In 2021, the leading causes of death in Africa were lower respiratory infections, malaria, and stroke. That year, lower respiratory infections resulted in around 65 deaths per 100,000 population in Africa. Leading causes of death in Africa vs the world Worldwide, the top three leading causes of death in 2021 were heart disease, COVID-19, and stroke. At that time, some of the leading causes of death in Africa, such as lower respiratory infections and stroke, were among the leading causes worldwide, but there were also stark differences in the leading causes of death in Africa compared to the leading causes worldwide. For example, malaria, diarrheal disease, and preterm birth complications were among the top ten leading causes of death in Africa, but not worldwide. Furthermore, HIV/AIDS was the eighth leading cause of death in Africa at that time, but was not among the top ten leading causes worldwide. HIV/AIDS in Africa Although HIV/AIDS impacts every region of the world, Africa is still the region most impacted by this deadly virus. Worldwide, there are around 40 million people currently living with HIV, with about 20.8 million found in Eastern and Southern Africa and 5.1 million in Western and Central Africa. The countries with the highest HIV prevalence worldwide include Eswatini, Lesotho, and South Africa, with the leading 20 countries by HIV prevalence all found in Africa. However, due in part to improvements in education and awareness, the prevalence of HIV in many African countries has decreased. For example, in Botswana, the prevalence of HIV decreased from 26.1 percent to 16.6 percent in the period from 2000 to 2023.

Lower respiratory infections were the leading cause of death in Africa in 2021. Lower respiratory infections accounted for 8.6 percent of all deaths in Africa that year, followed by malaria, which was responsible for 6.5 percent of deaths. Although HIV is not one of the leading causes of death worldwide, it remains within the top 10 leading causes of death in Africa. As of 2023, the top 15 countries with the highest prevalence of new HIV infections are all found in Africa. HIV/AIDS HIV (human immunodeficiency virus) is an infectious sexually transmitted disease that is transmitted via exposure to infected semen, blood, vaginal and anal fluids and breast milk. HIV weakens the human immune system, resulting in the affected person being unable to fight off opportunistic infections. HIV/AIDS was the eighth leading cause of death in Africa in 2021, accounting for around 4.6 percent of all deaths, or around 405,790 total deaths. HIV Treatment Although there is currently no effective cure for HIV, death can be prevented by taking HIV antiretroviral therapy (ART). Access to ART worldwide has increased greatly over the last decade; however, there are still barriers to access in some of the countries most impacted by HIV. The African countries with the highest percentage of HIV infected children who were receiving antiretroviral treatment were Eswatini, Lesotho, and Uganda.

Latest data from 2017 show that Tuberculosis was with approximately 28,700 cases the leading cause of death in South Africa. Diabetes mellitus caused 25 thousand casualties and was the second highest underlying cause of death, whereas 22,259 people passed away due to Cerebrovascular diseases (e.g. stroke, carotid stenosis). HIV/AIDS was the fifth ranked disease, causing 21,439 casualties. In total, roughly 20.6 million people in East and Southern Africa lived with HIV in 2018, causing over 300,000 AIDS-related deaths.

Abstract

This cumulative dataset contains statistics on mortality and causes of death in South Africa covering the period 1997-2019. The mortality and causes of death dataset is part of a regular series published by Stats SA, based on data collected through the civil registration system. This dataset is the most recent cumulative round in the series which began with the separately available dataset Recorded Deaths 1996.

The main objective of this dataset is to outline emerging trends and differentials in mortality by selected socio-demographic and geographic characteristics for deaths that occurred in the registered year and over time. Reliable mortality statistics, are the cornerstone of national health information systems, and are necessary for population health assessment, health policy and service planning; and programme evaluation. They are essential for studying the occurrence and distribution of health-related events, their determinants and management of related health problems. These data are particularly critical for monitoring the Sustainable Development Goals (SDGs) and Agenda 2063 which share the same goal for a high standard of living and quality of life, sound health and well-being for all and at all ages. Mortality statistics are also required for assessing the impact of non-communicable diseases (NCD's), emerging infectious diseases, injuries and natural disasters.

Geographic coverage

The survey has national coverage.

Analysis unit

Individuals

Universe

This dataset is based on information on mortality and causes of death from the South African civil registration system. It covers all death notification forms from the Department of Home Affairs for deaths that occurred in 1997-2019, that reached Stats SA during the 2020/2021 processing phase.

Kind of data

Administrative records

Mode of data collection

Other

Research instrument

The registration of deaths is captured using two instruments: form BI-1663 and form DHA-1663 (Notification/Register of death/stillbirth).

Data appraisal

This cumulative dataset is part of a regular series published by Stats SA and includes all previous rounds in the series (excluding Recorded Deaths 1996). Stats SA only includes one variable to classify the occupation group of the deceased (OccupationGrp) in the current round (1997-2018). Prior to 2016, Stats SA included both occupation group (OccupationGrp) and industry classifcation (Industry) in all previous rounds. Therefore, DataFirst has made the 1997-2015 cumulative round available as a separately downloadable dataset which includes both occupation group and industry classification of the deceased spanning the years 1997-2015.

The leading causes of death among Black residents in the United States in 2022 included diseases of the heart, cancer, unintentional injuries, and stroke. The leading causes of death for African Americans generally reflects the leading causes of death for the entire United States population. However, a major exception is that death from assault or homicide is the seventh leading cause of death among African Americans, but is not among the ten leading causes for the general population. Homicide among African Americans The homicide rate among African Americans has been higher than that of other races and ethnicities for many years. In 2023, around 9,284 Black people were murdered in the United States, compared to 7,289 white people. A majority of these homicides are committed with firearms, which are easily accessible in the United States. In 2022, around 14,189 Black people died by firearms. However, suicide deaths account for over half of all deaths from firearms in the United States. Cancer disparities There are also major disparities in access to health care and the impact of various diseases. For example, the incidence rate of cancer among African American males is the greatest among all ethnicities and races. Furthermore, although the incidence rate of cancer is lower among African American women than it is among white women, cancer death rates are still higher among African American women.

Abstract

This dataset contains statistics on deaths in South Africa in 2009. The registration of deaths in South Africa is regulated by the Births and Deaths Registration Act, 51 of 1992. The South African Department of Home Affairs (DHA) is responsible for the registration of deaths in South Africa. The data is collected with two instruments: The death register and the medical certificate in respect of death. The staff of the DHA Registrar of Deaths section fills in the former while the medical practitioner attending to the death completes the latter. Causes of death are coded by the Department of Home Affairs according to the tenth revision of the International Classification of Diseases (ICD-10) ICD-10, as required by the World Health Organization for their member countries. The data is used by the Department of Home Affairs to update the Population Register. The forms are sent to Statistics South Africa (Stats SA) for their use for statistical purposes. From the two forms sent to Stats SA, the following data items of the deceased are extracted: place of residence, place of death, date of death, month and year of registration, sex, marital status, occupation, underlying cause of death, whether or not the death was certified by a medical practitioner, and whether or not the deceased died in a health institution or nursing home. From 1991 death notifications do not require data on population group, and therefore this dataset includes death data for all population groups. This dataset excludes 2010 deaths that were not registered, and late registrations which would not have been available to Stats SA in time for the production of the dataset.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The data covers all deaths that occurred in 2009 and registered at the Department of Home Affairs.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

The data is collected with notification / death register / still birth instrument.

The leading cause of death in low-income countries worldwide in 2021 was lower respiratory infections, followed by stroke and ischemic heart disease. The death rate from lower respiratory infections that year was 59.4 deaths per 100,000 people. While the death rate from stroke was around 51.6 per 100,000 people. Many low-income countries suffer from health issues not seen in high-income countries, including infectious diseases, malnutrition and neonatal deaths, to name a few. Low-income countries worldwide Low-income countries are defined as those with per gross national incomes (GNI) per capita of 1,045 U.S. dollars or less. A majority of the world’s low-income countries are located in sub-Saharan Africa and South East Asia. Some of the lowest-income countries as of 2023 include Burundi, Sierra Leone, and South Sudan. Low-income countries have different health problems that lead to worse health outcomes. For example, Chad, Lesotho, and Nigeria have some of the lowest life expectancies on the planet. Health issues in low-income countries Low-income countries also tend to have higher rates of HIV/AIDS and other infectious diseases as a consequence of poor health infrastructure and a lack of qualified health workers. Eswatini, Lesotho, and South Africa have some of the highest rates of new HIV infections worldwide. Likewise, tuberculosis, a treatable condition that affects the respiratory system, has high incident rates in lower income countries. Other health issues can be affected by the income of a country as well, including maternal and infant mortality. In 2023, Afghanistan had one of the highest rates of infant mortality rates in the world.

A database detailing the top twenty single underlying causes of death in South Africa, with separate rankings for males and females.

Two datasets that explore causes of death due to cancer in South Africa, drawing on data from the Revised Burden of Disease estimates for the Comparative Risk Factor Assessment for South Africa, 2000.

The number and percentage of deaths due to cancer by cause are ranked for persons, males and females in the tables below.

Lung cancer is the leading cause of cancer in SA accounting for 17% of all cancer deaths. This is followed by oesophagus Ca which accounts for 13%, cervix cancer accounting for 8%, breast cancer accounting for 8% and liver cancer which accounts for 6% of all cancers. Many more males suffer from lung and oesophagus cancer than females.

UNICEF's country profile for South Africa, including under-five mortality rates, child health, education and sanitation data.

This statistic presents the death rates for the five leading causes of deaths among adolescents aged 10 to 19 years in each WHO region in 2015 (per 100,000 population). In low- and middle-income countries in Africa the leading cause of death among those aged 10 to 19 years was lower respiratory infections with a death rate of 21.8 per 100,000 population. In high income WHO countries road injury was the leading cause of death among adolescents with a rate of 4.6. Road injury was the only cause to be in the five leading causes of death among adolescents in every WHO region.

BackgroundAround 8.8 million children under-five die each year, mostly due to infectious diseases, including malaria that accounts for 16% of deaths in Africa, but the impact of international financing of malaria control on under-five mortality in sub-Saharan Africa has not been examined. Methods and FindingsWe combined multiple data sources and used panel data regression analysis to study the relationship among investment, service delivery/intervention coverage, and impact on child health by observing changes in 34 sub-Saharan African countries over 2002–2008. We used Lives Saved Tool to estimate the number of lives saved from coverage increase of insecticide-treated nets (ITNs)/indoor residual spraying (IRS). As an indicator of outcome, we also used under-five mortality rate. Global Fund investments comprised more than 70% of the Official Development Assistance (ODA) for malaria control in 34 countries. Each $1 million ODA for malaria enabled distribution of 50,478 ITNs [95%CI: 37,774–63,182] in the disbursement year. 1,000 additional ITNs distributed saved 0.625 lives [95%CI: 0.369–0.881]. Cumulatively Global Fund investments that increased ITN/IRS coverage in 2002–2008 prevented an estimated 240,000 deaths. Countries with higher malaria burden received less ODA disbursement per person-at-risk compared to lower-burden countries ($3.90 vs. $7.05). Increased ITN/IRS coverage in high-burden countries led to 3,575 lives saved per 1 million children, as compared with 914 lives in lower-burden countries. Impact of ITN/IRS coverage on under-five mortality was significant among major child health interventions such as immunisation showing that 10% increase in households with ITN/IRS would reduce 1.5 [95%CI: 0.3–2.8] child deaths per 1000 live births. ConclusionsAlong with other key child survival interventions, increased ITNs/IRS coverage has significantly contributed to child mortality reduction since 2002. ITN/IRS scale-up can be more efficiently prioritized to countries where malaria is a major cause of child deaths to save greater number of lives with available resources.

The number of deaths registered in Africa was projected at 11.7 million in 2023. This was a decline from the past two year, when the continent experienced rising COVID-19 related deaths. Furthermore, 2021 had the highest registered death number with slightly over 12 million deaths.

This statistic shows the 20 countries* with the highest infant mortality rate in 2024. An estimated 101.3 infants per 1,000 live births died in the first year of life in Afghanistan in 2024. Infant and child mortality Infant mortality usually refers to the death of children younger than one year. Child mortality, which is often used synonymously with infant mortality, is the death of children younger than five. Among the main causes are pneumonia, diarrhea – which causes dehydration – and infections in newborns, with malnutrition also posing a severe problem. As can be seen above, most countries with a high infant mortality rate are developing countries or emerging countries, most of which are located in Africa. Good health care and hygiene are crucial in reducing child mortality; among the countries with the lowest infant mortality rate are exclusively developed countries, whose inhabitants usually have access to clean water and comprehensive health care. Access to vaccinations, antibiotics and a balanced nutrition also help reducing child mortality in these regions. In some countries, infants are killed if they turn out to be of a certain gender. India, for example, is known as a country where a lot of girls are aborted or killed right after birth, as they are considered to be too expensive for poorer families, who traditionally have to pay a costly dowry on the girl’s wedding day. Interestingly, the global mortality rate among boys is higher than that for girls, which could be due to the fact that more male infants are actually born than female ones. Other theories include a stronger immune system in girls, or more premature births among boys.

The project, based at the University of Greenwich, UK and Stellenbosch University, South Africa, aimed to examine epidemiologic transitions by identifying and quantifying the drivers of change in CVD risk in the middle-income country of South Africa compared to the high-income nation of England. The project produced a harmonised dataset of national surveys measuring CVD risk factors in South Africa and England for others to use in future work. The harmonised dataset includes microdata from nationally-representative surveys in South Africa derived from the Demographic and Health Surveys, National Income Dynamics Study, South Africa National Health and Nutrition Examination Survey and Study on Global Ageing and Adult Health, covering 11 cross-sections and approximately 156,000 individuals aged 15+ years, representing South Africa’s adult population from 1998 to 2017.

Data for England come from 17 Health Surveys for England (HSE) over the same time period, covering over 168,000 individuals aged 16+ years, representing England’s adult population.

This study uses existing data to identify drivers of recent health transitions in South Africa compared to England. The global burden of non-communicable diseases (NCDs) on health is increasing. Cardiovascular diseases (CVD) in particular are the leading causes of death globally and often share characteristics with many major NCDs. Namely, they tend to increase with age and are influenced by behavioural factors such as diet, exercise and smoking. Risk factors for CVD are routinely measured in population surveys and thus provide an opportunity to study health transitions. Understanding the drivers of health transitions in countries that have not followed expected paths (eg, South Africa) compared to those that exemplified models of 'epidemiologic transition' (eg, England) can generate knowledge on where resources may best be directed to reduce the burden of disease. In the middle-income country of South Africa, CVD is the second leading cause of death after HIV/AIDS and tuberculosis (TB). Moreover, many of the known risk factors for NCDs like CVD are highly prevalent. Rates of hypertension are high, with recent estimates suggesting that over 40% of adults have high blood pressure. Around 60% of women and 30% of men over 15 are overweight in South Africa. In addition, excessive alcohol consumption, a risk factor for many chronic diseases, is high, with over 30% of men aged 15 and older having engaged in heavy episodic drinking within a 30-day period. Nevertheless, infectious diseases such as HIV/AIDS remain the leading cause of death, though many with HIV/AIDS and TB also have NCDs. In high-income countries like England, by contrast, NCDs such as CVD have been the leading causes of death since the mid-1900s. However, CVD and risk factors such as hypertension have been declining in recent decades due to increased prevention and treatment. The major drivers of change in disease burden have been attributed to factors including ageing, improved living standards, urbanisation, lifestyle change, and reduced infectious disease. Together, these changes are often referred to as the epidemiologic transition. However, recent research has questioned whether epidemiologic transition theory accurately describes the experience of many low- and middle-income countries or, in fact, of high-income nations such as England. Furthermore, few studies have empirically tested the relative contributions of demographic, behavioural, health and economic factors to trends in disease burden and risk, particularly on the African continent. In addition, many social and environmental factors are overlooked in this research. To address these gaps, our study will use population measurements of CVD risk derived from surveys in South Africa over nearly 20 years in order to examine whether and to what extent demographic, behavioural, environmental, medical, social and other factors contribute to recent health trends and transitions. We will compare these trends to those occurring in England over the same time period. Thus, this analysis seeks to illuminate the drivers of health transitions in a country which is assumed to still be 'transitioning' to a chronic disease profile but which continues to have a high infectious disease burden (South Africa) as compared to a country which is assumed to have already transitioned following epidemiological transition theory (England). The analysis will employ modelling techniques on pooled cross-sectional data to examine how various factors explain the variation in CVD risk over time in representative population samples from South Africa and England. The results of this analysis may help to identify some of the main contributors to recent changes in CVD risk in South Africa and England. Such information can be used to pinpoint potential areas for intervention, such as social policy and services, thereby helping to set priorities for governmental and...

Background: Stroke is a leading cause of death and disability in sub-Saharan Africa with increasing incidence. In Kenya, it is a neglected condition with a paucity of evidence despite its need for urgent care and hefty economic burden. Therefore, we reviewed studies on stroke epidemiology, care, and outcomes in Kenya to highlight existing evidence and gaps on stroke in Kenya.Methods: We reviewed all published studies on epidemiology, care, and outcomes of stroke in Kenya between 1 January 1990 to 31 December 2020 from PubMed, Web of Science, EBSCOhost, Scopus, and African journal online. We excluded case reports, reviews, and commentaries. We used the Newcastle-Ottawa scale adapted for cross-sectional studies to assess the quality of included studies.Results: Twelve articles were reviewed after excluding 111 duplicates and 94 articles that did not meet the inclusion criteria. Five studies were of low quality, two of medium quality, and five of high quality. All studies were hospital-based and conducted between 2003 and 2017. Of the included studies, six were prospective and five were single-center. Stroke patients in the studies were predominantly female, in their seventh decade with systemic hypertension. The mortality rate ranged from 5 to 27% in-hospital and 23.4 to 26.7% in 1 month.Conclusions: Our study highlights that stroke is a significant problem in Kenya, but current evidence is of low quality and limited in guiding policy development and improving stroke care. There is thus a need for increased investment in hospital- and community-based stroke care and research.

This dataset contains data from WHO's data portal covering the following categories:

Adolescent, Ageing, Air pollution, Assistive technology, Child, Child mortality, Cross-cutting, Dementia diagnosis, treatment and care, Environment and health, Foodborne Diseases Estimates, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, Global Patient Safety Observatory, Global strategy, HIV, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, Malaria, Maternal and newborn, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, SDG Target 3.8 | Achieve universal health coverage (UHC), Sexually Transmitted Infections, Tobacco control, Tuberculosis, Vaccine-preventable communicable diseases, Violence prevention, Water, sanitation and hygiene (WASH), World Health Statistics.

For links to individual indicator metadata, see resource descriptions.

Trends in the mortality rates and mean age at death of endometrial cancer in South Africa (1999–2018).

The impacts of high ambient temperatures on mortality in humans and domestic animals are well understood. However, much less is known about how hot weather affects mortality in wild animals. High ambient temperatures have been associated with African wild dog Lycaon pictus pup mortality, suggesting that high temperatures might also be linked to high adult mortality. We analysed mortality patterns in African wild dogs radio-collared in Kenya (0°N), Botswana (20°S), and Zimbabwe (20°S), to examine whether ambient temperature was associated with adult mortality. We found that high ambient temperatures were associated with increased adult wild dog mortality at the Kenya site, and there was some evidence for temperature associations with mortality at the Botswana and Zimbabwe sites. At the Kenya study site, which had the highest human impact, high ambient temperatures were associated with increased risks of wild dogs being killed by people, and by domestic dog diseases. In contrast, temperature was not associated with the risk of snare-related mortality at the Zimbabwe site, which had the second-highest human impact. Causes of death varied markedly between sites. Pack size was positively associated with survival at all three sites. These findings suggest that while climate change may not lead to new causes of mortality, rising temperatures may exacerbate existing anthropogenic threats to this endangered species, with implications for conservation. This evidence suggests that temperature-related mortality, including interactions between temperature and other anthropogenic threats, should be investigated in a greater number of species to understand and mitigate the likely impacts of climate change. Methods Study sites We analysed adult African wild dog mortality at three sites: the Ewaso ecosystem, Kenya; the Okavango Delta, Botswana; and Savé Valley Conservancy, Zimbabwe. All three study sites fall within semi-arid savanna ecosystems.

Field Data Collection At the Kenya study site 130 African wild dogs (56 female, 74 male) from 41 packs were monitored using either Vectronics GPS collars (GPS Plus, Vectronic Aerospace GmbH), Televilt GPS collars (GPS-Posrec, Televilt, Lindesberg, Sweden), Berlin, Germany), or VHF radio-collars (Telonics, Mesa AZ, USA). All three collar types included a mortality sensor programmed to emit a characteristic radio signal if stationary for ≥4h. At the Zimbabwe study site, 59 African wild dogs (22 female, 37 male) from 34 packs were monitored using either radio collars or GPS collars (African Wildlife Tracking, Rietondale, Pretoria, South Africa). Using radio-collars (Sirtrack, Havelock West, New Zealand) 31 African wild dogs (10 female, 21 male) from 16 packs were monitored at the Botswana site. Collars were fitted using the procedures outlined in McNutt (1996), Woodroffe (2011) and Jackson et al. (2017). At all three sites, packs were located every 1-2 weeks where possible. Any collared animal found dead was carefully examined with the aim of establishing a cause of death. At the Kenya site necropsies were carried out on all dead individuals located. At the Botswana site cause of death was only recorded in cases where the death was directly observed, or during disease outbreaks, and therefore the majority of causes of death were unconfirmed. Most deaths at the Botswana site are likely to be due to natural causes given the low human activity in this area. For all three sites, the date of first detection of a mortality signal from the collar was used to estimate the date of death when not observed directly, and where this was not possible an estimated date of mortality was made based on the date midway between the last sighting, or the last detection of the radio-collar without a mortality signal, and the discovery of the carcass or collar. If any study animal was not observed in its resident pack for over 30 days, no mortality signal was detected, and no carcass was found, it was considered lost from the study and censored from the day of the last observation (Kenya: n=51, Zimbabwe: n=34, Botswana: n=8). If a carcass or collar was discovered more than 30 days after the last sighting (n=2), the animal was considered lost from the study due to the inaccuracy of the date of death and was censored from the date of the last sighting. Group and individual characteristics were recorded at each site. At all three sites dispersal status of the individual was recorded. Individuals were defined as dispersing if they left their pack for multiple days and did not return, otherwise they were defined as resident (Woodroffe et al. 2019b). Group size – either the pack size for resident individuals or the dispersal group size for dispersing individuals – was recorded for each individual, and was defined as the number of adults (>12 months in age) in the group. African wild dog pup-rearing involves the pups being left at a den site for the first three months of life while the majority of the rest of the pack hunt daily, bringing food back to provision the pups. This pup rearing period is referred to as denning. For each pack, denning periods were identified using either direct observations or GPS-collar data. At the Kenya site a number of other individual and pack characteristics were also monitored. Individuals’ alpha status was inferred based on consistent close association with a specific individual of the opposite sex, coordinated scent marking, and reproductive activity; all animals not identified as alpha were considered subdominant. African wild dog age was known for many individuals, otherwise it was estimated from tooth wear when the individual was collared (Woodroffe et al. 2019b). Age range at collaring ranged from 1 to 7 years old (mean: 2.43 ±1.27). The age of the majority of individuals at the Zimbabwe and Botswana sites was not known. Weather data is from weather stations within the field site at Mpala research station at the Kenya site (detailed in Caylor K., Gitonga, J. and Martins 2016), 30km outside the study site at Maun airport for the Botswana site and the Middle Sabi Research Station, 12km from the study area boundary at the Zimbabwe research site.

Data Processing The average mean temperature was taken on a 90 day rolling average at the Kenya and Zimbabwe sites, and a 30 day rolling average at the Botswana site. Rainfall was summed over a 30 day rolling time period at the Kenya and Botswana sites and a 90 day rolling period at the Zimbabwe site.

UNICEF's country profile for Chad, including under-five mortality rates, child health, education and sanitation data.