Table 1.
Descriptions of studies
| Author, publication year | Data sources | Study design, number in study sample, and time period | Target population | Algorithm development method | Reported algorithm performance metrics |
|---|---|---|---|---|---|
| Birt et al. (2014) | Prescription drug claims data from a large, commercial insurance plans (Market Scan database) | Cohort; 6 291 810 patients; 2008–2009 | Individual medication | Compare Lorenz curves at 1% and 50% values, medication possession ratio, and proportion of days covered values for medications prone to nonmedical use with medications that are not | Discriminatory ability of metric using C-statistic Concordance of medication possession ratio vs proportion of days covered, vs Lorenz 1% and Lorenz 50% |
| Parente et al. (2004) | Prescription drug claims data from a multistate database with a mix of health plan types, including indemnity fee-for-service plans, preferred provider organizations, independent practice associations, and health maintenance organizations | Cohort; 7 million patients; 2000 | Patients (primarily) or prescribers | Controlled substance patterns of utilization requiring evaluation system, top 10 patterns evaluated | Algorithm sensitivity Pseudo R2 of model |
| Sullivan et al. (2010) | Administrative claims data, including demographic, clinical, and drug utilization data; commercial insurance and Medicaid insurance databases were used | Cohort; 31 845; 2000–2005 | Patients | Polytomous logistic regression; test for linear trend | Criterion validity: test for linear trend |
| Yang et al. (2015) | Prescription drug claims data from a multistate Medicaid database | Cohort; 90 010 patients; 2008–2010 | Patients | 2 indicators: pharmacy shopping indicator, overlapping prescription indicator diagnostic odds ratio, defined as the ratio of the odds of being identified as having nonmedical use when the patient actually has nonmedical use (true positives) divided by the odds of being identified as having nonmedical use when the patient does not have nonmedical use (false positives) | Criterion validity of the 2 indicators |
| Mailloux et al. (2010) | Prescription drug claims data from Wisconsin’s Medicaid database | Cohort; 190 patients; 1998–1999 | Patients | Decision tree | Sensitivity, specificity, positive predictive value, negative predictive value, validation attempts (concordance between results and gold standard as defined) |
| Iyengar et al. (2014) | Prescription drug and medical claims data from a large health insurance plan | Cohort; 2.3 million patients; 99 000 prescribers; 2011 | Patients and providers | Auditing analysis | Area under receiving operating curve (C-statistic) |
| White et al. (2009) | Administrative claims data, including demographic, clinical, and drug utilization data from a private health insurance plan | Case-control; 632 000 patients; 2005–2006 | Patients | Logistic regression | C-statistic Pseudo R2Model parsimony |
| Rice et al. (2012) | Administrative claims data, including demographic, clinical, and drug utilization data from a private health insurance plan | Case-control; 821 916 patients; 1999–2009 | Patients | Logistic regression | C-statistic |
| Cochran et al. (2014) | Administrative claims data, including demographic, clinical, and utilization data from a large health insurance plan | Case-control; 2 841 793 patients; 2000–2008 | Patients | Logistic regression | Sensitivity |
| Dufour et al. (2014) | Administrative claims data, including demographic, clinical, and drug utilization data from Humana and Truven | Case-control; 3567 patients 2009–2011 | Patients | Logistic regression | Sensitivity, specificity, positive predictive value, negative predictive value, validation attempts (concordance between results and gold standard as defined) |
| Carrell et al. (2015) | Free text from electronic health records; computer-assisted manual review of clinical notes | Descriptive; 22 142 patients; 2006–2012 | Patients | Natural language processing plus computer-assisted manual record review | False positive rate that compared natural language processing plus computer-assisted manual record review approach to traditional diagnostic codes in electronic health records |
| Hylan et al. (2015) | Free text from electronic health records | Cohort; 2752 patients; 2008–2012 | Patients | Logistic regression | Sensitivity, specificity, positive predictive value, negative predictive value C-statistic |
| Epstein et al. (2011) | Automated drug dispensing carts and anesthesia information management systems from a hospital | Case series; 158 providers 2007–2011 | Providers | Data mining | Sensitivity, specificity, positive predictive value, negative predictive value |
| Ringwalt et al. (2015) | Prescription drug monitoring program data from North Carolina | Cohort; 33 635 providers; 2009–2013 | Providers | Subject matter/clinical expertise | Criterion validity |
| Derrington et al. (2015) | ICD-9-CM codes from hospital discharge data in Massachusetts | Descriptive; 1 728 027 patients; 2002–2008 | Patients | Factor analysis | Number, percentage, and 95% confidence intervals for substance use disorders identified by algorithm compared to gold standard |