The Level and Risk of Out-of-Pocket Health Care Spending Michael D. Hurd RAND and NBER Susann Rohwedder RAND Financial support from the Social Security Administration via a research grant to the Michigan Retirement Research Center is gratefully acknowledged. Additional support from NIA for data development is gratefully acknowledged
Of many issues connected with health care spending here are two:
1. Health care spending and economic preparation for retirement Center for Retirement Research at Boston College • Nearly 45 percent of households are "at risk" of not having enough to maintain their living standards in retirement. • Explicitly including health care in the Index drives up the share of households ‘at risk’ to 61 percent. Yet, Hurd and Rohwedder: actual spending in a life-cycle context find much higher rates of adequate economic preparation
Part of difference may be estimations and projections of out- of-pocket spending health care costs
2. Estimations of models of economic behavior that account for risk (dynamic programming models) Entire distribution of costs, not just mean or median Depending on method, estimations quite sensitive to large outliers
Health and Retirement Study is only adequate vehicle for such studies Want to examine HRS measures of out-of-pocket spending on health care: mean etc but also “outliers,” …large enough to influence mean Do not include spending for health insurance which is predictable (just another life-cycle expense).
Out-of-pocket spending (1000s), mean and percentile points, 2003$. HRS 2004. N = 9089 mean p50 p90 p95 p99 max 65-69 2.1 0.7 3.8 5.9 25.6 420.0 70-74 2.4 0.8 4.5 7.2 28.8 218.3 75-79 2.6 0.9 4.2 6.4 30.1 268.3 80-84 3.0 1.0 5.2 11.3 36.4 180.8 85+ 4.4 1.0 9.6 24.5 60.6 127.2 Total 2.7 0.8 4.8 8.1 36.0 420.0 Can these large values be valid?
Two-year average out-of-pocket spending by households between years t-2 and t and income and wealth (1000s 2003$). HRS 2004. N= 9089 top 1% top 10 obs Mean Median Mean Median OOP spending 115.9 90.2 477.3 434.2 Household income 38.7 24.2 48.9 13.6 Household wealth at t-2 407.1 145 282.9 113.9 Household wealth at t 383.8 134.9 328.8 78.3
Two-year average out-of-pocket spending by households between years t-2 and t and income and wealth top 1% top 10 obs Mean Median Mean Median OOP spending 115.9 90.2 477.3 434.2 Household income 38.7 24.2 48.9 13.6 Household wealth at t-2 407.1 145 282.9 113.9 Household wealth at t 383.8 134.9 328.8 78.3 2*income – Δ W 100.7 58.5 51.9 62.8 Large values cannot be correct: spending could not be financed.
Issues to be investigated • Imputation for missing values • Spending on drugs…hard to measure • Comparison with other surveys • Moment-in-time spending versus panel spending
Role of imputation HRS method 1. Ask whether particular service used (doctor visit). 2. Ask about out-of-pocket spending for that service. 3. If nonresponse with respect to amount spent, amount is bracketed 4. Amount imputed using covariates and bracket Is imputation responsible for outliers?
HRS rate of imputation is 23% in middle of spending distribution. (Any imputation among number of spending categories)(
Two-year out-of-pocket spending, income and wealth of households by top 1% of spenders by whether any spending was imputed in $1000s. Age 65 or older N spending income wealth wealth t-1 t Means no imputations 213 120.4 46.6 537.3 537.0 some imputations 244 90.2 32.1 333.7 282.3 Medians no imputations 213 86.9 34.0 215.1 236.3 some imputations 244 77.3 19.5 87.8 86.3 Large outliers whether imputations or not. Imputations associated with low income and wealth
Spending on drugs particularly difficult to measure. Episodic for some One-year recall best… but recall error Very regular for others Monthly recall best HRS question (hopes to do both) On average, about how much have you paid out-of-pocket per month for these prescriptions in the last two years?
Median annual spending, total and excluding drugs, HRS 2004 1200 1000 800 total 600 exclude drugs 400 200 0 65-69 70-74 75-79 80-84 85+ Total At median most of spending is from drugs
95th percentile of spending 30000 25000 20000 total 15000 exclude drugs 10000 5000 0 65-69 70-74 75-79 80-84 85+ Total Smaller differences as percent, but large differences in absolute value
99th percentile of spending 30000 25000 20000 total 15000 exclude drugs 10000 5000 0 65-69 70-74 75-79 80-84 85+ Total
Out-of-pocket spending on drugs responsible for (some) large values
Compare with other data Medical Expenditure Panel Survey, but noninstitutionalized population only Medicare Current Beneficiary Survey, but age 65 or older only However, both focus on health and health care spending Use greater survey effort Comparison with HRS
Spending by non-nursing home populaton. 65-69 25000 20000 15000 HRS MEPS MCBS 10000 5000 0 mean p90 p95 p99 Mean HRS about twice as large
70-74 30000 25000 20000 HRS 15000 MEPS MCBS 10000 5000 0 mean p90 p95 p99
75-79 25000 20000 15000 HRS MEPS MCBS 10000 5000 0 mean p90 p95 p99
80-84 35000 30000 25000 HRS 20000 MEPS 15000 MCBS 10000 5000 0 mean p90 p95 p99
85+ 30000 25000 20000 HRS 15000 MEPS MCBS 10000 5000 0 mean p90 p95 p99
HRS consistently higher values than either MEPS or MCBS. Comparison between MEPS and MCBS shows no particular pattern
Non-drug spending by non-nursing home population. 65-69 10000 9000 8000 7000 6000 HRS 5000 MEPS MCBS 4000 3000 2000 1000 0 mean p90 p95 p99
70-74 10000 9000 8000 7000 6000 HRS 5000 MEPS MCBS 4000 3000 2000 1000 0 mean p90 p95 p99
75-79 12000 10000 8000 HRS 6000 MEPS MCBS 4000 2000 0 mean p90 p95 p99
80-84 12000 10000 8000 HRS 6000 MEPS MCBS 4000 2000 0 mean p90 p95 p99
85+ 20000 18000 16000 14000 12000 HRS 10000 MEPS MCBS 8000 6000 4000 2000 0 mean p90 p95 p99
Now HRS and MCBS mostly in agreement and higher than MEPS. Same populations? Samples recruited in different ways MEPS much smaller sample sizes N = 367 age 85 or older MCBS: N= 1600 (non-nursing home) HRS : N = 1200 (non-nursing home)
But spending by nursing home population is important Cannot use MEPS
Annual spending including nursing home residents 6000 Means 5000 4000 HRS Medians 3000 MCBS 2000 1000 0 65-69 70-74 75-79 80-84 85+ Total 65-69 70-74 75-79 80-84 85+ Total
Total spending including nursing home population. Age 65-69 30000 25000 20000 HRS 15000 MCBS 10000 5000 0 p50 p90 p95 p99
70-74 35000 30000 25000 20000 HRS MCBS 15000 10000 5000 0 p50 p90 p95 p99
75-79 35000 30000 25000 20000 HRS MCBS 15000 10000 5000 0 p50 p90 p95 p99
80-84 40000 35000 30000 25000 HRS 20000 MCBS 15000 10000 5000 0 p50 p90 p95 p99
85 or older 70000 60000 50000 40000 HRS MCBS 30000 20000 10000 0 p50 p90 p95 p99
HRS higher than MEPS at the upper percentiles in lower age groups. Leads to differences in means but not medians. At higher ages spending on nursing homes relatively more important.
Spending over time Transitions between spending quartiles Use spending transitions to get a qualitative idea of stability of spending
Percent distribution of spending in wave t conditional on spending quartile in wave t-1, HRS waves 1998-2004. Single persons quartile in wave t quartile in lowest 2nd 3rd highest all wave t-1 lowest 58.8 20.8 11.8 8.7 100.0 2 nd 19.9 41.2 24.7 14.1 100.0 3 rd 9.3 23.9 39.9 26.9 100.0 highest 8.6 12.3 24.7 54.5 100.0
Percent distribution of spending in wave t conditional on spending quartile in wave t-1, HRS waves 1998- 2004. Married persons quartile in wave t quartile in lowest 2nd 3rd highest all wave t-1 lowest 47.1 26.4 15.6 11.0 100.0 2 nd 22.2 33.0 26.1 18.8 100.0 3 rd 13.3 24.1 34.3 28.2 100.0 highest 10.9 17.4 26.5 45.1 100.0 Moderate stability at lower and upper quartiles
Conclusions Compared with MEPS and MCBS spending on drugs overstated in HRS. • median, mean and upper percentiles • reason can be traced to survey methods Other types of spending consistent with those surveys HRS modified questions about drugs in 2006, apparently reducing values.
Conclusions (cont.) Impact of spending needs to be put in life-cycle perspective: some wave-to-wave persistence but not complete. Life-cycle risk
Conclusions (cont.) In future data • For over 65 o link to Part D data. But would lack data on Medicare Advantage plans or employer provided insurance) o high end spending reduced by Part D insurance (but not all persons covered) • Under 65 o Further improvements in HRS questionnaire
Conclusions (cont.) Use of past data • Pay attention to outliers • Bayesian shrinking o But not necessarily case that MEPS or MCBS is accurate at high end
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