Cost-benefit analysis (CBA) provides a clear decision rule: undertake an intervention if the monetary value of its benefits exceed its costs. However, due to a reluctance to characterize health benefits in monetary terms, users of cost-utility and cost-effectiveness analyses must rely on arbitrary standards (e.g., < $50,000 per QALY) to deem a program "cost-effective." Moreover, there is no consensus regarding the appropriate dollar value per QALY gained upon which to base resource allocation decisions. To address this, the authors determined the value of a QALY as implied by the value-of-life literature and compared this value with arbitrary thresholds for cost-effectiveness that have come into common use. A literature search identified 42 estimates of the value of life that were appropriate for inclusion. These estimates were classified by method: human capital (HK), contingent valuation (CV), revealed preference/job risk (RP-JR) and revealed preference/non-occupational safety (RP-S), and by U.S. or non-U.S. origin. After converting these value-of-life estimates to 1997 U.S. dollars, the life expectancy of the study population, age-specific QALY weights, and a 3% real discount rate were used to calculate the implied value of a QALY. An ordinary least-squares regression of the value of a QALY on study type and national origin explained 28.4% of the variance across studies. Most of the explained variance was attributable to study type; national origin did not significantly affect the values. Median values by study type were $24,777 (HK estimates), $93,402 (RP-S estimates), $161,305 (CV estimates), and $428,286 (RP-JR estimates). With the exception of HK, these far exceed the "rules of thumb" that are frequently used to determine whether an intervention produces an acceptable increase in health benefits in exchange for incremental expenditures.
Long-term care resources would be allocated more cost-effectively if care planning and medical/functional eligibility decisions were grounded more firmly in extant evidence regarding the risk of nursing home placement, hospitalization, functional impairment, and mortality. This article synthesizes the studies that longitudinally assess the predictors of each of these outcomes for the 65 and older population in the United States. A database was assembled containing 167 multivariate analyses abstracted from 78 journal articles published between 1985 and 1998. Findings show that 22 risk factors consistently predict two or more outcomes, including three that predict all four: worse performance on physical function measures not based on activities of daily living, greater illness severity, and prior hospital use. Findings should help prioritize variable selection choices of those setting eligibility criteria, allocating care resources, and doing descriptive studies. Gaps are shown to exist in the understanding of outcome effects of facility, market, policy, and other system attributes.
For almost three decades researchers have sought to quantify the benefits of home and community care for the elderly, invariably assuming that such care would be an economical substitute for institutionalization. Twenty-seven studies that met rigorous criteria of design, size, and subject were analyzed and the results were synthesized to address the effects on institutional utilization and expenditures, and patient health status and well-being. Home- and community-based health care services are shown to raise overall utilization and costs. Health status effects are limited primarily to patient and caregiver contentment and reduction of unmet needs. Recommendations are made for reaping this considerable benefit more efficiently.
The Arizona Long-Term Care System is the first capitated, long-term care Medicaid program in the nation to operate statewide. It promotes an extensive home and community-based services program intended to lower long-term care costs by substituting home care for institutional care. Because the program is statewide, finding a suitable control group to evaluate it was a serious problem. A substitute strategy was chosen that compares actual costs incurred to an estimate of what costs would have been in the absence of home and community-based (HCB) services. To estimate the likelihood of institutionalizing clients in the absence of HCB services, coefficients for institutionalization risk factors were estimated in a logistic regression model developed using national data. These were applied to characteristics of Arizona clients. The model assigned approximately 75 percent of the program's clients to a category with traits that were determined to resemble nursing home residents' traits. A similar methodology was used to estimate lengths of nursing home stays. Lengths of stay by the program's nursing home patients were regressed on their characteristics using an event history analysis model. Coefficients for these characteristics from the regression analysis were then applied to HCB services clients to estimate how long their nursing home stays would have lasted, had they been institutionalized. These estimated nursing home stays were generally shorter than these same patients' observed home and community stays. Risk of institutionalization was then multiplied by estimated length of stay and by monthly nursing home costs to estimate what costs would have been without the HCB services option. The expected costs were compared to actual costs to judge cost savings. Home and community-based services appeared to save substantial amounts on costs of nursing home care. Estimates of savings were very robust and did not appear to be declining as the program matured. Savings probably came from several sources: the assessment teams that judged client eligibility were employed by a state agency and thus were independent from the program contractors; clients were required to be in need of at least a three-month nursing home stay; a cap was placed on the number of HCB services clients contractors were allowed to serve each month; the capitated payment methodology forced managed care contractors to hold down average HCB services costs or lose money; and the HCB services and nursing home costs were blended in the capitated rate, so that plans that failed to place clients in HCB services would lose money by using more nursing home days than their monthly capitated rate allowed.
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