Utilization Management in the Medicare Part D Program and Prescription Drug Utilization
-
Martin S. Andersen
Abstract
Medicare Part D has significantly enhanced access to prescription drugs among Medicare beneficiaries. However, the recent rapid rise of utilization management policies in the Medicare Part D program may have adversely affected access to prescription drugs. I study the effects of expected and observed exposure to utilization management in prescription drug utilization using Medicare Part D claims data from 2009 to 2016 and an instrumental variables strategy based on the interaction of lagged health status and the set of plans available to each beneficiary. I find that the expected share of spending subject to utilization management increases the observed share, with the smallest effect for prior authorization. Increases in the expected share of drug spending subject to prior authorization increases Part D spending by $122.27 per percentage point, with almost three-quarters of this increase being paid by the Medicare program, rather than beneficiaries or plans. Comparable increases in step therapy and quantity limit exposure increase spending by $46 and decrease spending by $31, respectively. Interestingly, increased exposure to prior authorization and quantity limits increases the average price per 30-day prescription.
Appendix
Simulator correlations.
| Year | Raw correlation | Adjusted correlation | ||||
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| Spending | Out-of-pocket | TrOOP | Spending | Out-of-pocket | TrOOP | |
| 2010 | 0.716 | 0.930 | 0.931 | 0.985 | 0.940 | 0.940 |
| 2011 | 0.687 | 0.949 | 0.967 | 0.974 | 0.974 | 0.980 |
| 2012 | 0.519 | 0.905 | 0.94 | 0.952 | 0.967 | 0.973 |
| 2013 | 0.747 | 0.930 | 0.955 | 0.862 | 0.950 | 0.965 |
| 2014 | 0.722 | 0.892 | 0.934 | 0.841 | 0.925 | 0.951 |
| 2015 | 0.765 | 0.889 | 0.934 | 0.940 | 0.940 | 0.961 |
| 2016 | 0.694 | 0.850 | 0.911 | 0.916 | 0.937 | 0.958 |
-
Source—Author’s analysis of Medicare Part D claims files. Each cell is the correlation coefficient between the observed and simulated values for the column variable in the row year. The adjusted correlation is after restricting the sample to beneficiaries who are never in the top or bottom 0.1 percentile of errors on each of the three listed measures. “TrOOP” is the true out-of-pocket, which account for spending in the Medicare Part D coverage gap.
References
Andersen, M., V. Lorenz, A. Pant, J. Bray, and G. C. Alexander. 2022. “Association of Opioid Utilization Management ith Prescribing and Overdose.” American Journal of Managed Care 28 (2): e63–8.10.37765/ajmc.2022.88829Suche in Google Scholar
Andersen, M., and A. Pant. 2022. “Effects of Utilization Management on Health Outcomes: Evidence from Urinary Tract Infections and Community-Acquired Pneumonia.” Expert Review of Pharmacoeconomics & Outcomes Research 22 (6): 981–92, https://doi.org/10.1080/14737167.2022.2067529.Suche in Google Scholar
Congressional Budget Office. 2020. “Medicare—CBO’s Baseline as of March 6, 2020,” p. 4.Suche in Google Scholar
Cubanski, J., T. Neuman, and M. Freed. 2019. “The Facts on Medicare Spending and Financing.” The Henry J. Kaiser Family Foundation, https://www.kff.org/medicare/issue-brief/the-facts-on-medicare-spending-and-financing/ (Accessed September 22, 2022).Suche in Google Scholar
Duggan, M., P. Healy, and F. S. Morton. 2008. “Providing Prescription Drug Coverage to the Elderly: America’s Experiment with Medicare Part D.” Journal of Economic Perspectives 22 (4): 69–92, https://doi.org/10.1257/jep.22.4.69.Suche in Google Scholar
Engelhardt, G. V., and J. Gruber. 2011. “Medicare Part D and the Financial Protection of the Elderly.” American Economic Journal: Economic Policy 3 (4): 77–102, https://doi.org/10.1257/pol.3.4.77.Suche in Google Scholar
Finkelstein, A., S. Taubman, B. Wright, M. Bernstein, J. Gruber, J. Newhouse, H. Allen, and K. Baicker, Oregon Health Study Group. 2012. “The Oregon Health Insurance Experiment: Evidence from the First Year.” The Quarterly Journal of Economics 127 (3): 1057–106, https://doi.org/10.1093/qje/qjs020.Suche in Google Scholar
Geweke, J., G. Gowrisankaran, and R. J. Town. 2003. “Bayesian Inference for Hospital Quality in a Selection Model.” Econometrica 714: 1215–38, https://doi.org/10.1111/1468-0262.00444.Suche in Google Scholar
Gilman, B. H., and J. Kautter. 2008. “Impact of Multitiered Copayments on the Use and Cost of Prescription Drugs Among Medicare Beneficiaries.” Health Services Research 43 (2): 478–95, https://doi.org/10.1111/j.1475-6773.2007.00774.x.Suche in Google Scholar
Hartung, D. M., D. R. Touchete, K. L. Ketchum, D. G. Haxby, and B. W. Goldberg. 2004. “Effects of a Prior-Authorization Policy for Celecoxib on Medical Service and Prescription Drug Use in a Managed Care Medicaid Population.” Clinical Therapeutics 26 (9): 1518–32, https://doi.org/10.1016/j.clinthera.2004.09.013.Suche in Google Scholar
Huskamp, H. A., R. G. Frank, K. A. McGuigan, and Y. Zhang. 2005. “The Impact of a Three-Tier Formulary on Demand Response for Prescription Drugs.” Journal of Economics and Management Strategy 14 (3): 729–53, https://doi.org/10.1111/j.1530-9134.2005.00080.x.Suche in Google Scholar
MedPAC. 2015. Health Care Spending and the Medicare Program. Tech. rep. Washington.Suche in Google Scholar
Ketcham, J. D., C. Lucarelli, and C. A. Powers. 2015. “Paying Attention or Paying Too Much in Medicare Part D.” The American Economic Review 1051: 204–33, https://doi.org/10.1257/aer.20120651.Suche in Google Scholar
Law, M. R., C. Y. Lu, S. B. Soumerai, A. J. Graves, R. F. LeCates, F. Zhang, D. Ross-Degnan, and A. S. Adams. 2010. “Impact of Two Medicaid Prior-Authorization Policies on Antihypertensive Use and Costs Among Michigan and Indiana Residents Dually Enrolled in Medicaid and Medicare: Results of a Longitudinal, Population-Based Study.” Clinical Therapeutics 32 (4): 729–41, https://doi.org/10.1016/j.clinthera.2010.04.007.Suche in Google Scholar
Law, M. R., D. Ross-Degnan, and S. B. Soumerai. 2008. “Effect of Prior Authorization of Second-Generation Antipsychotic Agents on Pharmacy Utilization and Reimbursements.” Psychiatric Services 59 (5): 540–6, https://doi.org/10.1176/ps.2008.59.5.540.Suche in Google Scholar
Morley, C. P., D. J. Badolato, J. Hickner, and J. W. Epling. 2013. “The Impact of Prior Authorization Requirements on Primary Care Physicians’ Offices: Report of Two Parallel Network Studies.” The Journal of the American Board of Family Medicine 26 (1): 93–5, https://doi.org/10.3122/jabfm.2013.01.120062.Suche in Google Scholar
Newhouse, J. P. 1993. Free for All? Lessons from the RAND Health Insurance Experiment. Cambridge, MA: Harvard University Press.Suche in Google Scholar
Wooldridge, J. M. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge: MIT Press.Suche in Google Scholar
Zhang, Y., J. M. Donohue, J. R. Lave, G. O’Donnell, and J. P. Newhouse. 2009. “The Effect of Medicare Part D on Drug and Medical Spending.” New England Journal of Medicine 361 (1): 52–61, https://doi.org/10.1056/nejmsa0807998.Suche in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
