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

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

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.

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.

The dataset contains risk-adjusted mortality rates, quality ratings, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 3 procedures performed (Carotid Endarterectomy, Pancreatic Resection, and Percutaneous Coronary Intervention) in California hospitals. The 2023 IMIs were generated using AHRQ Version 2024, while previous years' IMIs were generated with older versions of AHRQ software (2022 IMIs by Version 2023, 2021 IMIs by Version 2022, 2020 IMIs by Version 2021, 2019 IMIs by Version 2020, 2016-2018 IMIs by Version 2019, 2014 and 2015 IMIs by Version 5.0, and 2012 and 2013 IMIs by Version 4.5). The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to statewide table for California overall rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings/resource/af88090e-b6f5-4f65-a7ea-d613e6569d96

Analysis of ‘Heart Failure Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/andrewmvd/heart-failure-clinical-data on 28 January 2022.

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

About this dataset

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.

Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.

People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.

How to use this dataset

• Create a model for predicting mortality caused by Heart Failure.
• Your kernel can be featured here!
• More datasets

Acknowledgements

If you use this dataset in your research, please credit the authors

Citation

Davide Chicco, Giuseppe Jurman: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020). (link)

License

CC BY 4.0

Splash icon

Icon by Freepik, available on Flaticon.

Splash banner

Wallpaper by jcomp, available on Freepik.

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

About this dataset

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.

Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.

People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.

How to use this dataset

• Create a model for predicting mortality caused by Heart Failure.
• Your kernel can be featured here!
• More datasets

Acknowledgements

If you use this dataset in your research, please credit the authors

Citation

Davide Chicco, Giuseppe Jurman: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020). (link)

License

CC BY 4.0

Splash icon

Icon by Freepik, available on Flaticon.

Splash banner

Wallpaper by jcomp, available on Freepik.

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

Quick Start 🚀: If you're not up for reading all of this, head straight to the file section. There, you'll find detailed explanations of the files and all the variables you need.

This dataset contains the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile has 13 clinical features.

Dataset Characteristics: Multivariate

Subject Area: Health and Medicine

Associated Tasks: Classification, Regression, Clustering

Feature Type: Integer, Real

Instances: 299

Features: 12

Dataset Information

A detailed description of the dataset can be found in the Dataset section of the following paper:

Title: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Authors:

Davide Chicco Giuseppe Jurman Source: BMC Medical Informatics and Decision Making 20, 16 (2020)

DOI: https://doi.org/10.1186/s12911-020-1023-5

Dataset Details

Statistical quantitative description of the category features

number of patients. %: percentage of patients. Full sample: 299 individuals. Dead patients: 96 individuals. Survived patients: 203 individuals.

Bangladesh BD: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 18.100 % in 2021. This records a decrease from the previous number of 18.900 % for 2020. Bangladesh BD: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 21.400 % from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 24.700 % in 2000 and a record low of 18.100 % in 2021. Bangladesh BD: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: 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;This is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/].

BackgroundRheumatic heart disease (RHD) remains the leading cause of preventable death and disability in children and young adults, killing an estimated 320,000 individuals worldwide yearly.Materials and methodsWe utilized the Bayesian age-period cohort (BAPC) model to project the change in disease burden from 2020 to 2030 using the data from the Global Burden of Disease (GBD) Study 2019. Then we described the projected epidemiological characteristics of RHD by region, sex, and age.ResultsThe global age-standardized prevalence rate (ASPR) and age-standardized incidence rate (ASIR) of RHD increased from 1990 to 2019, and ASPR will increase to 559.88 per 100,000 population by 2030. The global age-standardized mortality rate (ASMR) of RHD will continue declining, while the projected death cases will increase. Furthermore, ASPR and cases of RHD-associated HF will continue rising, and there will be 2,922,840 heart failure (HF) cases in 2030 globally. Female subjects will still be the dominant population compared to male subjects, and the ASPR of RHD and the ASPR of RHD-associated HF in female subjects will continue to increase from 2020 to 2030. Young people will have the highest ASPR of RHD among all age groups globally, while the elderly will bear a greater death and HF burden.ConclusionIn the following decade, the RHD burden will remain severe. There are large variations in the trend of RHD burden by region, sex, and age. Targeted and effective strategies are needed for the management of RHD, particularly in female subjects and young people in developing regions.

Objectives: Early in the COVID-19 pandemic, people with underlying comorbidities were overrepresented in hospitalised cases of COVID-19, but the relationship between comorbidity and COVID-19 outcomes was complicated by potential confounding by age. This review therefore sought to characterise the international evidence base available in the early stages of the pandemic on the association between comorbidities and progression to severe disease, critical care, or death, after accounting for age, among hospitalised patients with COVID-19.Methods: We conducted a rapid, comprehensive review of the literature (to 14 May 2020), to assess the international evidence on the age-adjusted association between comorbidities and severe COVID-19 progression or death, among hospitalised COVID-19 patients – the only population for whom studies were available at that time.Results: After screening 1,100 studies, we identified 14 eligible for inclusion. Overall, evidence for obesity and cancer increasing risk of severe disease or death was most consistent. Most studies found that having at least one of obesity, diabetes mellitus, hypertension, heart disease, cancer, or chronic lung disease was significantly associated with worse outcomes following hospitalisation. Associations were more consistent for mortality than other outcomes. Increasing numbers of comorbidities and obesity both showed a dose-response relationship. Quality and reporting were suboptimal in these rapidly conducted studies, and there was a clear need for additional studies using population-based samples.Conclusions: This review summarises the most robust evidence on this topic that was available in the first few months of the pandemic. It was clear at this early stage that COVID-19 would go on to exacerbate existing health inequalities unless actions were taken to reduce pre-existing vulnerabilities and target control measures to protect groups with chronic health conditions.

Particulate matter (PM) comprises solid and liquid particles in the air. PM2.5 particles have a diameter less than 2.5 micrometres. They can be inhaled and deposited deep in the lungs where air-gas exchange occurs.

Short- and long-term exposure to PM2.5, even at low levels, is linked to respiratory and cardiovascular disease, and increased risk of premature death, especially in vulnerable people (the young, the elderly, and people with respiratory illness). Emerging evidence points to possible links with cognitive function, neuro-development, and diabetes.

In New Zealand, most PM2.5 in the air results from combustion (for example, burning wood for home heating), and to a lesser extent, from reactions in the atmosphere (secondary PM), and from naturally occurring sea salt.

This dataset reports on the seasonal trends assessed for the period 2011-2020.

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Abstract INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 has infected more than 9,834,513 Brazilians up to February 2021. Knowledge of risk factors of coronavirus disease among Brazilians remains scarce, especially in the adult population. This study verified the risk factors for intensive care unit admission and mortality for coronavirus disease among 20-59-year-old Brazilians. METHODS: A Brazilian database on respiratory illness was analyzed on October 9, 2020, to gather data on age, sex, ethnicity, education, housing area, and comorbidities (cardiovascular disease, diabetes, and obesity). Multivariate logistic regression analysis was performed to identify the risk factors for coronavirus disease. RESULTS: Overall, 1,048,575 persons were tested for coronavirus disease; among them, 43,662 were admitted to the intensive care unit, and 34,704 patients died. Male sex (odds ratio=1.235 and 1.193), obesity (odds ratio=1.941 and 1.889), living in rural areas (odds ratio=0.855 and 1.337), and peri-urban areas (odds ratio=1.253 and 1.577) were predictors of intensive care unit admission and mortality, respectively. Cardiovascular disease (odds ratio=1.552) was a risk factor for intensive care unit admission. Indigenous people had reduced chances (odds ratio=0.724) for intensive care unit admission, and black, mixed, East Asian, and indigenous ethnicity (odds ratio=1.756, 1.564, 1.679, and 1.613, respectively) were risk factors for mortality. CONCLUSIONS: Risk factors for intensive care unit admission and mortality among adult Brazilians were higher in men, obese individuals, and non-urban areas. Obesity was the strongest risk factor for intensive care unit admission and mortality.

Background: Hospitalized patients with acute decompensated heart failure (ADHF) frequently exhibit aggravating mitral regurgitation (MR). Those patients do not always undergo surgical mitral valve repair, but particularly in the elderly, they are often treated by conservative medical therapy. This study was aimed to investigate factors affecting 6-month outcomes in hospitalized patients with heart failure (HF) harboring surgically untreated MR.Methods: We screened the presence of MR in hospitalized patients with HF between September 2017 and May 2020 in the Yamaguchi Prefectural Grand Medical (YPGM) center. At the time of discharge of these patients, individuals with surgically unoperated MR, including primary and secondary origin, were consequently recruited to this single-center prospective cohort study. The patients with severe MR who undergo surgical mitral valve treatment were not included in this study. The primary endpoint was all-cause readmission or all-cause death and the secondary endpoint was HF-related endpoint at 6 months after discharge. The Cox proportional hazard regression analyses were employed to assess the predictors for the composite endpoint.Results: Overall, 489 patients with ADHF were admitted to the YPGM center. Of those, 146 patients (30% of total patients with HF) (median age 83.5 years, 69 men) were identified as harboring grade II MR or greater. Consequently, all the recruited patients were diagnosed as functional MR. During a median follow-up of 186.0 days, a total of 55 patients (38%) reached the primary or secondary endpoints (HF death and readmission in 31 patients, other in 24 patients). As a result of multivariate analysis, geriatric nutritional risk index [hazard ratio (HR) = 0.932; 95% CI = 0.887–0.979, p = 0.005], age (HR = 1.058; 95% CI = 1.006–1.112, p = 0.027), and left ventricular ejection fraction (HR = 0.971; 95% CI = 0.945–0.997, p = 0.030) were independent predictors of all-cause death or all-cause admission. Body mass index (HR = 0.793; 95% CI = 0.614–0.890, p = 0.001) and ischemic heart disease etiology (HR = 2.732; 95% CI = 1.056–7.067, p = 0.038) were also independent predictors of the HF-related endpoints.Conclusion: Malnutrition and underweight were substantial predictors of adverse outcomes in elderly patients with HF harboring surgically untreated moderate-to-severe functional MR.

Etiology of heart failure.

The share of the population with overweight in the United States was forecast to continuously increase between 2024 and 2029 by in total 1.6 percentage points. After the fifteenth consecutive increasing year, the overweight population share is estimated to reach 77.43 percent and therefore a new peak in 2029. Notably, the share of the population with overweight of was continuously increasing over the past years.Overweight is defined as a body mass index (BMI) of more than 25.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the share of the population with overweight in countries like Canada and Mexico.