LDI Research Seminar 2_18_11 The Diversity of Concentrated Prescribing Behavior V4
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  • Risperdal has also lost patent protection in 2008.
  • Despite the controversy over relative efficacy and cost-effectiveness of typicals vs. atypicals, based on a 10% random sample of antipsychotic prescribers, the number of atypical Rxs dispensed between 1996 and 2007 increased sevenfold from about 400K in 1996 to 2,800K in 2007, even as the number of conventional or typical Rxs fell 45% from 1,100,000 in 1996 to about 500K in 2003, and has stabilized since then. Atypical share has tripled from about 27% in 1996 to 85% in 2007. Also notable is the fact that despite all the concerns about the safety and efficacy of antipsychotics, total number of antipsychotics dispensed – typical plus atypical – more than doubled between 1996 and 2007, from about 1.5m to about 3.3m, an AAGR of 7.4%. Expanded sales in part due to additional FDA indication approvals. Approved by FDA for additional indications -- mania phase of bipolar II, and for depression. Clozapine approved for prevention of suicidality.
  • HHI for atypicals at end of previous slide is 2443. Note sure why it isn’t higher, given next slide. Typicals 15%, next slide for all antipsychotics?
  • At the individual prescriber level, prescribing behavior is very concentrated (HHIs almost 5000), but there is also substantial variability, with the COV just over 0.5. However, as one aggregates into larger regions, not only is less concentrated prescribing behavior observed, but so too is less relative variability, particularly as one moves from the county to the HRR geographical aggregate. In particular, 95% of the difference in mean HHI between individual prescriber and national level shares disappears at the HRR level, and 99% disappears at the state level. Within Phelps’ classification scheme, antipsychotic prescribing behavior is highly variable at the individual and county level, but is low variability behavior at the HRR and larger regional aggregates.
  • Gibbons and Henderson have looked at phenomenon of persistent differences in other context.
  • Administration could be a plausible phenomenon in a number of drug classes where there are strict formulary restrictions that vary across insurers, but for antipsychotics formularies have not only been open, but there’s been a substantial number of drugs available at each copayment tier (private sector), and for Medicaid, hardly any copayment or formulary restrictions for antipsychotics. So we rule “administration” out as a plausible explanatory factor.
  • This leaves us with three possibilities. While we won’t entirely rule out perception and motivation, here we focus on inspiration.
  • In the bandit model we have one learner who does not know the efficacy of each drug. Like a bandit model, our model has Bayesian learning, but our learning is about how to use a drug, rather than about its underlying efficacy for given symptoms. That is, the maximum potential benefit Bsd is known in our model. Full Comment from Bob: In the bandit model we have one learner who does not know the efficacy of each drug. The economists and statisticians who develop and apply models like this usually (to my knowledge) do not have in mind that another learner is seeing the decisions of the first learner. In our setting, however, one might think that a prescriber could see the nation-wide market shares of the drugs, which would give that prescriber information about what others had decided (and, implicitly, about what others had learned). As is mentioned in Section D below, this would be something like “herding,” except in this case prescribers would herd towards the correct answer (whereas the point of models like Banerjee and Bikhchandani-Hirshliefer-Welch is that people herd toward the wrong answer). Of course, our main empirical finding is quite the opposite of herding (whether to the right or the wrong answer): prescribers persist in doing different things. We therefore choose a model where these differences in behavior persist even if prescribers observe nation-wide market shares. Like a bandit model, ours does have Bayesian learning, but our learning is about how to use a drug, rather than about its underlying efficacy for given symptoms. That is, Bsd is known in our model. As a result, our model has the same reduced form as another class of models called “learning” models, namely models of “learning curves” or “learning by doing,” where costs fall as experience rises. For what it is worth, note that these models are (typically) deterministic; they do not have Bayesian learning. As with bandit models, I’m not sure that people who play with “learning curve” models typically think much about whether you can benefit from my learning. Or, I guess what I should say is that they implicitly or explicitly assume that you cannot benefit from my learning (i.e., to reduce your costs, you have to go through your own experience), but they don’t say much or anything about why this is the case. What I think is good about our model, relative to both bandit models and “learning curve” models, is that we are explicit about why seeing nation-wide market shares won’t change the behavior of a given prescriber: you have to learn how to use a drug, and no amount of being told that other people have learned how to use it can teach you how to use it; you have to do it yourself. In short, our model can be seen as micro foundations for “learning curve” models. Hope this helps 
  • We assume θ d and ε dp are normally distributed random variables with mean zero and variances σ 2 d and σ 2 ε , respectively, for all d, p . In addition we assume θ d and ε dp are independently distributed from one another. We also assume θ d is independent across drugs and ε dp is independent across drugs and across patients.
  • Re bullet 2: For exposition, could also say “but not on the symptoms” – but not necessary assumption Re bullet 4: For exposition, could also say “but not on the patient’s presentation of symptoms for whom the physician prescribes this drug – but not necessary assumption
  • Note: The way this model is set up, there are no principal agent problems here. All patient encounters are weighted equally, subject to discounting.
  • In analysis only use physicians who dispensed at least 10 antipsychotic scrips in 2007.
  • More than half the sample PCPs, but relative to NPs and NEU, and especially PSY, PCPs write relatively few antipsychotic prescriptions. PCPs also prescribe surprising variety of distinct antipsychotics, although their HHI is quite high.
  • Since we are dealing with a psychopharmaceutical product, appropriate that we denote our measure of difference from regional shares as deviance (deviants?).

LDI Research Seminar 2_18_11 The Diversity of Concentrated Prescribing Behavior V4 Presentation Transcript

  • 1. The Diversity of Concentrated Prescribing Behavior: An Application to Antipsychotics Anna Levine Taub, Anton D. Kolotilin, Robert S. Gibbons and Ernst R. Berndt Special thanks to the IMS Health Services Research Network for data support The usual disclaimers apply. This research has not been sponsored.
  • 2. Scenario: Physician sees patient…
    • Patient has confirmed diagnosis for which alternative FDA approved treatments are available, but patient response (efficacy, side effects) is idiosyncratic and unpredictable
    • Little evidentiary literature available for physician to base ex ante treatment choice
    • How best can physician learn regarding efficacy and tolerability of alternative drugs?
    • Concentrate or diversify on drug treatment? What complementary actions can be utilized?
  • 3. Issues We Address
    • Trade-off of exploiting or exploring, concentrating or diversifying, prescribing “ready-to-wear” vs. “custom-made” treatments (Frank-Zeckhauser 2007)
    • How does physician’s location along the treatment diversity continuum vary by physician’s specialty, patient volume, age, gender and training?
    • If physicians concentrate, is there convergence and unanimity on choice of favorite drug, or is concentration non-uniform? If set of drugs prescribed is diverse, does physician’s distribution mimic regional or national shares? How to model?
  • 4. Related Literature and Findings
    • Frank and Zeckhauser [2007]: 1372 PCPs – find that share of most prescribed medicine to treat four acute and five chronic conditions is high – average 60% -- but 13% less for chronic conditions
    • Patient clinical factors played a “startlingly minor role”, physicians practiced “ready-to-wear” treatment norms, sometimes “sensible” but in other cases “disturbing”
    • We focus on antipsychotics that treat primarily chronic conditions, and examine prescriber behavior across a much larger number of prescribers and variety of specialties, but only have data on prescribers, not data on the patients they treat (unlike Frank-Zeckhauser)
  • 5. Outline of Remaining Presentation
    • Alternative antipsychotics for treatment of schizophrenia and related conditions (brief)
    • Preliminary evidence on concentrated vs. diversified prescribing behavior (the setting)
    • A model of prescriber learning and treatment behavior, and some predictions of the model
    • Empirical findings on consistency of model predictions with observed prescriber behavior
    • Limitations and discussion
  • 6. Schizophrenia
    • Incurable mental illness, positive and negative symptoms, 1-2% prevalence, strikes males in late teens and early 20s, females slightly later, unknown etiology (genetics?)
    • Patients experience unemployment, lose family contact, become homeless, substantial portion experience incarceration for some time periods
    • Goals of treatment are to reduce/eliminate symptoms, maximize quality of life and adaptive functioning, maintain recovery to maximum extent possible
    • In US, Medicaid is the largest payer of medical and drug benefits to people with schizophrenia (Duggan)
  • 7. Available Treatments for Schizophrenia
    • From 1955 thru early 1990s, pharmacological treatments called typical , conventional, and/or neuroleptic antipsychotics – better for +ve than –ve symptoms, but for some patients have lasting extrapyramidal side effects (e.g., tardive dyskinesia) for which there’s no treatment
    • Clozaril (clozapine) introduced in 1989, still considered most effective antipsychotic drug, but for 1-2% side effect of agranulocytosis (can be lethal), so use requires frequent white blood cell count monitoring
    • Clozaril dubbed first generation atypical drug – “FGA”
  • 8. Second Generation Atypicals (SGAs)
    • Five SGAs introduced in US 1993-2002
      • Risperdal (risperidone) – 1993
      • Zyprexa (olanzapine) – 1996
      • Seroquel (quetiapine) – 1997
      • Geodon (ziprasidone) – 2001
      • Abillify (aripiprazole) -- 2002
    • Initially SGAs perceived as similar to typicals for +ve but better for –ve symptoms, without typicals’ extrapyramical side effects, but since 2001-2 concern has grown over SGA metabolic syndrome and weight gain side effects
    • Typicals and clozapine now off patent, SGAs very costly
    • CATIE [2005] and related studies questioned relative efficacy and cost-effectiveness of atypicals vs. typicals
  • 9. Typical and Atypical Prescriptions Annually 1996-2007
  • 10. Preliminary Evidence on Concentration
    • By 2007, five years after launch of last SGA, among those writing at least one antipsychotic Rx, mean share of prescriber’s favorite drug was 66% (greater than in Frank-Zeckhauser [2007]).
    • However, concentration is diverse – when we limit 2007 sample further to highly concentrated prescribers (> 75% favorite ), 55% chose Seroquel as favorite drug, 28% Risperdal, 13% Zyprexa, 3% Abilify, 2% Geodon and 0.4% clozapine.
    • 2007 respective national atypical market shares were 36%, 27%, 13%, 14%, 7% and 2%.
  • 11. Regional Geographic Variations as in Dartmouth Studies? No!!!
    • Compute Herfindahl measure of concentration HHI = Σ j s j 2 , 0 ≤ s j ≤ 100, so HHI ranges up to 10,000 for a monopoly (if ten drugs had equal shares, HHI would be 10*(10) 2 = 1000)
    • Calculate HHI at various levels -- individual prescriber, county, HRR (hospital referral region, Dartmouth Atlas Project), state and nation
    • Also compute coefficient of variation (COV), with Phelps [1992] saying COV in 0.1 – 0.2 range reveals “low variability”, and > 0.4 “highly variable” practice of medicine
  • 12. Antipsychotic HHIs in 2007 at Alternative Geographic Aggregates Means, Standard Deviations and Coefficients of Variation
    • Geographical Aggregate Mean HHI Std. Dev. Coef. Var . N
    • Individual Prescriber 4946 2499 0.505 19537
    • County 3234 1773 0.548 1904
    • Hospital Referral Region 1989 359 0.180 306
    • State (plus District of Columbia) 1859 16 0.008 51
    • Nation 1825 na na 1
    • Since the prescriber data indicate that there’s little variability in HHI at the geographic HHR level and higher (unlike Wennberg and colleagues), whereas there is very substantial heterogeneity at the level of the individual prescriber, here we will focus on factors affecting prescribing behavior of individual prescriber. Will look at county “spillovers” at future date.
  • 13. How can we explain the wide variance in the way physicians respond to patients?
    • Perception (Don’t think we’re behind.)
      • Differing mental models / categories / priors
      • Example: Two physicians read different articles arriving at different conclusions
    • Motivation (Don’t want to do it.)
      • Weak competition, agency issues
      • Example: MD - Pharma contacts are influential and heterogeneous
    • Administration (Don’t know to get there.)
      • Know where you want to go but can’t get there due to financial or administrative constraints
      • Limited formulary coverage
    • Inspiration (Don’t know what to do.)
      • Bandit problem, learning model
      • Don’t know about the efficacy of the drug, complex treatment regime and need to learn how to use the drug effectively
  • 14. How can we explain the wide variance in the way physicians respond to patients?
    • Perception (Don’t think we’re behind.)
      • Differing mental models / categories / priors
      • Ex. Two doctors read different articles arriving at different conclusions
    • Motivation (Don’t want to do it.)
      • Weak competition, agency issues
      • Ex. Phy - Pharma contacts are influential and heterogeneous
    • Administration (Don’t know to get there.)
      • Know where you want to go but can’t get there due to financial or administrative constraints
      • Limited formulary coverage
    • Inspiration (Don’t know what to do.)
      • Bandit problem, learning model
      • Don’t know about the efficacy of the drug, complex treatment regime and need to learn how to use the drug effectively
  • 15. How can we explain the wide variance in the way physicians respond to patients?
    • Perception (Don’t think we’re behind.)
      • Differing mental models / categories / priors
      • Ex. Two doctors read different articles arriving at different conclusions
    • Motivation (Don’t want to do it.)
      • Weak competition, agency issues
      • Ex. Phy - Pharma contacts are influential and heterogeneous
    • Administration (Don’t know to get there.)
      • Know where you want to go but can’t get there due to financial or administrative constraints
      • Limited formulary coverage
    • Inspiration (Don’t know what to do.)
      • Bandit problem, learning models
      • Don’t know about the efficacy of the drug, complex treatment regime and need to learn how to use the drug effectively
  • 16. How can we explain the wide variance in the way physicians respond to patients?
    • Perception (Don’t think we’re behind.)
      • Differing mental models / categories / priors
      • Ex. Two doctors read different articles arriving at different conclusions
    • Motivation (Don’t want to do it.)
      • Weak competition, agency issues
      • Ex. Phy - Pharma contacts are influential and heterogeneous
    • Administration (Don’t know to get there.)
      • Know where you want to go but can’t get there due to financial or administrative constraints
      • Limited formulary coverage
    • Inspiration (Don’t know what to do.)
      • bandit problem, learning models
      • Don’t know about the efficacy of the drug, complex treatment regime and need to learn how to use the drug effectively
  • 17. Learning to Concentrate
    • Patients p = 1, 2, … arrive sequentially
    • Symptoms s = 1, …, S randomly drawn
    • Drugs d = 1, …, D d * (s)
    • Time btwn patients w Arrival at time 0, w, 2w, …
    • Discount rate r Continuous time
    • Maximum potential benefit of drug d for symptom s = B sd
        • Example: B sd*(s) > B sd for all d ≠ d * (s)
    • Therapy = drug (d) and unobserved complementary actions (a)
        • Ideal effectiveness requires ideal complementary actions (uncertain)
          • Ex. Dosage of the drug (titrating), actions that effect adherence (Management and Communication about possible side effects and their duration)
    • Realized effectiveness = b sdp = B sd [1 – (a – x dp ) 2 ]
        • x dp = θ d + ε dp where θ d and ε dp are independently normal for all d,p
    • Provider observes x dp , learns about θ d
  • 18. Learning to Concentrate: Intuition
    • The physician learns by combining different complementary actions a when prescribing drug d , and observes how patients respond x dp .
    • The best action that the physician can potentially learn to make, θ d , depends only on the drug prescribed
    • Symptoms determine which drug has the highest potential for giving a patient the best outcomes, d*(s).
    • Speed of learning the complementary action θ d for each drug d depends only on how often the physician prescribes drug d
  • 19. Learning to Concentrate
    • The physician’s dynamic strategy is to choose a drug, d p (s, h p-1 ) , and complementary actions, a p (d, h p-1 ) , for each patient p with symptom s and each history h p-1 , where history is determined by the physician’s strategy implementation in prior patient encounters.
    • Assuming that the physician cares only about maximizing the expected discounted patient benefit of all patients s/he expects to see over time in the future, the physician’s optimization problem, provided that e -rw >0, is to solve:
  • 20. Learning to Concentrate: Intuition Cont’d
    • Suppose first that w is large, i.e., patients are infrequently seen by the physician (the physician is a small volume prescriber):
      • In this case over time the physician will concentrate on only a subset of drugs.
      • Number and identity of the drugs for which the physician concentrates depend on the initial history of symptom presentation to the physician -- idiosyncratic.
    • As w is decreased (i.e., the volume of patients seen by the physician increases):
      • Physicians will have a larger incentive to invest in learning how to use new or different drugs effectively, future patients will benefit.
      • The set of drugs a physician uses will still depend on the initial history of symptoms of the patients the physician has seen; however, this dependence will be weaker.
    • As w decreases to zero (the physician sees patients very frequently – almost continuously):
      • The physician will eventually learn a great deal about optimal complementary actions θ d for each drug d in D and prescribe d*(s) for every s.
      • To the extent the symptom set s presented to the physician is heterogeneous, given patient volume so too will be the range of drugs prescribed.
  • 21. Predictions From This Model
    • Concentration decreases with volume
    • Variability of treatment regimes from national norms is greater among low volume prescribers (greater dependence on idiosyncratic random history)
    • Prescribers with higher discount rates have more concentrated prescribing behavior (those near retirement or likely to leave the labor force?)
  • 22. Limitations of This Model
    • Physicians know B sd, maximum potential benefit of each drug d given symptoms s , but don’t know optimal complementary actions, and learn only from their own experience with drug d treating symptoms s
    • Don’t learn about drugs not prescribed, nor infer anything from national market shares – richer model would include costs and benefits of learning about a drug from other sources
  • 23. Physician Prescribing Data
    • IMS Health Xponent™ monthly data tracks prescriptions dispensed at retail and mail order by NDC code, links to prescriber ID, and links to American Medical Association directory of physicians
    • We take 10% random sample of all prescribers who wrote at least one antipsychotic Rx in 1996, refresh each year with 10% random sample of “new” prescribers, but here we only utilize 2007 data – five years since introduction of last new antipsychotic
    • Only data on prescriber, no data on patients
  • 24. Five Prescriber Groups
    • Primary care physicians (PCP) – internal medicine, family medicine and practice, pediatrics, and general practice prescribers
    • Psychiatrists (PSY) – general, child-adolescent and geriatric psychiatry
    • Neurologists (NEU) – general, geriatric and child neurologists
    • Non-physicians (NP) – primarily nurse practitioners and physician assistants
    • Other (OTH) – all other prescribers
  • 25. Mean Values of Characteristics of 2007 Prescriber Sample, by Prescriber Specialty Specialty Group Number of Prescribers Antipsychotic Annual Rx Atypical Annual Rx No. Distinct Antipsychotics No. Distinct Atypicals Antips-ychotic HHI Atypical HHI PSY 3463 609.56 551.37 7.46 4.71 3,245 3,661 NEU 728 97.64 82.30 3.60 2.33 5,657 7,025 PCP 9544 68.03 52.82 4.31 2.73 4,612 5,915 OTH 4161 54.24 29.53 2.76 1.65 6,912 7,081 NP 1641 174.85 155.30 4.29 2.88 5,181 5,633
  • 26. Prescriber Data
    • AMA directory of physicians does not have any data on NPs
    • For MDs/DOs, have data on age (divide sample into quartiles), specialty, hospital or office based, zip code of practice, group or solo practice, population size of county in which physician practices, gender, and whether physician allows prescribing behavior data to be used by pharmaceutical or other for-profit organizations
  • 27. Additional Measure of Diversity
    • Theory suggests we not only look at concentration, but also diversity of concentration (should decrease with volume)
    • Physician i prescribing drug j in region r – share of Rx’s for j is s ijr , whereas regional share for j is m jr . Our measure of deviance D ijr is:
    • D ijr = Σ j (s ijr – m jr ) 2
  • 28. Alternative Dependent Variables
    • Various dependent variables at level of individual prescriber – number of distinct antipsychotic drug molecules prescribed (Poisson), number of distinct atypical molecules prescribed (Poisson), share of all antipsychotic Rxs that are atypicals (Tobit), log antipsychotic HHI (Tobit), log atypical HHI (Tobit), and log deviance (OLS)
    • Drop NPs from sample, yields n = 17,652
  • 29. Common Set of Explanatory Variables in Regression Equations
    • Various dependent variables are function of (where bold denotes reference case)
      • Age quartiles ( < 43 , 43-50, 51-58, 59+ )
      • Volume interacted with specialty ( OTH , PCP, PSY and NEU)
      • County population (< 150K , 150K-500K, 500K-1M, and > 1m)
      • Male /Female, Group /Solo practice, Office /Hospital base, MD /DO flag and Physician Rx Data Can Be Used /Opt Out
  • 30. Summary Statistics Variable Obs Mean Std. Dev. Minimum Maximum Deviation of Physician's Antipsychotic prescribing from HRR Shares 17,652 2,660 2,441 5 10,321 Deviation of Physician's Antipsychotic prescribing from National Market Shares 17,652 2,735 2,499 30 10,051 HHI of Individual Physician's Antipsychotic Prescribing 17,652 4,920 2,484 1,196 10,000 HHI of Individual Physician's Atypical Prescribing 16,262 5,708 2,498 1,701 10,000 % of Prescriptions for Antipsychotics that were for Atypicals 17,652 71.46 32.60 0 100 Number of Different Antipsychotics Prescribed 17,652 4.54 2.70 1 17 Number of Different Atypicals Prescribed 17,652 2.85 1.64 0 6 Total Yearly Antipsychotic Prescriptions 17,652 171.80 431.35 12 7,186 Total Yearly Atypical Antipsychotic Prescriptions 17,652 145.92 388.51 0 6,780 Prescriber Age 17,652 50.37 10.80 26 92 PCP 17,652 0.54 0.50 0 1 PSY 17,652 0.19 0.40 0 1 NEU 17,652 0.04 0.20 0 1 OTH 17,652 0.23 0.42 0 1 Solo Practice 17,652 0.20 0.40 0 1 Population (county) 17,652 1,065,738 1,810,008 1,299 9,734,701 Female 17,652 0.26 0.44 0 1 Hospital Based Physician 17,652 0.08 0.27 0 1 DO Flag 17,652 0.09 0.28 0 1 Physician Opt Out 17,652 0.03 0.18 0 1
  • 31. Results: Variety of Drugs Prescribed
    • Number distinct antipsychotics – Given volume, PSY>PCP>NEU>OTH; effect of volume +ve for OTH, +ve but very small for PSY, with PCP and NEU in between; effect of age +ve for two middle quartiles, else zero; female –ve as is population, but flat after 500K; solo, hospital and MD opt out insignificant, but DO +ve
    • Number distinct atypicals – same results except –ve for oldest age quartile (cohort effect?)
    • Interpretation: PSY training a substitute for volume experienced by other specialties?
  • 32. Poisson Regressions: Number of Distinct Antipsychotic Molecules and Distinct Atypical Molecules prescribed in 2007 Number of Distinct Antipsychotics Number of Distinct Atypicals   Coefficient Standard Error P>|z| Coefficient Standard Error P>|z| Total Yearly Antipsychotic Prescriptions 0.001039 0.000048 <.001 0.001059 0.000061 <.001 PCP*Total Yearly Antipsychotic Prescriptions -0.000366 0.000051 <.001 -0.000405 0.000066 <.001 PSY*Total Yearly Antipsychotic Prescriptions -0.000792 0.000049 <.001 -0.000937 0.000062 <.001 NEU*Total Yearly Antipsychotic Prescriptions -0.000472 0.000071 <.001 -0.000478 0.000089 <.001 Age Quartile 43-50* 0.0374 0.0102 <.001 0.0113 0.0127 0.375 Age Quartile 51-58* 0.0513 0.0101 <.001 0.0152 0.0126 0.230 Age Quartile 59+* 0.0186 0.0107 0.081 -0.0423 0.0134 0.002 PCP* 0.4520 0.0115 <.001 0.5093 0.0148 <.001 PSY* 0.8965 0.0129 <.001 1.0558 0.0166 <.001 NEU* 0.2608 0.0233 <.001 0.3502 0.0292 <.001 Female* -0.0899 0.0084 <.001 -0.0308 0.0105 0.003 Population 150,000-500,000 (county)* -0.0357 0.0099 <.001 -0.0460 0.0126 <.001 Population 500,000-1,000,000 (county)* -0.0742 0.0105 <.001 -0.0900 0.0132 <.001 Population more than 1,000,000 (county)* -0.0692 0.0101 <.001 -0.0868 0.0127 <.001 Solo Practice* -0.0019 0.0091 0.831 -0.0086 0.0115 0.452 Hospital Based Physician* 0.0085 0.0125 0.497 -0.0124 0.0160 0.437 DO Flag* 0.0334 0.0129 0.010 0.0518 0.0161 0.001 Physician Opt Out* 0.0120 0.0190 0.530 0.0348 0.0238 0.144 Cons 0.9876 0.0141 <.001 0.4958 0.0179 <.001 Number of Observations= 17,652 Pseudo R^2= 0.145 Pseudo R^2= 0.1045
  • 33. Results: Old vs. New Antipsychotic Drugs
    • Share of antipsychotic Rx’s that are atypical (new): Marginal effects evaluated at variable means – Given volume, PSY>NEU>PCP>OTH; effect of volume insignificant for OTH and NEU, +ve for PCP, and –ve but small for PSY; three older age quartiles –ve, especially oldest; female +ve and large, population monotonically -ve as is hospital based and MD opt out, but solo and DO flag not significant
    • Greater use of older antipsychotics in more populous areas, particularly by PSYs – specialization in more urban areas serving heterogeneous populations?
  • 34. Tobit Regression (Marginal Effects Estimated at Variable Means) on Percent of All Antipsychotic Prescriptions written for Atypicals in 2007    dy/dx Standard Error P>|z| Mean Value Total Yearly Antipsychotic Prescriptions 0.0108 0.0061 0.074 171.80 PCP*Total Yearly Antipsychotic Prescriptions 0.0154 0.0070 0.028 36.44 PSY*Total Yearly Antipsychotic Prescriptions -0.0155 0.0061 0.011 118.84 NEU*Total Yearly Antipsychotic Prescriptions 0.0032 0.0094 0.737 3.99 Age Quartile 43-50* -1.77 0.760 0.020 0.252 Age Quartile 51-58* -1.42 0.761 0.063 0.262 Age Quartile 59+* -2.00 0.810 0.014 0.222 PCP* 17.02 0.793 <.001 0.536 PSY* 44.72 1.035 <.001 0.194 NEU* 27.66 1.660 <.001 0.041 Female* 6.68 0.640 <.001 0.263 Population 150,000-500,000 (county)* -3.55 0.760 <.001 0.262 Population 500,000-1,000,000 (county)* -5.65 0.796 <.001 0.225 Population more than 1,000,000 (county)* -7.30 0.769 <.001 0.264 Solo Practice* 0.310 0.682 0.650 0.199 Hospital Based Physician* -2.67 0.995 0.007 0.081 DO Flag* 0.103 0.970 0.915 0.086 Physician Opt Out* 2.74 1.486 0.065 0.034 Number of Observations= 17,652 Pseudo R ^2 = 0.017 Left Censored=0 Right Censored=3,353 * dy/dx is for a discrete change of a dummy variable from 0 to 1
  • 35. Prescriber Concentration: HHI for All Antipsychotics and Just Atypicals
    • For all antipsychotics, given volume, HHI is lowest for PSY<PCP<NEU <OTH; effect of volume is –ve for OTH, PCP and NEU, but essentially zero for PSY; age quartiles U-shaped; population effect +ve but peaks at 1m; female +ve, others not significant
    • For atypicals, similar results except oldest age quartile has large +ve effect unlike younger quartiles (no effect), while DO, MD opt out -ve
  • 36. Tobit Regressions (Marginal Effects Evaluated at Variable Means) on Log (Antipsychotic Prescription HHI for 2007) and Log (Atypical Antipsychotic Prescription HHI for 2007) Antipsychotic HHI Atypical HHI   dy/dx Standard Error P>|z| Mean Value dy/dx Standard Error P>|z| Mean Value Total Yearly Antipsychotic Prescriptions -0.000821 0.000080 <.001 171.80 -0.001026 0.000080 <.001 182.67 PCP*Total Yearly Antipsychotic Prescriptions -0.000149 0.000090 0.089 36.44 -0.000051 0.000090 0.557 39.04 PSY*Total Yearly Antipsychotic Prescriptions 0.000626 0.000080 <.001 118.84 0.000859 0.000080 <.001 128.99 NEU*Total Yearly Antipsychotic Prescriptions 0.000359 0.000120 0.002 3.99 0.000384 0.000110 0.001 4.30 Age Quartile 43-50* -0.029 0.010 0.002 0.252 0.002 0.009 0.869 0.252 Age Quartile 51-58* -0.039 0.010 <.001 0.262 0.008 0.009 0.424 0.262 Age Quartile 59+* -0.019 0.010 0.067 0.222 0.055 0.010 <.001 0.223 PCP* -0.435 0.010 <.001 0.536 -0.206 0.011 <.001 0.563 PSY* -0.761 0.013 <.001 0.194 -0.708 0.013 <.001 0.210 NEU* -0.238 0.021 <.001 0.041 -0.011 0.021 0.592 0.043 Female* 0.057 0.008 <.001 0.263 0.034 0.008 <.001 0.267 Population 150,000-500,000 (county)* 0.020 0.010 0.037 0.262 0.013 0.009 0.159 0.261 Population 500,000-1,000,000 (county)* 0.040 0.010 <.001 0.225 0.052 0.010 <.001 0.225 Population more than 1,000,000 (county)* 0.032 0.010 0.001 0.264 0.047 0.010 <.001 0.262 Solo Practice* -0.005 0.009 0.582 0.199 0.017 0.009 0.046 0.201 Hospital Based Physician* 0.019 0.013 0.119 0.081 -0.003 0.013 0.805 0.079 DO Flag* -0.023 0.012 0.056 0.086 -0.029 0.012 0.014 0.088 Physician Opt Out* -0.035 0.019 0.056 0.034 -0.037 0.018 0.043 0.035 Number of Observations= 17,652 Number of Observations= 16,262 Pseudo R^2= 0.218 Pseudo R^2= 0.22 Left Censored=0 Right Censored=1,571 Left Censored=0 Right Censored=2,606 * dy/dx is for a discrete change of a dummy variable from 0 to 1
  • 37. Deviance from National and HRR Market Share Norms
    • Similar results for national and HRR shares – only show national share results
    • Given volume, deviance of PSY<PCP<NEU <OTH; effect of volume –ve for OTH and PCP, less so for NEU and especially PSY; effect of age quartiles U-shaped; population effect +ve but peaks at 1m; female effect +ve; other variables not significant
  • 38. Linear Regression on Log (Deviance in Physician Antipsychotic Prescribing from National Market Shares)   Coefficient Standard Error P>|z| Total Yearly Antipsychotic Prescriptions -0.00131 0.00014 <.001 PCP*Total Yearly Antipsychotic Prescriptions -0.00079 0.00016 <.001 PSY*Total Yearly Antipsychotic Prescriptions 0.00079 0.00014 <.001 NEU*Total Yearly Antipsychotic Prescriptions 0.00059 0.00022 0.008 Age Quartile 43-50* -0.013 0.018 0.456 Age Quartile 51-58* -0.007 0.018 0.674 Age Quartile 59+* 0.054 0.019 0.005 PCP* -0.661 0.019 <.001 PSY* -1.458 0.024 <.001 NEU* -0.499 0.039 <.001 Female* 0.045 0.015 0.003 Population 150,000-500,000 (county)* 0.021 0.018 0.228 Population 500,000-1,000,000 (county)* 0.101 0.019 <.001 Population more than 1,000,000 (county)* 0.085 0.018 <.001 Solo Practice* 0.012 0.016 0.469 Hospital Based Physician* 0.025 0.023 0.283 DO Flag* -0.036 0.023 0.110 Physician Opt Out* -0.052 0.035 0.134 Cons 8.204 0.023 <.001 Number of Observations= 17,652 R^2= 0.345 * indicates dummy variable
  • 39. Robustness Checks
    • Results essentially unchanged when we add a quadratic term in volume, and when adding volume quadratic interacted with specialties – so volume-specialty findings are not due to simple nonlinearity
    • If we restrict sample to 3K+ PSYs, still get same volume, age and gender results, but not as significant, and other variables not as significant
    • When 305 HRR dummies are added, while jointly highly significant, increase in R 2 de minimus -- <4% -- variability is at prescriber level, not at HRR level
  • 40. Discussion: I
    • Our results largely complement and extend those reported by Frank-Zeckhauser [2007] – here a chronic maintenance medication, a greater range and number of specialty prescribers – rational prescribing and “sensible use of norms”
    • Our model entirely ignores learning from others, spillovers, and is inconsistent with herding behavior by physicians, as modeled and estimated in Chandra and Staiger [2007]
    • Results different from large Dartmouth Atlas HRR and state level literature – pharmaceuticals differ from procedures (Zhang, Baicker, Newhouse [2010]); non-Medicare differs from Medicare (Rettenmeier- Saving [2009]), and under age 65 from 65+ (Gawande [2009] and Franzini, Mikhail and Skinner [2010]) in Medicare vs. private insurance – McAllen vs. El Paso
  • 41. Discussion: II
    • Higher volume physicians use wider variety of drugs, mimic more closely national norms – have most to learn and benefit from exploring vs. exploiting
    • Oldest quartile and female MDs most concentrated – discount future at greater rate?
    • Volume matters most for PCPs and those trained in specialties not typically prescribing antipsychotics in large volumes, and least for PSY – specialty training and volume prescribing are substitutes for learning about drug characteristics
  • 42. Discussion: III
    • Deviance from national norms also decreases with volume
    • Major limitation is that we don’t observe patient data – but finding of dominant role of physician over patient is frequent (Frank-Zeckhauser [2007], Hellerstein [1997], Zhang, Baicker and Newhouse [2010], Schneeweis, Glynn, Avorn and Solomon [2005])
    • How generalizable to other medication classes? Non-physician prescribers?
    • Our 2007 analysis is a single cross-section
  • 43. Discussion: IV
    • Atypical
    • Market Share 2002 2008
    • Seroquel 21% 37%
    • Abilify 0 16
    • Geodon 4 7
    • Risperdal 35 26
    • Zyprexa 34 12
    • Other 6 2
    • Who switched most rapidly? High or low volume prescribers? PCPs or specialists? Old vs. young, male or female, group or solo practice MDs? Variation across HRRs? Who responds most/least to published clinical trial results, FDA warnings?