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Opioid prescribing patterns and overdose deaths in Texas.

Tiffany Champagne-LangabeerRenita MaduSharmila GiriAngela L StottsJames R. Langabeer II
Published in: Substance abuse (2019)
Opioid use disorder has recently been declared a public health emergency, yet it is unknown whether opioid prescribing patterns have changed over time. Our objective is to examine opioid prescribing behavior and overdose fatalities in one large state prior to state-mandated usage of a prescription drug monitoring program (PDMP). Methods: We relied on de-identified longitudinal data from state and national databases for opioid prescriptions and overdose deaths in Texas between 2013 and 2017. Descriptive statistics and trend analyses were used to assess proportional differences and changes over time. Results: Prescriptions for opioids represented over 45% of the total controlled medications dispensed across the entire period. This equates to roughly 17.7 million opioid prescriptions dispensed per year, or 63.7 opioid prescriptions per 100 persons, slightly less than the reported national average. Hydrocodone was the most widely prescribed opioid (32.9%), followed by tramadol (26.9%) and codeine (21.5%). The overall controlled substance prescribing rate appears to be decreasing in the latest year, and the composition of opioids has shifted. We found a reduction in schedule II medications (such as hydrocodone and fentanyl) and increase in schedule IV medications such as tramadol. At the same time, total overdose fatalities increased 42% during this time, and population-adjusted rates increased 34% to 5.87 deaths per 100,000 persons. Conclusions: While prescribing rates have decreased in Texas, overdose deaths from both legal and illicit opioids are rising, suggesting that changing physician prescribing behavior alone may not be sufficient to curb the epidemic. Policies and community interventions should be considered to address increases in both prescription and illicit opioid deaths.
Keyphrases
  • chronic pain
  • pain management
  • primary care
  • public health
  • emergency department
  • adverse drug
  • healthcare
  • quality improvement
  • mental health
  • cross sectional
  • big data
  • machine learning