental disorder (both p

The dataset contains data for US workers who resided and died due to a cardiovascular disease during the periods 2007-2010, in one of 25 US States. Mortality described through proportionate mortality ratios (PMRs) along with the number of deaths is described by the type of cardiovascular disease, gender and age-group of workers, as well as by the industry they worked in.

2020 - 2022, county-level U.S. stroke death rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. stroke death rates by county. Data can be stratified by age, race/ethnicity, and sex.Visit the CDC Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I60-I69; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC 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

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

BackgroundCardiovascular diseases (CVD) are the underlying cause 1.6 million deaths per year in the Americas, accounting for 30% of total mortality and 38% of by non-communicable deaths diseases (NCDs). A 25% reduction in premature mortality due four main NCDs was targeted by the 2011 High-level Meeting of the General Assembly on the Prevention and Control of NCDs. While overall CVD mortality fell in the Americas during the past decade, trends in premature CVD mortality during the same period have not been described, particularly in the countries of Latin America and the Caribbean.MethodsThis is a population-based trend-series study based on a total of 6,133,666 deaths to describe the trends and characteristics of premature mortality due to CVD and to estimates of the average annual percentage of change during the period 2000–2010 in the Americas.FindingsPremature mortality due to CVD in the Americas fell by 21% in the period 2000–2010 with a -2.5% average annual rate of change in the last 5 year—a statistically significant reduction of mortality—. Mortality from ischemic diseases, declined by 25% - 24% among men and 26% among women. Cerebrovascular diseases declined by 27% -26% among men and 28% among women. Guyana, Trinidad and Tobago, the Dominican Republic, Bahamas, and Brazil had CVD premature mortality rates over 200 per 100,000 population, while the average for the Region was 132.7. US and Canada will meet the 25% reduction target before 2025. Mexico, Costa Rica, Venezuela, Dominican Republic, Panama, Guyana, and El Salvador did not significantly reduce premature mortality among men and Guyana, the Dominican Republic, and Panama did not achieve the required annual reduction in women.ConclusionsTrends in premature mortality due to CVD observed in last decade in the Americas would indicate that if these trends continue, the Region as a whole and a majority of its countries will be able to reach the goal of a 25% relative reduction in premature mortality even before 2025.

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

BackgroundNighttime physical activity (PA) has significant effects on human health. Whether excessive nighttime PA is associated with adverse long-term prognosis remains unknown.MethodsThree thousand six hundred ninety adults from the US National Health and Nutrition Examination Survey (NHANES) 2003–2006 with accelerometer monitor recording PA data were included. Nighttime PA was quantified by the nighttime to all-day PA intensity ratio (NAPAIR). Participants with the NAPAIR above the population median (0.17) were defined as the nighttime active population (NAP), otherwise as the daytime active population. All-cause and cardiovascular disease mortality status was acquired from the US National Death Index from their interview and physical examination date through December 31, 2015.ResultsAmong 3690 adults (weighted mean age 48.1 years), 1781 (weighted proportion 48.8%) were females. One thousand eight hundred six (48.9%) were determined as the NAP. During the follow-up period of up to 13.1 years (median, 10.7 years), 639 deaths occurred (heart diseases, 114). Multivariable Cox proportional hazards model showed that the NAP was associated with higher risks of all-cause (hazard ratio [HR], 1.46; 95% confidence interval [CI], 1.22–1.75) and cardiovascular disease (HR, 1.58; 95% CI, 1.03–2.41) mortality compared with the daytime active population, and each 0.1 increase in the NAPAIR was associated with 15% increased all-cause mortality risks.ConclusionIn this nationally representative prospective cohort study of a sample of United States adults, excessive nighttime PA was associated with a higher risk of death from all causes and cardiovascular disease.

BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This is one of the datasets provided by the National Cardiovascular Disease Surveillance System. The system is designed to integrate multiple indicators from many data sources to provide a comprehensive picture of the public health burden of CVDs and associated risk factors in the United States. The data are organized by location (national, regional, state, and selected sites) and indicator, and they include CVDs (e.g., heart failure) and risk factors (e.g., hypertension). The data can be plotted as trends and stratified by age group, sex, and race/ethnicity.

Community Health Status Indicators (CHSI) to combat obesity, heart disease, and cancer are major components of the Community Health Data Initiative. This dataset provides key health indicators for local communities and encourages dialogue about actions that can be taken to improve community health (e.g., obesity, heart disease, cancer). The CHSI report and dataset was designed not only for public health professionals but also for members of the community who are interested in the health of their community. The CHSI report contains over 200 measures for each of the 3,141 United States counties. Although CHSI presents indicators like deaths due to heart disease and cancer, it is imperative to understand that behavioral factors such as obesity, tobacco use, diet, physical activity, alcohol and drug use, sexual behavior and others substantially contribute to these deaths.

This dataset contains the raw files exported from the CDC-WONDER database, and selections needed on the CDC-WONDER Multiple Causes of Death database in order to access and replicate our data and findings.

IntroductionCompared with sleep disorders, no consensus has been reached on whether a subjective complaint of having trouble sleeping is associated with increased all-cause and heart disease mortality risk. Previous studies displayed considerable heterogeneity in population disease characteristics and duration of follow-up. Therefore, the aims of this study were to examine the relationship between sleep complaints and all-cause and heart disease mortality and whether the associations were influenced by follow-up time and population disease characteristics. In addition, we aimed to figure out the influence of the joint effects of sleep duration and sleep complaints on mortality risk.MethodsThe present study utilized data from five cycles of the National Health and Nutrition Examination Survey (NHANES) (2005~2014) linked with the most updated 2019 National Death Index (NDI). Sleep complaints were determined by answers to “Have you ever told a doctor or other health professional that you have trouble sleeping?” and “Have you ever been told by a doctor or other health professional that you have a sleep disorder?”. Those who answered ‘Yes' to either of the aforementioned two questions were considered as having sleep complaints.ResultsA total of 27,952 adult participants were included. During a median follow-up of 9.25 years (interquartile range, 6.75–11.75 years), 3,948 deaths occurred and 984 were attributable to heart disease. A multivariable-adjusted Cox model revealed that sleep complaints were significantly associated with all-cause mortality risk (HR, 1.17; 95% CI, 1.07–1.28). Subgroup analysis revealed that sleep complaints were associated with all-cause (HR, 1.17; 95% CI, 1.05–1.32) and heart disease (HR, 1.24; 95% CI, 1.01–1.53) mortality among the subgroup with cardiovascular disease (CVD) or cancer. In addition, sleep complaints were more strongly associated with short-term mortality than long-term mortality. The joint analysis of sleep duration and sleep complaints showed that sleep complaints mainly increased the mortality risk in those with short (< 6 h/day, sleep complaints HR, 1.40; 95% CI, 1.15–1.69) or recommended (6–8 h/day, sleep complaints HR, 1.15; 95% CI, 1.01–1.31) sleep duration group.DiscussionIn conclusion, sleep complaints were associated with increased mortality risk, indicating a potential public benefit of monitoring and managing sleep complaints in addition to sleep disorders. Of note, persons with a history of CVD or cancer may represent a potentially high-risk group that should be targeted with a more aggressive intervention of sleep problems to prevent premature all-cause and heart disease death.

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

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