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The relationships of characteristics of the initial opioid prescription and pain etiology with the probability of opioid discontinuation were explored in this retrospective cohort study using health insurance claims data from a nationally representative database of commercially insured patients in the U.S. We identified 1,353,902 persons aged ≥14 with no history of cancer or substance abuse, with new opioid use episodes and categorized them into 11 mutually exclusive pain etiologies. Cox Proportional Hazards models were estimated to identify factors associated with time to opioid discontinuation. After accounting for losses to follow-up, the probability of continued opioid use at one year was 5.3% across all subjects. Patients with chronic pain had the highest probability for continued opioid use followed by patients with inpatient admissions. Patients prescribed doses above 90 morphine milligram equivalents (HR=0.91, CI: 0.91–0.92); initiated on tramadol (HR=0.90, CI: 0.89–0.91) or long-acting opioids (HR=0.78, CI: 0.75–0.80); were less likely to discontinue opioids. Increasing days’ supply of the first prescription was consistently associated with a lower likelihood of opioid discontinuation (HRs, CIs: 3–4 days’ supply = 0.70, 0.70–0.71; 5–7 days’ supply = 0.48, 0.47–0.48; 8–10 days’ supply = 0.37, 0.37–0.38; 11–14 days’ supply = 0.32, 0.31–0.33; 15–21 days’ supply = 0.29, 0.28– 0.29; ≥22 days supplied = 0.20, 0.19–0.20). The direction of this relationship was consistent across all pain etiologies. Clinicians should initiate patients with the lowest supply of opioids to mitigate unintentional long term opioid use.

Introduction The COVID-19 pandemic is a global crisis impacting population health and the economy. We describe a cost-effectiveness framework for evaluating acute treatments for hospitalized patients with COVID-19, considering a broad spectrum of potential treatment profiles and perspectives within the US healthcare system to ensure incorporation of the most relevant clinical parameters, given evidence currently available. Methods A lifetime model, with a short-term acute care decision tree followed by a post-discharge three-state Markov cohort model, was developed to estimate the impact of a potential treatment relative to best supportive care (BSC) for patients hospitalized with COVID-19. The model included information on costs and resources across inpatient levels of care, use of mechanical ventilation, post-discharge morbidity from ventilation, and lifetime healthcare and societal costs. Published literature informed clinical and treatment inputs, healthcare resource use, unit costs, and utilities. The potential health impacts and cost-effectiveness outcomes were assessed from US health payer, societal, and fee-for-service (FFS) payment model perspectives. Results Viewing results in aggregate, treatments that conferred at least a mortality benefit were likely to be cost-effective, as all deterministic and sensitivity analyses results fell far below willingness-to-pay thresholds using both a US health payer and FFS payment perspective, with and without societal costs included. In the base case, incremental cost-effectiveness ratios (ICER) ranged from $22,933 from a health payer perspective using bundled payments to $8028 from a societal perspective using a FFS payment model. Even with conservative assumptions on societal impact, inclusion of societal costs consistently produced ICERs 40–60% lower than ICERs for the payer perspective. Conclusion Effective COVID-19 treatments for hospitalized patients may not only reduce disease burden but also represent good value for the health system and society. Though data limitations remain, this cost-effectiveness framework expands beyond current models to include societal costs and post-discharge ventilation morbidity effects of potential COVID-19 treatments. Supplementary Information The online version contains supplementary material available at 10.1007/s12325-021-01654-5.

Background and Purpose— The objective of the study is to compare the cost-effectiveness of oral anticoagulants among atrial fibrillation patients at an increased stroke risk. Methods— A Markov model was constructed to project the lifetime costs and quality-adjusted survival (QALYs) of oral anticoagulants using a private payer’s perspective. The distribution of stroke risk (CHADS 2 score: congestive heart failure, hypertension, advanced age, diabetes mellitus, stroke) and age of the modeled population was derived from a cohort of commercially insured patients with new-onset atrial fibrillation. Probabilities of treatment specific events were derived from published clinical trials. Event and downstream costs were determined from the cost of illness studies. Drug costs were obtained from 2015 National Average Drug Acquisition Cost data. Results— In the base case analysis, warfarin was the least costly ($46 241; 95% CI, 44 499–47 874) and apixaban had the highest QALYs (9.38; 95% CI, 9.24–9.48 QALYs). Apixaban was found to be a cost-effective strategy over warfarin (incremental cost-effectiveness ratio=$25 816) and dominated other anticoagulants. Probabilistic sensitivity analysis showed that apixaban had at least a 61% chance of being the most cost-effective strategy at willingness to pay value of $100 000 per QALY. Among patients with CHADS 2 ≥3, dabigatran was the dominant strategy. The model was sensitive to efficacy estimates of apixaban, dabigatran, and edoxaban and the cost of these drugs. Conclusions— All the newer oral anticoagulants compared were more effective than adjusted dosed warfarin. Our model showed that apixaban was the most effective anticoagulant in a general atrial fibrillation population and has an incremental cost-effectiveness ratio <$50 000/QALY. For those with higher stroke risk (CHADS 2 ≥3), dabigatran was the most cost-effective treatment option.

OBJECTIVE: To determine the association of medical marijuana legalization with prescription opioid utilization. METHODS: A 10% sample of a nationally representative database of commercially insured population was used to gather information on opioid use, chronic opioid use, and high-risk opioid use for the years 2006-2014. Adults with pharmacy and medical benefits for the entire calendar year were included in the population for that year. Multilevel logistic regression analysis, controlling for patient, person-year, and state-level factors, were used to determine the impact of medical marijuana legalization on the three opioid use measures. Subgroup analysis among cancer-free adults and cancer-free adults with at least one chronic non-cancer pain condition in the particular year were conducted. Alternate regression models were used to test the robustness of our results including a fixed effects model, an alternate definition for start date for medical marijuana legalization, a person-level analysis, and a falsification test. RESULTS: The final sample included a total of 4,840,562 persons translating into 15,705,562 person years. Medical marijuana legalization was found to be associated with a lower odds of any opioid use: OR = 0.95 (0.94-0.96), chronic opioid use: OR = 0.93 (0.91-0.95), and high-risk opioid use: OR = 0.96 (0.94-0.98). The findings were similar in both the subgroup analyses and all the sensitivity analyses. The falsification tests showed no association between medical marijuana legalization and prescriptions for antihyperlipidemics (OR = 1.00; CI 0.99-1.01) or antihypertensives (OR = 1.00; CI 0.99-1.01). CONCLUSIONS: In states where marijuana is available through medical channels, a modestly lower rate of opioid and high-risk opioid prescribing was observed. Policy makers could consider medical marijuana legalization as a tool that may modestly reduce chronic and high-risk opioid use. However, further research assessing risk versus benefits of medical marijuana legalization and head to head comparisons of marijuana versus opioids for pain management is required.

Providing stress ulcer prophylaxis with H2RA therapy may reduce costs, increase survival, and avoid complications compared with PPI therapy. This finding is highly sensitive to the pneumonia and stress-related mucosal bleeding rates and whether observational data are used to inform the model.

Clinicians should evaluate opioid use in participants with CNCP as opioid use is not correlated with better HRQoL.

IntroductionThe risk of rheumatoid arthritis (RA) associated with dipeptidyl peptidase-4 inhibitor (DPP-4i) use is unclear. This study assesses the RA risk associated with DPP-4i use among a diabetic cohort initiating second-line therapy.MethodsThis was a nested case–control study, using the adult diabetic population starting second-line antidiabetic therapy from IMS LifeLink Plus® database (2006–2015). Cases were those with two or more RA diagnosis, at least one prescription, and 180 days enrollment prior to the event date (earliest of the two: first RA diagnosis, first RA prescription). Controls were drawn from the nest after matching (1:15) with cases on index date (± 90 days), age (± 5 years), sex, and event date (imputed to have the same time difference between cohort entry and event date as the matched case). Exposure and covariate information was gathered from the 180-day period prior to event date. Conditional logistic regression was used to assess exposure among cases and controls. Adjusted analysis was carried out after controlling for important medications and comorbidities.ResultsThe final sample consists of 790 cases and 11,850 controls; of these, 151 cases (19.11%) and 2177 controls (18.37%) had DPP-4i claims during the exposure assessment period. DPP-4i therapy was not significantly associated with the development of RA after adjusting for covariates (OR = 1.156, 95% CI 0.936–1.429). Changing the exposure definition or exposure window to 1 year and subgroup analyses yielded similar results except for the non-insulin-using subgroup (OR = 1.299, 95% CI 1.001–1.985) which showed a significant positive association.ConclusionDPP-4i were not significantly associated with the risk of RA compared with other second-line antidiabetic therapies.