HealthHealth

Children living far from clinics

Author/s: Katharine Hall
Date: August 2024

Definition

This indicator reflects the distance from a child’s household to the health facility they normally attend. Distance is measured as the length of time travelled to reach the health facility, by whatever form of transport is usually used. The health facility is regarded as “far” if a child would have to travel more than 30 minutes to reach it, irrespective of mode of transport.

Data


Data Source

Statistics South Africa (2003 - 2023) General Household Survey 2002 - 2022. Pretoria, Cape Town: Statistics South Africa.
Analysis by Katharine Hall, Children's Institute, UCT.  

Notes
  1. Children are defined as persons aged 0 – 17 years.
  2. Population numbers have been rounded off to the nearest thousand.
  3. Sample surveys are always subject to error, and the proportions simply reflect the mid-point of a possible range. The confidence intervals (CIs) indicate the reliability of the estimate at the 95% level. This means that, if independent samples were repeatedly taken from the same population, we would expect the proportion to lie between upper and lower bounds of the CI 95% of the time. The wider the CI, the more uncertain the proportion. Where CIs overlap for different sub-populations or time periods we cannot be sure that there is a real difference in the proportion, even if the mid-points differ. CIs are represented in the bar graphs by vertical lines at the top of each bar.

What do the numbers tell us?

A review of international evidence suggests that universal access to key preventive and treatment interventions could avert up to two-thirds of under-five deaths in developing countries.1 Preventative measures include promotion of breast and complementary feeding, micronutrient supplements (vitamin A and zinc), immunisation, and the prevention of mother-to-child transmission of HIV, amongst others. Curative interventions provided through the government’s Integrated Management of Childhood Illness strategy include oral rehydration, infant resuscitation and the dispensing of medication.

According to the United Nations Committee on Economic, Social and Cultural Rights, primary health care should be available (in sufficient supply), accessible (easily reached), acceptable and of good quality.2  In 1996, primary level care was made free to everyone in South Africa, but the availability and physical accessibility of health care services remain a problem, particularly for people living in remote areas.

Physical inaccessibility poses particular challenges when it comes to health services because the people who need these services are often unwell or injured or need to be carried because they are too young, too old or too weak to walk. Physical inaccessibility can be related to distance, transport options and costs, or road infrastructure. Physical distance and poor roads also make it difficult for mobile clinics and emergency services to reach outlying areas. Within South Africa, the extent to which patients use health care services is influenced by the distance to the health service provider: those who live further from their nearest health facility are less likely to use the facility. This ‘distance decay’ is found even in the uptake of services that are required for all children, including immunisation and maintaining the Road-to-Health Book.3

In 2022, 20% of South Africa's children lived far from the primary health care facility they normally use. Analyses from previous surveys shows that over 90% of children live in households where members attended the health facility closest to their home. The main reasons for attending a more remote health service relate to perceptions of service quality; a preference for private health services (36%), and other specific quality complaints including long waiting times (19%); the unavailability of medication (8%) and rude or uncaring staff (4%). Cost considerations also inform choices, and 12% of households that did not use their nearest facility chose to travel further in order to access cheaper medical care or free government health services.4 Unfortunately these questions were dropped from the GHS in 2022

In total, over 4 million children travel more than 30 minutes to reach their usual health care service provider. This is a significant improvement since 2002, when 36% (or 6.6 million children) lived far from their nearest health facility. Improvements in the accessibility of health services are probably related both to the roll-out of additional facilities since 2002, and to increased urbanisation and greater population density in the areas around existing health infrastructure. While it is easier to deliver services in areas of greater population density, it may lead to greater pressure on health facilities if their capacity is not increase alongside a growing client population.

It is encouraging that the greatest improvements in health facility accessibility have been made in provinces which performed worst in 2002: the Eastern Cape (where the share of children with poor access to health facilities dropped from 53% in 2002 to 25% in 2022), KwaZulu-Natal (down from 48% to 28%), and Limpopo (from 42% to 25%). Provinces with the highest rates of access are the largely metropolitan provinces of the Western Cape and Gauteng, where only 6–7% of children live more than 30 minutes from their usual health care service.

Over twenty percent of African children travel far to reach their usual health care facility, compared with between 6 and 10% of Coloured, Indian and White children. Racial inequalities are amplified by access to transport: if in need of medical attention, 94% of White children would be transported to their health facility in a private car, compared with only 13% of African children. Only 3% of the poorest children (quintile 1) travel to their health facility in a private car, while 58% walk.

Poor children bear the greatest burden of disease, due to undernutrition and poorer living conditions and access to services (water and sanitation). Yet health facilities are least accessible to the poor. More than a quarter of children (28%) in the poorest 20% of households travel far to access health care, compared with 7% of children in the richest quintile.

There are no significant differences in patterns of access to health facilities when comparing children of different sex and age groups.

 
1 Jones G, Steketee RW, Black RE, Bhutta ZA, Morris SS & Bellagio Child Survival Study Group (2003) How many deaths can we prevent this year? The Lancet, 362(9977): 65-71.
2 United Nations Economic and Social Council (2000) International Covenant on Economic, Social and Cultural Rights, Article 12: The Right to the Highest Attainable Standard of Health: General Comment No. 14. Geneva: Committee on Economic, Social and Cultural Rights.
3 McLaren Z, Ardington C & Leibbrandt M (2013) Distance as a Barrier to Health Care Access in South Africa. A Southern Africa Labour and Development Research Unit Working Paper 97. Cape Town: SALDRU, UCT.
4 Statistics South Africa: General Household Survey 2018 (K Hall analysis).

Technical notes

The General Household Survey asks: “How long does it take when using the usual means of transport to get to the health facility that your household normally goes to?”

For purposes of this indicator, where respondents indicate that children would have to travel more than 30 minutes to their usual health facility, the distance is categorised as “far”. In cases where children would spend 30 minutes or less to reach their health care facility, the distance is categorised as “not far”. Amongst households with children, only 8% do not usually attend their nearest health facility. And within the poorest 40% of households, only 5% do not use their nearest facility. The main reasons for attending a more distant health service relate to choices based on perceptions of quality: preference for a private doctor, long waiting times at clinics, non-availability of medicines.

Data from 2009 onwards may not be be directly comparable with that of previous years, due to a change in question formulation in the General Household Survey. From 2002 to 2008, the survey asked: "How long in minutes does it take or would it take, from here [home] to reach the nearest clinic using the usual means of transport?" 

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

Strengths and limitations of the data

The numbers are derived from the General Household Survey, 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 GHS uses a Master Sample frame which has been developed as a general-purpose household survey frame that can be used by all other Stats SA household-based surveys that have design requirements that are reasonably compatible with the GHS. The sample is drawn from Census enumeration areas using a stratified two-stage design with probability proportional to size sampling of PSUs in teh first stage, and sampling of dwelling units with systematic sampling in the second stage. The resulting sample consists of just over 20,000 households with around 70,000 individuals, and should be representative of all households in South Africa. It is also designed to be representative at provincial level and within provinces at metro/non-metro levels and three geography types (urban areas, rural areas under traditional authority, and farms).

The 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 probably do not have a noticeable impact on the findings in respect of children.

Changes in sample frame and stratification
Since 2014 the GHS has been based on the 2013 master sample that that is, in turn, based on information collected during the 2011 Population Census. The previous master sample for the GHS was used for the first time in 2008, and the one before that in 2004. These again differed from the master sample used in the first two years of the GHS: 2002 and 2003. Thus there have been four different sampling frames during history of the annual GHS, with the changes occurring in 2004, 2008 and 2013. In addition, there have been changes in the method of stratification over the years. These changes could compromise comparability across iterations of the survey to some extent, although it is common practice to use the GHS for longitudinal monitoring and many of the official trend analyses are drawn from this survey.

Weights
Person and household weights are provided by Stats SA and are applied in Children Count analyses to give population estimates on the indicators. The GHS weights are derived from Stats SA’s mid-year population estimates for the relevant year. The population estimates are based on a model that is revised from time to time when it is possible to calibrate the population model to Census data and larger population surveys such as the Community Survey.

In 2017, Stats SA revised its demographic model to produce a new series of mid-year population estimates and the GHS data were re-released with the revised population weights. All the Children Count indicators were re-analysed retrospectively, using the revised weights provided by Stats SA, based on the 2013 model. The estimates are therefore comparable over all years. The revised weights particularly affected estimates for the years 2002 – 2007.

The 2017 model drew on the 2011 census, along with vital registration, antenatal and other administrative data, but was a “smoothed” model that did not mimic the unusual shape of the age distribution found in the census. The results of the 2011 census were initially distrusted because it seemed to over-count children in the 0 – 4 age group and under-count children in the 4 – 14-year group. It is now thought that the fertility rates recorded in the 2011 population census may have been an accurate reflection of demopraphic trends, with an unexplained upswing in fertility around 2009 after which fertility rates declined again gradually. Similar patterns were found in the vital registration data as more births were reported retrospectively to the Department of Home Affairs, and in administrative data from schools, compiled by the Department of Basic Education. In effect, this means that there may be more children in South Africa than appear from the analyses presented in these analyses, where we have applied weights based on a model that it is now known to be inaccurate.

Stats SA has subsequently developed a new population model - the 2022 series, which provides revised mid-year population estimates back to 2002 and projected to 2032. However, the GHS series has not yet been reweighted.The population estimates in Children Count are therefore based on weights derived from outdated population model (2017). It is not yet clear when and how the population model will be revised again following the 2022 Census, as there are concerns around census under-count and plausibility of its findings.

Disaggregation
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.

Reporting error
Error may be present due to the methodology used, i.e. 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).

For more information on the methods of the General Household Survey, see the metadata for the respective survey years, available on Nesstar  or DataFirst