Examining the influence of family physician supply on district health system performance in South Africa: An ecological analysis of key health indicators

Background: The supply of appropriate health workers is a key building block in the World Health Organization’s model of effective health systems. Primary care teams are stronger if they contain doctors with postgraduate training in family medicine. The contribution of such family physicians to the performance of primary care systems has not been evaluated in the African context. Family physicians with postgraduate training entered the South African district health system (DHS) from 2011. Aim: This study aimed to evaluate the impact of family physicians within the DHS of South Africa. The objectives were to evaluate the impact of an increase in family physician supply in each district (number per 10 000 population) on key health indicators. Setting: All 52 South African health districts were included as units of analysis. Methods: An ecological study evaluated the correlations between the supply of family physicians and routinely collected data on district performance for two time periods: 2010/2011 and 2014/2015. Results: Five years after the introduction of the new generation of family physicians, this study showed no demonstrable correlation between family physician supply and improved health indicators from the macro-perspective of the district. Conclusion: The lack of a measurable impact at the level of the district is most likely because of the very low supply of family physicians in the public sector. Studies which evaluate impact closer to the family physician’s circle of control may be better positioned to demonstrate a measurable impact in the short term.

This ecological study was informed by a pilot study conducted in the Western Cape, South Africa.32 A retrospective cohort design was used, whereby data were collected for the period 2010/2011 as a baseline and 2014/2015 representing 5 years post-deployment of the new generation of family physicians. The STROBE statement’s checklist for reporting cohort studies was used as standard for presenting this research.33 This study evaluated all 52 health districts across all nine provinces of South Africa (a national study frame, see Figure 1) for two time periods. Map of South Africa depicting its 52 districts. All 52 South African health districts were included as units of analysis. A national dataset, the District Health Barometer (DHB), contributed the data on district performance for two time periods: 2010/2011 and 2014/2015.35,36 The DHB draws data from several data sources provided by the NDoH. Compilation of the DHB is guided by an advisory committee made up of managers from the NDoH, as well as health experts from Health Systems Trust (HST). The DHB is designed to assist the NDoH in monitoring health service delivery at district level for all of South Africa’s health districts. Furthermore, the HST encourages providers, managers, researchers and policy-makers to use DHB information by making the publication and its data freely available online on their website. Table 1 presents the list of DHB indicators used. The DHB system of categorising the indicators was used throughout (ranging from financial indicators to clinical process and outcome indicators). The official DHB indicator descriptions are also presented in Table 1. List of DHB data indicators arranged by DHB categories.35 Source: The definitions of the indicators were adopted from Massyn35 ALOS, average length of stay; ANC, antenatal care; ART, antiretroviral therapy; BAS, Basic Accounting System; BUR, bed utilisation rate; CFRs, case fatality rates; CHC, community health centre; CDC, community day centre; C-section, caesarean section; CYPR, couple year protection rate; DHIS, District Health Information Software; DHB, District Health Barometer; DHS, District Health System; ENDR, early neonatal death rate; ETR.Net, Electronic TB Register; ICDR, inpatient crude death rate; LG, local government; MMR, maternal mortality ratio; OPD, outpatient department; PCR, polymerase chain reaction; PCV, pneumococcal vaccine; PDE, patient day equivalent; PHC, primary health care; PMTCT, prevention of mother-to-child transmission; PN, professional nurse; RV, Rota virus; SAM, severe acute malnutrition; TB, tuberculosis; WHO, World Health Organization. For the family physician supply, public sector family physicians working in joint appointments (with the universities) or non-joint appointments and employed at facility-, sub-district and district levels (including district office and district clinical specialist team appointments) were included. Those family physicians employed at regional or tertiary hospitals in full-time academic positions or in the private sector were excluded. The data on family physician supply per district for these two time periods were obtained from all nine academic institutions involved with postgraduate family medicine training in South Africa and who were familiar with the health system in their catchment area. The absolute numbers of family physicians were converted to family physician supply per 10 000 population (using the DHB population data for the respective time periods). The DHB data, as well as data on family physician supply, were entered into an Excel sheet and subsequently converted into IBM SPSS version 23 for descriptive and inferential analyses.37 The data analysis included all 52 units of analysis and commenced with descriptive analysis of the independent and dependent variables. Subsequently, the correlation between change in family physician supply and change in the indicators available for both time periods (37 indicators) was analysed. In addition, a cross-sectional correlation analysis was performed for time period 2 (2014/2015) on the remaining DHB data set (data for 12 indicators were available only for time period 2). Simple scatterplots of the bivariate correlations were inspected to identify the nature of each relationship. A non-parametric test, Spearman’s rho, was selected to test for correlation between the independent and dependent variables, because of the non-parametric distribution of the data as well as the presence of outliers (especially in reference to the independent variable). The level of significance chosen was p < 0.05. For those relationships found to be linear and showing at least a low-to-moderate correlation coefficient (see interpretation guide below), further regression analysis was performed using a generalised linear model (GLM), to control for the effect of available confounders, namely province and socio-economic quintile (SEQ) of the districts. Using GLMs with province as covariate created better regression models as opposed to GLMs with SEQ as covariate (using the omnibus test and its likelihood ratio Chi-square value as guide). Correlation values may be interpreted as:32,38 This study was approved by the Health Research Ethics Committee, Stellenbosch University (reference S15/01/003) and HST also confirmed their permission for use of the open access data.

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