Aims To identify childhood and adolescent factors differentiating heavy alcohol users in early adulthood from more moderate users or abstainers. Design Low-income participants followed from birth to age 28 years. Participants A total of 178 adults (95 males) who were first-born children of low-income mothers recruited in Minneapolis, Minnesota, during their third trimester of pregnancy. Measurements Maternal hostility (24/42 months), externalizing and internalizing behavior problems (9 years), peer acceptance and academic achievement (12 years), maternal alcohol use and participants' drinking behavior (16 years), quantity of alcohol use per occasion (19, 23 and 26 years), alcohol use disorders (28 years). Findings For men: (i) higher amounts of alcohol consumption at age 16 increased the odds of being a heavy drinker compared to an abstainer (age 19) and a moderate drinker (ages 23 and 26); (ii) lower achievement scores at age 12 and having a mother who drank more when the participant was age 16 increased the odds of being a heavy drinker compared to moderate drinker (age 26). Higher levels of externalizing behavior problems at age 9 and drinking more when the participants were age 16 increased the odds that men would have a current alcohol use disorder at age 28. For women: (i) drinking more at age 16 increased the odds of being a heavy drinker compared to being either an abstainer or a moderate drinker (age 26); (ii) having higher levels of achievement at age 12 increased the odds of being a heavy drinker compared to an abstainer at age 23. Adolescent alcohol use mediated the relation between externalizing behavior at age 9 and alcohol use at age 26 for women. Conclusions Problem drinking may be the result of a long-term developmental process wherein childhood externalizing behavior problems sets a pathway leading to heavy drinking during and after adolescence.
Concerns about opioid-related adverse events, including overdose, prompted the Veterans Health Administration (VHA) to launch an Opioid Safety Initiative and Overdose Education and Naloxone Distribution program. To mitigate risks associated with opioid prescribing, a holistic approach that takes into consideration both risk factors (e.g., dose, substance use disorders) and risk mitigation interventions (e.g., urine drug screening, psychosocial treatment) is needed. This article describes the Stratification Tool for Opioid Risk Mitigation (STORM), a tool developed in VHA that reflects this holistic approach and facilitates patient identification and monitoring. STORM prioritizes patients for review and intervention according to their modeled risk for overdose/suicide-related events and displays risk factors and risk mitigation interventions obtained from VHA electronic medical record (EMR)-data extracts. Patients' estimated risk is based on a predictive risk model developed using fiscal year 2010 (FY2010: 10/1/2009-9/30/2010) EMR-data extracts and mortality data among 1,135,601 VHA patients prescribed opioid analgesics to predict risk for an overdose/suicide-related event in FY2011 (2.1% experienced an event). Cross-validation was used to validate the model, with receiver operating characteristic curves for the training and test data sets performing well (>.80 area under the curve). The predictive risk model distinguished patients based on risk for overdose/suicide-related adverse events, allowing for identification of high-risk patients and enrichment of target populations of patients with greater safety concerns for proactive monitoring and application of risk mitigation interventions. Results suggest that clinical informatics can leverage EMR-extracted data to identify patients at-risk for overdose/suicide-related events and provide clinicians with actionable information to mitigate risk. (PsycINFO Database Record
Objective To examine the associations between stopping treatment with opioids, length of treatment, and death from overdose or suicide in the Veterans Health Administration. Design Observational evaluation. Setting Veterans Health Administration. Participants 1 394 102 patients in the Veterans Health Administration with an outpatient prescription for an opioid analgesic from fiscal year 2013 to the end of fiscal year 2014 (1 October 2012 to 30 September 2014). Main outcome measures A multivariable Cox non-proportional hazards regression model examined death from overdose or suicide, with the interaction of time varying opioid cessation by length of treatment (≤30, 31-90, 91-400, and >400 days) as the main covariates. Stopping treatment with opioids was measured as the time when a patient was estimated to have no prescription for opioids, up to the end of the next fiscal year (2014) or the patient’s death. Results 2887 deaths from overdose or suicide were found. The incidence of stopping opioid treatment was 57.4% (n = 799 668) overall, and based on length of opioid treatment was 32.0% (≤30 days), 8.7% (31-90 days), 22.7% (91-400 days), and 36.6% (>400 days). The interaction between stopping treatment with opioids and length of treatment was significant (P<0.001); stopping treatment was associated with an increased risk of death from overdose or suicide regardless of the length of treatment, with the risk increasing the longer patients were treated. Hazard ratios for patients who stopped opioid treatment (with reference values for all other covariates) were 1.67 (≤30 days), 2.80 (31-90 days), 3.95 (91-400 days), and 6.77 (>400 days). Descriptive life table data suggested that death rates for overdose or suicide increased immediately after starting or stopping treatment with opioids, with the incidence decreasing over about three to 12 months. Conclusions Patients were at greater risk of death from overdose or suicide after stopping opioid treatment, with an increase in the risk the longer patients had been treated before stopping. Descriptive data suggested that starting treatment with opioids was also a risk period. Strategies to mitigate the risk in these periods are not currently a focus of guidelines for long term use of opioids. The associations observed cannot be assumed to be causal; the context in which opioid prescriptions were started and stopped might contribute to risk and was not investigated. Safer prescribing of opioids should take a broader view on patient safety and mitigate the risk from the patient’s perspective. Factors to address are those that place patients at risk for overdose or suicide after beginning and stopping opioid treatment, especially in the first three months.
Substance use disorders are highly prevalent, debilitating conditions for which effective pharmacotherapies exist with a broad evidence base, yet pharmacotherapy for the treatment of addiction disorders is underutilized. The goals of this review are to describe the barriers that may contribute to poor adoption and utilization of pharmacotherapy for alcohol and opioid dependence at the system, provider, and patient level and to discuss ways to overcome those barriers. Multifaceted efforts directed at all three levels may be needed to speed pharmacotherapy adoption. More research is needed to help us better understand barriers from patients’ perspectives. Strategies to promote adoption of pharmacotherapy for addiction disorders should be modified to fit the needs of the practice, system, and individual patients. Pharmacotherapy is a valuable tool in the clinical armamentarium of addiction treatment; thus, overcoming barriers to implementation may improve clinical and social outcomes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.