Is it Cost-Effective to Pay People to Stop Using Illicit Drugs?

1. Is it Cost-Effective to Pay People to Stop Using Illicit Drugs? Jody Sindelar, PhD, Yale Todd Olmstead, PhD, Yale Nancy Petry, PhD, UCONN October 25, 2005 We acknowledge financial support from the National Institute on Drug Abuse (NIDA RO1-DA14471). Thank CTN esp Dr. Stitzer.

2. Overview • Cost effectiveness analyses (CEA) of a CTN effectiveness trial • Multi-site trial • Low cost, prize based Contingency Management (CM) • Focus on policy implications • Note- DRAFT not for circulation

3. How we add to the literature. • One of the first CEA of CM, • CM an important intervention, we provide critical policy relevant info- (LR goals of our research agenda is to further study CM) • CM Perfect for CEA • Add acceptability curves- new method for SAT- gives policy relevant info, accounts for uncertainty • Other strengths, large sample, multi-site

4. Background: NIDA Clinical Trials Network (CTN) • Network of researchers and providers; conduct trials to assess effectiveness of promising TX • Community based settings • Multiple sites, geographically disperse, more generalizable • CM was one of the first selected • Two CM trials- this DF, companion MM

5. Background: CM reinforces behavior with tangible incentives • CM has been found to be effective in previous literature • Reinforcing desirable behavior- abstaining. • Escalating payments • Often pay vouchers (eg $2) • Previous TX for illicit drug use were relatively expensive, adds costs on to usual care • As high as $1000 paid to successful patients

6. Intermittent CM –used in these CTN studies • Draw prizes from an urn if drug free • Is a relatively low cost CM by using intermittent reinforcement (Petry) • Not all draws earn a prize • Can vary the expected value of prizes earned conditional on being drug free • Can ask, how much do you need to pay (Petry et al effectiveness; Sindelar, Petry, Elbel, CEA)

7. Effectiveness Study in brief • (Drs. Petry, Pierce, Stizer and CTN) • Design – Random assignment of 412 stimulant abusers to UC or UC+CM for 12 weeks • Setting – 8 community-based outpatient psychosocial SAT programs • Test for- stimulants • Primary outcome measures- Retention, counseling attendance, # neg urines, longest duration abstinent

8. Incentives – Intermittent reinforcement • Chance to win prizes for stimulant-negative samples (2X per week) • # draws earned increases with continuous abstinence • # draws resets to zero with positive or missing sample • 500 chips in urn • 250 (50%) = “good job” => $0 • 209 (41.8%) = small prize => $1 • 40 (8%) = large prize => $20 • 1 (0.2%) = jumbo prize => $100

9. Effectiveness Study Findings Those in CM arm have better outcomes: LDA, retention, counseling attendance, # neg urines ( But, percentage negative was low overall but not different by UC and CM; we think that this is a poor measure of success!)

10. It is effective, but • Is it cost-effective? • Do you get your money’s worth? • Should ‘society’ pay for adding CM?

11. Our CEA study • Objective – Evaluate the cost-effectiveness of the prize-based intervention (CM) added to usual care (UC) • Secondary- to look at site differences, but not report on here.

12. Methods • Calculate incremental cost-effectiveness ratios- (ICERs) • Change in costs of adding CM/ change in effectiveness gained • Use trial data on effectiveness • Outcomes • Longest duration abstinent in study (LDA – weeks) • # of negative urine samples in study • Length of stay in study (LOS – weeks) • Collect data on unit costs, calculate incremental costs: unit cost* resources used; Conduct a survey.

13. Cost categories • Counseling (session time + admin time) • Individual • Group • Family • Testing (materials + time) • Urinalysis • Breathalyzer • Prizes • Drawing time • Value of prizes themselves • Administration time to run the prizes system , eg stocking time

14. Clinic cost survey • Survey 8 clinics (14 between the two trials) • Aim to obtain unit prices of counseling, testing, and prize admin • RA administered the survey, asked key people eg CEO, CFA, Medical director • Paid clinic $100 for completed survey

15. Cost data and calculations • Ask questions such as, hourly wage rate of counselors, fringe benefits; no. of clients in a group session; admin time re session; admin time of running the prize system, etc • Calculate unit costs • Multiply number of units of inputs by unit costs • Derive cost of variable inputs to UC and CM • Calculate incremental costs (and effects), ICERs

16. Results – Overall *p-value < .1; ** p-value <.05; ***p-value <.01

17. Interpretation of results • Find testing costs are high. • Prizes add costs too. Is it worth it? • How to interpret $231 more per additional week of consecutive abstaining (LDA)? Worth it? • No thresholds available.

18. Acceptability curve • Provides policy relevant interpretation of results • Provides measure of uncertainty (Difficult to calculate s.e. of ICER; denominator may be 0)

19. Acceptability curve-how to • Bootstrap 1000 replicates from sample • Consider correlation of changes in incremental effects and outcomes • Scatterplot of ICERs of 1000 replicates • Plot acceptability curves:

20. Acceptability curves • Plot of • Probability that the ICER that is found is acceptable at a range of society’s willingness to pay (WTP) • Problem is that we do not have a measure of society’s maximum WTP for a given outcome in SAT • (use QALYs in other areas; not good for SAT as not include extranalities- crime, spread of disease, work)

21. Results – Acceptability Curve – LDA Overall-percent and $WTP

22. Interpretation • If society is WTP about $270 per extra consecutive week abstaining, then it is 90% likely that society should accept the additional expense of CM- used in this way • As society is WTP more for the outcome, the probability of acceptance increases.

23. Next steps • Derive some bounds of WTP per extra week, eg. consider values of reduced crime, spread of AIDS/HIV due to longer abstaining. Is society’s WTP $270 or more? • Sensitivity analysis (eg price of testing, running the prize system at full levels)

24. Also, • Examine difference by site with goal of understanding how to interpret for policy purposes. • Glad to have comments, suggestions, CM is a cont. interest of our research.

25. Strengths and weaknesses • Strengths- • Large sample, multi-site thus generalizable, community based, trial implies causality, one of the first CEA of CM, CM an important intervention • Weaknesses- Missing obs, Need longer follow-up, patient costs CBA instead?,need more compete data, crime, spread of AIDS/HIV, etc

26. Future analyses • Sensitivity analyses: robust to different assumptions- tests are dropping in price • What if it were operating at full capacity- costs would decline • What to make of site differences- possible policy conundrums

27. Further work Analyze CEA of other CM trials, determine what accounts for variability/ develop thresholds Analyze policy options More comprehensive outcomes- crime, drugs

28. Results – Acceptability Curves by Site

29. *p-value < .1; ** p-value <.05; ***p-value <.01