Three Essays on Physician Prescribing BehaviorBrian K. ChenOrals ExaminationDecember 4, 2006Haas School of BusinessUniversity of California at Berkeley

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

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?

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

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

Background Quick statistics

Land Area: 13,823 square milesPopulation (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 billionNational Health Insurance, virtually 100%

coverage5.7 hospital beds per 1,000 people, 1.4

physicians trained in Western medicine for every 1,000

Salient Features of Taiwan’s Health Care System

Closed SystemPhysicians are employees

Freedom of Choice Lack of system of referrals Commingling of diagnostic and

dispensing services

◄Chapter 1►Adoption/Diffusion of New

Therapeutic Agents

Literature Review: Adoption of New Drugs

“Epidemic” studiesMenzel (1955), Coleman (1957), Peay

(1988), Denig (1991) Nair (2006) (related) “Firm Heterogeneity” studies

Steffesen (1999), Tamblyn (2003), Dybahl (2005)

Bayesian model of adoptionCoscelli (2004)

Motivation to Prescribe

Firm heterogeneity model: there exist characteristics that predict adoption of new technologyWhat these characteristics are remains

an open empirical questionAre these characteristics constant across

new drugs? Patient Demand Marketing Activities

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

Top Prescription Drugs in Taiwan by Sales, 2004

Rank Drug Name Indication Claims ($New Taiwan Dollar)* Growth Rate1 AMLODIPINE (Norvasc) Hypertension/Angina 2,717,461,581 13.7%2 VALSARTAN (Diovan) Hypertension 1,438,903,860 21.3%3 ATORVASTATIN (Lipitor) Cholesterol 1,279,879,641 44.5%4 FELODIPINE (Plendil/Lexxel) Hypertension 1,174,234,843 20.1%5 ROSIGLITAZONE (Avandia) Diabetes (Type 2) 1,027,488,496 29.9%6 CLOPIDOGREL (Plavix) Heart Failure/Stroke 1,011,987,948 62.1%7 LOSARTAN (Cozaar) Hypertension 1,003,209,815 11.0%8 GLICLAZIDE Diabetes (Type 2) 1,001,107,515 9.2%9 METFORMIN Diabetes (Type 2) 983,970,085 11.6%10 NIFEDIPINE (Adalat/Procardia) Hypertension 973,298,312 2.4%11 FACTOR VIII Anemia (Test) 891,448,733 6.9%12 ENALAPRIL (Vaseretic/Vasotec) Hypertension/Heart Failure 852,860,619 8.0%13 ZOLPIDEM (Ambien) Insomnia 848,801,879 34.7%14 CEFAZOLIN (Ancef) Infection (Bacterial) 791,589,797 -5.3%15 CIPROFLOXACIN (Ciloxan) Infection (Bacterial, of the eye) 774,191,733 10.6%16 ALBUMIN Liver/Kidney Disease (Test) 734,444,611 7.5%17 GLIMEPIRIDE (Amaryl) Diabetes (Type 2) 713,329,867 36.0%18 IRBESARTAN (Avapro) Hypertension 707,404,461 54.5%19 CELECOXIB (Celebrex) Arthritis 674,028,771 36.1%20 SIMVASTATIN (Zocor) Cholesterol 667,612,759 13.2%21 CARVEDILOL (Coreg) Heart Failure/Hypertension 665,092,124 18.5%22 RISPERIDONE (Risperdal) Schizophrenia 641,028,107 21.8%23 LOVASTATIN (Advicor) Cholesterol 622,792,048 46.5%24 ATENOLOL Hypertension 600,122,674 5.4%25 DOXAZOSIN (Cardura) Enlarged Prostate 590,823,465 19.8%

Source: National Health Insurance Bureau, Taiwan*1 U.S. Dollar = 33 New Taiwan Dollars in 2004

Top Drugs by Sales in Taiwan, 2004

Top ICD-9-CM codes in TaiwanRank ICD9CM/ACODE Disease Name Frequency

1 A312 Acute sinitis 1348973 461.92 4659 Upper respiratory infection 12911303 460 Common cold 3350474 4660 Acute bronchitis 2938055 A311 2357976 4019 Hypertension, essential 2130797 463 Acute tonsillitis 2000348 4619 Acute sinitis 1996619 A320 Acute bronchitis 190653 46610 462 Pharyngitis 15793411 A233 Acute conjuntivitis 148104 37212 A429 Atopic dermatitis 141388 706.113 A322 Influenza 140634 487.114 A346 Constipation 139419 56415 A420 Cellulitis and abscess 137914 682.916 4650 Frequent colds 125392 460 or 465, 465.0, 465.8, 465.917 5589 Acute gastroenteritis 12210618 A314 Chronic rhinitis 120790 47219 A310 Acute tonsillitis 119881 46320 6929 Contact dermatitis 11961521 25000 Diabetes mellitus 11138922 7890 Abdominal pain 9343123 4871 Influenza 8983724 A323 Asthma 8188525 A239 Diabetes mellitus with retinopathy 80829 250.50+362.0126 7061 Acne vulgaris 7941027 7804 Dizziness and giddiness 7843628 4658 Frequent colds 78127 460 or 465, 465.0, 465.8, 465.929 A269 Hypertension, essential 76532 401.930 4640 Acute laryngitis 75676

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

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)

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

Hypertension HHD* Cholesterol Diabetes Lipitor Takers** Population*** Percentage1 0 0 0 1719 26956 0.0637705890 0 1 0 1400 10316 0.1357115160 1 0 0 1046 12739 0.0821100560 0 0 1 1661 44792 0.0370825151 0 0 1 781 5614 0.1391164941 0 1 0 829 3756 0.2207135251 1 0 0 58 720 0.0805555560 0 1 1 837 4098 0.2042459740 1 0 1 401 2554 0.1570086140 1 1 0 482 2004 0.2405189621 0 1 1 402 1499 0.2681787861 1 0 1 19 70 0.2714285711 1 1 0 15 46 0.3260869570 1 1 1 191 645 0.296124031

combinations

code combinations from visit to visit, the 14 combinations are not mutually exclusive

*HHD = Hypertensive Heart Disease**A patient is considered a Lipitor-taker if he or she has been prescribed Lipitor at least once***"Population" is the number of unique individuals with at least one visit with the relevant disease code

These figures are drawn from the master data file. Because an individual may have different diagnosis

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)

Summary Statistics - HypertensionVariable Obs Unique Mean Min Max Label

FEE_YM 310805 49 514.367 491 539 Claims DateHOSP_ID 310805 4846 . . . Hospital IDID 310805 22594 . . . Patient IDID_BMDY 310805 12997 -7605.51 -24029 14636 Patient BirthdateID_SEX 310780 3 . . . Patient SexFUNC_MDY 310805 1462 15670.3 14975 16436 Office Visit DateFUNC_TYP 308578 41 . . . Department VisitedCASE_TYP 310805 25 . . . Reimbursement TypePRSN_ID 310805 13400 . . . Physician IDDRUG_DAY 310805 56 24.17292 1 90 Prescription Durationnum_drug 310805 26 4.212822 1 27 Number of DrugsICD9_1 310805 1851 . . . Diagnosis 1ICD9_2 256681 1947 . . . Diagnosis 2ICD9_3 185677 1813 . . . Diagnosis 3DRUG_AMT 310805 6986 1142.13 0 75787 Drug Amountdrug_cpy 310805 11 90.38072 0 200 Patient Drug Copay AmountPART_NO 310805 42 . . . Patient Copay CodePART_AMT 310805 80 162.9943 0 930 Patient Total Copay T_AMT 310805 8516 1505.093 16 77037 Total Claims Amountper_lipitor_mo 310805 49 0.019745 0 0.039856 Percent of patient-visits with Lipitor by monthper_doc_lip_mo 310805 49 0.03059 0 0.062314 Percent of physicians who prescribed Lipitor by monthlip_pat_vst 310805 2 0.01887 0 1 Equals 1 if Lipitor receivedper_lipitor 310805 211 0.047465 0 1 Percentage of total visits with Lipitor (by Patient)d_acadhosp 310805 2 0.036322 0 1 Equals 1 if academic hospitalurban 310805 2 0.489632 0 1 Equals 1 if urbanage 310805 99 63.75954 3 107 Patient AgePRSN_SEX 308207 4 . . . Physician Sexprsn_age 307688 80 44.70343 -798 92 Physician Ageprsn_exper 308178 55 6.250401 -828 68 Physician Experience (Years since certification)prsn_tenure 279917 18 2.245155 -2 26 Physician Tenure (Years at work place)prac_vol 225871 92 4780.519 500 69500 Physician Practice Volumedayssince 301660 2127 68.72651 1 2869 Days between visitshosp_adms_hy 310805 12 0.181358 0 11 Number of Hospital Admissions in last 6 monthsavg_medamt_hy 310805 9715 5551.92 0 1214610 Average Hospital Exp. In last 6 monthsavg_partamt_hy 310805 5126 338.1398 0 225604 Average Inpatient Copay in last 6 monthsavg_stay_hy 310805 315 0.991601 0 84 Average length of stay in last 6 months

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?

kjikijii uHospPhysPatAdoption ,,,,1Pr

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?

1,1 tt

1,1

1

u

ut

Diffusion patternLipitor (for Hypertensive Patients Only)

Preliminary Results – Panel DataLikelihood of Lipitor AdoptionOLS/Random Effects R.E. Logit (Panel), Odds Ratio R.E. Logit (Panel), Marginal Effects R.E. Probit (Panel), Marginal Effects

Dep var: adoption Coef. Std. Err. OR Std. Err. dy/dx Std. Err. X dy/dx Std. Err. Xfamily practice -0.0023039** 0.0012944 0.7885027*** 0.0615047 -0.2376194*** 0.078 0.150923 -0.1277919*** 0.04393 0.150923public -0.0053071*** 0.0019605 0.5598564*** 0.0337863 -0.580075*** 0.06035 0.263539 -0.2406292*** 0.03407 0.263539clinic -0.014053*** 0.0014696 0.0932925*** 0.0087427 -2.372015*** 0.09371 0.3185 -1.046141*** 0.04508 0.3185academic hospital 0.0152431*** 0.00592 1.758961*** 0.1570815 0.5647234*** 0.0893 0.040609 0.2879713*** 0.05537 0.040609urban 0.0033508*** 0.0014845 1.509043*** 0.0816024 0.4114759*** 0.05408 0.483731 0.1503279*** 0.03058 0.483731patient age 0.0001778*** 0.0000483 1.001481 0.0020495 0.0014795 0.00205 63.7609 0.0015028 0.00121 63.7609patient sex 0.003106*** 0.0013404 1.355327*** 0.0667762 0.3040428*** 0.04927 0.525462 0.1387352*** 0.02902 0.525462serious -0.0077691*** 0.0022525 0.2406417*** 0.0594353 -1.424446*** 0.24699 0.027459 -0.5539457*** 0.11864 0.027459poor -0.0019786 0.0058783 0.3819593*** 0.1185521 -0.9624411*** 0.31038 0.010389 -0.3743522*** 0.18029 0.010389d_hlc 0.0643002** 0.0379062 4.696839*** 2.227083 1.54689*** 0.47417 0.000545 0.8631665*** 0.2681 0.000545d_hl 0.0792764*** 0.0056185 11.75206*** 0.6187012 2.464028*** 0.05265 0.069652 1.262681*** 0.03184 0.069652physician sex 0.0091677*** 0.0025417 1.862283*** 0.1276077 0.6218033*** 0.06852 0.067642 0.2963631*** 0.04025 0.067642physician age -0.000077* 0.0000494 0.9918423*** 0.0015784 -0.0081912*** 0.00159 44.6498 -0.0042903*** 0.0009 44.6498physician experience -0.0000333 0.0000486 0.997841 0.0024619 -0.0021613 0.00247 6.51965 -0.0016277 0.00117 6.51965physician tenure 0.0019483*** 0.0002169 1.142842*** 0.0076847 0.1335182*** 0.00672 2.19627 0.0693052*** 0.00371 2.19627practice volume 0.000000258 1.86E-07 1.000017*** 6.66E-06 0.0000174*** 0.00001 4457.04 0.00000825*** 0 4457.04ER visits -0.0099165*** 0.0012375 0.2882533*** 0.0612194 -1.243916*** 0.21238 0.021999 -0.5823007*** 0.10103 0.021999hospital admissions -0.0015808*** 0.00076 0.8719133*** 0.0462464 -0.1370653*** 0.05304 0.185314 -0.0661082*** 0.02798 0.185314average stay 0.00000355 0.0001058 1.003719 0.0077491 0.0037122 0.00772 1.00633 0.0025798 0.00401 1.00633_cons 0.0011855 0.0042243

N 253,088 253,143 253,143 253,143R2 0.0389Chi2 3746.96 3746.96 2845.04Dep. Var.: Adoption = 1 if patient received Lipitor during the visit, 0 otherwise

Preliminary Results – “Pooled” DataLikelihood of Lipitor AdoptionProbit: Marginal Effects Logit Regression Marginal Effects

Dep Var: Adoption dy/dx Std. Err. X Odds Ratio Std. Err. dy/dx Std. Err. Xfamily practice -0.0056235*** 0.00258 0.153412 0.7880558** 0.0997434 -0.0044693*** 0.0022 0.153412public -0.0054855*** 0.00214 0.223603 0.7577589*** 0.0755574 -0.0052383*** 0.00176 0.223603clinic -0.0343888*** 0.00223 0.360295 0.1750072*** 0.0250033 -0.031211*** 0.00211 0.360295academic hospital 0.0239782*** 0.00719 0.035014 1.987782*** 0.3049082 0.0191818*** 0.00575 0.035014urban 0.0016535 0.00223 0.470821 1.090944 0.1001267 0.0017691 0.00187 0.470821patient age 0.0003363*** 0.00006 60.522 1.014505*** 0.0030491 0.0002919*** 0.00006 60.522patient sex 0.0050531*** 0.00191 0.50589 1.19617*** 0.0943077 0.0036313*** 0.0016 0.50589serious -0.0024749 0.0065 0.015851 0.9247244 0.2872006 -0.00153 0.00585 0.015851poor -0.0147356*** 0.00546 0.007546 0.3689485 0.2661378 -0.0130552*** 0.00566 0.007546d_hlc 0.3558736*** 0.16508 0.000434 23.38733*** 17.17291 0.3098128** 0.16249 0.000434d_hl 0.2285449*** 0.01403 0.057706 14.97437*** 1.290243 0.1952882*** 0.01361 0.057706physician sex 0.0182379*** 0.00533 0.05928 1.749463*** 0.2256541 0.0144977*** 0.00422 0.05928physician age -0.0002237** 0.00012 44.8525 0.9913706*** 0.0040963 -0.0001757*** 0.00008 44.8525physician experience -0.0001103** 0.00007 6.46414 0.9951993 0.0034832 -0.0000975 0.00007 6.46414physician tenure 0.003362*** 0.00034 1.59079 1.151739*** 0.0156969 0.0028635*** 0.00029 1.59079practice volume -0.000000221 0 4385.67 0.9999912 0.0000114 -0.000000178 0 4385.67ER visits -0.030924*** 0.00664 0.050649 0.272909*** 0.0841252 -0.0263218*** 0.00617 0.050649hospital admissions 0.0003737 0.00172 0.208186 1.032864 0.0846072 0.0006554 0.00166 0.208186average stay -0.0001204 0.00026 1.11786 0.9956885 0.0122598 -0.0000876 0.00025 1.11786_cons

N 18,421 18,421Chi2 1392.25 1590.20Pseudo R2 0.2368 0.2350

Dep. Var.: Adoption = 1 if patient received Lipitor during the visit, 0 otherwise

Preliminary Results – Year by YearLikelihood of Lipitor Adoption2001 2002 2003 2004

Dep. Var: Adoption Odds Ratio Std. Err. Odds Ratio Std. Err. Odds Ratio Std. Err. Odds Ratio Std. Err.family practice 0.6302883 0.2275293 0.9523267 0.1820943 0.7280197* 0.127925 0.9902941 0.1694217public 0.9075834 0.2226236 0.6728642*** 0.1019138 0.6207893*** 0.0809304 0.8926806 0.1217703clinic 0.2663509*** 0.0929507 0.1620778*** 0.040325 0.1715873*** 0.0354593 0.1475397*** 0.0307479academic hospital 1.020202 0.5056124 1.268227 0.307244 1.460769** 0.2864846 1.4422* 0.3017895urban 1.197663 0.3095196 1.940949*** 0.2974637 1.597852*** 0.2019848 1.380867*** 0.163111patient age 1.010161 0.0083097 1.002437 0.0047581 1.008308** 0.0040193 1.005365 0.0042933patient sex 1.039622 0.2103167 1.065695 0.1292174 1.229874** 0.1253863 1.390368*** 0.1503748serious 0.4865983 0.4997534 0.7187478 0.338731 0.3261428** 0.1676118 0.3395853** 0.1751703poor 0.5536279 0.5681946 0.2523154 0.2585209 0.3717542 0.3803973d_hlc 23.84015*** 27.12236 5.493735 5.888289 14.79736*** 11.58786d_hl 14.47979*** 3.028132 10.62204*** 1.351887 9.562978*** 1.041586 10.2344*** 1.16819physician sex 1.981936** 0.6188189 1.66949*** 0.3227004 1.522148*** 0.2492969 1.4698** 0.2791801physician age 0.9946476** 0.0024587 0.9938469** 0.0026193 0.9698728*** 0.0067897 0.9748898*** 0.0071935physician experience 1.0144 0.0184827 1.0144 0.0117581 1.01343 0.009481 1.002569 0.007345physician tenure 1.119848*** 0.0469927 1.035121 0.0252343 1.040202** 0.0198238 1.003511 0.0194944practice volume 0.9999812 0.0000358 0.9999842 0.0000209 1.000017 0.0000169 1.000019 0.0000171ER visits 0.3484968 0.3548526 0.2186974** 0.1570677 0.4469373** 0.1725207 0.3189974** 0.1591509hospital admissions 1.020416 0.2478937 0.8712102 0.1320062 0.9838344 0.1178092 1.027071 0.1085529average stay 1.000033 0.035679 1.004319 0.0175388 0.9936948 0.0161526 0.9904582 0.01867

N 10300 10690 11121 8272Chi2 210.87 532.87 730.05 695.80Pseudo R2 0.1758 0.1857 0.1881 0.2044

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

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?

◄Chapter 2►Do new drugs lead to better

health outcomes?

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)?

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

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

Conceptual Framework

Empirical question: Estimation of Average Treatment EffectAre the high cost of new drugs justified

based on their health outcome impact?Lichtenberg studies do not address

selection bias in treatment

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

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

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

Empirical Strategy

Naïve Fixed Effects Regression

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Threats to Identification

Selection for treatment most likely not random

Selection Bias in TreatmentPerhaps physicians assign nonrandom

populations to treatmentPerhaps patients seek physicians who

prescribe new drugs (e.g., Lipitor)

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)?

Correction for Selection Bias Selection on Observables

Propensity Score MatchingAnalysis of the Effects of

Unobservables?

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

Distribution of new Lipitor takers

“Treatment” vs. “Non-Treatment”

Variable Obs Mean Std. Dev. Min MaxTreatment = 0 (received Lipitor < 50% of visits after first Lipitor)Age 17770 61.99561 11.14983 25 198Total number of visits 17770 69.03523 37.57343 6 246number of visits after Lipitor 17770 21.39961 12.30476 3 68Number of visits with Lipitor 17770 4.650366 3.463945 1 18How long on Lipitor (days) 17770 190.9992 251.5393 0 1214How long since first visit (ever) 17770 2313.325 525.1061 49 2872serious 17770 0.0270681 0.1622865 0 1Treatment = 1 (received Lipitor > 50% of visits after first Lipitor)Age 23691 62.32628 10.75469 3 198Total number of visits 23691 58.21945 32.66184 1 192number of visits after Lipitor 23691 12.67751 10.39991 1 51Number of visits with Lipitor 23691 9.914356 8.203874 1 46How long on Lipitor (days) 23691 335.9669 314.1132 0 1295How long since first visit (ever) 23691 2196.106 650.6024 0 2901serious 23691 0.0054451 0.0735913 0 1

Graphical Evidence – ER visitsNo adjustment for selection bias

Graphical Evidence –ER visits (1009 Lipitor takers)

Graphical Evidence –Smoothed ER visits (1009 Lipitor takers)

Graphical Evidence – ER visits (656 consistent takers)

Graphical Evidence – Smoothed ER visits (656 consistent takers)

Graphical Evidence - Hospitalization

Graphical Evidence –Hospitalization (1009 Lipitor takers)

Graphical Evidence –Smoothed Hospitalization (1009 takers)

Graphical Evidence – Hospitalization(656 consistent takers)

Graphical Evidence – Smoothed Hospitalization(656 consistent takers)

Graphical Evidence – Average Length of Stay

Graphical Evidence – Average Length of Stay (1009 Lipitor takers)

Graphical Evidence – Smoothed average lengths of stay (1009 Lipitor takers)

Graphical Evidence – Average lengths of stay (656 consistent takers)

Graphical Evidence – Smoothed average lengths of stay (656 consistent takers)

Graphical Evidence – Average Hospital Expenditures

Graphical Evidence – Average expenditures (1009 takers)

Graphical Evidence – Smoothed average expenditures (1009 takers)

Graphical Evidence –Average expenditures (656 consistent takers)

Graphical Evidence –Smoothed average expenditures (656 consistent takers)

Discussion Consistent Lipitor use does lead to better health

outcomes? Not just selection bias if consistency does improve health

outcome? But suggestive evidence that healthier patients are more

likely to receive Lipitor consistently? But: numerous possibilities for errors while merging 2004 data consistently slightly “bizarre” Treatment indicator very rough. 1 prescription of Lipitor over

2 visits = 50% treatment Need to consider consistency over a prescribed period of

time: 3 months? What did they take before Lipitor?

Need to include all indications for use of Lipitor Need to adjust for patient heterogeneity SELECTION ISSUES

Future Agenda

Better understandDrug adoption decisions, based on

chapter 1Quest for proper instrumentHypertension, Hypertensive Heart

Diseases, Diabetes, High CholesterolClinical trial results for the new drugs

Thank you for your attention

and valuable assistance

◄Chapter 3►Do physicians consider patient out-of-pocket expenses when

prescribing drugs?

Research Question

Do physicians consider patient out-of-pocket expenses? In August 1999, Taiwan implemented a modest, linear

prescription drug copayment system Patients with one of 97 chronic conditions can be

exempt from outpatient prescription copayments if physicians give an “chronic illness extended prescription certificate”

As of 2005, only 13% of eligible patient-visits receive the extended prescription certificate

Do physicians with high practice volume only give the extended prescription certificate? Or do patients have to demand the certificate?

Literature Review

Three bodies of literature: Impact of cost-sharing on patients’ drug utilization

choice: Soumerai et al (1987, 1991, 1994), Nelson (1984),

Tamblyn (2001). Patient-Physician Principal-Agent Relationship

Especially: Supplier-induced demand (SID): Rice (1983), Yip (1998)

Physician consideration of patient out-of-pocket expenses

Only survey studies available:

Contribution and Limitation Contribution

As far as I know, first paper to investigate through non-survey data whether physicians consider patient out-of-pocket expenses in their prescribing behavior

Policy implications: Greater payment for physicians to give certificate; or

greater effort to inform patients of their financial rights

Limitation Copayment is insignificant (capped at $3.33

USD until 2001, then capped at $6.66 USD) Generalizability? Correlation Study

Conceptual Framework

Physicians generally earn greater income through increased practice volumePhysicians give certificates if the already

have high practice volumeOr patients may demand certificate: proxied

by competition and patient sophisticationOr both

Empirical Strategy

First: Fixed Effects Regression Investigate effects of copayment on number

of drugs, prescription duration, adjusted drug amount, and adjusted drug quantity

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visitsofnumber cumulative logged_log

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Empirical Strategy

Second: Logit/Probit EstimationEffects of physician practice volume,

patient sophistication level, and market competition on the likelihood of giving “extended prescription certificate”

Data Files

Ambulatory Care Expenditures by Visit Details of Ambulatory Care Orders Inpatient Expenditures by Admission Details of Inpatient Orders Expenditures for Prescriptions Dispensed

at Contracted Pharmacies Details of Prescriptions Dispensed at

Contracted Pharmacies

Ambulatory Care Expenditures by VisitAmbulatory Care Expenditures by Admission

Field Number Variable Name Description Note

1 FEE_YM Month and Year of Application2 APPL_TYPE Application Type3 HOSP_ID Hospital ID4 APPL_DATE Application Date5 CASE_TYPE Case Type Western Medicine, Tradition, Dentistry, etc.6 SEQ_NO Sequence Number7 CURE_ITEM_NO1 Code for Chronic illnesses Some chronic illnesses are exempt from8 CURE_ITEM_NO2 certain payments. 9 CURE_ITEM_NO310 CURE_ITEM_NO411 FUNC_TYPE Hospital Department Visited12 FUNC_DATE Date of Hospital Visit13 TREAT_END_DATE Treatment End Date14 ID_BIRTHDAY Patient's Birthdate15 ID Patient's ID Number16 CARD_SEQ_NO Patient's NHIB Card Sequence17 GAVE_KIND Type of Reimbursement18 PART_NO Copayment Code19 ACODE_ICD9_1 ICD-9-CM Code20 ACODE_ICD9_2 ICD-9-CM Code 221 ACODE_ICD9_3 ICD-9-CM Code 322 ICD_OP_CODE Surgical Code23 DRUG_DAY Prescription Duration (days)24 MED_TYPE How Prescription is Filled25 PRSN_ID Physician's ID Number26 PHAR_ID Pharmacist's ID Number27 DRUG_AMT Drug Expenditures28 TREAT_AMT Treatment Fee29 TREAT_CODE Treatment Code30 DIAG_AMT Diagnosis Fee31 DSVC_NO Drug Handling Fee Code32 DSVC_AMT Drug Handling Fee Fee33 BY_PASS_CODE DRG Code34 T_AMT Total Amount35 PART_AMT Copayment Amount36 T_APPL_AMT Total Amount Applied37 ID_SEX Patient's Gender

Details of Ambulatory Care Orders

Details of Ambulatory Care OrdersField Number Variable Name Description Note

1 FEE_YM Application Month and Year2 APPL_TYPE Application Type3 HOSP_ID Hospital ID4 APPL_DATE Application Date5 CASE_TYPE Case Type6 SEQ_NO Sequence Number7 ORDER_TYPE Order Type 1. Diagnostic test; 2: Prescription drugs; 3: Special Materials8 DRUG_NO Drug Code9 DRUG_USE Drug Dosage10 DRUG_FRE Drug Frequency11 UNIT_PRICE Unit Price of Drug12 TOTAL_QTY Total Quantity (of Drugs)13 TOTAL_AMT Total Amount

Inpatient Files

Inpatient Expenditures by AdmissionIdentification information; patient age

and gender; date of admission and release; ICD9CM codes, ICD operation codes, DRG code, various fees, various copay amounts

Details of Inpatient OrdersIdentification information; drug

dispensed or services rendered

Summary Statistics, Master FileVariable Obs Unique Mean Min Max Label

visit 3.76E+07 1.48E+07 6739285 1 1.48E+07 Visit Number*FEE_YM 3.76E+07 96 14964.93 13515 16406 Claims DateHOSP_ID 3.76E+07 24621 . . . Hospital IDID 3.76E+07 191525 . . . Patient IDID_BMDY 3.76E+07 31848 -477.187 -58438 296358 Patient BirthdateID_SEX 3.76E+07 3 . . . Patient Sex**FUNC_MDY 3.76E+07 2994 14979.37 -15276 233586 Office Visit DateFUNC_TYP 3.76E+07 62 . . . Department VisitedPRSN_ID 3.76E+07 46854 . . . Physician ID DRUG_NO 3.76E+07 31858 . . . Drug CodeUNIT_PRI 3.76E+07 5423 29.06636 0 61192 Drug Unit PriceTOTAL_QTY 3.76E+07 825 15.306 0 34083 Drug Total QuantityD_SUBTOT 3.76E+07 4857 108.4071 0 535605 Drug SubtotalDRUG_DAY 3.76E+07 87 8.283142 0 99 Prescription Durationnum_drug 3.76E+07 41 4.283822 1 41 Number of Different DrugsICD9_1 3.71E+07 12407 . . . Diagnosis Code 1ICD9_2 1.83E+07 11348 . . . Diagnosis Code 2ICD9_3 9508421 8517 . . . Diagnosis Code 3DRUG_AMT 3.76E+07 15094 448.6745 0 535605 Drug Amountdrug_cpy 3.71E+07 12 21.51059 0 200 Patient Drug CopaymentPART_NO 3.73E+07 69 . . . Patient Copayment CodePART_AMT 3.71E+07 308 86.68472 0 2030 Patient Copayment AmountT_AMT 3.71E+07 23033 863.5745 0 535863 Total Claims Amounttotal_vst 1.48E+07 709 132.8225 1 1531 Number of Total VisitsR_HOSP_ID 5905315 9428 . . . Prescribing Hospital IDR_CASE_T 5814920 37 . . . Reimbursement TypeDRUG_MDY 5905315 2923 15630.46 12848 123283 Date of Prescription

randomly selected individuals in the data set from 1997-2004

All monetary amounts in New Taiwan Dollars. US$1 = NTD$33*Each unique patient-visit receives a unique visit number

Master Data File includes all outpatient patient-visits with a prescription for at least one drug for all 200,000**A person is listed as unspecified if his/her gender is unknown. This represents only 0.15% of all patient-visits.