Sanitation aims to prevent the spread of disease and promotes health through safe and hygienic waste disposal. To do this, sanitation systems must break the cycle of disease. For example the toilet lid and fly screen in a ventilated pit latrine stop flies reaching human faeces and spreading disease. Good sanitation is not simply about access to a particular type of toilet. It is equally dependent on the safe use and maintenance of that technology, otherwise toilets break down, smell bad, attract insects and spread germs. 
 

Good sanitation is essential for safe and healthy childhoods. It is very difficult to maintain good hygiene without water and toilets. Poor sanitation is associated with diarrhoea, cholera, malaria, bilharzia, worm infestations, eye infections and skin disease. These illnesses compromise children’s nutritional status. Using public toilets and the open veld (fields) can also put children in physical danger. The use of the open veld and bucket toilets is also likely to have consequences for water quality in the area and to contribute to the spread of disease. Poor sanitation undermines children’s health, safety and dignity.

 

The data show a gradual and significant improvement in children’s access to sanitation over the period 2002 to 2010, although the proportion of children without adequate toilet facilities remains worryingly high. In 2002 less than half of all children (47%) had access to adequate sanitation. By 2010 the proportion of children with adequate toilets had risen to 67%. Over six million children still use unventilated pit latrines, buckets or open land, despite the state’s reiterated goals to provide adequate sanitation to all, and to eradicate the bucket system.

 

As with other indicators of living environments, there are great provincial disparities. In provinces with large metropolitan populations, like Gauteng and the Western Cape, over 90% of children have access to adequate sanitation, while provinces with large rural populations have the poorest sanitation. The provinces with the greatest improvements in sanitation services are the Eastern Cape (where the number of children with access to adequate sanitation increased by nearly 150% in nine years), KwaZulu-Natal (where adequate sanitation leves increased from 36% of children in 2002 to 61% in 2010) and the Free State (improved from 55% in 2022 to 79% in 2010).

Although there have also been significant improvements in sanitation provision in Limpopo, this province still lags behind, with only 38% of children living in households with adequate sanitation in 2010. It is unclear why the vast majority of children in Limpopo are reported to live in formal houses, yet access to basic sanitation is the poorest of all the provinces. Definitions of adequate housing such as those in the UN-HABITAT and the South Africa’s National Housing Code include a minimum quality for basic services, including sanitation.

 

The statistics on basic sanitation provide yet another example of persistent racial inequality: Over 95% of Indian, White and Coloured children had access to adequate toilets in 2010, while only 61% of African children had access to basic sanitation. This is a marked improvement from 38% of African children in 2002.

Children in relatively well-off households have better levels of access to sanitation than poorer children. Amongst the richest 20% of households, over 90% of children have adequate sanitation, while only 53% of children in the poorest households have this level of service.

Sanitation therefore includes infrastructure, service provisioning and behaviour. For the purposes of this indicator we use a narrow definition, based simply on the type of toilet available to each household. The General Household Survey asks about each household’s toilet facilities. The following facilities are included in the category of adequate sanitation: ‘flush on-site’, ‘flush off-site’ and ‘VIP’, standing for ventilated improved pit latrine. Inadequate sanitation includes the following: ‘chemical toilet’, ‘other pit’, ‘bucket’, ‘none’ and a small number of ‘unspecified’.

 

For purposes of measuring and monitoring persistent racial inequality, population groups are defined as 'African', 'Coloured', 'Indian', and 'White'.

The data are derived from the General Household Survey³, a multi-purpose annual survey conducted by the national statistical agency, Statistics South Africa, to collect information on a range of topics from households in the country’s nine provinces. The survey uses a sample of 30,000 households. These are drawn from Census enumeration areas using multi-stage stratified sampling and probability proportional to size principles. The resulting estimates should be representative of all households in South Africa.

 

The GHS sample consists of households and does not cover other collective institutionalised living-quarters such as boarding schools, orphanages, students’ hostels, old age homes, hospitals, prisons, military barracks and workers’ hostels. These exclusions should not have a noticeable impact on the findings in respect of children.

 

The current master sample was used for the first time in 2004, meaning that, for longitudinal analysis, 2002 and 2003 may not be easily comparable with later years as they are based on a different sampling frame. From 2006, the sample was stratified first by province and then by district council. Prior to 2006, the sample was stratified by province and then by urban and rural area. The change in stratification could affect the interpretation of results generated by these surveys when they are compared over time.

 

Provincial boundary changes occurred between 2002 and 2007, and slightly affect the provincial populations. Comparisons on provincial level should therefore be treated with some caution. The sample and reporting are based on the old provincial boundaries as defined in 2001 and do not represent the new boundaries as defined in December 2005.

 

Person and household weights are provided by Statistics South Africa and are applied in Children Count – Abantwana Babalulekile analyses to give estimates at the provincial and national levels. Survey data are prone to sampling and reporting error. Some of the errors are difficult to estimate, while others can be identified. One way of checking for errors is by comparing the survey results with trusted estimates from elsewhere. Such a comparison can give an estimate of the robustness of the survey estimates. For this project, GHS data were compared with estimates from the Statistics South Africa’s mid-year estimates, as well as the Actuarial Society of South Africa’s ASSA2003 AIDS and Demographic model.

 

Analyses of the seven surveys from 2002 to 2008 suggest that over- and under-estimation may have occurred in the weighting process:

The apparent discrepancies in the seven years of data may slightly affect the accuracy of the Children Count – Abantwana Babalulekile estimates. Since 2005 the male and female patterns vary in respect of a particular characteristic, which means that the total estimate for this characteristic will be somewhat slanted toward the male pattern. A similar slanting will occur where the pattern for 10 – 14-year-olds, for example, differs from that of other age groups. Furthermore, there are likely to be different patterns across population groups.

 

Statistics South Africa suggests caution when attempting to interpret data generated at low level disaggregation. The population estimates are benchmarked at the national level in terms of age, sex and population group while at provincial level, benchmarking is by population group only. This could mean that estimates derived from any further disaggregation of the provincial data below the population group may not be robust enough.

 

Error may be present due to the methodology used, ie the questionnaire is administered to only one respondent in the household who is expected to provide information about all other members of the household. Not all respondents will have accurate information about all children in the household. In instances where the respondent did not or could not provide an answer, this was recorded as “unspecified” (no response) or “don’t know” (the respondent stated that they didn’t know the answer).

 

² Department of Water Affairs and Forestry (2002) The policy on basic household sanitation made easy. Pretoria: DWAF

 

³ Statistics South Africa (2011). General Household Survey 2010 Metadata. Cape Town, Pretoria: Statistics South Africa.

Westwood A (2011) Diarrhoeal Disease in Stephen C, Bamford L, Patrick W & the MRC Unit for Maternal and Infant Health Care Strategies (eds) Saving Children 2009: Five years of data. A sixth survey of child healthcare in South Africa. Pretoria: Tshepesa Press, MRC, CDC; 2011.

World Health Organisation & Unicef (2010) Progress on sanitation and drinking water: 2010 update. Geneva: WHO Press.