• No results found

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

N/A
N/A
Protected

Academic year: 2021

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

Copied!
31
0
0

Loading.... (view fulltext now)

Full text

(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 2004-05, 4/10 hospital admissions and 1/4

inpatient days were private (NHS, 2004-05)

– Policy makers recognise the important role of

(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

2004-05)

(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

(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., 1998.

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

(14)

Econometric Framework

• System approach with partial observability

Latent form:

(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 2004-05 (14 970 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.

(17)
(18)

Results: Coefficients

YI 2.254 (0.563)** incdech4 0.366 (0.046)** 0.019 (0.051) 0.183 (0.135) age30 0.204 (0.042) ** -0.096 ( 0.045)** 0.265 (0.125)** incdech5 0.393 (0.047)** 0.147 (0.053)** 0.250 (0.131)* age40 0.480 (0.043) ** -0.211 ( 0.047)** 0.104 (0.131) incdech6 0.525 (0.050)** 0.123 (0.057)** 0.193 (0.153) age50 0.792 (0.047) ** -0.238 ( 0.050)** 0.280 (0.161)* incdech7 0.625 (0.050)** 0.126 (0.058)** 0.243 (0.160) age60 1.135 (0.056) ** -0.263 ( 0.056)** 0.533 (0.226)** incdech8 0.793 (0.051)** 0.077 (0.061) 0.172 (0.183) age70+ 1.118 (0.062) ** -0.207 ( 0.059)** 0.690 (0.228)** incdech9 0.991 (0.053)** 0.177 (0.061)** 0.238 (0.200) male -0.067 (0.027) ** -0.133 ( 0.029)** -0.148 (0.077)* incdech10 1.346 (0.056)** 0.187 (0.061)** 0.241 (0.228) married 0.222 (0.028) ** 0.072 ( 0.027)** 0.120 (0.079) concess - 0.482 (0.040)** 0.108 (0.044)** 0.105 (0.123) profeng 0.370 (0.081) ** 0.153 ( 0.090)* 0.195 (0.204) excelh 0.233 (0.063)** -0.741 (0.062)** depkid 0.157 (0.036) ** 0.001 (0.103) vgoodh 0.224 (0.060)** -0.667 (0.056)** sinpar -0.158 (0.064) ** 0.035 (0.167) goodh 0.141 (0.057)** -0.460 (0.052)** majcity 0.120 (0.033) ** -0.004 ( 0.037) -0.056 (0.090) athritis 0.013 (0.032) 0.075 (0.032)** inregn 0.111 (0.038) ** 0.026 ( 0.042) 0.036 (0.098) cancer 0.097 (0.075) 0.478 (0.070)** workft -0.349 (0.065) ** -0.324 ( 0.046)** -0.096 (0.201) heart 0.056 (0.029)* 0.143 (0.030)** workpt -0.294 (0.063) ** -0.141 ( 0.045)** -0.220 (0.205) diabetes - 0.027 (0.054) 0.191 (0.052)** workstud -0.187 (0.112) * -0.144 ( 0.117) -0.011 (0.276) asthm a 0.025 (0.040) 0.073 (0.039)* studyft 0.439 (0.081) ** -0.324 ( 0.089)** -0.433 (0.276) osteo 0.167 (0.056)** 0.048 (0.057) unemp -0.221 (0.102) ** -0.242 ( 0.093)** 0.011 (0.299) smokedly - 0.393 (0.031)** prof 0.448 (0.057) ** 0.187 (0.180) alchirsk - 0.144 (0.062)** trades 0.235 (0.066) ** 0.043 (0.200) overwt - 0.024 (0.025) cler k 0.598 (0.097) ** 0.264 (0.308) noexcise - 0.082 (0.026)** intsales 0.321 (0.061) ** 0.377 (0.202)* copay - 0.006 (0.001)** -0.002 (0.003) prodtran 0.156 (0.072) ** 0.132 (0.220) bed - 0.023 (0.019) -0.025 (0.060) elsales 0.131 (0.071) * 0.082 (0.214) Constant - 1.644 (0.129)** -0.474 (0.119)** -2.614 (1.351)* degree 0.314 (0.042) ** 0.042 ( 0.044) 0.162 (0.108) ΞIH 0.085 (0.018)** tafe 0.096 (0.035) ** 0.106 ( 0.036)** 0.138 (0.092) ΞIP - 0.245 (0.272) year12 0.213 (0.034) ** 0.015 ( 0.036) 0.171 (0.091)* ΞHP 0.392 (0.150)**

Standard er rors are given in parentheses. *significant at 10% level; **si gnificant at 5% level.

YH YP

(19)

Results: Marginal Effects

YI 0.757 (0.095)** tafe 0.038 (0.014)** 0.026 (0.009)** 0.055 (0.026)** age30 0.081 (0.017)** -0.024 (0.011)** 0.143 (0.033)** year12 0.084 (0.013)** 0.004 (0.009) 0.110 (0.025)** age40 0.189 (0.017)** -0.052 (0.012)** 0.198 (0.033)** incdech4 0.144 (0.018)** 0.005 (0.013) 0.160 (0.036)** age50 0.312 (0.018)** -0.058 (0.012)** 0.345 (0.035)** incdech5 0.155 (0.018)** 0.036 (0.013)** 0.173 (0.034)** age60 0.448 (0.022)** -0.065 (0.014)** 0.521 (0.044)** incdech6 0.207 (0.020)** 0.030 (0.014)** 0.201 (0.038)** age70+ 0.441 (0.024)** -0.051 (0.014)** 0.551 (0.043)** incdech7 0.247 (0.020)** 0.031 (0.014)** 0.245 (0.039)** male -0.026 (0.011)** -0.033 (0.007)** -0.046 (0.021)** incdech8 0.313 (0.020)** 0.019 (0.015) 0.284 (0.042)** married 0.088 (0.011)** 0.018 (0.007)** 0.093 (0.021)** incdech9 0.391 (0.021)** 0.044 (0.015)** 0.352 (0.045)** profeng 0.146 (0.032)** 0.038 (0.022)* 0.151 (0.056)** incdech10 0.531 (0.022)** 0.046 (0.015)** 0.462 (0.051)** depkid 0.062 (0.014)** 0.049 (0.028)* concess -0.190 (0.016)** 0.026 (0.011)** -0.133 (0.034)** sinpar -0.062 (0.025)** -0.040 (0.048) excelh 0.092 (0.025)** -0.182 (0.015)** 0.149 (0.038)** majcity 0.047 (0.013)** -0.001 (0.009) 0.023 (0.023) vgoodh 0.089 (0.023)** -0.164 (0.014)** 0.139 (0.035)** inregn 0.044 (0.015)** 0.006 (0.010) 0.041 (0.028) goodh 0.055 (0.022)** -0.113 (0.013)** 0.091 (0.027)** workft -0.138 (0.026)** -0.079 (0.011)** -0.100 (0.054)* athritis 0.005 (0.013) 0.018 (0.008)** -0.004 (0.011) workpt -0.116 (0.025)** -0.034 (0.011)** -0.135 (0.055)** cancer 0.038 (0.030) 0.117 (0.017)** -0.019 (0.035) workstud -0.074 (0.045)* -0.035 (0.029) -0.046 (0.079) heart 0.022 (0.012)* 0.035 (0.007)** 0.003 (0.012) studyft 0.173 (0.032)** -0.080 (0.022)** 0.056 (0.072) diabetes -0.011 (0.022) 0.047 (0.013)** -0.028 (0.019) unemp -0.087 (0.040)** -0.059 (0.023)** -0.041 (0.082) asthma 0.010 (0.016) 0.018 (0.010)* 0.000 (0.013) prof 0.177 (0.022)** 0.188 (0.048)** osteo 0.066 (0.022)** 0.012 (0.014) 0.047 (0.020)** trades 0.093 (0.026)** 0.084 (0.055) smokedly -0.155 (0.012)** -0.122 (0.022)** clerk 0.236 (0.038)** 0.255 (0.088)** alchirsk -0.057 (0.024)** -0.045 (0.021)** intsales 0.127 (0.024)** 0.199 (0.053)** overwt -0.009 (0.010) -0.007 (0.008) prodtran 0.062 (0.028)** 0.083 (0.061) noexcise -0.032 (0.010)** -0.025 (0.009)** elsales 0.052 (0.028)* 0.062 (0.060) copay -0.003 (0.000)** -0.002 (0.001)** degree 0.124 (0.017)** 0.010 (0.011) 0.136 (0.030)** bed -0.009 (0.007) -0.014 (0.016) P(.|x) 0.441 (0.005)** 0.162 (0.003)** 0.433 (0.033)** YH YP |YH = 1 YI YH YP |YH = 1 YI
(20)

Age

a ge30

0.081 (0.01 7)**

-0.024 (0 .01 1)**

0.1 43 (0.033 )**

a ge40

0.189 (0.01 7)**

-0.052 (0 .01 2)**

0.1 98 (0.033 )**

a ge50

0.312 (0.01 8)**

-0.058 (0 .01 2)**

0.3 45 (0.035 )**

a ge60

0.448 (0.02 2)**

-0.065 (0 .01 4)**

0.5 21 (0.044 )**

a ge70 +

0.441 (0.02 4)**

-0.051 (0 .01 4)**

0.5 51 (0.043 )**

Y

I

Y

H

Y

P

|Y

H

=

1

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

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.

workft

-0.138 (0.02 6)**

-0.079 (0 .01 1)**

- 0.1 00 (0.054 )*

workp t

-0.116 (0.02 5)**

-0.034 (0 .01 1)**

- 0.1 35 (0.055 )**

workstud

-0.074 (0.04 5)*

-0.035 (0 .02 9)

- 0.0 46 (0.079 )

studyft

0.173 (0.03 2)**

-0.080 (0 .02 2)**

0.0 56 (0.072 )

u nemp

-0.087 (0.04 0)**

-0.059 (0 .02 3)**

- 0.0 41 (0.082 )

p rof

0.177 (0.02 2)**

0.1 88 (0.048 )**

trad es

0.093 (0.02 6)**

0.0 84 (0.055 )

cler k

0.236 (0.03 8)**

0.2 55 (0.088 )**

in tsale s

0.127 (0.02 4)**

0.1 99 (0.053 )**

p rodtran

0.062 (0.02 8)**

0.0 83 (0.061 )

e lsa les

0.052 (0.02 8)*

0.0 62 (0.060 )

Y

I

Y

H

Y

P

|Y

H

=

1

(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

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.

e xcelh

0.092 (0.02 5)**

-0.182 (0 .01 5)**

0.1 49 (0.038 )**

vgoo dh

0.089 (0.02 3)**

-0.164 (0 .01 4)**

0.1 39 (0.035 )**

g oodh

0.055 (0.02 2)**

-0.113 (0 .01 3)**

0.0 91 (0.027 )**

(25)

Objective measures of Health

• 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.

a thr itis

0.005 (0.01 3)

0.018 (0 .00 8)**

- 0.0 04 (0.011 )

can ce r

0.038 (0.03 0)

0.117 (0 .01 7)**

- 0.0 19 (0.035 )

h eart

0.022 (0.01 2)*

0.035 (0 .00 7)**

0.0 03 (0.012 )

d iabetes

-0.011 (0.02 2)

0.047 (0 .01 3)**

- 0.0 28 (0.019 )

a sthma

0.010 (0.01 6)

0.018 (0 .01 0)*

0.0 00 (0.013 )

o steo

0.066 (0.02 2)**

0.012 (0 .01 4)

0.0 47 (0.020 )**

Y

I

Y

H

Y

P

|Y

H

=

1

(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.

(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.

References

Related documents

● No one did this ​ : The remaining 28 activities were not mentioned for pilot dataset #1: Authentication, Cease Data Curation, Chain of custody, Code review, Conversion

Sharing practices embedded in collaborative economy occur in different ways (Botsman 2013) in respond to different motivations (Belk 2010).. Even the word “sharing” through

- Toxic when absorbed by skin - Cause severe irritation of eyes - Potential carcinogen1. - Excessive exposure may cause drowsiness, headache, nausea

Normally Open (N.O.) auxiliary contact is provided for remote indication of High Inlet Dewpoint Alarm. See wiring diagram for actual terminal numbers. Relay is energized during

Significant differences prior to administration of midazolam and at intervals of 5 minutes up to 30 minutes were observed for blood pressure and heart rate, but not for

This paper discusses the portfolio optimisation models and compares the performance as well as portfolio composition of the mean-variance model with other downside risk models which

Among di ff erent dietary components, phenolic compounds, including phenolics acids, hydroxycinnamic acids and flavonoids, including proanthocyanidins, have been shown to be related

(There will in such studies be a need to consider the impact of policies which affect the movement of freight within the urban area, such as lorry restrictions or time-specific