Opioid therapy trajectories of patients with chronic non-cancer pain over 1 year of follow-up after initiation of short-acting opioid formulations.

OBJECTIVE: This study compared opioid utilization trajectories of persons initiating tramadol, short-acting hydrocodone, or short-acting oxycodone, and it characterized opioid dose trajectories and type of opioid in persistent opioid therapy subsamples. METHODS: A retrospective cohort study of adults with chronic non-cancer pain who were initiating opioid therapy was conducted with the IQVIA PharMetrics® Plus for Academics data (2008-2018). Continuous enrollment was required for 6 months before ("baseline") and 12 months after ("follow-up") the first opioid prescription ("index date"). Opioid therapy measures were assessed every 7 days over follow-up. Group-based trajectory modeling (GBTM) was used to identify trajectories for any opioid and total morphine milligram equivalent measures, and longitudinal latent class analysis was used for opioid therapy type. RESULTS: A total of 40 276 tramadol, 141 023 hydrocodone, and 45 221 oxycodone initiators were included. GBTM on any opioid therapy identified 3 latent trajectories: early discontinuers (tramadol 39.0%, hydrocodone 54.1%, oxycodone 61.4%), late discontinuers (tramadol 37.9%, hydrocodone 39.4%, oxycodone 33.3%), and persistent therapy (tramadol 6.7%, hydrocodone 6.5%, oxycodone 5.3%). An additional fourth trajectory, intermittent therapy (tramadol 16.4%), was identified for tramadol initiators. Of those on persistent therapy, 2687 individuals were on persistent therapy with tramadol, 9169 with hydrocodone, and 2377 with oxycodone. GBTM on opioid dose resulted in 6 similar trajectory groups in each persistent therapy group. Longitudinal latent class analysis on opioid therapy type identified 6 latent classes for tramadol and oxycodone and 7 classes for hydrocodone. CONCLUSION: Opioid therapy patterns meaningfully differed by the initial opioid prescribed, notably the presence of intermittent therapy among tramadol initiators and higher morphine milligram equivalents and prescribing of long-acting opioids among oxycodone initiators.

Telehealth Utilization for Opioid Use Disorder: A Nationwide Analysis Before and After the COVID-19 Public Health Emergency Declaration.

Introduction: The COVID-19 pandemic has led to the rapid and widespread adoption of telehealth services. Telehealth may aid in bridging gaps in access to care. The specific impact of telehealth on opioid use disorder (OUD) and its treatment remains uncertain. Methods: A retrospective review of commercial insurance claim records within the United States was conducted to investigate the association between the COVID-19 pandemic and changes in the rates of(a) OUD treatments with and without telehealth support and (b) prescriptions for medications for opioid use disorder (MOUD) with and without telehealth support among individuals diagnosed with OUD. Results: In a study population of 1,340,506 individuals, OUD diagnosis rates were 5 per 1,000 in-person and 1 per 1,000 via telehealth. COVID-19 decreased in-person OUD diagnoses by 0.89 per 1,000, while telehealth diagnoses increased by 0.83 per 1,000. In-person MOUD treatment rates increased by 0.07 per 1,000 during COVID-19, while telehealth rates remained low. The onset of COVID-19 saw a 1.13 per 1,000 higher increase in telehealth-supported MOUD treatment compared to solely in-person treatment. Conclusions: A retrospective review of commercial insurance claim records within the United States was conducted to investigate the association between the COVID-19 pandemic and changes in the rates of (a) OUD treatments with and without telehealth support and (b) prescriptions for MOUD with and without telehealth support among individuals diagnosed with OUD.

Association of Remote Patient Monitoring with Mortality and Healthcare Utilization in Hypertensive Patients: a Medicare Claims-Based Study.

BACKGROUND: Hypertension management is complex in older adults. Recent advances in remote patient monitoring (RPM) have warranted evaluation of RPM use and patient outcomes. OBJECTIVE: To study associations of RPM use with mortality and healthcare utilization measures of hospitalizations, emergency department (ED) utilization, and outpatient visits. DESIGN: A retrospective cohort study. PATIENTS: Medicare beneficiaries aged ≥65 years with an outpatient hypertension diagnosis between July 2018 and September 2020. The first date of RPM use with a corresponding hypertension diagnosis was recorded (index date). RPM non-users were documented from those with an outpatient hypertension diagnosis; a random visit was selected as the index date. Six months prior continuous enrollment was required. MAIN MEASURES: Outcomes studied within 180 days of index date included (i) all-cause mortality, (ii) any hospitalization, (iii) cardiovascular-related hospitalization, (iv) non-cardiovascular-related hospitalization, (v) any ED, (vi) cardiovascular-related ED, (vii) non-cardiovascular-related ED, (viii) any outpatient, (ix) cardiovascular-related outpatient, and (x) non-cardiovascular-related outpatient. Patient demographics and clinical variables were collected from baseline and index date. Propensity score matching (1:4) and Cox regression were performed. Hazard ratios (HR) and 95% confidence intervals (CI) are reported. KEY RESULTS: The matched sample had 16,339 and 63,333 users and non-users, respectively. Cumulative incidences of mortality outcome were 2.9% (RPM) and 4.3% (non-RPM), with a HR (95% CI) of 0.66 (0.60-0.74). RPM users had lower hazards of any [0.78 (0.75-0.82)], cardiovascular-related [0.79 (0.73-0.87)], and non-cardiovascular-related [0.79 (0.75-0.83)] hospitalizations. No significant association was observed between RPM use and the three ED measures. RPM users had higher hazards of any [1.10 (1.08-1.11)] and cardiovascular-related outpatient visits [2.17 (2.13-2.19)], while a slightly lower hazard of non-cardiovascular-related outpatient visits [0.94 (0.93-0.96)]. CONCLUSIONS: RPM use was associated with substantial reductions in hazards of mortality and hospitalization outcomes with an increase in cardiovascular-related outpatient visits.

Predictors of fatal and nonfatal overdose after prescription of opioids for chronic pain: a systematic review and meta-analysis of observational studies.

BACKGROUND: Higher doses of opioids, mental health comorbidities, co-prescription of sedatives, lower socioeconomic status and a history of opioid overdose have been reported as risk factors for opioid overdose; however, the magnitude of these associations and their credibility are unclear. We sought to identify predictors of fatal and nonfatal overdose from prescription opioids. METHODS: We systematically searched MEDLINE, Embase, CINAHL, PsycINFO and Web of Science up to Oct. 30, 2022, for observational studies that explored predictors of opioid overdose after their prescription for chronic pain. We performed random-effects meta-analyses for all predictors reported by 2 or more studies using odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Twenty-eight studies (23 963 716 patients) reported the association of 103 predictors with fatal or nonfatal opioid overdose. Moderate- to high-certainty evidence supported large relative associations with history of overdose (OR 5.85, 95% CI 3.78-9.04), higher opioid dose (OR 2.57, 95% CI 2.08-3.18 per 90-mg increment), 3 or more prescribers (OR 4.68, 95% CI 3.57-6.12), 4 or more dispensing pharmacies (OR 4.92, 95% CI 4.35-5.57), prescription of fentanyl (OR 2.80, 95% CI 2.30-3.41), current substance use disorder (OR 2.62, 95% CI 2.09-3.27), any mental health diagnosis (OR 2.12, 95% CI 1.73-2.61), depression (OR 2.22, 95% CI 1.57-3.14), bipolar disorder (OR 2.07, 95% CI 1.77-2.41) or pancreatitis (OR 2.00, 95% CI 1.52-2.64), with absolute risks among patients with the predictor ranging from 2-6 per 1000 for fatal overdose and 4-12 per 1000 for nonfatal overdose. INTERPRETATION: We identified 10 predictors that were strongly associated with opioid overdose. Awareness of these predictors may facilitate shared decision-making regarding prescribing opioids for chronic pain and inform harm-reduction strategies SYSTEMATIC REVIEW REGISTRATION: Open Science Framework (https://osf.io/vznxj/).

A State Profile of Disparities in Telehealth Utilization Among Medicare Beneficiaries: An Intersection Between Race/Ethnicity, Rurality, and Chronic Conditions-Arkansas, 2019.

Introduction: The COVID-19 pandemic brought about renewed interest and investment in telehealth, while also highlighting persistent health disparities in the Southern states. Little is known about the characteristics of those utilizing telehealth services in Arkansas, a rural Southern state. We sought to compare the characteristics of telehealth utilizers and nonutilizers among Medicare beneficiaries in Arkansas before the COVID-19 public health emergency to provide a baseline for future research investigating disparities in telehealth utilization. Methods: We used Arkansas Medicare beneficiary data (2018-2019) to model telehealth use. We included interactions to assess how the association between the number of chronic conditions and telehealth was moderated by race/ethnicity and rurality, adjusted for covariates. Results: Overall telehealth utilization in 2019 was low (n = 4,463; 1.1%). The adjusted odds of utilizing telehealth was higher for non-Hispanic Black/African Americans (vs. white, adjusted odds ratio [aOR] = 1.34, 95% confidence interval [CI] = 1.17-1.52), rural beneficiaries (aOR = 1.99, 95% CI = 1.79-2.21), and those with more chronic conditions (aOR = 1.23, 95% CI = 1.21-1.25). Race/ethnicity and rurality were significant moderators, such that the association between the number of chronic conditions and telehealth was strongest among white and among rural beneficiaries. Discussion: Among the 2019 Arkansas Medicare beneficiaries, having more chronic conditions was most strongly associated with telehealth use among white and rural individuals, while the effect was not as pronounced for Black/African American and urban individuals. Our findings suggest that advances in telehealth are not benefiting all Americans equally, with aging minoritized communities continuing to engage with more strained and underresourced health systems. Future research should investigate how upstream factors such as structural racism perpetuate poor health outcomes.

Utilization of Remote Patient Monitoring Within the United States Health Care System: A Scoping Review.

Introduction: Limited information exists on the landscape of studies and policies for remote patient monitoring (RPM) in the United States. Methods: We conducted a scoping review to assess (1) for which adult patient populations and health care needs is RPM being used and (2) the landscape of national- and state-level reimbursement policies for RPM. This study was guided by the Arksey and O'Malley methodological framework for scoping reviews and the Joanna Briggs Institute Manual for Evidence Synthesis. Results: A total of 399 articles were included in our final sample: 268 study articles and 131 articles of gray literature (e.g., websites, legislative bills). RPM-related articles rose drastically from 2015 to 2021, and the vast majority of articles were peer-reviewed journal articles. Of the study articles, prospective cohort studies were the most common study method, with m-health/smart watches being the most common RPM modality. RPM was found to be most commonly tested within patients with cardiovascular diseases, and the most common outcomes measured were usability and feasibility. Gray literature found 36 U.S. state Medicaid programs had reimbursement policies for RPM in 2021; however, 28 of those had at least one restriction on reimbursement (e.g., limited to specific providers). Conclusions: Despite the rapid growth in the literature on RPM and the adoption of reimbursement policies, retrospective, population-level studies, large randomized controlled trials, studies with a focus on additional favorable outcomes (e.g., quality of life), and studies evaluating trends in RPM reimbursement policies are lacking in the current literature.

Factors Associated with Remote Patient Monitoring Services Provision by Hospitals and Health Care Systems in the United States.

Purpose: To assess the factors associated with offering remote patient monitoring (RPM) services. Methods: We integrated three datasets: (1) 2019-2020 Area Health Resource Files, (2) 2019 American Community Survey, and (3) 2019 American Hospitals Association annual survey using county Federal Information Processing Standards code to evaluate associations between hospital characteristics and county-level demographic factors with provision of (1) post-discharge, (2) chronic care, (3) other RPM services, and (4) any of these three RPM service categories. These outcomes were analyzed using multi-level, mixed-effects multivariate logistic regression modeling to account for county-level clustering of hospitals. Findings: Among 3,381 hospitals, 1,354 (40.0%) provided any RPM services. Being part of a clinically integrated network (CIN) and private, non-profit (vs. public) ownership were respectively associated with 104.5% (95% confidence interval [CI]: 69.4-146.8%; p < 0.001) and 30.4% (95% CI: 2.5-66.0%; p = 0.031) higher odds of providing any RPM services. Critical access hospital (CAH) designation, for-profit (vs. public) ownership, and location in the South (vs. Northeast) were associated with significantly lowering odds of providing any RPM services by 36.2% (95% CI: 14.2-52.6%; p = 0.003), 70.1% (95% CI: 56.0-79.6%; p < 0.001), and 34.0% (95% CI: 2.8-55.1%; p = 0.035), respectively. Similar trends were found with the various RPM service categories. Conclusions: The factors most associated with provision of any RPM services were hospital-level factors. Specifically, being part of a CIN and private, non-profit ownership had the highest positive associations with offering RPM services whereas location in the South and CAH designation had the strongest negative associations. Further studies are needed to understand the reasons behind these associations.

Socioeconomic Determinants of Remote Patient Monitoring Implementation Among Rural and Urban Hospitals.

Introduction: Remote patient monitoring (RPM) is a form of telehealth that improves quality of care for chronic disease treatment and reduces hospital readmission rates. Geographical proximity to health care is important for individuals of low socioeconomic status (SES) who face additional financial and transportation barriers. The goal of this study was to assess the association between social determinants of health and adoption of RPM. Methods: This cross-sectional study analyzed data from hospitals that responded to the American Hospital Association's Annual Survey (2018) and spatially linked census tract-level environmental and social determinants of health obtained from the Social Vulnerability Index (2018). Results: A total of 4,206 hospitals (1,681 rural and 2,525 urban hospitals) met study criteria. Rural hospitals near households in the lower middle quartile SES were associated with a 33.5% lower likelihood of having adopted RPM for chronic care management compared with rural hospitals near households in the highest quartile SES (adjusted odds ratios [aOR] = 0.665; 95% confidence interval [CI]: 0.453-0.977). Urban hospitals near households in the lowest quartile SES were associated with a 41.9% lower likelihood of having adopted RPM for chronic care management compared with urban hospitals near households in the highest quartile SES (aOR = 0.581; 95% CI: 0.435-0.775). Similar trends in accessibility were found with RPM for postdischarge services among urban hospitals. Conclusion: Our findings highlight the importance of hospital responsibility and state and federal policy approaches toward ensuring equitable access to RPM services for patients characterized by lower SES.

Association between opioid therapy trajectories and potential opioid-related adverse health events.

PURPOSE: We identified associations between membership in seven group-based trajectories based on supply of filled opioid prescriptions and potential opioid-related adverse health events over a 720-day window. METHODS: We identified two veteran cohorts with chronic non-cancer pain who initiated treatment with long-term opioid therapy between 2008 and 2015, excluding those with prior substance use disorder (n = 373 941) or non-SUD, opioid-related adverse outcome (n = 405 631) diagnoses. Outcomes of interest included opioid use disorder, non-opioid drug use disorder, and alcohol use disorder for the first cohort; or accidents resulting in wounds or injuries, self-inflicted injuries, opioid-related accidents and overdoses, alcohol and non-opioid drug-related accidents and overdoses, and violence-related injuries for the second cohort. Using a cross-sectional design, Veterans were followed until the specific outcome of interest was diagnosed, they died, the study ended, or they were lost to follow up. Accelerated failure time models were estimated for each outcome. RESULTS: Membership in persistent moderate days covered and persistent modest days covered trajectories was associated with decreased risk of opioid use disorder (Moderate: θ = 0.59, 95%CI:0.54, 0.64; Modest: θ = 0.54, 95%CI:0.50, 0.59) and opioid overdose (Moderate: θ = 0.67,95%CI: 0.57, 0.79; Modest: θ = 0.72, 95%CI:0.61, 0.85) versus higher-utilizing persistent users. Rapid discontinuation was associated with decreased risk of opioid use disorder (θ = 0.86, 95% CI:0.77, 0.95) and opioid overdose (θ = 0.54, 95%CI:0.41, 0.71), but increased risk of alcohol use disorder (θ = 1.07, 95%CI:1.00, 1.15) and other substance use disorders. Delayed discontinuation or delayed reduction was associated with increased risk for most opioid related adverse health events. CONCLUSION: Persistent use trajectories with low levels of opioid utilization were associated with lower risks of potential opioid-related adverse health events.

Using data science to improve outcomes for persons with opioid use disorder.

Medication treatment for opioid use disorder (MOUD) is an effective evidence-based therapy for decreasing opioid-related adverse outcomes. Effective strategies for retaining persons on MOUD, an essential step to improving outcomes, are needed as roughly half of all persons initiating MOUD discontinue within a year. Data science may be valuable and promising for improving MOUD retention by using "big data" (e.g., electronic health record data, claims data mobile/sensor data, social media data) and specific machine learning techniques (e.g., predictive modeling, natural language processing, reinforcement learning) to individualize patient care. Maximizing the utility of data science to improve MOUD retention requires a three-pronged approach: (1) increasing funding for data science research for OUD, (2) integrating data from multiple sources including treatment for OUD and general medical care as well as data not specific to medical care (e.g., mobile, sensor, and social media data), and (3) applying multiple data science approaches with integrated big data to provide insights and optimize advances in the OUD and overall addiction fields.

Cost-Effectiveness of Intranasal Naloxone Distribution to High-Risk Prescription Opioid Users.

OBJECTIVES: To determine the cost-effectiveness of pharmacy-based intranasal naloxone distribution to high-risk prescription opioid (RxO) users. METHODS: We developed a Markov model with an attached tree for pharmacy-based naloxone distribution to high-risk RxO users using 2 approaches: one-time and biannual follow-up distribution. The Markov structure had 6 health states: high-risk RxO use, low-risk RxO use, no RxO use, illicit opioid use, no illicit opioid use, and death. The tree modeled the probability of an overdose happening, the overdose being witnessed, naloxone being available, and the overdose resulting in death. High-risk RxO users were defined as individuals with prescription opioid doses greater than or equal to 90 morphine milligram equivalents (MME) per day. We used a monthly cycle length, lifetime horizon, and US healthcare perspective. Costs (2018) and quality-adjusted life-years (QALYs) were discounted 3% annually. Microsimulation was performed with 100 000 individual trials. Deterministic and probabilistic sensitivity analyses were conducted. RESULTS: One-time distribution of naloxone prevented 14 additional overdose deaths per 100 000 persons, with an incremental cost-effectiveness ratio (ICER) of $56 699 per QALY. Biannual follow-up distribution led to 107 additional lives being saved with an ICER of $84 799 per QALY compared with one-time distribution. Probabilistic sensitivity analyses showed that a biannual follow-up approach would be cost-effective 50% of the time at a willingness-to-pay (WTP) threshold of $100 000 per QALY. Naloxone effectiveness and proportion of overdoses witnessed were the 2 most influential parameters for biannual distribution. CONCLUSION: Both one-time and biannual follow-up naloxone distribution in community pharmacies would modestly reduce opioid overdose deaths and be cost-effective at a WTP of $100 000 per QALY.

Impact of Medical Marijuana Legalization on Opioid Use, Chronic Opioid Use, and High-risk Opioid Use.

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. Sub-group 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 sub-group 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.

Doctor hopping and doctor shopping for prescription opioids associated with increased odds of high-risk use.

PURPOSE: Early detection of risky behaviors involving prescription opioids can assist prescribers in implementing safer prescribing. Patient-to-prescriber travel patterns may indicate potential opioid misuse. We introduce doctor hopping, patients bypassing nearby prescribers in favor of more distant ones, as a new spatial estimation of potentially risky behavior, and compare with traditional doctor shopping metrics. METHODS: We examined all filled opioid prescriptions between 2015 and 2016 from the Arkansas Prescription Drug Monitoring Program. We calculated patient-to-prescriber travel times and number of prescribers bypassed for each prescription, adjusted for payment method. Opioid recipients traveling further than the nearest urban area and bypassing more prescribers than 99% of other recipients from the same zip code were identified as doctor hoppers. We calculated odds ratios to evaluate how doctor hopping and doctor shopping correspond to high-risk opioid uses. RESULTS: Approximately 0.72% of all opioid recipients in Arkansas engaged in doctor hopping two or more times during the study period. Rates of doctor hopping varied spatially but were more common in rural areas. Doctor shopping was more common in urban areas. Both hopping and shopping were significantly associated with higher odds of engaging in high-risk opioid use. The combination of doctor hopping and doctor shopping metrics can predict high-risk use better than either metric alone and may allow for earlier detection than doctor shopping alone. CONCLUSIONS: Doctor hopping is positively associated with high-risk opioid use and is distinct from and complementary to doctor shopping. We recommend Prescription Drug Monitoring Program (PDMP) vendors incorporate similar spatial analyses into their systems.

Assessment of Reliability and Validity of SF-12v2 among a Diabetic Population.

OBJECTIVES: To validate the Medical Outcomes Study Short Form version 2 (SF-12v2) in diabetic patients. METHODS: Adults with self-reported diabetes from the Medical Expenditure Panel Survey (2011-2013) were identified. Reliability (internal consistency and test-retest) and validity (construct, concurrent, criterion, and predictive) of the SF-12v2 were assessed. The SF-12v2 consists of two normalized composite scores: the physical component summary score (PCS12) and the mental component summary score (MCS12). Confirmatory factor analysis was conducted to assess the instrument structure. Concurrent (convergent and discriminant) validity was assessed by a multitrait-multimethod matrix using the Patient Health Questionnaire, the Kessler Scale, and perceived health and mental health questions. The predictive validity was assessed by estimating future limitations. The concurrent validity was tested by comparing the MCS12, PCS12, and utility scores (six-dimensional health state short form) across comorbidity scores. RESULTS: The final sample comprised 2214 diabetic patients with mean normalized (population mean 50; range 0-100) PCS12 and MCS12 scores of 40.81 (standard error 0.33) and 49.82 (standard error 0.26), respectively. The PCS12 and MCS12 scores showed good internal consistency (Cronbach α: PCS12 0.85; MCS12 0.83) and acceptable test-retest reliability (intraclass correlation coefficient: PCS12 0.72; MCS12 0.63) and produced acceptable goodness-of-fit indices (normed fit index 0.95; comparative fit index 0.95; root mean square error of approximation 0.11 [95% confidence interval 0.1017-0.1188]). The PCS12 and MCS12 were moderately correlated with perceived health and perceived mental health. The MCS12 was highly correlated with the Patient Health Questionnaire and the Kessler Scale. Both the PCS12 and the MCS12 could predict the future health limitations. The PCS12, MCS12, and utility scores demonstrated sensitivity to the presence of comorbidity scores. CONCLUSIONS: The SF-12v2 is a valid generic instrument for measuring quality of life in diabetic patients.

Evaluation of opioid use among patients with back disorders and arthritis.

PURPOSE: Long-term opioid use for chronic pain has increased, but limited evidence exists on its benefits. Evaluation of long-term benefits in pain is based on patient-reported measures such as health-related quality of life (HRQoL). This study examined the long-term effects of opioid use on HRQoL and its subdomains in patients with back pain or arthritis by comparing opioid users to non-opioid users for three metrics: (1) any opioid use, (2) duration of opioid use, and (3) average daily morphine equivalent dose. METHODS: A nationally representative sample of cancer-free adults with chronic back pain or arthritis was selected. Using the 12-Item Short Form Survey, HRQoL measures of Mental Component Score (MCS), Physical Component Score (PCS), and individual subdomains were assessed at baseline and 1 year later. Opioid users were matched to non-opioid users in a 1:1 greedy match using propensity scores estimated based on many patient demographics and baseline HRQoL measures. RESULTS: At year one, PCS was significantly lower among opioid users, mostly driven by bodily pain subdomain; MCS was not different. Short-term opioid users (< 1 month) had higher MCS while long-term users (≥ 1 month) had lower PCS. Low-dose [< 20 morphine milligram equivalents (MME)/day] opioid use was associated with lower PCS, while no difference was found between high dose (≥ 20 MME/day) and non-opioid users. However, most differences were not clinically significant. CONCLUSIONS: Long-term opioid use is not associated with improvements in HRQoL. Clinicians should carefully evaluate the need for opioid use, especially long-term use in managing chronic back pain and arthritis.

Characteristics of Initial Prescription Episodes and Likelihood of Long-Term Opioid Use - United States, 2006-2015.

Because long-term opioid use often begins with treatment of acute pain (1), in March 2016, the CDC Guideline for Prescribing Opioids for Chronic Pain included recommendations for the duration of opioid therapy for acute pain and the type of opioid to select when therapy is initiated (2). However, data quantifying the transition from acute to chronic opioid use are lacking. Patient records from the IMS Lifelink+ database were analyzed to characterize the first episode of opioid use among commercially insured, opioid-naïve, cancer-free adults and quantify the increase in probability of long-term use of opioids with each additional day supplied, day of therapy, or incremental increase in cumulative dose. The largest increments in probability of continued use were observed after the fifth and thirty-first days on therapy; the second prescription; 700 morphine milligram equivalents cumulative dose; and first prescriptions with 10- and 30-day supplies. By providing quantitative evidence on risk for long-term use based on initial prescribing characteristics, these findings might inform opioid prescribing practices.

Cost-Effectiveness of Oral Anticoagulants for Ischemic Stroke Prophylaxis Among Nonvalvular Atrial Fibrillation Patients.

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 (CHADS2 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 CHADS2 ≥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 (CHADS2≥3), dabigatran was the most cost-effective treatment option.

The influence of propoxyphene withdrawal on opioid use in veterans.

PURPOSE: Our aim is to determine if propoxyphene withdrawal from the US market was associated with opioid continuation, continued chronic opioid use, and secondary propoxyphene-related adverse events (emergency department visits, opioid-related events, and acetaminophen toxicity). METHODS: Medical service use and pharmacy data from 19/11/08 to 19/11/11 were collected from the national Veterans Healthcare Administration healthcare databases. A quasi-experimental pre-post retrospective cohort design utilizing a historical comparison group provided the study framework. Logistic regression controlling for baseline covariates was used to estimate the effect of propoxyphene withdrawal. RESULTS: There were 24,328 subjects (policy affected n = 10,747; comparison n = 13,581) meeting inclusion criteria. In the policy-affected cohort, 10.6% of users ceased using opioids, and 26.6% stopped chronic opioid use compared with 3.8% and 13.5% in the historical comparison cohort, respectively. Those in the policy-affected cohort were 2.7 (95%CI: 2.5-2.8) and 3.2 (95%CI: 2.9-3.6) times more likely than those in the historical comparison cohort to discontinue chronic opioid and any opioid use, respectively. Changes in adverse events and Emergency Department (ED) visits were not different between policy-affected and historical comparison cohorts (p > 0.05). CONCLUSIONS: The withdrawal of propoxyphene-containing products resulted in rapid and virtually complete elimination in propoxyphene prescribing in the veterans population; however, nearly 90% of regular users of propoxyphene switched to an alternate opioid, and three quarters continued to use opioids chronically.

The association between rurality, dual Medicare/Medicaid eligibility and chronic conditions with telehealth utilization: An analysis of 2019-2020 national Medicare claims.

INTRODUCTION: Telehealth services have the potential to increase healthcare access among underserved populations, such as rural residents and racial/ethnic minority groups. The COVID-19 public health emergency led to unprecedented growth in telehealth utilization, but evidence suggests the growth has not been equitable across all patient populations. This study aimed to explore whether telehealth utilization and expansion changed equitably from 2019 to 2020 among sub-groups of Medicare beneficiaries. METHODS: We conducted an analysis of telehealth utilization among a 20% random sample of 2019-2020 Medicare beneficiaries on a national level. We fit multivariable logistic regression models and calculated average marginal effects (AME) to assess the association between demographic and clinical characteristics on telehealth utilization. RESULTS: We found telehealth utilization was less likely among non-Hispanic Black/African-American (2019: adjusted odds ratio [aOR] = 0.77, AME = -0.15; 2020: aOR = 0.85, AME = -3.50) and Hispanic (2019: aOR = 0.79, AME = -0.13; 2020: aOR = 0.87, AME = -2.89) beneficiaries, relative to non-Hispanic White beneficiaries in both 2019 and 2020, with larger disparities in 2020. Rural beneficiaries were more likely to utilize telehealth than urban beneficiaries in 2019 (aOR = 2.62, AME = 0.84), but less likely in 2020 (aOR = 0.57, AME = -14.47). In both years, dually eligible Medicare/Medicaid beneficiaries were more likely than non-dually eligible beneficiaries to utilize telehealth (2019: aOR = 4.75, AME = 0.84; 2020: aOR = 1.34, AME = 2.25). However, the effects of dual eligibility and rurality changed in both models as the number of chronic conditions increased. DISCUSSION: We found evidence of increasing disparities in telehealth utilization among several Medicare beneficiary sub-groups in 2020 relative to 2019, including individuals of minority race/ethnicity, rural residents, and dually eligible beneficiaries, with disparities increasing among individuals with more chronic conditions. Although telehealth has the potential to address health inequities, our findings suggest that many of the patients in greatest need of healthcare are least likely to utilize telehealth services.

Comparative Study of Opioid Initiation With Tramadol, Short-acting Hydrocodone, or Short-acting Oxycodone on Opioid-related Adverse Outcomes Among Chronic Noncancer Pain Patients.

OBJECTIVE: To compare the safety profiles of low and high-dose tramadol, short-acting hydrocodone, and short-acting oxycodone therapies among chronic noncancer pain individuals. MATERIALS AND METHODS: A retrospective cohort study of individuals with back/neck pain/osteoarthritis with an initial opioid prescription for tramadol, hydrocodone, or oxycodone was conducted using IQVIA PharMetrics Plus claims for Academics database (2006 to 2020). Two cohorts were created for separately studying opioid-related adverse events (overdoses, accidents, self-inflicted injuries, and violence-related injuries) and substance use disorders (opioid and nonopioid). Patients were followed from the index date until an outcome event, end of enrollment, or data end. Time-varying exposure groups were constructed and Cox regression models were estimated. RESULTS: A total of 1,062,167 (tramadol [16.5%], hydrocodone [61.1%], and oxycodone [22.4%]) and 986,809 (tramadol [16.5%], hydrocodone [61.3%], and oxycodone [22.2%]) individuals were in the adverse event and substance use disorder cohorts. All high-dose groups had elevated risk of nearly all outcomes, compared with low-dose hydrocodone. Compared with low-dose hydrocodone, low-dose oxycodone was associated with a higher risk of opioid overdose (hazard ratio: 1.79 [1.37 to 2.33]). No difference in risk was observed between low-dose tramadol and low-dose hydrocodone (hazard ratio: 0.85 [0.64 to 1.13]). Low-dose oxycodone had higher risks of an opioid use disorder, and low-dose tramadol had a lower risk of accidents, self-inflicted injuries, and opioid use disorder compared with low-dose hydrocodone. DISCUSSION: Low-dose oxycodone had a higher risk of opioid-related adverse outcomes compared with low-dose tramadol and hydrocodone. This should be interpreted in conjunction with the benefits of pain control and functioning associated with oxycodone use in future research.