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Aims: To compare healthcare costs and use between United States (US) Veterans Health Administration (VHA) patients with opioid use disorder (OUD) who experienced an opioid overdose (OD cohort) and patients with OUD who did not experience an opioid overdose (non-OD cohort)., Design: This is a retrospective cohort study of administrative and clinical data., Setting: The largest integrated national health-care system is the US Veterans Health Administration's healthcare systems., Participants: We included VHA patients diagnosed with OUD from October 1, 2017 through September 30, 2018. We identified the index date of overdose for patients who had an overdose. Our control group, which included patients with OUD who did not have an overdose, was randomly assigned an index date. A total of 66 513 patients with OUD were included for analysis (OD cohort: n = 1413; non-OD cohort: n = 65 100)., Measurements: Monthly adjusted healthcare-related costs and use in the year before and after the index date. We used generalized estimating equation models to compare patients with an opioid overdose and controls in a difference-in-differences framework., Findings: Compared with the non-OD cohort, an opioid overdose was associated with an increase of $16 890 [95% confidence interval (CI) = $15 611-18 169; P < 0.001] in healthcare costs for an estimated $23.9 million in direct costs to VHA (95% CI = $22.1 million, $25.7 million) within the 30 days following overdose after adjusting for baseline characteristics. Inpatient costs ($13 515; 95% CI = $12 378-14 652; P < 0.001) reflected most of this increase. Inpatient days (+6.15 days; 95% CI, = 5.33-6.97; P < 0.001), inpatient admissions (+1.01 admissions; 95% CI = 0.93-1.10; P < 0.001) and outpatient visits (+1.59 visits; 95% CI = 1.34-1.84; P < 0.001) also increased in the month after opioid overdose. Within the overdose cohort, healthcare costs and use remained higher in the year after overdose compared with pre-overdose trends., Conclusions: The US Veterans Health Administration patients with opioid use disorder (OUD) who have experienced an opioid overdose have increased healthcare costs and use that remain significantly higher in the month and continuing through the year after overdose than OUD patients who have not experienced an overdose., (© 2023 The Authors. Addiction published by Society for the Study of Addiction on behalf of Society for the Study of Addiction. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Objective: The complete capture of all values for each variable of interest in pharmacy research studies remains aspirational. The absence of these possibly influential values is a common problem for pharmacist investigators. Failure to account for missing data may translate to biased study findings and conclusions. Our goal in this analysis was to apply validated statistical methods for missing data to a previously analyzed data set and compare results when missing data methods were implemented versus standard analytics that ignore missing data effects., Design: Using data from a retrospective cohort study, the statistical method of multiple imputation was used to provide regression-based estimates of the missing values to improve available data usable for study outcomes measurement. These findings were then contrasted with a complete-case analysis that restricted estimation to subjects in the cohort that had no missing values. Odds ratios were compared to assess differences in findings of the analyses. A nonadjusted regression analysis ("crude analysis") was also performed as a reference for potential bias., Setting: Veterans Integrated Systems Network that includes VA facilities in the Southern California and Nevada regions., Patients: New statin users between November 30, 2006, and December 2, 2007, with a diagnosis of dyslipidemia., Main Outcome Measure: We compared the odds ratios (ORs) and 95% confidence intervals (CIs) for the crude, complete-case, and multiple imputation analyses for the end points of a 25% or greater reduction in atherogenic lipids., Results: Data were missing for 21.5% of identified patients (1665 subjects of 7739). Regression model results were similar for the crude, complete-case, and multiple imputation analyses with overlap of 95% confidence limits at each end point. The crude, complete-case, and multiple imputation ORs (95% CIs) for a 25% or greater reduction in low-density lipoprotein cholesterol were 3.5 (95% CI 3.1-3.9), 4.3 (95% CI 3.8-4.9), and 4.1 (95% CI 3.7-4.6), respectively. The crude, complete-case, and multiple imputation ORs (95% CIs) for a 25% or greater reduction in non-high-density lipoprotein cholesterol were 3.5 (95% CI 3.1-3.9), 4.5 (95% CI 4.0-5.2), and 4.4 (95% CI 3.9-4.9), respectively. The crude, complete-case, and multiple imputation ORs (95% CIs) for 25% or greater reduction in TGs were 3.1 (95% CI 2.8-3.6), 4.0 (95% CI 3.5-4.6), and 4.1 (95% CI 3.6-4.6), respectively., Conclusion: The use of the multiple imputation method to account for missing data did not alter conclusions based on a complete-case analysis. Given the frequency of missing data in research using electronic health records and pharmacy claims data, multiple imputation may play an important role in the validation of study findings., (© 2015 Pharmacotherapy Publications, Inc.)

Study Objective: To investigate the association between statin adherence and changes in lipid panel outcomes from baseline in a veteran population., Design: Retrospective cohort study using multiple linear regression models., Setting: Veterans Affairs health care system within the Veterans Integrated Service Network 22, a network of Veterans Affairs facilities in the southwest region of the United States that includes Los Angeles, San Diego, Loma Linda, and Long Beach, California, and Las Vegas, Nevada, with an enrollment of approximately 1.4 million veterans., Patients: A total of 5365 patients who were new statin users between December 1, 2006, and November 30, 2007; 2674 patients were in the adherent group and 2691 were in the nonadherent group., Measurements and Main Results: Adherence was determined by the medication possession ratio. Patients were categorized as adherent if the medication possession ratio at follow-up was 0.80 or more. Adherent patients exhibited significant differences in baseline demographic and clinical characteristics than nonadherent patients in our study sample. Baseline laboratory values for adherent patients were significantly lower for low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), non-high-density lipoprotein cholesterol (non-HDL), and total cholesterol levels. The primary outcome was change in LDL level from baseline at 12 months. Secondary outcomes were changes in non-HDL and total cholesterol levels from baseline at 12 months. Independent variables controlled for in the multiple linear regression included age, sex, body mass index, race-ethnicity, baseline lipid panel (LDL, HDL, total cholesterol, and triglycerides), statin copayment status, income quintile (according to ZIP code median household income), baseline medication count, statin prescribed, and comorbidities. Multiple linear regression revealed that adherent patients demonstrated significantly greater reductions in LDL of 20.98 mg/dl versus nonadherent patients (p<0.0001). Adherent patients similarly demonstrated larger reductions of 24.31 mg/dl in non-HDL and 24.06 mg/dl in total cholesterol versus nonadherent patients (p<0.0001 for both comparisons)., Conclusion: Patients adherent to statin therapy had significant associations with clinically relevant reductions in LDL, non-HDL, and total cholesterol from baseline at 12 months compared with nonadherent patients when controlling for potential confounders. Adherence to statin therapy may have important consequences in decreasing clinical outcomes such as myocardial infarctions, strokes, and mortality due to large reductions in lipid panel outcomes from baseline at 12 months., (© 2013 Pharmacotherapy Publications, Inc.)

Objective: To evaluate the cost-effectiveness of intravitreal bevacizumab to ranibizumab in patients with neovascular age-related macular degeneration (AMD)., Methods: A cost-utility analysis using a Markov model was performed to evaluate incremental cost-effectiveness ratio [ICER, $US per quality-adjusted life year (QALY) gained] between bevacizumab and ranibizumab from a US payer perspective. Transition probabilities for ranibizumab and bevacizumab were extrapolated from published studies and local institutional data. Utility values, likewise, were obtained from another published study. Mortality rates were determined from the Centers for Disease Control 2003 Life Tables. Resource utilization and total direct costs were estimated using the Centers for Medicare and Medicaid Services and the Veterans Affairs Decision Support System. A hypothetical cohort of 1000 patients was simulated through the model for 20 years. Sensitivity analyses were performed using univariate and probabilistic sensitivity analysis (PSA) on all costs, transition probabilities and utility values. An acceptability curve was generated to illustrate the cost-effectiveness probability of bevacizumab to ranibizumab with increasing willingness-to-pay (WTP)., Results: The cost-effectiveness ratios (CER) for bevacizumab and ranibizumab were $1405 per QALY and $12,177 per QALY, respectively. The ICER for bevacizumab was dominant compared to ranibizumab. The base-case CER was sensitive to drug costs of the study medications with a breakeven point of $44 for ranibizumab and $2666 for bevacizumab. PSA revealed a 95% probability of bevacizumab being more cost-effective than ranibizumab at a WTP of $50,000 per QALY gained., Conclusion: Based on a WTP defined at $50,000 per QALY gained, bevacizumab was cost-effective versus ranibizumab 95% of the time because of lower acquisition costs and increased efficacy., (© 2010 Blackwell Publishing Ltd.)

Objective: To examine patient and provider perspectives about the impact of having diabetes, hypertension and/or dyslipidemia (DHD) on health-related quality of life (HRQL) in order to identify domains for an HRQL instrument focusing on specific chronic diseases (DHD)., Study Design: Exploratory qualitative design was used to elicit patient and provider responses. Patient survey contained 11 demographic and nine content questions inquiring how the disease(s) affected patient HRQL. Provider survey examined how the disease(s) affected their patient's HRQL and crucial factors for improvement. Patients were included if they had DHD and spoke English. Providers working with DHD patients were identified among university faculty and community practitioners., Results: Thirty-eight patients (24.7%) completed the interviews. Three judges content analysed patient responses and proposed seven categories (patients ranked physical functioning first followed by diet, emotional well-being (E), general well-being, social functioning (S), 'no change in HRQL' and medications). One question about medications had four categories (medication inconvenience, medication side effects, forgetfulness and general well-being). Index for reliability was greater than 80% for all categories. Providers ranked lifestyle modification (LM), first followed by complications, medication, self-care, E, education, S, physical activity and resources (ability to pay) as components that impact HRQL., Conclusions: Patient and provider perspectives were similar in four areas (E, S, medication and diet); however, the differences were in how these impacted HRQL - patients focused on activities relevant to daily life that were affected, while providers based it on processes of therapy management and self-care (education and implementation of LM).

Purpose: To evaluate the cost-effectiveness of three antipsychotic medications (olanzapine, risperidone and haloperidol) in the treatment of schizophrenia using the Positive and Negative Symptom Scale., Study Design and Methods: A decision analysis model was created to evaluate the cost-effectiveness of two atypical antipsychotics (risperidone and olanzapine) and haloperidol. Outcome probabilities were determined from published clinical trials. The main dependent variable of interest was to compare the incremental cost-effectiveness ratios (ICER) of the atypical antipsychotic with haloperidol, and also to compare the ICER of olanzapine and risperidone. Sensitivity analyses were conducted for olanzapine and risperidone to determine the effects of altering drug cost, efficacy and re-hospitalization rate on total costs., Results: Risperidone and olanzapine were dominant strategies compared with haloperidol (less costly and more effective). Risperidone was also dominant when compared with olanzapine. A one-way sensitivity analysis for efficacy indicated that the efficacy of risperidone would need to decrease by approximately 3% from the base-case in order for olanzapine and risperidone to have equal total costs. In a two-way sensitivity analysis varying both the cost of olanzapine and risperidone, the difference in drug costs between them would have to increase from $2.12 per day to $4.12 per day in order to have equal total costs. In terms of varying re-hospitalization rates, the re-hospitalization rate for risperidone would have to increase from 3% to 33% greater than the re-hospitalization rate for olanzapine in order to have equal total direct costs., Conclusion: Based on this decision model, atypical antipsychotics were a dominant strategy over haloperidol primarily because of increased efficacy and lower re-hospitalizations. The ICER indicated that risperidone was dominant over olanzapine because of lower drug costs and increased number of responders, which leads to decreased total costs.