Estimating naloxone need in the USA across fentanyl, heroin, and prescription opioid epidemics: a modelling study

BACKGROUND

The US overdose crisis is driven by fentanyl, heroin, and prescription opioids. One evidence-based policy response has been to broaden naloxone distribution, but how much naloxone a community would need to reduce the incidence of fatal overdose is unclear. We aimed to estimate state-level US naloxone need in 2017 across three main naloxone access points (community-based programmes, provider prescription, and pharmacy-initiated distribution) and by dominant opioid epidemic type (fentanyl, heroin, and prescription opioid).

METHODS

In this modelling study, we developed, parameterised, and applied a mechanistic model of risk of opioid overdose and used it to estimate the expected reduction in opioid overdose mortality after deployment of a given number of two-dose naloxone kits. We performed a literature review and used a modified-Delphi panel to inform parameter definitions. We refined an established model of the population at risk of overdose by incorporating changes in the toxicity of the illicit drug supply and in the naloxone access point, then calibrated the model to 2017 using data obtained from proprietary data sources, state health departments, and national surveys for 12 US states that were representative of each epidemic type. We used counterfactual modelling to project the effect of increased naloxone distribution on the estimated number of opioid overdose deaths averted with naloxone and the number of naloxone kits needed to be available for at least 80% of witnessed opioid overdoses, by US state and access point.

FINDINGS

Need for naloxone differed by epidemic type, with fentanyl epidemics having the consistently highest probability of naloxone use during witnessed overdose events (range 58-76% across the three modelled states in this category) and prescription opioid-dominated epidemics having the lowest (range 0-20%). Overall, in 2017, community-based and pharmacy-initiated naloxone access points had higher probability of naloxone use in witnessed overdose and higher numbers of deaths averted per 100 000 people in state-specific results with these two access points than with provider-prescribed access only. To achieve a target of naloxone use in 80% of witnessed overdoses, need varied from no additional kits (estimated as sufficient) to 1270 kits needed per 100 000 population across the 12 modelled states annually. In 2017, only Arizona had sufficient kits to meet this target.

INTERPRETATION

Opioid epidemic type and how naloxone is accessed have large effects on the number of naloxone kits that need to be distributed, the probability of naloxone use, and the number of deaths due to overdose averted. The extent of naloxone distribution, especially through community-based programmes and pharmacy-initiated access points, warrants substantial expansion in nearly every US state.

FUNDING

National Institute of Health, National Institute on Drug Abuse.

Implementation and maintenance of an emergency department naloxone distribution and peer recovery specialist program

STUDY OBJECTIVE

Emergency department (ED)-based naloxone distribution and peer-based behavioral counseling have been shown to be feasible, but little is known about utilization maintenance over time and clinician, patient, and visit level factors influencing implementation.

METHODS

We conducted a retrospective cohort study of an ED overdose prevention program providing take-home naloxone, behavioral counseling, and treatment linkage for patients treated for an opioid overdose at two Rhode Island EDs from 2017 to 2020: one tertiary referral center and a community hospital. Utilizing a Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, we evaluated program reach, adoption, implementation modifiers, and maintenance using logistic and Poisson regression.

RESULTS

Seven hundred forty two patients were discharged after an opioid overdose, comprising 966 visits (median: 32 visits per month; interquartile range: 29, 41). At least one intervention was provided at most (86%, 826/966) visits. Take-home naloxone was provided at 69% of visits (637/919). Over half (51%, 495/966) received behavioral counseling and treatment referral (65%, 609/932). Almost all attending physicians provided take-home naloxone (97%, 105/108), behavioral counseling (95%, 103/108), or treatment referral (95%, 103/108) at least once. Most residents and advanced practice practitioners (APPs) provided take home naloxone (78% residents; 72% APPs), behavioral counseling (76% residents; 67% APPs), and treatment referral (80% residents; 81% APPs) at least once. Most clinicians provided these services for over half of the opioid overdose patients they cared for. Patients were twice as likely to receive behavioral counseling when treated by an attending in combination with a resident and/or APP (adjusted odds ratio: 2.29; 95% confidence interval, 1.68, 3.12) compared to an attending alone. There was no depreciation in use over time.

CONCLUSIONS

ED naloxone distribution, behavioral counseling, and referral to treatment can be successfully integrated into usual emergency care and maintained over time with high reach and adoption. Further work is needed to identify low-cost implementation strategies to improve services use and dissemination across clinical settings.

Forecasted and Observed Drug Overdose Deaths in the US During the COVID-19 Pandemic in 2020

This cross-sectional study uses data from the National Vital Statistics System to compare forecasted numbers of drug overdose deaths in the US in the latter 43 weeks of 2020 with the observed number of overdose deaths in that period.

Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

A Risk Prediction Model for Long-term Prescription Opioid Use

BACKGROUND

Tools are needed to aid clinicians in estimating their patients' risk of transitioning to long-term opioid use and to inform prescribing decisions.

OBJECTIVE

The objective of this study was to develop and validate a model that predicts previously opioid-naive patients' risk of transitioning to long-term use.

RESEARCH DESIGN

This was a statewide population-based prognostic study.

SUBJECTS

Opioid-naive (no prescriptions in previous 2 y) patients aged 12 years old and above who received a pill-form opioid analgesic in 2016-2018 and whose prescriptions were registered in the California Prescription Drug Monitoring Program (PDMP).

MEASURES

A multiple logistic regression approach was used to construct a prediction model with long-term (ie, >90 d) opioid use as the outcome. Models were developed using 2016-2017 data and validated using 2018 data. Discrimination (c-statistic), calibration (calibration slope, intercept, and visual inspection of calibration plots), and clinical utility (decision curve analysis) were evaluated to assess performance.

RESULTS

Development and validation cohorts included 7,175,885 and 2,788,837 opioid-naive patients with outcome rates of 5.0% and 4.7%, respectively. The model showed high discrimination (c-statistic: 0.904 for development, 0.913 for validation), was well-calibrated after intercept adjustment (intercept, -0.006; 95% confidence interval, -0.016 to 0.004; slope, 1.049; 95% confidence interval, 1.045-1.053), and had a net benefit over a wide range of probability thresholds.

CONCLUSIONS

A model for the transition from opioid-naive status to long-term use had high discrimination and was well-calibrated. Given its high predictive performance, this model shows promise for future integration into PDMPs to aid clinicians in formulating opioid prescribing decisions at the point of care.

Variation in emergency department visit rates for opioid use disorder: Implications for quality improvement initiatives

STUDY OBJECTIVE

Emergency departments (ED) are critical touchpoints for encounters among patients with opioid use disorder (OUD), but implementation of ED initiated treatment and harm reduction programs has lagged. We describe national patient, visit and hospital-level characteristics of ED OUD visits and characterize EDs with high rates of OUD visits in order to inform policies to optimize ED OUD care.

METHODS

We conducted a descriptive, cross-sectional study with the 2017 Nationwide Emergency Department Sample (NEDS) from the Healthcare Cost and Utilization Project, using diagnostic and mechanism of injury codes from ICD-10 to identify OUD related visits. NEDS weights were applied to generate national estimates. We evaluated ED visit and clinical characteristics of all OUD encounters. We categorized hospitals into quartiles by rate of visits for OUD per 1000 ED visits and described the visit, clinical, and hospital characteristics across the four quartiles.

RESULTS

In 2017, the weighted national estimate for OUD visits was 1,507,550. Overdoses accounted for 295,954. (19.6%) of visits. OUD visit rates were over 8× times higher among EDs in the highest quartile of OUD visit rate (22.9 per 1000 total ED visits) compared with EDs in the lowest quartile of OUD visit rate (2.7 per 1000 ED visits). Over three fifths (64.2%) of all OUD visits nationwide were seen by the hospitals in the highest quartile of OUD visit rate. These hospitals were predominantly in metropolitan areas (86.2%), over half were teaching hospitals (51.7%), and less than a quarter (23.3%) were Level 1 or Level 2 trauma centers.

CONCLUSION

Targeting initial efforts of OUD care programs to high OUD visit rate EDs could improve care for a large portion of OUD patients utilizing emergency care.

VOICES: An efficacious trauma-informed, gender-responsive cannabis use intervention for justice and school-referred girls with lifetime substance use history

BACKGROUND

Girls have unique developmental pathways to substance use and justice system involvement, warranting gender-responsive intervention. We tested the efficacy of VOICES (a 12-session, weekly trauma-informed, gender-responsive substance use intervention) in reducing substance use and HIV/STI risk behaviors among justice- and school-referred girls.

METHODS

Participants were 113 girls (M_age = 15.7 years, SD = 1.4; 12 % White, 19 % Black, 15 % multi-racial; 42 % Latinx) with a history of substance use referred from juvenile justice (29 %) and school systems (71 %). Study assessments were completed at baseline, 3-, 6- and 9-months follow-up. Primary outcomes included substance use and HIV/STI risk behaviors; secondary outcomes included psychiatric symptoms (including posttraumatic stress) and delinquent acts. We hypothesized that girls randomized to the VOICES (n = 51) versus GirlHealth (attention control; n = 62) condition would report reduced alcohol, cannabis and other substance use, HIV/STI risk behaviors, psychiatric symptoms, and delinquent acts.

RESULTS

Girls randomized to VOICES reported significantly less cannabis use over 9-month follow-up relative to the control condition (time by intervention, p < .01), but there were no between group differences over time in HIV/STI risk behavior. Girls in both conditions reported fewer psychiatric symptoms and delinquent acts over time.

CONCLUSIONS

Data support the use of a trauma-informed, gender-responsive intervention to reduce cannabis use among girls with a substance use history and legal involvement; reducing cannabis use in this population has implications for preventing future justice involvement and improving public health outcomes for girls and young women, who are at disproportionate health and legal risk relative to their male counterparts.

Attributing health benefits to preventing HIV infections versus improving health outcomes among people living with HIV: an analysis in six US cities

OBJECTIVE

Combination strategies generate health benefits through improved health outcomes among people living with HIV (PLHIV) and prevention of new infections. We aimed to determine health benefits attributable to improved health among PLHIV versus HIV prevention for a set of combination strategies in six US cities.

DESIGN

A dynamic HIV transmission model.

METHODS

Using a model calibrated for Atlanta, Baltimore, Los Angeles, Miami, New York City (NYC) and Seattle, we assessed the health benefits of city-specific optimal combinations of evidence-based interventions implemented at publicly documented levels and at ideal (90% coverage) scale-up (2020-2030 implementation, 20-year study period). We calculated the proportion of health benefit gains (measured as quality-adjusted life-years) resulting from averted and delayed HIV infections; improved health outcomes among PLHIV; and improved health outcomes due to medication for opioid use disorder (MOUD).

RESULTS

The HIV-specific proportion of total benefits ranged from 68.3% (95% credible interval: 55.3-80.0) in Seattle to 98.5% (97.5-99.3) in Miami, with the rest attributable to MOUD. The majority of HIV-specific health benefits in five of six cities were attributable HIV prevention, and ranged from 33.1% (26.1-41.1) in NYC to 83.1% (79.6-86.6) in Atlanta. Scaling up to ideal service levels resulted in three to seven-fold increases in additional health benefits, mostly from MOUD, with HIV-specific health gains primarily driven by HIV prevention.

CONCLUSION

Optimal combination strategies generated a larger proportion of health benefits attributable to HIV prevention in five of six cities, underlining the substantial benefits of antiretroviral therapy engagement for the prevention of HIV transmission through viral suppression. Understanding to whom benefits accrue may be important in assessing the equity and impact of HIV investments.

Machine learning takes a village: Assessing neighbourhood-level vulnerability for an overdose and infectious disease outbreak

BACKGROUND

Multiple areas in the United States of America (USA) are experiencing high rates of overdose and outbreaks of bloodborne infections, including HIV and hepatitis C virus (HCV), due to non-sterile injection drug use. We aimed to identify neighbourhoods at increased vulnerability for overdose and infectious disease outbreaks in Rhode Island, USA. The primary aim was to pilot machine learning methods to identify which neighbourhood-level factors were important for creating "vulnerability assessment scores" across the state. The secondary aim was to engage stakeholders to pilot an interactive mapping tool and visualize the results.

METHODS

From September 2018 to November 2019, we conducted a neighbourhood-level vulnerability assessment and stakeholder engagement process named The VILLAGE Project (Vulnerability Investigation of underlying Local risk And Geographic Events). We developed a predictive analytics model using machine learning methods (LASSO, Elastic Net, and RIDGE) to identify areas with increased vulnerability to an outbreak of overdose, HIV and HCV, using census tract-level counts of overdose deaths as a proxy for injection drug use patterns and related health outcomes. Stakeholders reviewed mapping tools for face validity and community distribution.

RESULTS

Machine learning prediction models were suitable for estimating relative neighbourhood-level vulnerability to an outbreak. Variables of importance in the model included housing cost burden, prior overdose deaths, housing density, and education level. Eighty-nine census tracts (37%) with no prior overdose fatalities were identified as being vulnerable to such an outbreak, and nine of those were identified as having a vulnerability assessment score in the top 25%. Results were disseminated as a vulnerability stratification map and an online interactive mapping tool.

CONCLUSION

Machine learning methods are well suited to predict neighborhoods at higher vulnerability to an outbreak. These methods show promise as a tool to assess structural vulnerabilities and work to prevent outbreaks at the local level.

Improving health equity and ending the HIV epidemic in the USA: a distributional cost-effectiveness analysis in six cities

BACKGROUND

In the USA, Black and Hispanic or Latinx individuals continue to be disproportionately affected by HIV. Applying a distributional cost-effectiveness framework, we estimated the cost-effectiveness and epidemiological impact of two combination implementation approaches to identify the approach that best meets the dual objectives of improving population health and reducing racial or ethnic health disparities.

METHODS

We adapted a dynamic, compartmental HIV transmission model to characterise HIV micro-epidemics in six US cities: Atlanta, Baltimore, Los Angeles, Miami, New York, and Seattle. We considered combinations of 16 evidence-based interventions to diagnose, treat, and prevent HIV transmission according to previously documented levels of scale-up. We then identified optimal combination strategies for each city, with the distribution of each intervention implemented according to existing service levels (proportional services approach) and the racial or ethnic distribution of new diagnoses (between Black, Hispanic or Latinx, and White or other ethnicity individuals; equity approach). We estimated total costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios of strategies implemented from 2020 to 2030 (health-care perspective; 20-year time horizon; 3% annual discount rate). We estimated three measures of health inequality (between-group variance, index of disparity, Theil index), incidence rate ratios, and rate differences for the selected strategies under each approach.

FINDINGS

In all cities, optimal combination strategies under the equity approach generated more QALYs than those with proportional services, ranging from a 3·1% increase (95% credible interval [CrI] 1·4-5·3) in New York to more than double (101·9% [75·4-134·6]) in Atlanta. Compared with proportional services, the equity approach delivered lower costs over 20 years in all cities except Los Angeles; cost reductions ranged from $22·9 million (95% CrI 5·3-55·7 million) in Seattle to $579·8 million (255·4-940·5 million) in Atlanta. The equity approach also reduced incidence disparities and health inequality measures in all cities except Los Angeles.

INTERPRETATION

Equity-focused HIV combination implementation strategies that reduce disparities for Black and Hispanic or Latinx individuals can significantly improve population health, reduce costs, and drive progress towards Ending the HIV Epidemic goals in the USA.

FUNDING

National Institute on Drug Abuse.

Comparison of Characteristics of Deaths From Drug Overdose Before vs During the COVID-19 Pandemic in Rhode Island

Importance

The rate of deaths from overdose has increased during the COVID-19 pandemic, and recent US overdose mortality rates have been markedly high. However, scant data are available on the causes of this increase or subpopulations at elevated risk.

Objective

To evaluate the rates and characteristics of deaths from drug overdose before vs during the COVID-19 pandemic.

Design, Setting, and Participants

This retrospective, population-based cohort study used data from 4 statewide databases linked at the person level via the Rhode Island Data Ecosystem on adults with deaths due to overdose in Rhode Island from January 1 to August 31, 2019, and January 1 to August 31, 2020.

Main Outcomes and Measures

The rates of unintentional deaths from drug-related overdose during the 2019 and 2020 observation periods overall and by sociodemographic characteristics, drugs contributing to the cause of death, location of death, and socioeconomic factors were evaluated. In subgroup analyses restricted to Medicaid beneficiaries (n = 271), the proportions of deaths from overdose by behavioral health treatment and diagnosis claims in the year before death were also examined.

Results

A total of 470 adults who died of drug overdose were included in the analysis (353 men [75%]; mean [SD] age, 43.5 [12.1] years). The rate of deaths from overdose in Rhode Island increased 28.1%, from 29.2 per 100 000 person-years in 2019 to 37.4 per 100 000 person-years in 2020 (P = .009). Compared with 2019, rates of deaths due to overdose during 2020 were higher among men (43.2 vs 59.2 per 100 000 person-years; P = .003), non-Hispanic White individuals (31.0 vs 42.0 per 100 000 person-years; P = .005), single individuals (54.8 vs 70.4 per 100 000 person-years; P = .04), deaths involving synthetic opioids (20.8 vs 28.3 per 100 000 person-years; P = .005), and deaths occurring in a personal residence (13.2 vs 19.7 per 100 000 person-years; P = .003). A decrease in the proportion of deaths from overdose involving heroin (11 of 206 [5%] vs <2% [exact value suppressed]; P = .02) and an increase among persons experiencing job loss (16 of 206 [8%] vs 41 of 264 [16%]; P = .01) from 2019 to 2020 were observed. Among individuals who died of overdose and were Medicaid beneficiaries, the proportions of those aged 50 to 59 years with anxiety (11 of 121 [9%] vs 29 of 150 [19%]; P = .03), men with depression (27 of 121 [22%] vs 57 of 150 [38%]; P = .008), and men with anxiety (28 of 121 [23%] vs 55 of 150 [37%]; P = .02) increased during 2020 compared with 2019.

Conclusions and Relevance

In this cohort study, during the first 8 months of 2020, the rate of deaths from overdose increased in Rhode Island compared with the same period in 2019, and several emerging characteristics of deaths from drug overdose during the first year of the COVID-19 pandemic were identified. These findings may inform interventions that address macroenvironmental changes associated with the pandemic.

Cannabis Use Among Court-Involved Minority Sexual Orientation and Gender Identity Adolescents

We examined the effects of family functioning and beliefs regarding peers' cannabis use among minority (n = 112) and non-minority (n = 275) sexual orientation and gender identity (SOGI), first-time court-involved adolescents. We examined longitudinally the effects of baseline general family functioning and peer cannabis use beliefs on self-reported cannabis use and cannabis-related consequences after 12 months. At baseline, 39.2 percent of adolescents reported using cannabis. Minority SOGI adolescents reported worse family functioning (p = .017) and higher peer cannabis use beliefs (p = .047). Higher peer cannabis use beliefs at baseline predicted recent cannabis use at the 12-month assessment for both minority and non-minority SOGI adolescents. Better family functioning predicted a lower likelihood of recent cannabis use at 12 months for non-minority SOGI adolescents, but not for minority SOGI adolescents. Baseline peer cannabis use beliefs and family functioning predicted cannabis-related consequences for both cohorts at 12 months when accounting for intermediate (i.e., four-month and eight-month) data. Among all first-time court-involved adolescents, those who believed greater cannabis use among their peers reported more subsequent cannabis use themselves. Conversely, higher general family functioning may be less of a protective factor for minority SOGI adolescents. These results suggest the utility of feedback interventions to modify peer norm beliefs among first-time court-involved adolescents.

Long-term Patterns of Self-reported Opioid Use, VACS Index, and Mortality Among People with HIV Engaged in Care

Longitudinal analyses of opioid use and overall disease severity among people with HIV (PWH) are lacking. We used joint-trajectory and Cox proportional hazard modeling to examine the relationship between self-reported opioid use and the Veterans Aging Cohort Study (VACS) Index 2.0, a validated measure of disease severity and mortality, among PWH engaged in care. Using data from 2002 and 2018, trajectory modeling classified 20% of 3658 PWH in low (i.e., lower risk of mortality), 40% in moderate, 28% in high, and 12% in extremely high VACS Index trajectories. Compared to those with moderate VACS Index trajectory, PWH with an extremely high trajectory were more likely to have high, then de-escalating opioid use (adjusted odds ratio [AOR], 95% confidence interval [CI] 5·17 [3·19-8·37]) versus stable, infrequent use. PWH who report high frequency opioid use have increased disease severity and mortality risk over time, even when frequency of opioid use de-escalates.

Emulating Target Trials to Improve Causal Inference From Agent-Based Models

Agent-based models are a key tool for investigating the emergent properties of population health settings, such as infectious disease transmission, where the exposure often violates the key "no interference" assumption of traditional causal inference under the potential outcomes framework. Agent-based models and other simulation-based modeling approaches have generally been viewed as a separate knowledge-generating paradigm from the potential outcomes framework, but this can lead to confusion about how to interpret the results of these models in real-world settings. By explicitly incorporating the target trial framework into the development of an agent-based or other simulation model, we can clarify the causal parameters of interest, as well as make explicit the assumptions required for valid causal effect estimation within or between populations. In this paper, we describe the use of the target trial framework for designing agent-based models when the goal is estimation of causal effects in the presence of interference, or spillover.

Prevalence and Correlates of Self-Injurious Behaviors Among Justice-Involved Youth

An estimated one in five adolescents exhibit self-injurious behavior (SIB), which poses serious public health concerns. The present analysis aims to describe the prevalence and correlates of lifetime SIB among first-time offending court-involved nonincarcerated youth. Baseline data from 412 youth enrolled in Epidemiological Project Involving Children in the Court (EPICC), a longitudinal cohort study, were analyzed to identify the prevalence and correlates of lifetime SIB. Almost a quarter (22.4%) of youth self-reported lifetime SIB. Participants who were female, bisexual, and those with more severe post-traumatic stress symptoms had higher prevalence odds of lifetime SIB. These findings suggest the importance of screening for SIB among youth and may provide guidance in the development of interventions designed to improve health outcomes of adolescents who come into first contact with the juvenile justice system.

Rhode Island's Opioid Overdose Hospital Standards and Emergency Department Naloxone Distribution, Behavioral Counseling, and Referral to Treatment

STUDY OBJECTIVE

We sought to determine the influence of the Levels of Care for Rhode Island Emergency Departments and Hospitals for Treating Overdose and Opioid Use Disorder (Levels of Care) on emergency department (ED) provision of take-home naloxone, behavioral counseling, and referral to treatment.

METHODS

A retrospective analysis of Rhode Island ED visits for opioid overdose from 2017 to 2018 was performed using data from a statewide opioid overdose surveillance system. Changes in provision of take-home naloxone, behavioral counseling, and referral to treatment before and after Levels of Care implementation were assessed using interrupted time series analysis. We compared outcomes by hospital type using multivariable modified Poisson regression models with generalized estimating equation estimation to account for hospital-level variation.

RESULTS

We analyzed 245 overdose visits prior to Levels of Care implementation (January to March 2017) and 1340 overdose visits after implementation (hospital certification to December 2018). After implementation, the proportion of patients offered naloxone increased on average by 13% (95% confidence interval [CI] 5.6% to 20.4%). Prior to implementation, the proportion of patients receiving behavioral counseling and treatment referral was declining. After implementation, this decline slowed and stabilized, and on average 18.6% more patients received behavioral counseling (95% CI 1.3% to 35.9%) and 23.1% more patients received referral to treatment (95% CI 2.7% to 43.5%). Multivariable analysis showed that after implementation, there was a significant increase in the likelihood of being offered naloxone at Level 1 (adjusted relative risk [aRR] 1.31 [95% CI 1.06 to 1.61]) and Level 3 (aRR 3.13 [95% CI 1.08 to 9.06]) hospitals and an increase in referrals for medication for opioid use disorder (from 2.5% to 17.8%) at Level 1 hospitals (RR 7.73 [95% CI 3.22 to 18.55]). Despite these increases, less than half of the patients treated for an opioid overdose received behavioral counseling or referral to treatment CONCLUSION: The establishment of ED policies for treatment and services after opioid overdose improved naloxone distribution, behavioral counseling, and referral to treatment at hospitals without previously established opioid overdose services. Future investigations are needed to better characterize implementation barriers and evaluate policy influence on patient outcomes.

Sociodemographic and clinical correlates of gabapentin receipt with and without opioids among a national cohort of patients with HIV

Gabapentin is commonly prescribed for chronic pain, including to patients with HIV (PWH). There is growing concern regarding gabapentin's potential for harm, particularly in combination with opioids. Among PWH, we examined factors associated with higher doses of gabapentin receipt and determined if receipt varied by opioid use. We examined data from the Veterans Aging Cohort Study, a national prospective cohort including PWH, from 2002 through 2017. Covariates included prescribed opioid dose, self-reported past year opioid use, and other sociodemographic and clinical variables. We used multinomial logistic regression to determine independent predictors of gabapentin receipt. Among 3,702 PWH, 902 (24%) received any gabapentin during the study period at a mean daily dose of 1,469 mg. In the multinomial model, high-dose gabapentin receipt was associated with high-dose benzodiazepine receipt (adjusted odds ratio [aOR], 95% confidence interval [CI]= 1.53, [1.03-2.27]), pain interference (1.65 [1.39-1.95]), and hand or foot pain (1.81, [1.45-2.26]). High-dose gabapentin receipt was associated with prescribed high-dose opioids receipt (2.66 [1.95-3.62]) but not self-reported opioid use (1.03 [0.89-1.21]). PWH prescribed gabapentin at higher doses are more likely to receive high-dose opioids and high-dose benzodiazepines, raising safety concerns.

The Potential Epidemiological Impact of Coronavirus Disease 2019 (COVID-19) on the Human Immunodeficiency Virus (HIV) Epidemic and the Cost-effectiveness of Linked, Opt-out HIV Testing: A Modeling Study in 6 US Cities

Background

Widespread viral and serological testing for SARS-CoV-2 may present a unique opportunity to also test for HIV infection. We estimated the potential impact of adding linked, opt-out HIV testing alongside SARS-CoV-2 testing on HIV incidence and the cost-effectiveness of this strategy in six US cities.

Methods

Using a previously-calibrated dynamic HIV transmission model, we constructed three sets of scenarios for each city: (1) sustained current levels of HIV-related treatment and prevention services (status quo); (2) temporary disruptions in health services and changes in sexual and injection risk behaviours at discrete levels between 0%-50%; and (3) linked HIV and SARS-CoV-2 testing offered to 10%-90% of the adult population in addition to scenario (2). We estimated cumulative HIV infections between 2020-2025 and incremental cost-effectiveness ratios of linked HIV testing over 20 years.

Results

In the absence of linked, opt-out HIV testing, we estimated a total of 16.5% decrease in HIV infections between 2020-2025 in the best-case scenario (50% reduction in risk behaviours and no service disruptions), and 9.0% increase in the worst-case scenario (no behavioural change and 50% reduction in service access). We estimated that HIV testing (offered at 10%-90% levels) could avert a total of 576-7,225 (1.6%-17.2%) new infections. The intervention would require an initial investment of $20.6M-$220.7M across cities; however, the intervention would ultimately result in savings in health care costs in each city.

Conclusions

A campaign in which HIV testing is linked with SARS-CoV-2 testing could substantially reduce HIV incidence and reduce direct and indirect health care costs attributable to HIV.

Disseminated Effects in Agent-Based Models: A Potential Outcomes Framework and Application to Inform Preexposure Prophylaxis Coverage Levels for HIV Prevention

Preexposure prophylaxis (PrEP) for prevention of human immunodeficiency virus (HIV) infection may benefit not only the person who uses it but also their uninfected sexual risk contacts. We developed an agent-based model using a novel trial emulation approach to quantify disseminated effects of PrEP use among men who have sex with men in Atlanta, Georgia, from 2015 to 2017. Model components (subsets of agents connected through partnerships in a sexual network but not sharing partnerships with any other agents) were first randomized to an intervention coverage level or the control group; then, within intervention components, eligible agents were randomized to receive or not receive PrEP. We calculated direct and disseminated (indirect) effects using randomization-based estimators and report corresponding 95% simulation intervals across scenarios ranging from 10% coverage in the intervention components to 90% coverage. A population of 11,245 agents was simulated, with an average of 1,551 components identified. When comparing agents randomized to no PrEP in 70% coverage components with control agents, there was a 15% disseminated risk reduction in HIV incidence (risk ratio = 0.85, 95% simulation interval: 0.65, 1.05). Persons not on PrEP may receive a protective benefit by being in a sexual network with higher PrEP coverage. Agent-based models are useful for evaluating possible direct and disseminated effects of HIV prevention modalities in sexual networks.

"Ending the Epidemic" Will Not Happen Without Addressing Racial/Ethnic Disparities in the United States Human Immunodeficiency Virus Epidemic

We estimated human immunodeficiency virus incidence and incidence rate ratios (IRRs) for black and Hispanic vs white populations in 6 cities in the United States (2020-2030). Large reductions in incidence are possible, but without elimination of disparities in healthcare access, we found that wide disparities persisted for black compared with white populations in particular (lowest IRR, 1.69 [95% credible interval, 1.19-2.30]).

Ending the HIV Epidemic Among Persons Who Inject Drugs: A Cost-Effectiveness Analysis in Six US Cities

BACKGROUND

Persons who inject drugs (PWID) are at a disproportionately high risk of HIV infection. We aimed to determine the highest-valued combination implementation strategies to reduce the burden of HIV among PWID in 6 US cities.

METHODS

Using a dynamic HIV transmission model calibrated for Atlanta, Baltimore, Los Angeles, Miami, New York City, and Seattle, we assessed the value of implementing combinations of evidence-based interventions at optimistic (drawn from best available evidence) or ideal (90% coverage) scale-up. We estimated reduction in HIV incidence among PWID, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) for each city (10-year implementation; 20-year horizon; 2018 $ US).

RESULTS

Combinations that maximized health benefits contained between 6 (Atlanta and Seattle) and 12 (Miami) interventions with ICER values ranging from $94 069/QALY in Los Angeles to $146 256/QALY in Miami. These strategies reduced HIV incidence by 8.1% (credible interval [CI], 2.8%-13.2%) in Seattle and 54.4% (CI, 37.6%-73.9%) in Miami. Incidence reduction reached 16.1%-75.5% at ideal scale.

CONCLUSIONS

Evidence-based interventions targeted to PWID can deliver considerable value; however, ending the HIV epidemic among PWID will require innovative implementation strategies and supporting programs to reduce social and structural barriers to care.

Predictors of enrollment in opioid agonist therapy after opioid overdose or diagnosis with opioid use disorder: A cohort study

BACKGROUND

Medicaid recipients have a high burden of opioid overdose and opioid use disorder (OUD). Opioid agonist therapies are an effective treatment for OUD, but there is a wide and persisting gap between those who are indicated and those who receive treatment. The objective of this study was to identify the predictors of enrollment in opioid agonist therapy within 6 months of an opioid overdose or OUD diagnosis in a cohort of Medicaid recipients.

METHODS

Using multiple linked, state-level databases, we conducted a retrospective cohort study of 17,449 Medicaid recipients in Rhode Island who had an opioid overdose or an OUD diagnosis between July 2013 and June 2018.

RESULTS

The majority (58 %) of Medicaid recipients did not enroll in opioid agonist therapy within 6 months. In adjusted models, having one or more prior overdose (adjusted risk ratio [ARR] = 0.33, 95 % CI: 0.28, 0.38), alcohol use disorder (ARR = 0.56, 95 % CI: 0.52, 0.60), or back problems (ARR = 0.58, 95 % CI: 0.55, 0.61) were strong predictors of non-enrollment. Conversely, emergency department (ARR = 1.31, 95 % CI: 1.28-1.34) and primary care provider (ARR = 1.03, 95 % CI: 1.01-1.34) visit frequency above the 75th percentile were associated with timely enrollment in opioid agonist therapy.

CONCLUSIONS

Our findings underscore the need to enhance pathways to treatment for OUD through varied nodes of engagement with healthcare systems. Interventions to improve screening for OUD and referrals to opioid agonist therapies should include high-impact settings, such as treatment programs for alcohol and substance use disorders, pain clinics, and outpatient behavioral care settings.

Polysubstance Involvement in Opioid Overdose Deaths in Adolescents and Young Adults, 1999-2018

This cross-sectional study examines US trends in polysubstance-involved opioid overdose deaths among adolescents and young adults aged 13 to 25 years.

Incidence and time-varying predictors of HIV and sexually transmitted infections among male sex workers in Mexico City

Background

Male sex workers are at high-risk for acquisition of sexually transmitted infections (STIs), including human immunodeficiency virus (HIV). We quantified incidence rates of STIs and identified their time-varying predictors among male sex workers in Mexico City.

Methods

From January 2012 to May 2014, male sex workers recruited from the largest HIV clinic and community sites in Mexico City were tested for chlamydia, gonorrhea, syphilis, hepatitis, and HIV at baseline, 6-months, and 12-months. Incidence rates with 95% bootstrapped confidence limits were calculated. We examined potential time-varying predictors using generalized estimating equations for a population averaged model.

Results

Among 227 male sex workers, median age was 24 and baseline HIV prevalence was 32%. Incidence rates (per 100 person-years) were as follows: HIV [5.23; 95% confidence interval (CI): 2.15–10.31], chlamydia (5.15; 95% CI: 2.58–9.34), gonorrhea (3.93; 95% CI: 1.88–7.83), syphilis (13.04; 95% CI: 8.24–19.94), hepatitis B (2.11; 95% CI: 0.53–4.89), hepatitis C (0.95; 95% CI: 0.00–3.16), any STI except HIV (30.99; 95% CI: 21.73–40.26), and any STI including HIV (50.08; 95% CI: 37.60–62.55). In the multivariable-adjusted model, incident STI (excluding HIV) were lower among those who reported consistently using condoms during anal and vaginal intercourse (odds ratio = 0.03, 95% CI: 0.00–0.68) compared to those who reported inconsistently using condoms during anal and vaginal intercourse.

Conclusions

Incidence of STIs is high among male sex workers in Mexico City. Consistent condom use is an important protective factor for STIs, and should be an important component of interventions to prevent incident infections.

Implementation of Syringe Services Programs to Prevent Rapid Human Immunodeficiency Virus Transmission in Rural Counties in the United States: A Modeling Study

BACKGROUND

Syringe services programs (SSPs) are effective venues for delivering harm-reduction services to people who inject drugs (PWID). However, SSPs often face significant barriers to implementation, particularly in the absence of known human immunodeficiency virus (HIV) outbreaks.

METHODS

Using an agent-based model, we simulated HIV transmission in Scott County, Indiana, a rural county with a 1.7% prevalence of injection drug use. We compared outcomes arising in the absence of an SSP, in the presence of a pre-existing SSP, and with implementation of an SSP after the detection of an HIV outbreak among PWID over 5 years following the introduction of a single infection into the network.

RESULTS

In the absence of an SSP, the model predicted an average of 176 infections among PWID over 5 years or an incidence rate of 12.1/100 person-years. Proactive implementation averted 154 infections and decreased incidence by 90.3%. With reactive implementation beginning operations 10 months after the first infection, an SSP would prevent 107 infections and decrease incidence by 60.8%. Reductions in incidence were also observed among people who did not inject drugs.

CONCLUSIONS

Based on model predictions, proactive implementation of an SSP in Scott County had the potential to avert more HIV infections than reactive implementation after the detection of an outbreak. The predicted impact of reactive SSP implementation was highly dependent on timely implementation after detecting the earliest infections. Consequently, there is a need for expanded proactive SSP implementation in the context of enhanced monitoring of outbreak vulnerability in Scott County and similar rural contexts.