↓ Skip to main content

Healthcare costs and utilization associated with high-risk prescription opioid use: a retrospective cohort study

Overview of attention for article published in BMC Medicine, May 2018
Altmetric Badge

Mentioned by

2 tweeters


18 Dimensions

Readers on

56 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Healthcare costs and utilization associated with high-risk prescription opioid use: a retrospective cohort study
Published in
BMC Medicine, May 2018
DOI 10.1186/s12916-018-1058-y
Pubmed ID

Hsien-Yen Chang, Hadi Kharrazi, Dave Bodycombe, Jonathan P. Weiner, G. Caleb Alexander


Previous studies on high-risk opioid use have only focused on patients diagnosed with an opioid disorder. This study evaluates the impact of various high-risk prescription opioid use groups on healthcare costs and utilization. This is a retrospective cohort study using QuintilesIMS health plan claims with independent variables from 2012 and outcomes from 2013. We included a population-based sample of 191,405 non-elderly adults with known sex, one or more opioid prescriptions, and continuous enrollment in 2012 and 2013. Three high-risk opioid use groups were identified in 2012 as (1) persons with 100+ morphine milligram equivalents per day for 90+ consecutive days (chronic users); (2) persons with 30+ days of concomitant opioid and benzodiazepine use (concomitant users); and (3) individuals diagnosed with an opioid use disorder. The length of time that a person had been characterized as a high-risk user was measured. Three healthcare costs (total, medical, and pharmacy costs) and four binary utilization indicators (the top 5% total cost users, the top 5% pharmacy cost users, any hospitalization, and any emergency department visit) derived from 2013 were outcomes. We applied a generalized linear model (GLM) with a log-link function and gamma distribution for costs while logistic regression was employed for utilization indicators. We also adopted propensity score weighting to control for the baseline differences between high-risk and non-high-risk opioid users. Of individuals with one or more opioid prescription, 1.45% were chronic users, 4.81% were concomitant users, and 0.94% were diagnosed as having an opioid use disorder. After adjustment and propensity score weighting, chronic users had statistically significant higher prospective total (40%), medical (3%), and pharmacy (172%) costs. The increases in total, medical, and pharmacy costs associated with concomitant users were 13%, 7%, and 41%, and 28%, 21% and 63% for users with a diagnosed opioid use disorder. Both total and pharmacy costs increased with the length of time characterized as high-risk users, with the increase being statistically significant. Only concomitant users were associated with a higher odds of hospitalization or emergency department use. Individuals with high-risk prescription opioid use have significantly higher healthcare costs and utilization than their counterparts, especially those with chronic high-dose opioid use.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 56 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 21%
Student > Bachelor 9 16%
Student > Master 8 14%
Student > Ph. D. Student 6 11%
Student > Postgraduate 4 7%
Other 9 16%
Unknown 8 14%
Readers by discipline Count As %
Medicine and Dentistry 20 36%
Pharmacology, Toxicology and Pharmaceutical Science 7 13%
Nursing and Health Professions 4 7%
Economics, Econometrics and Finance 4 7%
Agricultural and Biological Sciences 3 5%
Other 10 18%
Unknown 8 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 May 2018.
All research outputs
of 15,142,643 outputs
Outputs from BMC Medicine
of 2,343 outputs
Outputs of similar age
of 278,507 outputs
Outputs of similar age from BMC Medicine
of 1 outputs
Altmetric has tracked 15,142,643 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,343 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 36.9. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 278,507 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them