Examining the Feasibility of Long Term Care Insurance

Examining the Feasibility of Long Term Care Insurance
David Cullen Principal Economist National Disability Insurance Agency

Disclaimer The views presented in this paper are those of the author. They should not be interpreted as the views of the National Disability Insurance Agency or of the Australian Government.

Thanks I’d like to thank three ANU students who have worked with me on the issues that I discuss in this paper as interns at the National Disability Insurance Agency: Xi Xin Tianxiao Pan Ge Zhan I’d also like to thank the staff of the National Aged Care Data Clearing House at the Australian Institute for Health and Welfare for providing the two large data sets analysed in this paper. Shorthand PRAC = Permanent Residential Aged Care

Overview Examine the feasibility of long term care insurance by investigating the three key components of an abstract insurance scheme The probability of a claim Lifetime risk of entering permanent residential aged care The likely value of the claim Distribution of length of stay in permanent residential aged care When the claim can be expected to occur - Average age of first admission to permanent residential aged care The product of the first two statistical distributions determines the expected expenses of the scheme The third distribution, together with the premium price, determines the expected revenue of the scheme

Outline Policy context
Estimate the average age of first admission to permanent residential aged care Estimate the lifetime risk of entering permanent residential aged care Examine the distribution of length of stay in permanent residential aged care Discuss the implications for the design of a long term care insurance scheme

Policy context – Growing Share of GDP
1.3 million older Australians were supplied with aged care services over 2,000 service providers total revenue of those providers was $19.7 billion $15.2 billion (77.2 per cent) provided by the Australian Government 1.2 per cent of GDP. By Australian Government expenditure on aged care will be 1.7 per cent of GDP. The sector as whole will account for 2.2 per cent of GDP.

Policy context – Current funding policy
Government contribution comes from general taxation Individuals make their contributions from their government pensions, superannuation and earnings on savings, and by drawing down on savings. Private long term care insurance does not currently play a role in the financing of aged care in Australia Section 72-1(1)(c)(i) of the Private Health Insurance Act 2007 says that health insurers are not permitted to offer insurance cover for the cost of care and accommodation in an aged care service.

Policy context – Current funding policy
The prohibition on long term care insurance dates from 1981. The then Minister for Health, in justifying the introduction of the prohibition to the Parliament, stated that: “Nursing home patients are, in the strict sense, uninsurable through voluntary insurance in which guaranteed benefits without discrimination or exclusions are made mandatory; and the arrangement represents an inequitable liability for the bulk of people with hospital insurance”. This is the issue that I want to test in this paper.

Average Age of First Admission – The Data Set
Every PRAC episode that ended between 1 July 1998 and 30 June 2015. Inclusive of those episodes that were incomplete on 30 June 2015. 852,790 individuals 1,295,221 episodes of care 9 variables Unique person identifiers - allowing multiple episodes to be identified Sex Month and year of birth Month and year of admission Month and year of discharge Reason for discharge

Average Age of First Admission - Results
Females Males

In : 85.0 for women 82.6 for men Has been increasing for men and women Average annual increase is 0.2% for men and women since The rate of increase is slowing Average annual increase in the first five years was 0.3% for women (0.4% for men) Average annual increase in the last five years was 0.1% for men and women In any case: 90% of male first admissions are after age 69 95% of female first admissions are after age 69 That is, the ‘trigger event’ for the ‘insurance product’ overwhelmingly occurs in the retirement phase.

Average Age of First Admission - Implications
Implications for the design of a long term care insurance scheme If we assume premiums are paid only during working life (say from age 25 to age 65) then we can be very confident about the number of years of premium income from each individual The trigger event will almost always occur after the premium period If we instead price premiums so that they are paid until the trigger event then there is a high degree of uncertainty as to how long an given individual will pay premiums Accounting for the distribution of this risk, and its implications for scheme liquidity, means the scheme would need to be more conservative in premium setting under this arrangement.

Lifetime risk The lifetime risk of entry to PRAC is not well understood. Cross-sectional data tends to understate the likelihood that an individual will require PRAC at some time in their life. Share of population receiving PRAC, June 2014

Lifetime risk Lifetable approach
Mason F, Liu Z and Braun P. (2001). The probability of using an aged care home over a lifetime ( ). Welfare Division Working Papers. Canberra, Australian Institute of Health and Welfare. Cullen DJ. (2006). ‘Factoring the cost of aged care into retirement planning’. Paper delivered at the 14th Colloquium of Superannuation Researchers, Centre for Pensions and Superannuation, University of New South Wales. Cullen DJ. (2007). ‘The financial impact of entering aged care’. Australasian Journal on Ageing 26(3):145–147. Cullen DJ. (2011). Technical Paper on the changing dynamics of residential aged care. Canberra: Productivity Commission.

Lifetime Risk – Building the Life Table
Same data set used for estimating the average age of first admission. 852,790 individuals Derive three observed variables - all by sex by single year of age, where Age is as at the start of the financial year ORpopx – Observed population in PRAC at the start of each financial year OFadmx – Observed first admissions to PRAC during each financial year ORdx – Observed deaths to PRAC during each financial year ABS Data OPopx - Observed Australian population at the start of the financial year, by sex by single year of age

Calculated variables First admission rate 𝑅𝑎𝑡𝑒𝐹𝑎𝑑𝑚 𝑥 = 𝑂𝐹𝑎𝑑𝑚 𝑥 ( 𝑂𝑝𝑜𝑝 𝑥 − 𝑂𝑅𝑝𝑜𝑝 𝑥 ) Death rate in care 𝑅𝑎𝑡𝑒𝑅𝑞 𝑥 = 𝑂𝑅𝑑 𝑥 𝑂𝑅𝑝𝑜𝑝 𝑥 Australian life table for the relevant financial year Number of people in the stationary population at the start of the year 𝑙 𝑥 𝑅𝑎𝑡𝑒𝐹𝑎𝑑𝑚 𝑥 and 𝑅𝑎𝑡𝑒𝑅𝑞 𝑥 are smoothed by averaging three years of admission and death rates – in line with the Australian life table

Population without an admission (not in care and never previously admitted) 𝐶𝑙 𝑥+1 =𝐶𝑙 𝑥 − 𝐶𝑑 𝑥 − 𝐴𝑑𝑚 𝑥 Population with an admission (in care or previously admitted and now in the community) 𝑅𝑙 𝑥+1 =𝑅𝑙 𝑥 − 𝑅𝑑 𝑥 + 𝐴𝑑𝑚 𝑥 First admissions 𝐴𝑑𝑚 𝑥 =𝑅𝑎𝑡𝑒𝐴𝑑𝑚 𝑥 × 𝐶𝑙 𝑥 Deaths in care 𝑅𝑑 𝑥 =𝑅𝑎𝑡𝑒𝑅𝑑 𝑥 × 𝐶𝑙 𝑥 +0.5× 𝐴𝑑𝑚 𝑥 Deaths in the community 𝐶𝑑 𝑥 = 𝑙 𝑥 − 𝑙 𝑥+1 −𝑅𝑑 𝑥 Calculating Lifetime Risk at age x 𝑳𝑹 𝒙 = 𝒙 𝟏𝟎𝟎 𝑨𝒅𝒎 𝒙 𝑪𝒍 𝒙 Note: The calculations assume that people who have been admitted to PRAC and then discharged into the community have the same mortality rates as people in PRAC (rather than as people who have never been admitted).

First Admission rates (aged x) Death rate in care (aged x)
Survivors in Stationary Population (aged x) Survivors without a nursing home admission (at age x) Survivors with a nursing home admission (at age x) Estimated first admissions in SP (at age x) Deaths without an admission (at age x) Deaths after having at least one admission (at age x) Lifetime risk of first admission (at age x) x Rate - Fadmx Rate - Rdx lx Clx Rlx Fadmx Cdx Rdx LRx 0.00% 100,000 601 29.29% 1 99,399 45 29.47% 65 0.13% 20.72% 84,673 84,231 442 158 1,117 108 33.51% 66 0.23% 24.16% 83,448 82,956 492 193 1,199 142 33.84% 67 0.26% 22.30% 82,107 81,564 543 216 1,322 145 34.18% 80 2.45% 37.18% 50,980 48,946 2,034 2,450 979 41.88% 81 2.91% 39.26% 47,551 45,297 2,254 1,316 2,400 1,143 42.61% 82 3.38% 39.38% 44,008 41,581 2,427 1,406 2,405 1,233 43.25% 90 9.34% 43.42% 15,516 12,733 2,783 1,189 1,467 46.00% 91 11.51% 43.20% 12,727 10,221 2,506 1,177 1,112 1,337 45.67% 92 12.14% 42.70% 10,278 7,932 2,346 963 895 1,207 44.02% 99 9.35% 47.75% 1,716 1,327 389 124 213 215 12.36% 100 4.04% 56.18% 1,288 990 298 40 950 338

Lifetime Risk - Results

Lifetime Risk - Results
Lifetime risk is high and higher for women In : 55.29% for women at birth (59.12% at age 65) 38.29% for men at birth (42.70% at age 65) Lifetime risk is increasing In : 49.60% for women at birth (53.77% at age 65) 29.29% for men at birth (33.51% at age 65) The gap between men and women is closing

Lifetime Risk – Drivers of the increase in risk

Lifetime Risk - Drivers of the increase in risk
Males Females Lifetime risk at age 0 in 2000 29.3% 49.6% Lifetime risk at age 0 in 2014 38.3% 55.3% Total change 9.0 p.p (30.7%) 5.7 p.p. (11.5%) Change due to: -- Change in life expectancy 10.6 p.p. (36.1%) 9.4 p.p. (18.9%) -- Change in death rates in care 0.4 p.p. (1.3%) 1.7 p.p. (3.4%) -- Change in First admission rates -2.0 p.p. (-6.9%) -4.9 p.p. (-10.0%) -- Change in Other factors 0.0 p.p. (0.2%) -0.4 p.p. (-0.8%) The increase in lifetime risk is mainly driven by increases in life expectancy For men - the change at any age under 80 is almost entirely due to increased life expectancy For women - there is a smaller positive effect as growth in life expectancy was smaller Lower First admission rates at most ages (up to 90) put downwards pressure on lifetime risk, offset by increasing first admission rates among the very old

Lifetime Risk - Drivers of the increase in risk
First Admission Rates - Females First Admission Rates - Males

Lifetime Risk – Future Risk
The estimates of lifetime risk developed in this paper are ‘cohort estimates’ That is, they are driven by past experience and assume that people born today will experience the same mortality rates and rates of admissions to permanent residential aged care as the current population For insurance purposes we need ‘period estimates’ These estimates project forward trends in current observed rates to predict the mortality rates and rates of admissions to permanent residential aged care that people born today will experience in the future.

As change in life expectancy is the largest driver of change in lifetime risk, we adjust for this factor in the following estimates of ‘period lifetime risk’. We use the Improvement Factors published by the Australian Government Actuary to project future age specific mortality rates, which we then use to build a new stationary lifetable in which people experience the projected age specific mortality rates This new stationary lifetable for the Australian population is then used in a new lifetime risk lifetable. We adjust for changes in first admission rates similarly We ignore future changes in mortality rates in care – as these are of second order.

Adjusted for improved life expectancy – AGA 125 year Improvement Factors

Adjusted for improved life expectancy and First Admission Rates – AGA 125 year Improvement Factors

Lifetime Risk - Implications
The probability that the insurance coverage will be triggered is high for both men and women – and getting higher. At age 25 it is currently 62% for women 48% for men The closer this risk is to 100% the more the product is concerned purely with dealing with ‘longevity’ or ‘severity’ risk – the size of the claim rather than whether or not a claim will be made.

Length of Stay – The Initial Data Set
Every episode of residential aged care that ended between 1 July and 30 June 2016 (inclusive), including: Episodes that were open on 1 July 1998 Episodes that were open on 30 June 2016. 1,008,998 individuals (1,008,335 after cleaning) 1,294,324 episodes of care (1,292,454 after cleaning) 16 variables Unique person identifiers - allowing multiple episodes to be identified Demographic information Severity information Episode information

Length of Stay – The Initial Data Set
Unique person identifier Year of admission (1959 – 2016) Month of admission Age at admission (3 – 112) Sex Age at discharge (4 – 114) Length of stay (0 – 20,341 days) Discharge reason (Death, Other, Open) Frailty Information RCS assessment information RCS Category (S1 – S8) RCS Score (0 – 104) ACFI assessment Information ADL Category (N, L, M, H) ADL Score (0 – 99.99) BEH Category (N, L, M, H) BEH Score (0 – ) CHC Category (N, L, M, H) CHC Score (0 – 3.00)

Length of Stay – Cleaning the data
Discarded episodes with length of stay = 0 1,840 records deleted (1,833 individuals) Checked for possible duplicates No records deleted Checked for overlapping episodes 30 records deleted Rescale all frailty scores (RCS, ADL, BEH, CHC) to be 0-100 Create Subsidy variable ($0-$ per day) by calculating subsidy payable under each frailty scoring system Create a single Severity variable by rescaling Subsidy (0-100)

Length of Stay– Cleaning the data
Possible duplicates None deleted ID_number sex age_at_admission age_at_discharge month_of_admission year_of_admission length_of_stay 348525 Male 77 1 2016 21 415857 Female 90 8 2002 3 961192 91 5 2012 6 962515 82 2014 2

Length of Stay – Cleaning the Data
Overlapping episodes 30 records deleted ID_number sex age_at_admission age_at_discharge month_of_admission year_of_admission length_of_stay Ob_number Action 19106 Female 77 78 4 2006 154 24440 87 9 3563 24441 85 3 2014 827 24442 DELETE 28063 Male 71 1997 5938 35819 73 90 1999 6317 35820 7 2013 1095 35821 76117 86 91 2011 1906 97731 8 1787 97732 157139 1 97 201790 12 201791

Length of Stay – Lifetime Use Analysis
1,008,335 individuals Join all episodes for an individual together and add lengths of stay 20 variables Unique person identifier Demographic information (at first admission) Severity information (at first admission) Subsidy information (at first admission) Number of Episodes Total Length of Stay

Unique person identifier Year of first admission (1959 – 2016) Month of first admission Financial year of first admission ( ) Age at first admission (3 – 112) Sex Age at last discharge (4 – 114) Number of episodes (1 – 14) Total Length of stay (0 – 20,341 days) Last Discharge reason (Death, Other, Open) Frailty Information RCS assessment information RCS Category (S1 – S8) RCS Score (0 – ) ACFI assessment Information ADL Category (N, L, M, H) ADL Score (0 – ) BEH Category (N, L, M, H) BEH Score (0 – ) CHC Category (N, L, M, H) CHC Score (0 – ) Severity ( ) Subsidy ( )

Distribution of LOS (all records) Distribution of LOS (under 100 days)

Length of stay data is both left and right censored Right censoring – some episodes uncompleted on 30 June 2016. These are known and can be dealt with through standard statistical methods Left censoring – some individuals might have had episodes of care that ended before 1 July 1998. Given the distribution of the number of episodes and the distribution of the gaps between episodes it is unlikely that individuals whose first recorded admission was on or after 1 July had an earlier episode.

We deal with the unknown right censoring issue by restricting our analysis to individuals who first record admission is in FY2000 or later. These individuals are very unlikely to have had a previous episode of care The majority of individuals only have one episode where an individual does have more than one episode of care then the gap between episodes tends to be short Data set had 875,874 individuals Left censoring is deal with using the standard statistical methods

Lifetime Use Survival by Sex
Survival curves for each strata are pairwise statistically different Log-Rank Wilcoxon -2 Log (LR)

Lifetime Use Survival by Age (1)
Survival curves for most strata are pairwise statistically different Exception and groups

Lifetime Use Survival by Age (2)
Survival curves for most strata are pairwise statistically different Exception – 0-64 and groups Tukey-Kramer Adjustment for Multiple Comparisons for the Wilcoxon Test This remains true if also stratified by sex

Lifetime Use Survival by ADL
Survival curves for each strata are pairwise statistically different This remains true if also stratified by sex

Lifetime Use Survival by BEH

Lifetime Use Survival by CHC

Lifetime Use Survival by Severity
Survival curves for each strata are pairwise statistically different This remains true if also stratified by sex (Hazard function shows some of the issues with the broad severity measure.)

Lifetime Use Survival by FY of Admission
Survival curves for different Financial Years are often pairwise statistically different (except if very close)

Lifetime Use – Weibull Model
Analysis of Maximum Likelihood Estimates Parameter DF Estimate Standard Error 95% Confidence Limits Chi-Square Pr > ChiSq Intercept 1 2.1647 2945.7 <.0001 sex Female 0.5033 0.0044 0.4948 0.5119 Male . Age at first admission 0.0003 Financial Year of first admission 0.0637 0.0011 0.0616 0.0658 ADL Score at first admission 0.0002 BEH Score at first admission 0.0017 0.0001 0.0015 0.002 192.77 CHC Score at first admission -0.008 Severity Score at first admission 0.0004 0.001 0.74 0.3896 Scale 1.1207 0.0018 1.1172 1.1241 Weibull Shape (1 = exponential) 0.8923 0.0014 0.8896 0.8951

Lifetime Use – Exponential Model
Analysis of Maximum Likelihood Estimates Parameter DF Estimate Standard Error Chi-Square Pr > ChiSq Hazard Ratio sex Female 1 <.0001 0.629 Age at first admission 1.031 Financial Year of first admission 0.961 ADL Score at first admission 1.013 BEH Score at first admission 0.999 CHC Score at first admission 1.007 Severity Score at first admission 4.1425 0.0418

Females Males

Lifetime Use – Exponential Model
Age at first admission ADL at first admission BEH at first admission CHC at first admission Severity at first admission Financial Year Annual Growth in covariate 0.2% 2.5% 2.7% 6.1% 3.2% 1 LN(Hazard Ratio) increase per pp 0.0072 Effect of each covariate on LN(Hazard Ratio) Change in Hazard Ratio Year on Year

Implications Key points about the premium equation
Premium payment period can be thought of as constant Lifetime risk is close to 1 The key distribution is the distribution of costs – driven by length of stay The distribution of claims is essentially exponential Mean (currently) = 1084 days About $200,000 (only insuring the care cost) Prudential arrangements require insurers to deal with the 75 percentile Ln(4)*Mean = $275,000 Even assuming 40 year premium period - $6,000 per year

Implications Lots of moral hazard Compare two types of insurance
The distribution of claims depends on factors that clients may know about Especially if the premiums are paid over the entire lifetime Compare two types of insurance A policy purchased now to cover costs in 40 or more years time A policy purchased once long temr care has commenced The first product is essentially uninsurable The confidence intervals on projections so far in advance are too wide Making contributions compulsory and publicly financing the scheme (to deal with the liquidity issue) decreases costs of the scheme by at least a third, but still problematic. The second product is easier to price and can use all available information to accurately place a perosnon the right risk curve and so appropriate price

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