Distance to the nearest clinic

Author/s: Katharine Hall
Date: October 2016


This indicator reflects the distance from a child’s household to the health facility they normally attend. Distance is measured through a proxy indicator: 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. 


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Data Source

Statistics South Africa (2003 - 2015) General Household Survey 2002 - 2014. Pretoria, Cape Town: Statistics South Africa.
Analysis by Katharine Hall & Winnie Sambu, Children's Institute, UCT.  

  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.

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 UN Committee on Economic, Social and Cultural Rights, primary health care should be available (in sufficient supply), accessible (easily reached), affordable, 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, patterns of health care utilisation are 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 clinic card (Road-to-Health booklet).3  

Over a fifth (21%) of South Africa's children live far from the primary health care facility they normally use, and 95% attend the facility closest to their home. Amongst households with children, only 7% do not usually attend their nearest health facility, and within the poorest 40% of households, only 3% do not use their nearest facility, while 11% of children in upper quintile households (the richest 20%) travel beyond their nearest health facility to seek care. The main reasons for attending a more distant health service relate to choices based on perceptions of quality: preference for a private doctor, non-availability of medicines, and long waiting times at clinics.4

In total, 4 million children travel more than 30 minutes to reach their usual health care service provider. This is a significant improvement since 2002, when 37% (or 6.9 million children) lived far from their nearest clinic. 

It is encouraging that the greatest improvements in access have been made in provinces which performed worst in 2002: the Eastern Cape (where the proportion of children with poor access to health facilities dropped from 55% in 2002 to 36% in 2014), KwaZulu-Natal (down from 49% to 27%), Limpopo (from 43% to 24%) and North West (from 39% to 26%) over the 13-year period. Provinces with the highest rates of access are the largely metropolitan provinces of Gauteng and the Western Cape, both at 8%.

There are also significant differences between population groups. Close to a quarter (24%) of African children travel far to reach a health care facility, compared with only 1 – 10% of Indian, White and Coloured children. Racial inequalities are amplified by access to transport: if in need of medical attention, 95% of White children would be transported to their health facility in a private car, compared with only 10% of African children and 31% of Coloured children. 

Poor children bear the greatest burden of disease, partly due to poorer living conditions and levels of services (water and sanitation). Yet health facilities are least accessible to the poor. Close to a third of children (32%) in the poorest 20% of households have to travel far to access health care, compared with 4% of children in the richest 20% of households.

There are no significant differences in patterns of access to health facilities when comparing children of different sex and age groups.
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'.
The data are derived from the General Household Survey5, 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.
Changes in sample frame and stratification
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
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 Stats SA 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. The GHS weights are derived from Stats SA’s mid-year population estimates. For this project, weighted GHS population numbers were compared with population projections from the Actuarial Society of South Africa’s ASSA2008 AIDS and Demographic model.

Analyses of the ten surveys from 2002 to 2011 suggest that some over- and under-estimation may have occurred in the weighting process: 

§  When comparing the weighted 2002 data with the ASSA2008 AIDS and Demographic model estimates, it seems that the number of children was under-estimated by 5% overall. The most severe under-estimation is in the youngest age group (0 – 9 years) where the weighted numbers of boys and girls yield under-estimations of 15% and 16% respectively. The next age group (5 – 9 years) is also under-estimated for both boys and girls, at around 7% each. The difference is reduced in the 10 – 14-year age group, although boys are still under-estimated by around 1% and girls by 3%. In contrast, the weighted data yield over-estimates of boys and girls in the upper age group (15 – 17 years), with the GHS over-counting these children by about 5%. The pattern is consistent for both sexes, resulting in fairly equal male-to-female ratios of 1.02, 1.01, 1.03 and 1.01 for the four age groups respectively. 

§  Similarly in 2003, there was considerable under-estimation of the youngest age groups (0 – 4 years and 5 – 9 years) and over-estimation of the oldest age group (15 – 17 years). The pattern is consistent for both sexes. Children in the youngest age group are under-estimated by as much as 16%, with under-estimates for babies below two years in the range 19 – 30%. The results also show that the over-estimation of males in the 15 – 17-year age group (9%) is much more severe than the over-estimation for females in this age range (1.4%), resulting in a male-to-female ratio of 1.09 in this age group, compared with ratios around 1.02 in the younger age groups. 

§  In the 2004 results, all child age groups seem to have been under-estimated, with the under-estimate being more severe in the upper age group (15 – 17 years). This is the result of severe under-estimation in the number of girls, which outweighs the slight over-estimation of boys in all age groups. Girls are under-estimated by around 6%, 8%, 8% and 12% respectively for the four age bands, while over-estimation in the boys’ age bands is in the range of 2 – 3%, with considerable variation in the individual years. This results in male-to-female ratios of 1.10, 1.11, 1.12 and 1.14 for the four age groups. 

§  In 2005, the GHS weights seem to have produced an over-estimate of the number of males and an under-estimate of the number of females within each five-year age group. The extent of under-estimation for girls (by 7% overall) exceeds that of the over-estimation for boys (at 2% overall). These patterns result in male-to-female ratios of 1.06, 1.13, 1.10 and 1.13 respectively for the four age groups covering children. 

§  The 2006 weighting process yields different patterns from other years when compared to population estimates for the same year derived from ASSA2008, in that it yielded an under-estimation of both females and males. The under-estimation of females is greatest in the 0 – 4 and 5 – 9-year age groups, while the under-estimation of males is in the range 3 – 10% in the 5 – 9 age group and 1 – 6% in the 10 – 14-year age group. This results in male-to-female ratios of 1.09, 0.99, 0.96 and 1.00 respectively for the four age groups covering children. 

§  The 2007 weighting process produced an over-estimation for boys and an under-estimation for girls. The under-estimation of females is in the range of 4 – 8% while the over-estimation for boys is in the range of 1 – 5%. This results in male-to-female ratios of 1.07, 1.06, 1.08 and 1.06 respectively for the four age groups covering children. 

§  In 2008, the GHS weighted population numbers when compared with ASSA2008 over-estimated the number of boys aged 10 and over, in the range of 3% for the 10 – 14 age group, and 8% for the 15 – 17 age group. The total weighted number of girls is similar to the ASSA population estimate for girls, but this belies an under-estimate of female babies below two years (by 7 – 8%), and an over-estimate of young teenage girls. The GHS 2008 suggests a male-to-female ratio of 1.03 for children aged 0 – 4 years, which is higher than that of the ASSA2008 model. 

§  A comparison of the GHS and ASSA for 2009 suggests a continuation of the general pattern from previous years, which is that GHS weights result in an under-estimation of children in the 0 – 4 age group (especially infants), and an over-estimate of older children. In 2009 the under-estimation in the 0 – 4 age group ranges up to 4% for boys and 5% for girls. In the 15 – 17 age group, the GHS-weighted data produce population numbers that are 7% higher than ASSA for boys, and 3% higher for girls. The male-to-female ratios in 2009 are in keeping with those in ASSA2008, with the exception of the 15 – 17 age group where the GHS-derived ratio is higher, at 1.08, compared to 1.00 in ASSA. 

§  In 2010, the GHS weights again produce an underestimation of children in the 0 – 4 age group and an over-estimate of children aged 15 – 17 years. For the middle age groups, and for the child age group as a whole, there is less than 1% difference in the estimates from the two sources. For the 0 – 4 age group the under-estimate is lower than previously, at 2%, but for the oldest age group there is an over-estimate of 5%. The male-to-female ratios are similar across the two sources, although the ratio is 1.00 for all but the 0 – 4 age group in ASSA as against 1.01 for the youngest age group in ASSA and for all age groups in the GHS. 

§  A comparison of the GHS2011 to ASSA2008 (projected to 2011) suggests an under-estimation of children below two years and an over-estimation of children aged 14 – 17 years in the Stats SA survey. This pattern holds for both boys and girls. The under-estimation is particularly pronounced for babies under a year, at 8%. The male-to-female ratio for all children under 17 is 1.00 in ASSA, and 1.01 in the GHS. 

The apparent discrepancies in the ten years of data may slightly affect the accuracy of the Children Count – Abantwana Babalulekile estimates. From 2005 to 2008, consistently distorted male- to-female ratios means that the total estimates for certain characteristics would be somewhat slanted toward the male pattern. This effect is reduced from 2009, where more even ratios are produced, in line with the modelled estimates. 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.
Reporting error
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).

 1Jones 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.

2United 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.

 3McLaren 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 no. 97. Cape Town: SALDRU, UCT.

 4K Hall & W Sambu Analysis of General Household Survey 2014, Children’s Institute, UCT.

5Statistics South Africa (2003-2015). General Household Survey 2002-2014 Metadata. Cape Town, Pretoria: Statistics South Africa


International Conference on Primary Health Care (1978) Declaration of Alma-Ata. International Conference on Primary Health Care, Alma-Ata, USSR, 6 – 12 September 1978.