Physician Drug Selection in Oncology Aaron Mitchell, MD Aaron Winn, - - PowerPoint PPT Presentation

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Physician Drug Selection in Oncology Aaron Mitchell, MD Aaron Winn, - - PowerPoint PPT Presentation

Jan 04, 2024 176 likes •475 views

Pharmaceutical Industry Payments and Physician Drug Selection in Oncology Aaron Mitchell, MD Aaron Winn, MPP Stacie Dusetzina, PhD 1 Background: conflicts of interest Common in US Potential unintended consequences DeJong et al, JAMA

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Pharmaceutical Industry Payments and Physician Drug Selection in Oncology Aaron Mitchell, MD Aaron Winn, MPP Stacie Dusetzina, PhD

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  • Common in US
  • Potential unintended consequences

DeJong et al, JAMA Internal Medicine, 2016

Background: conflicts of interest

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  • Conflicts within oncology practice
  • Granularity: different types of conflicts

Overall Research Goals

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1) Test for an association between receipt of payments from a pharmaceutical company and prescription of that company’s cancer drug 2) Test for an association with different payment categories, General Payments (GP) and Research Payments (RP)

  • Sponsored meals
  • Consulting
  • Speaker fees
  • Travel/lodging
  • Gifts
  • Direct research grants
  • Funding going to PI

institution

Study Goals

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  • 2. Data on industry payments to oncologists:
  • 1. Data on oncologists’ prescribing patterns:

Data Sources

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  • 1. Define cancer types of interest
  • Those for which oncologists have several FDA-approved,

guideline-recommended, orally-administered drugs to choose from

Renal Cell Carcinoma (RCC): Sunitinib Sorafenib Pazopanib Chronic Myeloid Leukemia (CML): Imatinib Dasatinib Nilotinib

  • 2. Define cohort of oncologists prescribing these drugs
  • At least 20 filled claims among the “drugs of interest”

for each cancer type, during 2014

  • Specialty listed as “oncology”

Methods

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Primary independent variable: Receipt of payment (binary yes/no) from the manufacturer of one of the cancer drugs in 2013 Adjusted covariates: Geographic region, physician gender, year of medical school graduation, practice size, prescribing volume, GP or RP Primary dependent variable: Odds of prescribing, in 2014, the drug made by the manufacturer from which physician had received payment[s] during 2013 Model structure: McFadden conditional logit

Analytic Strategy

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Drug A Drug B Drug C Drug A Drug B Drug C

Control Index

Analytic Strategy

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Physicians in Open Payments

  • Received payments from

manufacturer of drug of interest

  • Specialty is oncology
  • Duplicates excluded

N = 12,991

Physicians in Medicare Part D

  • 20 or more claims among

drugs of interest

  • Specialty is oncology
  • Duplicates excluded

N = 2,440

Merge by name + practice location In both datasets

N = 1,634

In Part D, NOT in Open Payments

N = 803

In Open Payments, NOT in Part D

N = 11,351

Physicians who received money from the relevant drug companies, but did not prescribe RCC or CML drugs Physicians prescribing RCC and/or CML drugs who DID receive industry payments Physicians prescribing RCC and/or CML drugs who DID NOT receive industry payments

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Results

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Physician cohort RCC (n = 356) CML (n = 2,140)

Group practice size by number of physicians, % 1 7.3 6.8 2-10 12.9 17.4 11-50 15.5 20.5 >50 64.3 55.3 Received general payments in 2013, % 36.0 45.1 Received general payments in 2014, % 53.7 52.4 Received research payments in 2013, % 9.0 6.3 Received research payments in 2014, % 13.8 8.9 Mean dollar value of all general payments in 2013 (SD)* $ 566 (1,143) $ 166 (775) Mean dollar value of all research payments in 2013 (SD)* $ 33,391 (60,950) $ 185,763 (718,595)

* Among those physicians who received payments

Results

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Odds ratio (95% CI) RCC CML 1.78 (1.23 – 2.57) 1.29 (1.13 – 1.48) 2.13 (1.13 – 4.00) 1.10 (0.83 – 1.45)

GP RP

Received payment in:

Index group Control group

2013 2014 +/- +/-

Index group Control group

+/- +/-

Results: base case analysis

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Odds ratio (95% CI) RCC CML 2.64 (1.51 – 4.61) 1.25 (1.08 – 1.47) 2.14 (0.93 – 4.91) 0.95 (0.70 – 1.30) Received payment in:

Index group Control group

2013 2014 +/- +/-

GP RP

Index group Control group

Results: sensitivity analysis

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Odds ratio (95% CI) RCC CML 2.87 (1.70 – 4.86) 1.21 (1.05 – 1.42) 2.48 (1.15 – 5.34) 1.08 (0.82 – 1.43) Received payment in:

Index group Control group

2013 2014

GP RP

Index group Control group

Results: sensitivity analysis

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0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

PAZOPANIB SORAFENIB SUNITINIB

Adjusted fraction of prescriptions for each drug

DASATINIB IMATINIB NILOTINIB

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

PAZOPANIB SORAFENIB SUNITINIB

Adjusted fraction of prescriptions for each drug

DASATINIB IMATINIB NILOTINIB

* * * * General Payments Research Payments RCC CML

No Payments Received Payments

Results: individual drug effects

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Accuracy of Open Payments Generalizability Drug indications Correlation and causation

Limitations

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Consistent association between general payments and increased prescribing Inconsistent association between research payments and increased prescribing

Conclusions

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Questions?

@TheWonkologist

This research was partially supported by a National Service Research Award Post- Doctoral Traineeship from the Agency for Healthcare Research and Quality sponsored by the Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Grant No. 5T32 HS000032-28.

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Slides in reserve

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Results: continuous variable

Odds ratio* (95% CI) RCC CML 1.13 (1.05 – 1.22) 1.06 (1.03 – 1.10) 1.08 (1.01 – 1.16) 1.01 (0.98 – 1.03) Received payment in: 2013 2014 Index group +/- Index group +/- GP RP

Industry payments modeled as a continuous variable on logged-odds scale

* Odds ratio associated with a 10-fold increase in dollar value of payments

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Financial relationships with industry among National Comprehensive Cancer Network (NCCN) guideline authors

Mitchell et al, JAMA Oncology, 2016 16% 10% 18% 29% 24% 3%

0% 5% 10% 15% 20% 25% 30% 35%

0 - 99 100 - 999 1,000 - 9,999 10,000 - 49,999 >=50,000 Percentage of Authors

General payments received (inclusive of consulting fees, meals, travel, lodging), USD

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Perlis RH and Perlis CS, PLOS One, 2016

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Iria Puyosa, 2009

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Analytic Strategy

Identify scenarios in which oncologists have several drugs to choose from, and hence any preferences are more likely to be detectable Stage III colon cancer

Surgical resection + FOLFOX chemo

Stage IV kidney cancer

Sunitinib

  • r

Sorafenib

  • r

Pazopanib

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Physician characteristics

mRCC (n = 356) CML (n = 2,140) Male sex, % 82.3 81.5 Group practice size by number of physicians, % 1 7.3 6.8 2-10 12.9 17.4 11-50 15.5 20.5 >50 64.3 55.3 Year of Medical School Graduation, mean (SD of number of years) 1990 (9.5) 1987 (9.8) US Geographical Region, % Northeast 14.0 15.3 Midwest 20.5 26.5 South 39.3 39.8 West 26.1 18.4 Mean number of claims for all drugs

  • f interest per MD (SD)

33.4 (18.3) 36.3 (17.5) Received GP in 2013, % 36.0 45.1 Received GP in 2014, % 53.7 52.4 Received GP in 2013 & 2014, % 31.7 39.0 Mean dollar value of all GP in 2013 from the manufacturer of the drug of interest, among MDs who received at least one GP (SD) $ 566 (1,143) $ 166 (775) 25th percentile $ 16 $ 16 50th percentile $ 30 $ 20 75th percentile $ 235 $ 24 Received RP in 2013, % 9.0 6.3 Received RP in 2014, % 13.8 8.9 Received RP in 2013 & 2014, % 8.7 5.2 Mean dollar value of all RP in 2013 among MDs who received at least

  • ne RP (SD)

$ 33,391 (60,950) $ 185,763 (718,595) 28