1 MODELLING THE COVERAGE OF PRIVATE HEALTH INSURANCE IN AUSTRALIA IN 1995 Richard Percival, Deborah Schofield and Simon Fischer Technical Paper No. 12 May 1997
2 National Centre for Social and Economic Modelling Faculty of Management University of Canberra The National Centre for Social and Economic Modelling was established on 1 January 1993, following a contract between the University of Canberra and the then federal Department of Health, Housing, Local Government and Community Services (now Health and Family Services). NATSEM aims to enhance social and economic policy debate and analysis by developing high quality models, applying them in relevant research and supplying consultancy services. NATSEM s key area of expertise lies in developing and using microdata and microsimulation models for a range of purposes, including analysing the distributional impact of social and economic policy. The NATSEM models are usually based on individual records of real (but unidentifiable) Australians. This base produces great flexibility, as results can be derived for small subgroups of the population or for all of Australia. NATSEM ensures that the results of its work are made widely available by publishing details of its products and research findings. Its technical and discussion papers are produced by NATSEM s research staff or visitors to the centre, are the product of collaborative efforts with other organisations and individuals, or arise from commissioned research (such as conferences). Discussion papers present preliminary research findings and are only lightly refereed. Its policy papers are designed to provide rapid input to current policy debates and are not externally refereed. It must be emphasised that NATSEM does not have views on policy and that all opinions are the authors own. Director: Ann Harding
3 National Centre for Social and Economic Modelling Faculty of Management University of Canberra MODELLING THE COVERAGE OF PRIVATE HEALTH INSURANCE IN AUSTRALIA IN 1995 Richard Percival, Deborah Schofield and Simon Fischer Technical Paper No. 12 May 1997
4 ISSN ISBN NATSEM s STINMOD Technical Paper series has been renamed the Technical Paper series, which incorporates technical papers across the range of NATSEM s work. It includes material that would previously have appeared in the DYNAMOD Technical Paper series and the Dynamic Modelling Working Paper series. NATSEM, University of Canberra 1998 National Centre for Social and Economic Modelling GPO Box 563 Canberra ACT 2601 Australia Phone: Fax: Client services General World Wide Web site Core funding for NATSEM is provided by the federal Department of Health and Family Services.
5 iii Abstract This paper describes how microsimulation techniques were used to create a dataset of individual s private health insurance information. The dataset was based on the national health survey (NHS) conducted by the Australian Bureau of Statistics. Data from other sources were also used when it was necessary to impute information not recorded in the NHS. While the NHS was the most comprehensive microdata source with information on private health insurance, health characteristics and health services usage in Australia, the latest survey in this series was undertaken in This means that much of its information is now out of date. In particular, the significant changes in the incidence of private health insurance since then mean that its description of the incidence of this insurance is no longer valid and the accurate modelling of possible changes in health insurance policy is not possible. The methods used to age the demographic, socioeconomic and insurance usage characteristics of the NHS records to 1995 are described, and the accuracy of the variables in the new dataset is assessed by comparing them with benchmark data.
6 iv Author note Richard Percival is a Senior Research Fellow and Simon Fischer a Research Officer at the National Centre for Social and Economic Modelling. Deborah Schofield was a Research Fellow at NATSEM during much of the period when this project was undertaken. Acknowledgments The authors would like thank Brian Richings and staff of the Health Section of the Australian Bureau of Statistics for their assistance in preparing data from the private health insurance surveys. Thanks also go to Jenny Badham and Sidney Sax for their helpful comments on the paper and to David Pederson for his advice on aspects of the statistical modelling used in the study. Responsibility for the study s direction, analysis and any errors remains, of course, with the authors. General caveat NATSEM research findings are generally based on estimated characteristics of the population. Such estimates are usually derived from the application of microsimulation modelling techniques to microdata based on sample surveys. These estimates may be different from the actual characteristics of the population because of sampling and nonsampling errors in the microdata and because of the assumptions underlying the modelling techniques. The microdata do not contain any information that enables identification of the individuals or families to which they refer.
7 v Contents Abstract Author note Acknowledgments General caveat... iii...iv...iv...iv 1 Introduction Changes in private health insurance Data sources National health survey Health insurance surveys Survey of income and housing costs and amenities Labour force survey Developing the base population Information included in the PHIDB Derived variables Ageing the NHS population Updating NHS incomes Wages and salaries Income from self-employment Interest, dividends and income from rent Workers compensation, superannuation, pensions, benefits and other income Uprating methodology Uprating outcomes Updating private health insurance usage Imputing private health insurance premiums Summary and conclusions Appendix A PHIDB data dictionary Appendix B Regression equation to estimate proportions of persons insured References... 36
8 1 1 Introduction Private health insurance is an important feature in Australian health policy, especially as it is a significant source of funding for health services. However, very little current information on its distribution is available. This paper describes how microsimulation techniques were used to construct a dataset that simulates individual level information on private health insurance cover in November Most knowledge of the characteristics of the private health insurance users comes from large scale sample surveys that are regularly conducted by the Australian Bureau of Statistics (ABS). However, it is a common feature of sample surveys that the information they contain becomes out of date virtually from the moment it is collected. In many circumstances this does not present a problem, either because very little time has passed since the survey was held or the information itself is not likely to have changed. In other instances, however, information of particular interest may have been collected some time ago and it may also be known to have changed significantly since then. This is the case when detailed information on the incidence of private health insurance in Australia is required to model changes to health insurance arrangements and their relationship to other aspects of the health system. The most comprehensive source of information on both private health insurance and health service use in Australia is the national health survey (NHS), which is conducted by the ABS every five years. The most recent NHS was undertaken in Since then there have been a host of socioeconomic and demographic changes to the Australian population. In addition, there is evidence of a steady decline in the number of persons holding private health insurance. The remainder of the paper describes the construction of an up-to-date private health insurance dataset (henceforth referred to as the PHIDB), based on the 1990 NHS. [An more detailed analysis of the changing composition of private health insurance between 1983 and 1995 is presented in Schofield, Fischer and Percival (1997).] As is the case with the NHS, the PHIDB is a file of individuals. However, where appropriate, information that is common to all family members is also included on each individual record.
9 2 Technical Paper No Changes in private health insurance There has been a steady decline in the proportion of Australian families holding private health insurance since the introduction of Medicare in A study by the Private Insurance Taskforce (1993) found that the sharpest decline was during 1983 and 1984 (from 64 per cent to 50 per cent). An analysis of data from the Private Health Insurance Administration Council (PHIAC) showed that from 1984 to 1993 the average annual decrease in the proportion of the population insured was 2.6 per cent (figure 1). However, between 1991 and 1995 the average rate of decline was almost 5.5 per cent (Private Health Insurance Administration Council, various dates). The more recent steeper decline appears to have coincided with the recession of the early 1990s. A state-by-state analysis indicated that the fall was largest in Victoria and South Australia, the states worst affected by the recession (Australian Council of Social Service 1994). A decline in the incidence of private health insurance during a recession is not surprising as income has been found to be one of the most significant predictors of health insurance coverage (ABS 1993, p. 1; Schofield 1997). The decline in the incidence of health insurance has not been even across different age groups. In particular, it has been reported that people under the age of 65 years have been dropping out of health insurance at Figure 1 Basic hospital private health insurance coverage, Australia Percentage of population % Data source: Private Health Insurance Administration Council (various dates).
10 Modelling the Coverage of Private Health Insurance in Australia in a faster rate than those over 65 (Private Insurance Taskforce 1993). As a result, the age structure of the insured population has changed. Willcox (1991) found that between 1983 and 1990, while the incidence of private health insurance among those aged years declined from 70 to 40 per cent, it remained constant for persons aged 65 years and over (at 45 per cent for year olds and 37 per cent for those 70 years and over). The reduction in the number of younger people with private health insurance, together with the ageing of the population, has resulted in an increasing pool of insured who are 60 years of age or over increasing from 13.5 per cent in 1983 to 22.1 per cent in 1990 (Willcox 1991). An examination of the pattern of the decline in health insurance among the younger population over the period to suggested that, in the earlier period of a family s life cycle, families are becoming less likely to be insured (Private Insurance Taskforce 1993, p. 6). If this trend continues, it is likely that holders of private health insurance will be even more highly concentrated among the older population, which is significant since aged people are the highest users of health services. Apart from the introduction of Medicare and the impact of the recession, there have been several other reasons suggested for the decline in health insurance coverage. These have included economic pressures on contributors as premiums have risen and the unpredictable and sometimes large out-of-pocket costs associated with private hospital admittance. For these reasons, it has been suggested that private health insurance may no longer be seen to provide value for money, particularly when an economic recession was already placing some insured people under financial pressure (Australian Institute of Health and Welfare 1994, p. 139; Private Insurance Taskforce 1993). 3 Data sources As noted, before NHS data can be used to model aspects of private health insurance arrangements, information it contains needs to be updated to Because of the range of information that required updating (incomes, demographics and health insurance status), a variety of data sources were used in either the construction or the calibration of the PHIDB. The most important of these data sources are now described.
11 4 Technical Paper No National health survey The principal data source used in this study was the NHS (ABS 1990b). The NHS provides a useful resource for analysing private health insurance because of its comparatively large size (about persons) and the large number of important socioeconomic, health insurance, health risk and health service usage variables it contains. In addition, the NHS is available as a unit record dataset each record containing information about each respondent to the survey. (Stringent confidentiality measures undertaken by the ABS ensure that it is not possible to identify any individuals or families.) Each record has a weight attached, indicating how many similar Australians (of the same age and sex and state of residence) the record represents. The survey obtained information from residents of private dwellings (houses, flats, etc.) and non-private dwellings (hotels, motels, caravan parks, etc.). Households were selected from all states and territories. The only exclusions were non-australian diplomatic personnel and members of non-australian defence forces, persons holidaying in Australia, students at boarding schools and institutionalised persons (ABS 1990c, p. 125). 3.2 Health insurance surveys In the past the ABS has conducted irregular health insurance surveys for example, in 1983, 1986, 1988, 1990 and 1992 (ABS 1983, 1986, 1988, 1990a, 1992b). These surveys recorded information on the level and type of health insurance cover, contribution rates and the location, composition and incomes of contributor units 1. Unfortunately, this survey series was discontinued after 1992, and no other sufficiently detailed and more recent source of data is available. Accordingly, current analysis has to be based on simulated data. Data from the health insurance surveys were used to analyse the decline and changing composition of private health insurance between 1983 and 1 A contributor unit is defined by the ABS as a contributor plus all persons in the same family who are covered by the health insurance arrangements of the contributor (ABS 1992b, p. 1).
12 Modelling the Coverage of Private Health Insurance in Australia in and to provide estimates of the 1995 private health insurance incidence for selected population subgroups. This information was subsequently used to age the incidence of private health insurance in the NHS prior to its inclusion in the PHIDB. 3.3 Survey of income and housing costs and amenities The survey of income and housing costs and amenities (commonly known as the income distribution survey) was used to capture the demographic changes that have occurred since the NHS was undertaken in Until recently, the income distribution survey was conducted every four years by the ABS, with the latest in 1990 (ABS 1990d). The income distribution survey provides detailed data on the sources of income, labour force status and participation, and housing characteristics of its respondents. The latest income distribution survey was undertaken over the period October December It includes details on persons in households. Data are made available as a file of unit records, at both an individual and an income unit level. (Again, it is not possible to identify any individuals or households.) As both the income distribution survey and the NHS were conducted in 1990, the former s more detailed income information can reasonably be taken to apply also to the latter. As chapter 4 describes, this allows the incomes from the income distribution survey to be aged in a comparatively sophisticated manner, with the results then being applied to the less detailed NHS incomes. 3.4 Labour force survey The labour force survey is a comparatively large-scale survey (a multistaged sample of about households) that is conducted every month by the ABS. The survey provides accurate and very recent estimates of labour force participation by a range of socioeconomic indicators, including state, sex, age, occupation, industry, family status and education status. Accordingly, the labour force survey was chosen as the source from which to derive population benchmarks that could be
13 6 Technical Paper No. 12 used to adjust the NHS to capture population changes between and For the most part, the NHS and the labour force survey have a very similar scope and coverage, principally because the NHS is a special supplementary survey that is run off the monthly labour force survey (ABS 1992a, p. 163). However, one important difference is that, while the NHS contains individual records for persons of all ages, the labour force survey collects information on only persons aged 15 years and older. As chapter 4 explains, this has important implications when the labour force survey is used to age the NHS. 4 Developing the base population 4.1 Information included in the PHIDB As noted, the NHS was selected as the dataset on which to build the PHIDB because it contained a broad range of information on private health insurance, as well as other characteristics desirable for later research using the database. The NHS variables included in the database were selected because they were important in determining who had private health insurance and in defining the type of health services they used. [See Schofield (1997) for a more detailed description of the determinants of private health insurance.] These variables fell into the following broad categories: health insurance: type and contribution rate; health status: self-reported health status and number of chronic conditions; health service use: indicators of the use of a range of health services including hospitals, doctors and allied health services; health risk factors: alcohol consumption and body mass; and socioeconomic characteristics: personal characteristics such as income, family type, age, location of residence, sex, educational attainment, immigration characteristics and occupation. A full listing of the variables included in the PHIDB is provided in appendix A.
14 Modelling the Coverage of Private Health Insurance in Australia in Derived variables While most of the population characteristics selected to be included in the PHIDB were available as variables in the NHS, some had to be derived from other NHS variables. These included individual health insurance status, family income, individual health concession card status and income unit identification number. Information on these characteristics was derived as follows. Individual health insurance status: In the NHS, not all family members were recorded as having private health insurance, even when the contribution rate indicated that all family members were covered (for example, the two parents might be recorded as insured, but not the children). Accordingly, where families reported contributing at a family rate, the insurance cover was extended to all members of the family. Family income: The definition of income provided in the NHS is annual gross income recorded for each person across 11 income ranges. Gross income includes regular income from all sources. However, in the PHIDB, family rather than individual income was included as it provides a better indication of available financial resources. To derive family income, the midpoint of each income range was calculated (as an estimate of each person s income) and the incomes for the reference person and spouse summed (as a measure of family income). As this method could not be used for persons in the top income range, their incomes were estimated using the mean incomes of persons in the 1990 income distribution survey who had incomes of $ or more. Individual health concession card status: Health concession card status was defined as having a health concession card or not having a health concession card. Individuals were defined as having a health card if, on the NHS, they reported having a Veteran s Affairs or Social Security health concession card. As was the case for individual health insurance status, not all family members were recorded in the NHS as being covered by a health card. However, as in practice, when one family member held a health concession card, the concessions associated with the health card were extended to the whole family.
15 8 Technical Paper No. 12 Income unit identification number: An income unit (or family) identification number was attached to each record so that information could be aggregated across families for subsequent family level analysis. 5 Ageing the NHS population The first step in making the NHS more current was to update its demographic characteristics from to This was achieved by reweighting individual records on the original data file. As noted, the weight attached to each record represents the likelihood of finding persons with a similar set of characteristics in the Australian population. For example, a record with a weight of 300 is estimated to represent 300 comparable individuals in the Australian population. Reweighting the NHS data thus involved adjusting the weight of each record, in line with the movements over time in the characteristics of the subpopulations that each represents. In the study these subpopulations were defined by the following: sex: male, female; state: New South Wales, Victoria, Queensland, Western Australia, South Australia, Tasmania, ACT and the Northern Territory; age: 0 4 years, 5 9 years, years, years, years, years, years, years, years, years, years, years, years, years, 75 or more years; labour force status: employed full-time, employed part-time, unemployed, not in the labour force; and family status: sole parent, couple only, couple with dependants, single person. This defined a total of 2363 subpopulations of between 63 and members. The methodology adopted to reweight the NHS was to adjust the weights of the records to match those indicated in an appropriate external benchmark data source in this instance, the labour force survey.
16 Modelling the Coverage of Private Health Insurance in Australia in New weights were calculated by dividing the benchmark (labour force survey) population estimates for predetermined subgroups by the number of records in the NHS that also belonged to the subgroup. For example, if the labour force survey estimated that there were 1000 unemployed single males aged between 30 and 34 years in South Australia and the NHS had sampled 10 persons with these characteristics, then each would be given a new weight of 100. [See Percival (1994) and Landt, Harding, Percival and Sadkowsky (1994) for a fuller discussion of reweighting methods.] Sampling differences between the two surveys meant that not all NHS records were able to be matched to the labour force survey estimates at such a detailed level. Accordingly, the reweighting matrix was progressively collapsed, by first dropping family status and then labour force status until all records were matched. As information on under 15 year olds was not recorded in the labour force survey, a simpler reweighting methodology was used for this group. Aggregate age rate changes for persons aged 0 14 years were derived using ABS population estimates for 1990 and 1995 (ABS 1996c). This rate was then used to inflate the original NHS weights. Tables 1 and 2 compare the reweighted NHS population with the benchmark data. As the tables show, the reweighting methodology was able to achieve a very high level of accuracy, with all age groups varying from the benchmark data by less than 1 per cent. When the estimates of family types and labour force status were compared, it was found that all were within a variation of 5 per cent again, a very acceptable outcome, given that some variation was inevitable as it was not possible to match all records at the most disaggregated level. Figure 2 shows the shift in the distribution of population ages between and 1995, as estimated using the original and reweighted NHS population numbers. In 1995 there were proportionately fewer persons in the younger age ranges and more in the middle age ranges (particularly as the baby boomer cohorts move through).
17 10 Technical Paper No. 12 Table 1 Reweighted NHS population estimates compared with benchmark data, by age, Australia, 1995 Age Labour force survey and ABS population projections PHIDB Difference years no. no. % All Sources: The labour force survey provided population data at November 1995 (ABS 1996d) for persons aged 15 years or more; the population projections were to June 1995 (ABS 1996c) for persons 0 14 years. Table 2 Reweighted NHS population estimates and labour force survey benchmarks, by family status and labour force status, Australia, November 1995 Labour force survey National health survey Difference no. no. % Sex Male Female Labour force status Employed full-time Employed part-time Unemployed Not in labour force Family status Sole parent Couple only Couple with dependants Single person All Note: Results include only persons aged 15 years and over. Sources: ABS (1996d); PHIDB.
18 Modelling the Coverage of Private Health Insurance in Australia in Figure 2 Distribution of the population by age, and November NHS PHIDB 1995 % Age Updating NHS incomes One of the major tasks involved in making the private health contributor units in the PHIDB a more accurate representation of the current world is to uprate (that is, inflate or deflate) the income values recorded in the NHS. However, the NHS recorded incomes only as an aggregate value, gross annual income (that is, private income plus government cash transfers), defined as falling within 11 broad income ranges. In reality, the incomes of many people are often composed of several components from, for example, wages, investments and rent. As the average value of each component is likely to have changed over time at a different rate, no single inflator or deflator is able to accurately capture the cumulative change. The uprating strategy in this study used income information from the 1990 income distribution survey (being coterminous with the NHS) to estimate the changing proportion of the population that would fall within the same income ranges after its annual income components were uprated to As previously noted, the income distribution survey contains detailed information on income sources and amounts for each person surveyed
19 12 Technical Paper No. 12 (except persons under 15 years of age and school students years old). Current weekly income and annual income for the previous financial year are reported, both disaggregated into income components and expressed as a total. For consistency with the NHS, only the incomes of adults were used in the calculations. Annual income (for the purpose of uprating) was defined as annual taxable income, and included income from the following sources: wages and salaries; own nonlimited liability business or trust (pretax but after expenses deducted); service pension; sickness benefit; unemployment benefit; sole parent s benefit; wife or carer s pension; special benefit; AUSTUDY payments; age pension; widow s pension; interest, dividends and rent; superannuation; workers compensation, accident or sickness insurance; road accident compensation; financial support from relatives; and any other regular source. The inflators that were used to uprate the individual components of the incomes were selected from available indexes to best capture the economic changes since These are now described. [For additional information and discussion on the methods used, see Percival (1994).] 6.1 Wages and salaries Wages and salaries were uprated using information from the ABS Weekly Earnings of Employees (Distribution) (WEEDs) (ABS 1991, 1996h) and Average Weekly Earnings (AWE) (ABS 1996a).
20 Modelling the Coverage of Private Health Insurance in Australia in WEEDs information was used to estimate the average movement in wages and salaries between August 1990 and August 1995 for groups defined by income quintile and whether working part-time or full-time. AWE estimates were then used to inflate, for each group, the average movements in incomes between August and November 1990 and, similarly, to inflate those between August and November Income from self-employment Income from self-employment was inflated using an inflator derived from the labour force survey (ABS 1996d, 1996e) and National Accounts data (ABS 1996f). The inflator used was the estimated average annual income per self-employed person in both farm and non-farm sectors. The estimate of average self-employed income was calculated by dividing the national income for unincorporated non-farm enterprises by the number of self-employed persons recorded by the labour force survey. The definition of self-employed persons included both full-time and part-time self-employed, but excluded anyone in the public administration industry. 6.3 Interest, dividends and income from rent Income received from interest, dividends and rents was uprated using the change recorded in the ABS National Accounts estimates for each income source (ABS 1996g). The estimates were provided from the seasonally adjusted Households Income and Outlay Account item Income of other unincorporated enterprises and from dwellings and interest and dividends. Dividends included dividends from life offices and superannuation funds. Rent included nondwelling and dwelling rent and interest. 6.4 Workers compensation, superannuation, pensions, benefits and other income Income from workers compensation, superannuation, annual pensions and benefits and the catch-all group, other incomes was uprated using