Presentation on theme: "Weighing Benefits, Harms and Costs: What is the Right Number Needed to Treat? George F. Sawaya, MD Professor Department of Obstetrics, Gynecology and Reproductive."— Presentation transcript:
Weighing Benefits, Harms and Costs: What is the Right Number Needed to Treat? George F. Sawaya, MD Professor Department of Obstetrics, Gynecology and Reproductive Sciences Department of Epidemiology and Biostatistics University of California, San Francisco
Objectives Understand population effects of interventions (e.g., screening tests) in terms of benefits (effectiveness) and harms (costs and patient morbidity) and how these might be weighed Appreciate the need to systematically synthesize evidence about benefit and harms using decision analyses (e.g., outcomes tables) and cost-effectiveness analyses Know the difference between cost-benefit, cost- effectiveness and cost-utility analyses Understand efficiency curves in cost-effectiveness analyses
An example: recent changes in cervical cancer screening In November 2009, the American College of Obstetricians and Gynecologists (ACOG, Practice Bulletin 109) changed the recommendation from screening every 1-3 years among women aged 21 and older to the following: Sawaya GF N Engl J Med 2009 361;26 2503-2505
Possible reasons why less screening was recommended
How to determine the optimal balance of benefits and harms?
Consider… Two currently available treatments: Treatment A and B Treatment A has a 90% cure rate for Disease X. Treatment B has a 95% cure rate for Disease X. What would you want to know to decide to offer B over A? Treatment A costs $1,000 and Treatment B costs $10,000 Treatment B cost $100,000 or $10 million? What if the cure rate with Treatment B were 90.1% and Disease X was cancer? Should an insurance plan cover Treatment B?
Weighing benefits and harms: possible approaches Shared decision making (example: amniocentesis benefits and harms) Decision analyses: outcomes tables of both benefits and harms Cost analyses: assigning values to both benefits and harms in terms of both quantity and quality of life (life years, quality-adjusted life years)
What is a life year? What is a quality-adjusted life year? Life year a measure of the quantity of life lived used in decision analyses to compare benefits of various health intervention strategies may be expressed as life years expected per 1000 people for each intervention strategy Quality-adjusted life year (QALY) a measure of both the quantity and quality of life lived (i.e., a year of life adjusted for its quality or value). A year in perfect health is equal to 1.0 QALY; a year in ill health would be discounted (e.g., a year bedridden might be equal to 0.5 QALY). Perfect health is highly subjective; some individuals may believe that certain health states are worse than death.
Decision analysis Estimates the outcomes of different clinical decisions Breaks down problem into components: treatment options, outcome probabilities with each option (both benefits and harms); uses systematic reviews and meta-analyses Applies to large, theoretic cohorts of individuals going forward in time (effectiveness) Example: chemoprophylaxis of breast cancer (tamoxifen) 1) what are the benefits and harms specific to this question? 2) how likely are they to occur? 3) how to weigh these and make recommendations?
1,000 45-year-old women: estimated 5-year benefits and harms of tamoxifen USPSTF Evidence Report, 2001
1,000 65-year-old women: estimated 5-year benefits and harms of tamoxifen USPSTF Evidence Report, 2001
Current USPSTF Recommendation The USPSTF recommends against… use of tamoxifen or raloxifene for the primary prevention of breast cancer in women at low or average risk for breast cancer. D recommendation The USPSTF recommends that clinicians discuss chemoprevention with women at high risk for breast cancer and at low risk for adverse effects of chemoprevention. B recommendation
Cost analyses: assigning values to both benefits and harms Cost-benefit Cost-effectiveness Cost-utility
Cost-benefit analyses assigns a monetary value to both interventions and outcomes for each decision option ($ for $) Example: Is it cost-beneficial to have publicly-funded contraception programs in the State of California?
Impact of publicly funded contraceptive services on unintended pregnancies and implications for Medicaid expenditures for every dollar spent to provide publicly-funded contraceptive services, an average of $3 was saved in Medicaid costs for pregnancy-related health care and medical care for newborns. Family Planning Perspectives, 28:188-195, 1996
Cost-effectiveness analyses assigns a monetary value only to the intervention; the outcome is expressed on another scale (e.g., life years saved). allows quantitative comparisons of various medical decisions (a measure of value or bang for the buck) Example: Should we continue to screen women annually for cervical cancer once they have had 3+ normal Pap tests?
A real life example CDC has a national program that screens low- income women for cervical and breast cancer Resources are constrained but were expended in recalling women for additional Pap tests after normal tests have been documented. What is the most cost-effective screening strategy for cervical cancer in women with 0, 1, 2 and 3+ prior normal Pap tests (annual, biennial, triennial testing)? Sawaya et al NEJM 2003:349;1501-9
Kulasingam et al Obstet Gynecol 2006 Feb;107(2):321-328
Analysis: effectiveness Interventions that extend life an average of 30 days are thought to be effective.
Effectiveness: Results, long-term (weighted average of all ages, 100,000 women with 3 prior normal Pap tests, screened to age 65) 2 hours, 53 minutes 18.09310Pap q 1 year 2 hours, 36 minutes 18.09277Pap q 2 years 1 day, 42 minutes 18.09247Pap q 3 years -18.08965No screening Incremental life- years Expected life- years (3% discount) Strategy
Cost-effectiveness analyses Threshold values of $50,000 - $100,000 per year of life saved have been described as being cost-effective but… Greatest value is in comparing interventions
Cost-effectiveness: Results, long-term (weighted average of all ages, 100,000 women with 3 prior normal Pap tests, screened to age 65 and followed to 85) StrategyExpected life- years (3% discount) Incremental cost- effectiveness No screening18.08965- Pap q 3 years18.09247$115,429 Pap q 2 years18.09277$460,422 Pap q 1 year18.09310$1,192,770
Costs vs. Benefits: The Big Picture Benefits Costs Day 1 Day 2 Marginal (or incremental) benefit
Kulasingam et al Obstet Gynecol 2006 Feb;107(2):321-328 Efficiency curves: women aged 30-44, by prior Pap test results Diamond=0 prior nl tests Circle=3+ prior nl tests
How to best balance these benefits and harms for a population? Reasoned judgment? Cost-based benchmarks and precedence? All of the above?
Cost-utility analyses like cost-effectiveness analyses, but adjusts years of life saved for quality of life (quality-adjusted life-years-- how long someone will live, with an adjustment for quality of life -- healthy years count more, sick or disabled years, less). Unit of measure is a utility Example: Should we recommend screening for prostate cancer?
Prostate cancer screening: a decision analysis Optimal decision dependent on utilities of most common adverse outcomes: impotence and urethral stricture (incontinence) When adverse outcomes of treatment ignored, screening favored (24.86 vs 24.22 life year expectancy) No screening preferred to screening when patients' utilities considered (24.14 vs 23.47 quality-adjusted life years expected) When quality-of-life preferences of men considered, prostate cancer screening not recommended J Fam Pract 1995 Jul;41(1):33-41
Note the differences Cost-benefit $ spent per $ saved Cost-effectiveness $ spent per life-year saved Cost-utility$ spent per quality- adjusted life-year saved
Conflict: how should we optimally weigh benefits and harms for screening/prevention recommendations? Outcomes tables Pros: transparent Cons: heavy reliance on judgment Cost analyses Pros: levels the playing field to allow comparisons; can quantify and project population effects of beneficial and harmful outcomes over a lifetime Cons: opaque and complex, monetary basis Shared decision making
Summary Technological advances in medicine make tests more sensitive, often less specific and certainly more costly Large-scale trials (comparative effectiveness) may prove small benefits of statistical significance, but of unclear clinical value You must be aware of harms incurred in pursuit of marginal benefits Many forces are at play DAs and CEAs can be useful tools to judge the balance of benefits and harms for population-based recommendations, but neither is perfect Judgment = controversy (stay tuned for breast cancer screening)
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