Measuring and Valuing Health Outcomes
Expert-defined terms from the Advanced Certificate in Economic Evaluation in Public Health Interventions course at LearnUNI. Free to read, free to share, paired with a professional course.
Absolute Risk Reduction (ARR) #
The difference in event rates between a control group and an intervention group. Related terms: Relative Risk Reduction (RRR), Number Needed to Treat (NNT). ARR quantifies the actual decrease in risk attributable to an intervention, expressed as a proportion. Example: If 10 % of patients in a control arm experience a heart attack versus 6 % in the treatment arm, the ARR is 4 % (0.04). Practical application: ARR is used to translate statistical effects into concrete health benefits for decision‑makers, informing resource allocation and patient counseling. Challenges: ARR can appear small even when relative effects are large; it is sensitive to baseline risk, which may vary across populations, complicating generalisation.
Adverse Event (AE) #
Any undesirable experience associated with the use of a health intervention. Related terms: Serious Adverse Event (SAE), Adverse Drug Reaction (ADR). AEs are measured through surveillance systems, clinical trials, or post‑marketing registries. Example: A vaccine trial records mild fever as an AE, while anaphylaxis would be classified as an SAE. Practical application: Incorporating AE costs and disutility into economic evaluations ensures that harms are weighed against benefits. Challenges: Under‑reporting, varying severity grading, and assigning appropriate utility decrements can bias cost‑effectiveness results.
Aggregate Preference #
The summed preferences of a population for different health states, often derived from utility elicitation techniques. Related terms: Individual Preference, Social Welfare Function. Aggregate preference is used to produce population‑level utility weights for measures such as QALYs. Example: A national health agency conducts a population‑based EQ‑5D survey, aggregating individual utilities to obtain average values for each health state. Practical application: Provides a common metric for comparing interventions across disease areas. Challenges: Heterogeneity in values, cultural differences, and the ethical debate over whether individual utilities should be summed without weighting.
Benefit–Cost Ratio (BCR) #
The ratio of the monetary value of benefits to the monetary cost of an intervention. Related terms: Cost‑Benefit Analysis (CBA), Net Benefit. BCR > 1 indicates that benefits exceed costs. Example: A smoking‑cessation program yields $2.5 million in health savings for a $1 million investment, giving a BCR of 2.5. Practical application: Policymakers use BCR to prioritise projects when budgets are constrained. Challenges: Valuing health benefits in monetary terms often relies on willingness‑to‑pay estimates, which can be controversial and sensitive to income distribution.
Cost #
Effectiveness Analysis (CEA): An economic evaluation that compares the relative costs and outcomes (often expressed in natural units) of two or more interventions. Related terms: Cost‑Utility Analysis (CUA), Incremental Cost‑Effectiveness Ratio (ICER). CEA calculates the additional cost per additional unit of effect, such as cost per life‑year saved. Example: Comparing a new antihypertensive drug to standard therapy yields an ICER of $15 000 per life‑year gained. Practical application: CEA informs reimbursement decisions and guideline development. Challenges: Selecting appropriate outcome measures, handling uncertainty, and choosing a willingness‑to‑pay threshold that reflects societal preferences.
Cost #
Utility Analysis (CUA): A form of CEA that uses utility‑based outcomes, typically QALYs or DALYs, to capture both quantity and quality of life. Related terms: Quality‑Adjusted Life Year (QALY), Disability‑Adjusted Life Year (DALY). CUA expresses results as cost per QALY gained. Example: An oral vaccine costs $200 per dose and yields 0.03 QALYs per child, producing an ICER of $6 667 per QALY. Practical application: CUA is the preferred method for health technology assessment agencies in many countries. Challenges: Utility measurement methods vary, and societal willingness‑to‑pay thresholds differ across jurisdictions.
Cost‑Benefit Analysis (CBA) #
An evaluation that translates both costs and health outcomes into monetary terms, allowing direct comparison. Related terms: Benefit–Cost Ratio (BCR), Willingness‑to‑Pay (WTP). CBA often uses contingent valuation or revealed preference methods to estimate the monetary value of health gains. Example: A community water fluoridation program is assigned a monetary benefit of $5 million from reduced dental disease, against a cost of $1 million, yielding a net benefit of $4 million. Practical application: CBA can be integrated into broader public‑policy appraisals that include non‑health sectors. Challenges: Monetisation of health outcomes can be ethically contentious and may undervalue benefits for low‑income groups.
Cost‑Effectiveness Threshold (CET) #
The maximum amount a decision‑maker is willing to pay for a unit of health gain (e.g., per QALY). Related terms: Willingness‑to‑Pay (WTP), Incremental Cost‑Effectiveness Ratio (ICER). CETs guide interpretation of ICERs; an ICER below the CET is considered cost‑effective. Example: In the United Kingdom, the National Institute for Health and Care Excellence commonly uses £20 000–£30 000 per QALY as the CET. Practical application: Provides a benchmark for health technology assessment bodies. Challenges: CETs may not reflect opportunity costs, can be politically driven, and differ between regions, leading to inconsistent decisions.
Cost‑Effectiveness Plane #
A graphical representation that plots incremental costs on the vertical axis and incremental effectiveness on the horizontal axis. Related terms: Incremental Cost‑Effectiveness Ratio (ICER), Dominance. The plane divides outcomes into four quadrants, indicating whether an intervention is more effective, less costly, or both. Example: An intervention located in the north‑east quadrant is more effective but also more costly; its ICER determines acceptability. Practical application: Visualises uncertainty through cost‑effectiveness acceptability curves. Challenges: Interpretation requires statistical expertise, and the plane does not capture multi‑dimensional outcomes such as equity.
Cost‑Effectiveness Acceptability Curve (CEAC) #
A plot that shows the probability an intervention is cost‑effective across a range of willingness‑to‑pay thresholds. Related terms: Probabilistic Sensitivity Analysis (PSA), Cost‑Effectiveness Plane. CEACs are derived from Monte Carlo simulations of uncertain parameters. Example: A CEAC indicates a 75 % probability of cost‑effectiveness at $30 000 per QALY. Practical application: Helps decision‑makers assess robustness of results under uncertainty. Challenges: CEACs can be misinterpreted as providing definitive thresholds; they depend on the chosen distribution of parameters.
Cost‑Effectiveness Frontier #
The set of non‑dominated interventions that provide the greatest health benefit for each level of cost. Related terms: Dominance, Incremental Cost‑Effectiveness Ratio (ICER). The frontier is identified by ordering interventions by increasing cost and eliminating those that are less effective and more costly than another option. Example: In a vaccination programme, three vaccines are compared; the frontier includes the cheapest vaccine with acceptable efficacy and the most effective vaccine despite higher cost. Practical application: Guides resource allocation by highlighting efficient choices. Challenges: Data quality, heterogeneity in study populations, and changes in technology can shift the frontier over time.
Cost‑Effectiveness Threshold (CET) – Country‑Specific #
Specific monetary values established by national health authorities. Related terms: Willingness‑to‑Pay (WTP), Opportunity Cost. For instance, Canada commonly references CAD 50 000 per QALY, while Brazil may use three times the gross domestic product per capita. Example: An intervention with an ICER of CAD 45 000 per QALY would be deemed cost‑effective in Canada. Practical application: Aligns economic evaluation with local fiscal constraints. Challenges: Thresholds may become outdated as health systems evolve, and they may not reflect true opportunity costs or equity considerations.
Cost‑Effectiveness Threshold – Incremental #
A dynamic threshold that reflects the marginal productivity of the health system rather than a fixed monetary value. Related terms: Opportunity Cost, Marginal Cost‑Effectiveness Ratio. This approach estimates the health gain forgone by allocating resources to a new intervention. Example: If the average cost per QALY in current programmes is $12 000, the incremental threshold would be set around this figure. Practical application: Provides a more realistic benchmark for new technologies. Challenges: Requires robust data on existing programme efficiency, which may be unavailable in many settings.
Cost‑Effectiveness Threshold – Willingness‑to‑Pay (WTP) #
The amount an individual or society is prepared to exchange for a health gain. Related terms: Contingent Valuation, Discrete Choice Experiment (DCE). WTP is elicited through surveys that ask respondents how much they would pay for a specified health improvement. Example: A DCE finds an average WTP of $25 000 for a one‑year gain in perfect health. Practical application: Informs the setting of CETs where no official threshold exists. Challenges: Responses can be biased by income, framing effects, and hypothetical bias.
Cost‑Effectiveness Threshold – Opportunity Cost #
The health outcomes forgone when resources are diverted from existing services to a new intervention. Related terms: Marginal Productivity, Budget Impact Analysis. Estimating opportunity cost requires knowledge of the health system’s current efficiency. Example: If the health system can produce 0.5 QALYs per $10 000 spent, the opportunity cost of a new drug costing $20 000 per QALY is 0.25 QALYs. Practical application: Aligns decisions with the principle of maximizing health gain from limited resources. Challenges: Data scarcity, variation across regions, and the difficulty of quantifying non‑health benefits.
Cost‑Effectiveness Threshold – Societal Perspective #
A threshold that incorporates both health sector costs and broader societal costs (e.g., productivity losses). Related terms: Societal Willingness‑to‑Pay, Full Economic Evaluation. When a societal perspective is adopted, the threshold may be higher because it captures additional benefits. Example: A mental‑health intervention that reduces absenteeism may be deemed cost‑effective at a higher threshold due to productivity gains. Practical application: Encourages inclusion of cross‑sectoral impacts in decision‑making. Challenges: Valuing non‑health outcomes consistently and avoiding double‑counting.
Cost‑Utility Ratio (CUR) #
The ratio of cost to utility‑based outcomes, such as cost per QALY or cost per DALY averted. Related terms: Incremental Cost‑Effectiveness Ratio (ICER), Cost‑Effectiveness Analysis (CEA). CUR provides a single figure summarising the efficiency of an intervention. Example: A surgical procedure costs $30 000 and yields 2 QALYs, giving a CUR of $15 000 per QALY. Practical application: Facilitates comparison across interventions with different clinical endpoints. Challenges: Sensitivity to utility measurement, discounting, and the choice of time horizon.
Cost‑Utility Analysis (CUA) – Markov Model #
The use of a Markov modelling framework to estimate costs and QALYs over time, accounting for transitions between health states. Related terms: Markov Process, Transition Probabilities. Markov models are especially useful for chronic diseases with recurring events. Example: A CUA of a hepatitis‑C therapy models yearly transitions among “chronic infection,” “cirrhosis,” “liver cancer,” and “death.” Practical application: Captures long‑term costs and health outcomes beyond trial follow‑up. Challenges: Requires reliable transition data, can become complex with many health states, and may suffer from the “memoryless” assumption.
Cost‑Utility Analysis – Time Horizon #
The period over which costs and health outcomes are measured in a CUA. Related terms: Discount Rate, Lifetime Horizon. A longer horizon captures more downstream benefits but increases uncertainty. Example: A vaccine’s benefits may accrue over a lifetime, so a lifetime horizon is appropriate. Practical application: Ensures that interventions with delayed benefits are not undervalued. Challenges: Selecting an appropriate horizon, handling extrapolation, and applying consistent discount rates.
Cost‑Utility Analysis – Discount Rate #
The annual rate used to convert future costs and health outcomes into present values. Related terms: Time Preference, Present Value. Standard practice often applies a 3 % discount rate to both costs and QALYs, though some guidelines recommend differential rates. Example: Discounting a QALY gained ten years from now at 3 % reduces its present value to about 74 % of its nominal value. Practical application: Reflects societal preference for immediate benefits over future ones. Challenges: Choice of rate influences results; higher rates penalise interventions with long‑term benefits, potentially biasing against preventive measures.
Cost‑Utility Analysis – Sensitivity Analysis #
Examination of how results change when key parameters vary. Related terms: Deterministic Sensitivity Analysis, Probabilistic Sensitivity Analysis (PSA). Sensitivity analysis identifies drivers of uncertainty and tests robustness. Example: Varying the utility weight for a health state from 0.7 to 0.9 alters the ICER by 15 %. Practical application: Provides decision‑makers with a range of plausible outcomes. Challenges: Requires transparent reporting, selection of appropriate distributions, and may be computationally intensive for complex models.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) Threshold #
The monetary amount a society is prepared to pay for one additional QALY. Related terms: Cost‑Effectiveness Threshold (CET), Incremental Cost‑Effectiveness Ratio (ICER). The WTP threshold determines whether an ICER is acceptable. Example: An ICER of $45 000 per QALY is deemed cost‑effective in a country with a $50 000 WTP threshold. Practical application: Aligns economic evaluation with policy criteria. Challenges: Determining an appropriate threshold that reflects budget constraints and societal values.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) Elicitation #
Methods used to estimate how much individuals value health gains. Related terms: Contingent Valuation, Discrete Choice Experiment (DCE). Common techniques include surveys that ask respondents to state a maximum price they would pay for a health improvement. Example: A DCE finds an average WTP of $30 000 for a 0.5 QALY gain. Practical application: Supplies empirical data for setting CETs in the absence of official benchmarks. Challenges: Respondent bias, income effects, and the hypothetical nature of stated preferences.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Risk Reduction #
The monetary value individuals assign to a reduction in the probability of adverse health outcomes. Related terms: Value of a Statistical Life (VSL), Risk Premium. WTP for risk reduction can be derived by dividing the monetary amount by the risk reduction magnitude. Example: If a person would pay $100 to lower their 1 % chance of disease, the implied value per statistical life is $10 000. Practical application: Used in environmental health, occupational safety, and public‑health policy to monetize risk reductions. Challenges: Small risk changes may be difficult for respondents to comprehend, leading to inconsistent valuations.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Cost‑Effectiveness Threshold (CET), Value of a Statistical Life Year (VSLY). WTP per QALY can be derived by dividing the VSL by the average remaining life expectancy. Example: If VSL is $5 million and average remaining life expectancy is 30 years, the implied WTP per QALY is about $166 667. Practical application: Provides a theoretical basis for setting CETs. Challenges: VSL estimates vary widely across studies, and translating VSL to QALY values may oversimplify complex preferences.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Preventive Interventions… #
Related terms: Preventive Value, Discounted WTP. Preventive WTP often exceeds the cost of the intervention, justifying investment. Example: A community receives a survey indicating a $200 WTP for a daily vitamin D supplement that reduces osteoporosis risk. Practical application: Supports funding for vaccination, screening, and health‑promotion programmes. Challenges: Anticipated benefits may be uncertain, and respondents may overstate willingness due to social desirability bias.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Treatment #
The monetary amount individuals would allocate to obtain a therapeutic benefit. Related terms: Treatment Value, Health State Valuation. WTP for treatment can be elicited directly or inferred from market prices. Example: Patients with chronic pain indicate a $500 monthly WTP for a new analgesic that offers a 0.2 QALY gain per year. Practical application: Helps gauge patient‑centred value and informs price negotiations. Challenges: Heterogeneity in ability to pay, ethical concerns about equity, and potential for preference reversal.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Treatment Adherence #
The monetary value individuals assign to interventions that improve medication adherence. Related terms: Adherence Incentives, Behavioural Economics. WTP can be used to design incentive schemes. Example: A study finds patients are willing to pay $30 per month for a reminder app that improves adherence by 15 %. Practical application: Guides design of cost‑effective adherence programmes. Challenges: Measuring actual adherence improvements, accounting for long‑term benefits, and ensuring incentives do not create unintended consequences.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Telehealth #
The amount patients or societies would pay for remote health services. Related terms: Digital Health Valuation, Access to Care. WTP studies often compare telehealth to in‑person visits. Example: Rural patients indicate a $40 WTP per virtual consultation, reflecting saved travel time and costs. Practical application: Supports reimbursement policies for telemedicine. Challenges: Variation in technology acceptance, quality perception, and the need to adjust for internet access disparities.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Vaccination #
The monetary amount individuals would allocate to receive a vaccine. Related terms: Vaccine Acceptance, Risk Perception. WTP can be influenced by disease severity, vaccine efficacy, and trust in health authorities. Example: During an influenza season, surveys reveal a $25 WTP per vaccine dose among adults aged 65+. Practical application: Informs pricing strategies and subsidy levels. Challenges: Seasonal variability, misinformation, and differing health literacy levels.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Wellness Programs #
The amount participants would pay for programmes that promote health and prevent disease. Related terms: Health Promotion, Preventive Valuation. WTP may be measured through contingent valuation or revealed preference approaches. Example: Employees report a $150 WTP for a corporate wellness package that includes fitness classes and nutrition counseling. Practical application: Helps employers decide on investment levels for employee health initiatives. Challenges: Attribution of health benefits to specific components, potential selection bias, and variability in perceived value.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Whole‑Population Interve… #
Related terms: Public Goods Valuation, Collective Benefit. WTP can be estimated using travel cost methods, hedonic pricing, or contingent valuation. Example: Residents of a city express a $10 million WTP for a new public park that improves air quality and recreation. Practical application: Justifies public‑sector investment in infrastructure that yields health benefits. Challenges: Capturing non‑market benefits, dealing with free‑rider problems, and ensuring representativeness of survey samples.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Life‑Extending Treatment… #
Related terms: Value of a Statistical Life (VSL), Life‑Year Valuation. WTP can be derived from labour market data or survey methods. Example: A study estimates that individuals would pay $50 000 for a therapy that extends life expectancy by one year in a terminal illness. Practical application: Informs pricing of high‑cost oncology drugs. Challenges: Ethical concerns about placing a price on life, heterogeneity in preferences, and the influence of cultural attitudes toward death.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Cost‑Effectiveness Threshold (CET), Value of a Statistical Life Year (VSLY). WTP per QALY can be estimated by dividing VSL by average remaining life expectancy. Example: If VSL is $6 million and average remaining life expectancy is 40 years, the implied WTP per QALY is $150 000. Practical application: Provides empirical grounding for CET setting. Challenges: VSL estimates vary across studies, may not reflect health‑specific preferences, and can be sensitive to income distribution.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Equity‑Weighted WTP, Threshold Adjustments. Empirical studies often find lower WTP per QALY compared with high‑income nations. Example: A study in a low‑income country reports a WTP of $500 per QALY, reflecting limited disposable income. Practical application: Guides appropriate threshold selection for health technology assessment in low‑resource settings. Challenges: Ensuring that low WTP values do not perpetuate inequities, accounting for purchasing power parity, and integrating societal preferences beyond income.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Equity‑Weighted Cost‑Effectiveness, Social Value Judgment. Equity weights can be applied to QALYs to reflect societal preferences for reducing health inequalities. Example: An equity weight of 1.5 is applied to QALYs gained by low‑income populations, effectively raising the WTP per QALY for those groups. Practical application: Supports decision‑makers who aim to balance efficiency with fairness. Challenges: Determining appropriate equity weights, potential conflict with efficiency goals, and the difficulty of obtaining consensus on societal values.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Present Value of QALYs, Time Preference. Discounting reduces the present value of QALYs accrued in later years, affecting cost‑effectiveness calculations. Example: A QALY gained ten years from now, discounted at 3 %, is valued at 0.74 of its undiscounted value, lowering the implied WTP per QALY for long‑term interventions. Practical application: Ensures consistency between cost and outcome discounting. Challenges: Choosing an appropriate discount rate, handling divergent discount rates for costs versus health outcomes, and communicating the effect of discounting to stakeholders.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Probabilistic Sensitivity Analysis (PSA), Threshold Analysis. Analysts test a range of WTP values to see at which points an intervention becomes cost‑effective. Example: A PSA shows a 60 % probability that a new drug is cost‑effective at a WTP of $30 000 per QALY, rising to 85 % at $50 000 per QALY. Practical application: Provides decision‑makers with a nuanced view of uncertainty. Challenges: Communicating probabilistic results, selecting a plausible range of WTP values, and avoiding misinterpretation of probability as certainty.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Subgroup Analysis, Preference Heterogeneity. Different age, gender, or cultural groups may assign distinct monetary values to QALYs. Example: Elderly respondents express a higher WTP per QALY ($60 000) than younger adults ($30 000). Practical application: Enables tailored threshold setting for specific programmes (e.g., geriatric care). Challenges: Data collection burden, risk of inequitable resource allocation, and the need for transparent weighting procedures.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Value of a Statistical Life Year (VSLY), Health Economic Valuation. VSL provides a monetary value for mortality risk reductions; dividing by average life expectancy yields a per‑QALY figure. Example: A VSL of $7 million and an average remaining life expectancy of 35 years imply a WTP of $200 000 per QALY. Practical application: Offers a theoretically grounded approach to setting CETs when direct WTP data are unavailable. Challenges: VSL estimates vary widely, may not reflect health‑specific preferences, and can be influenced by income and cultural factors.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Contingent Valuation, Discrete Choice Experiment (DCE), Standard Gamble. Each method has strengths and limitations regarding realism, cognitive burden, and bias. Example: A DCE yields a WTP per QALY of $45 000, while a contingent valuation approach produces $55 000 for the same health state. Practical application: Enables selection of appropriate elicitation technique based on context and resources. Challenges: Methodological consistency, respondent understanding, and translating stated preferences into reliable threshold values.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
g., lifestyle changes). Related terms: Non‑Monetary Benefits, Opportunity Cost. Even zero‑cost interventions can have associated resource use (e.g., time). Example: A community walking programme requires volunteer time, which can be valued using a WTP approach to estimate the implicit cost. Practical application: Captures the full economic impact of preventive measures. Challenges: Assigning monetary values to time, dealing with intangible benefits, and ensuring comparability with costed interventions.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Present Value of QALYs, Discount Rate. Discounting reduces the present value of future QALYs, thereby affecting the implied WTP per QALY. Example: A QALY gained 15 years from now, discounted at 3 %, is worth 0.64 of its undiscounted value, lowering the effective WTP per QALY for long‑term interventions. Practical application: Aligns WTP estimates with standard economic evaluation practice. Challenges: Selecting appropriate discount rates, handling divergent rates for costs versus outcomes, and communicating the impact of discounting to non‑technical audiences.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Equity‑Weighted Cost‑Effectiveness, Social Value Judgment. Equity weights can be applied to QALYs gained by disadvantaged groups, effectively raising the WTP for those gains. Example: An equity weight of 1.3 is applied to QALYs for low‑income populations, increasing the effective WTP from $30 000 to $39 000 per QALY. Practical application: Supports policy decisions that balance efficiency with fairness. Challenges: Determining appropriate equity weights, potential conflict with efficiency goals, and achieving consensus on societal values.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Differential Discounting, Time Preference. Some guidelines recommend a lower discount rate for health benefits than for costs. Example: Applying a 1.5 % discount rate to QALYs and a 3 % rate to costs can increase the cost‑effectiveness of preventive interventions. Practical application: Reflects the societal value placed on future health. Challenges: Lack of consensus on appropriate rates, increased complexity in analysis, and potential for inconsistent comparisons across studies.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Threshold Analysis, Incremental Cost‑Effectiveness Ratio (ICER). Analysts may present results for multiple thresholds to illustrate robustness. Example: An intervention with an ICER of $40 000 per QALY is cost‑effective at a $50 000 threshold but not at $30 000. Practical application: Provides policymakers with transparent information on the impact of threshold selection. Challenges: Communicating uncertainty, avoiding arbitrary thresholds, and ensuring thresholds reflect budgetary reality.
Cost‑Utility Analysis – Willingness‑to‑Pay (WTP) for Quality‑Adjusted Life Ye… #
Related terms: Contingent Valuation, Discrete Choice Experiment (DCE). WTP for health gains can be expressed per QALY, enabling comparison across interventions. Example: A DCE finds an average WTP of $35 000 for a one‑QALY improvement. Practical application: Supplies empirical evidence for setting cost‑effectiveness thresholds. Challenges: Potential bias in stated preferences, income effects, and ensuring respondents understand abstract health concepts.
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