Ontology highlight
ABSTRACT: Objective
To estimate the own-price elasticity of demand for naloxone, a prescription medication that can counter the effects of an opioid overdose, and predict the change in pharmacy sales following a conversion to over-the-counter status.Data sources/study setting
The primary data source was a nationwide prescription claims dataset for 2010-2017. The data cover 80 percent of US retail pharmacies and account for roughly 90 percent of prescriptions filled. Additional covariates were obtained from various secondary data sources.Study design
We estimated a longitudinal, simultaneous equation model of naloxone supply and demand. Our primary variables of interest were the quantity of naloxone sold, measured as total milligrams sold at pharmacies, and the out-of-pocket price paid per milligram, both measured per ZIP Code and quarter-year.Data collection/extraction methods
Primary data came directly from payers and processors of prescription drug claims.Principal findings
We found that, on average, a 1 percent increase in the out-of-pocket price paid for naloxone would result in a 0.27 percent decrease in pharmacy sales. We predict that the total quantity of naloxone sold in pharmacies would increase 15 percent to 179 percent following conversion to over-the-counter status.Conclusions
Naloxone is own-price inelastic, and conversion to over-the-counter status is likely to lead to a substantial increase in total pharmacy sales.
SUBMITTER: Murphy SM
PROVIDER: S-EPMC6606536 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature

Health services research 20190220 4
<h4>Objective</h4>To estimate the own-price elasticity of demand for naloxone, a prescription medication that can counter the effects of an opioid overdose, and predict the change in pharmacy sales following a conversion to over-the-counter status.<h4>Data sources/study setting</h4>The primary data source was a nationwide prescription claims dataset for 2010-2017. The data cover 80 percent of US retail pharmacies and account for roughly 90 percent of prescriptions filled. Additional covariates w ...[more]