Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Apr 1.
Published in final edited form as: Int J Drug Policy. 2024 Mar 5;126:104371. doi: 10.1016/j.drugpo.2024.104371

Volatile Drug Use and Overdose During the First Year of the COVID-19 Pandemic in the United States

Kristin E Schneider 1, Emily M Martin 1, Sean T Allen 1, Miles Morris 1, Katherine Haney 1, Brendan Saloner 2, Susan G Sherman 1
PMCID: PMC11056297  NIHMSID: NIHMS1972939  PMID: 38447262

Abstract

Background:

Overdose deaths in the United States rose substantially during the COVID-19 pandemic. Disruptions to the drug supply and service provision introduced significant instability into the lives of people who use drugs (PWUD), including volatility in their drug use behaviors.

Methods:

Using data from a multistate survey of PWUD, we examined sociodemographic and drug use correlates of volatile drug use during COVID-19 using multivariable linear regression. In a multivariable logistic regression model, we assessed the association between volatile drug use and past month overdose adjusting for sociodemographic and other drug use characteristics.

Results:

Among participants, 52% were male, 50% were white, 29% had less than a high school education, and 25% were experiencing homelessness. Indicators of volatile drug use were prevalent: 53% wanted to use more drugs; 45% used more drugs; 43% reported different triggers for drug use, and 23% used drugs that they did not typically use. 14% experienced a past-month overdose. In adjusted models, hunger (β=0.47, 95% CI: 0.21-0.72), transactional sex (β=0.50, 95% CI: 0.06-0.94), and the number of drugs used (β=0.16, 95% CI: 0.07-0.26) were associated with increased volatile drug use. Volatile drug use was associated with increased overdose risk (aOR=1.42, 95% CI: 1.17-1.71) in the adjusted model.

Conclusions:

Volatile drug use during the COVID-19 pandemic was common, appeared to be driven by structural vulnerability, and was associated with increased overdose risk. Addressing volatile drug use through interventions that ensure structural stability for PWUD and a safer drug supply is essential for mitigating the ongoing overdose crisis.

Introduction

Overdose deaths have risen dramatically since the onset of the COVID-19 pandemic, exacerbating the longstanding trend of increasing overdose mortality. Overdose deaths increased 30% between 2019 and 2020 in the United States (US), and fatalities increased another 15% to 107,622 deaths in 2021 and remain stable at 109,846 deaths in 2022 (Ahmad et al., 2021; F. B. Ahmad et al., 2023). Similar trends in overdose fatalities have been observed in Canada (Federal, 2023). Rising overdose rates have been attributed to a range of drivers related to the erratic illicit drug supply and changes in drug use patterns during the COVID-19 pandemic. While adulterants have long been present in the illicit drug supply, their presence was rapidly proliferated during the pandemic due to changes in drug trafficking related to travel restrictions and border control (European Monitoring Centre for Drugs and Drug Addiction and Europol, 2020; Grebely et al., 2020; Nguyen and Buxton, 2021). During this time, drug supplies were of varying potency, quality, and accessibility with higher prices (Ali et al., 2021; Bennett et al., 2022; European Monitoring Centre for Drugs and Drug Addiction and Europol, 2020). Adulterants or cutting agents that contribute to both overdose and other harms have been increasingly identified by drug checking programs across the US and Canada (Karamouzian et al., 2018; Maghsoudi et al., 2022; Tupper et al., 2018). In particular, xylazine, a veterinary tranquilizer, has entered the national spotlight due to being identified in a range of US drug markets (Bowles et al., 2021; Friedman et al., 2022; Johnson et al., 2021; Thangada et al., 2021). Xylazine has been implicated in both overdose and the development of severe wounds and skin lesions (Alexander et al., 2022; Bowles et al., 2021; Friedman et al., 2022; Gupta et al., 2023; Wei et al., 2023). While xylazine has received the most media attention, other adulterants, such as levamisole (a veterinary anti-parasite medication the causes wounds in humans), medetomidine (another veterinary tranquilizer), benzodiazepines, and nitazenes (highly potent synthetic opioids), are regularly identified in illicit drug samples (Blanckaert et al., 2020; Delaney et al., 2023; Fiorentin et al., 2019; Krotulski et al., 2020; Midthun et al., 2021; Montanari et al., 2022; Sisco and Appley, 2023; Solomon and Hayes, 2017; Larnder et al., 2022). The proliferation of these adulterants increases opacity in the drug market, compounding that from previously identified adulterants like fentanyl. However, the presence of fentanyl has not remained stable either; data from Canada has also demonstrated increasing fentanyl concentrations in illicit drug samples over the course of the pandemic (Tobias et al., 2023). These quantitative data are mirrored by qualitative findings that document increased fears about overdose due to drug supply changes among PWUD (McNeil et al., 2022). As a result, PWUD do not truly know what they are consuming when using drugs, creating unintended volatility in drug use at the individual level.

Interruptions to harm reduction and drug treatment service delivery, as well as mutual support group access, further contributed to the instability in PWUD’s lives during the height of the COVID-19 pandemic. Harm reduction services were highly disrupted in the US, with approximately 15-25% of syringe services programs discontinuing services completely early in the pandemic, and the majority that remained open had reduced hours of operation (Bartholomew et al., 2020; Glick et al., 2020). Drug treatment services in the US underwent significant changes during the pandemic to mitigate COVID-19 transmission risk while still aiming to retain patients in care. Early in the pandemic, many programs discontinued or significantly reduced in person services (Blanco et al., 2021; Kleykamp et al., 2020). The Department of Health and Human Services enacted emergency permissions to increase the use of telehealth services for medication for opioid use disorder (MOUD) induction and continued prescribing in place of in person appointments that were previously mandatory (Alexander et al., 2020). However, PWUD still experienced significant disruptions, especially those who relied upon mutual support meetings. Individuals who utilized self-help groups or counseling reported barriers such as a lack of internet access, discomfort or lack of ability with online groups, and decreases in meeting frequency (Russell et al., 2021). Collectively, these service disruptions had mixed effects for PWUD depending on the type and modality of services they utilized. For PWUD who experienced challenges with accessing care through telehealth, the pandemic destabilized their access to life-saving resources and put them at heightened risk for overdose.

The pandemic has broadly increased instability in the lives of PWUD. This includes through exacerbating structural vulnerabilities like housing and food insecurity- known drivers of drug use and overdose risk (Walters et al., 2022; Zhen-Duan et al., 2022). Additionally, the pandemic exacerbated mental health issues and other existing structural issues (Heimer et al., 2020; Russell et al., 2021; Walters et al., 2022). For example, PWUD have reported increased rates of poor mental health outcomes, stress, job loss, and other negative impacts from the societal impacts of COVID-19, which led to increased drug use as a coping mechanism (Ali et al., 2021; Bennett et al., 2022; Czeisler et al., 2020; Stack et al., 2021). Without intervention, pandemic-related stressors and conditions not only increased substance use and related issues, but also decreased PWUD’s abilities to mitigate their own substance use-related risks (Heimer et al., 2020; Russell et al., 2021).

Structural vulnerability, which is defined as the risk of negative health outcomes generated by a person or group’s socioeconomic, political, or cultural position within society (Bourgois and Hart, 2011; Bourgois et al., 2017; Holmes, 2011), is a known risk factor for overdose (Milaney et al., 2021; Park et al., 2018; Pérez-Figueroa et al., 2022; Walters et al., 2022; Yamamoto et al., 2019; Zhen-Duan et al., 2022). During the pandemic, time-limited emergency housing and nutrition interventions emerged to address structural vulnerability. Programs using empty hotel rooms as quarantine spaces or emergency housing for people experiences homelessness were implemented in many cities (Chin et al., 2022; Fuchs et al., 2021; Robinson et al., 2022; Rosecrans et al., 2022). Housing interventions established during the COVID-19 pandemic showed that providing stable housing is associated with protective factors for overdose, including decreased substance use, MOUD adherence, strengthening in social and familial relationships, and better opportunity to finding more permanent housing options (Heimer et al., 2020; Scallan et al, 2022). Economic stimulus payments from the federal government helped temporarily mitigate food insecurity (Cooney and Shaefer, 2021). Changes to federal policies for the Supplemental Nutrition Assistance Program (SNAP) expanded resources to qualifying participants and allowed for greater flexibility in how programs were administered (Caspi et al., 2022). As a result of this additional flexibility, many food provision programs targeted toward children, including those administered through school systems, were expanded to include families and address other barriers to food access (Jablonski et al., 2021; McLoughlin et al., 2020). Unfortunately, many of these programs have since been discontinued or policies have been reverted to pre-pandemic states; emergency housing in unoccupied hotel rooms, moratoriums on housing evictions, economic stimulus payments from the federal government, and SNAP benefit increases have all been discontinued and the associated federal policies have expired (NHLP, 2021; Rosenbaum et al., 2023; USDA 2023). Many PWUD remain unable to access needed services and in turn are left with a risk environment that is highly conducive to increased substance use and overdose.

The current analysis aims to explore pandemic-related changes in volatile drug use among a multi-state sample of PWUD. We will further assess if volatile drug use was associated with recent overdose experiences.

Methods

Data Source.

Data were derived from the COVID Harm Reduction and Treatment programs Survey (COVID-HARTS) Study. The full details of the COVID-HARTS study have been reported previously (Saloner et al., 2022). Participants were recruited from 21 drug treatment and harm reduction programs across 9 states and the District of Columbia in the US between August 2020 and January 2021. Data collection occurred early in the pandemic and was completed before vaccines were widely available. Participants were referred to the study by program staff, who distributed recruitment cards to clients. The recruitment cards provided a study phone number and unique study identifier, and interested individuals were instructed to call in to be screened for eligibility. Eligibility criteria included being 18 years or older, a client of a participating organization, able to provide informed consent, and able to provide a valid study identifier from the recruitment card (to minimize duplicate and non-client participation). Eligible participants then completed the 1-hour survey via telephone and received a $40 incentive for participating (N=587). The COVID-HARTS Study was approved by the Johns Hopkins School of Public Health Institutional Review Board.

Measures.

Overdose.

Participants were asked how many times they had overdosed in the past month (“In the past month, how many times have yon overdosed to the point of passing out?”). We created a binary indicator for if a participant reported experiencing any overdoses in the past month, which is our primary outcome of interest for this analysis.

Volatile drug use.

We defined volatile drug use as changes to drug use behaviors and cravings. To measure volatile drug use, we created an index of four items that measured changes in amount of drugs used, types of drugs used, drug craving, and drug triggers during the COVID-10 pandemic. Potential scores ranged from 0 to 4. The four items (yes/no) that comprised this index began with a stem question asking if the participant had experienced any of the following since COVID-19 and the individual items asked if the participant wanted to use more drugs than before COVID-19, did use drugs more than before COVID-19, had different triggers for using drugs than normal, and had used drugs that they did not typically use.

Sociodemographic Characteristics.

Participants reported their age (in years), gender (man/male or woman/female), sexual orientation (heterosexual vs sexual minority), race (categorized as white, Black, or other), relationship status (single vs married/in a relationship), education (less than high school, high school diploma or equivalent, or some college or more), employment status (full time, part time, or not working), current homelessness (yes/no), experiencing hunger at least once a week (yes/no), and engaging in transactional sex. To assess urbanicity, we categorized participants’ reported state and county according to the National Center for Health Statistics Rural Classification Scheme and then created three categories of urbanicity based on these codes (large metropolitan, small metropolitan, and non-metropolitan).

Drug use and treatment.

We asked participants if they had used any of the following nine drugs in the past month (binary yes/no): fentanyl, heroin, methamphetamine, cocaine, prescription opioids (not as prescribed), buprenorphine/Suboxone (not as prescribed), sedative/tranquilizers, other stimulants, and other medications (not as prescribed). We also created a count variable for the number of drugs/drug combinations used. Participants also reported if they had injected any drugs in the past month (yes/no). We included three indicators for drug treatment utilization in the past month: any treatment, treatment with medication for opioid use disorder (MOUD), and any non-MOUD treatment (individual counseling with a therapist or addiction counselor, group counseling with a therapist or addiction counselor, consultation with a medical doctor or nurse practitioner, and mutual support meetings including Alcoholics Anonymous, Narcotics Anonymous, or SMART recovery).

Analysis.

For this analysis, we restricted the sample to participants who had used drugs in the past month (n=143 removed). We also removed four transgender participants from the analytic sample, as gender was a correlate of interest, and the sample size was too small for analysis. Finally, we removed 21 participants with missingness on the volatility measures, as it was the main measure of interest in this analysis. This process yielded a final sample of 419. We selected variables to include in bivariate analyses a priori from known and expected relationships based on the literature. We used chi-square and t-tests to assess the associations between each variable and past month overdose. We further estimated bivariate associations between the use volatility score and other variables, using t-tests and one-way ANOVAs for categorical variables and Pearson’s correlations for continuous ones. After assessing bivariate associations, we then estimated two multivariable regression models. First, we estimated a multivariable linear regression model to assess adjusted associations between correlates of interest and the volatile drug use scores. We then estimated a multivariable logistic regression model for overdose to test the association of volatile drug use with overdose when adjusting for other factors. For both regression models, we included all variables that were significantly associated with the outcome in the bivariate analyses at the p<0.1 level, apart from the substance use and treatment variables. For both models, we decided to include the drug count variable instead of a measure of each individual substance for parsimony. For the drug treatment variables, we included only the any treatment measure in the regression models. There was substantial overlap in participants who reported MOUD and non-MOUD treatment in the past month (94% reported either both or neither form of treatment, data not shown), so we included only the any treatment variable to avoid collinearity. We clustered standard errors by the organization participants were recruited from to account for the study design in the model. Statistical analyses were performed using Stata 17 (StataCorp, 2017).

Results

The sample was 52% male, 51% white, 49% single, 88% heterosexual, and 86% were not currently working (Table 1). The average age was 43 years old and roughly half (53%) of the sample lived in a large metropolitan area. One quarter of the sample reported that they were experiencing homelessness, 30% were experiencing hunger, and 14% had experienced at least one overdose in the past month. Heroin was the most commonly used drug (70%) followed by fentanyl (40%), methamphetamine (34%), and cocaine (34%). Sixty-two percent injected drugs in the past month. Over half (55%) of participants reported any drug treatment, 47% reported MOUD treatment, and 50% reported non-MOUD treatment.

Table 1.

Sample Characteristics and Bivariate Associations Between Overdose and Volatile Drug Use and Sociodemographic, Drug Use, and Treatment Characteristics (N=419).

Total N=419 Overdosed in the Past Month Volatile Drug Use
No Yes p M (SD) p
361 (86.2) 58 (13.8) -- 1.7 (1.4) --
Sociodemographic Characteristics
Age, M (SD) 43.0 (11.8) 43.4 (11.9) 40.4 ( 10.6) 0.070 −0.16* 0.001
Gender
  Woman 202 (48.3) 176 (87.1) 26 (12.9) 0.566 1.7 (1.4) 0.687
  Man 216 (51.7) 184 (85.2) 32 (14.8) 1.6 (1.4)
Race
  White 211 (50.5) 177 (83.9) 34 (16.1) 0.033 1.8 (1.4) 0.086
  Black 91 (21.8) 86 (94.5) 5 (5.5) 1.6 (1.4)
  Other 116 (27.8) 97 (83.6) 19 (16.4) 1.5 (1.3)
Sexual Minority
  No 367 (87.8) 316 (86.1) 51 (13.9) 0.974 1.7 (1.4) 0.928
  Yes 51 (12.2) 44 (86.3) 7 (13.7) 1.7 (1.4)
Education
  Less than High School 124 (29.6) 107 (86.3) 17 (13.7) 0.995 1.4 (1.4) 0.011
  High School Equivalent 178 (42.5) 153 (86.0) 25 (14.0) 1.7 (1.4)
  Some College or More 117 (27.9) 101 (86.3) 16 (13.7) 1.9 (1.4)
Employment
  Full time 21 (5.0) 17 (81.0) 4 (19.0) 0.704 2.2 (1.5) 0.205
  Part-time/Temporary 36 (8.6) 32 (88.9) 4 (11.1) 1.6 (1.2)
  Not working 362 (86.4) 312 (86.2) 51 (13.8) 1.6 (1.4)
Homeless
  No 313 (75.2) 276 (88.2) 37 (11.8) 0.029 1.7 (1.4) 0.732
  Yes 103 (24.8) 82 (79.6) 20 (20.4) 1.7 (1.4)
Hunger at least weekly
  No 294 (70.2) 259 (88.1) 35 (11.9) 0.078 1.5 (1.4) <0.001
  Yes 125 (29.8) 102 (81.6) 23 (18.4) 2.0 (1.3)
Single
  No 212 (50.8) 178 (84.0) 34 (16.0) 0.152 1.8 (1.4) 0.151
  Yes 205 (49.2) 182 (88.8) 23 (11.2) 1.6 (1.3)
Transactional Sex
  No 380 (90.7) 331 (87.1) 49 (12.6) 0.080 1.6 (1.4) <0.001
  Yes 39 (9.3) 33 (76.9) 9 (23.1) 2.4 (1.2)
Urbanicity
  Large Metropolitan 221 (53.0) 191 (86.4) 30 (13.6) 0.718 1.7 (1.4) 0.942
  Small Metropolitan 120 (28.8) 101 (84.2) 19 (15.8) 1.6 (1.4)
  Non-Metropolitan 76 (18.2) 67 (88.2) 9 (11.8) 1.7 (1.3)
Past Month Drug Use
Fentanyl
  No 248 (59.6) 225 (90.7) 23 (9.3) 0.001 1.5 (1.3) <0.001
  Yes 168 (40.4) 134 (79.8) 34 (20.2) 1.9 (1.4)
Heroin
  No 124 (29.7) 116 (93.5) 8 (6.5) 0.004 1.4 (1.3) 0.012
  Yes 294 (70.3) 244 (83.0) 50 (17.0) 1.8 (1.4)
Methamphetamine
  No 276 (66.0) 248 (89.9) 28 (10.1) 0.002 1.6 (1.3) 0.336
  Yes 142 (34.0) 112 (78.9) 30 (21.1) 1.8 (1.4)
Cocaine
  No 253 (66.0) 225 (88.9) 28 (11.1) 0.040 1.6 (1.4) 0.076
  Yes 165 (34.0) 139 (81.8) 30 (18.2) 1.8 (1.4)
Prescription Opioids
  No 349 (83.7) 307 (88.0) 42 (12.0) 0.028 1.6 (1.4) 0.005
  Yes 68 (16.3) 53 (77.9) 15 (22.1) 2.1 (1.3)
Buprenorphine/Suboxone
  No 384 (91.6) 338 (88.0) 46 (12.0) <0.001 1.6 (1.4) 0.271
  Yes 35 (8.5) 23 (65.7) 12 (34.3) 1.9 (1.5)
Sedatives/Tranquilizers
  No 316 (75.6) 275 (87.0) 41 (13.0) 0.348 1.5 (1.4) <0.001
  Yes 102 (24.4) 85 (83.3) 17 (16.7) 2.1 (1.4)
Stimulants
  No 403 (96.4) 350 (86.8) 53 (13.2) 0.026 1.6 (1.4) 0.083
  Yes 15 (3.6) 10 (66.7) 5 (33.3) 2.3 (1.5)
Other Medications
  No 369 (88.1) 321 (87.0) 48 (13.0) 0.179 1.6 (1.4) 0.002
  Yes 50 (11.9) 40 (80.0) 10 (20.0) 2.2 (1.3)
Drug Count, M (SD) 2.5 (1.7) 2.3 (1.7) 3.5 (1.8) <0.001 0.23* <0.001
Injected Drugs
  No 160 (38.2) 148 (92.5) 12 (7.5) 0.003 1.4 (1.3) 0.006
  Yes 259 (61.8) 213 (82.2) 46 (17.8) 1.8 (1.4)
Drug Treatment in the Past Month
Any Treatment
  No 189 (45.1) 153 (81.0) 36 (19.0) 0.005 1.7 (1.5) 0.536
  Yes 230 (54.9) 208 (90.4) 22 (9.6) 1.6 (1.3)
MOUD Treatment
  No 219 (52.8) 182 (83.1) 37 (16.9) 0.070 1.7 (1.4) 0.648
  Yes 196 (47.2) 175 (89.3) 21 (10.7) 1.6 (1.3)
Non-MOUD Treatment
  No 207 (50.0) 170 (82.1) 37 (17.9) 0.015 1.7 (1.4) 0.671
  Yes 207 (50.0) 187 (90.3) 20 (9.7) 1.6 (1.3)
Open in a new tab
*

indicates a Pearson’s correlation coefficient

Volatile drug use was common, with 54% wanting to use drugs more, 46% using drugs more, 44% reporting different triggers for using, and 24% using drugs that they did not typically use. The average use volatility score was 1.7. In the bivariate analyses, education (less than high school: 1.4, high school equivalent: 1.7, some college or more: 1.9, p=0.011), experiencing hunger (2.0 vs 1.5, p<0.001), engaging in transactional sex (2.4 vs 1.6, p<0.001), and injecting drugs (1.8 vs 1.4, p=0.006) were associated with more volatile drug use. Volatile drug use scores also differed marginally by race (white: 1.8, Black: 1.6, another race: 1.5, p=0.086). Age was negatively correlated with volatile drug use (r=-0.16, p=0.001). The number of drugs used was positively correlated with volatile drug use (r=0.23, p<0.001). In a multivariable linear regression model (Table 2), experiencing weekly hunger (β=0.43, 95% Confidence Interval: 0.16, 0.70), transactional sex (β=0.57, 95% CI: 0.07, 1.07), having some college education or more (β=0.48, 95% CI: 0.13, 0.84), and using a greater number of drugs (β=0.14, 95% CI: 0.04, 0.24) remained significantly associated with higher volatile drug use scores. Older age (β=-0.02, 95% CI: −0.03, −0.00) remained significantly associated with lower volatile drug use scores.

Table 2.

Multivariable Linear Regression of Volatile Drug Use Scores on Sociodemographic and Drug Use Characteristics.

Beta p-value 95% Confidence Interval
Age −0.02 0.021 −0.03, −0.00
Race
  White Reference -- --
  Black 0.19 0.427 −0.29, 0.67
  Other −0.15 0.333 −0.46, 0.16
Education
  Less than HS Reference -- --
  HS equivalent 0.28 0.174 −0.13, 0.70
  Some college or more 0.48 0.010 0.13, 0.84
Hunger 0.43 0.003 0.16, 0.70
Transactional sex 0.57 0.026 0.07, 1.07
Number of drugs used 0.14 0.007 0.04, 0.24
Injected drugs −0.07 0.708 −0.48, 0.33
Open in a new tab

Reported overdoses were significantly less common among Black participants than among other groups (Black: 5%, white: 16%, other: 16%, p=0.033). Reported overdoses were significantly more common among persons who reported experiencing homelessness (20% vs 12%, p=0.029) than those who did not. Reporting an overdose was significantly associated with using more drugs (3.5 vs 2.3, p<0.001) and injecting drugs (18% vs 8%, p=0.003). Treatment was associated with a lower prevalence of overdose (any treatment: 10% vs 19%, p=0.005; MOUD: 11% vs 17%, p=0.070; non-MOUD: 10% vs 18%, p=0.015). In the multivariable analysis, volatile drug use (adjusted Odds Ratio (aOR) = 1.42, 95% CI: 1.22, 1.65) and the number of drugs used (aOR = 1.32, 95% CI: 1.13, 1.54) were associated with higher odds of overdose (Table 3).

Table 3.

Multivariable Logistic Regression of Overdose on Volatile Drug Use, Sociodemographic Characteristics, and Other Drug Use Characteristics.

Adjusted Odds Ratio p-value 95% Confidence Interval
Volatile Drug Use 1.42 <0.001 1.22, 1.65
Age 1.02 0.188 0.99, 1.05
Race
  White Reference -- --
  Black 0.36 0.028 0.14, 0.90
  Other 1.44 0.266 0.75, 2.77
Homelessness 1.52 0.209 0.79, 2.91
Hunger 1.01 0.983 0.52, 1.94
Transactional sex 1.42 0.427 0.60, 3.40
Number of drugs used 1.32 0.001 1.13, 1.54
Injected drugs 0.98 0.945 0.60, 1.60
Any drug treatment 0.61 0.174 0.30, 1.24
Open in a new tab

Discussion

Among a multisite sample of service-engaged PWUD, we documented different aspects of volatile drug use during the first year of the COVID-19 pandemic. More than half (54%) the sample reported increased desires for drugs, 46% used more drugs, 44% had different triggers for drug use, and 24% reported using different drugs than before the pandemic. Using more drugs was associated with both more volatile drug use and overdose risk. Volatile drug use during the pandemic was significantly associated with experiencing an overdose in the past month. For each additional volatility indicator endorsed, participants had 43% increased odds of experiencing an overdose in the past month. Volatile drug use appears to be an important driver of overdose risk during the COVID-19 pandemic.

Structural vulnerabilities had a significant association with volatile drug use within this sample. Both food insecurity and engaging in transactional sex were significantly associated with more volatile drug use. Structural vulnerability, captured through food insecurity and homelessness, was also significantly associated with overdose risk in bivariate analyses. These findings are consistent with existing literature on the role of structural vulnerability in overdose (Walters et al., 2022; Zhen-Duan et al., 2022) (Pérez-Figueroa et al., 2022) (Milaney et al., 2021; Park et al., 2018; Yamamoto et al., 2019). Collectively, these findings highlight how structural vulnerabilities and drug market factors intersect to increase overdose and other risks for the most marginalized PWUD. Public health efforts must address these underlying basic needs through low threshold housing and nutrition programs to impact downstream health consequences and reduce overdose rates among PWUD. During COVID-19, many time limited programs were introduced and later removed that targeted housing and food insecurity, however these programs were broadly insufficient to address the population level needs in the US. Long-term investment into a range of housing, nutrition, and employment programs are essential structural solutions to key drivers of drugs use and overdose. Housing first programs and supportive housing models are evidence-based strategies to reduce housing insecurity and improve health (Rogg et al., 2014; Woodhall-Melnik & Dunn, 2016). Large scale implementation of such housing programs is needed to reduce the burden of homelessness on PWUD. At a policy level, exclusion of PWUD from public housing due to criminal records is well documented to increase recidivism (Whittle, 2016). Removal of bans on public housing access are critical policy changes needed to increase housing access among PWUD. Similarly, drug-related criminal records are a barrier to government assistance and employment opportunities. As of April 2022, 21 states had full or partial bans on previously incarcerated individuals with felony convictions receiving Temporary Assistance for Needy Families (TANF) and 24 states had similar bans on the receipt of Supplemental Nutrition Assistance Program (SNAP) benefits (Burnside, 2022), despite evidence that accessing such benefits decreases recidivism (Harding et al., 2014; Holtfreter et al., 2004; Tuttle, 2019; Yang, 2017). SNAP beneficiaries are also able to receive employment and training services aimed at increasing job readiness and employment under the SNAP E&T program. The exclusion of PWUD with criminal convictions from these programs is a critical barrier to reducing poverty and improving health that needs to be addressed through policy change.

Ensuring a safer and consistent drug supply is a key intervention to address market-driven volatile drug use and reduce overdose rates. Safer supply interventions are a harm reduction strategy where organizations provide a safe and consistent supply of unadulterated prescription drugs, including opioids, stimulants and other drugs, to PWUD to use rather than illicit drugs (Ivsins et al., 2020; Tyndall, 2020a). Such interventions work to curb overdose rates and other drug-related harms by eliminating harmful adulterants from the drug supply (Ivsins et al., 2020; Tyndall, 2020b). In the case of opioids, this often means ensuring access to opioids free from illicitly manufactured fentanyl. Recent studies have shown that safer supply interventions confer a range of benefits to participants, including improved self-reported health and wellbeing, reduced drug use risk behaviors, reduced overdose risk, and fewer hospitalizations (Gomes et al., 2022; Ivsins et al., 2021; McNeil et al., 2022; Schmidt et al., 2023). While most existing safer supply interventions have focused on opioids, these models can be applied to a broader range of substances to mitigate the harms of volatile drug use incited by drug market factors. As fentanyl continues to adulterate both stimulant and opioid supplies amongst others, safer supply programs could be utilized across several types of substances (Fleming et al., 2020; Ivsins et al., 2020; Tyndall, 2020b). Given that the results from this study showed that PWUD are tending to use more drugs as well as use drugs that they were not typically using, safer supply would decrease one’s risk of overdose or using adulterated drugs across different substances, which would address both increases in frequency of use or unfamiliarity with use. It is important that safer supply interventions meet the full spectrum of needs of PWUD to maximize their effect, as programs that do not adequately address needs like pain management will require participants to supplement with illicit drugs, undermining potential benefits (McNeil et al., 2022).

Limitations.

First, participants were recruited through harm reduction and treatment providers, meaning the sample only reflects service engaged PWUD. This is likely not representative of PWUD who are disconnected from services. Our results may also be affected by highly vulnerable PWUD being disconnected from services due to broad service disruptions caused by the pandemic. PWUD who are most marginalized may have been more likely to be disengaged from programs and would therefore have been missed by our survey. Recall bias may be present in this study as the data was cross-sectional. There are also limitations to the volatile drug use measure. We created a composite score for four binary variables, which may not adequately capture individual differences in volatile drug use. Individuals may have experienced differing degrees of changes, which would have been missed by the binary measures. Further work with more nuanced measures is needed to fully understand the role of volatile drug use in overdose risk. Measuring volatility in the context of polysubstance use is also challenging. Changes in the number of drugs used may not reflect volatility in the same way for PWUD seeking to use multiple types of drugs as it would for PWUD seeking only one drug type.

Conclusions.

Volatile drug use appears to be an important contributor to overdose risk in the context of the COVID-19 pandemic. This is particularly relevant in the context of an opaque unregulated drug market where additional volatility may be introduced by unintended consumption of adulterants or other unexpected substances. Structural vulnerability appeared to be a key driver of volatile drug use in this sample. Interventions to address underlying structural causes, as well as harmful characteristics of the drug supply, are needed to mitigate overdose fatality rates in the United States.

Funding:

The study was supported by Bloomberg Philanthropies. STA is also supported by the National Institutes of Health (K01DA046234). The funders were not involved in the collection of study data, the drafting of the manuscript, or the decision to submit the study for publication.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Disclosures: Dr. Sherman has served as an expert witness in opioid litigation cases. The authors have no other financial interests/conflicts of interest to disclose.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Dr. Sherman has served as an expert witness in opioid litigation cases. The authors have no other financial interests/conflicts of interest to disclose.

Ethics approval

The authors declare that they have obtained ethics approval from an appropriately constituted ethics committee/institutional review board where the research entailed animal or human participation.

Johns Hopkins Bloomberg School of Public Health Institutional Review Board (Study IRB00012280)

The authors declare that the work reported herein did not require ethics approval because it did not involve animal or human participation.

References

  1. Ahmad FB, Rossen LM, Sutton P, 2021. Provisional drug overdose death counts. National Center for Health Statistics. [Google Scholar]
  2. Alexander GC, Stoller KB, Haffajee RL, Saloner B, 2020. An epidemic in the midst of a pandemic: opioid use disorder and COVID-19. American College of Physicians, pp. 57–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alexander RS, Canver BR, Sue KL, Morford KL, 2022. Xylazine and overdoses: trends, concerns, and recommendations. American Journal of Public Health 112(8), 1212–1216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ali F, Russell C, Nafeh F, Rehm J, LeBlanc S, Elton-Marshall T, 2021. Changes in substance supply and use characteristics among people who use drugs (PWUD) during the COVID-19 global pandemic: A national qualitative assessment in Canada. International Journal of Drug Policy 93, 103237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bartholomew TS, Nakamura N, Metsch LR, Tookes HE, 2020. Syringe services program (SSP) operational changes during the COVID-19 global outbreak. The International journal on drug policy 83, 102821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bennett AS, Townsend T, Elliott L, 2022. The COVID-19 pandemic and the health of people who use illicit opioids in New York City, the first 12 months. International Journal of Drug Policy 101, 103554. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Blanckaert P, Cannaert A, Van Uytfanghe K, Hulpia F, Deconinck E, Van Calenbergh S, Stove C, 2020. Report on a novel emerging class of highly potent benzimidazole NPS opioids: chemical and in vitro functional characterization of isotonitazene. Drug Testing and Analysis 12(4), 422–430. [DOI] [PubMed] [Google Scholar]
  8. Blanco C, Compton WM, Volkow ND, 2021. Opportunities for research on the treatment of substance use disorders in the context of COVID-19. JAMA psychiatry 78(4), 357–358. [DOI] [PubMed] [Google Scholar]
  9. Bourgois P, Hart LK, 2011. Commentary on Genberg et al. (2011): the structural vulnerability imposed by hypersegregated US inner-city neighborhoods--a theoretical and practical challenge for substance abuse research. Addiction (Abingdon, England) 106(11), 1975–1977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bourgois P, Holmes SM, Sue K, Quesada J, 2017. Structural vulnerability: operationalizing the concept to address health disparities in clinical care. Academic medicine: journal of the Association of American Medical Colleges 92(3), 299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Bowles JM, McDonald K, Maghsoudi N, Thompson H, Stefan C, Beriault DR, Delaney S, Wong E, Werb D, 2021. Xylazine detected in unregulated opioids and drug administration equipment in Toronto, Canada: clinical and social implications. Harm reduction journal 18(1), 1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Burnside A. (2022). No More Double Punishments: Lifting the Ban on SNAP and TANF for People with Prior Felony Drug Convictions. Center for Law and Social Policy, April, https://www.clasp.org/wp-content/uploads/2022/04/2022Apr_No-More-Double-Punishments.pdf [Google Scholar]
  13. Caspi C, Seligman H, Berge J, Ng S, Krieger J, 2022. COVID-19 pandemic-era nutrition assistance: impact and sustainability. Health Affairs. [Google Scholar]
  14. Chin I, Karb R, Goldman RE, 2022. Rhode Island’s Response for COVID-Positive Individuals Experiencing Homelessness and Housing Insecurity: A Qualitative Process Evaluation. Journal of Health Care for the Poor and Underserved 33(2), 857–869. [DOI] [PubMed] [Google Scholar]
  15. Cooney P, Shaefer HL, 2021. Material hardship and mental health following the COVID-19 relief bill and American rescue plan act. Ann Arbor, Michigan: Poverty Solutions, University of Michigan. [Google Scholar]
  16. Czeisler MÉ, Lane RI, Petrosky E, Wiley JF, Christensen A, Njai R, Weaver MD, Robbins R, Facer-Childs ER, Barger LK, 2020. Mental health, substance use, and suicidal ideation during the COVID-19 pandemic—United States, June 24–30, 2020. Morbidity and Mortality Weekly Report 69(32), 1049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Delaney SR, Konforte D, Stefan C, Palaty J, Sun D, McDonald K, Thompson H, Werb D, Beriault DR, 2023. Drug checking services as a surveillance tool for clinical laboratories: Examining trends in the unregulated fentanyl supply. Clinical Biochemistry 111, 11–16. [DOI] [PubMed] [Google Scholar]
  18. European Monitoring Centre for Drugs and Drug Addiction and Europol, 2020. EU Drug Markets:Impact of COVID-19. Publications Office of the European Union, Luxembourg. [Google Scholar]
  19. Ahmad FB, Cisewski JA, Rossen LM, Sutton P, 2023. Provisional drug overdose death counts. Federal, provincial, and territorial Special Advisory Committee on the Epidemic of Opioid Overdoses, 2023. Opioid- and Stimulant-related Harms in Canada. https://health-infobase.canada.ca/substance-related-harms/opioids-stimulants/ [Google Scholar]
  20. Fiorentin TR, Krotulski AJ, Martin DM, Browne T, Triplett J, Conti T, Logan BK, 2019. Detection of cutting agents in drug-positive seized exhibits within the United States. Journal of forensic sciences 64(3), 888–896. [DOI] [PubMed] [Google Scholar]
  21. Fleming T, Barker A, Ivsins A, Vakharia S, McNeil R, 2020. Stimulant safe supply: a potential opportunity to respond to the overdose epidemic. Harm Reduction Journal 17(1), 1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Friedman J, Montero F, Bourgois P, Wahbi R, Dye D, Goodman-Meza D, Shover C, 2022. Xylazine spreads across the US: a growing component of the increasingly synthetic and polysubstance overdose crisis. Drug and alcohol dependence 233, 109380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fuchs JD, Carter HC, Evans J, Graham-Squire D, Imbert E, Bloome J, Fann C, Skotnes T, Sears J, Pfeifer-Rosenblum R, 2021. Assessment of a hotel-based COVID-19 isolation and quarantine strategy for persons experiencing homelessness. JAMA network open 4(3), e210490–e210490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Glick SN, Prohaska SM, LaKosky PA, Juarez AM, Corcorran MA, Des Jarlais DC, 2020. The impact of COVID-19 on syringe services programs in the United States. AIDS and Behavior 24(9), 2466–2468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Gomes T, Kolia G, McCormack D, Sereda A, Kitchen S, Antoniou T, 2022. Clinical outcomes and health care costs among people entering a safer opioid supply program in Ontario. CMAJ : Canadian Medical Association journal = journal de l’Association medicale canadienne 194(36), E1233–E1242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Grebely J, Cerdá M, Rhodes T, 2020. COVID-19 and the health of people who use drugs: What is and what could be? International Journal of Drug Policy 83, 102958. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gupta R, Holtgrave DR, Ashburn MA, 2023. Xylazine—Medical and Public Health Imperatives. New England Journal of Medicine. [DOI] [PubMed] [Google Scholar]
  28. Harding DJ, Wyse JJ, Dobson C, & Morenoff JD (2014). Making ends meet after prison. Journal of Policy Analysis and Management, 33(2), 440–470. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Heimer R, McNeil R, Vlahov D, 2020. A community responds to the COVID-19 pandemic: a case study in protecting the health and human rights of people who use drugs. Journal of Urban Health 97, 448–456. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Holtfreter K, Reisig MD, & Morash M (2004). Poverty, state capital, and recidivism among women offenders. Criminology & Public Policy, 3(2), 185–208. [Google Scholar]
  31. Holmes SM, 2011. Structural vulnerability and hierarchies of ethnicity and citizenship on the farm. Medical anthropology 30(4), 425–449. [DOI] [PubMed] [Google Scholar]
  32. Ivsins A, Boyd J, Beletsky L, McNeil R, 2020. Tackling the overdose crisis: the role of safe supply. International Journal of Drug Policy 80, 102769. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ivsins A, Boyd J, Mayer S, Collins A, Sutherland C, Kerr T, McNeil R, 2021. “I’s helped me a lot, just like to stay alive”: a qualitative analysis of outcomes of a novel hydromorphone tablet distribution program in Vancouver, Canada. Journal of Urban Health 98(1), 59–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Jablonski BB, Casnovsky J, Clark JK, Cleary R, Feingold B, Freedman D, Gray S, Romeiko X, Olabisi LS, Torres M, 2021. Emergency food provision for children and families during the COVID-19 pandemic: examples from five US cities. Applied Economic Perspectives and Policy 43(1), 169–184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Johnson J, Pizzicato L, Johnson C, Viner K, 2021. Increasing presence of xylazine in heroin and/or fentanyl deaths, Philadelphia, Pennsylvania, 2010–2019. Injury prevention 27(4), 395–398. [DOI] [PubMed] [Google Scholar]
  36. Karamouzian M, Dohoo C, Forsting S, McNeil R, Kerr T, Lysyshyn M, 2018. Evaluation of a fentanyl drug checking service for clients of a supervised injection facility, Vancouver, Canada. Harm reduction journal 15(1), 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kleykamp BA, Guille C, Barth KS, McClure EA, 2020. Substance use disorders and COVID-19: the role of telehealth in treatment and research. Journal of social work practice in the addictions 20(3), 248–253. [Google Scholar]
  38. Krotulski AJ, Papsun DM, Kacinko SL, Logan BK, 2020. Isotonitazene quantitation and metabolite discovery in authentic forensic casework. Journal of Analytical Toxicology 44(6), 521–530. [DOI] [PubMed] [Google Scholar]
  39. Larnder A, Saatchi A, Borden SA, Moa B, Gill CG, Wallace B, Hore D, 2022. Variability in the unregulated opioid market in the context of extreme rates of overdose. Drug and Alcohol Dependence 235, 109427. [DOI] [PubMed] [Google Scholar]
  40. Maghsoudi N, Tanguay J, Scarfone K, Rammohan I, Ziegler C, Werb D, Scheim AI, 2022. Drug checking services for people who use drugs: a systematic review. Addiction 117(3), 532–544. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. McLoughlin GM, McCarthy JA, McGuirt JT, Singleton CR, Dunn CG, Gadhoke P, 2020. Addressing food insecurity through a health equity lens: a case study of large urban school districts during the COVID-19 pandemic. Journal of Urban Health 97, 759–775. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. McNeil R, Fleming T, Mayer S, Barker A, Mansoor M, Betsos A, Austin T, Parusel S, Ivsins A, Boyd J, 2022. Implementation of safe supply alternatives during intersecting COVID-19 and overdose health emergencies in British Columbia, Canada, 2021. American journal of public health 112(S2), S151–S158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Midthun KM, Nelson LS, Logan BK, 2021. Levamisole—a toxic adulterant in illicit drug preparations: a review. Therapeutic Drug Monitoring 43(2), 221–228. [DOI] [PubMed] [Google Scholar]
  44. Milaney K, Passi J, Zaretsky L, Liu T, O’Gorman CM, Hill L, Dutton D, 2021. Drug use, homelessness and health: responding to the opioid overdose crisis with housing and harm reduction services. Harm reduction journal 18(1), 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Montanari E, Madeo G, Pichini S, Busardò FP, Carlier J, 2022. Acute intoxications and fatalities associated with benzimidazole opioid (nitazene analog) use: a systematic review. Therapeutic drug monitoring 44(4), 494–510. [DOI] [PubMed] [Google Scholar]
  46. National Housing Law Project. (2021). Federal Moratorium on Evictions for Nonpayment of Rent. https://nlihc.org/sites/default/files/Overview-of-National-Eviction-Moratorium.pdf [Google Scholar]
  47. Nguyen T, Buxton JA, 2021. Pathways between COVID-19 public health responses and increasing overdose risks: A rapid review and conceptual framework. International Journal of Drug Policy 93, 103236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Park JN, Weir BW, Allen ST, Chaulk P, Sherman SG, 2018. Fentanyl-contaminated drugs and non-fatal overdose among people who inject drugs in Baltimore, MD. Harm reduction journal 15(1), 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Pérez-Figueroa R, Obonyo D, Santoscoy S, Surratt H, Lekas H, Lewis C, Lyons J, Amesty S, 2022. Housing instability, structural vulnerability, and non-fatal opioid overdoses among people who use heroin in Washington Heights, New York City. Behavioral Medicine 48(4), 320–330. [DOI] [PubMed] [Google Scholar]
  50. Robinson L, Schlesinger P, Keene DE, 2022. “You Have a Place to Rest Your Head in Peace”: use of hotels for adults experiencing homelessness during the COVID-19 pandemic. Housing Policy Debate 32(6), 837–852. [Google Scholar]
  51. Rosecrans AM, Moen MA, Harris RE, Rice MS, Augustin VS, Stracker NH, Burns KD, Rives ST, Tran KM, Callahan CW, 2022. Implementation of Baltimore City’s COVID-19 isolation hotel. American Journal of Public Health 112(6), 876–880. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Rosenbaum D, Bergh K, Hall L (2023). Temporary Pandemic SNAP Benefits Will End in Remaining 35 States in March 2023. Center on Budget and Policy Priorities. Washington, DC. https://www.cbpp.org/research/food-assistance/temporary-pandemic-snap-benefits-will-end-in-remaining-35-states-in-march. [Google Scholar]
  53. Rog DJ, Marshall T, Dougherty RH, George P, Daniels AS, Ghose SS, & Delphin-Rittmon ME (2014). Permanent supportive housing: assessing the evidence. Psychiatric Services, 65(3), 287–294. [DOI] [PubMed] [Google Scholar]
  54. Russell C, Ali F, Nafeh F, Rehm J, LeBlanc S, Elton-Marshall T, 2021. Identifying the impacts of the COVID-19 pandemic on service access for people who use drugs (PWUD): A national qualitative study. Journal of substance abuse treatment 129, 108374. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Saloner B, Krawczyk N, Solomon K, Allen ST, Morris M, Haney K, Sherman SG, 2022. Experiences with substance use disorder treatment during the COVID-19 pandemic: Findings from a multistate survey. International Journal of Drug Policy 101, 103537. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Scallan E, Bodkin C, Wiwcharuk J, O’Shea T, Lennox R, 2022. Finding stability amidst the COVID-19 pandemic: The impact of emergency temporary housing for people who use drugs. Drug and Alcohol Review 41(1), 7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Schmidt RA, Kaminski N, Kryszajtys DT, Rudzinski K, Perri M, Guta A, Benoit AC, Bayoumi AM, Challacombe L, Hales J, 2023. T don’t chase drugs as much anymore, and I’m not dead’: Client reported outcomes associated with safer opioid supply programs in Ontario, Canada. Drug and Alcohol Review 42(7), 1825–1837. [DOI] [PubMed] [Google Scholar]
  58. Sisco E, Appley M, 2023. Identification of the veterinary sedative medetomidine in combination with opioids and xylazine in Maryland. Journal of Forensic Sciences. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Solomon N, Hayes J, 2017. Levamisole: a high performance cutting agent. Academic forensic pathology 7(3), 469–476. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Stack E, Leichtling G, Larsen JE, Gray M, Pope J, Leahy JM, Gelberg L, Seaman A, Korthuis PT, 2021. The impacts of COVID-19 on mental health, substance use, and overdose concerns of people who use drugs in rural communities. Journal of addiction medicine 15(5), 383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. StataCorp, L., 2017. Stata statistical software (version release 15). College Station, TX: StataCorp LP. [Google Scholar]
  62. Thangada S, Clinton HA, Ali S, Nunez J, Gill JR, Lawlor RF, Logan SB, 2021. Notes from the field: xylazine, a veterinary tranquilizer, identified as an emerging novel substance in drug overdose deaths—Connecticut, 2019–2020. Morbidity and Mortality Weekly Report 70(37), 1303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Tobias S, Grant CJ, Laing R, Lysyshyn M, Buxton JA, Tupper KW, Wood E, Ti L (2023). What impact did the COVID-19 pandemic have on the variability of fentanyl concentrations in the Vancouver, Canada illicit drug supply? An interrupted time-series analysis. BMJ Public Health, 1(1). [Google Scholar]
  64. Tupper KW, McCrae K, Garber I, Lysyshyn M, Wood E, 2018. Initial results of a drug checking pilot program to detect fentanyl adulteration in a Canadian setting. Drug and alcohol dependence 190, 242–245. [DOI] [PubMed] [Google Scholar]
  65. Tuttle C (2019). Snapping back: Food stamp bans and criminal recidivism. American Economic Journal: Economic Policy, 11(2), 301–327. [Google Scholar]
  66. Tyndall M, 2020a. A safer drug supply: a pragmatic and ethical response to the overdose crisis. CMAJ : Canadian Medical Association journal = journal de l’Association medicale canadienne 192(34), E986–E987. [Google Scholar]
  67. Tyndall M, 2020b. Safer opioid distribution in response to the COVID-19 pandemic. International Journal of Drug Policy 83, 102880. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. United States Department of Agriculture. (2023). SNAP Benefits - COVID-19 Pandemic and Beyond. https://www.fns.usda.gov/snap/benefit-changes-2021. Accessed February 12, 2024.
  69. Walters SM, Bolinski RS, Almirol E, Grundy S, Fletcher S, Schneider J, Friedman SR, Ouellet LJ, Ompad DC, Jenkins W, 2022. Structural and community changes during COVID-19 and their effects on overdose precursors among rural people who use drugs: a mixed-methods analysis. Addiction Science & Clinical Practice 17(1), 1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Wei J, Wachuku C, Berk-Krauss J, Steele KT, Rosenbach M, Messenger E, 2023. Severe cutaneous ulcerations secondary to xylazine (tranq): A case series. JAAD Case Reports 36, 89–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Whittle TN (2018). Felony collateral sanctions effects on recidivism: A literature review. Criminal Justice Policy Review, 29(5), 505–524. [Google Scholar]
  72. Woodhall-Melnik JR, & Dunn JR (2016). A systematic review of outcomes associated with participation in Housing First programs. Housing Studies, 31(3), 287–304. [Google Scholar]
  73. Yamamoto A, Needleman J, Gelberg L, Kominski G, Shoptaw S, Tsugawa Y, 2019. Association between homelessness and opioid overdose and opioid-related hospital admissions/emergency department visits. Social science & medicine 242, 112585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Yang CS (2017). Does public assistance reduce recidivism?. American Economic Review, 107(5), 551–555. [DOI] [PubMed] [Google Scholar]
  75. Zhen-Duan J, Gade N, Falgas-Bagué I, Sue KL, DeJonckheere M, Alegría M, 2022. Using a structural vulnerability framework to understand the impact of COVID-19 on the lives of Medicaid beneficiaries receiving substance use treatment in New York City. Health Services Research 57(5), 1104–1111. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES