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Estimating price elasticities of demand for pain relief drugs: evidence from Medicare Part D

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Abstract

Overdose deaths from prescription opioids remain elevated, and policymakers seek solutions to curb opioid misuse. Recent proposals call for price-based solutions, such as opioid taxes and removal of opioids from insurance formularies. However, there is limited evidence on how opioid consumption responds to price stimuli. This study addresses that gap by estimating the effects of prices on the utilization of opioids, as well as other prescription painkillers. I use nationally representative individual-level data on prescription drug purchases and exploit the introduction of Medicare Part D in 2006 as an exogenous change in out-of-pocket drug prices. I find that new users have a relatively high price elasticity of demand for prescription opioids, and that consumers treat over-the-counter painkillers as substitutes for prescription painkillers. My results suggest that increasing out-of-pocket prices of opioids, through formulary design or taxes, may be effective in reducing new opioid use.

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Fig. 1
Fig. 2

Source: Author’s calculations based on Nielsen Household Consumer Panel 2004–2016. Figures display mean annual spending per household, adjusted by Nielsen survey weights. Spending outcomes have been adjusted for inflation

Fig. 3

Source: Author’s calculations based on data from the Henry J. Kaiser Family Foundation

Fig. 4

Source: Author’s calculations based on Medical Expenditure Panel Survey 2000–2009. Figures display the mean OOP spending per day supply across NDCs, weighted by 2003 utilization of the NDC. Prices are adjusted to 2009 dollars using the Bureau of Labor Statistics’ Pharmaceutical Producer Price Index

Fig. 5

Source: Author’s calculations based on Medical Expenditure Panel Survey 2000–2009. Sample is restricted to adults aged 55–74 (N = 50,579). Figures display the mean annual number of days supplied per person, adjusted by MEPS survey weights

Fig. 6

Source: Author’s calculations based on Nielsen Household Consumer Panel 2004–2009. Figure displays the mean annual number of days supplied per household, adjusted by Nielsen survey weights

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Notes

  1. Most other prescription drugs are exempt from sales tax in all states, except Illinois (where they are taxed at 1% at the state level but exempt from local sales tax) and Louisiana (where they are tax-exempt at the state level, but local areas can opt to tax). In contrast, over the counter (OTC) drugs are subject to sales tax in most states.

  2. For example, in 2018, Kentucky voted on an opioid tax which would have levied a 25-cent tax on drug distributors for each dose sent to the state. Although the bill eventually failed to pass in the state Senate, the House voted in favor of the tax, suggesting that there was some legislative support for the measure.

  3. In Online Appendix Table 4 I also estimate the price elasticity of demand of all prescription drugs using a similar empirical approach as the approach used in the main analysis of this paper. I obtain an elasticity of − 0.45, which is similar to that obtained in previous studies.

  4. My data measures the utilization of drugs, which may not be synonymous with demand. Utilization is based on patients’ demand for the drug, as well as physicians’ willingness to write prescriptions. A reduction in OOP price can increase utilization in three ways: (1) encourage patients to seek prescriptions by increasing physician visits, (2) increase the number of prescriptions written by physicians, and (3) increase the number of prescriptions that are filled (compliance).

  5. The Prescribed Medicines file consists of only outpatient prescription drug purchases; it excludes prescription drugs administered in hospitals, clinics, or physicians’ offices.

  6. The data has been verified by the prescribing pharmacy only for those who consented to release their pharmacy records. For those who did not consent, expenditures are based on self-reported expenditures that have been adjusted for outliers and imputations from the pharmacy data.

  7. See Online Appendix A-3 for additional details on the imputation process.

  8. Although NSAIDs have no known potential for addiction, they are not without risk. Side-effects of prolonged NSAID use include liver damage and GI bleeding. Nevertheless, most studies find that opioids represent a substantially higher risk of death and adverse events than NSAIDs (Solomon et al., 2010).

  9. Table 3 provides an abridged version of the painkiller classification. See Online Appendix Table 2 for the complete classification.

  10. See Online Appendix A-4 for additional background on Medicare Part D.

  11. Other researchers have used the RAND Health Insurance Experiment (HIE) to estimate the price elasticity of demand for health care overall (Manning et al., 1987). While the HIE is useful in identifying the effects of cost sharing for most medical services, plan design did not differ independently for drug coverage, making it difficult to isolate the impact of drug price changes. Moreover, the HIE data is from the 1980s, whereas prescription painkillers became more popular in the late 1990s; consumer preferences for painkillers were likely very different in the 1980s than in more recent years.

  12. Although 74% of the elderly had prescription drug coverage even before 2006, this coverage was often less than adequate. We may expect drug utilization to increase even for those who had coverage before 2006 if Part D coverage was more generous than previous drug plans, e.g. offered lower cost-sharing, fewer restrictions such as prior authorization, or more medications covered in formularies.

  13. While Part D is an older policy, it is still a topic of discussion in the current literature because it provides a valuable context for studying the causal effects of increased pharmaceutical access (Bradford & Bradford, 2016; Buchmueller & Carey, 2018; Carey, 2017; Dunn & Shapiro, 2019; Huh & Reif, 2017; Kaplan & Zhang, 2017; Powell et al., 2020). The purpose of the current analysis is not to evaluate the impact of Part D as a policy, but rather to understand more generally how utilization of prescription painkillers responds to prices. Part D merely serves an identification strategy.

  14. While this classification of treatment and control groups works in the MEPS, the NHCP is a household-level dataset, and households can consist of individuals of differing ages. Nevertheless, the NHCP provides detailed ages of each household member, so I define the treatment group as households with at least one member aged 65–74, and the control group as households with all members < 65.

  15. Part D was signed into law at the end of 2003. The year 2003 is therefore unlikely to be biased by possible anticipation effects (Alpert, 2016).

  16. Weighting by the 2003 level of utilization helps us isolate price changes resulting from changes in actual OOP or list prices (which is what our elasticity measure needs to capture) from price changes resulting in people shifting consumption from cheaper to more expensive drugs in response to increased coverage. This weighting approach is common in the literature (Chandra et al., 2010; Contoyannis et al., 2005; Landsman et al., 2005). See Section Impact of price changes on prescription painkiller utilization.A for additional details.

  17. Previous studies suggest that the form of the policy matters. Consumers underreact to price changes that are not salient (Chetty et al., 2009).

  18. Einav et al. reports that the average Medicare Part D enrollee spends $1910 on drugs per year, but the spending level to enter the donut hole (i.e. the Einav et al. study sample) is $2250–$2840, which is around the 75th percentile of the expenditure distribution.

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Acknowledgements

I am grateful to Kosali Simon, Jeffrey Prince, Haizhen Lin, Daniel Sacks, John Cawley, Christopher Carpenter, Molly Schnell, and seminar participants at American University, Boston University, the Food and Drug Administration, George Washington University, Indiana University, IUPUI, the University of Pennsylvania, the University of Utah, the 2018 Conference of the American Society of Health Economists, the 2018 Conference of the Association of Public Policy Analysis and Management, the 2018 Southern Economic Association Conference, and the 2019 American Economic Association Conference. Aeric Koerner and Alexandra Rakus provided excellent research assistance. Researcher(s)' own analyses calculated (or derived) based in part on data from Nielsen Consumer LLC and marketing databases provided through the NielsenIQ Datasets at the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. The conclusions drawn from the NielsenIQ data are those of the researcher(s) and do not reflect the views of NielsenIQ. NielsenIQ is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein.

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    Aparna Soni

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Soni, A. Estimating price elasticities of demand for pain relief drugs: evidence from Medicare Part D. Int J Health Econ Manag. (2024). https://doi.org/10.1007/s10754-024-09382-3

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