This study aimed to examine the opioid prescribing patterns and trends relative to the newly implemented PDMP in Pennsylvania. This study found that from Q3 in 2016 to Q1 in 2020, there was a 38% decrease in opioid prescriptions. Hydrocodone and oxycodone had the largest decrease in the number of prescriptions, decreasing from 2016 to 2020 by 295,729 and 330,284, respectively. Fentanyl, meperidine, and oxymorphone had the largest decrease in percentage, decreasing by 51%, 89%, and 77%, respectively. The composition of drugs being prescribes saw slight changes from 2016 to 2020. Fentanyl and hydrocodone were the two opioids that saw the largest decrease between 2016 and 2020 but decreasing by only 0.9% and 3.5%, respectively. Morphine and oxycodone saw the largest increase among the opioids, increasing by 1.8% and 3.2%, respectively.
This study is one of the first to examine Pennsylvania's opioid prescribing trends. Pennsylvania is a large, diverse state with one of the largest cities in the country in Philadelphia, several other large metropolitan areas, and large rural areas. National trends show the total number of prescriptions dispensed peaked in 2012, at more than 255 million and a dispensing rate of 81.3 prescriptions per 100 persons. While opioid prescriptions have decreased to 153 million, dispensing at 46.7 prescriptions per 100 persons nationally, they are still being prescribed almost three times more than in 1999 (11, 12). Pennsylvanians similarly continue to receive a high number of opioid prescriptions, but there is a downward trajectory demonstrated in the four years since PDMP data has become available.
In addition to the decrease noted in the number of opioid prescriptions written across Pennsylvania during the study period after implementing the PDMP, there are also some interesting findings about the type of opioids being prescribed. Specifically, Pennsylvania prescribers decreased their overall opioid prescription numbers; the greatest decrease was among opioids with the highest morphine milligram equivalents (MME). For instance, while hydrocodone and oxycodone had the largest absolute decrease in the number of prescriptions over the study period, fentanyl, meperidine, and oxymorphone had the largest decreases percentage-wise. These three medications also happen to have the highest MME.
Upon review of the current literature, no other studies were identified that examined opioid prescribing patterns at a state level. However, one study was identified that evaluated Texas' opioid prescribing patterns that reported a reduction in schedule II medications being prescribed and an increase in schedule IV medications like tramadol (13). While this cannot be directly compared to the Pennsylvania PDMP because it does not record schedule IV medications like tramadol, it is speculated that tramadol would similarly have increased over the study period in Pennsylvania.
The decrease in opioid prescriptions from 2016 to 2020 saw higher MME opioids being prescribed less. Nevertheless, when looking at each quarter and its composition of opioids being prescribed, only slight changes were seen from 2016 to 2020. Fentanyl and hydrocodone saw the largest decrease, 0.9% and 3.5%, respectively. Morphine and oxycodone saw the largest increase, 1.8% and 3.2%, respectively. While the percent composition of medications remained relatively consistent from quarter to quarter, the total number of prescriptions decreased. When this article was written, no previous research that evaluated the composition of opioids being prescribed had been published. While a firm conclusion cannot be drawn about what is responsible for the decreased opioid prescriptions, the Pennsylvania PDMP could be anticipated to have played a role in helping prescribers make informed decisions regarding patients resulting in decreased opioid prescriptions.
While the PDMP allows better tracking of opioids and, at least in Pennsylvania, documents a decreasing trend in prescribing, Finley et al. found that studies examining the association between PDMP implementation and opioid-related outcomes did not display a constant pattern (14). When Florida and New York evaluated their PDMPs, they found evidence that opioid prescribing was reduced (15, 16). Pennsylvania aligns with Florida and New York in their reduction of opioid prescribing. However, when North Carolina analyzed their prescribing results, they found no significant trends in opioid prescribing (17). The variation between these states could be from the variation in study design and methods. However, another factor that could have affected these results is that PDMPs vary considerably between states. Specifically, each state PDMP program is managed by varying regulatory agencies, collects different data types, requires data to be updated at different times, allows access to different groups of people, and does not require all prescribers to register and use the PDMP.
While the implementation of the PDMP has allowed for better tracking of opioid prescriptions, Pennsylvania continues to suffer from being the third-highest age-adjusted state for drug overdose death, with a rate of 36.1 per 100,000 standard population, only behind Delaware and Maryland (4). Pennsylvania is also classified as a problematic state because of lower numbers of opioid treatment programs (18). While the evaluation of PDMPs is still in an early stage, the current evidence has shown that PDMPs may have other unintended consequences. First, PDMPs are serving as a more comprehensive tool for prescribers; Feldman et al. found that 93.6% of physicians that accessed Ohio's PDMP reported it influenced the type and quantity of medication they prescribed (19). Another reported PDMP outcome is that it may cause a "chilling effect," dissuading prescribers from prescribing opioids that might deny patients adequate pain control (20, 21), or other unclear long-term unintended consequences (14, 22). Some evidence also suggests that patients with reduced access to opioids may turn to illegal options like heroin; two studies found an increase in mortality from heroin, morphine, and fentanyl in some states with PDMPs (15,23).
This study had several limitations. First, patient and provider demographic data was not available for review and correlation. Therefore, a sub-analysis based on patient age, gender, race, and other demographics was impossible. Similarly, no prescriber data was available, and therefore no analysis of prescriber characteristics, including specialization and training, was not possible. Lastly, this study represents a specific period in time of analysis without control for other variables, and therefore conclusions on opioid prescribing characteristics can only be related to the state's PDMP, but direct causality cannot be proven.