Three Essays on Physician Prescribing Behavior

1. Three Essays on Physician Prescribing Behavior Brian K. Chen Orals Examination December 4, 2006 Haas School of Business University of California at Berkeley

2. MotivationPrescription drug expenditures: the fastest growing component of health care expenditures • 1990s: Prescription drug expenditures grew by 5% to 23% annually in most industrialized countries • United States: Fastest growing component of $1.9 trillion health care industry • In 2004: Third largest component in the US health care expenditures at 9%, following hospital (31%) and physician (22%) services • In 2002: Drug expenditures up by 15.3%, outstripping expenditures in hospital (9.5%) and physician (7.7%) services • By 2001: US$607 billion spent on prescription drugs worldwide • In nominal terms, top 20 GDP in the world • New drugs explain up to 40% of annual drug expenditures growth

3. Dissertation Outline and Research Questions • Chapter 1: What are the determinants in the adoption decision of new drugs? • Do physician, patient, and hospital characteristics matter in the likelihood/rate of adoption of new drugs? • Chapter 2: What is the health outcome impact of new drugs? • Do new drugs lead to better health outcomes? • Chapter 3: If high costs to patient affect drug use, do physicians take patient costs into consideration?

4. Why are these questions important? • Numerous implications • Who gets new drugs? Who prescribes new drugs? • Theoretical interest – consistent early adopters? • Policy interest • As first stage analysis for second essay • Are new drugs worth their cost? • If yes, what are the cost savings? How to encourage appropriate use of new drugs • If no, what are the additional costs compared to older, just-as-effective drugs? • If financial burden prevents access to new drugs, do physicians take this into consideration? • If only marginal improvement, physicians should prescribe older drugs to the financially burdened • If substantial improvement, implications for drug copayment policies

5. Contribution • Chapter 1: • Very little known about physician adoption of new drugs • But I need: theoretical framework? • Chapter 2: • No strong empirical evidence on the effectiveness of new drugs versus older drugs that corrects for selection bias • But I need: INSTRUMENT for treatment

6. Background • Quick statistics • Land Area: 13,823 square miles • Population (2006): 23,000,000 • 2005 GDP: $U.S. 611.5 billion ($U.S. 326.5 billion) • 2005 Per Capita GDP: $U.S. 26,700 ($14,200) • Health Care in Taiwan: • 2003: $U.S. 11 billion • National Health Insurance, virtually 100% coverage • 5.7 hospital beds per 1,000 people, 1.4 physicians trained in Western medicine for every 1,000

7. Salient Features of Taiwan’s Health Care System • Closed System • Physicians are employees • Freedom of Choice • Lack of system of referrals • Commingling of diagnostic and dispensing services

8. ◄Chapter 1►Adoption/Diffusion of New Therapeutic Agents

9. Literature Review: Adoption of New Drugs • “Epidemic” studies • Menzel (1955), Coleman (1957), Peay (1988), Denig (1991) Nair (2006) (related) • “Firm Heterogeneity” studies • Steffesen (1999), Tamblyn (2003), Dybahl (2005) • Bayesian model of adoption • Coscelli (2004)

10. Motivation to Prescribe • Firm heterogeneity model: there exist characteristics that predict adoption of new technology • What these characteristics are remains an open empirical question • Are these characteristics constant across new drugs? • Patient Demand • Marketing Activities

11. Conceptual Framework: Predictions • Physician characteristics: • Prime age  greater adoption • Gender  unclear, general view is male  greater adoption • Past practice volume  greater adoption? • Type of doctor  family practice  less adoption • *Past use of drugs manufactured by same company  greater adoption • Patient characteristics • Age  unclear; depends on drug • Gender  unclear • *Higher Education  greater adoption • Condition severity  unclear • Hospital characteristics • Academic  greater adoption • Urban  greater adoption • Family practice  less adoption at hospitals • *Drug characteristics • New drug-action mechanism?  slower adoption for agents with new mechanism

12. Top Prescription Drugs in Taiwan by Sales, 2004

13. Top ICD-9-CM codes in Taiwan

14. Drugs introduced between 1997-2004 • Atorvastatin(Lipitor) (19114/2097) • Date of introduction: November 1, 2000 • Therapeutic class: statins • Indication: to lower cholesterol and thereby reduce cardiovascular disease. • With 2005 sales of US$12.2 billion under the brand name Lipitor, it is the largest selling drug in the world • Rosiglitazone (Avandia) (15281/1052) • Date of introduction: March 1, 2001 • Therapeutic Class: thiazolidinedione • Indication: Anti-diabetic drug (Diabetes Type II) • Clopidogrel (Plavix) (7378/728) • Date of introduction: January 1, 2001 • Therapeutic Class: Antiplatelet agent • Indication: is a potent oral antiplatelet agent often used in the treatment of coronary artery disease, peripheral vascular disease, and cerebrovascular disease. • In 2005 it was the world's second highest selling pharmaceutical with sales of US$5.9 billion

15. Other new drugs • Celecoxib (Celebrex) • Arthritis/Pain (April 1, 2001) (but: side effects) (15574/3952) • Esomeprazole (Nexium) • Heartburn/Acid Reflux (January 1, 2002) (4250) • Olanzapine (Zyprexa) • Schizophrenia/Bipolar (February 1, 1999) (5284) • Venlafaxine (Effexor) • Antidepressant ( October 1, 2000) (2296) • Montelukast (Singulair) • Asthma (July 1, 2001) (2489) • Quetiapine (Seroquel) • Schizophrenia/Bipolar (April 1, 2000) (2795)

16. Disease Code Combinationsonly < 1% of visits have no ICD9 code

17. Description of Data • Panel Data • Eight years of complete medical claims data for a random selection of 200,000 individuals from Taiwan’s population of 23 million • HOSB, PER, DOC and ID files • The age, gender, and expenditures of the randomly selected individuals do not differ significantly from the population • Time Series (Random Subsamples) • Outpatient Expenditures • Inpatient Expenditures • Prescription Drugs at Contracted Pharmacies (complete)

18. Summary Statistics - Hypertension

19. Empirical Strategy – Likelihood of adoption • Probit/Logit Model • Pat: Patient Characteristics: age, gender, past number of visits, ER visits, hospitalizations, multiple conditions? • Phys: Physician Characteristics: age, gender, experience, tenure, past prescription pattern • Hosp: Hospital Characteristics: Academic, urban, family practice • Endogenous variable? Omitted variables (Neglected heterogeneity)? New diagnoses?

20. Empirical Strategy – Duration to Adoption • Right-Censored Duration Model • Continuous or Discrete Time-Scaling • Nonparametric or parametric functional form?: • Weibull (increasing) • Log-logistic • Effect of Covariates (same as previous slide) • Proportional Hazard (+coeff  - hazard / +duration) • Accelerated Lifetime Hazard (1 unit +coeff  % +duration) • Other issues • Multiple spells? Time-varying covariates? (move from one hospital to another?) Unobserved Heterogeneity?

21. Diffusion patternLipitor (for Hypertensive Patients Only)

22. Preliminary Results – Panel DataLikelihood of Lipitor Adoption

23. Preliminary Results – “Pooled” DataLikelihood of Lipitor Adoption

24. Preliminary Results – Year by YearLikelihood of Lipitor Adoption

25. Discussion • Need to reconstruct data from scratch • Different types of severity • multiplicity of conditions, or severity of a single condition • Not surprising: • academic, urban providers more likely to adopt, patients with multiple indications more likely to be given Lipitor • A little surprising? • Female physicians more likely to adopt (probably problem from merged data); female patients more likely to receive • Quite surprising? • More serious patients less likely to be given Lipitor

26. Future Agenda • Better understand • What factors lead to CONSISTENT adoption? • Disease conditions • Patients’ disease progression • Drug action mechanism • Physician decision-making process • Drug sales representatives’ activities • Future Research • Random Utility Model of Prescribing Behavior? • Spillover effects • Opinion Leaders • Ethnolinguistic differences • Celebrex study: when do physicians reject new drugs?

27. ◄Chapter 2►Do new drugs lead to better health outcomes?

28. Research Question • Do new drugs lead to better health outcomes? • More specifically, do patients who take Lipitor, Avandia, or Plavix experience a reduction in • ER visits, hospital admissions, hospital lengths of stay (problem?), and/or medical expenditures (compared to patients taking older drugs)?

29. Quote • “Too often,” says Robert Seidman, chief pharmacy officer at health insurer WellPoint, “we're choosing the newer, pricier drug without considering whether older drugs would get the job done just as well” • Lipitor: $612/180 20mg tablets • Zocor: $799/180 20mg tablets  but soon generics • Mevacor: $228.31/180 20mg tablets • www.drugstore.com prices

30. Literature Review • Lichtenberg (1996) • Number of hospital bed-days declined most rapidly for those diagnoses with the greatest change in the total number of drugs prescribed and greatest change in the distribution of drugs (proxy for novelty) • Lichtenberg (2001) • Patients who consume newer drugs experience fewer work-loss days than patients who consume older drugs; and the former tend to have lower non-drug expenditures, reducing total expenditures • Lichtenberg (2002) • With larger dataset, and 3 years instead of 1 year of observation, Lichtenberg argues that a reduction in the age of drugs decreased non-drug expenditures 7.2 times as much as it increased drug expenditures. (8.3 times for Medicare population) • Lichtenberg (2005) • Effect of the launch of new drugs: Average 1 week increase in life expectancy in the entire population

31. Conceptual Framework • Empirical question: Estimation of Average Treatment Effect • Are the high cost of new drugs justified based on their health outcome impact? • Lichtenberg studies do not address selection bias in treatment

32. Atorvastatin (Lipitor): Clinical Research • Collaborative Atorvastatin Diabetes Study (CARDS), • 2,800 patients with type-2 diabetes, no history of heart disease, and relatively-low levels of cholesterol, • Positive Health outcome: • patients who took Lipitor had a 37 percent reduction in major cardiovascular events • which included heart attacks, stroke, chest pain that required hospitalization, cardiac resuscitation, and coronary revascularization procedures. • 48 percent fewer Lipitor treated patients experienced strokes compared to those who received placebo • overall mortality rate for Lipitor patients was 27 percent lower than for those on placebo. • But: Study Sponsored by Pfizer / No comparison with older drugs / Relatively Healthy Population

33. Atorvastatin (Lipitor)Clinical Research - Hypertension • LIPITOR significantly reduced the rate of coronary events • either fatal coronary heart disease (46 events in the placebo group vs 40 events in the LIPITOR group) • or nonfatal MI (108 events in the placebo group vs 60 events in the LIPITOR group)] • relative risk reduction of 36% (based on incidences of 1.9% for LIPITOR vs 3.0% for placebo), p=0.0005 • The risk reduction was consistent regardless of age, smoking status, obesity or presence of renal dysfunction. The effect of LIPITOR was seen regardless of baseline LDL levels. Due to the small number of events, results for women were inconclusive. • N = 10,305 (Anglo-Scandinavian Cardiac Outcomes Trial) • Source: www.lipitor.com

34. Mixed Results for Lipitor Vs. ZocorBy THERESA AGOVINO, AP Business WriterTuesday, November 15, 2005 06 57 PM • High doses of the cholesterol-lowering drug Lipitor were no better at preventing major heart problems than regular doses of rival Zocor, according to the latest study on efforts to aggressively treat the conditions released Tuesday. • Lipitor outperformed Zocor on several fronts such as lowering cholesterol and preventing nonfatal heart attacks. The findings will continue to give it an advantage in the market even if generic Zocor is less expensive, some doctors said. • But: HIGH DOSE OF LIPITOR vs. REGULAR DOSE OF ZOCOR • What about LIPITOR vs. MEVACOR, PRAVACHOL, LESCOL, CRESTOR

35. Empirical Strategy • Naïve Fixed Effects Regression

36. Threats to Identification • Selection for treatment most likely not random • Selection Bias in Treatment • Perhaps physicians assign nonrandom populations to treatment • Perhaps patients seek physicians who prescribe new drugs (e.g., Lipitor)

37. Correction for Selection Bias • Instrumental Variable Approach • Gives internally valid causal effects for individuals whose treatment status is manipulable by the instrument • Candidates: the combination of covariates from Chapter 2 as an instrument for the treatment (i.e., use of new drug, such as Lipitor) • With patient’s pre-adoption status in the instruments to avoid patient self-selection • However, may reduce statistical power • Note: we can see if patients actually self-select into treatment • But: instruments (predicts adoption) may also affect the dependent variable (measures for health outcome)?

38. Correction for Selection Bias • Selection on Observables • Propensity Score Matching • Analysis of the Effects of Unobservables?

39. Cost Analysis • Lipitor Costs (Taiwan NHID formulary 2004, in USD): • $1.04 per 10 mg tablet; $1.40 per 20 mg; $1.75 per 40 mg • What are the cost savings? • If new drug reduces emergency and hospital services • Savings = reduced cost in emergency and hospital services – increased drug costs • What are the additional costs? • If new drug has not health outcome impact? • Additional cost = difference in price of new and old drugs

40. Distribution of new Lipitor takers

41. “Treatment” vs. “Non-Treatment”

42. Graphical Evidence – ER visitsNo adjustment for selection bias

43. Graphical Evidence –ER visits (1009 Lipitor takers)

44. Graphical Evidence –Smoothed ER visits (1009 Lipitor takers)

45. Graphical Evidence – ER visits (656 consistent takers)

46. Graphical Evidence – Smoothed ER visits (656 consistent takers)

47. Graphical Evidence - Hospitalization

48. Graphical Evidence –Hospitalization (1009 Lipitor takers)

49. Graphical Evidence –Smoothed Hospitalization (1009 takers)

50. Graphical Evidence – Hospitalization(656 consistent takers)