Dataset Information

Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

ABSTRACT: Objective:Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use. Materials and Methods:We searched PubMed and Embase for articles describing automatable algorithms that used electronic health care claims or records to identify patients or prescribers with likely nonmedical opioid use. We assessed algorithm development, validation, and performance characteristics and the settings where they were applied. Study variability precluded a meta-analysis. Results:Of 15 included algorithms, 10 targeted patients, 2 targeted providers, 2 targeted both, and 1 identified medications with high abuse potential. Most patient-focused algorithms (67%) used prescription drug claims and/or medical claims, with diagnosis codes of substance abuse and/or dependence as the reference standard. Eleven algorithms were developed via regression modeling. Four used natural language processing, data mining, audit analysis, or factor analysis. Discussion:Automated algorithms can facilitate population-level surveillance. However, there is no true gold standard for determining nonmedical opioid use. Users must recognize the implications of identifying false positives and, conversely, false negatives. Few algorithms have been applied in real-world settings. Conclusion:Automated algorithms may facilitate identification of patients and/or providers most likely to need more intensive screening and/or intervention for nonmedical opioid use. Additional implementation research in real-world settings would clarify their utility.

SUBMITTER: Canan C

PROVIDER: S-EPMC7651982 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

Canan Chelsea C Polinski Jennifer M JM Alexander G Caleb GC Kowal Mary K MK Brennan Troyen A TA Shrank William H WH

Journal of the American Medical Informatics Association : JAMIA 20171101 6

<h4>Objective</h4>Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use.<h4>Materials and methods</h4>We searched PubMed and Embase for artic ...[more]

PMID: 29016967

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Project description:ImportanceBetween 1997 and 2017, the United States saw increases in nonmedical prescription opioid use and its consequences, as well as changes in marijuana policies. Ecological-level research hypothesized that medical marijuana legalization may reduce prescription opioid use by allowing medical marijuana as an alternative.ObjectivesTo investigate the association of state-level medical marijuana law enactment with individual-level nonmedical prescription opioid use and prescription opioid use disorder among prescription opioid users and to determine whether these outcomes varied by age and racial/ethnic groups.Design, setting, and participantsThis cross-sectional study used restricted data on 627 000 individuals aged 12 years and older from the 2004 to 2014 National Survey on Drug Use and Health, a population-based survey representative of the civilian population of the United States. Analyses were completed from March 2018 to May 2018.ExposuresTime-varying indicator of state-level medical marijuana law enactment (0 = never law enactment, 1 = before law enactment, and 2 = after law enactment).Main outcomes and measuresPast-year nonmedical prescription opioid use and prescription opioid use disorder among prescription opioid users. Odds ratios of nonmedical prescription opioid use and prescription opioid use disorder comparing the period before and after law enactment were presented overall, by age and racial/ethnic group, and adjusted for individual- and state-level confounders.ResultsThe study sample included 627 000 participants (51.51% female; 9.88% aged 12-17 years, 13.30% aged 18-25 years, 14.30% aged 26-34 years, 25.02% aged 35-49 years, and 37.50% aged ≥50 years; the racial/ethnic distribution was 66.97% non-Hispanic white, 11.83% non-Hispanic black, 14.47% Hispanic, and 6.73% other). Screening and interview response rates were 82% to 91% and 71% to 77%, respectively. Overall, there were small changes in nonmedical prescription opioid use prevalence after medical marijuana law enactment (4.32% to 4.86%; adjusted odds ratio, 1.13; 95% CI, 1.06-1.20). Prescription opioid use disorder prevalence among prescription opioid users decreased slightly after law enactment, but the change was not statistically significant (15.41% to 14.76%; adjusted odds ratio, 0.95; 95% CI, 0.81-1.11). Outcomes were similar when stratified by age and race/ethnicity.Conclusions and relevanceThis study found little evidence of an association between medical marijuana law enactment and nonmedical prescription opioid use or prescription opioid use disorder among prescription opioid users. Further research should disentangle the potential mechanisms through which medical marijuana laws may reduce opioid-related harm.

Project description:ImportanceThere is concern that nonmedical prescription opioid use is associated with an increased risk of later heroin use initiation in adolescents, but to our knowledge, longitudinal data addressing this topic are lacking.ObjectiveTo determine whether nonmedical prescription opioid use is associated with subsequent initiation of heroin use in adolescents.Design, setting, and participantsThis prospective longitudinal cohort study conducted in 10 high schools in Los Angeles, California, administered 8 semiannual surveys from 9th through 12th grade that assessed nonmedical prescription opioid use, heroin use, and other factors from October 2013 to July 2017. Students were baseline never users of heroin recruited through convenience sampling. Cox regression models tested nonmedical prescription opioid use statuses at survey waves 1 through 7 as a time-varying and time-lagged regressor and subsequent heroin use initiation across waves 2 to 8 as the outcome.ExposuresSelf-reported nonmedical prescription opioid use (past 30-day [current] use vs past 6-month [prior] use without past 30-day use vs no past 6-month use) at each wave from 1 to 7.Main outcomes and measuresSelf-reported heroin use initiation (yes/no) during waves 2 to 8.ResultsOf 3298 participants, 1775 (53.9%) were adolescent girls, 1563 (48.3%) were Hispanic, 548 (17.0%) were Asian, 155 (4.8%) were African American, 529 (16.4%) were non-Hispanic white, and 220 (6.8%) were multiracial. Among baseline never users of heroin in ninth grade with valid data (3298 [97% of cohort enrollees]; mean [SD] age, 14.6 [0.4] years), the number of individuals with outcome data available at each follow-up ranged from 2987 (90.6%) to 3200 (97.0%). The mean per-wave prevalence of prior and current nonmedical prescription opioid use from waves 1 to 7 was 1.9% and 2.7%, respectively. Seventy students (2.1%) initiated heroin use during waves 2 to 8. Prior vs no (hazard ratio, 3.59; 95% CI, 2.14-6.01; P < .001) and current vs no (hazard ratio, 4.37; 95% CI, 2.80-6.81; P < .001) nonmedical prescription opioid use were positively associated with subsequent heroin use initiation. For no, prior, and current nonmedical prescription opioid use statuses at waves 1 to 7, the estimated cumulative probabilities of subsequent heroin use initiation by wave 8 (42-month follow-up) were 1.7%, 10.7%, and 13.1%, respectively. In covariate-adjusted models, associations were attenuated but remained statistically significant and current nonmedical prescription opioid use risk estimates were stronger than corresponding associations of nonopioid substance use with subsequent heroin use initiation.Conclusions and relevanceNonmedical prescription opioid use was prospectively associated with subsequent heroin use initiation during 4 years of adolescence among Los Angeles youth. Further research is needed to understand whether this association is causal.

Project description:ContextThe opioid epidemic has been well-documented in the general population, but the literature pertaining to opioid use and misuse in the athletic population remains limited.ObjectivesThe objectives of this study were to seek answers to the following questions: (1) what are the rates of opioid use and misuse among athletes, (2) do these rates differ compared with the nonathletic population, and (3) are there specific subgroups of the athletic population (eg, based on sport, level of play) who may be at higher risk?Data sourcesThe Embase, MEDLINE, and PubMed were used for the literature search.Study selectionRecords were screened in duplicate for studies reporting rates of opioid use among athletes. All study designs were included.Study designSystematic review.Level of evidenceLevel 4.Data extractionData regarding rates of opioid use, medication types, prescription patterns, and predictors of future opioid use were collected. Study quality was assessed using the Methodological Index for Non-Randomized Studies (MINORS) criteria for clinical studies and 5 key domains previously identified for survey studies.ResultsA total of 11 studies were eligible for inclusion (N = 226,256 athletes). Studies included survey studies and retrospective observational designs. Opioid use among professional athletes at any given time, as reported in 2 different studies, ranged from 4.4% to 4.7%, while opioid use over a National Football League career was 52%. High school athletes had lifetime opioid use rates of 28% to 46%. Risk factors associated with opioid use included Caucasian race, contact sports (hockey, football, wrestling), postretirement unemployment, and undiagnosed concussion. Use of opioids while playing predicted use of opioids in retirement.ConclusionOverall, opioid use is prevalent among athletes, and use during a playing career predicts postretirement use. This issue exists even at the high school level, with similar rates to professional athletes. Further higher quality observational studies are needed to better define patterns of opioid use in athletes.

Dataset Information

Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

Publications

Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

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