Impact of HIV on South African Demographics and Global Health Trends

The study focuses on the demographic impacts of HIV/AIDS in South Africa, utilizing data from the World Health Organization (WHO) and other global databases.
It specifically examines how high-prevalence diseases alter population structures, mortality rates, and life expectancy.

General Demographic and Socioeconomic Data (2021):
- Total Population: $59.392$ million.
- Gross National Income (GNI) per capita, PPP: $14\,140$ US dollars.
- Maternal Mortality: $119$ deaths per $100\,000$ live births (based on $2017$ data).
- Total Health Expenditure: $9\%$ of Gross Domestic Product (GDP) (based on $2019$ data).
- Health Expenditure per capita: $547$ US dollars (based on $2019$ data).
- Life Expectancy at Birth: $43.5$ years.
- Total Fertility Rate (TFR): $2.4$ births per woman.
- Human Development Index (HDI) Value: $0.709$ (based on $2019$ data).

Target Framework: The Joint United Nations Programme on HIV/AIDS (UNAIDS) set the following benchmarks for $2025$ :
- First 95: $95\%$ of people living with HIV (PLHIV) should know their status.
- Second 95: $95\%$ of people living with HIV who know their status should be receiving Antiretroviral Therapy (ART).
- Third 95: $95\%$ of people on treatment should have suppressed viral loads.
Ultimate Goals for 2025:
- Success in these targets results in $90\%$ of all HIV-positive individuals being on treatment.
- Success results in $86\%$ of all HIV-positive individuals achieving viral load suppression.
South Africa's Health Sector Cascade (2021 Status):
- Knowing Status: $94\%$ of PLHIV (Estimated $7\,032\,814$ people).
- Receiving ART: $74\%$ of PLHIV (Estimated $5\,549\,989$ people).
- Viral Load Suppression: $67\%$ of PLHIV (Estimated $5\,052\,165$ people).

Vertical Transmission Prevention:
- Estimated percentage of pregnant women living with HIV who received Antiretrovirals (ARVs) for Prevention of Mother-to-Child Transmission (PMTCT) in $2021$ : $96\%$ .
Treatment Coverage:
- Estimated ART coverage for both children and adults in $2021$ : $48\%$ .
Trend in New Infections (2010–2021):
- There has been a significant reduction in the number of people newly infected with HIV, showing a $-50\%$ trend since $2010$ .
- In $2010$ , the point estimate was approximately $400\,000$ new infections.
- By $2021$ , new infections decreased significantly below the $200\,000$ threshold.
Trend in HIV-Related Deaths (2010–2021):
- High rates of improvement recorded, with a $-72\%$ reduction in deaths since $2010$ .
- In $2010$ , the point estimate for HIV-related deaths was near $200\,000$ .
- By $2021$ , this value dropped well below $50\,000$ .

Population Size and Distribution Changes:
- A comparison of population pyramids for South Africa between the years $2000$ and $2025$ demonstrates the profound impact of AIDS.
- Actual and Projected Population: Represented by actual estimates including the impact of AIDS.
- Hypothetical Population: Represents the projected size of the population in the absence of AIDS (represented by red bars in visualizations provided by the UN Population Division).
Observations on Age-Group Distribution:
- Significant gaps between the hypothetical (without AIDS) and actual population sizes are visible in middle-age cohorts ( $25\text{--}49$ years) and infant/childhood cohorts ( $0\text{--}9$ years).
- These gaps illustrate both the direct mortality from the disease and the indirect loss of births due to high adult mortality and infection rates.

Correlation between Income and Infection:
- Analysis of Adult HIV prevalence (ages $15\text{--}49$ ) versus Income per person (PPP).
- High Prevalence Cluster (Mainly Southern Africa): Includes Lesotho, Zimbabwe, Swaziland (Eswatini), Botswana, Namibia, Zambia, Malawi, Mozambique, and South Africa.
- Low Prevalence and High Income: Countries like Australia, Singapore, United Kingdom, and the United States show prevalence rates significantly below $0.1\%$ .
Geographic Data Points:
- South Africa: Characterized by high prevalence (approx. $15\text{--}20\%$ in $2007$ ) and mid-range income (approx. $10\,000$ per year).
- Russia/Ukraine/Thailand: Middle-income nations with prevalence rates clustered between $0.4\%$ and $2\%$ .
- India/Vietnam/China: Large population sizes with low prevalence rates relative to Sub-Saharan Africa.

Mortality Probabilities ( $q_x$ ): Use line graphs to compare mortality probability values across the years $1990$ , $2000$ , $2011$ , and $2019$ . Determine two primary differences identifying variations within and between countries like South Africa and Australia.
Population Pyramids: Construct synthetic cohorts and population pyramids specifically for South African data to compare changes in age-sex profiles over time. Contrast these with Australian profiles, noting differences in early-life mortality and structural shifts.
Age-Specific Expectation of Life ( $e_x$ ): Compare life expectancy at various ages for the years $1990$ , $2000$ , $2011$ , and $2019$ between countries.
Impact of Therapy: Analyze how changes in HIV prevalence, incidence, and access to anti-HIV therapy directly impact mortality and life expectancy values.
Hypothetical Future Scenario: If anti-HIV drug coverage reached $100\%$ by $2025$ , evaluate the predicted effects on demographic values by the year $2045$ .
COVID-19 Impact: Use line graphs to compare overall life expectancy ( $e_x$ ) at birth and at age $60$ from $2000\text{--}2021$ . Assess how the COVID-19 pandemic likely altered these existing mortality and life expectancy profiles.