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

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.

Home infusion: Safe, clinically effective, patient preferred, and cost saving.

BACKGROUND: As the U.S. healthcare payment system shifts from volume to value, identifying care approaches that improve outcomes while lowering costs are essential. We sought to understand the utility of home infusion versus medical-setting infusion as a mechanism to affect the three-part aim: better care, better health outcomes, and lower costs. STUDY DESIGN: Systematic review. METHODS: We searched MEDLINE, EMBASE, and Science Citation Index for articles related to the safety, clinical effectiveness, quality of life and satisfaction, and/or costs of home infusion as compared with infusion in an outpatient medical facility or hospital. RESULTS: Of 253 potentially relevant articles, 13 met all inclusion criteria. Study design, disease state, and outcomes varied considerably. As compared to medical setting infusion patients, home infusion patients were no more likely to experience adverse drug events or side effects (all p>0.05). Clinical outcomes were as good or better, e.g., for patients with hemophilia, a 40% (0.50-0.70) reduced likelihood of hospitalization for bleeding complications. Patients overwhelmingly preferred home infusion, reporting significantly better physical and mental well being and less disruption of family and personal responsibilities. Home infusion costs were significantly lower than medical setting infusion costs, with savings between $1928 and $2974 per treatment course. CONCLUSIONS: Home infusion care can provide safe, clinically effective care improve patients' quality of life and reduce healthcare costs. As the overhaul of the healthcare payment system gains momentum, the home infusion care delivery model offers strong promise as one in a set of approaches that can improve care and lower costs.

Prescription drug insurance coverage and patient health outcomes: a systematic review.

Previous reviews have shown that changes in prescription drug insurance benefits can affect medication use and adherence. We conducted a systematic review of the literature to identify studies addressing the association between prescription drug coverage and health outcomes. Studies were included if they collected empirical data on expansions or restrictions of prescription drug coverage and if they reported clinical outcomes. We found 23 studies demonstrating that broader prescription drug insurance reduces use of other health care services and has a positive impact on patient outcomes. Coverage gaps or caps on drug insurance generally led to worse outcomes. States should consider implementing the Affordable Care Act expansions in drug coverage to improve the health of low-income patients receiving state-based health insurance.

Validity of maternal and infant outcomes within nationwide Medicaid data.

PURPOSE: The aim of this study is to assess the validity of preeclampsia, congenital cardiac malformations, and persistent pulmonary hypertension of the newborn (PPHN) diagnoses in the US Medicaid Analytic eXtract (MAX), a nationwide health care utilization database that may be useful for perinatal research. METHODS: Using the 2000-2007 MAX, we identified more than 1 million pregnancies ending in live birth. We identified potential cases based on claims, reviewed their hospital medical records, and calculated the positive predictive values (PPVs) and 95% confidence intervals (CIs) using records as the reference. RESULTS: Among 183 women with any preeclampsia diagnoses, the PPV was 66.5% (53.6, 77.4%), but it increased to 94.5% (84.0, 98.3%) for inpatient preeclampsia diagnoses. The PPV for inpatient PPHN diagnoses (N = 82) was 68.3% (57.6, 77.4%), but it increased to 89.6% (CI: 77.8, 95.5%) when restricting to infants not transferred to another facility shortly after birth (N = 48). The PPV for cardiac malformations was 77.6% (65.7, 86.2%) when requiring inpatient codes on more than one date (N = 63). CONCLUSIONS: These PPVs are conservative, particularly when patients were transferred or received outpatient diagnoses, because we reviewed records from a single hospitalization only. PPVs improve with stringent identification criteria, at the cost of sensitivity, and can be used to correct for measurement error.

Harnessing the Medicaid Analytic eXtract (MAX) to Evaluate Medications in Pregnancy: Design Considerations.

BACKGROUND: In the absence of clinical trial data, large post-marketing observational studies are essential to evaluate the safety and effectiveness of medications during pregnancy. We identified a cohort of pregnancies ending in live birth within the 2000-2007 Medicaid Analytic eXtract (MAX). Herein, we provide a blueprint to guide investigators who wish to create similar cohorts from healthcare utilization data and we describe the limitations in detail. METHODS: Among females ages 12-55, we identified pregnancies using delivery-related codes from healthcare utilization claims. We linked women with pregnancies to their offspring by state, Medicaid Case Number (family identifier) and delivery/birth dates. Then we removed inaccurate linkages and duplicate records and implemented cohort eligibility criteria (i.e., continuous and appropriate enrollment type, no private insurance, no restricted benefits) for claim information completeness. RESULTS: From 13,460,273 deliveries and 22,408,810 child observations, 6,107,572 pregnancies ending in live birth were available after linkage, cleaning, and removal of duplicate records. The percentage of linked deliveries varied greatly by state, from 0 to 96%. The cohort size was reduced to 1,248,875 pregnancies after requiring maternal eligibility criteria throughout pregnancy and to 1,173,280 pregnancies after further applying infant eligibility criteria. Ninety-one percent of women were dispensed at least one medication during pregnancy. CONCLUSIONS: Mother-infant linkage is feasible and yields a large pregnancy cohort, although the size decreases with increasing eligibility requirements. MAX is a useful resource for studying medications in pregnancy and a spectrum of maternal and infant outcomes within the indigent population of women and their infants enrolled in Medicaid. It may also be used to study maternal characteristics, the impact of Medicaid policy, and healthcare utilization during pregnancy. However, careful attention to the limitations of these data is necessary to reduce biases.

Cardiovascular risk in rheumatoid arthritis: comparing TNF-α blockade with nonbiologic DMARDs.

BACKGROUND: Elevated tumor necrosis factor (TNF)-α likely contributes to the excess cardiovascular risk observed in rheumatoid arthritis. We compared the cardiovascular risk in rheumatoid arthritis patients starting a TNF-α blocking agent versus a nonbiologic disease-modifying antirheumatic drug (nbDMARD). METHODS: Subjects with rheumatoid arthritis participating in several different US insurance programs between 1998 and 2007 who received methotrexate were eligible. Those who added a TNF-α blocking agent were compared with subjects who added a nbDMARD in Cox regression models stratified by propensity score decile and adjusted for oral glucocorticoid dosage. We examined the composite cardiovascular end point of myocardial infarction, stroke, or coronary re-vascularization after 6 months. RESULTS: We compared 8656 new users of a nbDMARD with 11,587 new users of a TNF-α blocking agent with similar baseline covariates. Incidence rates per 100 person-years for the composite cardiovascular end point were 3.05 (95% confidence interval [CI], 2.54-3.65) for nbDMARDs and 2.52 (95% CI, 2.12-2.98) for TNF-α blocking agents. The hazard ratio (HR) for the TNF-α blocking agent compared with nbDMARD carrying the first exposure forward was 0.80 (95%, CI 0.62-1.04), while the HR for the as-treated analysis was 0.71 (95% CI, 0.52-0.97). The potential cardiovascular benefit of TNF-α blocking agents was strongest among individuals ≥65 years of age (HR 0.52; 95% CI, 0.34 -0.77; P for interaction = 0.075). CONCLUSION: Among subjects with rheumatoid arthritis, TNF-α blocking agents may be associated with a reduced risk of cardiovascular events compared with an nbDMARD. Randomized controlled clinical trials should be considered to test this hypothesis.

Changing interactions between physician trainees and the pharmaceutical industry: a national survey.

BACKGROUND: Increasingly, medical school policies limit pharmaceutical representatives' access to students and gifts from drugmakers, but little is known about how these policies affect student attitudes toward industry. OBJECTIVE: To assess interactions between trainees and the pharmaceutical industry, and to determine whether learning environment characteristics influence students' practices and attitudes. DESIGN, PARTICIPANTS: We conducted a cross-sectional survey with a nationally-representative sample of first- and fourth-year medical students and third-year residents, stratified by medical school, including ≥ 14 randomly selected trainees at each level per school. MAIN MEASURES: We measured frequency of industry interactions and attitudes regarding how such interactions affect medical training and the profession. Chi-squared tests assessed bivariate linear trend, and hierarchical logistic regression models were fitted to assess associations between trainees' attitudes and their schools' National Institutes of Health (NIH) funding levels and American Medical Student Association (AMSA) PharmFree Scorecard grades reflecting industry-related conflict of interest policies. KEY RESULTS: Among 1,610 student (49.3 % response rate) and 739 resident (43.1 %) respondents, industry-sponsored gifts were common, rising from 33.0 % (first-year students) to 56.8 % (fourth-year students) and 54 % (residents) (p < 0.001). These gifts included meals outside the hospital (reported by 5 % first-year students, 13.4 % fourth-year students, 27.5 % residents (p < 0.001)) and free drug samples (reported by 7.4 % first-year students, 14.1 % fourth-year students, 14.3 % residents (p < 0.001)). The perception that industry interactions lead to bias was prevalent, but the belief that physicians receive valuable education through these interactions increased (64.1 % to 67.5 % to 79.8 %, p < 0.001). Students in schools receiving more NIH funding reported industry gifts less often (OR = 0.51, 95 % CI: 0.38-0.68, p < 0.001), but the strength of institutional conflict of interest policies was not associated with this variable. CONCLUSIONS: Despite recent policy changes, a substantial number of trainees continue to receive gifts from pharmaceutical representatives. We found no relation between these outcomes and a school's policies concerning interactions with industry.

Availability of comparative efficacy data at the time of drug approval in the United States.

CONTEXT: Comparative effectiveness is taking on an increasingly important role in US health care, yet little is known about the availability of comparative efficacy data for drugs at the time of their approval in the United States. OBJECTIVE: To quantify the availability of comparative efficacy data for new molecular entities (NMEs) approved in the United States. DATA SOURCES: Approval packages publicly available through the online database of drug products approved by the US Food and Drug Administration (FDA). STUDY SELECTION: Identification of efficacy studies that supported approval of each NME approved by FDA between 2000 and 2010. DATA EXTRACTION: We determined whether eligible studies were head-to-head active controlled trials and whether the results of such studies were available in the approval packages. We recorded the approved indication, whether the NME was an orphan product, whether the NME had undergone priority review, and whether the control group was a specific active comparator or standard care. RESULTS: Of 197 NMEs identified that met eligibility criteria, 100 (51% [95% confidence interval {CI}, 44%-58%]) met criteria for having comparative efficacy data available at the time of market authorization. After excluding NMEs designated as orphan products (n = 37) and those approved for indications for which no alternative treatments existed (n = 17), this proportion increased to 70% (95% CI, 62%-77%). The proportions of NMEs with available comparative efficacy data varied widely by therapeutic area, from 33% (95% CI, 9%-67%) for hormones and contraceptives to 89% (95% CI, 56%-99%) for diabetes medications. CONCLUSION: Publicly available FDA approval packages contain comparative efficacy data for about half of NMEs recently approved in the United States and for more than two-thirds of NMEs for which alternative treatment options exist. We did not investigate the extent to which available comparative efficacy information is useful for clinical guidance.