Jeffery, Molly M.

OBJECTIVE Sustained postoperative opioid use after elective surgery is a matter of growing concern. Herein, the authors investigated incidence and predictors of long-term opioid use among patients undergoing elective lumbar spine surgery, especially as a function of opioid prescribing practices at postoperative discharge (dose in morphine milligram equivalents [MMEs] and type of opioid). METHODS The OptumLabs Data Warehouse (OLDW) was queried for postdischarge opioid prescriptions for patients undergoing elective lumbar decompression and discectomy (LDD) or posterior lumbar fusion (PLF) for degenerative spine disease. Only patients who received an opioid prescription at postoperative discharge and those who had a minimum of 180 days of insurance coverage prior to surgery and 180 days after surgery were included. Opioid-naive patients were defined as those who had no opioid fills in 180 days prior to surgery. The following patterns of long-term postoperative use were investigated: additional fills (at least one opioid fill 90–180 days after surgery), persistent fills (any span of opioid use starting in the 180 days after surgery and lasting at least 90 days), and Consortium to Study Opioid Risks and Trends (CONSORT) criteria for persistent use (episodes of opioid prescribing lasting longer than 90 days and 120 or more total days’ supply or 10 or more prescriptions in 180 days after the index fill). Multivariable logistic regression was performed to identify predictors of long-term use. RESULTS A total of 25,587 patients were included, of whom 52.7% underwent PLF (n = 13,486) and 32.5% (n = 8312) were opioid-naive prior to surgery. The rates of additional fills, persistent fills, and CONSORT use were 47%, 30%, and 23%, respectively, after PLF and 35.4%, 19%, and 14.2%, respectively, after LDD. The rates among opioid-naive patients were 18.9%, 5.6%, and 2.5% respectively, after PLF and 13.3%, 2.0%, and 0.8%, respectively, after LDD. Using multivariable logistic regression, the following were identified to be significantly associated with higher risk of long-term opioid use following PLF: discharge opioid prescription ≥ 500 MMEs, prescription of a long-acting opioid, female sex, multilevel surgery, and comorbidities such as depression and drug abuse (all p < 0.05). Elderly (age ≥ 65 years) and opioid-naive patients were found to be at lower risk (all p < 0.05). Similar results were obtained on analysis for LDD with the following significant additional risk factors identified: discharge opioid prescription ≥ 400 MMEs, prescription of tramadol alone at discharge, and inpatient surgery (all p < 0.05). CONCLUSIONS In an analysis of pharmacy claims from a national insurance database, the authors identified incidence and predictors of long-term opioid use after elective lumbar spine surgery.

Objectives Evaluate associations between ACE inhibitors (ACEis) and angiotensin receptor blockers (ARBs) and clinical outcomes in acute viral respiratory illness (AVRI). Design Retrospective cohort analysis of claims data. Setting The USA; 2018–2019 influenza season. Participants Main cohort: people with hypertension (HTN) taking an ACEi, ARB or other HTN medications, and experiencing AVRI. Falsification cohort: parallel cohort receiving elective knee or hip replacement. Main outcome measures Main cohort: hospital admission, intensive care unit, acute respiratory distress (ARD), ARD syndrome and all-cause mortality. Falsification cohort: complications after surgery and all-cause mortality. Results The main cohort included 236 843 episodes of AVRI contributed by 202 629 unique individuals. Most episodes were in women (58.9%), 81.4% in people with Medicare Advantage and 40.3% in people aged 75+ years. Odds of mortality were lower in the ACEi (0.78 (0.74 to 0.83)) and ARB (0.64 (0.61 to 0.68)) cohorts compared with other HTN medications. On all other outcomes, people taking ARBs (but not ACEis) had a >10% reduction in odds of inpatient stays compared with other HTN medications. In the falsification analysis (N=103 353), both ACEis (0.89 (0.80 to 0.98)) and ARBs (0.82 (0.74 to 0.91)) were associated with decreased odds of complications compared with other HTN medications; ARBs (0.64 (0.47 to 0.87)) but not ACEis (0.79 (0.60 to 1.05)) were associated with lower odds of death compared with other HTN medications. Conclusions Outpatient use of ARBs was associated with better outcomes with AVRI compared with other medications for HTN. ACEis were associated with reduced risk of death, but with minimal or no reduction in risk of other complications. A falsification analysis conducted to provide context on the possible causal implications of these findings did not provide a clear answer. Further analysis using observational data will benefit from additional approaches to assess causal relationships between these drugs and outcomes in AVRI.

AbstractObjectiveTo describe physicians’ variation in de‐adopting concurrent statin and fibrate therapy for type 2 diabetic patients following a reversal in clinical evidence.Data SourcesWe analyzed 2007‐2015 claims data from OptumLabs® Data Warehouse, a longitudinal, real‐world data asset with de‐identified administrative claims and electronic health record data.Study DesignWe modeled fibrate use among Medicare Advantage and commercially insured type 2 diabetic statin users before and after the publication of the ACCORD lipid trial, which found statins and fibrates were no more effective than statins alone in reducing cardiovascular events among type 2 diabetic patients. We modeled fibrate use trends with physician random effects and physician characteristics such as age and specialty.Data ExtractionWe identified patient‐year‐quarters with one year of continuous insurance enrollment, type 2 diabetes diagnoses, and fibrate use. We designated the physician most responsible for patients’ diabetes care based on evaluation and management visits and prescriptions of glucose‐lowering drugs.Principal FindingsFibrate use increased by 0.12 percentage points per quarter among commercial patients (95% CI, 0.10 to 0.14) and 0.17 percentage points per quarter among Medicare Advantage patients (95% CI, 0.13 to 0.20) before the trial and then decreased by 0.16 percentage points per quarter among commercial patients (95% CI, −0.18 to −0.15) and 0.05 percentage points per quarter among Medicare Advantage patients (95% CI, −0.06 to −0.03) after the trial. However, 45% of physicians treating commercial patients and 48% of physicians treating Medicare Advantage patients had positive trends in prescribing following the trial. Physicians’ characteristics did not explain their variation (pseudo R2 = 0.000).ConclusionOn average, physicians decreased fibrate prescribing following the ACCORD lipid trial. However, many physicians increased prescribing following the trial. Observable physician characteristics did not explain variations in prescribing. Future research should examine whether physicians vary similarly in other de‐adoption settings.

EXECUTIVE SUMMARY We sought to determine emergency medicine physicians’ accuracy in designating patients’ disposition status as “inpatient” or “observation” at the time of hospital admission in the context of Medicare’s Two-Midnight rule and to identify characteristics that may improve the providers’ predictions. We conducted a 90-day observational study of emergency department (ED) admissions involving adults aged 65 years and older and assessed the accuracy of physicians’ disposition decisions. Logistic regression models were fit to explore associations and predictors of disposition. A total of 2,257 patients 65 and older were admitted through the ED. The overall error rate in physician designation of observation or inpatient was 36%. Diagnoses most strongly associated with stays lasting less than two midnights included diverticulitis, syncope, and nonspecific chest pain. Diagnoses most strongly associated with stays lasting two or more midnights included orthopedic fractures, biliary tract disease, and back pain. ED physicians inaccurately predicted patient length of stay in more than one third of all patients. Under the Two-Midnight rule, these inaccurate predictions place hospitals at risk of underpayment and patients at risk of significant financial liability. Further work is needed to increase providers’ awareness of the financial repercussions of their admission designations and to identify interventions that can improve prediction accuracy.

Objective To identify the relationships between county-level area deprivation and patterns of both opioid prescriptions and drug-poisoning mortality. Design, setting and participants For this retrospective cross-sectional study, we used the IQVIA Xponent data to capture opioid prescriptions and Centres for Disease Control and Prevention National Vital Statistics System to assess drug-poisoning mortality. The Area Deprivation Index (ADI) is a composite measure of social determinants of health comprised of 17 US census indicators, spanning four socioeconomic domains. For all US counties with available opioid prescription (2712 counties) and drug-poisoning mortality (3133 counties) data between 2012 and 2017, we used negative binomial regression to examine the association between quintiles of county-level ADI and the rates of opioid prescriptions and drug-poisoning mortality adjusted for year, age, race and sex. Primary outcome measures County-level opioid prescription fills and drug-poisoning mortality. Results Between 2012 and 2017, overall rates of opioid prescriptions decreased from 96.6 to 72.2 per 100 people, while the rates of drug-poisoning mortality increased from 14.3 to 22.8 per 100 000 people. Opioid prescription and drug-poisoning mortality rates were consistently higher with greater levels of deprivation. The risk of filling an opioid prescription was 72% higher, and the risk of drug-poisoning mortality was 36% higher, for most deprived compared with the least deprived counties (both p<0.001). Discussion Counties with greater area-level deprivation have higher rates of filled opioid prescriptions and drug-poisoning mortality. Although opioid prescription rates declined across all ADI quintiles, the rates of drug-poisoning mortality continued to rise proportionately in each ADI quintile. This underscores the need for individualised and targeted interventions that consider the deprivation of communities where people live.

Objective: Experts cautioned that patients affected by the November 2010 withdrawal of the opioid analgesic propoxyphene might receive riskier prescriptions. To explore this, we compared drug receipts and outcomes among propoxyphene users before and aftermarket withdrawal. Study Design: Using OptumLabs data, we studied 3 populations: commercial, Medicare Advantage (MA) aged (age 65+ y) and MA disabled (age below 65 y) enrollees. The exposed enrollees received propoxyphene in the 3 months before market withdrawal (n=13,622); historical controls (unexposed) received propoxyphene 1 year earlier (n=9971). Regression models estimated daily milligrams morphine equivalent (MME), daily prescription acetaminophen dose, potentially toxic acetaminophen doses, nonopioid prescription analgesics receipt, emergency room visits, and diagnosed falls, motor vehicle accidents, and hip fractures. Principal Findings: Aged MA enrollees illustrate the experience of all 3 populations examined. Following the market withdrawal, propoxyphene users in the exposed cohort experienced an abrupt decline of 69% in average daily MME, compared with a 14% decline in the unexposed. Opioids were discontinued by 34% of the exposed cohort and 18% of the unexposed. Tramadol and hydrocodone were the most common opioids substituted for propoxyphene. The proportion of each group receiving ≥4 g of prescription acetaminophen per day decreased from 12% to 2% in the exposed group but increased from 6% to 8% among the unexposed. Adverse events were rare and not significantly different in exposed versus unexposed groups. Conclusions: After propoxyphene market withdrawal, many individuals experienced abrupt discontinuation of opioids. Policymakers might consider supporting appropriate treatment transitions and monitoring responses following drug withdrawals.