Back

Cross-state variation in opioid use disorder among a privately insured nonelderly population in the United States

Jiang, B.; Wang, L.; Leslie, D.

2020-06-12 health economics
10.1101/2020.06.09.20121269 medRxiv
Show abstract

BackgroundAlthough cross-state variation of the opioid epidemic in the United States are well documented in general, little are known about the epidemic in privately insured individuals. ObjectivesTo describe cross-state variation in Opioid Use Disorder (OUD) among privately insured individuals in the US for the years 2005-2015 and investigate demographic differences of OUD patients between a group of hard-hit states and the rest states. MethodsThe MarketScan(R) Commercial Claims and Encounters database was used to calculate prevalence of opioid use disorder for the 50 states in the US, respectively. We analyzed level and change of OUD prevalence in each state from 2005 to 2015 and identified the states which were affected most by the epidemic. One-sided exact fisher test was used to analyze demographic difference of the epidemic in the hard-hit states and the remaining states. ResultsCross-state variations of the opioid epidemic among privately insured population were substantial, both in terms of severity and acceleration of the epidemic. Demographic patterns of the epidemic were similar across states. The 18-34 age group was the most affected group with the highest prevalence. The 55-64 group experienced the most rapid increase of OUD prevalence, especially in states that suffered most in the epidemic. ConclusionsResults can assist policy makers to design better clinical and policy interventions on the opioid epidemic, especially on privately insured individuals. Drastic increase of OUD prevalence among the 55-64 group might indicate the need to improve prescription drug monitoring programs for chronic pain, especially in states more affected by the epidemic.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.