How far does family physician supply correlate with district health system performance?

Background: Since 2011, a new cadre of family physicians, with 4 years of postgraduate training, was deployed in the district health services of the Western Cape, and tasked with a considerable range of duties aimed at a general improvement in care and health outcomes. There is a need to evaluate the contribution of these family physicians to the district health system. Aim: To develop a methodology for describing the correlation between family physician supply and district health system performance, clinical processes and outcomes, and to measure this correlation at baseline. Method: A cross-sectional study was undertaken that analysed data at an ecological level for the period of 01 April 2011 to 31 March 2012. This was a pilot project analysing data from the first year of a 4-year project. The correlations between family physician supply and 18 health system indicators were assessed within a logic model. The supplies of other categories of staff were also measured. Results: Although most of the correlations with family physicians were positive, the study was unable to demonstrate any strong or statistically significant correlations at baseline. There were significant correlations with other categories of staff. Conclusions: This study developed a methodology for monitoring the relationship between family physician supply using routinely collected indicators of health system performance, clinical processes and outcomes over time. Additional research will also be needed to investigate the impact of family physicians and triangulate findings as this methodology has many limitations and potential confounding factors.

This is a cross-sectional ecological study that explores the baseline associations within a broader prospective study that ran from 2011 to 2014. This study focused on the first year from 01 April 2011 to 31 March 2012. The exploratory analysis on the baseline data of the larger study also served as a pilot of the methodology in the South African setting. Data were collected from the existing health information system used by the Department of Health on the number of family physicians as of 2011, other health workers, key clinical processes, key health system functions, community indicators and health outcomes, and then aggregated to subdistrict and district level. The number of family physicians per 10 000 people was the measure of ‘supply’. The Western Cape Province is made up of six health districts: Cape Metropole, West Coast, Cape Winelands, Overberg, Eden and Central Karoo. The Cape Metropole, which represents the city of Cape Town, is further split into four substructures and each substructure is split into two subdistricts. The Western Cape has ‘aligned its Comprehensive Service Plan with the model of having a family physician at each district hospital (> 50 beds) and each community health centre (> 30 000 people served)’.9,21 Although there were only about 20 family physicians (both old and newly trained) in the province in 2011, the overall perception was that they made a difference.9 Prior to 2011 family physicians were trained in part-time training programmes, which varied in terms of their learning outcomes and quality. Many of the family physicians employed by 2011 came from these earlier programmes. As the new family physicians graduate from 2011 onwards, it is expected that the number of family physicians across the province will increase to between 60 and 80 over the next 5 years.10 The Western Cape was estimated to have a population of 5 755 607 in 2011 of whom approximately 83% were dependent on public health services. Relative to other provinces, the Western Cape had good access to basic amenities (e.g. 94% of households were electrified), but still had inequities within and between districts.22,23 The province shared the same quadruple burden of disease as the rest of the country: HIV-related disease and TB, interpersonal violence and trauma, maternal and child health problems and non-communicable chronic diseases.24 The units of analysis included the five rural districts and the eight Cape Town metropolitan subdistricts in the Western Cape. This mix of a total of 13 organisational units, described in Table 1, provided for similar-sized units for analysis. Whilst this is a small sample size, it is finite in that it includes collated data for the entire Western Cape geographic region for a period of 1 year. Description of units of analysis. The study included data on the Western Cape population (Table 1) and DHS. Data from central, specialised and regional hospitals were excluded. Private and community-based service data were also excluded. Data from the following types of facilities within the DHS were included: Data from the following types of facilities within the DHS were excluded: Home-based care services, whilst part of DHS, were excluded because of differential procedures in data collection and resource allocation between the units of analysis in this study. Data were collected in respect of the number of family physicians, other health workers, key clinical processes, key health system performance, community indicators and health outcomes. A conceptual model that guided the evaluation and illustrates the inter-relationships between various elements is set out in Figure 1 and elaborated on below.25 The model, based on a modified Donabedian causal chain, was used to make sense of the complexity of the health system and to provide a rationale for the selection of indicators and identification of confounders. Indicators assessed were grouped according to the following categories: Modified Donabedian causal chain – Interventions at structural (policy) and generic service level can achieve effects through intervening variables further down the chain to result in particular health outcomes.25 Policy intervention and structure: Changes in the policy applied to the DHS were monitored qualitatively during the study. Generic interventions such as human resources impact across a wide range of processes. In this case introducing a new cadre of family physicians was seen as a generic intervention. The study measured the number of family physicians and other practitioners (nurses, doctors and other specialists) in the DHS per 10 000 dependent population. Targeted interventions were aimed at improving specific clinical processes via training, audit cycles or other clinical governance methods. Clinical interventions directly impacted clinical processes through the provision of new drugs, devices, procedures or therapies. Health system performance: Kringos et al. have identified key aspects of the primary care system upon which the family physician can be expected to have some impact.26 These include access to, continuity of, coordination of, comprehensiveness of, quality of and efficiency of care. Clinical processes: Family physicians as expert generalists should impact across the full range of clinical processes. Health outcomes: Key facility-based outcome indicators such as perinatal mortality. Tarimo defines the DHS as a ‘well-defined population living within a clearly delineated administrative and geographic area. It includes all the relevant health care activities in the area, whether governmental or otherwise’.27 However, for this study the DHS definition was restricted to services rendered by the vertical funding programme 2: ‘District Health Services’ in the annual performance plan, which includes governmental primary health care facilities and district hospitals.10 Population-based indicators were expressed per ‘dependent population’ (Table 1). The dependent population is an estimate of the proportion of the population with insufficient household income to afford private medical care, whether by out-of-pocket payment or by medical insurance. It is therefore different from the ‘uninsured population’. Whilst the dependent population provides for a denominator of the population likely to utilise public health services, it can also be used as a proxy for deprivation.28 The final set of indicators that had to be collected was defined during the first 6 months of the project through a collaborative process between the researchers, current family physicians, district managers and the directorate for health impact assessment within the Western Cape Department of Health. Indicators were selected according to the conceptual framework (Figure 1) and in terms of their availability, credibility and expected impact by family physicians. The aforementioned indicators were given shorter names as variables for convenience (Tables 2 and ​and3).3). For this study we chose four generic intervention variables (Table 2) and 18 proxy variables for clinical processes, health system performance and health outcomes (Table 3). Definitions of independent variables. Definitions of dependent variables. PHC, primary health care, TB, tuberculosis. Staff numbers were expressed as ‘full-time equivalents’ (FTE) (Table 4). A FTE is a representation of the time spent by a particular staff category in rendering designated services during the total number of working hours for the financial year. Numbers of full-time equivalents per staff category in each district or subdistrict. Data were collected from routine health and human resource management information systems as follows: Persal: human resource management tool, used to establish the numbers of ‘generic interventions’ such as various categories of staff. Sinjani: routine monitoring and reporting tool, used to collate all health facility routine data in the province. ETR.net: electronic TB registers, used to collate and report cohort data of TB patients. Chronic Diseases Audit: annual provincially coordinated audit on the quality of care for chronic non-communicable diseases in primary health care facilities. Staff Satisfaction Survey: biennial provincial audit of staff satisfaction. The Centre for Statistical Consultation at Stellenbosch University was consulted to assist with data analysis. Data for the 22 variables were collated using Microsoft Excel™ 2011. Data were then exported to and analysed in Stata™ version 13.1. The mean and standard deviations (s.d.) were calculated for each of the variables. As this was an ecological study these would be the ‘means of means’. The s.d. was preferred to the 95% confidence interval to describe the variance in the data, rather than the precision of the means, as the results came from a finite data set.29 The median and interquartile ranges (IQR) were also calculated for each variable. Simple correlation, Spearman’s rho, was used to describe the relationship between the number of family physicians per 10 000 people and key health system performance, clinical processes and health outcomes for 2011. The socio-economic differences between subdistricts and districts were included by expressing population-based data according to ‘dependence’ (a measure of income inequality). Data were analysed for all 13 organisational units. The level of significance chosen was p < 0.05. We undertook correlation analysis between the family physician supply and the 18 dependant variables listed in Table 3. Correlation values can be interpreted as:30 0.90–1.00 (−0.9 to −1.00) Very high positive (negative) correlation 0.70–0.90 (−0.70 to −0.90) High positive (negative) correlation 0.50–0.70 (−0.50 to −0.70) Moderate positive (negative) correlation 0.30–0.50 (−0.30 to −0.50) Low positive (negative) correlation 0.00–0.30 (0.00 to −0.30) Negligible correlation. Graphs were generated using Stata™ version 13.1 to illustrate the relationship between the data points and the correlation (or regression) line. The data points were assigned unique colours and shapes to distinguish rural districts from urban subdistricts. This study was approved by the Health Research Ethics Committee (HREC) at Stellenbosch University, protocol number N11/10/012, and was conducted according to accepted and applicable national and international ethical guidelines and principles, including those of the International Declaration of Helsinki October 2008. Permission was obtained from the Provincial Health Research Committee (PHRC) to conduct the research and to provide the routine data required. Data used in this research were collated at the subdistrict level and did not involve individual patient identifiers.

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