person:cerdam01 or freids01 or hamill07 or krawcn01
A Taxonomy of Hospital-Based Addiction Care Models: a Scoping Review and Key Informant Interviews
BACKGROUND:There is pressing need to improve hospital-based addiction care. Various models for integrating substance use disorder care into hospital settings exist, but there is no framework for describing, selecting, or comparing models. We sought to fill that gap by constructing a taxonomy of hospital-based addiction care models based on scoping literature review and key informant interviews. METHODS:Methods included a scoping review of the literature on US hospital-based addiction care models and interventions for adults, published between January 2000 and July 2021.Â We conducted semi-structured interviews with 15 key informants experienced in leading, implementing, evaluating, andpracticing hospital-based addiction care to explore model characteristics, including their perceived strengths, limitations,Â and implementation considerations. We synthesized findings from the literature review and interviews to construct a taxonomy of model types. RESULTS:Searches identified 2,849 unique abstracts. Of these, we reviewed 280 full text articles, of which 76 were included in the final review. We added 8 references from reference lists and informant interviews, and 4 gray literature sources. We identified six distinct hospital-based addiction care models. Those classified as addiction consult models include (1) interprofessional addiction consult services, (2) psychiatry consult liaison services, and (3) individual consultant models. Those classified as practice-based models, wherein general hospital staff integrate addiction care into usual practice, include (4) hospital-based opioid treatment and (5) hospital-based alcohol treatment. The final type was (6) community-based in-reach, wherein community providers deliver care. Models vary in their target patient population, staffing, and core clinical and systems change activities. Limitations include that some models have overlapping characteristics and variable ways of delivering core components. DISCUSSION/CONCLUSIONS:A taxonomy provides hospital clinicians and administrators, researchers, and policy-makers with a framework to describe, compare, and select models for implementing hospital-based addiction care and measure outcomes.
Does recreational cannabis legalization change cannabis use patterns? Evidence from secondary school students in Uruguay
BACKGROUND AND AIMS/OBJECTIVE:In 2013, Uruguay became the first country to legalize and regulate the production and distribution of cannabis for recreational use. We measured whether Uruguay's non-commercial model of recreational cannabis legalization was associated with changes in the prevalence of risky and frequent cannabis use among secondary school students. DESIGN/METHODS:We used data from repeated cross-sectional surveys of secondary students in Uruguay and Chile (2007-2018). Using a difference-in-difference approach, we evaluated changes in the prevalence of past-year, past-month, any risky and frequent cannabis use following enactment (2014) and implementation (2016) of cannabis legalization among the full sample of secondary students and among students who reported past-year/month use. We examined changes separately for students aged 12-17, and students for whom cannabis became legally accessible, ages 18-21. SETTING/METHODS:Uruguay and Chile (2007-2018). PARTICIPANTS/METHODS:grade (n=204,730). MEASUREMENTS/METHODS:Past-year and past-month cannabis use; any risky cannabis use measured with the Cannabis Abuse Screening Test (CAST); and frequent cannabis use (10+ days in the past-month). FINDINGS/RESULTS:We found a decrease in past-year and past-month use following enactment or implementation. Among students ages 18-21, post-enactment, we observed a transitory increase in 2014 that decreased thereafter for: any risky use among those who reported past-year use (prevalence difference [PD]=13.5%; 95% confidence interval [CI]: 2.0, 24.9), frequent use in the full sample (PD=4.5%; 95%CI: 1.0, 8.1), and frequent use among those who reported past-month use (PD=16.8%; 95%CI: 1.9, 31.8). CONCLUSION/CONCLUSIONS:The legalization of recreational cannabis in Uruguay was not associated with overall increases in either past-year/past-month cannabis use or with multi-year changes in any risky and frequent cannabis use among young people.
Substance use disorders and COVID-19: An analysis of nation-wide Veterans Health Administration electronic health records
BACKGROUND:Substance use disorders (SUD) elevate the risk for COVID-19 hospitalization, but studies are inconsistent on the relationship of SUD to COVID-19 mortality. METHODS:Veterans Health Administration (VHA) patients treated in 2019 and evaluated in 2020 for COVID-19 (n=5,556,315), of whom 62,303 (1.1%) tested positive for COVID-19 (COVID-19+). Outcomes were COVID-19+ by 11/01/20, hospitalization, ICU admission, or death within 60 days of a positive test. Main predictors were any ICD-10-CM SUDs, with substance-specific SUDs (cannabis, cocaine, opioid, stimulant, sedative) explored individually. Logistic regression produced unadjusted and covariate-adjusted odds ratios (OR; aOR). RESULTS:Among COVID-19+ patients, 19.25% were hospitalized, 7.71% admitted to ICU, and 5.84% died. In unadjusted models, any SUD and all substance-specific SUDs except cannabis use disorder were associated with COVID-19+(ORs=1.06-1.85); adjusted models produced similar results. Any SUD and all substance-specific SUDs were associated with hospitalization (aORs: 1.24-1.91). Any SUD, cocaine and opioid disorder were associated with ICU admission in unadjusted but not adjusted models. Any SUD, cannabis, cocaine, and stimulant disorders were inversely associated with mortality in unadjusted models (OR=0.27-0.46). After adjustment, associations with mortality were no longer significant. In ad hoc analyses, adjusted odds of mortality were lower among the 49.9% of COVID-19+ patients with SUD who had SUD treatment in 2019, but not among those without such treatment. CONCLUSIONS:In VHA patients, SUDs are associated with COVID-19 hospitalization but not COVID-19 mortality. SUD treatment may provide closer monitoring of care, ensuring that these patients received needed medical attention, enabling them to ultimately survive serious illness.
The impact of syringe services program closure on the risk of rebound HIV outbreaks among people who inject drugs: A modeling study
OBJECTIVE:Despite their effectiveness in preventing the transmission of HIV among people who inject drugs (PWID), syringe services programs (SSPs) in many settings are hampered by social and political opposition. We aimed to estimate the impact of closure and temporary interruption of SSP on the HIV epidemic in a rural US setting. METHODS:Using an agent-based model calibrated to observed surveillance data, we simulated HIV risk behaviors and transmission in adult populations who inject and do not inject drugs in Scott County, Indiana. We projected HIV incidence and prevalence between 2020-2025 for scenarios with permanent closure, delayed closure (one additional renewal for 24â€Šmonths before closure), and temporary closure (lasting 12â€Šmonths) of an SSP in comparison to persistent SSP operation. RESULTS:With sustained SSP operation, we projected an incidence rate of 0.15 per 100 person-years among the overall population [95% simulation interval: 0.06-0.28]. Permanently closing the SSP would cause an average of 58.4% increase in the overall incidence rate during 2021-2025, resulting in a higher prevalence of 60.8% [50.9%-70.6%] (18.7% increase) among PWID by 2025. A delayed closure would increase the incidence rate by 38.9%. A temporary closure would cause 12 (35.3%) more infections during 2020-2021. CONCLUSIONS:Our analysis suggests that temporary interruption and permanent closure of existing SSPs operating in rural US may lead to "rebound" HIV outbreaks among PWID. To reach and sustain HIV epidemic control, it will be necessary to maintain existing and implement new SSPs in combination with other prevention interventions.
Outcomes of a NYC Public Hospital System Low-Threshold Tele-Buprenorphine Bridge Clinic at 1 Year
National Trends and Disparities in Bullying and Suicidal Behavior Across Demographic Subgroups of US Adolescents
OBJECTIVE:Suicidal behavior and bullying victimization are important indicators of adolescent psychological distress, and are patterned by sex, race/ethnicity and sexual identity. This study aimed to estimate trends and disparities in these factors along key demographics. METHOD/METHODS:Youth Risk Behavior Survey data (2015-2019, N=44,066) were collected biennially through national cross-sectional surveys of US school-attending adolescents. Survey-weighted logistic regressions examined disparities in past-year bullying and suicidal behavior, overall and by demographics. RESULTS:Bullying in 2019 was highest for female (vs. male) students (OR=1.82, 95% CI:[1.62, 2.06]), American Indian/Alaskan Native (vs White) students (OR= 1.48, [0.91, 2.41], p>.05), and gay/lesbian (vs heterosexual) students (OR= 2.81, [2.07, 3.81]). Suicidal behavior disparities affected similar groups. There was minimal evidence for shifts in disparities since 2015, with the exception of bullying for gay/lesbian adolescents. The prevalence of bullying victimization among gay and lesbian adolescents went from 31.6% to 44.5% between 2015 and 2019, surpassing the bisexual and "Not Sure" groups to be the sexual identity group with the highest rate of bullying victimization. CONCLUSION/CONCLUSIONS:Interventions that operate on multiple structural levels and empower marginalized youth are needed.
Structural and community changes during COVID-19 and their effects on overdose precursors among rural people who use drugs: a mixed-methods analysis
BACKGROUND:Drug overdose rates in the United States have been steadily increasing, particularly in rural areas. The COVID-19 pandemic and associated mitigation strategies may have increased overdose risk for people who use drugs by impacting social, community, and structural factors. METHODS:The study included a quantitative survey focused on COVID-19 administered to 50 people who use drugs and semi-structured qualitative interviews with 17 people who use drugs, 12 of whom also participated in the quantitative survey. Descriptive statistics were run for the quantitative data. Qualitative coding was line-by-line then grouped thematically. Quantitative and qualitative data were integrated during analysis. RESULTS:Findings demonstrate how COVID-19 disruptions at the structural and community level affected outcomes related to mental health and drug use at the individual level. Themes that emerged from the qualitative interviews were (1) lack of employment opportunities, (2) food and housing insecurity, (3) community stigma impacting health service use, (4) mental health strains, and (5) drug market disruptions. Structural and community changes increased anxiety, depression, and loneliness on the individual level, as well as changes in drug use patterns, all of which are likely to increase overdose risk. CONCLUSION:The COVID-19 pandemic, and mitigation strategies aimed at curbing infection, disrupted communities and lives of people who use drugs. These disruptions altered individual drug use and mental health outcomes, which could increase risk for overdose. We recommend addressing structural and community factors, including developing multi-level interventions, to combat overdose. Trial registration Clinicaltrails.gov: NCT04427202. Registered June 11, 2020: https://clinicaltrials.gov/ct2/show/NCT04427202?term=pho+mai&draw=2&rank=3.
Cycles of Chronic Opioid Therapy Following Mandatory Prescription Drug Monitoring Program Legislation: A Retrospective Cohort Study
BACKGROUND:Mandates for prescriber use of prescription drug monitoring programs (PDMPs), databases tracking controlled substance prescriptions, are associated with reduced opioid analgesic (OA) prescribing but may contribute to care discontinuity and chronic opioid therapy (COT) cycling, or multiple initiations and terminations. OBJECTIVE:To estimate risks of COT cycling in New York City (NYC) due to the New York State (NYS) PDMP mandate, compared to risks in neighboring New Jersey (NJ) counties. DESIGN/METHODS:We estimated cycling risk using Prentice, Williams, and Peterson gap-time models adjusted for age, sex, OA dose, payment type, and county population density, using a life-table difference-in-differences design. Failure time was duration between cycles. In a subgroup analysis, we estimated risk among patients receiving high-dose prescriptions. Sensitivity analyses tested robustness to cycle volume considering only first cycles using Cox proportional hazard models. PARTICIPANTS/METHODS:The cohort included 7604 patients dispensed 12,695 prescriptions. INTERVENTIONS/METHODS:The exposure was the August 2013 enactment of the NYS PDMP prescriber use mandate. MAIN MEASURES/METHODS:We used monthly, patient-level data on OA prescriptions dispensed in NYC and NJ between August 2011 and July 2015. We defined COT as three sequential months of prescriptions, permitting 1-month gaps. We defined recurrence as re-initiation of COT after at least 2 months without prescriptions. The exposure was enactment of the PDMP mandate in NYC; NJ was unexposed. KEY RESULTS/RESULTS:Enactment of the NYS PDMP mandate was associated with an adjusted hazard ratio (HR) for cycling of 1.01 (95% CI, 0.94-1.08) in NYC. For high-dose prescriptions, the risk was 1.16 (95% CI, 1.01-1.34). Sensitivity analyses estimated an overall risk of 1.01 (95% CI, 0.94-1.11) and high-dose risk of 1.09 (95% CI, 0.91-1.31). CONCLUSIONS:The PDMP mandate had no overall effect on COT cycling in NYC but increased cycling risk among patients receiving high-dose opioid prescriptions by 16%, highlighting care discontinuity.
Dynamics of drug overdose in the 20th and 21st centuries: The exponential curve was not inevitable, and continued increases are preventable
Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial
BACKGROUND AND AIMS/OBJECTIVE:In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning-based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level. DESIGN/METHODS:Randomized, population-based, community intervention trial. SETTING/METHODS:Rhode Island, USA. PARTICIPANTS/METHODS:All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes. INTERVENTION/METHODS:Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web-based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community-based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions. MEASUREMENTS/METHODS:The primary outcome is the municipal-level rate of fatal and non-fatal drug overdoses. Fatal overdoses will be defined as unintentional drug-related death; non-fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non-fatal overdose rates in treatment vs. control municipalities. COMMENTS/CONCLUSIONS:The findings will inform the utility of predictive modelling as a tool to improve public health decision-making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services.