Prevalence and correlates of using opioids alone among individuals in a residential treatment program in Michigan: implications for overdose mortality prevention

Sex disparities in outcome of medication-assisted therapy of opioid use disorder: Nationally representative outpatient clinic data

QUESTION

The opioid epidemic causes massive morbidity, and males have substantially greater overdose mortality rates than females. It is unclear whether there are sex-related disparities at different stages in the trajectory of opioid use disorders (OUD), from large samples in the community.

GOAL

To determine sex disparities in non-medical opioid use (NMOU) at the end of treatment with medications for opioid use disorder (MOUD), using national data.

DESIGN

Observational study of outpatient MOUD programs in the "Treatment episode data set-discharges" (TEDS-D) for 2019.

PARTICIPANTS

Persons aged ≥ 18 in their first treatment episode, in outpatient MOUD-based therapy for heroin or other opioids (N = 10,065). The binary outcome was presence/absence of NMOU in the month prior to discharge.

RESULTS

In univariate analyses, males had higher odds of NMOU compared to females (odds ratio=1.22; p = 6.84 ×10-5 after Bonferroni correction). A multivariable logistic regression detected a relatively small male>female odds ratio of 1.14 (p = 0.0039), surviving adjustment for demographic variables and social determinants of health. Several specific conditions were revealed in which males had greater odds of NMOU compared to females (e.g., if they were in the white racial category, and were not of hispanic ethnicity). Also, using by smoking, inhalation or injection routes (versus oral) was associated with greater odds of NMOU, irrespective of sex.

CONCLUSIONS

This national community sample shows that males overall have greater odds of NMOU in their first treatment episode with MOUD, a potential indicator of more unfavorable outcomes. Further analyses should examine the underpinnings of this disparity, including clinical severity features.

Measuring network dynamics of opioid overdose deaths in the United States

The US opioid overdose epidemic has been a major public health concern in recent decades. There has been increasing recognition that its etiology is rooted in part in the social contexts that mediate substance use and access; however, reliable statistical measures of social influence are lacking in the literature. We use Facebook's social connectedness index (SCI) as a proxy for real-life social networks across diverse spatial regions that help quantify social connectivity across different spatial units. This is a measure of the relative probability of connections between localities that offers a unique lens to understand the effects of social networks on health outcomes. We use SCI to develop a variable, called "deaths in social proximity", to measure the influence of social networks on opioid overdose deaths (OODs) in US counties. Our results show a statistically significant effect size for deaths in social proximity on OODs in counties in the United States, controlling for spatial proximity, as well as demographic and clinical covariates. The effect size of standardized deaths in social proximity in our cluster-robust linear regression model indicates that a one-standard-deviation increase, equal to 11.70 more deaths per 100,000 population in the social proximity of ego counties in the contiguous United States, is associated with thirteen more deaths per 100, 000 population in ego counties. To further validate our findings, we performed a series of robustness checks using a network autocorrelation model to account for social network effects, a spatial autocorrelation model to capture spatial dependencies, and a two-way fixed-effect model to control for unobserved spatial and time-invariant characteristics. These checks consistently provide statistically robust evidence of positive social influence on OODs in US counties. Our analysis provides a pathway for public health interventions informed by social network structures. The statistical robustness of our primary variable of interest, deaths in social proximity, supports the hypothesis of a social network effect on OODs. Using agent-based modeling (ABM) to simulate social networks can offer an effective method to design interventions that incorporate the dynamics of social networks for maximum impact.

Sex disparities in outcome of medication-assisted therapy of opioid use disorder: Nationally representative study

Question

The opioid epidemic causes massive morbidity, and males have substantially greater overdose mortality rates than females. It is unclear whether there are sex-related disparities at different stages in the trajectory of opioid use disorders, in "real world" settings.

Goal

To determine sex disparities in non-medical opioid use (NMOU) at the end of outpatient medication-assisted treatment (MAT), using nationally representative data.

Design

Observational epidemiological study of publicly funded outpatient MAT programs in the national "Treatment episode data set-discharges" (TEDS-D) for 2019.

Participants

Persons aged ≥18 in their first treatment episode, in outpatient MAT for use of heroin or other opioids (N=11,549). The binary outcome was presence/absence of NMOU.

Results

In univariate analyses, males had significantly higher odds of NMOU, compared to females (odds ratio=1.27; Chi2 [df:1]=39.08; uncorrected p<0.0001; p=0.0041 after Bonferroni correction). A multivariable logistic regression detected a male>female odds ratio of 1.19 (95%CI=1.09-1.29; p<0.0001), adjusting for socio-demographic/clinical variables. Several specific conditions were revealed in which males had greater odds of NMOU compared to females (e.g., at ages 18-29 and 30-39; corrected p=0.012, or if they used opioids by inhalation; corrected p=0.0041).

Conclusions

This nationally representative study indicates that males have greater odds of NMOU in their first episode of MAT, indicating more unfavorable outcomes. The study reveals specific socio-demographic and clinical variables under which this sex disparity is most prominent.

Evaluation of Strategies to Enhance Community-Based Naloxone Distribution Supported by an Opioid Settlement

Importance

Many US states are substantially increasing community-based naloxone distribution, supported in part through settlements from opioid manufacturers and distributors.

Objectives

To evaluate the potential impact of increased naloxone availability on opioid overdose deaths (OODs) and explore strategies to enhance this impact by integrating interventions to address solitary drug use.

Design, Setting, and Participants

This decision analytical modeling study used PROFOUND (Prevention and Rescue of Fentanyl and Other Opioid Overdoses Using Optimized Naloxone Distribution Strategies), a previously published simulation model, to forecast annual OODs between January 2023 and December 2025. The simulated study population included individuals from Rhode Island who misused opioids and stimulants and were at risk for opioid overdose.

Exposures

The study modeled expanded naloxone distribution supported by the state's opioid settlement (50 000 naloxone nasal spray kits each year). Two approaches to expanding naloxone distribution were evaluated: one based on historical spatial patterns of naloxone distribution (supply-based approach) and one based on the spatial distribution of individuals at risk (demand-based approach). In addition, hypothetical interventions to enhance the likelihood of witnessed overdoses in private or semiprivate settings were considered.

Main Outcomes and Measures

Annual number of OODs and ratio of fatal to nonfatal opioid overdoses.

Results

Modeling results indicated that distributing more naloxone supported by the state's opioid settlement could reduce OODs by 6.3% (95% simulation interval [SI], 0.3%-13.7%) and 8.8% (95% SI, 1.8%-17.5%) in 2025 with the supply-based and demand-based approaches, respectively. However, increasing witnessed overdoses by 20% to 60% demonstrated greater potential for reducing OODs, ranging from 8.5% (95% SI, 0.0%-20.3%) to 24.1% (95% SI, 8.6%-39.3%). Notably, synergistic associations were observed when combining both interventions: increased naloxone distribution with the 2 approaches and a 60% increase in witnessed overdoses could reduce OODs in 2025 by 33.5% (95% SI, 17.1%-50.4%) and 37.4% (95% SI, 19.6%-56.3%), respectively.

Conclusions and Relevance

These findings suggest that interventions to address solitary drug use are needed to maximize the impact of continued efforts to increase community-based naloxone distribution, which may be particularly important for jurisdictions that have strong community-based naloxone distribution programs.

Mortality risk and causes of death among people who use opioids in a take-home naloxone cohort

BACKGROUND

People who use opioids (PWUO) have an excess mortality from a range of causes. The cumulative effect of behavioral, social, and health risk factors complicates the interpretation of the effects of suitable interventions. This study explores mortality causes among a cohort of PWUO in the take-home naloxone (THN) program.

METHODS

This was a prospective cohort study of PWUO who received THN between 2015 and 2023. Participant data was linked with death registry data. Crude mortality rates and standardized mortality ratios (SMRs) were calculated for all causes, internal causes, and accidental opioid overdoses (AOOs). In addition to age and gender, risk factors like main route of administration, polydrug use, self-experienced overdoses, and using while alone were fitted in a Cox Regression model to explore factors associated with mortality.

RESULTS

The 2194 participants had a considerably higher mortality ratio for all causes investigated when compared to the general population (SMR=10.9, 95 % CI = 9.3,12.6). AOOs were the most prevalent cause of death (49 %). Those who reported frequent opioid use while alone had an elevated risk of dying of 2.6 (95 % CI = 1.1,6.4) compared to those who never used while alone.

CONCLUSIONS

Frequent opioid use while alone was associated with elevated mortality risk, supporting the importance of overdose prevention education with naloxone distribution, and additional efforts to improve environmental and social areas for safer drug-use practices among PWUO. The variety of mortality causes found in this study illustrates the need for multifaceted and targeted interventions for people at risk of overdosing.

The Need to Rethink Harm Reduction for People Using Drugs Alone to Reduce Overdose Fatalities

Background: During the ongoing opioid epidemic, Cuyahoga County (second largest in Ohio) reported overdose mortality rates (54/per 100,000) higher than the national average. Prior research demonstrates that people who use drugs often use alone but there is minimal research on people who died of overdose while using alone. The objective of this study is to examine sociodemographic, toxicologic, and injury characteristics, and emergency medical response to overdose decedents who died using drugs alone. Method: Data from the Cuyahoga County Medical Examiner's Office (2016-2020, N = 2944) on unintentional overdose deaths in adults was tabulated including socio-demographic, toxicologic, and injury-related information. Decedents using drugs alone were identified and compared to those not using alone via Chi-square and Fisher's exact tests. We further fit a multivariate logistic regression model to evaluate socio-demographic, toxicologic, and injury-related factors associated with increased odds of using alone. All results are reported with 95% confidence intervals. Result: Among decedents, 75% (n = 2205) were using drugs alone. Decedents using alone were more likely to be using drugs at home (p = 0.001) or be found dead at the scene (p < 0.001) and less likely to receive naloxone (p < 0.001) have other person/bystander, not using, present (p = 0.002). Using drugs at home (aOR = 1.61[1.19-2.20]) was associated with higher odds of using alone; and being married (aOR = 0.57[0.38-0.86]), having history of illicit drug use (aOR = 0.25[0.08-0.81]) and other person present, who was not using (aOR = 0.58[0.42-0.79]) were associated with lower odds of using alone. Conclusion: New harm reduction approaches targeting people using drugs alone are needed to reduce overdose deaths.