In 2021, South Korea had the lowest rate of death from ischemic heart disease among OECD countries, with around ** deaths per 100,000 inhabitants. In comparison, there were around *** deaths due to ischemic heart disease per 100,000 population in Lithuania. Cardiovascular disease worldwide Fatty deposits accumulating in the inner wall of the coronary artery which restrict blood flow to the heart cause ischemic heart disease (IHD) and can also precipitate heart attacks and strokes. Cardiovascular risk factors such as smoking, heavy alcohol use, and unhealthy diet are more prevalent in Eastern European countries, contributing to a much higher burden of cardiovascular diseases and deaths. In Russia, the general public greatly underestimates the burden of cardiovascular diseases with the actual number of deaths over ** percent higher than what people estimate. Prevention and intervention Invasive interventions for heart disease can include surgical procedures such as heart bypass surgery- where blood is diverted around clogged parts of major arteries- which ranges in cost around the world. Other medical interventions include the use of prescribed or over-the-counter drugs, such as prescription nitrates or beta blockers, or OTC medications like aspirin. Lifestyle factors to lower blood pressure and cholesterol levels can help decrease risk of heart attacks and other cardiovascular diseases, including maintaining a healthy diet, regular physical activity, and smoking and alcohol cessation.

In 2021, it was estimated that the Pacific island country Nauru had the highest death rate from ischemic heart disease in the world, with around 405 deaths per 100,000 population. In 2021, ischemic heart disease was the leading cause of death worldwide, resulting in over nine million deaths.

Rheumatic heart disease (RHD), the principal long-term sequel of acute rheumatic fever (ARF), has been a major contributor to cardiac-related mortality in general population, especially in developing countries. With improvement in health and sanitation facilities across the globe, there has been almost a 50% reduction in mortality rate due to RHD over the last 25 years. However, recent estimates suggest that RHD still results in more than 300,000 deaths annually. In India alone, more than 100,000 deaths occur due to RHD every year (Watkins DA et al., N Engl J Med, 2017). Children and adolescents (aged below 15 years) constitute at least one-fourth of the total population in India. Besides, ARF is, for the most part, a pediatric disorder. The pediatric population, therefore, requires special consideration in developing countries to reduce the burden of RHD. In the developed world, Kawasaki disease (KD) has emerged as the most important cause of acquired heart disease in children. Mirroring global trends over the past two decades, India also has witnessed a surge in the number of cases of KD. Similarly, many regions across the globe classified as “high-risk” for ARF have witnessed an increasing trend in the incidence of KD. This translates to a double challenge faced by pediatric health care providers in improving cardiac outcomes of children affected with ARF or KD. We highlight this predicament by reviewing the incidence trends of ARF and KD over the last 50 years in ARF “high-risk” regions.

This statistic displays the mortality rate from coronary heart disease in the United Kingdom in 2022, by country. In that year, Scotland had the highest death rate from the disease, with *** deaths per 100,000 population.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)





  Dataset Description

This dataset provides country-level data for the indicator "3.4.1 Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)" across African nations, sourced from… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/mortality-rate-attributed-to-cardiovascular-disease-cancer-d-for-african-countries.

In 2021, it was estimated that Pakistan had the highest death rate from rheumatic heart disease worldwide, with around 18 deaths per 100,000 population. This was followed by Nepal and India, with both countries estimated to have around 14 deaths from rheumatic heart disease per 100,000 population.

Abstract Background Heart Failure with mid-range Ejection Fraction (HFmEF) was recently described by European and Brazilian guidelines on Heart Failure (HF). The ejection fraction (EF) is an important parameter to guide therapy and prognosis. Studies have shown conflicting results without representative data from developing countries. Objective To analyze and compare survival rate in patients with HFmEF, HF patients with reduced EF (HFrEF), and HF patients with preserved EF (HFpEF), and to evaluate the clinical characteristics of these patients. Methods A cohort study that included adult patients with acute HF admitted through the emergency department to a tertiary hospital, reference in cardiology, in south Brazil from 2009 to 2011. The sample was divided into three groups according to EF: reduced, mid-range and preserved. A Kaplan-Meier curve was analyzed according to the EF, and a logistic regression analysis was done. Statistical significance was established as p < 0.05. Results A total of 380 patients were analyzed. Most patients had HFpEF (51%), followed by patients with HFrEF (32%) and HFmEF (17%). Patients with HFmEF showed intermediate characteristics related to age, blood pressure and ventricular diameters, and most patients were of ischemic etiology. Median follow-up time was 4.0 years. There was no statistical difference in overall survival or cardiovascular mortality (p=.0031) between the EF groups (reduced EF: 40.5% mortality; mid-range EF 39.7% and preserved EF 26%). Hospital mortality was 7.6%. Conclusion There was no difference in overall survival rate between the EF groups. Patients with HFmEF showed higher mortality from cardiovascular diseases in comparison with HFpEF patients. (Arq Bras Cardiol. 2021; 116(1):14-23)

BackgroundGlobal and regional projections of mortality and burden of disease by cause for the years 2000, 2010, and 2030 were published by Murray and Lopez in 1996 as part of the Global Burden of Disease project. These projections, which are based on 1990 data, continue to be widely quoted, although they are substantially outdated; in particular, they substantially underestimated the spread of HIV/AIDS. To address the widespread demand for information on likely future trends in global health, and thereby to support international health policy and priority setting, we have prepared new projections of mortality and burden of disease to 2030 starting from World Health Organization estimates of mortality and burden of disease for 2002. This paper describes the methods, assumptions, input data, and results. Methods and FindingsRelatively simple models were used to project future health trends under three scenarios—baseline, optimistic, and pessimistic—based largely on projections of economic and social development, and using the historically observed relationships of these with cause-specific mortality rates. Data inputs have been updated to take account of the greater availability of death registration data and the latest available projections for HIV/AIDS, income, human capital, tobacco smoking, body mass index, and other inputs. In all three scenarios there is a dramatic shift in the distribution of deaths from younger to older ages and from communicable, maternal, perinatal, and nutritional causes to noncommunicable disease causes. The risk of death for children younger than 5 y is projected to fall by nearly 50% in the baseline scenario between 2002 and 2030. The proportion of deaths due to noncommunicable disease is projected to rise from 59% in 2002 to 69% in 2030. Global HIV/AIDS deaths are projected to rise from 2.8 million in 2002 to 6.5 million in 2030 under the baseline scenario, which assumes coverage with antiretroviral drugs reaches 80% by 2012. Under the optimistic scenario, which also assumes increased prevention activity, HIV/AIDS deaths are projected to drop to 3.7 million in 2030. Total tobacco-attributable deaths are projected to rise from 5.4 million in 2005 to 6.4 million in 2015 and 8.3 million in 2030 under our baseline scenario. Tobacco is projected to kill 50% more people in 2015 than HIV/AIDS, and to be responsible for 10% of all deaths globally. The three leading causes of burden of disease in 2030 are projected to include HIV/AIDS, unipolar depressive disorders, and ischaemic heart disease in the baseline and pessimistic scenarios. Road traffic accidents are the fourth leading cause in the baseline scenario, and the third leading cause ahead of ischaemic heart disease in the optimistic scenario. Under the baseline scenario, HIV/AIDS becomes the leading cause of burden of disease in middle- and low-income countries by 2015. ConclusionsThese projections represent a set of three visions of the future for population health, based on certain explicit assumptions. Despite the wide uncertainty ranges around future projections, they enable us to appreciate better the implications for health and health policy of currently observed trends, and the likely impact of fairly certain future trends, such as the ageing of the population, the continued spread of HIV/AIDS in many regions, and the continuation of the epidemiological transition in developing countries. The results depend strongly on the assumption that future mortality trends in poor countries will have a relationship to economic and social development similar to those that have occurred in the higher-income countries.

The file presents additional information for the article, Acharya, A. et al., Cardiovascular disease mortality based on verbal autopsy in low- and middle-income countries: a systematic review, Bulletin of the World Health Organization 1) Inclusion and Exclusion Criteria 2) Risk of bias assessment of individual studies

In 2019, the leading causes of death worldwide were ischemic heart disease, stroke, and chronic obstructive pulmonary disease (COPD). That year, ischemic heart disease and stroke accounted for a combined ** percent of all deaths worldwide. Although the leading causes of death worldwide vary by region and country, heart disease is a consistent leading cause of death regardless of income, development, size, or location. Heart disease In 2019, around **** million people worldwide died from ischemic heart disease. In comparison, around **** million people died from lung cancer that year, while *** million died from diabetes. The countries with the highest rates of death due to heart attack and other ischemic heart diseases are Lithuania, Russia, and Slovakia. Although some risk factors for heart disease, such as age and genetics, are unmodifiable, the likelihood of developing heart disease can be greatly reduced through a healthy lifestyle. The biggest modifiable risk factors for heart disease include smoking, an unhealthy diet, being overweight, and a lack of exercise. In 2019, it was estimated that around *** million deaths worldwide due to ischemic heart disease could be attributed to smoking. The leading causes of death in the United States Just as it is the leading cause of death worldwide, heart disease is also the leading cause of death in the United States. In 2023, heart disease accounted for ** percent of all deaths in the United States. Cancer was the second leading cause of death in the U.S. that year, followed by accidents. As of 2023, the odds that a person in the United States will die from heart disease is * in *. However, rates of death due to heart disease have actually declined in the U.S. over the past couple decades. From 2000 to 2022, there was a *** percent decline in heart disease deaths. On the other hand, deaths from Alzheimer’s disease saw an increase of *** percent over this period. Alzheimer’s disease is currently the sixth leading cause of death in the United States, accounting for **** deaths per 100,000 population in 2023.

As of 2023, the countries with the highest death rates worldwide were Monaco, Bulgaria, and Latvia. In these countries, there were ** to ** deaths per 1,000 people. The country with the lowest death rate is Qatar, where there is just *** death per 1,000 people. Leading causes of death The leading causes of death worldwide are, by far, cardiovascular diseases, accounting for ** percent of all deaths in 2021. That year, there were **** million deaths worldwide from ischaemic heart disease and **** million from stroke. Interestingly, a worldwide survey from that year found that people greatly underestimate the proportion of deaths caused by cardiovascular disease, but overestimate the proportion of deaths caused by suicide, interpersonal violence, and substance use disorders. Death in the United States In 2023, there were around **** million deaths in the United States. The leading causes of death in the United States are currently heart disease and cancer, accounting for a combined ** percent of all deaths in 2023. Lung and bronchus cancer is the deadliest form of cancer worldwide, as well as in the United States. In the U.S. this form of cancer is predicted to cause around ****** deaths among men alone in the year 2025. Prostate cancer is the second-deadliest cancer for men in the U.S. while breast cancer is the second deadliest for women. In 2023, the tenth leading cause of death in the United States was COVID-19. Deaths due to COVID-19 resulted in a significant rise in the total number of deaths in the U.S. in 2020 and 2021 compared to 2019, and it was the third leading cause of death in the U.S. during those years.

BackgroundHypertensive heart disease (HHD) is a major global public health issue resulting from hypertension-induced end-organ damage. The aim of this study was to examine the global impact, risk factors, and age-period-cohort (APC) model of HHD from 1990 to 2019.MethodsData from the 2019 Global Burden of Disease were used to assess age-adjusted HHD prevalence, disability-adjusted life years (DALYs), mortality rates, and contributions of HHD risk factors with 95% uncertainty intervals (UIs). APC models were used to analyze global age, period, and cohort mortality trends for HHD.ResultsIn 2019, 18.6 million prevalent HHD cases led to 1.16 million fatalities and 21.51 million DALYs. Age-adjusted rates were 233.8 (95%UI = 170.5–312.9) per 100,000 individuals for prevalence, 15.2 (11.2–16.7) for mortality, and 268.2 (204.6–298.1) for DALYs. Regionally, the Cook Islands (703.1), Jordan (561.6), and Kuwait (514.9) had the highest age-standardized incidence of HHD in 2019. There were significant increases in HHD prevalence in Andean Latin America (16.7%), western sub-Saharan Africa (5.6%), and eastern sub-Saharan Africa (4.6%). Mortality rate varied widely among countries. Risk factors like elevated systolic blood pressure and high body mass index significant influenced DALY rates, especially in females. The APC model revealed an association between mortality rates and age, with a decreasing mortality risk over time and improved survival rates for a later birth cohort.ConclusionsDespite the reduction in prevalence, HHD remains a significant public health issue, particularly in nations with low sociodemographic indices. To alleviate the impact of HHD, prevention efforts should concentrate on the management of hypertension, weight loss, and lifestyle improvement.

Summary statistics for natural disaster impacts and mortality and YLL rates due to IHD during two periods in 193 countries.

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.

Ischemic heart disease (IEC-9: 410-414 and CIE10: I20-I25)* was responsible for 7.8 % of deaths in the study period. In order of frequency it ranked second among the causes of death and from 1990 to 2004 it experienced an average annual decrease of 3.3 %. The distribution of mortality in the CAPV does not respond to an obvious geographical pattern, showing scattered high and low-risk sections.

Afghanistan Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 32.700 % in 2021. This records a decrease from the previous number of 34.800 % for 2020. Afghanistan Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 37.400 % from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 43.500 % in 2001 and a record low of 32.700 % in 2021. Afghanistan 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 Afghanistan – Table AF.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/].

Complication-related characteristics in relation to cumulative incidence of death and survival probability of patients admitted to the pediatric ICU of ACSH, N = 223.

Organ/System-affected related characteristics in relation to cumulative incidence of death and survival probability of patients admitted to the pediatric ICU of ACSH, N = 223.

Context

The data are simulated based on real hospital administrative data for England called Hospital Episodes Statistics. Every public (National Health Service, NHS) hospital in the country must submit records for every admission; private hospitals also submit records for any NHS patients that they treat. Your simulated extract contains a random sample of emergency (unplanned) admissions for heart failure (ICD10 code I50). Here's a list of the fields and an explanation for some of them. Many of the fields are comorbidities coded as 0/1, where 1 indicates that the patient had it recorded.

Content :

death (0/1)

los (hospital length of stay in nights)

age (in years)

gender (1=male, 2=female)

cancer

cabg (previous heart bypass)

crt (cardiac resynchronisation device - a treatment for heart failure)

defib (defibrillator implanted)

dementia

diabetes (any type)

hypertension

ihd (ischaemic heart disease)

mental_health (any mental illness)

arrhythmias

copd (chronic obstructive lung disease)

obesity

pvd (peripheral vascular disease)

renal_disease

valvular_disease (disease of the heart valves)

metastatic_cancer

pacemaker

pneumonia

prior_appts_attended (number of outpatient appointments attended in the previous year)

prior_dnas (number of outpatient appointments missed in the previous year)

pci (percutaneous coronary intervention)

stroke (history of stroke)

senile

quintile (socio-economic status for patient's neighbourhood, from 1 (most affluent) to 5 (poorest))

ethnicgroup (see below for categories)

fu_time (follow-up time, i.e. time in days since admission to hospital)

Ethnic group has the following categories in this extract:

1=white

2=black

3=Indian subcontinent

8=not known

9=other

Inspiration :

Factors associated with hospital emergency readmission and mortality rates in patients with heart failure.

Acute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach. The purpose of this research is to implement the principles of explainable artificial intelligence (XAI) in the analysis of hematological predictors for AMI. In this retrospective analysis, 477 (48.6%) patients with AMI and 504 (51.4%) healthy individuals were enrolled and assessed in predicting AMI. Of the patients with AMI, 182 (38%) had an ST-segment elevation MI (STEMI), and 295 (62%) had a non-ST-segment elevation MI (NSTEMI). Demographic and hematological information of the patients was analyzed to determine AMI. The XAI approach combined with machine learning approaches (Extreme Gradient Boosting, XGB; Adaptive Boosting, AB; Light Gradient Boosting Machine, LGBM) was applied for the estimation of AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). The SHAP approach was used to explain the predictions intuitively. After selecting the 10 most important hematological parameters for AMI, the LGBM model achieved 83% and 74% accuracy for prediction of AMI, and distinguishing subgroups of AMI (STEMI and NSTEMI), respectively. SHAP results showed that neutrophil (NEU), white blood cell (WBC), platelet width of distribution (PDW), and basophil (BA) were the most important for AMI prediction. Mean corpuscular volume (MCV), BA, monocytes (MO), and lymphocytes (LY) were the most important hematological parameters that distinguish STEMI from NSTEMI. The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up of patients with AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). Analyzing readily accessible hemogram parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals.