National Health Insurance
Evidence of the Prevalence of Chronic Disease in Medical Schemes
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The purpose of this series of policy briefs on National Health Insurance (NHI) and the related IMSA web-site is to put in the public domain material and evidence that will progress the technical work of developing a National Health Insurance system in South Africa. This includes tools for costing NHI and evidence on where savings could be achieved in moving to a future mandatory system with universal coverage.
This policy brief builds on the first two dealing with the population to be covered in order to estimate the impact of 25 chronic diseases on a future NHI. The important topic of HIV is not dealt with fully here and is the subject of a separate policy brief.
The process of developing a formula for risk adjustment between medical schemes 1-3 has provided exceptionally good data on the prevalence of chronic disease in medical schemes by age and gender. The study to develop the risk adjustment tables from 2007 onwards (the REF Study 2005)3 was done on data from 2005 from the four largest administrators who provided services to 4.249 million lives or 63.4% of the medical scheme beneficiaries in that year. The study had 49.847 million member months of data or the equivalent of 4.154 million full member years of data. This was described as “an extraordinary data set to work with” and provided many new insights into disease prevalence in medical schemes. The graph below illustrates the rates of total chronic disease, multiple chronic disease and HIV on anti-retroviral medicine (ARVs).
Figure 1 : Rate of Chronic Disease in Medical Schemes Expected for 2009
The diseases covered in the graph above are the Chronic Disease List (CDL) diseases that must be covered by medical schemes as part of the Prescribed Minimum Benefit package. These numbers of people with these 25 diseases (listed in Table 1) and numbers with HIV who are on treatment with anti-retroviral medicines are among the risk factors used in the design of the Risk Equalisation Fund (REF). Since the inclusion of these diseases in PMBs and the REF data collection, the understanding of these diseases in medical schemes has been greatly improved.
The choice of the 25 diseases for the minimum package remains contentious. The original philosophy underlying the PMBs used a clear method for rationing and determining the package. The initial package of diagnosis-treatment pairs was perceived by many funds to cover only hospital-based treatment and several funds altered their chronic medicine benefits to reduce or completely remove cover for chronic diseases. The policy response was legislation in 2002 to mandate a package of diagnosis, treatment and medicine for 25 chronic conditions but implementation was delayed to enable the industry to develop therapeutic algorithms which came into effect from January 2004. However the methodology for determining which diseases were included in the Chronic Disease List was not published and has been described as 25 “common conditions”. Even this is in doubt as diseases like Addison’s are more rare and less costly than cystic fibrosis which was not included. Research on the prevalence and cost of the CDL diseases showed that 77.1% of registrations for chronic medicine in 2001 were for at least one of the CDL conditions.
Data has been collected on a monthly basis on the 25 CDL diseases (plus HIV) in medical schemes since January 2005. Concern was expressed in the original formula report about the ability to reliably measure the chronic disease factors and about the ability to audit this data. It was seen as critical that there was a trusted and fair way to determine the numbers with chronic disease. The Risk Equalisation Technical Advisory panel and a clinical team drawn from the Council for Medical Schemes and industry experts developed a comprehensive manual of Verification Criteria that is now in its fourth iteration.
The Verification Criteria have been developed with the emphasis on the verifiability of cases of chronic disease. There are two elements to the criteria:
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the diagnosis of a particular disease, which includes specification of applicable ICD-10 codes and limitations on the practitioners that may diagnose certain complex conditions. here may be mandatory tests to differentiate between diseases and results must be retained by the fund; and
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a proof of treatment element which is based on paid claims data. Data for at least two of the three calendar months prior to the month of submission is typically required in order to demonstrate proof of treatment. The applicable medicines that can be used as proof are classified using the Anatomical Therapeutic Chemical (ATC) classification and payment must have been made from the risk pool, not personal medical savings accounts
For the REF Study 2005 two sets of data were extracted: the first used Version 2 of the Verification Criteria and was called the “Treated Patient” data”; the second set was extracted without the test for “treated patient” and can be called the “Diagnosed Cases” data. This provided a powerful tool to investigate the impact if more people in future fall within the definition of “treated patient”.
Each disease has its own unique pattern by age and gender. The patterns from the “Treated Patient” data were compared to the original study in 2002 and found to be very close for most diseases. This was a useful finding in that the original study occurred before there were incentives to inflate chronic disease and the effect of the Verification Criteria were shown to produce similar results.
One of the most interesting findings from the REF Study 2005 was the number of people who are diagnosed with a chronic disease but who are not receiving treatment at the levels required for “treated patient” status. Figure 2 illustrates the data available using one disease, diabetes mellitus Type 2. This condition is more prevalent in males than females, as clearly illustrated.
Figure 2 : Rate of Diabetes Mellitus Type 2 in Medical Schemes
The authors of the REF Study 2005 argued that the prevalence that should be used in the normal context of prevalence data should be the “treated patient” prevalence. The “diagnosed cases” data is critically dependant on the correct ICD10 code being allocated by the doctor or treating specialist. For “treated patient” status there is the additional confirmatory evidence that a particular class of drugs (relevant to that disease) was dispensed on a regular basis. Compulsory ICD10 coding was in its introduction phase during the REF Study 2005 and there may be some diseases where the diagnosed cases are over-reported. However studies in medical scheme administrators have for many years shown that people diagnosed with a chronic condition do not always continue on the medicines prescribed. This is particularly the case in diseases where the symptoms are not readily apparent, like hypertension. There may also be only intermittent drug use for asthma so that the person does not meet the “treated patient” criteria of using the drug for one out of every three months.
Each of the CDL diseases has a unique shape by age and differences by gender. The graph above shows that with a very large study, the shapes for a disease form smooth curves. Slides for each of the CDL diseases and spreadsheets of the values are given on the IMSA NHI web-site . These shapes can be used with other populations (like the public sector by age and gender) to estimate the possible prevalence in the new population. Epidemiological data is often reported as a total prevalence rate for a particular population (not by age and gender) but this total could be compared to that from the estimate in order to calibrate the shapes to the new population .
For example, we may want to increase the prevalence of respiratory diseases in the public sector above that of medical schemes or increase the prevalence of diabetes in some population groups. Cardiac disease for the population as a whole may decrease relative to that found in medical schemes. A simple percentage increase or reduction to the age-gender shape would be a reasonable starting point until more is known about the age-gender shape in the new population.
ICD-10 means the International Classification of Diseases, version 10. This alpha-numeric diagnosis coding standard is owned and maintained by the World Health Organization and is a standard diagnostic classification system used internationally. See http://www.who.int/classifications/icd/en/
It is used in both the private and public sectors in South Africa and since July 2005 it has been mandatory for accounts from healthcare practitioners to medical schemes to contain ICD-10 codes.
The ATC Index is issued annually by the WHO Collaborating Centre for Drug Statistics. See http://www.whocc.no
Discovery Health (Pty) Ltd, Medscheme (Pty) Ltd, Old Mutual Healthcare (Pty) Ltd and Metropolitan Health Group (Pty) Ltd.
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