In 2024, Pietermaritzburg in South Africa ranked first in the crime index among African cities, scoring **** index points. The six most dangerous areas on the continent were South African cities. Furthermore, Pretoria and Johannesburg followed, with a score of **** and **** points, respectively. The index estimates the overall level of crime in a specific territory. According to the score, crime levels are classified as very high (over 80), high (60-80), moderate (40-60), low (20-40), and very low (below 20). Contact crimes are common in South Africa Contact crimes in South Africa include violent crimes such as murder, attempted murder, and sexual offenses, as well as common assault and robbery. In fiscal year 2022/2023, the suburb of Johannesburg Central in the Gauteng province of South Africa had the highest number of contact crime incidents. Common assault was the main contributing type of offense to the overall number of contact crimes. Household robberies peak in certain months In South Africa, June, July, and December experienced the highest number of household robberies in 2023. June and July are the months that provide the most hours of darkness, thus allowing criminals more time to break in and enter homes without being detected easily. In December, most South Africans decide to go away on holiday, leaving their homes at risk for a potential break-in. On the other hand, only around ** percent of households affected by robbery reported it to the police in the fiscal year 2022/2023.

In 2024, South Africa ranked first in the crime index among African countries, with a score of **** index points. Nigeria was the second most dangerous country on the continent, obtaining **** points. The index evaluates the overall crime levels in a specific country. Several African countries scored between ** and ** points, indicating high crime levels. Escalating concerns: South Africans worry about crime and violence In 2024, South Africa had one of the highest proportions of respondents expressing concerns about crime and violence compared to other countries participating in an online study. Throughout the period examined, the percentage of participants worried about violence peaked at ** percent in March 2023. The escalating levels of violent crime currently witnessed in the country has caused this significant rise in concerned respondents. South Africa's organized crime landscape In 2023, South Africa ranked the ************* in organized crime compared to its African counterparts. The continent's most prevalent organized criminal activity was **************************************. Moreover, from a regional perspective, Southern African countries had the lowest organized crime rate.

BackgroundOf the total 56 million deaths worldwide during 2012, 38 million (68%) were due to noncommunicable diseases (NCDs), particularly cardiovascular diseases (17.5 million deaths) cancers (8.2 million) which represents46.2% and 21.7% of NCD deaths, respectively). Nearly 80 percent of the global CVD deaths occur in low- and middle-income countries. Some of the major CVDs such as ischemic heart disease (IHD) and stroke and CVD risk conditions, namely, hypertension and dyslipidaemia share common modifiable risk factors including smoking, unhealthy diets, harmful use of alcohol and physical inactivity. The CVDs are now putting a heavy strain of the health systems at both national and local levels, which have previously largely focused on infectious diseases and appalling maternal and child health. We set out to estimate district-level co-occurrence of two cardiovascular diseases (CVDs), namely, ischemic heart disease (IHD) and stroke; and two major risk conditions for CVD, namely, hypertension and dyslipidaemia in South Africa.MethodThe analyses were based on adults health collected as part of the 2012 South African National Health and Nutrition Examination Survey (SANHANES). We used joint disease mapping models to estimate and map the spatial distributions of risks of hypertension, self-report of ischaemic heart disease (IHD), stroke and dyslipidaemia at the district level in South Africa. The analyses were adjusted for known individual social demographic and lifestyle factors, household and district level poverty measurements using binary spatial models.ResultsThe estimated prevalence of IHD, stroke, hypertension and dyslipidaemia revealed high inequality at the district level (median value (range): 5.4 (0–17.8%); 1.7 (0–18.2%); 32.0 (12.5–48.2%) and 52.2 (0–71.7%), respectively). The adjusted risks of stroke, hypertension and IHD were mostly high in districts in the South-Eastern parts of the country, while that of dyslipidaemia, was high in Central and top North-Eastern corridor of the country.ConclusionsThe study has confirmed common modifiable risk factors of two cardiovascular diseases (CVDs), namely, ischemic heart disease (IHD) and stroke; and two major risk conditions for CVD, namely, hypertension and dyslipidaemia. Accordingly, an integrated intervention approach addressing cardiovascular diseases and associated risk factors and conditions would be more cost effective and provide stronger impacts than individual tailored interventions only. Findings of excess district-level variations in the CVDs and their risk factor profiles might be useful for developing effective public health policies and interventions aimed at reducing behavioural risk factors including harmful use of alcohol, physical inactivity and high salt intake.

Abstract

South Africa has 230 operating mines located in diverse natural and social settings. Over 6 million people live in urban and rural mining host communities who will be significantly affected by mine closure. The national, provincial and local governments need guidance in identifying high-risk areas and relevant policy and programmatic interventions. This dataset includes data collected on mines in South Africa for the development of a quantitative mine closure risk rating system that assesses the likelihood of mine closure, the risk of social impact and the risk of environmental impact of mine closure for every operating mine in the country. The rating system was tested with 10 mines and 19 mining experts. The shape files for maps resulting from the research are also included in the dataset.The data is also available for viewing on the South African Mine Closure Risk and Opportunity Atlas https://experience.arcgis.com/experience/f8bbd7f733674b9d99cd320b4f5b98f5/ Data projection: GCS_WGS_1984 The data provides evidence for the research publication Cole, M.J. A Mine Closure Risk Rating System for South Africa. Mining 2024, 4, 58-78. https://doi.org/10.3390/mining4010005

Analysis unit

Institutions

Kind of data

Other

Mode of data collection

Other

Data were extracted from alien species databases on alien species in South Africa, on their pathways and dates of introduction, their regions of origin, South African distribution and invasion status. These data were used to assess the information content of South African alien species databases and to assess the introduction pathways of alien species in South Africa. The stowaway pathway for alien species was then assessed in more detail, and the role that shipping plays in the introduction of stowaway organisms to South Africa was evaluated. Finally, a simple, rapid and inexpensive method for developing invasive species watch lists was created and was tested using South Africa as a case study.

GLM outputs of the probability of High Risk flights.

Wildfire Risk APIs for Mortgages Market Outlook

According to our latest research, the Global Wildfire Risk APIs for Mortgages market size was valued at $412 million in 2024 and is projected to reach $1.21 billion by 2033, expanding at a CAGR of 12.8% during 2024–2033. The primary growth driver for this market is the increasing frequency and severity of wildfires globally, which has heightened the need for advanced risk assessment tools in the mortgage industry. Mortgage lenders, insurers, and real estate professionals are increasingly integrating wildfire risk APIs into their workflows to improve decision-making, mitigate financial losses, and comply with evolving regulatory requirements. The adoption of these APIs enables more precise property-level risk insights, ultimately supporting better loan underwriting, property valuation, and insurance integration processes.

Regional Outlook

North America holds the largest share of the Wildfire Risk APIs for Mortgages market, accounting for over 45% of the global revenue in 2024. This dominance is primarily attributed to the mature mortgage and insurance sectors in the United States and Canada, coupled with the high incidence of wildfires in regions such as California, Oregon, and British Columbia. The presence of advanced technology infrastructure, robust data ecosystems, and proactive government policies supporting climate risk management further fuel the demand for wildfire risk APIs. Major banks, mortgage lenders, and insurance companies in North America are early adopters of these solutions, leveraging them to enhance their risk assessment capabilities and comply with stringent regulatory mandates related to climate risk disclosure.

The Asia Pacific region is poised to be the fastest-growing market, with a projected CAGR of 15.6% during the forecast period. Rapid urbanization, expanding real estate markets, and increasing investments in digital transformation across countries like Australia, Japan, and China are key drivers. Notably, Australia’s vulnerability to devastating bushfires has accelerated the adoption of wildfire risk APIs by local banks and insurance companies. Additionally, regional governments are investing in climate resilience and encouraging the use of advanced data analytics in financial services. This has spurred a wave of partnerships between technology vendors and financial institutions aiming to integrate wildfire risk intelligence into their risk management frameworks.

Emerging economies in Latin America and Middle East & Africa are beginning to recognize the value of wildfire risk APIs, although adoption remains at a nascent stage. Challenges such as limited access to high-quality geospatial data, lower levels of digitalization in the mortgage sector, and varying regulatory landscapes have slowed penetration. However, localized demand is growing, particularly in wildfire-prone areas of Brazil, Chile, and South Africa. Governments and industry associations are increasingly promoting the use of technology to enhance climate resilience, which is expected to drive gradual adoption of wildfire risk APIs in these regions over the next decade.

Report Scope

South African Crime Statistics Dataset

This dataset encompasses crime statistics for South Africa, spanning from the financial year 2011/2012 to 2022/2023. The data has been released by the South African Police Services (SAPS) and includes various categories of crime incidents.

SAPS Objective

The South African Police Service has accepted a new and challenging objective to align its crime statistics with international best practices. This commitment is aimed at enhancing the credibility and accuracy of the crime data.

Importance of SAPS Crime Statistics

The crime statistics generated by SAPS play a crucial role in the statistics system's value chain. They inform policy development and planning within the criminal justice system. The collaboration with StatsSA (Statistics South Africa) is seen as a significant step towards improving the integrity of SAPS crime statistics, ensuring that policy-makers have access to high-quality data for informed decision-making.

Dataset structure

.
├── categoris/ 
│  ├── aggravated_robbery_incidents_by_category.csv 
│  ├── contact_crime_incidents_by_category.csv 
│  ├── contact_related_crime_incidents_by_category.csv 
│  ├── crimes_detected_as_a_result_of_police_action.csv 
│  ├── other_serious_crime_incidents_by_category.csv 
│  ├── property_related_crime_incidents_by_category.csv 
│  └── sexual_offence_incidents_by_category.csv 
└── crime_incidents_by_category.csv

Geographical Distribution

The dataset covers various provinces in South Africa, with each province represented by its respective code:

• ZA: South Africa
• EC: Eastern Cape
• FS: Free State
• GT: Gauteng
• KZN: KwaZulu-Natal
• LIM: Limpopo
• MP: Mpumalanga
• NW: North West (Bokone Bophirima)
• NC: Northern Cape (Kapa Bokone)
• WC: Western Cape (Kapa Bodikela)

In FY 2022/2023, the suburb of Johannesburg Central in the Gauteng province of South Africa had the highest number of contact crime recorded incidents, with ***** reports. Nyanga (Western Cape) followed with ***** reports. Combining the number of incidents in all ** hot-spot areas accounts for around eight percent of the total number of contact crimes in the observed period.

Modelled annual fatalities of pelicans under various conditions.

EMOTION was the first project to utilize human centered design (HCD) to develop branding, messaging and packaging for microbicide products in order to enhance awareness, desire, and demand for microbicides and lead to greater uptake and proper adherence by high-risk end users. The HCD methodology was used as the basis for this project, as it complements and expands upon existing sociobehavioral and acceptability research in the HIV prevention field. The goal of Project EMOTION is to increase uptake and correct and consistent use of antiretroviral (ARV)-based HIV prevention products by women at high risk of HIV infection using an end-user centered strategy. Selected products, brands, packaging and communication materials were manufactured for brand positioning, refinement, and message clarification in accordance with Social and Behavioral Change theory principals for Project Siyavala (October-November 2016 in Western Cape, Johannesburg, and KZN in collaboration with Abt Associates and Matchboxology). The team interviewed young women, men, health care providers and community leaders in both urban and rural regions in South Africa. Different advertisements/posters were presented, and participant reactions were analyzed to understand appeal, clarity, cultural appropriateness and other related factors. Further analysis disentangled the visual and messaging stimuli that resonated by group, region, and age range. These included facilitated group discussion, small group work ranking the messages from best to worst, and categorization of appealing messages by the key target audience segments of consumers and Health Care Providers (HCPs).

Post-Fire Erosion Risk via Satellite Market Outlook

According to our latest research, the Global Post-Fire Erosion Risk via Satellite market size was valued at $1.2 billion in 2024 and is projected to reach $3.9 billion by 2033, expanding at a robust CAGR of 13.8% during the forecast period of 2025–2033. A primary factor propelling this market’s expansion is the increasing frequency and severity of wildfires worldwide, which has driven urgent demand for advanced, real-time monitoring and predictive solutions to assess and mitigate post-fire erosion risks. The integration of high-resolution satellite imagery with AI-powered analytics is rapidly transforming how stakeholders manage land rehabilitation, disaster response, and environmental conservation, positioning satellite-based post-fire erosion risk assessment as a critical tool in climate-resilient land management strategies.

Regional Outlook

North America commands the largest share of the global Post-Fire Erosion Risk via Satellite market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the region’s mature satellite infrastructure, high incidence of wildfires in the western United States and Canada, and proactive governmental policies supporting wildfire management and environmental monitoring. Established agencies such as NASA and the US Forest Service have been early adopters of satellite-based erosion risk assessment, leveraging advanced remote sensing data and analytics platforms. The presence of leading technology providers and strong public-private collaboration further accelerates the development and deployment of innovative solutions, making North America a benchmark for technological integration and policy-driven market growth.

Asia Pacific is emerging as the fastest-growing region in the Post-Fire Erosion Risk via Satellite market, with a projected CAGR of 16.2% from 2025 to 2033. Rapid urbanization, deforestation, and climate change-induced wildfire events in countries such as Australia, China, and Indonesia are spurring significant investments in satellite monitoring and analytics capabilities. Regional governments are increasingly recognizing the socio-economic and environmental costs of post-fire erosion, leading to the adoption of advanced monitoring platforms and strategic partnerships with international satellite data providers. The region’s growth is further fueled by expanding disaster response frameworks, rising environmental awareness, and policy incentives aimed at building climate resilience across vulnerable landscapes.

Emerging economies in Latin America, the Middle East, and Africa are witnessing growing interest in satellite-based post-fire erosion risk solutions, albeit from a lower base. Adoption challenges persist due to limited technical expertise, budget constraints, and fragmented policy frameworks. However, localized demand is rising in wildfire-prone areas such as Brazil’s Cerrado, South Africa’s fynbos, and Mediterranean ecosystems in North Africa. International aid programs, cross-border research collaborations, and capacity-building initiatives are gradually bridging the technology gap, enabling these regions to leverage satellite imagery analytics for sustainable land rehabilitation and disaster risk reduction, despite persistent infrastructural and regulatory hurdles.

Report Scope

The rivers of KwaZulu-Natal, South Africa, are being impacted by various anthropogenic activities that threaten their sustainability. Our study demonstrated how Bayesian networks could be used to conduct an environmental risk assessment of macroinvertebrate biodiversity and their associated ecosystem to assess the overall effects of these anthropogenic stressors in the rivers. We examined the exposure pathways through various habitats in the study area using a conceptual model that linked the sources of stressors through cause-effect pathways. A Bayesian network was constructed to represent the observed complex interactions and overall risk from water quality, flow and habitat stressors. The model outputs and sensitivity analysis showed ecosystem threat and river health (represented by macroinvertebrate assessment index – MIRAI) could have high ecological risks on macroinvertebrate biodiversity and the ecosystem, respectively. The results of our study demonstrated that Bayesian networks can be used to calculate risk for multiple stressors and that they are a powerful tool for informing future strategies for achieving best management practices and policymaking. Apart from the current scenario, which was developed from field data, we also simulated three other scenarios to predict potential risks to our selected endpoints. We further simulated the low and high risks to the endpoints to demonstrate that the Bayesian network can be an effective adaptive management tool for decision making.

Estimated pelican population growth rates under various combinations of bird- and turbine-related parameters.

Abstract

In 2003, the Institute for Security Studies (ISS) in South Africa conducted a national victims of crime survey with the aim of measuring crime trends in the country, public perceptions about crime and safety, as well as confidence in the criminal justice system. The study was planned and carried out to allow direct comparisons with a victim of crime survey conducted in 1998 by Statistics SA for the Department of Safety and Security and the United Nations Interregional Crime and Justice Research Institute (UNICRI). The survey was conducted between September and October 2003. Households were randomly selected across the country based on the 2001 Census, and a national sample of 4,860 people, over the age of 16 years, was realised. The sample was stratified by province and urban/rural areas, and the data was weighted to reflect the actual composition of the population.

Geographic coverage

The survey had national coverage

Analysis unit

The units of analysis in the study were individuals and households

Universe

The survey covered all households in South Africa

Kind of data

Sample survey data [ssd]

Sampling procedure

Multi-stage cluster sampling was utilised, with Enumerator Areas (EAs) from the 2001 Census selected at the first stage of the sampling, households within the EAs at the second stage, and individuals within the household at the third stage. Based on the total number of households in South Africa (identified by the 2001 Census as 11,205,705), a total of 80,787 EAs were allocated. The total sample size was determined to be 4,050 households. The sample was calculated at a 95% confidence interval, and with a design effect of two. Ten interviews were collected from each EA. The distribution of the sample through the provinces is reflected in the table below. In total, a sample of 4,860 was realised. Households were selected randomly in the following manner: Maps of the EAs were obtained prior to entry of the EA, and random starting points selected. The household nearest to the starting point was selected, and a household interval randomly selected by the supervisor using the day, week and month of the interview. Every nth household was then completed until the target number of interviews was obtained. Respondents over the age of 16 years were then randomly selected for interview using a KISH grid.

Mode of data collection

Face-to-face [f2f]

Research instrument

In order to ensure comparability with the 1998 national Victims of Crime survey, as well as ISS surveys in other African countries that were being conducted at the same time as the national survey, specific aspects of the survey design were predetermined. These included certain components of the questionnaire as well as the sample design. A basic questionnaire, based on those used in the ICVS, previous ISS studies, concurrent regional studies, and the 1998 Victims of Crime survey was used as a starting point. To enable comparison of the 1998 and 2003 data sets, the definitions used in the study were kept identical to those used in the 1998 survey, with the exception of attempted car hijackings and attempted housebreakings. In this study, car hijacks specifically only included successful incidents, rather than including attempts as was the case in the 1998 study, while attempted housebreakings were excluded from this study. For methodological purposes, the types of crimes were divided into those that affect the household, and those that affect individuals.

In order to minimise fieldworker error, and coding and capturing error, as well as any ambiguities in responses, the instrument was largely pre-coded. A number of drafts were prepared and presented internally to the ISS, and then to a stakeholder committee for input, before finalisation. The questionnaire was not translated into any vernacular languages. However, the introduction and definitions of crimes used by enumerators were translated and back-translated from English into Afrikaans, Zulu, and Sotho. Although every precaution was taken during fieldworker training to ensure that interpretations were correct, it is not inconceivable that errors may have occurred. Cross-checks were carried out on 20% of the questionnaires to ensure that the responses given were an accurate reflection of respondents’ opinions. These cross-checks did not reveal any consistent error from the latitude ordinarily accorded interviewers in translating the original questionnaire

Cleaning operations

Capture was undertaken on Epi-Info. A process of double capture was undertaken in order to eliminate capture error.

: In South Africa, the interlacing socio-economic challenges of crime and high unemployment have long been a cause for concern. These challenges have also prompted extensive attention from scholars. Thus, the relationship between crime and unemployment emerges as one of the important niche areas of inquiry. The study used time series data from 1995 to 2020 to address this nexus. An Autoregressive Distributive Lag was deemed fit for this study as the data properties point to its use.

Abstract

The ExPoSE 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 data 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. Data for England can be obtained from the UK Data Service. Files made available include the code used within the scope of the ExPoSE Project https://www.exposeproject.net to import and harmonise microdata on cardiovascular risk factors and other information from a set of population surveys conducted in South Africa and England between 1997 and 2017.

Geographic coverage

The harmonised data comes from national surveys conducted in South Africa and England

Analysis unit

Individuals

Kind of data

Survey data

Mode of data collection

Other

Description: The guardian data of the SABSSM 2002 study cover information from the parents or care givers of children 2-11 years on matters ranging from biographical information of the child and parent/guardian, the child's home environment, care and protection, sources of information on HIV and AIDS, media impact and the health status of the child. The data set contains 380 variables and 2732 cases. Abstract: Background: This is the first in a series of national HIV household surveys conducted in South Africa. The survey was commissioned by the Nelson Mandela Children's Fund and the Nelson Mandela Foundation. The key aims were to determine the HIV prevalence in the general population, identify risk factors that increase vulnerability of South Africans to HIV infections, to identify the contexts within which sexual behaviour occurs and the obstacles to risk reduction and to determine the level of exposure of all sectors of society to current prevention. The Nelson Mandela Children's Fund requested the HSRC to assess the impact of current HIV and AIDS education and awareness programmes designed to slow down the epidemic, including infection rates, stigma, care and support for affected individuals and families. Methodology: Sampling methods: multi-stage cluster stratified sample stratified by province, settlement geography (geotype) and predominant race group in each area. A systematic sample of 15 households was drawn from each of 1 000 census enumeration areas (EAs). In each household, one person was randomly selected in each of four mutually exclusive age groups (2-11 years; 12-14 years; 15-24 years; 25+ years). Field workers administered questionnaires to selected respondents and also collected oral fluid specimens for HIV testing. Results: This study sampled a cross-section of 9 963 South Africans aged two years and older. HIV is a generalised epidemic in South Africa that extends to all age groups, geographic areas and race groups. It showed 11.4 % were HIV positive, 15.6 per cent of them aged between 15 and 49. Women (12.8% HIV positive) were more at risk of infection than men (9.5% HIV positive). Urban informal settlements have the highest incidence of HIV infection (21.3%). Free State showed the highest prevalence (14.9%) with Eastern Cape having the lowest (6.6%). Higher rates of infection (5.6%) are also found in children aged 2-14 and Africans (10.2%). Awareness of HIV status was low. Only 18.9% reported that they were previously tested. Fewer women (3.9%) reported more than one sexual partner as compared to men (13.5%). Condom use at last sex was low among both women (24.7%) and men (30.3%). Knowledge of HIV and AIDS is generally high, with sexual behaviour changes taking root in encouragingly low numbers of sexual partners and high levels of abstinence among the youth. There is still great uncertainty of the relationship between HIV and AIDS and popular myths. South Africans from all walks of life are at risk. In particular, wealthy Africans have the same levels of risk as poorer Africans - whereas in other race groups, poorer people are more vulnerable to infection. Conclusions: The study recommended the expansion of voluntary counseling and testing. Prevention programmes ought to focus on reduction on multiple partners and increased condom use. It further recommended, inter alia, that HIV/AIDS prevention programmes be intensified for people living in informal settlements, campaigns be implemented using mass media to address myths and misconceptions and that information needs in rural communities and poorer households due to lack of access to mass media channels, should be attended to. Clinical measurements Face-to-face interview Focus group Observation South African population, 2 years and older from urban formal, urban informal, rural formal, rural informal settlements. This project used the HSRC's master sample (HSRC 2002). A master sample is defined as a selection, for the purpose of repeated community or household surveys, of a probability sample of census enumeration areas throughout South Africa that are representative of the country's provincial, settlement and racial diversity. The sampling frame that was used in the design of the master sample was the 2001 census Enumerator Areas (EAs) from Statistics South Africa (Stats SA). The target population for this study were all people in South Africa, excluding persons in so-called special institutions (e.g. hospitals, military camps, old age homes, schools and university hostels). The EAs were used as the Primary Sampling Units (PSUs). Although the 2001 census data are not yet available, it was decided to use the 2001 EAs for the master sample because the sampling units would remain relevant for future surveys conducted by the HSRC within five to ten years' time. In addition, the HSRC would soon have access to the most recent census statistics over this period for weighting of future survey results, including this study. The sample was designed with two main explicit strata, namely, provinces and the geography type (geotype) of the EA. In the 2001 census, the four geotypes are urban formal, urban informal, rural formal (including commercial farms) and tribal areas (i.e. the deep rural areas). In the formal urban areas, race was also used as a third stratification variable. What this means is that the Master Sample has been designed to allow reporting of results (i.e. reporting domain) at a provincial, geotype and race level. A reporting domain is defined as that domain at which estimates of a population characteristic or variable should be of an acceptable precision for the presentation of survey results. The census 2001 EA data provided by Stats SA for drawing the sample contained an estimate of the number of dwelling units (DUs) or visiting points (VPs). A visiting point is defined as a separate (non-vacant) residential stand, address, structure, and flat in a block of flats or homestead. The 2001 estimate of visiting points was used as the Measure of Size (MOS) in the drawing of the sample. The visiting point is the Secondary Sampling Unit (SSU) in each of the selected PSUs. In this study, all people in all the households resident at the visiting point were initially listed, after which the eligible individual was randomly selected in each of the following three age groups 2-14, 15-24 and 25 years and older. These individuals constituted the Ultimate Sampling Units (USUs) of this study. Having completed the sample design, the sample was drawn with 1 000 PSUs or EAs being selected throughout South Africa (see Figure 2). These PSUs were allocated to each of the explicit strata. With a view to obtaining an approximately self-weighting sample of visiting points (i.e. SSUs), (a) the EAs were drawn with probability proportional to the size of the EA using the 2001 estimate of the number of visiting points in the EA database as a measure of size (MOS) and (b) to draw an equal number of visiting points (i.e. SSUs) from each drawn EA. An acceptable precision of estimates per reporting domain requires that a sample of sufficient size be drawn from each of the reporting domains. Consequently, a cluster of 11 VP was systematically selected on the aerial photography produced for each of the EAs in the master sample. Since it is not possible to determine on an aerial photograph whether a 'dwelling unit' is indeed a residential structure or whether it was occupied (i.e. people sleeping there), it was decided to form clusters of 11 dwelling units per PSU, allowing on average for one invalid dwelling unit in the cluster of 11 dwelling units. Previous experience at Statistics SA indicated a sample size of 10 households per PSU to be very efficient, balancing cost and efficiency. Overall, a total of 14 450 potential participants composed of 4 001 children, 3 720 youths and 6 729 adults were selected for the survey and 13 518 (93.6%) were actually visited. A small proportion (6.4%) of potential respondents could not be approached due to logistic constraints that were unavoidable in a study of such magnitude. Among the 13 518 individuals who were selected and contacted for the survey, 9 963 (73.7%) persons agreed to be interviewed, and 8 840 (65.4%) agreed to also give a specimen for an HIV test. The sample was designed with the view to enable reporting of the results on province level, on geography type area and on race of the respondent. The total sample size was limited by financial constraints, but based on other HSRC experience in sample surveys it was decided to aim at obtaining a minimum of 1 200 households per race group. In fact, the aim was to obtain 1 200 Indian households, 1 800 coloured households, 2 200 white households and 4 800 African households, a total thus of 10 000 households. The number of respondents per household for the study was expected to vary between one and three (one respondent in each of the three age groups). A 70% response rate was assumed and a HIV+ prevalence rate of 20%. However, the total refusal and noncontact rate was much higher than expected. Nevertheless, all cases where the interview could have been done were included in the analysis.