ZA: Prevalence of Stunting: Height for Age: % of Children Under 5 data was reported at 27.400 % in 2016. This records an increase from the previous number of 27.200 % for 2012. ZA: Prevalence of Stunting: Height for Age: % of Children Under 5 data is updated yearly, averaging 28.700 % from Dec 1994 (Median) to 2016, with 7 observations. The data reached an all-time high of 32.800 % in 2004 and a record low of 24.900 % in 2008. ZA: Prevalence of Stunting: Height for Age: % of Children Under 5 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. Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

South Africa ZA: Children Out of School: % of Primary School Age data was reported at 12.435 % in 2015. This records an increase from the previous number of 8.999 % for 2005. South Africa ZA: Children Out of School: % of Primary School Age data is updated yearly, averaging 7.755 % from Dec 1970 (Median) to 2015, with 13 observations. The data reached an all-time high of 35.097 % in 1970 and a record low of 5.884 % in 1999. South Africa ZA: Children Out of School: % of Primary School Age 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: Education Statistics. Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank sponsored Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect data on the conditions under which South Africans live in order to provide policymakers with the data necessary for development planning. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The survey covered all household members. Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn for the households in ESDs.

Kind of data

Sample survey data

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demographics, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.

In addition to the detailed household questionnaire, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.

A literacy assessment module (LAM) was administered to two respondents in each household, (a household member 13-18 years old and a one between 18 and 50) to assess literacy levels.

Data appraisal

The data collected in clusters 217 and 218 are highly unreliable and have therefore been removed from the dataset currently available on the portal. Researchers who have downloaded the data in the past should download version 2.0 of the dataset to ensure they have the corrected data. Version 2.0 of the dataset excludes two clusters from both the 1993 and 1998 samples. During follow-up field research for the KwaZulu-Natal Income Dynamics Study (KIDS) in May 2001 it was discovered that all 39 household interviews in clusters 217 and 218 had been fabricated in both 1993 and 1998. These households have been dropped in the updated release of the data. In addition, cluster 206 is now coded as urban as this was incorrectly coded as rural in the first release of the data. Note: Weights calculated by the World Bank and provided with the original data are NOT updated to reflect these changes.

Time series data for the statistic Fertility_Rate and country South Africa. Indicator Definition:Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.The statistic "Fertility Rate" stands at 2.22 births per woman 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 -0.4939 percent compared to the value the year prior.The 1 year change in percent is -0.4939.The 3 year change in percent is -1.82.The 5 year change in percent is -2.38.The 10 year change in percent is -8.73.The Serie's long term average value is 3.83 births per woman. It's latest available value, on 12/31/2023, is 42.16 percent lower, compared to it's long term average value.The Serie's change in percent 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 percent from it's maximum value, on 12/31/1960, to it's latest available value, on 12/31/2023, is -63.70%.

The School of Education at the University of Cape Town (UCT) investigated children’s learning through digital play. The aim of the study was to explore the intersection between child play, technology, creativity and learning among children aged between 3 and 11 years. The study also identified skills and dispositions children develop through both digital and non-digital play. The data shared emerged from a survey of parents of children in the stated age group, with particular reference to the parents views on children's play practices, including time parents spent playing with their children, concerns parents had on time children spend playing on various technologies, types of play children in South Africa engaged in and the concerns of parents when children played with some electronic devices. The following data files are shared:SA - Survey - Children, Technology and Play (CTAP) - Google Forms.pdfDescriptive Stats 2020.1.9 -Children Technology and Play SURVEY.xlsxParent Survey RAW PUBLIC DATA 2020.2.29 - Children Technology and Play Project.xlsxParent Survey RAW PUBLIC DATA 2020.2.29 - Children Technology and Play Project.csvParent Survey REPORT DATA 2020.2.29 - Children Technology and Play Project.xlsxParent Survey REPORT DATA 2020.2.29 - Children Technology and Play Project.csvParent Survey RAW and REPORT DATA SYNTAX 2020.2.29 - Children Technology and Play Project.spsNOTE: This survey was adapted from Marsh, J. Stjerne Thomsen, B., Parry, B., Scott, F. Bishop, J.C., Bannister, C., Driscoll, A., Margary, T., Woodgate, A., (2019) Children, Technology and Play. UK Survey Questions. LEGO Foundation.

Description: This data set contains responses from individuals who are 12 to 14 years old who self-reported on the indicators related to HIV/AIDS behaviour and testing. The respondents' biographical data, school attendance, questions on media, communication and norms, knowledge and perceptions of HIV and AIDS, home environment, care and protection at school, sexual debut, attitudes and knowledge towards sexual roles, health questions, male circumcision, crime and social norms were included. The data set contains 227 variables and 2273 cases. Refer to the user guide for information regarding guidance relating to data analysis. Subsequent to the dissemination of version 1 of the Child 12-14 data set the skip patterns for the Child data set was corrected, Version 2 of the data set is disseminated as: Human Sciences Research Council. South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey (SABSSM) 2012: Child 12-14 years - All provinces. [Data set]. SABSSM 2012 Child 12-14. Version 2.0. Pretoria South Africa: Human Sciences Research Council [producer] 2012, Human Sciences Research Council [distributor] 2016. http://dx.doi.org/doi:10.14749/1518167762. Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the World. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the fourth in a series of household surveys conducted by Human Sciences Research council (HSRC), that allow for tracking of HIV and associated determinants over time using a slightly same methodology used in 2002 and 2008 survey, making it the fourth national-level repeat survey. The 2002 and 2005 surveys included individuals aged 2+ years living in South Africa while 2008 and 2012 survey included individuals of all ages living in South Africa, including infants less than 2 years of age. The 2008 study included only four people per household, while in 2012 all members of the households participated. The interval of three years since 2002 allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The surveys provide the nationally representative HIV incidence estimates showing changes over time. The 2012 study key objectives were: to determine the proportion of PLHIV who are on Antiretroviral treatment (ART) in South Africa; to determine the prevalence and incidence of HIV infection in South Africa in relation to social and behavioural determinants; to determine the proportion of males in South Africa who are circumcised; to investigate the link between social values, and cultural determinants and HIV infection in South Africa; to determine the extent to which mother-child pairs include HIV-negative mothers and HIV-positive infants; to describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002 to 2012 collect data on the health conditions of South Africans; and contribute to the analysis of the impact of HIV/AIDS on society. In 2012, of the 15000 selected households or visiting points, 11079 agreed to participate in the survey, 42950 individuals (all household members were included) were eligible to be interviewed, and 38431 individuals completed the interview. Of the 38431 eligible individuals, 28997 agreed to provide a blood specimen for HIV testing and were anonymously linked to the behavioural questionnaires. The household response rate was 87.2% , the individual response rate was 89.5% and the overall response rate for HIV testing was 67.5% Clinical measurements Face-to-face interview Focus group Observation South African population. This project used the updated 2007-2011 HSRC's master sample. Aerial photographs drawn from Google Earth were utilised to ensure that the most up-to-date information was available sample. the 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) and the Secondary Sampling Units (SSUs) were the visiting points (VPs) or households (HHs). The Ultimate Sampling Units (USUs) were the individuals eligible to be selected for the survey. Any member of the household "who slept here last night", including visitors was an eligible household member for the interview. This sampling approach was used in the 2001 census and is a standard demographic household survey procedure. The sample was designed with two main explicit strata, the provinces and the geography types (geotype) of the EA. In the 2001 census, the four geotypes were urban formal, urban informal, rural formal (including commercial farms) and tribal areas (rural informal) (i.e. the deep rural areas). In the formal urban areas, race was used as a third stratification variable. What this means is that the Master Sample was 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. 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. A maximum of four visits were made to each VP to optimise response. Fieldworkers enumerated household members, using a random number generator to select the respondent and then preceded with the interview. All people in the households, resident at the visiting point were invited to participate in the study. These individuals constituted the USUs of this study. Having completed the sample design, the sample was drawn with 1 000 PSUs or EAs being selected throughout South Africa. 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 15 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 15 dwelling units per PSU, allowing on average for one invalid dwelling unit in the cluster of 15 dwelling units. Previous experience at Statistics SA indicated a sample size of 10 households per PSU to be very efficient, balancing cost and efficiency. The VP questionnaire was administered by the fieldworker, and in follow-up, participant selection was made by the supervisor. Participants aged 12 years and older who consented were all interviewed and also asked to provide dried blood spots (DBS) specimens for HIV testing. In case of 0-11 years, parents/guardians were interviewed but DBS specimens were obtained from the children. The sample size estimate for the 2012 survey was guided by the (1) requirement for measuring change over time in order to detect a change in HIV prevalence of 5 percentage points in each of the main reporting domains, namely gender, age-group, race, locality type, and province (5% level of significance, 80% power, two-sided test), and (2) the requirement of an acceptable precision of estimates per reporting domain; that is, to be able to estimate HIV prevalence in each of the main reporting domains with a precision level of less than ± 4%, which is equivalent to the expected width of the 95% confidence interval (z-score at the 95% level for two-sided test). A design effect of 2 was assumed. Overall, a total of 38 431 interviewed participants composed of 29.7% children (0-14 years), 19.3% youths (15-24 years), 35.6% adults (25-49 years), and 15.4% adults (50+ years ) were interviewed. 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. 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). More females (70.3%) than males (64.2%) were tested for HIV. The 15-24 year's age group was the most compliant (71.6%), and less than 2 years the least (51.6%). The highest testing response rate was found in rural formal settlements (80.8%) and the least in urban formal areas (59.7%).

This dataset consists of early learning development data collected using the Early Learning Outcomes Measure (ELOM) assessment tool. The ELOM was developed on behalf of Innovation Edge by Andrew Dawes, Linda Biersteker, Elizabeth Girdwood, Matthew Snelling and Colin Tedoux. Prior to the development of this tool, there was no validated South African instrument for measuring programme performance against early learning development standards. The ELOM is an age-normed, standardised instrument for use with children in two age groups: 50-59 months and 60-69 months. The tool covers both direct assessment of children’s performance and an assessment of the child’s social and emotional functioning and orientation to tasks.

Abstract

During October 1996 Statistics South Africa recorded the details of people living in more than nine million households in South Africa, as well as those in hostels, hotels and prisons. Census 1996 was the first nation wide census since the splitting up of the country under apartheid after 1970 and sought to apply the same methodology to everyone: visiting the household, and obtaining details about all its members from a representative who was either interviewed, or else filled in the questionnaire in their language of choice.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The survey covered households and household members in households in the nine provinces of South Africa.

Kind of data

Sample survey data

Sampling procedure

A sample of 1600 Enumerator Areas (EA's) was produced in conjunction with the sample for the 1996 Population Census post-enumeration survey. A two stage sampling procedure was applied in the following manner.

The first stratification was done by province, as well as by type of EA (formal or informal urban areas, commercial farms, traditional authority areas or other non-urban areas). Originally eight hundred EA's were allocated to each strata by province proportionately. Later some adjustments were made to ensure adequate representation of smaller provinces such as the Northern Cape. Independent systematic samples of EA's were drawn for each stratum within each province. The sampling frame that was used was constructed from the preliminary database of EA's which was established during the demarcation and listing phase of the 1996 population census. In the second phase 10 households were drawn from each EA on the western and eastern side of the EA drawn for the post enumeration survey. This meant 10 households per EA in 1600 different EA's, that is 16 000 households in total.

Mode of data collection

Face-to-face [f2f]

Research instrument

The data files in the October Household Survey 1996 (OHS 1996) correspond to the following sections in the questionnaire:

House: Data from FLAP, Section 1 and Section 7 Person: Data from Section 2 Worker: Data from Section 3 Migrant: Data from Section 4 Death: Data from Section 5 Births: Data from Section 6 - This data had a considerable number of problems and will not be published. Income: Data from Section 7 (included in House) Domestic: Data from Section 8

Data appraisal

Questionnaire: The October Household Survey 1996 questionnaire had incorrect FLAP data. No Population Group question was indicated on the FLAP. DataFirst notified Statistics SA who supplied a corrected questionnaire which is the one now available with the dataset.

Household IDs: In the previous version of the 1996 October Household Survey dataset archived by DataFirst the HHID were not unique. This was corrected in the first version disseminated by DataFirst, version 1. Version 1.1 keeps this correction, but data users should check versions not obtained from DataFirst and replace these with the latest version available from DataFirst.

Linking Files: The Metadata for the OHS 1996 provides an explanation for merging the files in the files in the OHS 1996 dataset: "The data from different files can be linked on the basis of the record identifiers. The record identifiers are composed of the first few fields in each file. Each record contains the three fields Magisterial District, Enumeration area, and Visiting point number. These eleven digits together constitute a unique household identifier. All records with a given household identifier, no matter which file they are in, belong to the same household. For individuals, a further two digits constituting the Person number, when added to the household identifier, creates a unique individual identifier. Again, these can be used to link records from the PERSON and WORK files. The syntax needed to merge information from different files will differ according to the statistical package used (October Household Survey 1996: Metadata: General Notes: 2).” According to the above, to generate household IDs it is necessary to use a combination of magisterial district number (mdnumber), enumeration area number (eanumber) and visiting point number (vpnumber). To generate person IDs it is necessary to use the above with the person number (personnu).

These variables are named as such in the OHS 1996 House, OHS 1996 Births, OHS 1996 Migrant, OHS 1996 Deaths, OHS 1996 Household Income Other, OHS 1996 Other, OHS 1996 Domestic and OHS 1996 Flap data files. However, in the OHS 1996 Worker and OHS 1996 Person data files the variable for magisterial district number is “distr”, the variable for Enumeration Area is “ea” and the variable for visiting point number is called "visp”. The variable for person number in these files is called “respno”.

The metadata provided to DataFirst with this dataset does not discuss these changes.

October Household Survey 1996 Births file: Births data was collected by Section 6 of the OHS 1996 questionnaire, completed for all women younger than 55 years who had ever given birth. The metadata for this survey from Statistics SA states that “This data had a considerable number of problems and will not be published” The dataset provided by DataFirst therefore does not include the original “births” file. Those in possession of this file from unofficial versions of the dataset should note the following problems with the data in the OHS 1996 births file:

Variable name: eegender Question 6.2: Is/was (the child) a boy or a girl? Valid range: 1 (boy) - 2 (girl) Data quality issue: There is a third response value of 0 with no description

Variable name: livinghh Question 6.4: If alive: Is (the child) currently living with this household? Valid range: 1 (yes) - 2 (no) Data quality issue: This variable has an additional response value (0), which has no description

Variable name: agealive Question 6.5: If alive: How old is he/she? This question was asked of all women younger than 55 years who have ever given birth to provide the age of their living children. Data quality issue: responses range from 0-77 for age of child (assuming age 99 is for missing responses) which is outside the plausible range.

Variable name: agenaliv Question 6.6: If dead: How old was (the child) when he/she died? Data quality issue: The format of the age at death variable is not clear

Variable name: datebirt Question 6.7: [All children]: In what year and month was (the child) born? Data quality issue: There are problems with the format of the date of birth variable

Variable name: wherebor Question 6.8: [All children]: Where was (the child) born? Data quality issue: There are only three options for the place of birth in the questionnaire (in a hospital, in a clinic and elsewhere), but the data has 10 response values (0-9) with no explanation for this in the metadata.

Variable name: regstere Question 6.9 [All children] Was the birth registered? Valid range: 1(yes) - 2 (no) Data quality issue: There are 4 response values (0-3) for this variable

To download the 2004 dataset go to: http://sds.ukzn.ac.za/default.php?11,0,0,0,0 The third round of the KwaZulu-Natal Income Dynamics Study (KIDS) dataset contains information on the socio-economic circumstances of households. This third round conducted in 2004 re-interviewed households contacted in 1993 and 1998. It is based on the Project for Statistics on Living Standards and Development (PSLSD). The 2004 questionnaire is based on the original 1993. It includes the collection of anthropometric data from children aged 6 years or less. New modules include the administration of a literacy test to children aged 7-9 years, a module on employment histories, and a module on the Child Support Grant (CSG). Also, several existing modules have been expanded or amended, including the information on deaths in the household, the module on health and caring, that on social capital and the information collected on children. The third round of the study interviewed 867 households containing core members from 760 of the households contacted in 1993. For 180 of these 760 ‘dynasties’, information was also collected on next generation households that had split off from them. Between 1993 and 2004, attrition rates appear to be within acceptable limits, although young adults and smaller, and perhaps poorer, households are underrepresented. The age distribution of the resident members of th e core and next generation households matches that of the African and Indian population of KwaZulu-Natal reported by Census 2001. The mortality results suggest that the proportion of people at ages 20-44 dying between the second and third rounds was nearly three times the proportion dying between the first two rounds. The pattern of income distribution is one of increasing poverty and inequality since 1993, although the partial reversal of these trends in the post-1998 period is hopeful as are signs of relative prosperity among those that established independent next-generation households. In addition, access to services has generally improved. The 2004 data collection was administered by researchers at the International Food Policy Research Institute (IFPRI), the University of KwaZulu-Natal (UKZN), the University of Wisconsin-Madison. The funding for the project was provided by the UK Department for International Development (DFID) through Department of Social Development (DSD), the National Research Foundation, the Norwegian Research Council, USAID, and the Mellon Foundation. The South Africa: KwaZulu-Natal I ncome Dynamics Study (KIDS), 2004 was a collaborative project of the International Food Policy Research Institute (IFPRI), the University of KwaZulu-Natal (UKZN), the University of Wisconsin-Madison, the London School of Hygiene and Tropical Medicine (LSHTM), and the Norwegian Institute of Urban and Regional Studies (NIBR).

The Cape Area Panel Study (CAPS) is a longitudinal study of the lives of youths and young adults in metropolitan Cape Town, South Africa. The first wave of the study collected interviews from about 4800 randomly selected young people age 14-22 in August-December, 2002. Wave 1 also collected information on all members of these young people’s households, as well as a random sample of households that did not have members age 14-22. A third of the youth sample was re-interviewed in 2003 (Wave 2a) and the remaining two thirds were re-visited in 2004 (Wave 2b). The full youth sample was then re-interviewed in 2005 (Wave 3), 2006 (Wave 4) and 2009 (Wave 5). Wave 3 includes interviews with approximately 2000 co-resident parents of young adults, while wave 4 also includes interviews with a sample of older adults (all individuals from the original 2002 households who were born on or before 1 January 1956) and all children born to the female young adults. The fifth wave comprises all respondents interviewed in any of the Waves 2a, 3 or 4. In 2010 there were telephonic follow-ups or proxy interviewed that tried to capture those that were not successfully interviewed during the course of the 2009 fieldwork. The study covers a wide range of outcomes, including schooling, employment, health, family formation, and intergenerational support systems. CAPS began in 2002 as a collaborative project of the Population Studies Center in the Institute for Social Research at the University of Michigan and the Centre for Social Science Research at the University of Cape Town (UCT). Other units involved in subsequent waves include UCT’s Southern African Labour and Development Research Unit and the Research Program in Development Studies at Princeton University. Primary funding is provided by the National Institute of Child Health and Human Development of the U.S. National Institutes of Health (NIH). Additional funding has been provided by the Office of AIDS Research, the Fogarty International Center, and the National Institute of Aging of NIH, and by grants from the Andrew W. Mellon Foundation to the University of Michigan and the University of Cape Town.

Abstract

This dataset consists of early learning development data collected using the Early Learning Outcomes Measure (ELOM) assessment tool. The ELOM was developed on behalf of Innovation Edge by Andrew Dawes, Linda Biersteker, Elizabeth Girdwood, Matthew Snelling and Colin Tedoux. Prior to the development of this tool, there was no validated South African instrument for measuring programme performance against early learning development standards. The ELOM is an age-normed, standardised instrument for use with children in two age groups: 50-59 months and 60-69 months. The tool covers both direct assessment of children’s performance and an assessment of the child’s social and emotional functioning and orientation to tasks.

Geographic coverage

The assessment was undertaken in schools in the Western Cape, KwaZulu Natal, and North West Provinces of South Africa

Analysis unit

Individuals

Universe

The assessment focused on children between the ages of 49-69 months in the selected schools.

Kind of data

Observation data/ratings [obs]

Mode of data collection

Computer Assisted Personal Interview [capi]

ZA: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 13.300 % in 2016. This records a decrease from the previous number of 17.200 % for 2012. ZA: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 13.300 % from Dec 1995 (Median) to 2016, with 6 observations. The data reached an all-time high of 19.200 % in 2004 and a record low of 10.300 % in 1995. ZA: Prevalence of Overweight: Weight for Height: % of Children Under 5 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.WDI: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

Description: This data set contains information on children aged 12 - 14 years; biographical data; media, communication and norms; knowledge and perceptions of HIV/AIDS; home environment; care and protection; sexual debut; condoms; attitudes and knowledge towards sexual roles; health; and violence in the community. The data set contains 394 variables and 1617 cases. Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the world. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the second in a series of household surveys conducted by the Human Sciences Research Council (HSRC), that allow for tracking of HIV and associated determinants over time using the same methodology used in the 2002 survey, thus making it the first national-level repeat survey. The interval of three years allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The survey provides the first nationally representative HIV incidence estimates. The study key objectives were to: Determine HIV prevalence and incidence as well as viral load in the population; Gather data to inform modelling of the epidemic; Identify risky behaviours that predispose the South African population to HIV infection; examine social, behavioural and cultural determinants of HIV; explore the reach of HIV/AIDS communication and the relationship of communication to response; assess the relationship between mental health and HIV/AIDS and establish a baseline; assess public perceptions of South Africans with respect to the provision of anti-retroviral (ARV) therapy for prevention of mother-to-child transmission and for treating people living with HIV/AIDS; understand public perceptions regarding aspects of HIV vaccines; and investigate the extent of the use of hormonal contraception and its relationship to HIV infection. In the 10 584 valid visiting points that agreed to participate in the survey, 24 236 individuals were eligible for interviews and 23 275 completed the interview. Of the 24 236 individuals, 15 851 agreed to HIV testing and were anonymously linked to the behavioural interviews. The household response rate was 84.1 % and the overall response rate for HIV testing was 55 %. Clinical measurements Face-to-face interview Focus group Observation South African population, 2 years and older from urban formal, urban informal, rural formal (farms), rural informal (tribal area) 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) and the Secondary Sampling Units (SSUs) were the visiting points (VPs) or households (HHs). The Ultimate Sampling Units (USUs) were the individuals eligible to be selected for the survey. Any member of the household 'who slept here last night', including visitors was an eligible household member for the interview. This sampling approach was used in the 2001 census and is a standard demographic household survey procedure. The sample was designed with two main explicit strata, the provinces and the geography types (geotype) of the EA. In the 2001 census, the four geotypes were urban formal, urban informal, rural formal (including commercial farms) and tribal areas (rural informal) (i.e. the deep rural areas). In the formal urban areas, race was used as a third stratification variable. What this means is that the Master Sample was 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. 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. A maximum of four visits were made to each VP to optimise response. Fieldworkers enumerated household members, using a random number generator to select the respondent and then proceeded with the interview. All people in the households, resident at the visiting point aged 2 years and older were initially listed, after which the eligible individual was randomly selected in each of the following three age groups 2-11, 12-14 and 15 years and older. These individuals constituted the USUs of this study. Having completed the sample design, the sample was drawn with 1 000 PSUs or EAs being selected throughout South Africa. 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 15 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 15 dwelling units per PSU, allowing on average for one invalid dwelling unit in the cluster of 15 dwelling units. Previous experience at Statistics SA indicated a sample size of 10 households per PSU to be very efficient, balancing cost and efficiency. The VP questionnaire was administered by the fieldworker, and in follow-up, participant selection was made by the supervisor. Participants aged 12 years and older who consented were all interviewed and also asked to provide dried blood spots (DBS) specimens for HIV testing. In case of 2-11 years, parents/guardians were interviewed but DBS specimens were obtained from the children. The sample size estimate for the 2005 survey was guided by (1) the requirement for measuring change over time and to be able to detect a change in HIV prevalence of 5 % points in each of the main reporting domains, and (2) the requirement of an acceptable precision of estimates per reporting domain, say a precision less than ?4% with a design effect of 2 units. Overall, a total of 23 275 participants composed of 6 866 children (2-14 years), 5 708 youths (15-24 years) and 10 687 adults (25+ years) were interviewed. 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. 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). More females (68.3%) than males (62.2%) were tested for HIV. The 25+ years age group was the most compliant (71.3%), and 2-14 years the least (54.6%). The highest response rates were found in rural formal locality types (74.5%) and the lowest in urban formal locality types (61.7%).

Abstract

The State of Giving project, established by the Centre for Civil Society (CCS) at the University of KwaZulu-Natal (UKZN), the Southern African Grantmakers’ Association (SAGA) and the National Development Agency (NDA), was initiated to generate information on and analyse the resource flows to poverty alleviation and development in South Africa. One component of the broader project was a focus on individual-level giving, which involved the design, implementation and analysis of a national sample survey on individual level giving behaviour. The sample, a random stratified one comprising 3000 respondents, is representative of all South Africans aged 18 and above. It thus speaks to both the urban and rural and the formal and informal dimensions of our social context. The survey collected data on who gives, why and how much they give, as well as what they give and the recipients of their giving.

Geographic coverage

National coverage

Analysis unit

Units of analysis in the survey were households and individuals

Universe

The population of interest in the survey was all South Africans aged 18 and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

A random stratified survey sample was drawn by Ross Jennings at S&T. The sample was stratified by race and province at the first level, and then by area (rural/urban/etc.) at the second level. The sample frame comprised 3000 respondents, yielding an error bar of 1.8%. The results are representative of all South Africans aged 18 and above, in all parts of the country, including formal and informal dwellings. Unlike many surveys, the project partners ensured that the rural component of the sample (commonly the most expensive for logistical reasons) was large and did not require heavy weighting (where a small number of respondents have to represent the views of a far larger community).

Randomness was built into the selection of starting points (from which fieldworkers begin their work) - every 5th dwelling was selected, after a randomly selected starting point had been identified - and into the selection of respondents, where the birthday rule was applied. That is, a household roster was completed, all those aged 18 and above were listed, and the householder whose birthday came next was identified as the respondent. Three call-backs were undertaken to interview the selected respondent; if s/he was unavailable, the household was substituted.

A second sample was drawn, specifically to boost the minority religious groups – namely Hindus, Jews and Muslims. They are separately analysed and reported as part of the broader project, since area sampling was used, disallowing us from incorporating them into the national survey dataset.

Mode of data collection

Face-to-face [f2f]

Research instrument

A set of focus groups were staged across the country in order to inform questionnaire design. Groups were recruited across a range of criteria, including demographic and religious differences, in order to ensure a wide range of views were canvassed. Direct input from focus group participants informed a series of robust design sessions with all the project partners, from which a draft questionnaire was designed. The questionnaire was piloted in two provinces, involving urban and rural respondents and covering all four race groups. The pilot included testing specific questions, and the overall methodological approach, namely our ability to quantify giving. After the pilot results had been assessed, the questionnaire was revised before going into field.

Data appraisal

1. "0" values in some variables Many of the variables have a "0" value in addition to the values for responses, e.g. variables with yes/no responses are coded "0" "1""2". There is no indication that the 0 represents "missing" (only Q75 specifies the use of "0" for none/nobody).

2. Variable Q9 (Question 9) Q8 lists the number of resident children under the age of 18. Q9 refers to this question with: "of these children aged below 16 living in your household". This should probably be "aged below 18", in line with Q8 The data only reflects children under 16, so the question should probably have been "of these children, how many below the age of 16 are (Q9A) children of the head of the household and (Q9B) children not born to the head of household, i.e. children born to others. It seems though, that Q8 and Q9 should match, with Q8 identifying children and Q9 identifying children of the household head. If specifying 16 rather than 18 in Q9 is an error, then this has been reflected in the data. This means that household members 17-18 years are listed, but the data does not record whether they are children of the household head.

3. Variable Q21 (Question 21) "What do you think is the most deserving cause that you support or would support if you could?" There are 14 values for Q21 (1-14).According to the report (Everatt, D. and G. Solanki. 2005. A Nation of givers: Social giving amongst South Africans) this and other open-ended questions were later categorised and given numeric codes. However, a codebook was not included with the documentation provided to DataFirst

4. Variable Q22 (Question 22) "Is there one cause or charity or organisation you would definitely NOT give money to?" There are 14 values for Q22 (1-14). Again, this requires a code list for explanation.

5. Variable Q29 (Question 29) Q28 deals with the giving of goods/food/clothes. Q29 provides a breakdown of these items, and Q28Q29L lists time/labour as one of these. It seems that Q29L is incorrectly listed as a sub-set of goods/food/clothes. Also, giving time to causes is dealt with extensively in Q30A-Q and Q31A-Q, so this variable seems out of place.

6. Variable Q39 (Question 36) This concerns the giving of food, goods, or other forms of help to beggars/street children/people asking for help, but the question text does not specifically mention these forms of help, so can be misleading.

7. Variable Q44 (Question 44) Q44 asks the respondent to complete the sentence "Help the poor because…." There are 8 values for this variable (0-7 and 11). Again, a code list is required to explain these values.

8. Variable Q59 (Question 59) This question has three coded responses (1-3) so should have three values (or 4, with a "missing" value). There are 12 values for this variable, though (59A-59L). It is possible that this variable has been swopped with Q60 (However, Q60 only has 11 options in the questionnaire)

9. Variable Q60 (Question 60) The variable from this question only has 4 values, but there are 11 possible responses to this question (60A-60K). This variable could have been swopped with Q59 (In which case, the extra value needs explanation, as Q59 only has 11 options in the questionnaire.

10. Variables Q67 - Q82 From this point on the order of variables seems wrong, as the responses don't match the number of values listed in the questionnaire. The variables seem to refer to the next question along, e.g. Variable Q67 seems to have data emanating from Question 68, and so on. The data in the revised dataset has been corrected to reflect this.

11. There is no variable Q83 in the dataset, although there is a question 83 in the questionnaire. This seems to support the above explanation. Data users are requested to provide any additional findings on this that come to light in their research.

Background: Typically, African healthcare providers use immunological reference intervals adopted from Europe and the United States (US). This may be inappropriate in a setting with many differences including exposure to different environmental stimuli and pathogens. We compared immunological reference intervals for children from Europe and the US with South African children to explore whether healthy children living in settings with high rates of infectious diseases have different baseline immunological parameters.Methodology: Blood was taken from 381 HIV-uninfected children aged between 2 weeks and 13 years of age from a Child Wellness Clinic in an informal settlement in Cape Town to establish local hematological and lymphocyte reference intervals for South African children. Flow-cytometry quantified percentage and absolute counts of the B-cells, NK-cells, and T-cells including activated, naïve, and memory subsets. These parameters were compared to three separate studies of healthy children in Europe and the US.Results: Increased activated T-cells, and natural killer cells were seen in the younger age-groups. The main finding across all age-groups was that the ratio of naïve/memory CD4 and CD8 T-cells reached a 1:1 ratio around the first decade of life in healthy South African children, far earlier than in resource-rich countries, where it occurs around the fourth decade of life.Conclusions: This is the largest data set to date describing healthy children from an African environment. These data have been used to create local reference intervals for South African children. The dramatic decline in the naïve/memory ratio of both CD4 and CD8 T-cells alongside increased activation markers may indicate that South African children are exposed to a wider range of environmental pathogens in early life than in resource-rich countries. These marked differences illustrate that reference intervals should be relevant to the population they serve. The implications for the developing pediatric immune system requires further investigation.

The UK Survey Data forms part of Children, Technology and Play (2019-2020), an 8-month co-produced study by academics from the University of Sheffield and University of Cape Town, South Africa, the LEGO Foundation and Dubit.

The study explored the contemporary play environments of children to identify the ways in which their play is shaped by technology, examine the relationship between digital play, learning and creativity, and explore the role of adults in mediating digital play.

As part of the UK research, an online survey was conducted by Dubit of 2,429 families with children aged 3-11 years across the UK. This dataset comprises analysis of the statistical data from the survey. The analysis was carried out using SPSS (Statistical Package for the Social Sciences) version 26. The analysis involved the creation of tables using either customised tables by direct coding (CTABLES) or by using the CROSSTABS procedure which produced the chi-squared statistics which determined any statistical significance in relation to age/gender/social class/ethnicity.

The project received ethical approval from the University of Sheffield (no. 028701).

The research tools, including the survey questions, and other datasets from the study are deposited elsewhere in ORDA and have been brought together in the Children, Technology and Play collection.

In the combined data set five individual data sets were combined, guardians for both infants younger than 2 years and children 2 to 11 years, children 12 to 14 years, youths and adults 15 years and older. The data set contains information on: biographical data, media, communication and norms, knowledge and perceptions of HIV/AIDS, male circumcision, sexual debut, partners and partner characteristics, condoms, vulnerability, HIV testing, alcohol and substance use, general perceptions about government, health and violence in the community. The data set contains 810 variables and 23369 cases. Subsequent to the dissemination of version 1 of this data set it was discovered that the data of the following variables were missing: rq240a - rq240f. This was corrected and additionally two variables without descriptions were removed from the data set. A new data set is disseminated as version 2. South Africa continues to have the largest number of people living with HIV/AIDS in the World. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the third in a series of household surveys conducted by Human Sciences Research Council (HSRC), that allow for tracking of HIV and associated determinants over time using a slightly same methodology used in 2002 and 2005 survey, making it the third national-level repeat survey. The 2002 and 2005 surveys included individuals aged 2+ years living in South Africa while 2008 survey included individuals of all ages living in South Africa, including infants younger than 2 years of age. The interval of three years since 2002 allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The survey provides the first nationally representative HIV incidence estimates. The study key objectives were to: determine the prevalence of HIV infection in South Africa; examine the incidence of HIV infection in South Africa; assess the relationship between behavioural factors and HIV infection in South Africa; describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002-2008; investigate the link between social, values, and cultural determinants and HIV infection in South Africa; assess the type and frequency of exposure to major national behavioural change communication programmes and assess their relationship to HIV prevention, AIDS treatment, care, and support; describe male circumcision practices in South Africa and assess its acceptability as a method of HIV prevention; collect data on the health conditions of South Africans; and contribute to the analysis of the impact of HIV/AIDS on society. In the 13440 valid households or visiting points, 10856 agreed to participate in the survey, 23369 individuals (no more than 4 per household, including infants under 2 years) were eligible to be interviewed, and 20826 individuals completed the interview. Of the 23369 eligible individuals, 15031 agreed to provide a blood specimen for HIV testing and were anonymously linked to the behavioural questionnaires. the household response rate was 80.8%, the individual response rate was 89.1% and the overall response rate for HIV testing was 64.3%.

BackgroundThe effectiveness of prenatal care for improving birth and subsequent child outcomes in low-income countries remains controversial, with much of the evidence to date coming from high-income countries and focused on early-life outcomes. We examined associations between prenatal care visits and birth weight, height-for-age at 24 months and attained schooling in four low- and middle-income countries.MethodsWe pooled data from prospective birth-cohort studies from Brazil, Guatemala, Philippines and South Africa. We created a prenatal care utilization index based on the number and timing of prenatal visits. Associations were examined between this index and birth weight, height-for-age at 24 months, and highest attained schooling grade until adulthood.ResultsAmong 7203 individuals in the analysis, 68.9% (Philippines) to 96.7% (South Africa) had at least one prenatal care visit, with most having at least four visits. Over 40% of Brazilians and Guatemalans had their first prenatal visit in the first trimester, but fewer Filipinos (13.9%) and South Africans (19.8%) did so. Prenatal care utilization was not significantly associated with birth weight (p>0.05 in pooled data). Each unit increase in the prenatal care utilization index was associated with 0.09 (95% CI 0.04 to 0.15) higher height-for-age z-score at 24 months and with 0.26 (95% CI 0.17 to 0.35) higher schooling grades attained. Although there was some heterogeneity and greater imprecision across sites, the results were qualitatively similar among the four different populations.ConclusionsWhile not related to birth weight, prenatal care utilization was associated with important outcomes later in life, specifically higher height-for-age at 24 months and higher attained school grades. These results suggest the relevance of prenatal care visits for human capital outcomes important over the lifecycle.

Abstract

The Demographic and Health Survey is mainly concerned with the determination of fertility, infant mortality rates and closely related issues. Questions surrounding respondent’s background, reproduction, contraception, health and breastfeeding, marriage, fertility preferences, and husband’s background and woman’s work were asked. This study consists of two datasets, one household dataset and the other an individual dataset, the respondent being a female of reproductive age that has already given birth or who is married or exposed to pregnancy. Females qualifying for the individual interview schedule were chosen from the responses to household (cover) questionnaire.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The universe of the survey was female housheold members aged 12 to 49, who had given birth or were pregnant, or had been/were married or in a "steady" relationship.

Kind of data

Sample survey data

Sampling procedure

Random samples of clusters of households, representative of the main lifestyles in every participating state or region were selected.

Mode of data collection

Face-to-face [f2f]

Research instrument

Structured interview schedule/questionnaire.

Description: The guardian data of the SABSSM 2008 study covers 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 243 variables and 4318 cases. Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the World. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the third in a series of household surveys conducted by Human Sciences Research Council (HSRC), that allow for tracking of HIV and associated determinants over time using a slightly same methodology used in 2002 and 2005 survey, making it the third national-level repeat survey. The 2002 and 2005 surveys included individuals aged 2+ years living in South Africa while 2008 survey included individuals of all ages living in South Africa, including infants younger than 2 years of age. The interval of three years since 2002 allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The survey provides the first nationally representative HIV incidence estimates. The study key objectives were to: determine the prevalence of HIV infection in South Africa; examine the incidence of HIV infection in South Africa; assess the relationship between behavioural factors and HIV infection in South Africa; describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002-2008; investigate the link between social, values, and cultural determinants and HIV infection in South Africa; assess the type and frequency of exposure to major national behavioural change communication programmes and assess their relationship to HIV prevention, AIDS treatment, care, and support; describe male circumcision practices in South Africa and assess its acceptability as a method of HIV prevention; collect data on the health conditions of South Africans; and contribute to the analysis of the impact of HIV/AIDS on society. In the 13440 valid households or visiting points, 10856 agreed to participate in the survey, 23369 individuals (no more than 4 per household, including infants under 2 years) were eligible to be interviewed, and 20826 individuals completed the interview. Of the 23369 eligible individuals, 15031 agreed to provide a blood specimen for HIV testing and were anonymously linked to the behavioural questionnaires. the household response rate was 80.8%, the individual response rate was 89.1% and the overall response rate for HIV testing was 64.3%. Clinical measurements Face-to-face interview Focus group Observation South African population of all individuals from urban formal, urban informal, rural formal (farms), rural informal (tribal area) settlements. As in previous surveys, a multi-stage disproportionate, stratified sampling approach was used. A total of 1 000 census enumeration areas (EAs) from the 2001 population census were selected from a database of 86 000 EAs and mapped in 2007 using aerial photography to create a new updated Master Sample as a basis for sampling visiting points/households. The selection of EAs was stratified by province and locality type. Locality types were identified as urban formal, urban informal, rural formal (including commercial farms), and rural informal. In the formal urban areas, race was also used as a third stratification variable (based on the predominant race group in the selected EA at the time of the 2001 census). The allocation of EAs to different stratification categories was disproportionate; that means, over-sampling or over-allocation of EAs was done, for example, in areas that were dominated by Indian, coloured or white race groups to ensure that the minimum required sample size in those smaller race groups was obtained. The Master Sample was 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. 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. A maximum of four visits were made to each VP to optimise response. Fieldworkers enumerated household members, using a random number generator to select the respondent and then preceded with the interview. All people in 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: under 2 years, 2-14 years, 15-24 years and 25+ years. These individuals constituted the USUs of this study. Having completed the sample design, the sample was drawn with 1 000 PSUs or EAs being selected throughout South Africa. 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 15 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 15 dwelling units per PSU, allowing on average for one invalid dwelling unit in the cluster of 15 dwelling units. Previous experience at Statistics SA indicated a sample size of 10 households per PSU to be very efficient, balancing cost and efficiency. The VP questionnaire was administered by the fieldworker, and in follow-up, participant selection was made by the supervisor. Participants aged 12 years and older who consented were all interviewed and also asked to provide dried blood spots (DBS) specimens for HIV testing. In case of 0-11 years, parents/guardians were interviewed but DBS specimens were obtained from the children. The sample size estimate for the 2008 survey was guided by the (1) requirement for measuring change over time in order to detect a change in HIV prevalence of 5 percentage points in each of the main reporting domains, namely gender, age-group, race, locality type, and province (5% level of significance, 80% power, two-sided test), and (2) the requirement of an acceptable precision of estimates per reporting domain; that is, to be able to estimate HIV prevalence in each of the main reporting domains with a precision level of less than 4%, which is equivalent to the expected width of the 95% confidence interval (z-score at the 95% level for two-sided test). A design effect of 2 was assumed. Overall, a total of 20826 interviewed participants composed of 4981 children (0-14 years), 5344 youths (15-24 years) and 10501 adults (25+ years) were interviewed. 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. 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). More females (68.9%) than males (62.02%) were tested for HIV. The 25+ years age group was the most compliant (68.8%), and 2-14 years the least (58.9%). The highest testing response rate was found in urban informal settlements (72.5%) and the lowest in urban formal areas (62.8%).