Modeling the impact of simulated educational interventions on the use and abuse of pharmaceutical opioids in the United States: a report on initial efforts

Scoping Literature Review of Disease Modeling of the Opioid Crisis

Opioid misuse continues to cause significant harm. To investigate current research, we conducted a scoping literature review of disease spread models of opioid misuse from January 2000 to December 2022. In total, 85 studies were identified and examined for the opioids modeled, model type, data sources used and model calibration and validation. Most of the studies (58%, 49) only modeled heroin; the next largest categories were prescription opioids and unspecified opioids which accounted for 9% (8) each. Most models were theoretical compartmental models (57) or applied compartmental models (21). Previously published research was the most used data source (38), and a majority of the model validation involved the researchers setting initial conditions to verify theoretical results (30). To represent typical opioid use more accurately, multiple opioids need to be incorporated into the disease spread models, and applying different modeling techniques may allow other insights into opioid misuse spread.

Clinician approaches to new spine pain cases in primary care: Balance of opioid prescribing and early linkage to exercise therapy and spinal manipulation

RATIONALE, AIMS AND OBJECTIVES

Spine pain (SP) is common and often disabling. Clinical practice guidelines discourage opioid treatment and outline the value of varied nonpharmacologic therapies (NPTs). This study elucidates the amount of variability in primary-care clinicians' (PCPs') prescribing of opioids and in their cases' receipt of the two most common NPTs (exercise therapy and spinal manipulation).

METHOD

The design was a retrospective cohort study examining variation in the treatment of PCPs' new SP cases, classified by receipt of (a) prescription of an opioid at the initial visit; (b) exercise therapy and/or spinal manipulation within 30 days of initial visit. The study was set in the primary care clinics at military treatment facilities of the US Military Health System in the period between October 2011 and September 2016.

RESULTS

The majority of cases did not receive a study treatment (66.3%); 19.6% of cases received only NPT within 30 days of initial visit; 11.5% were prescribed only an opioid at the initial visit with receipt of both NPT and opioid during early treatment rare (2.6%). Exercise therapy within 30 days exhibited more than a twofold difference in interquartile percentile rates (IQR) (median provision 15.8%, IQR 9.8%-22.1%). The other treatments exhibited even greater variation; specifically, spinal manipulation (median 8.5%, IQR 3.3%-15.8%), and opioid at initial visit (median 10.3%, IQR 4.4%-18.2%). The availability of physical therapists and doctors of chiropractic had significant association with several clinical provision rates.

CONCLUSION

Among providers of spine care for a sample of Army soldiers, there was substantial variation in the early provision of exercise therapy, spinal manipulation, and opioid prescriptions. The magnitude of the case-mix adjusted variation and its association with facility availability of providers suggests that quality of care initiatives may help reduce this variation.

Variation in prescribing of opioids for emergency department encounters: A cohort study in the Military Health System

RATIONALE, AIMS AND OBJECTIVES: Emergency department (ED) clinicians account for approximately 13% of all opioid prescriptions to opioid-naïve patients and variability in the rates of prescribing have been noted among individual clinicians and different EDs. This study elucidates the amount of variability within a unified health system (the U.S. Military Health System [MHS]) with the expectation that understanding the sources of variability will enable health system leaders to improve the quality of decision making.

METHODS

The design was a retrospective cohort study examining variation in opioid prescribing within EDs of the US MHS. Participants were Army soldiers who returned from a deployment and received care between October 2009 and September 2016. The exposure was ED encounters at a military treatment facility. Key measures were the proportion of ED encounters with an opioid prescription fill; total opioid dose of the fill (morphine milligram equivalent, MME); and total opioid days-supply of the fill.

RESULTS

The mean proportion of ED encounters with an opioid fill across providers was 19.7% (SD 8.8%), median proportion was 18.6%, and the distribution was close to symmetric with the 75th percentile provider prescribing opioids in 24.6% of their ED encounters and the 25th percentile provider prescribing in 13.4% of their encounters. The provider-level mean opioid dose per encounter was 113.1 MME (SD 56.0) with the 75th percentile (130.1) 50% higher than the 25th percentile (87.4). The mean opioid supply per encounter was 6.8 days (SD 3.9) with more than a twofold ratio between the 75th percentile (8.3) and the 25th (4.1). Using a series of multilevel regression models to examine opioid fills associated with ED encounters and their dose levels, the variation among providers within facilities was much larger in magnitude than the variation among facilities.

CONCLUSION

Among ED encounters of Army soldiers at military treatment facilities, there was substantial variation among providers in prescribing opioid prescriptions that were not explained by patient case-mix. These results suggest that programmes and protocols to address less than optimal prescribing in the ED should be initiated to improve the quality of care.

Insights from system dynamics applications in addiction research: A scoping review

BACKGROUND AND AIMS

Substance misuse and use disorders are dynamic and complex problems, situated within systems of interacting social, environmental, and neurobiological factors. System dynamics (SD) methods broaden, test, and improve understanding of complex systems and can help inform effective action. We sought to systematically review the use of SD tools in addiction-related research.

METHODS

Following PRISMA guidelines, we searched several databases from 1958 to 2019. We included studies focused on addiction-related screening and diagnosis, treatment, and return to use, as well as studies focused on earlier stages that may begin a path to addiction (e.g., experimentation, misuse onset).

RESULTS

We extracted information from 59 articles with a median publication year of 2014. In addition to using SD to understand the underlying complexity driving addiction-related trends, other commonly cited reasons for use of SD included assessing impacts of potential actions (n = 35), predicting future trends (n = 28), and supporting strategic planning processes (n = 22). Most studies included simulation models (n = 43); however, some presented insights from qualitative SD diagrams (n = 9) and concept models (n = 6). The majority of studies focused on stages leading to potential addiction: initiation/ experimentation (n = 42) and misuse onset (n = 38). One-third (n = 20) engaged persons with lived experience or other stakeholders during the modeling process.

CONCLUSIONS

Addiction-related SD research has increased over the last few decades with applications varying in several ways, from model purpose and types of data used to stakeholder involvement. Future applications should consider the benefits of stakeholder engagement throughout the modeling process and expanding models to include concomitant substance use.

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.

Improving the Delivery of Chronic Opioid Therapy Among People Living With Human Immunodeficiency Virus: A Cluster Randomized Clinical Trial

BACKGROUND

Chronic pain is prevalent among people living with human immunodeficiency virus (PLWH); managing pain with chronic opioid therapy (COT) is common. Human immunodeficiency virus (HIV) providers often diverge from prescribing guidelines.

METHODS

This 2-arm, unblinded, cluster-randomized clinical trial assessed whether the Targeting Effective Analgesia in Clinics for HIV (TEACH) intervention improves guideline-concordant care compared to usual care for PLWH on COT. The trial was implemented from 2015 to 2018 with 12-month follow-up at safety-net hospital-based HIV clinics in Boston and Atlanta. We enrolled 41 providers and their 187 patients on COT. Prescribers were randomized 1:1 to either a 12-month intervention consisting of a nurse care manager with an interactive electronic registry, opioid education, academic detailing, and access to addiction specialists or a control condition consisting of usual care. Two primary outcomes were assessed through electronic medical records: ≥2 urine drug tests and any early COT refills by 12 months. Other outcomes included possible adverse consequences.

RESULTS

At 12 months, the TEACH intervention arm had higher odds of ≥2 urine drug tests than the usual care arm (71% vs 20%; adjusted odds ratio [AOR], 13.38 [95% confidence interval {CI}, 5.85-30.60]; P < .0001). We did not detect a statistically significant difference in early refills (22% vs 30%; AOR, 0.55 [95% CI, .26-1.15]; P = .11), pain severity (6.30 vs 5.76; adjusted mean difference, 0.10 [95% CI, -1.56 to 1.75]; P = .91), or HIV viral load suppression (86.9% vs 82.1%; AOR, 1.21 [95% CI, .47-3.09]; P = .69).

CONCLUSIONS

TEACH is a promising intervention to improve adherence to COT guidelines without evident adverse consequences.

Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis

BACKGROUND

Significant uncertainty has existed about the safety of reopening college and university campuses before the COVID-19 pandemic is better controlled. Moreover, little is known about the effects that on-campus students may have on local higher-risk communities.

OBJECTIVE

We aimed to estimate the range of potential community and campus COVID-19 exposures, infections, and mortality under various university reopening plans and uncertainties.

METHODS

We developed campus-only, community-only, and campus × community epidemic differential equations and agent-based models, with inputs estimated via published and grey literature, expert opinion, and parameter search algorithms. Campus opening plans (spanning fully open, hybrid, and fully virtual approaches) were identified from websites and publications. Additional student and community exposures, infections, and mortality over 16-week semesters were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outliers. Sensitivity analyses were conducted to inform potential effective interventions.

RESULTS

Predicted 16-week campus and additional community exposures, infections, and mortality for the base case with no precautions (or negligible compliance) varied significantly from their medians (4- to 10-fold). Over 5% of on-campus students were infected after a mean of 76 (SD 17) days, with the greatest increase (first inflection point) occurring on average on day 84 (SD 10.2 days) of the semester and with total additional community exposures, infections, and mortality ranging from 1-187, 13-820, and 1-21 per 10,000 residents, respectively. Reopening precautions reduced infections by 24%-26% and mortality by 36%-50% in both populations. Beyond campus and community reproductive numbers, sensitivity analysis indicated no dominant factors that interventions could primarily target to reduce the magnitude and variability in outcomes, suggesting the importance of comprehensive public health measures and surveillance.

CONCLUSIONS

Community and campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Public health implications include the need for effective surveillance and flexible campus operations.

A Unified Theoretical Framework of Learning Theories to Inform and Guide Public Health Continuing Medical Education Research and Practice

Continuing medical education (CME) emerged at the start of the 20th century as a means of maintaining clinical competence among health care practitioners. However, evidence indicates that CME is often poorly developed and inappropriately used. Consequently, there has been increasing interest in the literature in evaluating wider contexts at play in CME development and delivery. In this article, the authors present a unified theoretical framework, grounded in learning theories, to explore the role of contextual factors in public health CME for health care practitioners. Discussion with pedagogical experts together with a narrative review of learning theories within medical and social science literature informed the framework's development. The need to consider sociocultural theories of learning within medical education restricted suitable theories to those that recognized contexts beyond the individual learner; adopted a systems approach to evaluate interactions between contexts and learner; and considered learning as more than mere acquisition of knowledge. Through a process of rigorous critical analysis, two theoretical models emerged as contextually appropriate: Biggs principle of constructive alignment and Bronfenbrenner bioecological model of human development. Biggs principle offers theoretical clarity surrounding interactive factors that encourage lifelong learning, whereas the Bronfenbrenner model expands on these factor's roles across multiple system levels. The authors explore how unification into a single framework complements each model while elaborating on its fundamental and practical applications. The unified theoretical framework presented in this article addresses the limitations of isolated frameworks and allows for the exploration of the applicability of wider learning theories in CME research.

Evaluation of public health interventions from a complex systems perspective: A research methods review

INTRODUCTION

Applying a complex systems perspective to public health evaluation may increase the relevance and strength of evidence to improve health and reduce health inequalities. In this review of methods, we aimed to: (i) classify and describe different complex systems methods in evaluation applied to public health; and (ii) examine the kinds of evaluative evidence generated by these different methods.

METHODS

We adapted critical review methods to identify evaluations of public health interventions that used systems methods. We conducted expert consultation, searched electronic databases (Scopus, MEDLINE, Web of Science), and followed citations of relevant systematic reviews. Evaluations were included if they self-identified as using systems- or complexity-informed methods and if they evaluated existing or hypothetical public health interventions. Case studies were selected to illustrate different types of complex systems evaluation.

FINDINGS

Seventy-four unique studies met our inclusion criteria. A framework was developed to map the included studies onto different stages of the evaluation process, which parallels the planning, delivery, assessment, and further delivery phases of the interventions they seek to inform; these stages include: 1) theorising; 2) prediction (simulation); 3) process evaluation; 4) impact evaluation; and 5) further prediction (simulation). Within this framework, we broadly categorised methodological approaches as mapping, modelling, network analysis and 'system framing' (the application of a complex systems perspective to a range of study designs). Studies frequently applied more than one type of systems method.

CONCLUSIONS

A range of complex systems methods can be utilised, adapted, or combined to produce different types of evaluative evidence. Further methodological innovation in systems evaluation may generate stronger evidence to improve health and reduce health inequalities in our complex world.

Data Needs in Opioid Systems Modeling: Challenges and Future Directions

INTRODUCTION

The opioid crisis is a pervasive public health threat in the U.S. Simulation modeling approaches that integrate a systems perspective are used to understand the complexity of this crisis and analyze what policy interventions can best address it. However, limitations in currently available data sources can hamper the quantification of these models.

METHODS

To understand and discuss data needs and challenges for opioid systems modeling, a meeting of federal partners, modeling teams, and data experts was held at the U.S. Food and Drug Administration in April 2019. This paper synthesizes the meeting discussions and interprets them in the context of ongoing simulation modeling work.

RESULTS

The current landscape of national-level quantitative data sources of potential use in opioid systems modeling is identified, and significant issues within data sources are discussed. Major recommendations on how to improve data sources are to: maintain close collaboration among modeling teams, enhance data collection to better fit modeling needs, focus on bridging the most crucial information gaps, engage in direct and regular interaction between modelers and data experts, and gain a clearer definition of policymakers' research questions and policy goals.

CONCLUSIONS

This article provides an important step in identifying and discussing data challenges in opioid research generally and opioid systems modeling specifically. It also identifies opportunities for systems modelers and government agencies to improve opioid systems models.

Economic Evaluation in Opioid Modeling: Systematic Review

OBJECTIVES

The rapid increase in opioid overdose and opioid use disorder (OUD) over the past 20 years is a complex problem associated with significant economic costs for healthcare systems and society. Simulation models have been developed to capture and identify ways to manage this complexity and to evaluate the potential costs of different strategies to reduce overdoses and OUD. A review of simulation-based economic evaluations is warranted to fully characterize this set of literature.

METHODS

A systematic review of simulation-based economic evaluation (SBEE) studies in opioid research was initiated by searches in PubMed, EMBASE, and EbscoHOST. Extraction of a predefined set of items and a quality assessment were performed for each study.

RESULTS

The screening process resulted in 23 SBEE studies ranging by year of publication from 1999 to 2019. Methodological quality of the cost analyses was moderately high. The most frequently evaluated strategies were methadone and buprenorphine maintenance treatments; the only harm reduction strategy explored was naloxone distribution. These strategies were consistently found to be cost-effective, especially naloxone distribution and methadone maintenance. Prevention strategies were limited to abuse-deterrent opioid formulations. Less than half (39%) of analyses adopted a societal perspective in their estimation of costs and effects from an opioid-related intervention. Prevention strategies and studies' accounting for patient and physician preference, changing costs, or result stratification were largely ignored in these SBEEs.

CONCLUSION

The review shows consistently favorable cost analysis findings for naloxone distribution strategies and opioid agonist treatments and identifies major gaps for future research.

Potential Community and Campus Covid-19 Outcomes Under University and College Reopening Scenarios

BACKGROUND

Significant uncertainty exists in many countries about the safety of, and best strategies for, reopening college and university campuses until the Covid-19 pandemic is better controlled. Little also is known about the effects on-campus students may have on local higher-risk communities. We aimed to estimate potential community and campus Covid-19 exposures, infections, and mortality due to various university reopening and precaution plans under current ranges of assumptions and uncertainties.

METHODS

We developed and calibrated campus-only, community-only, and campus-x-community epidemic differential equation and agent-based models. Input parameters for campus and surrounding communities were estimated via published and grey literature, scenario development, expert opinion, accuracy optimization algorithms, and Monte Carlo simulation; models were cross-validated against each other using February-June 2020 data from heterogeneous U.S. counties and states. Campus opening plans (spanning various fully open, hybrid, and fully virtual approaches) were identified from websites and publications. All scenarios were simulated assuming 16-week semesters and estimated ranges for Covid-19 prevalence among community residents and arriving students, precaution compliance, contact frequency, virus attack rates, and tracing and isolation effectiveness. Additional student and community exposures, infections, and mortality were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outlier scenarios. Factorial analyses were conducted to identify intervention inputs with largest and smallest effects.

RESULTS

As a base case with no precautions (or no compliance), predicted 16-week student infections and mortality under normal operations ranged significantly from 471 to 9,495 (median: 2,286, SD: 2,627) and 0 to 123 (median: 9, SD: 14) per 10,000 students, respectively. The maximum active exposures across a semester was 15.76% of all students warranting tracing. Total additional community exposures, infections, and mortality ranged from 1 to 187, 13 to 820, and 1 to 21 per 10,000 residents, respectively. 1% and 5% of on-campus students were infected after a mean (SD) of 11 (3) and 76 (17) days, respectively; >10% students infected by the end of a semester in 34.8% of scenarios, with the greatest increase (first inflection point) occurring on aver-age on day 84 (SD: 10.2 days). Common reopening precautions reduced infections by 24% to 26% and mortality by 36% to 50% in both populations. Uncertainties in many factors, however, produced tremendous variability in all results, ranging from medians by -67% to +342%.

CONCLUSIONS

Consequences on community and student Covid-19 exposures, infections, and mortality of reopening physical campuses are very highly unpredictable, depending on a combination of random chance, controllable (e.g. physical layouts), and uncontrollable (e.g. human behavior) factors. Implications include needs for criteria to adapt campus operations mid-semester, methods to detect when necessary, and contingency plans for doing so.

The opioid crisis: need for systems science research

The opioid epidemic in the United States has had a devastating impact on millions of people as well as on their families and communities. The increased prevalence of opioid misuse, use disorder and overdose in recent years has highlighted the need for improved public health approaches for reducing the tremendous harms of this illness. In this paper, we explain and call for the need for more systems science approaches, which can uncover the complexities of the opioid crisis, and help evaluate, analyse and forecast the effectiveness of ongoing and new policy interventions. Similar to how a stream of systems science research helped policy development in infectious diseases and obesity, more systems science research is needed in opioids.

Economic Evaluation of Interventions to Address Opioid Misuse: A Systematic Review of Methods Used in Simulation Modeling Studies

OBJECTIVES

Several evidence-based interventions exist for people who misuse opioids, but there is limited guidance on optimal intervention selection. Economic evaluations using simulation modeling can guide the allocation of resources and help tackle the opioid crisis. This study reviews methods employed by economic evaluations using computer simulations to investigate the health and economic effects of interventions meant to address opioid misuse.

METHODS

We conducted a systematic mapping review of studies that used simulation modeling to support the economic evaluation of interventions targeting prevention, treatment, or management of opioid misuse or its direct consequences (ie, overdose). We searched 6 databases and extracted information on study population, interventions, costs, outcomes, and economic analysis and modeling approaches.

RESULTS

Eighteen studies met the inclusion criteria. All of the studies considered only one segment of the continuum of care. Of the studies, 13 evaluated medications for opioid use disorder, and 5 evaluated naloxone distribution programs to reduce overdose deaths. Most studies estimated incremental cost per quality-adjusted life-years and used health system and/or societal perspectives. Models were decision trees (n = 4), Markov (n = 10) or semi-Markov models (n = 3), and microsimulations (n = 1). All of the studies assessed parameter uncertainty though deterministic and/or probabilistic sensitivity analysis, 4 conducted formal calibration, only 2 assessed structural uncertainty, and only 1 conducted expected value of information analyses. Only 10 studies conducted validation.

CONCLUSIONS

Future economic evaluations should consider synergies between interventions and examine combinations of interventions to inform optimal policy response. They should also more consistently conduct model validation and assess the value of further research.

A dynamic model of the opioid drug epidemic with implications for policy

Background: The U.S. opioid epidemic has caused substantial harm for over 20 years. Policy interventions have had limited impact and sometimes backfired. Experts recommend a systems modeling approach to address the complexities of opioid policymaking.Objectives: Develop a system dynamics simulation model that reflects the complexities and can anticipate intended and unintended intervention effects.Methods: The model was developed from literature review and data gathering. Its outputs, starting in 1990, were compared against 12 historical time series. Four illustrative interventions were simulated for 2020-2030: reducing prescription dosage by 20%, cutting diversion by 30%, increasing addiction treatment from 45% to 65%, and increasing lay naloxone use from 4% to 20%. Sensitivity testing was performed to determine effects of uncertainties. No human subjects were studied.Results: The model fits historical data well with error percentage averaging 9% across 201 data points. Interventions to reduce dosage and diversion reduce the number of persons with opioid use disorder (PWOUD) by 11% and 16%, respectively, but each of these interventions reduces overdoses by only 1%. Boosting treatment reduces overdoses by 3% but increases PWOUD by 1%. Expanding naloxone reduces overdose deaths by 12% but increases PWOUD by 2% and overdoses by 3%. Combining all four interventions reduces PWOUD by 24%, overdoses by 4%, and deaths by 18%. Uncertainties may affect these numerical results, but policy findings are unchanged.Conclusion: No single intervention significantly reduces both PWOUD and overdose deaths, but a combination strategy can do so. Entering the 2020s, only protective measures like naloxone expansion could significantly reduce overdose deaths.

Opioid agonist treatment scale-up and the initiation of injection drug use: A dynamic modeling analysis

BACKGROUND

Injection drug use (IDU) is associated with multiple health harms. The vast majority of IDU initiation events (in which injection-naïve persons first adopt IDU) are assisted by a person who injects drugs (PWID), and as such, IDU could be considered as a dynamic behavioral transmission process. Data suggest that opioid agonist treatment (OAT) enrollment is associated with a reduced likelihood of assisting with IDU initiation. We assessed the association between recent OAT enrollment and assisting IDU initiation across several North American settings and used dynamic modeling to project the potential population-level impact of OAT scale-up within the PWID population on IDU initiation.

METHODS AND FINDINGS

We employed data from a prospective multicohort study of PWID in 3 settings (Vancouver, Canada [n = 1,737]; San Diego, United States [n = 346]; and Tijuana, Mexico [n = 532]) from 2014 to 2017. Site-specific modified Poisson regression models were constructed to assess the association between recent (past 6 month) OAT enrollment and history of ever having assisted an IDU initiation with recently assisting IDU initiation. Findings were then pooled using linear mixed-effects techniques. A dynamic transmission model of IDU among the general population was developed, stratified by known factors associated with assisting IDU initiation and relevant drug use behaviors. The model was parameterized to a generic North American setting (approximately 1% PWID) and used to estimate the impact of increasing OAT coverage among PWID from baseline (approximately 21%) to 40%, 50%, and 60% on annual IDU initiation incidence and corresponding PWID population size across a decade. From Vancouver, San Diego, and Tijuana, respectively, 4.5%, 5.2%, and 4.3% of participants reported recently assisting an IDU initiation, and 49.4%, 19.7%, and 2.1% reported recent enrollment in OAT. Recent OAT enrollment was significantly associated with a 45% lower likelihood of providing recent IDU initiation assistance among PWID (relative risk [RR] 0.55 [95% CI 0.36-0.84], p = 0.006) compared to those not recently on OAT. Our dynamic model predicts a baseline mean of 1,067 (2.5%-97.5% interval [95% I 490-2,082]) annual IDU initiations per 1,000,000 individuals, of which 886 (95% I 406-1,750) are assisted by PWID. Based on our observed statistical associations, our dynamic model predicts that increasing OAT coverage from approximately 21% to 40%, 50%, or 60% among PWID could reduce annual IDU initiations by 11.5% (95% I 2.4-21.7), 17.3% (95% I 5.6-29.4), and 22.8% (95% I 8.1-36.8) and reduce the PWID population size by 5.4% (95% I 0.1-12.0), 8.2% (95% I 2.2-16.9), and 10.9% (95% I 3.2-21.8) relative to baseline, respectively, in a decade. Less impact occurs when the protective effect of OAT is diminished, when a greater proportion of IDU initiations are unassisted by PWID, and when average IDU career length is longer. The study's main limitations are uncertainty in the causal pathway between OAT enrollment and assisting with IDU initiation and the use of a simplified model of IDU initiation.

CONCLUSIONS

In addition to its known benefits on preventing HIV, hepatitis C virus (HCV), and overdose among PWID, our modeling suggests that OAT scale-up may also reduce the number of IDU initiations and PWID population size.

Evidence of Modeling Impact in Development of Policies for Controlling the Opioid Epidemic and Improving Public Health: A Scoping Review

BACKGROUND

Opioid addiction and overdose rates are reaching unprecedented levels in the U.S., with around 47,736 overdose deaths in 2017. Many stakeholders affect the opioid epidemic, including government entities, healthcare providers and policymakers, and opioid users. Simulation and conceptual modeling can help us understand the dynamics of the opioid epidemic by simplifying the real world and informing policymakers about different health interventions that could reduce the deaths caused by opioid overdose in the United States every year.

OBJECTIVES

To conduct a scoping review of simulation and conceptual models that propose policies capable of controlling the opioid epidemic. We demonstrate the strengths and limitations of these models and provide a framework for further improvement of future decision support tools.

METHODS

Using the methodology of a scoping review, we identified articles published after 2000 from eight electronic databases to map the literature that uses simulation and conceptual modeling in developing public health policies to address the opioid epidemic.

RESULTS

We reviewed 472 papers of which 14 were appropriate for inclusion. Each used either system dynamics simulation modeling, mathematical modeling, conceptual modeling, or agent-based modeling. All included studies tested and proposed strategies to improve health outcomes related to the opioid epidemic. Factors considered in the models included physicians prescribing opioids, trafficking, users recruiting new users, and doctor shopping; no model investigated the impact of age and spatial factors on the dynamics of the epidemic. Key findings from these studies were (1) prevention of opioid initiation is better than treatment of opioid addiction, (2) the analysis of an intervention's impact should include both benefits and harms, and (3) interventions with short-term benefits might have a counterproductive impact on the epidemic in long run.

CONCLUSIONS

While most studies examined the role of prescription opioids and trafficking on this epidemic, the transition of patients from prescription opioid use to nonprescription use including heroin and synthetic opioids such as fentanyl impacts the system significantly and results in an epidemic with quite different characteristics than what it had a decade ago. We recommend including the impact of age and geographic location on the opioid epidemic using modeling methods.

System Dynamics to Investigate Opioid Use and Chiropractic Care for Chronic Musculoskeletal Pain

OBJECTIVE

The purpose of this investigation was to create a system dynamics (SD) model, including published data and required assumptions, as a tool for future research identifying the role of chiropractic care in the management of chronic, nonmalignant pain in a Canadian population.

METHODS

We present an illustrative case description of how we evaluated the feasibility of conducting a large-scale clinical trial to assess the impact of chiropractic care in mitigating excessive opioid use in Canada. We applied SD modeling using current evidence and key assumptions where such evidence was lacking. Modeling outcomes were highlighted to determine which potential factors were necessary to account for compelling study designs.

RESULTS

Results suggest that a future clinical study diverting patients with nonmalignant musculoskeletal pain early to the chiropractic stream of care could be most effective. System dynamics modeling also highlighted design challenges resulting from unresearched assumptions that needed to be proxied for model completion. Assumptions included changing rates in opioid-associated deaths and rates of success in treatment management of addicted patients.

CONCLUSION

In this case, SD modeling identified current research gaps and strong contenders for appropriate follow-up questions in a clinical research domain, namely the role of chiropractic care in the management of chronic, nonmalignant pain in a Canadian population.

System dynamics applications to injury and violence prevention: a systematic review

Purpose of review

System dynamics (SD) is an approach to solving problems in the context of dynamic complexity. The purpose of this review was to summarize SD applications in injury prevention and highlight opportunities for SD to contribute to injury prevention research and practice.

Recent findings

While SD has been increasingly used to study public health problems over the last few decades, uptake in the injury field has been slow. We identified 18 studies, mostly conducted in the last 10 years. Applications covered a range of topics (e.g., road traffic injury; overdose; violence), employed different types of SD tools (i.e., qualitative and quantitative), and served a variety of research and practice purposes (e.g., deepen understanding of a problem, policy analysis).

Summary

Given the many ways that SD can add value and complement traditional research and practice approaches (e.g., through novel stakeholder engagement and policy analysis tools), increased investment in SD-related capacity building and opportunities that support SD use are warranted.

How the health-seeking behaviour of pregnant women affects neonatal outcomes: findings of system dynamics modelling in Pakistan

Background

Limited studies have explored how health-seeking behaviour during pregnancy through to delivery affect neonatal outcomes. We modelled health-seeking behaviour across urban and rural settings in Pakistan, where poor neonatal outcomes persist with wide disparities.

Methods and findings

A system dynamics model was developed and parameterised. Following validation tests, the model was used to determine neonatal mortality for pregnant women considering their decisions to access, refuse and switch antenatal care services in four provider sectors: public, private, traditional and charitable. Four health-seeking scenarios were tested across different pregnancy trimesters. Health-seeking behaviour in different subgroups by geographical locations and social network effect was modelled. The largest reduction in neonatal mortality was achieved with antenatal care provided by skilled providers in public, private or charitable sectors, combined with the use of institutional delivery. Women’s social networks had strong influences on if, when and where to seek care. Interventions by Lady Health Workers had a minimal impact on health-seeking behaviour and neonatal outcomes after trimester 1. Optimal benefits were achieved for urban women when antenatal care was accessed within trimester 2, but for rural women within trimester 1. Antenatal care access delayed to trimester 3 had no protective impact on neonatal mortality.

Conclusions

System dynamics modelling enables capturing the complexity of health-seeking behaviours and impact on outcomes, informing intervention design, implementation of targeted policies and uptake of services specific to urban/rural settings considering structural enablers/barriers to access, cultural contexts and strong social network influences.

Study protocol for the targeting effective analgesia in clinics for HIV (TEACH) study - a cluster randomized controlled trial and parallel cohort to increase guideline concordant care for long-term opioid therapy among people living with HIV

Background: People living with HIV (PLWH) frequently experience chronic pain and receive long-term opioid therapy (LTOT). Adherence to opioid prescribing guidelines among their providers is suboptimal. Objective: This paper describes the protocol of a cluster randomized trial, targeting effective analgesia in clinics for HIV (TEACH), which tested a collaborative care intervention to increase guideline-concordant care for LTOT among PLWH. Methods: HIV physicians and advanced practice providers (n = 41) were recruited from September 2015 to December 2016 from two HIV clinics in Boston and Atlanta. Patients receiving LTOT from participating providers were enrolled through a waiver of informed consent (n = 187). After baseline assessment, providers were randomized to the control group or the year-long TEACH intervention involving: (1) a nurse care manager and electronic registry to assist with patient management; (2) opioid education and academic detailing; and (3) facilitated access to addiction specialists. Randomization was stratified by site and LTOT patient volume. Primary outcomes (≥2 urine drug tests, early refills, provider satisfaction) were collected at 12 months. In parallel, PLWH receiving LTOT (n = 170) were recruited into a longitudinal cohort at both clinics and underwent baseline and 12-month assessments. Secondary outcomes were obtained through patient self-report among participants enrolled in both the cohort and the RCT (n = 117). Conclusions: TEACH will report the effects of an intervention on opioid prescribing for chronic pain on both provider and patient-level outcomes. The results may inform delivery of care for PLWH on LTOT for chronic pain at a time when opioid practices are being questioned in the US.

System dynamics modeling of public health services provided by China CDC to control infectious and endemic diseases in China

Background

Infectious and endemic diseases are a serious public health concern worldwide, and their prevention and treatment are globally controversial. This study aimed to establish an system dynamics (SD) model to analyze the factors influencing public health services provided by the Chinese Centers for Disease Control and Prevention (China CDC) to implement infectious and endemic disease control in China, by establishing more effective interventions to provide public health services and thus achieving the goal of controlling infectious and endemic diseases.

Materials and methods

An SD model was constructed using the Vensim DSS program. Intervention experiments were performed using the SD model, which reflected the influences on disease control by adjusting the governmental investment and compensation level for public health products.

Results

The experimental results showed that increasing the governmental investment in China CDC and compensation level for public health products will significantly increase the public health product rate provided by China CDC.

Discussion

Problems with infectious and endemic disease prevention and treatment are the result of the system’s incomplete functioning and limited health resources. To address the current problems and improve the system, the government should increase its investment in the public health service system and improve the compensation system to ensure smooth implementation of infectious and endemic disease prevention and treatment and, ultimately, improve public health in China.

Dynamic modeling approaches to characterize the functioning of health systems: A systematic review of the literature

Universal Health Coverage (UHC) is one of the targets for the United Nations Sustainable Development Goal 3. The impetus for UHC has led to an increased demand for time-sensitive tools to enhance our knowledge of how health systems function and to evaluate impact of system interventions. We define the field of "health system modeling" (HSM) as an area of research where dynamic mathematical models can be designed in order to describe, predict, and quantitatively capture the functioning of health systems. HSM can be used to explore the dynamic relationships among different system components, including organizational design, financing and other resources (such as investments in resources and supply chain management systems) - what we call "inputs" - on access, coverage, and quality of care - what we call "outputs", toward improved health system "outcomes", namely increased levels and fairer distributions of population health and financial risk protection. We undertook a systematic review to identify the existing approaches used in HSM. We identified "systems thinking" - a conceptual and qualitative description of the critical interactions within a health system - as an important underlying precursor to HSM, and collated a critical collection of such articles. We then reviewed and categorized articles from two schools of thoughts: "system dynamics" (SD)" and "susceptible-infected-recovered-plus" (SIR+). SD emphasizes the notion of accumulations of stocks in the system, inflows and outflows, and causal feedback structure to predict intended and unintended consequences of policy interventions. The SIR + models link a typical disease transmission model with another that captures certain aspects of the system that impact the outcomes of the main model. These existing methods provide critical insights in informing the design of HSM, and provide a departure point to extend this research agenda. We highlight the opportunity to advance modeling methods to further understand the dynamics between health system inputs and outputs.

Unused Opioid Pills After Outpatient Shoulder Surgeries Given Current Perioperative Prescribing Habits

BACKGROUND

In the past 16 years, the number of prescription opioids sold in the United States, as well as deaths from prescription opioids, has nearly quadrupled. However, the overall amount of pain reported by patients has not changed significantly. Specific information about opioid prescriptions in the perioperative period is lacking. Of the studies that have been published, investigators have shown that the majority of patients have unused postoperative opioid pills. Moreover, patients appear to lack information about disposal of unused opioid pills.

PURPOSE

To compare the number of pills prescribed versus the numbers left unused after outpatient shoulder surgeries at an orthopaedic surgery institution.

STUDY DESIGN

Case series; Level of evidence, 4.

METHODS

In this prospective, observational study, 100 patients (age >18 years) undergoing outpatient shoulder surgery (rotator cuff repair, labral repair, stabilization/Bankart repair, debridement) were enrolled. Follow-ups were conducted via surveys on postoperative days (PODs) 7, 14, 28, and 90. The primary outcome was the number of unused pills from the originally prescribed medication.

RESULTS

For all procedure types, the median (Q1, Q3) number of prescribed pills was 60 (40, 80). On POD 90, patients reported a median (Q1, Q3) of 13 (0, 32) unused pills; patients who underwent rotator cuff repairs had the lowest number of pills remaining (median [Q1, Q3], 0 [0, 16]), whereas patients who had stabilization/Bankart repairs had the highest number of unused pills (median [Q1, Q3], 37 [29, 50]). Patient satisfaction with pain management ranged from an average of 70% to 90%. Only 25 patients received instructions or education about opioid disposal.

CONCLUSION

Most outpatient shoulder surgery patients who underwent certain operations were prescribed more opioid analgesics than they consumed. Patient education regarding the disposal of opioids was lacking.

Systems science and systems thinking for public health: a systematic review of the field

OBJECTIVES

This paper reports on findings from a systematic review designed to investigate the state of systems science research in public health. The objectives were to: (1) explore how systems methodologies are being applied within public health and (2) identify fruitful areas of activity.

DESIGN

A systematic review was conducted from existing literature that draws on or uses systems science (in its various forms) and relates to key public health areas of action and concern, including tobacco, alcohol, obesity and the social determinants of health.

DATA ANALYSIS

117 articles were included in the review. An inductive qualitative content analysis was used for data extraction. The following were systematically extracted from the articles: approach, methodology, transparency, strengths and weaknesses. These were then organised according to theme (ie, commonalities between studies within each category), in order to provide an overview of the state of the field as a whole. The assessment of data quality was intrinsic to the goals of the review itself, and therefore, was carried out as part of the analysis.

RESULTS

4 categories of research were identified from the review, ranging from editorial and commentary pieces to complex system dynamic modelling. Our analysis of each of these categories of research highlighted areas of potential for systems science to strengthen public health efforts, while also revealing a number of limitations in the dynamic systems modelling being carried out in public health.

CONCLUSIONS

There is a great deal of interest in how the application of systems concepts and approach might aid public health. Our analysis suggests that soft systems modelling techniques are likely to be the most useful addition to public health, and align well with current debate around knowledge transfer and policy. However, the full range of systems methodologies is yet to be engaged with by public health researchers.

Key Data Gaps Regarding the Public Health Issues Associated with Opioid Analgesics

Most pharmaceutical opioids are used to treat pain, and they have been demonstrated to be effective medications for many. Their abuse and misuse pose significant public health concerns in the USA. Research has provided much insight into the prevalence, scope, and drivers of opioid abuse, but a holistic understanding is limited by a lack of available data regarding key aspects of this public health problem. Twelve data gaps were revealed during the creation of a systems-level computer model of medical use, diversion, nonmedical use, and the adverse outcomes associated with opioid analgesics in the USA. Data specific to these gaps would enhance the validity and real-world applications of systems-level models of this public health problem and would increase understanding of the complex system in which use and abuse occur. This paper provides an overview of these gaps, argues for the importance of closing them, and provides specific recommendations for future data collection efforts.

Dynamic model of nonmedical opioid use trajectories and potential policy interventions

BACKGROUND

Nonmedical use of pharmaceutical opioid analgesics (POA) increased dramatically over the past two decades and remains a major health problem in the United States, contributing to over 16 000 accidental poisoning deaths in 2010.

OBJECTIVES

To create a systems-oriented theory/model to explain the historical behaviors of interest, including the various populations of nonmedical opioid users and accidental overdose mortality within those populations. To use the model to explore policy interventions including tamper-resistant drug formulations and strategies for reducing diversion of opioid medicines.

METHODS

A system dynamics model was constructed to represent the population of people who initiate nonmedical POA usage. The model incorporates use trajectories including development of use disorders, transitions from reliance on informal sharing to paying for drugs, transition from oral administration to tampering to facilitate non-oral routes of administration, and transition to heroin use by some users, as well as movement into and out of the population through quitting and mortality. Empirical support was drawn from national surveys (NSDUH, TEDS, MTF, and ARCOS) and published studies.

RESULTS

The model was able to replicate the patterns seen in the historical data for each user population, and the associated overdose deaths. Policy analysis showed that both tamper-resistant formulations and interventions to reduce informal sharing could significantly reduce nonmedical user populations and overdose deaths in the long term, but the modeled effect sizes require additional empirical support.

CONCLUSION

Creating a theory/model that can explain system behaviors at a systems level scale is feasible and facilitates thorough evaluation of policy interventions.