Health Economics in a Nutshell: A Blood Banking perspective

1. Health Economics in a Nutshell: ABlood Banking perspective Evan M Bloch, MD, MS Associate Clinical Investigator, BSRI Assistant Adjunct Professor, UCSF Conferencia Regional Seguridad Sanguinea en America Latina Lima, Peru 30th March 2014

2. Health economics in 15minutes… • Similar to life in 35 seconds • Caveats: neither comprehensive nor complete • I’m no expert The principles of health economics • What and why? • Decision analysis • Basic terminology: Quality Adjusted Life Years and Health state utilities • How one evaluates “cost-effectiveness”? • Benefits and limitations Health economics in the context of blood banking • Successes and setbacks • Example: Babesia microti Today’s Presentation

3. Introduction to health economics: What and why? What is health economics? • Systematic identification, enumeration and valuation of costs and benefits (or consequences) of health care interventions or programs: ‘value for money’ • Welfare economics: Allocation of scarce resources in a way that maximizes benefit to society (social welfare theory) Why is it important? • Scarcity • insufficient resources for all activities or interventions • Choice • decisions between competing initiatives • by choosing to use resources in one way we forego using the same resources in other ways

4. Economics Do Matter Inflation Adjusted Red Cell Service FeesABC Newsletter 2008

5. Determinants of health prioritiesWhere does Health economics fit in?Robinson, Health Policy, 1999;49:13-26

6. Decision analysis is a systematic, quantitative, and explicit approach for assessing the relative value of different decision options How?Decision Analysis and Health Economics Decisions have to be made… Are there ways to optimize the outcome? • assesses the probability and value of multiple outcomes • enables incorporation of data from multiple sources, makes assumptions explicit, and quantifies the decision parameters • Highlights data strengths and deficiencies

7. Can we afford it? • Budget Impact Analysis (BIA) Is it Worth Doing? Cost-effectiveness: results expressed as a cost per natural unit e.g. infection prevented or lives saved Cost-utility analysis: cost per QALY Cost-benefit analysis: costs and benefits expressed as monetary values Health Economics Types of analyses…what do they mean?

8. Basic Terminology QALY:Quality-Adjusted Life Year • is a measure of disease burdenGain in QALYs • The QALY is based on the number of years of life that would be added by the intervention • both quality and quantity of life lived • QALY= year of life x health state utility Health state utility • Each year in perfect health is assigned the value of 1.0 down to a value of 0.0 for being dead • the extra years that are not lived in full health (e.g. Blindness, amputation) incur a utility of between 0 and 1 • Based on perception of outcomes • DALY:The Disability-Adjusted Life Year • alternative measure of overall disease burdenDALYs averted • expressed as the number of years lost due to ill-health, disability or early death

9. Terminology continued… CER: Cost-effectiveness Ratio CER is the ratio of the costs to benefits of an intervention e.g. treatment, testing etc. ICER: Incremental Cost-effectiveness Ratio ICER is the ratio of the change in costs to incremental benefits of a therapeutic intervention or treatment If there is nothing currently in place e.g. comparing the addition of new testing with no testing…CER and ICER will be the same

10. The analysis: getting startedThe Decision Tree Probability Probability Outcomes Outcomes Cost Option #1 Cost Option #2

11. The Decision Tree Disease progression/clinical sequelae Prevalence Donors≠ General population Donor deferral and loss Disposal of blood Transmissibility Complications ±death Infection averted Infection Treatment Performance characteristics Test Cost No Test Cost Loss of income Testing No testing

12. The Decision Tree Blood culture Investigation Treatment Febrile transfusion reactions  febrile reactions Probability of febrile reactions Health impact in utilities Cost No cost of Intervention Leukoreduction No leukoreduction

13. Additional considerationsPitfalls and the complexity of analysis Considerations • Life-expectancy in transfusion recipients • Risk varies by component Methodology • Health states are dynamic • Inflation • Discounting: adjusting future costs and outcomes to present day value (money worth more today than it is in the future)

14. Based on societal willingness to pay… Historically, $50-100,000 per QALY gained (or DALY averted) Per WHO, 3 x Gross Domestic Product (GDP) per capita • US (~$150,000 per QALY) Human component as to why one implements an intervention • Ethics of resource allocation to certain populations, diseases etc. • Childhood leukemia vs. myelodysplastic syndrome • Breast cancer vs. prostate cancer What constitutes “cost-effective” differs based on perspective • Blood center vs. hospital vs. patient vs. society Effects on blood banking decision making has been limited • Implicit threshold of $1 million per QALY in the United States How much is cost-effective?

15. Cost Utility and Blood TransfusionsCost Utility League Table of Blood Safety Interventions (USA Setting)

16. Successes and Setbacks (USA) Responding to emerging infectious diseases • West Nile Virus Epidemic (2002) • Risk per unit transfused during epidemic 2-5/10,000 • Within 1 year (2003) NAT testing initiated • Since NAT, transfusion transmission rare • 2003 to 2010: >3,000 WNV NAT-reactive units The cost utility analysis requires contemporary local or regional data • Trypansoma Cruzi – Chagas disease • Antibody screening for T. cruzi began in Jan 2007 • Rate of true positives is 1:30,000 units nationwide • Analysis post implementation of universal testing: • transfusion transmission very low • Shift to one time donor testing for T. cruzi • High cost and low enthusiasm for new tests

17. Quantifying the uncertaintyIt’s not all about cost • 1-way sensitivity analysis • Evaluating the impact of a single variable on the CER e.g. prevalence • Provides a high and low estimate of the CER • Tornado diagram • Series of 1-way sensitivity analyses, shown graphically • Monte Carlo method • Computer simulation to assess collective uncertainty across all parameters

18. Transfusion Transmitted BabesiosisA Contemporary Example of cost-utility analysis • Babesiosis = tick-borne Intra-erythrocytic protozoan infection • Majority of cases caused by B.microti • widely endemic North Eastern and Upper Midwestern US • Increase in naturally acquired and transfusion-transmitted babesiosis • Over 162 transfusion associated cases since 1979 with 12 fatalities • Any RBC containing product • Clinical • Mild febrile illness: immunocompetent • Severe disease: at extremes of age, asplenic and immunocompromised • hemolytic anemia, renal-, cardiorespiratory failure and death We DON’T tend to transfuse the healthy

19. Cost-effectiveness ratios (cost per QALY):screening vs no screening, stratified by test modality and extent of geographic inclusion Costs, consequences, and cost-effectiveness of strategies for Babesia microtiblood donor screening strategies the US blood supply (unpublished) Alex J Goodell, Evan M Bloch, Peter J Krause and Brian Custer The model highlights uncertainty surrounding estimates of transmissibility, disease progression, and epidemiology

20. Zero defect policy • The legacy of HIV and blood transfusion The lemming effect • Industry standards and the obligation to conform • Competitive environment Perception • Client hospitals and commercial ramifications of Transfusion transmitted infection • Public: increased awareness Fear • Wasted resources: lessons learned from T.cruzi • Implementation of testing with incomplete data and no exit strategy Emotional decision making and blood safety

21. Decision analysis/health economics valuable tool Quantifies value of a given intervention Informs rational resource allocation Cost analyses are only one source of data that will drive decision-making Not intended to be the single deciding factor Dynamic: changing over time It’s not all about the money Highlights gaps in knowledge Quantifies the uncertainty and the potential impact Conclusions