Factors associated with physicians' prescriptions for rheumatoid arthritis drugs not filled by patients.

BACKGROUND: This study estimated the extent and predictors of primary nonadherence (i.e., prescriptions made by physicians but not initiated by patients) to methotrexate and to biologics or tofacitinib in rheumatoid arthritis (RA) patients who were newly prescribed these medications. METHODS: Using administrative claims linked with electronic health records (EHRs) from multiple healthcare provider organizations in the USA, RA patients who received a new prescription for methotrexate or biologics/tofacitinib were identified from EHRs. Claims data were used to ascertain filling or administration status. A logistic regression model for predicting primary nonadherence was developed and tested in training and test samples. Predictors were selected based on clinical judgment and LASSO logistic regression. RESULTS: A total of 36.8% of patients newly prescribed methotrexate failed to initiate methotrexate within 2 months; 40.6% of patients newly prescribed biologics/tofacitinib failed to initiate within 3 months. Factors associated with methotrexate primary nonadherence included age, race, region, body mass index, count of active drug ingredients, and certain previously diagnosed and treated conditions at baseline. Factors associated with biologics/tofacitinib primary nonadherence included age, insurance, and certain previously treated conditions at baseline. The area under the receiver operating characteristic curve of the logistic regression model estimated in the training sample and applied to the independent test sample was 0.86 and 0.78 for predicting primary nonadherence to methotrexate and to biologics/tofacitinib, respectively. CONCLUSIONS: This study confirmed that failure to initiate new prescriptions for methotrexate and biologics/tofacitinib was common in RA patients. It is feasible to predict patients at high risk of primary nonadherence to methotrexate and to biologics/tofacitinib and to target such patients for early interventions to promote adherence.

Identifying Consistent High-cost Users in a Health Plan: Comparison of Alternative Prediction Models.

BACKGROUND: High-cost users in a period may not incur high-cost utilization in the next period. Consistent high-cost users (CHUs) may be better targets for cost-saving interventions. OBJECTIVES: To compare the characteristics of CHUs (patients with plan-specific top 20% medical costs in all 4 half-year periods across 2008 and 2009) and point high-cost users (PHUs) (top users in 2008 alone), and to build claims-based models to identify CHUs. RESEARCH DESIGN: This is a retrospective cohort study. Logistic regression was used to predict being CHUs. Independent variables were derived from 2007 claims; 5 models with different sets of independent variables (prior costs, medications, diagnoses, medications and diagnoses, medications and diagnoses and prior costs) were constructed. SUBJECTS: Three-year continuous enrollees aged from 18 to 62 years old from a large administrative database with $100 or more yearly costs (N=1,721,992). MEASURES: Correlation, overlap, and characteristics of top risk scorers derived from 5 CHUs models were presented. C-statistics, sensitivity, and positive predictive value were calculated. RESULTS: CHUs were characterized by having increasing total and pharmacy costs over 2007-2009, and more baseline chronic and psychosocial conditions than PHUs. Individuals' risk scores derived from CHUs models were moderately correlated (∼0.6). The medication-only model performed better than the diagnosis-only model and the prior-cost model. CONCLUSIONS: Five models identified different individuals as potential CHUs. The recurrent medication utilization and a high prevalence of chronic and psychosocial conditions are important in differentiating CHUs from PHUs. For cost-saving interventions with long-term impacts or focusing on medication, CHUs may be better targets.

Early opioid prescription and risk of long-term opioid use among US workers with back and shoulder injuries: a retrospective cohort study.

The number of prescription opioid overdose deaths has increased dramatically in recent years and many prescribers are unsure how to balance treatment of pain with secondary prevention. Guidelines recommend low-severity injury patients not receive opioids early in the course of their care, but evidence supporting this guideline is limited. Data from 123 096 workers' compensation claims with back and shoulder injuries were analysed to evaluate this guideline. Back and shoulder injury claimants with early opioid use (≤1 month after injury) had 33% lower (95% CI 24% to 41% lower) odds and 29% higher (95% CI 6% to 58% higher) odds, respectively, of long-term opioid use (>3 months) than claimants with late opioid use, after adjusting for key covariates. Stratified analyses indicate that early opioid use does not appear to increase the risk of long-term use except in cases where no diagnosis or only the diagnosis of unspecified shoulder pain is given prior to prescription.

Risk-adjusted resource allocation: using Taiwan's National Health Insurance as an example.

OBJECTIVES: To determine if access to medical services differed by regions and to demonstrate the extent of the differences of adopting a claims-based risk-adjustment system versus a demographic model for regional resource allocation. METHODS: The claims of a 1% random sample of Taiwan's National Health Insurance enrollees (N = 173 175) in 2002 was used. The number of visits and morbidity-adjusted resource consumption were calculated individually then collapsed regionally. Regional expected resource allocation was compared with actual consumption. RESULTS: After controlling for diagnosis-based health measures, the average numbers of visits were stable across regions. Two models were consistent in showing over- or underutilization; the overall difference between two models in resource allocation was 5.8% at the district level. We observed strong urban overutilization and rural underutilization. CONCLUSIONS: Access to medical services is similar across regions. The adoption of a diagnosis-based model over a demographic-adjusted budgeting method would affect resource allocation considerably.

Medication, diagnostic, and cost information as predictors of high-risk patients in need of care management.

OBJECTIVE: To contrast the advantages and limitations of using medication, diagnostic, and cost data to prospectively identify candidates for care management programs. METHODS: Risk scores from prior-cost information and a set of clinically based predictive models (PMs) derived from diagnostic and medication data sources, as well as from a combination of all 3 data sources, were assigned to a national sample of commercially insured, non-elderly adults (n = 2,259,584). Clinical relevance of risk groups and statistical performance using future costs as the outcome were contrasted across the PMs. RESULTS: Compared with prior cost, diagnostic and medication-based PMs identified high-risk groups with a higher burden of clinically actionable characteristics. Statistical performance was similar and in some cases better for the clinical PMs compared with prior cost. The best classification accuracy was obtained with a comprehensive model that united diagnostic, medication, and prior-cost risk factors. CONCLUSIONS: Clinically based PMs are a better choice than prior cost alone for programs that seek to identify high-risk groups of patients who are amenable to care management services.