In 2023, the average crude death rate in Africa was **** deaths per 1,000 people. The mortality rate on the continent has decreased gradually since the 2000s. In comparison, the death rate stood at roughly **** deaths per 1,000 population in 2000. Decreasing mortality, together with high fertility and rising life expectancy, is a key driver of Africa's population growth.

South Africa: Death rate, per 1000 people: The latest value from 2023 is 9.24 deaths per 1000 people, a decline from 9.4 deaths per 1000 people in 2022. In comparison, the world average is 7.70 deaths per 1000 people, based on data from 196 countries. Historically, the average for South Africa from 1960 to 2023 is 10.64 deaths per 1000 people. The minimum value, 8.23 deaths per 1000 people, was reached in 1990 while the maximum of 14.2 deaths per 1000 people was recorded in 1960.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

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.

The above patient numbers and models are the basis for crude and adjusted mortality rate ratios as presented in Table 2 and Figure 3 and the predictions in Figure 4. The associations in these models are presented as adjusted rate ratios with corresponding 95% CIs in parentheses.aDeaths in South Africa represent the estimated number of deaths after correction for mortality under-ascertainment through record linkage and re-weighting. The proportions of deaths that were documented prior to record linkage were 63%, 53%, 51%, 47%, and 43% for the successive durations on ART reflected in the table.Full multivariable models used to calculate mortality rate ratios comparing Europe and North America to South Africa by duration on antiretroviral therapy.

South Africa ZA: Mortality Rate: Under-5: per 1000 Live Births data was reported at 43.300 Ratio in 2016. This records a decrease from the previous number of 44.100 Ratio for 2015. South Africa ZA: Mortality Rate: Under-5: per 1000 Live Births data is updated yearly, averaging 66.000 Ratio from Dec 1974 (Median) to 2016, with 43 observations. The data reached an all-time high of 125.500 Ratio in 1974 and a record low of 43.300 Ratio in 2016. South Africa ZA: Mortality Rate: Under-5: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Health Statistics. Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

aAdjustments were for baseline gender, CD4 count, clinical stage, viral load, and calendar period and are detailed in Table 3.bMortality rates were predicted from a multivariable model for each region and ART duration for a common group of patients (women aged 30–45 starting ART with a CD4 count 100–199 cells/µl, advanced clinical stage, viral load 4–5 log10 copies/ml, and starting ART in 2004–2007), and then corrected by the inverse of the weighted self-assessed completeness of mortality ascertainment for each region.cBased on cohort-assessed completeness of mortality ascertainment and including seven European and six North American cohorts.dAll North American cohorts reported linking to death registries at least annually, whereas only three European cohorts provided linkage—one annually in patients

South Africa ZA: Mortality Rate: Infant: Female: per 1000 Live Births data was reported at 30.100 Ratio in 2016. This records a decrease from the previous number of 31.500 Ratio for 2015. South Africa ZA: Mortality Rate: Infant: Female: per 1000 Live Births data is updated yearly, averaging 33.000 Ratio from Dec 1990 (Median) to 2016, with 5 observations. The data reached an all-time high of 41.200 Ratio in 2000 and a record low of 30.100 Ratio in 2016. South Africa ZA: Mortality Rate: Infant: Female: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Health Statistics. Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female live births in a given year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted Average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

In 2023, the number of deaths per 1,000 inhabitants in South Africa was ****. Between 1960 and 2023, the figure dropped by ****, though the decline followed an uneven course rather than a steady trajectory.

BackgroundHigh early mortality in patients with HIV-1 starting antiretroviral therapy (ART) in sub-Saharan Africa, compared to Europe and North America, is well documented. Longer-term comparisons between settings have been limited by poor ascertainment of mortality in high burden African settings. This study aimed to compare mortality up to four years on ART between South Africa, Europe, and North America.Methods and FindingsData from four South African cohorts in which patients lost to follow-up (LTF) could be linked to the national population register to determine vital status were combined with data from Europe and North America. Cumulative mortality, crude and adjusted (for characteristics at ART initiation) mortality rate ratios (relative to South Africa), and predicted mortality rates were described by region at 0–3, 3–6, 6–12, 12–24, and 24–48 months on ART for the period 2001–2010. Of the adults included (30,467 [South Africa], 29,727 [Europe], and 7,160 [North America]), 20,306 (67%), 9,961 (34%), and 824 (12%) were women. Patients began treatment with markedly more advanced disease in South Africa (median CD4 count 102, 213, and 172 cells/µl in South Africa, Europe, and North America, respectively). High early mortality after starting ART in South Africa occurred mainly in patients starting ART with CD4 count

BackgroundStudies on the barriers migrant women face when trying to access healthcare services in South Africa have emphasized economic factors, fear of deportation, lack of documentation, language barriers, xenophobia, and discrimination in society and in healthcare institutions as factors explaining migrants’ reluctance to seek healthcare. Our study aims to visualize some of the outcome effects of these barriers by analyzing data on maternal death and comparing the local population and black African migrant women from the South African Development Countries (SADC) living in South Africa. The heightened maternal mortality of black migrant women in South Africa can be associated with the hidden costs of barriers migrants face, including xenophobic attitudes experienced at public healthcare institutions.MethodsOur analysis is based on data on reported causes of death (COD) from the South African Department of Home Affairs (DHA). Statistics South Africa (Stats SA) processed the data further and coded the cause of death (COD) according to the WHO classification of disease, ICD10. The dataset is available on the StatsSA website (http://nesstar.statssa.gov.za:8282/webview/) for research and statistical purposes. The entire dataset consists of over 10 million records and about 50 variables of registered deaths that occurred in the country between 1997 and 2018. For our analysis, we have used data from 2002 to 2015, the years for which information on citizenship is reliably included on the death certificate. Corresponding benchmark data, in which nationality is recorded, exists only for a 10% sample from the population and housing census of 2011. Mid-year population estimates (MYPE) also exist but are not disaggregated by nationality. For this reason, certain estimates of death proportions by nationality will be relative and will not correspond to crude death rates.ResultsThe total number of female deaths recorded from the years 2002 to 2015 in the country was 3740.761. Of these, 99.09% (n = 3,707,003) were deaths of South Africans and 0.91% (n = 33,758) were deaths of SADC women citizens. For maternal mortality, we considered the total number of deaths recorded for women between the ages of 15 and 49 years of age and were 1,530,495 deaths. Of these, deaths due to pregnancy-related causes contributed to approximately 1% of deaths. South African women contributed to 17,228 maternal deaths and SADC women to 467 maternal deaths during the period under study. The odds ratio for this comparison was 2.02. In other words, our findings show the odds of a black migrant woman from a SADC country dying of a maternal death were more than twice that of a South African woman. This result is statistically significant as this odds ratio, 2.02, falls within the 95% confidence interval (1.82–2.22).ConclusionThe study is the first to examine and compare maternal death among two groups of women, women from SADC countries and South Africa, based on Stats SA data available for the years 2002–2015. This analysis allows for a better understanding of the differential impact that social determinants of health have on mortality among black migrant women in South Africa and considers access to healthcare as a determinant of health. As we examined maternal death, we inferred that the heightened mortality among black migrant women in South Africa was associated with various determinants of health, such as xenophobic attitudes of healthcare workers toward foreigners during the study period. The negative attitudes of healthcare workers toward migrants have been reported in the literature and the media. Yet, until now, its long-term impact on the health of the foreign population has not been gaged. While a direct association between the heightened death of migrant populations and xenophobia cannot be established in this study, we hope to offer evidence that supports the need to focus on the heightened vulnerability of black migrant women in South Africa. As we argued here, the heightened maternal mortality among migrant women can be considered hidden barriers in which health inequality and the pervasive effects of xenophobia perpetuate the health disparity of SADC migrants in South Africa.

South African COVID-19 Provincial Data

Timeline of Confirmed Cases, Deaths, Recoveries and Testing Rates

By [source]

About this dataset

This dataset provides a detailed look into the ongoing COVID-19 pandemic in South Africa. It contains data on the number of confirmed cases, deaths, recoveries, and testing rates at both a provincial and national level. With this data set, users are able to gain insight into the current state and trends of the pandemic in South Africa. This provides essential information necessary to help fight the epidemic and make informed decisions surrounding its prevention. Using this set as a resource will allow users to monitor how this devastating virus has impacted communities, plans for containment and treatment strategies all while taking into account cultural, socioeconomic factors that can influence these metrics. This dataset is an invaluable tool for understanding not only South Africa’s specific current challenge with COVID-19 but is relevant on a global scale whenit comes to fighting back against this virus that continues to wreak havoc aroundthe worldl

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How to use the dataset

How to use This Dataset

This Kaggle dataset provides an overview of the South African COVID-19 pandemic situation. It contains data regarding the number of confirmed cases, deaths, recoveries, and testing rates for each province at both the provincial and national level. In order to understand this dataset effectively, it is important to know what each column represents in this dataset. The following is a description of all column names that are included:

Column Names

• EC: Number of confirmed cases in Eastern Cape province
• FS: Number of confirmed cases in Free State province
• GP: Number of confirmed cases in Gauteng province
• KZN: Number of confirmed cases in KwaZulu Natal province
• LP: Number of confirmed cases in Limpopo province
• MP: Number of confirmed cases in Mpumalanga Province

• NC: Number total number orconfirmed casews in Northern Cape Province

  • NW :Number total numberurceof confirmes ed cacasesin North WestProvince

  • WC :Number totaconsfirme dcasescinWestern CapProvincee

  • UNKNOWN :Number totalnumberorconfirmesdacsesinsUnknown locations

  • Total :Totalnumberofconfrmecase sacrosseSouthAfrica

  • Source :Sourecodataset fedzile_Dbi ejweleputswaMangaungXharie thabo_MofutsanyanaRecoveriesDeathsYYMMDD

Research Ideas

• Creating an interactive map to show the spread of COVID-19 over time, with up date information about confirmed cases, deaths, recoveries and testing rates for each province or district.
• Constructing a machine learning model to predict the likely number of future cases in each province based on previous data activities.
• Comparing different districts and provinces within South Africa and drawing out trends among them with comparative graphical representations or independent analyses

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: covid19za_provincial_cumulative_timeline_recoveries.csv | Column name | Description | |:--------------|:---------------------------------------------------------------| | date | Date of the data entry. (Date) | | YYYYMMDD | Date in YYYYMMDD format. (String) | | EC | Number of confirmed cases in Eastern Cape Province. (Integer) | | FS | Number of confirmed cases in Free State Province. (Integer) | | GP | Number of confirmed cases in Gauteng Province. (Integer) | | KZN | Number of confirmed cases in Kwazulu Natal Province. (Integer) | | LP | Number of confirmed cases in Limpopo Province. (Integer) | | MP | Number of confirmed cases in Mpumalanga Province. (Integer) | | NC | Number of confirmed cases in Northern Cape Province. (Integer) | | ...

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.

Kaggle Dataset Description

Title: Mortality Rate (Under-5, Per 1000 Live Births)
Subtitle: Exploring global trends in child survival and health advancements.

Detailed Description:
This dataset contains the under-5 mortality rate, measured as the number of deaths per 1,000 live births for children under five years of age. Sourced from the World Bank, it highlights progress in child survival and health outcomes globally over decades.

Key Highlights: - Annual data for countries worldwide. - Metric: Mortality rate (under-5, per 1000 live births). - Use cases: Analyze trends, compare regional disparities, and correlate mortality rates with health and economic indicators.

4. Exploratory Data Analysis (EDA)

Notebook Ideas

1. Data Cleaning:

   • Handle missing or inconsistent data.
   • Normalize data for comparison across regions.
   • Add calculated fields like regional averages or year-over-year changes.

2. Visualizations:

   • Line Graph: Trends in under-5 mortality rates over time for selected countries.
   • Heatmap: Mortality rates by region and year.
   • Scatterplot: Correlation between mortality rates and healthcare expenditure or GDP per capita.
   • Bar Chart: Top and bottom countries by under-5 mortality for a specific year.

3. Descriptive Analysis:

   • Highlight countries with the most significant reductions in mortality.
   • Analyze regional improvements over decades (e.g., Sub-Saharan Africa vs. South Asia).

5. Predictive Analysis (Optional)

• Use time-series forecasting (e.g., ARIMA or Prophet) to predict future mortality rates for specific countries or regions.
• Explore regression models to analyze the impact of factors like healthcare expenditure on mortality reduction.

6. Kaggle Notebook

Create a Kaggle notebook with: 1. Data Cleaning: Show how missing or inconsistent values are handled. 2. EDA: Include visualizations like heatmaps, scatterplots, and line charts. 3. Insights: Highlight significant findings, such as countries with notable improvements in child survival. 4. Optional Predictive Modeling: Use regression or time-series models to project future trends.

7. Call to Action

For GitHub:

• Share the GitHub repository link on LinkedIn, Twitter, and forums like Reddit (e.g., r/datascience).
• Invite collaboration:
  • "Fork this repository to add your analyses or insights!"

GitHub Link: https://github.com/yourusername/Under5_Mortality_Trends

For Kaggle:

• Encourage upvotes:
  • "If this dataset helps you, consider upvoting it to help others discover it!"
• Include questions to engage users:
  • "Which regions have made the most progress in reducing child mortality?"
  • "What correlations can be drawn between healthcare expenditure and mortality rates?"

Kaggle Link: https://www.kaggle.com/datasets/yourusername/under5-mortality-rate

8. LinkedIn Post

Post Title:
📉 Global Trends in Under-5 Mortality Rates 🌍

Post Body:
I’m excited to share my latest dataset on under-5 mortality rates (per 1,000 live births), sourced from the World Bank. This dataset highlights progress in global health and child survival, spanning decades and covering countries worldwide.

📂 Explore the Dataset:
- GitHub Repository: https://github.com/yourusername/Under5_Mortality_Trends
- Kaggle Dataset: https://www.kaggle.com/datasets/yourusername/under5-mortality-rate

Why It Matters:

Child survival is a fundamental measure of global health progress. This dataset is ideal for:
- Trend Analysis: Explore how under-5 mortality rates have evolved globally.
- Regional Comparisons: Identify disparities in child survival rates across regions.
- Correlations: Study the relationship between mortality rates and economic indicators like healthcare expenditure or GDP per capita.

📈 Get Involved:
- Use the dataset for your own analyses and visualizations.
- Share your insights and findings.
- Upvote the Kaggle dataset to help others discover it!

❓ What trends or correlations do you find in the data?
- Which country or region has shown the most improvement?
- What factors would you analyze further?

Let me know your thoughts, and feel free to share this resource with others who might benefit! 🌟

DataScience #ChildHealth #MortalityRates #WorldBankData #DataVisualization #GitHub #Kaggle #HealthAnalysis

Let me know if you'd like assistance with EDA or visualization templates!

BackgroundIncreased mortality among men on antiretroviral therapy (ART) has been documented but remains poorly understood. We examined the magnitude of and risk factors for gender differences in mortality on ART. Methods and FindingsAnalyses included 46,201 ART-naïve adults starting ART between January 2002 and December 2009 in eight ART programmes across South Africa (SA). Patients were followed from initiation of ART to outcome or analysis closure. The primary outcome was mortality; secondary outcomes were loss to follow-up (LTF), virologic suppression, and CD4+ cell count responses. Survival analyses were used to examine the hazard of death on ART by gender. Sensitivity analyses were limited to patients who were virologically suppressed and patients whose CD4+ cell count reached >200 cells/µl. We compared gender differences in mortality among HIV+ patients on ART with mortality in an age-standardised HIV-negative population. Among 46,201 adults (65% female, median age 35 years), during 77,578 person-years of follow-up, men had lower median CD4+ cell counts than women (85 versus 110 cells/µl, p<0.001), were more likely to be classified WHO stage III/IV (86 versus 77%, p<0.001), and had higher mortality in crude (8.5 versus 5.7 deaths/100 person-years, p<0.001) and adjusted analyses (adjusted hazard ratio [AHR] 1.31, 95% CI 1.22–1.41). After 36 months on ART, men were more likely than women to be truly LTF (AHR 1.20, 95% CI 1.12–1.28) but not to die after LTF (AHR 1.04, 95% CI 0.86–1.25). Findings were consistent across all eight programmes. Virologic suppression was similar by gender; women had slightly better immunologic responses than men. Notably, the observed gender differences in mortality on ART were smaller than gender differences in age-standardised death rates in the HIV-negative South African population. Over time, non-HIV mortality appeared to account for an increasing proportion of observed mortality. The analysis was limited by missing data on baseline HIV disease characteristics, and we did not observe directly mortality in HIV-negative populations where the participating cohorts were located. ConclusionsHIV-infected men have higher mortality on ART than women in South African programmes, but these differences are only partly explained by more advanced HIV disease at the time of ART initiation, differential LTF and subsequent mortality, and differences in responses to treatment. The observed differences in mortality on ART may be best explained by background differences in mortality between men and women in the South African population unrelated to the HIV/AIDS epidemic. Please see later in the article for the Editors' Summary.

Time series data for the statistic Birth_Rate_Crude_Per_1000_People and country South Africa. Indicator Definition:Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.The statistic "Birth Rate Crude Per 1000 People" stands at 18.78 per mille as of 12/31/2023, the lowest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -0.304 percentage points compared to the value the year prior.The 1 year change in percentage points is -0.304.The 3 year change in percentage points is -0.747.The 5 year change in percentage points is -1.08.The 10 year change in percentage points is -3.12.The Serie's long term average value is 29.26 per mille. It's latest available value, on 12/31/2023, is 10.49 percentage points lower, compared to it's long term average value.The Serie's change in percentage points from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0.The Serie's change in percentage points from it's maximum value, on 12/31/1966, to it's latest available value, on 12/31/2023, is -21.18.

In attaining UNAIDS targets of 90-90-90 to achieve epidemic control, understanding who the current utilizers of HIV treatment services are will inform efforts aimed at reaching those not being reached. A retrospective chart review of CAPRISA AIDS Treatment Program (CAT) patients between 2004 and 2013 was undertaken. Of the 4043 HIV-infected patients initiated on ART, 2586 (64.0%) were women. At ART initiation, men, compared to women, had significantly lower median CD4+ cell counts (113 vs 131 cells/mm3, p <0.001), lower median body mass index (BMI) (21.0 vs 24.2 kg/m2, p<0.001), higher mean log viral load (5.0 vs 4.9 copies/ml, p<0.001) and were significantly older (median age: 35 vs. 32 years, p<0.001). Men had higher mortality rates compared to women, 6.7 per 100 person-years (p-y), (95% CI: 5.8–7.8) vs. 4.4 per 100 p-y, (95% CI: 3.8–5.0); mortality rate ratio: 1.54, (95% CI: 1.27–1.87), p <0.001. Age-standardised mortality rate was 7.9 per 100 p-y (95% CI: 4.1–11.7) for men and 5.7 per 100 p-y (95% CI: 2.7 to 8.6) for women (standardised mortality ratio: 1.38 (1.15 to 1.70)). Mean CD4+ cell count increases post-ART initiation were lower in men at all follow-up time points. Men presented later in the course of their HIV disease for ART initiation with more advanced disease and experienced a higher mortality rate compared to women.

BackgroundMortality in HIV-infected patients who have access to highly active antiretroviral therapy (ART) has declined in sub-Saharan Africa, but it is unclear how mortality compares to the non-HIV–infected population. We compared mortality rates observed in HIV-1–infected patients starting ART with non-HIV–related background mortality in four countries in sub-Saharan Africa.Methods and FindingsPatients enrolled in antiretroviral treatment programmes in Côte d'Ivoire, Malawi, South Africa, and Zimbabwe were included. We calculated excess mortality rates and standardised mortality ratios (SMRs) with 95% confidence intervals (CIs). Expected numbers of deaths were obtained using estimates of age-, sex-, and country-specific, HIV-unrelated, mortality rates from the Global Burden of Disease project. Among 13,249 eligible patients 1,177 deaths were recorded during 14,695 person-years of follow-up. The median age was 34 y, 8,831 (67%) patients were female, and 10,811 of 12,720 patients (85%) with information on clinical stage had advanced disease when starting ART. The excess mortality rate was 17.5 (95% CI 14.5–21.1) per 100 person-years SMR in patients who started ART with a CD4 cell count of less than 25 cells/µl and World Health Organization (WHO) stage III/IV, compared to 1.00 (0.55–1.81) per 100 person-years in patients who started with 200 cells/µl or above with WHO stage I/II. The corresponding SMRs were 47.1 (39.1–56.6) and 3.44 (1.91–6.17). Among patients who started ART with 200 cells/µl or above in WHO stage I/II and survived the first year of ART, the excess mortality rate was 0.27 (0.08–0.94) per 100 person-years and the SMR was 1.14 (0.47–2.77).ConclusionsMortality of HIV-infected patients treated with combination ART in sub-Saharan Africa continues to be higher than in the general population, but for some patients excess mortality is moderate and reaches that of the general population in the second year of ART. Much of the excess mortality might be prevented by timely initiation of ART.Please see later in the article for Editors' Summary

As of November 16, 2020, a total of 17.577 COVID-19 related casualties were registered in South Africa. Some 14.1 percent of the deaths fell within the age group of 60 to 64 years with, whereas 12.6 percent of whom were aged 55 to 59 passed away due to the diseases caused by the coronavirus. Confirmed coronavirus cases per region in South Africa illustrated Gauteng was hit hardest. As of January 15, 2021, the region with Johannesburg as its capital registered 350,976 individuals with COVID-19 , whereas KwaZulu-Natal and Western Cape had dealt with less cases.

IntroductionStreptococcus pneumoniae is a leading cause of severe bacterial infections globally. A full understanding of the impact of pneumococcal conjugate vaccine (PCV) on pneumococcal disease burden, following its introduction in 2009 in South Africa, can support national policy on PCV use and assist with policy decisions elsewhere.MethodsWe developed a model to estimate the national burden of severe pneumococcal disease, i.e. disease requiring hospitalisation, pre- (2005–2008) and post-PCV introduction (2012–2013) in children aged 0–59 months in South Africa. We estimated case numbers for invasive pneumococcal disease using data from the national laboratory-based surveillance, adjusted for specimen-taking practices. We estimated non-bacteraemic pneumococcal pneumonia case numbers using vaccine probe study data. To estimate pneumococcal deaths, we applied observed case fatality ratios to estimated case numbers. Estimates were stratified by HIV status to account for the impact of PCV and HIV-related interventions. We assessed how different assumptions affected estimates using a sensitivity analysis. Bootstrapping created confidence intervals.ResultsIn the pre-vaccine era, a total of approximately 107,600 (95% confidence interval [CI] 83,000–140,000) cases of severe hospitalised pneumococcal disease were estimated to have occurred annually. Following PCV introduction and the improvement in HIV interventions, 41,800 (95% CI 28,000–50,000) severe pneumococcal disease cases were estimated in 2012–2013, a rate reduction of 1,277 cases per 100,000 child-years. Approximately 5000 (95% CI 3000–6000) pneumococcal-related annual deaths were estimated in the pre-vaccine period and 1,900 (95% CI 1000–2500) in 2012–2013, a mortality rate difference of 61 per 100,000 child-years.ConclusionsWhile a large number of hospitalisations and deaths due to pneumococcal disease still occur among children 0–59 months in South Africa, we found a large reduction in this estimate that is temporally associated with PCV introduction. In HIV-infected individuals the scale-up of other interventions, such as improvements in HIV care, may have also contributed to the declines in pneumococcal burden.