Hidden Costs, Value Lost Uninsurance in America (2003) / Chapter Skim
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Appendix B: Coverage Does Matter: The Value of Health Forgone by the Uninsured
Appendix B: Coverage Does Matter: The Value of Health Forgone by the Uninsured
Pages 129-169
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From page 129... ...
This paper examines the loss in health capital the imputed dollar value of health that individuals will have over their remaining lifetimes that accrues to society from this lack of health insurance. To measure this, I apply a variation of the methodology previously developed by Cutler and Richardson (1997)
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From page 130... ...
These numbers add up to an extremely large social cost. Reasonable, conservative estimates of the total cost to society offorgone health are between $250 billion and $3.3 trillion, depending on the assumptions about lifetime insurance status.
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From page 131... ...
I use the estimates from Fronstin to determine the size and age distribution of the population of interest, multiplying the proportion of individuals without insurance at a given age by the sex- and year of age-specific population for 2000 (CPS, 2001~. I assume that the age-group probability of being uninsured applies to the midpoint of the age range, and extrapolate linearly for individual years within categories.3 The comparison group of interest is individuals with private health insurance coverage.
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From page 132... ...
On the other hand, insurance status is not a random draw every year. The latter scenario captures the fact that the probability of being without health insurance is not constant across the lifespan.
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From page 133... ...
. A year in a health state with a particular HRQL weight is referred to as a "quality-adjusted life year," or QALY (Zeckhauser and Shepard, 1976~.
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From page 134... ...
We are making two different assumptions about future insurance status without the intervention. In the first case, we are assuming that an uninsured person would have remained in that state until he or she reaches age 65.
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From page 135... ...
Interest rates will be higher than the discount rate for utility for a number of reasons, including taxes and risk. I use a range of discount rates that are in line with the range considered appropriate in the health literature (Lipscomb et al., 1996~: 0 percent, 3 percent, and 6 percent.
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From page 136... ...
1OKeeler (2001) also points out that correcting for the value of leisure time brings human capital estimates of the value of a life close to estimates obtained from the revealed preference and contingent valuation methods.
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From page 137... ...
Furthermore, both labor and consumer market studies suffer from possible omitted variable bias due to the inherent difficulty in identifying other job or product amenities that may be highly correlated with lower risk jobs or products (Viscusi, 1993; Gerking et al., 1988~. For example, someone who buys a smoke detector is not simply purchasing a reduction in the risk of death, but also a decreased chance of injury, property damage, and psychological harm.
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From page 138... ...
the question be framed as a tax referendum or some other type of compulsory payment scheme. One criticism of both the human capital and contingent valuation methods is that they value individuals with low income or wealth less than those with high income or wealth.
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From page 139... ...
If age was not reported in the study, they assumed an average age of 40, with a sensitivity analysis ranging from 35 to 45. They then applied age-specific HRQL weights from the literature and assumed a 3 percent discount rate (with sensitivity analysis using 0, 5, and 7 percent)
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From page 140... ...
human capital method yields a value per life year of $41,000, while the revealedpreference-fortob-risk studies have a median value of $685,000 per life year. As discussed earlier, I believe that the contingent valuation is the theoretically correct methodology for valuing life and health changes and use a benchmark value of $160,000 for a year in perfect health.l3 Assuming a 3 percent discount rate and a life expectancy at birth of 76 years, this translates into a value of $4.8 million for a life.
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From page 141... ...
On one hand, people may opt out of insurance because they are relatively healthy and believe they do not need coverage; on the other hand, people may lose their health insurance as a result of being sick. Evidence from two studies that explicitly try to control for unobserved heterogeneity suggests that there is some causal effect of insurance status on mortality.
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From page 142... ...
finds no significant difference in infant mortality among unmarried mothers. She argues that the differences can be explained by better hospital resources overall, rather than better access to prenatal or postnatal care.l6 To incorporate mortality into the life tables, I assume that the cumulative risk of mortality is 25 percent higher for the uninsured of both sexes from age 1 until age 65.17 Because of the uncertainty in the literature about the causes of the infant mortality differential, I make the conservative assumption that there is no difference in infant mortality by insurance status.
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From page 143... ...
Table B.3 presents life expectancy conditional on reaching selected ages, by sex and insurance status. The first column is the comparison group, individuals who have private insurance coverage from age a until they reach 65.
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From page 144... ...
The magnitude of the difference declines with age because remaining life expectancy also declines with age. Using a 6 percent discount rate, an insured infant has between $1,000 and $14,000 more health capital than an uninsured infant.
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From page 146... ...
Men 0 270 41 14 2 18 238 37 21 4 25 222 32 26 5 35 199 26 35 5 45 167 21 45 7 113 14 45 6 Women 0 180 27 9 1 18 152 24 11 2 25 145 22 15 2 35 133 20 21 3 45 113 17 28 4 55 77 12 29 4 NOTE: Calculations assume a value of a life year of $160,000. the 3 percent discount rate, the differences actually increase with age as the mortality differentials become more imminent.
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From page 147... ...
147 _` so o o V, V, o s°¢ ¢ 4~ g V, .
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From page 148... ...
The last two columns of Table B.4 show the gain in health capital per year of health insurance provided for various ages, using the years of life approach. For a 25-year-old male, the gain in health capital for each year of insurance is $2,200 under the average probability of insurance scenario, and $3,000 under the continually uninsured scenario.
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From page 149... ...
Even if we control for a variety of characteristics, however, it is not necessarily the case that equalizing insurance status will eliminate the remaining difference in health capital. One reason for this is that we might expect some of the adverse effects of going without coverage to persist or have long-term implications.
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From page 150... ...
Despite the overwhelming evidence that being uninsured is not good for overall health or access to care, however, the literature that specifically addresses the components of health capital is relatively sparse. In particular, there are few high-quality studies that examine differences in the incidence or prevalence of disease by insurance status, or differences in quality of life for a particular disease or condition.
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From page 151... ...
Nevertheless, there may be unobservable characteristics that confound the relationship between insurance status and health outcomes. If the uninsured differ from the insured along dimensions that are correlated with worse health, then this approach is likely to yield estimates of the difference in health capital by insurance status that are too large.
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From page 152... ...
database are nationally representative sources of disease prevalence for measuring chronic conditions. The NHIS offers a cross-sectional sample of the noninstitutionalized U.S.
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From page 153... ...
One question of interest in this analysis is whether the observed burden of disease varies by insurance status. We can use the NHIS to observe net differences in reported disease prevalence by insurance status, although there is not enough information to examine the underlying mechanisms in detail.
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From page 154... ...
Disease prevalence differed significantly by insurance status in approximately one-quarter of the age-sex cells. In about half the significant cases the uninsured were significantly less likely to have a particular disease.
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From page 155... ...
I was not able to detect a statistically significant difference in prevalence by insurance status for most of the diseases, but the table generally supports the theory that the uninsured are more likely to have diseases that could be prevented or minimized with appropriate care. Stroke and emphysema are both much more common among the uninsured ages 45 to 54, and pain and bronchitis are more prevalent among the uninsured ages 35 to 44.
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From page 156... ...
The QALY literature often demonstrates that people in poor health rate their health status higher than do healthy people who are asked how they would rate their health if they were in that state (for example, see Epstein et al., 1989; Najman and Levine, 1981; and Sackett and Torrance, 1978~. In previous work, both of these problems were addressed by taking a different approach toward estimating the HRQL weights (Cutler and Richardson, 1997~.
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From page 157... ...
The condition pairs that were included in the final model were heart disease and joint pain, heart disease and other pain, heart disease and stroke, diabetes and stroke, joint pain and other pain, poor hearing and poor vision, poor hearing and diabetes, and poor hearing and cancer. It is also possible that the HRQL weights could vary by insurance status.
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From page 158... ...
The relative magnitudes of the HRQL weights look plausible. More serious conditions such as diabetes, stroke, and mental health disorders tend to have lower HRQL weights than less severe conditions such as poor vision or poor hearing.
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From page 159... ...
poor vision -0.097 (0.032) -0.01 Poor hearing*
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From page 160... ...
0.89 Mental health disorders -0.821 (0.038) 0.88 Uninsured (change in QALY weight)
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From page 162... ...
Don V~` ..\~4 Age FIGURE B.1 HRQL weights by insurance status, age 0-64. picking up the impact of omitted factors that are correlated with insurance status and health.
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From page 163... ...
Health Capital Estimates: Upper-Bound DALY Approach Table B.10 shows the analogous results to Table B.9 when we allow the HRQL weight to differ by insurance status. As expected, the differences in health
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From page 165... ...
These results are quite sensitive to the discount rate. The overall range of estimates we get by varying the discount rate from O to 6 percent widens to $8,000 to $354,000 for a newborn who will remain uninsured until age 65, and to a range of $1,000 to $55,000 for an uninsured newborn who faces the average probability of being uninsured each year.
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From page 166... ...
If we suddenly gave a lifetime of health insurance to the 40 million people who are currently uninsured, how much health would we gain? One key question is how much of the difference in health capital or other measures of health status would disappear and how much would persist.
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From page 167... ...
Health capital, YOL approach Health capital, lower-bound QALY approach Health capital, upper-bound QALY approach 0% discount rate Total cost ($ millions) Health capital, YOL approach Health capital, lower-bound QALY approach Health capital, upper-bound QALY approach 6% discount rate Total cost ($ millions)
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From page 168... ...
For example, the results are sensitive to the choice of discount rate, the value of a life year, the magnitude of any mortality reduction, and estimates of the HRQL weight. Also, we cannot be certain how much of the difference in observed health capital can truly be attributed to lack of insurance coverage.
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From page 169... ...
APPENDIX B 169 only divide the health capital estimate of interest by $160,000 and multiply it by a different value for a single life year. Even by conservative estimates, the lost health capital due to lack of health insurance is substantial.
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