“In 2017, approximately 47,600 individuals in the US died from an opioid overdose, and morbidity and mortality from the opioid epidemic continues to accrue.
[..] To fully estimate the epidemic’s scope and the impact of interventions to address it, it is essential to consider differences in individuals using prescription opioids vs heroin or illicit fentanyl, the increased risk of second overdose in people who have experienced an initial overdose, and the evolving time-dependent nature of the epidemic. Despite the contributions of prior models of the epidemic, most have not incorporated these elements, nor have they accounted for the more than 2.5 million individuals in the US who report lifetime—but not past year—opioid use disorder (OUD). In addition, earlier models have tended to regard treatment of OUD as a single entity rather than differentiating among different phases of treatment or recovery to allow for flexible modeling of different subpopulations, such as those receiving more or less intensive care.
[..] APOLLO’s conceptual framework was developed using an iterative process soliciting scientific and clinical input from experts in addiction and pain medicine, public health, health economics, epidemiologic factors, and health policy. The model consists of 32 compartments distinguishing 7 major populations: (1) no opioid use; (2) prescription opioid medical use; (3) prescription opioid nonmedical use; (4) use of heroin, illicit fentanyl, or other illicit opioids; (5) OUD from prescription opioids; (6) heroin use disorder with prior prescription opioid use (HUD-Rx); and (7) heroin use disorder without prior prescription opioid use (HUD-NonRx). Note that, in all compartments, heroin use includes use of illicit fentanyl and other nonprescription opioids. The model also provides for 7 subpopulations with prescription OUD, HUD-Rx, and HUD-NonRx based on degrees of clinical stability and treatment engagement.
[..] The model consisted of 109 monthly transitions between the 32 compartments. These included 25 time-dependent transitions reflecting changes in the epidemic, such as increased use of fentanyl and increasing lethality of opioids, decreased prescribing of opioid medications, increased access to medications for addiction treatment (MAT), and population growth in the US. We estimated transition probabilities from data sources including large national databases, peer-reviewed literature, and when necessary, expert opinion.
[..] We used 3 data sets to calibrate the model and derive information regarding key populations of interest. First, we used US Census data to derive information regarding the population of individuals in the US aged 12 years or older from 2010 to 2018. Second, we used NSDUH data to derive estimates of the numbers of people with prescription opioid nonmedical use, heroin use, and prescription OUD, adjusting for NSDUH’s double-counting of individuals who have both prescription OUD and HUD. Third, we used 2010 to 2017 CDC Wide-Ranging Online Data for Epidemiologic Research to capture estimates of opioid overdose deaths.
[..] First, we simulated the outcomes associated with reducing opioid prescribing by 5% annually from 2020 to 2029 beyond the status quo, which is equivalent to a total reduction of approximately 40% over 10 years. This intervention reflects policies such as reductions in marketing and promotion, stricter Drug Enforcement Agency quotas, increased promulgation of clinical guidelines, and any number of other local, state, and federal initiatives. Second, we simulated the outcomes associated with policies to expand the distribution and use of naloxone. To do so, we assumed a 5% annual reduction in overdose case fatality over 4 years (2020-2023) and then sustained for 6 years (2024-2029), which is equivalent to a total reduction of approximately 19% over 10 years. Third, we modeled the outcome of treatment expansion among individuals with OUD, evaluating a policy that increased the initiation of medications for MAT by 35% annually from 2020 to 2023 and then sustained these increases through 2029, which is equivalent to a tripling of MAT uptake over 10 years. Our MAT initiation begins with detoxification before an individual is prescribed MAT. We combined this latter intervention with one that decreased treatment relapse out of MAT by 50% for 10 years starting in 2020.
Cumulatively, the model projected 484,429 (95% confidence band, 390,543-576,631) overdose deaths from any opioid from 2020 to 2029, of which 155,628 (95% confidence band, 106,181-208,903) deaths (32.1%; 95% confidence band, 27.2%-36.2%) are from prescription opioid medical use, including both medical and nonmedical use, 199,751 (95% confidence band, 136,253-265,402) deaths (41.2%; 95% confidence band, 34.9%-46.0%) are from heroin use with previous prescription opioid use, and 129,050 (95% confidence band, 89,754-166,926) deaths (26.7%; 95% confidence band, 23.0%-28.9%) are from heroin use without previous prescription opioid use.
[..] Under the status quo, there were approximately 2.47 million individuals with active OUD in December 2019, of whom 2.17 million (87.9%) were not receiving treatment. By December 2029, the model predicted approximately 2.56 (95% confidence band, 2.41-2.75) million individuals with active OUD, of whom 2.25 (95% confidence band, 2.12, 2.42) million (87.9%; 95% confidence band, 87.7%-88.1%) were receiving on treatment.
[..] Reducing prescribing rates of opioids had a small association with opioid overdose deaths from 2020 to 2029 (0.3% reduction in all deaths, 3.4% reduction in prescription opioid overdose deaths) compared with status quo.
Increasing naloxone access was projected to be associated with a 15.4% reduction in cumulative overdose deaths, corresponding to 74,510 (95% confidence band, 60,310-87,894) fewer deaths from 2020 to 2029. Expanding MAT had the greatest projected association with cumulative opioid overdose deaths from 2020 to 2029, with a 25.3% (95% confidence band, 22.4%-27.7%) reduction (122,710 deaths; 95% confidence band, 95,451-148,335) compared with status quo, an outcome that reflects both increased MAT uptake (18.9% reduction in overdose deaths; 95% confidence band, 16.3-21.1) and reduced relapse (6.3% reduction in deaths; 95% confidence band, 5.5%-7.1%). MAT was associated with a 38.3% (95% confidence band, 34.4%-41.0%) projected reduction in cumulative HUD-Rx deaths (76,552 deaths; 95% confidence band, 51,827-100,320) compared with status quo. This association was due to relatively higher base rates of MAT uptake among HUD-Rx (5.3% individuals initiating MAT per month) compared with the prescription OUD population (3.0% per month).
Combining the interventions was associated with a projected reduction in the number of opioid overdose deaths by 37.0% (95% confidence band, 34.3%-38.4%) between 2020 and 2029 compared with status quo, representing 179,151 (140,696-211,323) deaths averted.
[..] Prescribing reductions and naloxone distribution have little association with the number of individuals with active OUD, increasing this estimate from 2.47 million in December 2019 to 2.51 million (95% confidence band, 2.40 million-2.68 million) for prescribing reductions and 2.59 million (2.44 million-2.78 million) for naloxone distribution in 2029.
[..] Increased MAT uptake and reduced relapse were projected to decrease the number of individuals with active OUD from 2.47 million in December 2019 to 1.81 million (95% confidence band, 1.72 million-1.93 million) by December 2029, a 26.7% (95% confidence band, 22.1%-30.2%) decrease. The number of individuals receiving MAT would increase from 431 282 in December 2019 to 1.09 million (95% confidence band, 0.98 million-1.21 million) in December 2029.
Our findings suggest that the opioid epidemic will exact a large burden of morbidity and mortality in the US for the coming decade, even in the face of decreasing prescription rates. While increased treatment uptake among individuals with OUD will reduce overdose rates, the public health benefits of increased treatment uptake are magnified with greater treatment continuity. Although the propensity for some level of reuse is a defining characteristic of OUD, well-described barriers also encourage dropout or hinder take-up in OUD treatment. These barriers include addiction; stigma; high out-of-pocket costs; logistical challenges, such as transportation and childcare; prior authorization reimbursement constraints; and poor interpersonal experiences with treatment professionals and staff. These and other barriers should be addressed through a number of measures, including improved training of service professionals and staff, strengthened linkages to required social services and peer supports, and more comprehensive public and private insurance coverage for OUD treatment.
[..] the findings of this study suggest that the epidemic is likely to continue to exact a large toll during the next decade. Our estimates suggest that aggressive deployment of evidence-supported practices, including expanded use of medications for addiction treatment and improved naloxone distribution, may save many lives. This public health opportunity should be seized to limit the harms associated with perhaps the most serious drug use epidemic in US history.”
Full article, Ballreich J, Mansour O, Hu E et al. JAMA Network Open 2020.11.4