Economic Evaluation in Opioid Modeling: Systematic Review

Opioid use surrounding diagnosis and follow-up in patients with ankylosing spondylitis, psoriatic arthritis, and rheumatoid arthritis: Results from US claims databases

OBJECTIVE

To describe patients' use of opioids in the year preceding and year following new diagnosis of ankylosing spondylitis (AS), psoriatic arthritis (PsA), or rheumatoid arthritis (RA), compared with patients without the/se diseases.

METHODS

This study used US IBM® MarketScan® Commercial Claims and Encounters (CCAE) and Medicaid data and included three cohorts, comprised of incident cases of AS, PsA, or RA (2010-2017). Three matched comparator patients (without the incident disease) were selected for each patient within the disease cohort. Opioid use and appropriate treatment exposure (as defined by US guideline recommendations) in the 12-month baseline and follow-up periods were evaluated using descriptive analyses.

RESULTS

Prevalence of claims for opioids was higher for disease cohorts vs. comparators in CCAE; 36.4% of patients with AS, 29.5% with PsA, and 44.4% with RA did not have any claim for guideline-appropriate therapy in follow-up. Prevalence of claims for opioids was also higher for disease cohorts vs. comparators in Medicaid; 30.6% of patients with AS, 36.6% with PsA, and 65.4% with RA did not have any claim for guideline-appropriate therapy in follow-up.

CONCLUSIONS

In patients with AS, PsA, or RA, there was high reliance on opioids at and around the time of diagnosis. Significant proportions of patients were not on appropriate treatment as defined by professional society post-diagnosis guidelines; this discordance between actual patient therapies and treatment recommendations may suggest a need for better awareness of appropriate pain management and treatment strategies in rheumatic diseases. Key Points • This study analysed opioid use among patients with ankylosing spondylitis (AS), psoriatic arthritis (PsA), or rheumatoid arthritis (RA), and adds to current knowledge by expanding beyond assessment of opioid use at diagnosis, to the year before and after diagnosis. • Opioid use was found to be highly prevalent in AS, PsA, and RA in the year prior to diagnosis and, interestingly, was still seen during the year after diagnosis. • Opioids are neither disease modifying, nor a targeted/recommended treatment for chronic autoimmune diseases. In addition to their association with significant economic costs, opioids are potentially hazardous and are not better than alternative treatments with superior safety profiles. • The reasons behind opioid prescribing patterns should be explored further to support movement to targeted therapies.

Economic evaluation of biological treatments in patients with severe asthma: a systematic review

BACKGROUND

Asthma is a highly prevalent disease, one of the chronic diseases with the highest economic costs; thus, it imposes a high economic burden on society, the healthcare system, patients, and third-party payers. Contrary to this study, until now, systematic reviews of economic evaluations (EEs) of treatments for severe asthma have not been exclusively focused on biological treatments, and have included a small number of studies and only model-based EEs.

METHODS

This study systematically reviews EEs of biological therapies for severe asthma published until December 2022 using PRISMA guidelines. The review analyzes the cost-effectiveness of biologicals in comparison to SOC, or SOC plus OCS. The quality of the EEs is assessed using Consensus on Health Economics Checklist extended (CHEC-extended).

RESULTS

Thirty-nine studies were eligible: 15 based on a Markov model, and 19 trial-based; eight adopting societal and NHS perspectives, and seven the payer's perspective. The reviewed EEs addressed cost-effectiveness, cost-utility, and incremental costs and outcomes comparison. Their findings were mainly expressed through ICER-incremental cost-effectiveness ratio (24 studies: 13 concluded that biological were cost-effective) and cost comparison analysis (14 studies: 6 concluded that biological were cost-effective), and were sensitive to a wide variety of factors (e.g. medication cost, treatment response, time horizon, utility benefits, mortality, exacerbation rate, discount rate, etc.).

CONCLUSIONS

There has been some ambiguity concerning the EE of biological therapies due to variation in choice of study design and contradictory results. Nevertheless, it can be concluded that biological treatments improve health outcomes, in many contexts at a high cost.

Opioid-related harms and care impacts of conventional and AI-based prescription management strategies: insights from leveraging agent-based modeling and machine learning

Introduction

Like its counterpart to the south, Canada ranks among the top five countries with the highest rates of opioid prescriptions. With many suffering from opioid use disorder first having encountered opioids via prescription routes, practitioners and health systems have an enduring need to identify and effectively respond to the problematic use of opioid prescription. There are strong challenges to successfully addressing this need: importantly, the patterns of prescription fulfillment that signal opioid abuse can be subtle and difficult to recognize, and overzealous enforcement can deprive those with legitimate pain management needs the appropriate care. Moreover, injudicious responses risk shifting those suffering from early-stage abuse of prescribed opioids to illicitly sourced street alternatives, whose varying dosage, availability, and the risk of adulteration can pose grave health risks.

Methods

This study employs a dynamic modeling and simulation to evaluate the effectiveness of prescription regimes employing machine learning monitoring programs to identify the patients who are at risk of opioid abuse while being treated with prescribed opioids. To this end, an agent-based model was developed and implemented to examine the effect of reduced prescribing and prescription drug monitoring programs on overdose and escalation to street opioids among patients, and on the legitimacy of fulfillments of opioid prescriptions over a 5-year time horizon. A study released by the Canadian Institute for Health Information was used to estimate the parameter values and assist in the validation of the existing agent-based model.

Results and discussion

The model estimates that lowering the prescription doses exerted the most favorable impact on the outcomes of interest over 5 years with a minimum burden on patients with a legitimate need for pharmaceutical opioids. The accurate conclusion about the impact of public health interventions requires a comprehensive set of outcomes to test their multi-dimensional effects, as utilized in this research. Finally, combining machine learning and agent-based modeling can provide significant advantages, particularly when using the latter to gain insights into the long-term effects and dynamic circumstances of the former.

Cost-effectiveness of Increasing Buprenorphine Treatment Initiation, Duration, and Capacity Among Individuals Who Use Opioids

Importance

Buprenorphine is an effective and cost-effective medication to treat opioid use disorder (OUD), but is not readily available to many people with OUD in the US. The current cost-effectiveness literature does not consider interventions that concurrently increase buprenorphine initiation, duration, and capacity.

Objective

To conduct a cost-effectiveness analysis and compare interventions associated with increased buprenorphine treatment initiation, duration, and capacity.

Design and Setting

This study modeled the effects of 5 interventions individually and in combination using SOURCE, a recent system dynamics model of prescription opioid and illicit opioid use, treatment, and remission, calibrated to US data from 1999 to 2020. The analysis was run during a 12-year time horizon from 2021 to 2032, with lifetime follow-up. A probabilistic sensitivity analysis on intervention effectiveness and costs was conducted. Analyses were performed from April 2021 through March 2023. Modeled participants included people with opioid misuse and OUD in the US.

Interventions

Interventions included emergency department buprenorphine initiation, contingency management, psychotherapy, telehealth, and expansion of hub-and-spoke narcotic treatment programs, individually and in combination.

Main Outcomes and Measures

Total national opioid overdose deaths, quality-adjusted life years (QALYs) gained, and costs from the societal and health care perspective.

Results

Projections showed that contingency management expansion would avert 3530 opioid overdose deaths over 12 years, more than any other single-intervention strategy. Interventions that increased buprenorphine treatment duration initially were associated with an increased number of opioid overdose deaths in the absence of expanded treatment capacity. With an incremental cost- effectiveness ratio of $19 381 per QALY gained (2021 USD), the strategy that expanded contingency management, hub-and-spoke training, emergency department initiation, and telehealth was the preferred strategy for any willingness-to-pay threshold from $20 000 to $200 000/QALY gained, as it was associated with increased treatment duration and capacity simultaneously.

Conclusion and Relevance

This modeling analysis simulated the effects of implementing several intervention strategies across the buprenorphine cascade of care and found that strategies that were concurrently associated with increased buprenorphine treatment initiation, duration, and capacity were cost-effective.

Healthcare Utilization of Patients with Opioid Use Disorder in US Hospitals from 2016 to 2019: Focusing on Racial and Regional Variances

BACKGROUND

There is a lack of US population-based research on healthcare utilization differences caused by opioid misuse.

OBJECTIVE

The aim of this study was to explore disparities in healthcare utilization by type of opioid use disorder, race, region, and other patient factors for a more targeted prevention and treatment program.

METHODS

The National Inpatient Sample of the United States was used to identify patients with opioid use disorder (n = 101,231, weighted n = 506,155) from 2016 to 2019. Type of opioid use disorder was defined as opioid dependence/unspecified use, adverse effects of opioids, opioid misuse, and opioid poisoning (also known as overdose). We examined the sample characteristics and the association between type of disorder, racial and regional variables, and healthcare utilization, measured by hospital charges and length of stay. The multivariate survey linear regression model was used.

RESULTS

Among 506,155 patients, most were categorized as opioid dependence/unspecified use (56.3%) and opioid poisoning (42.7%). The number of opioid use disorder patients during the study decreased; however, overall total charges and length of stay continuously increased. Survey linear results showed that opioid poisoning, adverse effects, and abuse were associated with higher hospital charges than opioid dependence; however, length of stay was significantly lower for these groups. White patients compared with minorities, and West, Northeast, and South regions were associated with higher hospital charges and length of stay.

CONCLUSION

Significant differences in healthcare utilization exist between type of disorder, race, and region. Such findings illustrate that tailored treatment regimens are required to bridge the gaps in care and combat the opioid crisis. Minorities with opioid use disorder utilize healthcare the least, possibly because of affordability, and need culturally sensitive and financially feasible treatment options.

Psychiatric Comorbidities Associated with Persistent Postoperative Opioid Use

PURPOSE OF REVIEW

This review outlines the psychiatric comorbidities associated with persistent opioid use in the postoperative period. We finish our analysis with evidence-based, patient-centered interventions that can be rendered in the perioperative setting to decrease postoperative opioid requirements.

RECENT FINDINGS

Opioids are overprescribed in the USA, especially in the postoperative setting. Excess opioids can result in diversion and contribute to the ongoing opioid epidemic. Mental health and substance use disorders can contribute to persistent postoperative opioid use. Adequately managing these disorders preoperatively promises to reduce persistent postoperative opioid use. Due to the lack of homogenous, evidence-based recommendations on the appropriate quantity and duration of postoperative opioid therapy, there is wide variability in provider prescribing habits. Further research is needed to establish surgery-specific postoperative opioid therapy protocols. Opioids continue to be a mainstay in the treatment of postoperative pain. Unmonitored postoperative opioid use can lead to opioid use disorder. Mental health disorders increase susceptibility to persistent postoperative opioid use. By managing these psychiatric illnesses preoperatively, clinicians have the ability to decrease opioid consumption postoperatively. Lastly, given the healthcare burden of opioid misuse and abuse, it is important to establish concrete protocols to guide provider-prescribing habits.

Identifying and Characterizing Medical Advice-Seekers on a Social Media Forum for Buprenorphine Use

Background: Online communities such as Reddit can provide social support for those recovering from opioid use disorder. However, it is unclear whether and how advice-seekers differ from other users. Our research addresses this gap by identifying key characteristics of r/suboxone users that predict advice-seeking behavior. Objective: The objective of this analysis is to identify and describe advice-seekers on Reddit for buprenorphine-naloxone use using text annotation, social network analysis, and statistical modeling techniques. Methods: We collected 5258 posts and their comments from Reddit between 2014 and 2019. Among 202 posts which met our inclusion criteria, we annotated each post to determine which were advice-seeking (n = 137) or not advice-seeking (n = 65). We also annotated each posting user’s buprenorphine-naloxone use status (current versus formerly taking and, if currently taking, whether inducting or tapering versus other stages) and quantified their connectedness using social network analysis. To analyze the relationship between Reddit users’ advice-seeking and their social connectivity and medication use status, we constructed four models which varied in their inclusion of explanatory variables for social connectedness and buprenorphine use status. Results: The stepwise model containing “total degree” (p = 0.002), “using: inducting/tapering” (p < 0.001), and “using: other” (p = 0.01) outperformed all other models. Reddit users with fewer connections and who are currently using buprenorphine-naloxone are more likely to seek advice than those who are well-connected and no longer using the medication, respectively. Importantly, advice-seeking behavior is most accurately predicted using a combination of network characteristics and medication use status, rather than either factor alone. Conclusions: Our findings provide insights for the clinical care of people recovering from opioid use disorder and the nature of online medical advice-seeking overall. Clinicians should be especially attentive (e.g., through frequent follow-up) to patients who are inducting or tapering buprenorphine-naloxone or signal limited social support.

Community Distribution of Naloxone: A Systematic Review of Economic Evaluations

BACKGROUND

As a core component of harm-reduction strategies to address the opioid crisis, several countries have instituted publicly funded programs to distribute naloxone for lay administration in the community. The effectiveness in reducing mortality from opioid overdose has been demonstrated in multiple systematic reviews. However, the economic impact of community naloxone distribution programs is not fully understood.

OBJECTIVES

Our objective was to conduct a review of economic evaluations of community distribution of naloxone, assessing for quality and applicability to diverse contexts and settings.

DATA SOURCES

The search strategy was performed on MEDLINE, Embase, and EconLit databases.

STUDY ELIGIBILITY CRITERIA AND INTERVENTIONS

Search criteria were developed based on two themes: (1) papers involving naloxone or narcan and (2) any form of economic evaluation. A focused search of the grey literature was also conducted. Studies exploring the intervention of community distribution of naloxone were selected.

STUDY APPRAISAL AND SYNTHESIS METHODS

Data extraction was done using the British Medical Journal guidelines for economic submissions, assigning quality levels based on the impact of the missing or unclear components on the strength of the conclusions.

RESULTS

A total of nine articles matched our inclusion criteria: one cost-effectiveness analysis, eight cost-utility analyses, and one cost-benefit analysis. Overall, the quality of the studies was good (six of high quality, two of moderate quality, and one of low quality). All studies concluded that community distribution of naloxone was cost effective, with an incremental cost-utility ratio range of $US111-58,738 (year 2020 values) per quality-adjusted life-year gained.

LIMITATIONS

Our search strategy was developed iteratively, rather than following an a priori design. Additionally, our search was limited to English terms.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

Based on this review, community distribution of naloxone is a worthwhile investment and should be considered by other countries dealing with the opioid epidemic.

Using Economic Evaluation to Inform Responses to the Opioid Epidemic in the United States: Challenges and Suggestions for Future Research

Background: Several aspects of the opioid epidemic and of public health care organization in the United States (US) make the conduct of economic evaluation and the design of policies to respond to this crisis particularly challenging. Objectives: This commentary offers suggestions for how economic evaluation may address and overcome four key features of the opioid epidemic: 1) its magnitude and geographical distribution, 2) its intersection with multiple epidemics, 3) its rapidly changing dynamics, 4) its multi-sectoral causes and consequences. Results: We first offer pragmatic suggestions to address the difficulties in delivering a coordinated response given the fragmented nature of health care in the US. In view of the broad suite of responses required to address opioid use disorder and its associated comorbidities, we highlight the need for economic evaluations which consider interventions throughout the continuum of care (i.e. primary, secondary and tertiary levels of prevention). We examine how the use of predictive modelling alongside economic evaluation might be adopted to address the rapidly evolving situation affecting distinct populations and geographic areas and encourage investments in epidemic preparedness. Finally, we propose methods to capture the interdependence of various sectors of government affected by the opioid crisis in economic evaluations to ensure optimal levels of investment towards a comprehensive response. Conclusions: The opioid epidemic in the US represents an unprecedented public health challenge, but sound epidemiological modelling and economic analysis can help to guide use of limited resources committed to addressing it in ways that can have greatest impact in limiting its adverse consequences.

A Systematic Review of Simulation Models to Track and Address the Opioid Crisis

The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models offer a tool to help us understand and address this complex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings. Further, we created a database of model parameters used for model calibration, and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and Agent-Based models (16%). Over a third evaluated intervention cost-effectiveness (40%), and another third (39%) focused on treatment and harm reduction services for people with opioid use disorder (OUD). More than half (61%) discussed calibrating their models to empirical data, and 31% discussed validation approaches used in their modeling process. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation/relapse, emergency medical services, and mortality parameters. This database offers a tool that future modelers can use to identify potential model inputs and evaluate comparability of their models to prior work. Future applications of simulation models to this field should actively tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.

Impact of Florida's prescription drug monitoring program on drug- related fatal vehicle crashes: a difference-in-differences approach

BACKGROUND

Prescription drug use has soared in the USA within the last two decades. Prescription drugs can impair motor skills essential for the safe operation of a motor vehicle, and therefore can affect traffic safety. As one of the epicentres of the opioid epidemic, Florida has been struck by high opioid misuse and overdose rates, and has concurrently suffered major threats to traffic disruptions safety caused by driving under the influence of drugs. To prevent prescription opioid misuse in Florida, Prescription Drug Monitoring Programs (PDMPs) were implemented in September 2011.

OBJECTIVE

To examine the impact of Florida's implementation of a mandatory PDMP on drug-related MVCs occurring on public roads.

METHODS

We employed a difference-in-differences approach to estimate the difference in prescription drug-related fatal crashes in Florida associated with its 2011 PDMP implementation relative to those in Georgia, which did not use PDMPs during the same period (2009-2013). The analyses were conducted in 2020.

RESULTS

In Florida, there was a significant decline in drug-related vehicle crashes during the 22 months post-PDMP. PDMP implementation was associated with approximately two (-2.21; 95% CI -4.04 to -0.37; p<0.05) fewer prescribed opioid-related fatal crashes every month, indicating 25% reduction in the number of monthly crashes. We conducted sensitivity analyses to investigate the impact of PDMP implementation on central nervous system depressants and stimulants as well as cocaine and marijuana-related fatal crashes but found no robust significant reductions.

CONCLUSIONS

The implementation of PDMPs in Florida provided important benefits for traffic safety, reducing the rates of prescription opioid-related vehicle crashes.