THE EFFECTIVENESS OF ENROLLMENT TYPE IN PRESCRIPTION DRUG MONITORING PROGRAMS- A RETROSPECTIVE STUDY

Author(s)

Summers C1, Mallow P2, Topmiller M2
1Xavier University, Fort mitchell, KY, USA, 2Xavier University, Cincinnati, OH, USA

BACKGROUND: There was a steady increase in the number of opioid prescriptions written from 2006 – 2012, which led to the development and modification of prescription drug monitoring programs (PDMPs) in most of the states.

OBJECTIVES: The purpose was to examine the relationship in prescribing and deaths related to opioid prescriptions in states with mandatory enrollment and those with no mandatory enrollment PDMPs.

METHODS: This observational study used prescription drug monitoring programs’ (PDMPs’) data, prescribing data from Brandeis University, and opioid related mortality data from the Centers for Disease Control and Prevention (CDC). The dependent variable were rates of opioid deaths and rates of prescriptions filled in the United States with mandatory enrollment and States that have no mandatory enrollment in 2015 and 2016 in the PDMP. The explanatory variable was PDMP status and enrollment type. The statistical model controlled for geography, interstate sharing, interoperability.

RESULTS: There were 24 states with mandatory enrollment and 9 states with no mandatory enrollment. Results revealed that enrollment type has little to no significant effect on deaths related to prescription opioids in the US. Also, 69% of States with Appalachian counties required both prescriber and dispenser to enroll in the program. However, in 2015, States that required both prescriber and dispenser to enroll had 3 times the total opioid prescribing rates per 100 residents than States with No mandatory enrollment. Appalachian States represent 50-60% of the top 10 highest opioid prescriptions per 100 residents between 2006-2016.

CONCLUSIONS: PDMP have been promising in identifying trends in prescribing and filling of medication. The findings in this study suggest that the effectiveness of enrollment status at reducing deaths related to prescription opioids could not be supported in 2015 and 2016. There’s a need for more research in this area.

Conference/Value in Health Info

2019-05, ISPOR 2019, New Orleans, LA, USA

Value in Health, Volume 22, Issue S1 (2019 May)

Code

PDG56

Topic

Methodological & Statistical Research, Real World Data & Information Systems

Topic Subcategory

Confounding, Selection Bias Correction, Causal Inference, Distributed Data & Research Networks

Disease

Drugs

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