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

56 million people died in 2017. What did they die from?

The Global Burden of Disease is a major global study on the causes of death and disease published in the medical journal The Lancet. These estimates of the annual number of deaths dataset are shown here.

Downloaded https://ourworldindata.org/causes-of-death dataset from first chart as CSV. Loaded the raw file in tableau prep for exploratory data distribution and applying some pivoting and cleaning. The output were uploaded in this dataset as well the original raw file.

Please notice the raw file have some country agrupations by region, but there is no data indicating it's an aggregation, so be careful analyzing the whole dataset guessing there are just countries as level of detail data. In order to be more accurate, I begin to analyze countries using the ISO Country code ("Code" named column). If you have no clue as me what country ZAF is, Google is your best friend (South Africa) 😉.

Abstract

This cumulative dataset contains statistics on mortality and causes of death in South Africa covering the period 1997-2015. The mortality and causes of death dataset are part of a regular series published by Stats SA, based on data collected through the civil registration system. The first dataset in the series is 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

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 1997-2015, that reached Stats SA during the 2016/2017 processing phase.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

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 (1997-2017). Prior to 2016, Stats SA included both occupation group (OccupationGrp) and industry classification (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.

In Nigeria, Chad, South Sudan, and the Central African Republic, the maternal mortality rate was over 650 per 100,000 live births in 2023, respectively. Nigeria recorded the highest rate on the continent. That year, for every 100,000 children, 993 mothers died from any cause related to or aggravated by pregnancy or its management. The maternal death rate in Chad equaled 748. South Sudan and the Central African Republic followed with 692 deaths per 100,000 live births each.

In 2022, the estimated number of deaths in South Africa reached *******. This was lower compared to the previous year when the deaths in the country reached the highest level since 2002, at *******. From 2006 onwards (except in 2015), the number of fatalities dropped annually until 2017. In 2021, however, the count of deaths jumped significantly due to the global coronavirus (COVID-19) pandemic.

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 nongovernmental action to control the CVD epidemic and improve health.

Male and female homicide rates in South African 2017 mortuary survey (weighted), compared to global rates from the Global Burden of Disease (GBD) study, 2017, [9] by external cause of death and age.

Descriptive male and female homicide victim characteristics in South Africa in 2017 by external cause of death, age, province, population group, month of year, day of week and alcohol-relatedness (weighted).

As of 2023, the mortality rate of infants aged under one-year-old in Nigeria was measured at 55.17. This means that there were about 55 deaths of children under the age of one year per 1,000 live births. Child mortality rates in Africa are very high. Among the countries with the highest infant mortality rate in the world, almost all of them are African countries. Similarly, maternal mortality rates are high. In 2017, Nigeria recorded 917 deaths of mothers per 100,000 live births.

Somalia is one of the countries with the highest terrorism threat levels in the world. In 2023, the terrorism index in this East-African country stood at 7.8 points, the third highest in Africa. In the same year, it recorded the eighth-largest number of deaths related to terrorism worldwide. The militant group Al-Shabaab, a jihadist fundamentalist group, was responsible for 99 percent of all terror-related deaths in Somalia in 2023, resulting in 429 deaths.

IntroductionSevere acute malnutrition (SAM) is a major cause of child mortality in sub-Saharan Africa, yet recent data from Mulanje District Hospital, Malawi, showed higher mortality among non-SAM under-five children. This unexpected trend highlights a knowledge gap, as no studies in Malawi have compared time to death and its predictors between SAM and non-SAM children. This study aimed to fill that gap by examining and comparing mortality timing and predictors in both groups.MethodsA retrospective cohort study was conducted using medical records of 454 randomly selected under-five children admitted to Mulanje District Hospital between January 2017 and February 2021. Data were collected using structured forms and analysed in STATA version 16. Cox proportional hazards regression was used to identify mortality predictors, with significance set at p 

Foodborne disease is a significant global health problem, with low- and middle-income countries disproportionately affected. Given that most fresh animal and vegetable foods in LMICs are bought in informal food systems, much the burden of foodborne disease in LMIC is also linked to informal markets. Developing estimates of the national burden of foodborne disease and attribution to specific food products will inform decision-makers about the size of the problem and motivate action to mitigate risks and prevent illness. This study provides estimates for the burden of foodborne disease caused by selected hazards in two African countries (Burkina Faso and Ethiopia) and attribution to specific foods. Country-specific estimates of the burden of disease in 2010 for Campylobacter spp., enterotoxigenic Escherichia coli (ETEC), Shiga-toxin producing E. coli and non-typhoidal Salmonella enterica were obtained from WHO and updated to 2017 using data from the Global Burden of Disease study. Attribution data obtained from WHO were complemented with a dedicated Structured Expert Judgement study to estimate the burden attributable to specific foods. Monte Carlo simulation methods were used to propagate uncertainty. The burden of foodborne disease in the two countries in 2010 was largely similar to the burden in the region except for higher mortality and disability-adjusted life years (DALYs) due to Salmonella in Burkina Faso. In both countries, Campylobacter caused the largest number of cases, while Salmonella caused the largest number of deaths and DALYs. In Burkina Faso, the burden of Campylobacter and ETEC increased from 2010 to 2017, while the burden of Salmonella decreased. In Ethiopia, the burden of all hazards decreased. Mortality decreased relative to incidence in both countries. In both countries, the burden of poultry meat (in DALYs) was larger than the burden of vegetables. In Ethiopia, the burdens of beef and dairy were similar, and somewhat lower than the burden of vegetables. The burden of foodborne disease by the selected pathogens and foods in both countries was substantial. Uncertainty distributions around the estimates spanned several orders of magnitude. This reflects data limitations, as well as variability in the transmission and burden of foodborne disease associated with the pathogens considered.

BackgroundCryptococcal meningitis (CM) remains a major cause of death among people living with HIV in rural sub-Saharan Africa. We previously reported that a CM diagnosis and treatment program (CM-DTP) improved hospital survival for CM patients in rural, northern Uganda. This study aimed to evaluate the impact on long-term survival.MethodsWe conducted a retrospective study at Lira Regional Referral Hospital in Uganda evaluating long-term survival (≥1 year) of CM patients diagnosed after CM-DTP initiation (February 2017-September 2021). We compared with a baseline historical group of CM patients before CM-DTP implementation (January 2015-February 2017). Using Cox proportional hazards models, we assessed time-to-death in these groups, adjusting for confounders.ResultsWe identified 318 CM patients, 105 in the Historical Group, and 213 in the CM-DTP Group. The Historical Group had a higher 30-day mortality of 78.5% compared to 42.2% in the CM-DTP Group. The overall survival rate for the CM-DTP group at three years was 25.6%. Attendance at follow-up visits (HR:0.13, 95% CI: [0.03–0.53], p