National Health Insurance
Estimate of Future Prevalence of Chronic Disease in South Africa
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The difficult technical issue in projecting future levels of chronic disease in South Africa is to what extent the excellent shapes by disease found in the medical schemes data can be applied to the public sector or to groups joining under a phased introduction of NHI.
It is difficult to get good income data in medical schemes to be able to produce curves of disease prevalence by income. However, using data from the first pricing of PMBs in medical schemes, an analysis of disease categories for higher and lower socio-economic groups or “clusters” was done. In essence, there was more respiratory and gastro-intestinal disease and obstetric events in the lower cluster and more cardiac-related conditions in the higher cluster, when patterns were considered by age and gender. This work was also partially reported in an appendix of the design of the Risk Equalisation Fund formula1.
Prof Alan Rothberg, who led the data extraction for the PMB pricing in 2001, argued that there were several forces at work in the differences in disease patterns. Age profile differences explain roughly two-thirds of difference in raw cluster prices. Other differences are probably due to a combination of what he called “the four P’s”:
- variation in Prevalence rates of important conditions;
- Presentation or manifestation of conditions (the severity by the time the person was seen);
- Provider choice (GP vs. specialist and the management or prescribing habits of each); and
- benefits available within the health care Plan.
After nearly two years of working with disease profiles by age and gender submitted by medical schemes in the REF shadow process, the overall sense is that the while there are differences amongst benefit options for each disease in isolation, the overall level of CDL chronic disease is about the same in each option. It is usually the age profile differences which make an option look like it has less disease, but when the shapes by age and gender are compared to the industry average, there are few differences. The cases where there are differences have on investigation turned out to be administration issues in the identification of chronic disease, like the so-called “auto-chronic” processes for identifying disease which have now been prohibited for REF data.
A critical issue to consider is that poorer communities may experience a greater burden of disease. A comprehensive technical report on the relationship between poverty and chronic disease has been produced by the Medical Research Council 18-20. These findings will be taken up again in Policy Brief 5. This issue has also received attention in work relating the need for health funding to deprivation by health district.
Any attempt to use current public sector epidemiological data to calibrate the medical scheme curves has several pitfalls: the public sector has become increasingly strained and under-resourced by nurses, doctors and pharmacists. Shortages of drugs were a problem at the beginning of the period after 1994 and have again been a problem in 2009, with provinces running out of budget to pay suppliers. On that basis, any published public sector prevalence figures may be understating the real prevalence of disease.
A further complication is evidence that as the public sector service levels fall, there is increasing ant-selection against medical schemes with more people with severe chronic diseases joining schemes. This may mean that the medical scheme disease prevalence figures are too high. However, the CDL disease curves understate total chronic disease. In section 1 the research was noted that showed that in medical schemes, the CDL conditions accounted for only 77.1% of all chronic conditions. The same research showed that people registered for any CDL condition accounted for 76.8% of people who claimed for any chronic condition. An adjustment of the order of 1/.77 or 130% is thus not unreasonable to estimate total chronic disease. It would not be correct to apply this to the whole age-gender curve and more research is underway to determine how to make the adjustment.
A longer term concern is the extent to which changes in mortality are accompanied by changes in the amount of disease or timing of disease experienced. This issue will be taken up in more detail in Policy Brief 5.
The graph below makes a simple assumption: that the overall level of CDL chronic disease by age and gender, as shown in Figure 1, can be applied to the historic and future population structure of the country as a whole. Some sensitivity in this assumption is shown by producing lines for a 10% increase and a 10% decrease in the prevalence curves by age and gender.
Figure 4: Estimated Numbers with Treated CDL Chronic Disease 1985 to 2025 showing Sensitivity to Prevalence Assumption
The graph above thus illustrates the effect that the aging of the population might have on the burden of chronic disease experienced in South Africa. The aging, combined with population growth since 1985, is significant: the total number with CDL chronic diseases might be:
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1985: 2.28 million
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1994: 2.99 million (131% of 1985 figure)
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2009: 4.12 million (138% of 1994 figure)
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2025: 5.13 million (172% of 1994 figure).
The implications of more people with chronic disease mean an increase in visits to clinics and GPs, an increase in the use of chronic medicine, an increase in the use of specialists and an increase in hospital events. Note that this analysis does not yet include the substantial additional burden from HIV which is dealt with in Policy Brief 4. The table below summarises the numbers expected in an NHI system for each of the CDL diseases, if the public sector prevalence is identical to that in medical schemes.
Table 1: Estimate of People Needing Treatment for Chronic Disease under National Health Insurance in South Africa
In preparing estimates at provincial level, a further complication arises. It is known that certain diseases are more prevalent in certain population groups, for example, diabetes mellitus is much more prevalent in the Indian community which should mean that KwaZulu-Natal has higher diabetes prevalence by age and gender than other provinces. This is offset though by very low diabetes prevalence amongst rural African Black lives. Much work still needs to be done to attempt to integrate the findings of the MRC report 13-16 with the private sector data for those diseases where there is some differentiation by age and gender or population group in the survey data.
There are often reduced benefits for benefit options designed for lower income groups which has the appearance of making the plan more affordable.
From January 2005, medical schemes submitted data on the REF risk factors to the Council for Medical Schemes. Monthly data was submitted on a quarterly basis. The data contained the aggregate number of beneficiaries in each benefit option by age, gender, numbers with the 25 CDL chronic diseases, numbers with HIV on anti-retroviral medication, numbers with multiple chronic conditions and the number of births.
Using medicine information to determine the diagnosis rather than capturing the diagnosis directly.
There was a rapid expansion of clinics that was not always met with an expansion in staffing or there were initial logistical problems.
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