HLTH 8451 Week 7 Health Outcome Valuation Critique Example

Twelve Million Dollars or Seven Hundred Thousand? Valuing Heat Deaths Avoided Among Frail Nursing Home Residents by Statistical Life and by Quality-Adjusted Life Year

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Walden University

HLTH 8451: Public Policy Analysis

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Twelve Million Dollars or Seven Hundred Thousand? Valuing Heat Deaths Avoided Among Frail Nursing Home Residents by Statistical Life and by Quality-Adjusted Life Year

Why Valuation Decides This Rule

Nearly all the monetized benefits of the heat refuge rule come from deaths avoided. In the provisional figures from the Week 6 design, the performance standard avoids about 27 deaths a year at an annualized cost of about $130 million. Valued at a composite central value per statistical life of $12 million, those deaths are worth about $327 million and net benefits are clearly positive. If the same deaths are instead valued by the life-years they add, the picture changes. Residents protected by the rule have short expected survival: in a national cohort of nursing home decedents, half had died within about five months of placement (Kelly et al., 2010). Assuming each death avoided adds 1.4 years of life and valuing each year at about $520,000, which is the composite $12 million spread over the discounted remaining life of a 40-year-old at 3 percent, each death avoided is worth about $730,000. Benefits then fall to about $21 million and net benefits become negative by roughly $110 million. The method does not refine the answer; it reverses it. This paper examines both approaches and chooses between them.

The Value per Statistical Life

The value per statistical life is not a price on any person's life. It is the sum of what many people would pay to lower their risk of death slightly, expressed per death avoided. If 10,000 people would each pay $1,200 to reduce their annual risk of death by 1 in 10,000, together they would pay $12 million to avoid one expected death. Federal agencies use the value to compare mortality risk reductions with costs, and HHS guidelines for regulatory impact analysis treat it as the standard measure for that purpose (Office of the Assistant Secretary for Planning and Evaluation, 2016).

The evidence behind the number comes mainly from two sources. Labor market studies estimate the wage premiums workers accept for jobs with higher fatal risk, and a large review of those studies found most estimates for US workers in a band of several million dollars (Viscusi & Aldy, 2003). Stated preference studies ask people directly what they would pay for risk reductions. Both draw largely on working-age adults. Workers choosing among jobs are healthy enough to work, decades younger than the residents this rule protects, whose mean age in recent nursing home studies is in the mid-80s (Katz et al., 2026). Applying the value to frail residents is therefore a transfer from one population to a very different one, and no direct study measures what frail nursing home residents would pay to reduce heat risk.

The Quality-Adjusted Life Year

This measure combines length and quality of life: a year of full health is scored 1, while a year in a state judged half as good is scored 0.5. QALYs are the usual outcome measure in cost-effectiveness analysis, which reports the cost per QALY gained rather than a net monetary benefit. They do not belong in cost-benefit analysis unless they are given a monetary value, and that conversion is itself contested. Hammitt (2002) shows that QALYs and willingness to pay rank health outcomes the same way only under restrictive assumptions about preferences, and that they answer different questions: QALYs ask how much health an intervention produces, while willingness to pay asks what that health is worth to those affected.

For this rule, QALYs make the frailty of the beneficiaries explicit. With 1.4 years of added life and a quality weight of 0.55, each death avoided yields about 0.77 QALYs. The performance standard would then gain about 21 QALYs a year at a net cost of about $6.2 million per QALY, far above any threshold commonly used in health technology assessment.

The Question of Age

Any attempt to adjust the value per statistical life for age meets strong objections. In the early 2000s a federal air quality analysis applied a lower value to deaths among people over 70, and public criticism of what was called a senior death discount led to its withdrawal. The empirical question is unsettled. Viscusi and Aldy (2007) found in labor market data that the value varies over the life cycle and declines for older workers, but their data end with working-age adults and say nothing directly about people in their 80s. Some economists argue that older people, with fewer years left, would pay less for risk reductions; others note that wealth and the value placed on remaining time may rise with age. The ethical objection is separate from the empirical one. Treating an 87-year-old's death as worth less than a 40-year-old's is a judgment many would reject regardless of what willingness-to-pay data show. This paper reports the controversy and does not adopt an age adjustment.

The Question of Disability

QALYs face a parallel objection. Because a year lived with impairment receives a quality weight below one, a treatment that extends the life of a person with a disability produces fewer QALYs than the same extension for a person without one. A federal disability council concluded that QALY-based analysis devalues the lives of people with disabilities and recommended against its use in federal health programs (National Council on Disability, 2019). Federal law already bars the Patient-Centered Outcomes Research Institute from developing or using a dollars-per-QALY threshold (42 U.S.C. § 1320e-1). Nearly all nursing home residents live with significant impairment, so this objection applies with full force here. A method that values their remaining time at roughly half of a year in full health builds a judgment about their lives into the calculation.

Choice and Its Costs

Table 1

Two Valuation Approaches Compared for the Heat Refuge Rule

Figures are from the provisional Week 6 model and use composite values.

The main analysis will use an unadjusted value per statistical life, consistent with HHS guidelines. It treats a reduction in the risk of death as equally valuable regardless of age or impairment, which matches how the rule's protection is offered. A QALY-based cost-effectiveness ratio will be reported beside it as a secondary measure. Reporting both will show readers that the verdict depends on the valuation method, which they would not learn if only one were shown.

The choice has costs. The main analysis almost certainly overstates what is gained in years of life, and a reader who believes that remaining life expectancy should count will find the net benefit figure misleading. The secondary measure answers that reader without adopting an adjustment the main analysis rejects. Week 9 will add a life-year approach to the sensitivity analysis so that the reversal shown at the start of this paper is tested directly.

References

Hammitt, J. K. (2002). QALYs versus WTP. Risk Analysis, 22(5), 985-1001. https://doi.org/10.1111/1539-6924.00265

Katz, G. M., Brown, K. A., Giannakeas, V., & Stall, N. M. (2026). Air conditioning in nursing homes and mortality during extreme heat. JAMA Internal Medicine, 186(2), 243-251. https://doi.org/10.1001/jamainternmed.2025.6595

Kelly, A., Conell-Price, J., Covinsky, K., Cenzer, I. S., Chang, A., Boscardin, W. J., & Smith, A. K. (2010). Length of stay for older adults residing in nursing homes at the end of life. Journal of the American Geriatrics Society, 58(9), 1701-1706. https://doi.org/10.1111/j.1532-5415.2010.03005.x

Limitations on certain uses of comparative clinical effectiveness research, 42 U.S.C. § 1320e-1 (2018).

National Council on Disability. (2019). Quality-adjusted life years and the devaluation of life with disability.

Office of the Assistant Secretary for Planning and Evaluation. (2016). Guidelines for regulatory impact analysis. U.S. Department of Health and Human Services.

Viscusi, W. K., & Aldy, J. E. (2003). The value of a statistical life: A critical review of market estimates throughout the world. Journal of Risk and Uncertainty, 27(1), 5-76. https://doi.org/10.1023/A:1025598106257

Viscusi, W. K., & Aldy, J. E. (2007). Labor market estimates of the senior discount for the value of statistical life. Journal of Environmental Economics and Management, 53(3), 377-392. https://doi.org/10.1016/j.jeem.2006.10.005