Impact of Private Health Insurance on the Choice of Public versus Private Hospital Services

Transcription

1 Impact of Private Health Insurance on the Choice of Public versus Private Hospital Services Preety Srivastava & Xueyan Zhao Centre for Health Economics Monash University 3 June 2008

2 Background Australian Health Care system Mix of public and private health services One of the highest % of private coverage across OECD countries In , 4/10 hospital admissions and 1/4 inpatient days were private (NHS, ) Policy makers recognise the important role of PHI funds in financing private hospitals.

3 Background PHI reforms after introduction of Medicare to ensure no crowding out of the private sector. Severe decline in PHI in the 90s leading to enormous pressure on public hospitals To stem erosion a package of initiatives introduced in late 90s: Tax penalty for high-income individuals without private cover 30% rebate on PHI premiums Lifetime health cover

4 Background Reforms strongly criticised by scholars Package of initiatives, in particular lifetime cover has increased PHI coverage but activity in the private sector not picked up. Not eased much pressure in public hospitals. money could have been better spent if applied directly toward enhancing capacity of public hospitals to meet the additional demand (Wilcox, 2001; Duckett and Jackson, 2000). PHI taken purely for financial reasons (Fiebig et al., 2006) and not necessarily to access private care.

5 Background More recently: Income threshold for Medicare surcharge penalty would be increased both for singles and families. This is also being criticised on the ground that a number of people are going to drop their PHI.

6 Background Concerns on equity of care-provision In terms of the disproportionate distribution of tax rebates to high-income earners (Hindle and McAuley, 2004; Butler, 2002; Wilcox, 2001). Subsidy is skewed to the more affluent. 80% (20%) of richest (poorest) 10% of Australians had PHI and nearly 75% (18%) admitted as private patients in 2005(NHS )

7 Objective of the study The objective of this study is to investigate the determinants of individuals choice between public and private hospital care and the role of PHI towards this decision.

8 Motivation and Contribution Demand for PHI has received ample attention in the literature but only a small body of research has examined its role in public/private health care utilisation (Fiebig et al., 2006; Rodriguez and Stoyanova, 2004; Savage and Wright, 2003; Propper, 2000) Also sheds light on the potential substitution between public and private hospital admissions in a system where PHI increases the chances of substitution by providing a duplicate coverage.

9 Motivation and Contribution Also makes a significant contribution in terms of the modelling approach. In most prior studies the 3 decisions i.e. to seek no care or private care or public care, has been modelled using a MNL model. In contrast, we model the hospital admission decision in two parts on the assumption that the decision to seek hospital care and the decision to get admitted as a public/private patient are two distinct processes. account for selectivity bias in the second stage given that the decision to get admitted as a public or private patient is only observed for those who visit a hospital. Also unlike prior studies, this study accounts for the endogeneity of PHI using a system approach instead of a two-step estimation.

10 Prior Studies Relationship between PHI and health service utilisation (Zhang and Zhao 2007) made no distinction between public and private admissions. Relationship between PHI and hospital admission (Fiebig et al. 2006) focus was more on the impact of insurance type - in terms of reasons for purchasing private health insurance - on the probability of hospital admission in Australia. Private health insurance participation and the duration of stay in private hospitals (Savage and Wright 2003) focus was on identifying any moral hazard behaviour and adverse selection in insurance purchase. Impact of PHI on hospital admission and hospital days (Cameron et al. 1988) made no distinction between public and private admissions. Overseas UK (Propper 2000) Spain (Rodriguez and Stoyanova 2004) Harmon and Nolan, 2001 and Holly et al., Estimation techniques two step estimation to account for endogeneity Accounted for endogeneity using FIML approach not distinguished between public and private service utilisation

11 Economic Framework Demand for health care=function of the value of benefits of treatment; quality of public care vs private care; attitude towards quality of care; cost of public health care (if any); cost of private health care

12 Economic Framework Value of benefits of treatment: Related to medical need which arises from the severity of illness and importance of good health. Importance of good health positively associated with education and socioeconomic factors and Negatively related to lifestyle factors such as drinking and smoking patterns, and exercise habits Quality of public care vs private care Reflected in waiting time, the ability to choose the doctor Attitude towards quality of care Quality measures such as waiting time or the inability to choose date and location of treatment may prove to be inconvenient. Since each person has his own valuation of time this may cause variations across people. A person s valuation of his time is usually a positive function of income and type of employment.

13 Economic Framework Cost of public health care (if any) Although public health care is free of user charges, travel and time costs are also important considerations, in particular for lower socioeconomic groups. Such factors are negatively associated with income. Cost of private health care Access costs to private health care depends mainly on price of health insurance and income Copayments can also represent a significant cost to access private health care, particularly in Australia. Access to health care can also vary across the population because of language or cultural differences. Such differences may result into a lower level of awareness of health care availability and efficacy or a shyness to use health services.

14 Econometric Framework System approach with partial observability Latent form: Endogeneity: Y I appears on the RHS

15 Econometric Framework Multivariate Probit (MVP) model. The system approach allows us to account for not only the effect of the observed variables and but also the effect of unobserved individual characteristics. This allows us to estimate a whole range of joint and conditional probabilities. We can also estimate the treatment effect of PHI, i.e. the effect of private insurance participation on the probability of visiting hospital or on the probability of seeking private care.

16 Data NHS ( Australian adults aged 18+) Contains a host of health related information (i.e. SRH, LT health conditions) Health service utilisation Other individual characteristics such as gender, marital status, income, level of education, main activity etc. Dependent variables: Y I : status of individuals who, at the time of the survey, had a private hospital cover Y H : whether an individual had at least one inpatient stay in a hospital and discharged in the 12 months prior to interview. Y P : whether he/she was admitted as a private or Medicare patient.

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18 Results: Coefficients Y I Y H Y P Y I Y P Y I (0.563)** incdech (0.046)** (0.051) (0.135) age (0.042)** (0.045)** (0.125)** incdech (0.047)** (0.053)** (0.131)* age (0.043)** (0.047)** (0.131) incdech (0.050)** (0.057)** (0.153) age (0.047)** (0.050)** (0.161)* incdech (0.050)** (0.058)** (0.160) age (0.056)** (0.056)** (0.226)** incdech (0.051)** (0.061) (0.183) age (0.062)** (0.059)** (0.228)** incdech (0.053)** (0.061)** (0.200) male (0.027)** (0.029)** (0.077)* incdech (0.056)** (0.061)** (0.228) married (0.028)** (0.027)** (0.079) concess (0.040)** (0.044)** (0.123) profeng (0.081)** (0.090)* (0.204) excelh (0.063)** (0.062)** depkid (0.036)** (0.103) vgoodh (0.060)** (0.056)** sinpar (0.064)** (0.167) goodh (0.057)** (0.052)** majcity (0.033)** (0.037) (0.090) athritis (0.032) (0.032)** inregn (0.038)** (0.042) (0.098) cancer (0.075) (0.070)** workft (0.065)** (0.046)** (0.201) heart (0.029)* (0.030)** workpt (0.063)** (0.045)** (0.205) diabetes (0.054) (0.052)** workstud (0.112)* (0.117) (0.276) asthm a (0.040) (0.039)* studyft (0.081)** (0.089)** (0.276) osteo (0.056)** (0.057) unemp (0.102)** (0.093)** (0.299) smokedly (0.031)** prof (0.057)** (0.180) alchirsk (0.062)** trades (0.066)** (0.200) overwt (0.025) clerk (0.097)** (0.308) noexcise (0.026)** intsales (0.061)** (0.202)* copay (0.001)** (0.003) prodtran (0.072)** (0.220) bed (0.019) (0.060) elsales (0.071)* (0.214) Constant (0.129)** (0.119)** (1.351)* degree (0.042)** (0.044) (0.108) Ξ IH (0.018)** tafe (0.035)** (0.036)** (0.092) Ξ IP (0.272) year (0.034)** (0.036) (0.091)* Ξ HP (0.150)** Standard errors are given in parentheses. *significant at 10% level; **significant at 5% level. Y H

19 Results: Marginal Effects Y I Y H Y P Y H = 1 Y I Y H Y P Y H = 1 Y I (0.095)** tafe (0.014)** (0.009)** (0.026)** age (0.017)** (0.011)** (0.033)** year (0.013)** (0.009) (0.025)** age (0.017)** (0.012)** (0.033)** incdech (0.018)** (0.013) (0.036)** age (0.018)** (0.012)** (0.035)** incdech (0.018)** (0.013)** (0.034)** age (0.022)** (0.014)** (0.044)** incdech (0.020)** (0.014)** (0.038)** age (0.024)** (0.014)** (0.043)** incdech (0.020)** (0.014)** (0.039)** male (0.011)** (0.007)** (0.021)** incdech (0.020)** (0.015) (0.042)** married (0.011)** (0.007)** (0.021)** incdech (0.021)** (0.015)** (0.045)** profeng (0.032)** (0.022)* (0.056)** incdech (0.022)** (0.015)** (0.051)** depkid (0.014)** (0.028)* concess (0.016)** (0.011)** (0.034)** sinpar (0.025)** (0.048) excelh (0.025)** (0.015)** (0.038)** majcity (0.013)** (0.009) (0.023) vgoodh (0.023)** (0.014)** (0.035)** inregn (0.015)** (0.010) (0.028) goodh (0.022)** (0.013)** (0.027)** workft (0.026)** (0.011)** (0.054)* athritis (0.013) (0.008)** (0.011) workpt (0.025)** (0.011)** (0.055)** cancer (0.030) (0.017)** (0.035) workstud (0.045)* (0.029) (0.079) heart (0.012)* (0.007)** (0.012) studyft (0.032)** (0.022)** (0.072) diabetes (0.022) (0.013)** (0.019) unemp (0.040)** (0.023)** (0.082) asthma (0.016) (0.010)* (0.013) prof (0.022)** (0.048)** osteo (0.022)** (0.014) (0.020)** trades (0.026)** (0.055) smokedly (0.012)** (0.022)** clerk (0.038)** (0.088)** alchirsk (0.024)** (0.021)** intsales (0.024)** (0.053)** overwt (0.010) (0.008) prodtran (0.028)** (0.061) noexcise (0.010)** (0.009)** elsales (0.028)* (0.060) copay (0.000)** (0.001)** degree (0.017)** (0.011) (0.030)** bed (0.007) (0.016) P(. x) (0.005)** (0.003)** (0.033)**

20 Age Y I Y H Y P Y H = 1 age (0.017)** (0.011)** (0.033)** age (0.017)** (0.012)** (0.033)** age (0.018)** (0.012)** (0.035)** age (0.022)** (0.014)** (0.044)** age (0.024)** (0.014)** (0.043)** Age is a significant in all three equations. The probability of purchase of PHI is found to increase with age with a slight drop-off for the 70+ age group. (similar evidence in prior studies) The probability of private care utilisation increases progressively as individuals get older In contrast, the probability of hospital admission has a U- shaped distribution with age, with the young and the old age groups more likely to get admitted.

21 Employment and Occupation Y I Y H Y P Y H = 1 workft (0.026)** (0.011)** (0.054)* workpt (0.025)** (0.011)** (0.055)** workstud (0.04 5)* (0.02 9) (0.079) studyft (0.032)** (0.022)** (0.072) unemp (0.040)** (0.023)** (0.082) prof (0.022)** (0.048)** trad es (0.02 6)** (0.055) clerk (0.038)** (0.088)** in tsale s (0.02 4)** (0.053)** p rodtran (0.02 8)** (0.061) elsales (0.028)* (0.060) when we control for other factors such as income and occupations, those who work are less likely to purchase PHI and use private health care than those NLF (base case) PHI purchase and use of private hospital care is also associated with individuals' occupations. Labourers (base case) have the lowest chances of purchasing PHI and opting for private hospital care than individuals in any other occupation.

22 Lifestyle factors, Household Characteristics Health related lifestyle factors such as heavy smoking, drinking at high risk levels, lack of exercise and being obese are all negatively related to insurance decision. More than poor health such factors indicate risk attitudes towards health. i.e. A decision-maker with such characteristics is less likely to indulge in a risk-averse behaviour such as PHI purchase. The presence of dependant kids is likely to be a significant stimulus for getting insured from both the risk averseness and financial point of view. The positive and significant coefficient on this indicator supports the hypothesis. On the other hand, single parents are found to be less likely to purchase PHI. Their decision to purchase insurance may be potentially constrained by their financial situations.

23 Education and Income Education is likely to increase individuals awareness of health care services and the benefits of purchasing a private health insurance. The insurance decision and private health care utilisation are both found to be strongly associated with education. degree holders are more likely to get insured and also more likely to use private health care than someone who has completed less than secondary education. Higher household income is associated with a higher probability of purchasing PHI and a higher probability of private health care utilisation. Note that tax incentives can be a significant stimulus for purchasing private health insurance. A flat Medicare levy with a progressive income taxation system encourages those on higher incomes to purchase private insurance irrespective of whether they would use private sector facilities (Fiebig et al 2006).

24 Self-Assessed Health Y I Y H Y P Y H = 1 e xcelh (0.02 5)** (0.01 5)** (0.038)** vgoo dh (0.02 3)** (0.01 4)** (0.035)** g oodh (0.02 2)** (0.01 3)** (0.027)** Medical need is a potential predictor of health care utilisation. Those who are in good health are less likely to access health care services. The results of the hospital utilisation equation support this hypothesis indicating that the less healthy individuals are, the more likely they are to get admitted into hospitals. However, we obtain a positive relationship between individuals self-assessed health and the probability of purchasing PHI and the probability of using private care. counter intuitive to the hypothesis of moral hazard and adverse selection into insurance such finding is not unusual and has been obtained in several previous studies and has often been associated with risk-related behaviours. i.e. people who are careful about their health are also more likely to engage into risk averse activities such as purchasing a PHI.

25 Objective measures of Health Y I Y H Y P Y H = 1 athritis (0.013) (0.008)** (0.011) cancer (0.030) (0.017)** (0.035) heart (0.012)* (0.007)** (0.012) diabetes (0.022) (0.013)** (0.019) asthma (0.016) (0.010)* (0.013) osteo (0.022)** (0.014) (0.020)** Some more objective measures of health status in terms of long-term conditions such as arthritis, cancer, heart disease, diabetes, asthma and osteoporosis. Not related to the choice of private health care or insurance purchase But significantly related with hospital utilisation.

26 Cost of insurance and cost of access to private hospitals No data on cost of insurance! Average state-level copayments are used as a measure of the cost of private care. Negative effect- the higher the copayments the lower is the probability of purchasing PHI or the probability of private care utilisation. Those who have concession cards have lower probability of insurance purchase and private hospital care.

27 Quality of health service The effect of the quality of public health care has been identified as an important determinant of insurance decision in previous studies A common measure of public hospital care is waiting list and queuing. Two different measures of waiting list at state level: average waiting time (i.e. days waited at 50th percentile)-insignificant effect the proportion of individuals who waited for more than a year for elective surgery- positively and significantly related to PHI purchase. Not included in final model - given the Australian waiting list data at state level is known to be inconsistent with regard to their collection and presentation (Hopkins and Kidd, 1996; AIHW, 2007)

28 Quality of health service Instead we use bed density and full-time equivalent (FTE) medical practitioners in public hospitals as alternate indicators of quality of public care. Measured at state level and by remoteness- more variation. The effects of both these variables are found to be negative with respect to both insurance purchase and private health care service utilisation (although mostly insignificant).

29 Effect of PHI Finally, private insurance is found to be an important determinant of private health care utilisation. In particular, those with private hospital cover are 76% more likely to seek private health care than use public health services. But is this high enough???

30 Predicted Probabilities and Treatment Effects

31 Conclusion This study attempts to provide insights on the role of PHI in the choice that an individual makes between public and private health care utilisation It uses a system of Probit models (MVP) to allow for potential endogeneity of private insurance participation. It also adjust for selection bias due to partial observability since we only observe individuals choices between P/P if they have visited a hospital. PHI has certainly been identified as an important determinant of private hospital care utilisation. However, other factors such as perceived quality of care in the public sector and cost of access were also found to have an impact on the use of private hospital care. This system approach allows predictions of a range of joint and conditional probabilities.