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BACKGROUND: Both increases and decreases in patients prescribed daily opioid dose have been linked to increased overdose risk, but associations between 30-day dose trajectories and subsequent overdose risk have not been systematically examined. OBJECTIVE: To examine the associations between 30-day prescribed opioid dose trajectories and fatal opioid overdose risk during the subsequent 15 days. DESIGN: Statewide cohort study using linked prescription drug monitoring program and death certificate data. We constructed a multivariable Cox proportional hazards model that accounted for time-varying prescription-, prescriber-, and pharmacy-level factors. PARTICIPANTS: All patients prescribed an opioid analgesic in California from March to December, 2013 (5,326,392 patients). MAIN MEASURES: Dependent variable: fatal drug overdose involving opioids. Primary independent variable: a 16-level variable denoting all possible opioid dose trajectories using the following categories for current and 30-day previously prescribed daily dose: 0-29, 30-59, 60-89, or ≥90 milligram morphine equivalents (MME). KEY RESULTS: Relative to patients prescribed a stable daily dose of 0-29 MME, large (≥2 categories) dose increases and having a previous or current dose ≥60 MME per day were associated with significantly greater 15-day overdose risk. Patients whose dose decreased from ≥90 to 0-29 MME per day had significantly greater overdose risk compared to both patients prescribed a stable daily dose of ≥90 MME (aHR 3.56, 95%CI 2.24-5.67) and to patients prescribed a stable daily dose of 0-29 MME (aHR 7.87, 95%CI 5.49-11.28). Patients prescribed benzodiazepines also had significantly greater overdose risk; being prescribed Z-drugs, carisoprodol, or psychostimulants was not associated with overdose risk. CONCLUSIONS: Large (≥2 categories) 30-day dose increases and decreases were both associated with increased risk of fatal opioid overdose, particularly for patients taking ≥90 MME whose opioids were abruptly stopped. Results align with 2022 CDC guidelines that urge caution when reducing opioid doses for patients taking long-term opioid for chronic pain.

BACKGROUND: Cannabis use disorder is associated with considerable comorbidity and impairment in functioning, and prevalence is increasing among adults with chronic pain. We aimed to assess the effect of introduction of medical cannabis laws (MCL) and recreational cannabis laws (RCL) on the increase in cannabis use disorder among patients in the US Veterans Health Administration (VHA). METHODS: Data from patients with one or more primary care, emergency, or mental health visit to the VHA in 2005-19 were analysed using 15 repeated cross-sectional VHA electronic health record datasets (ie, one dataset per year). Patients in hospice or palliative care were excluded. Patients were stratified as having chronic pain or not using an American Pain Society taxonomy of painful medical conditions. We used staggered-adoption difference-in-difference analyses to estimate the role of MCL and RCL enactment in the increases in prevalence of diagnosed cannabis use disorder and associations with presence of chronic pain, accounting for the year that state laws were enacted. We did this by fitting a linear binomial regression model stratified by pain, with time-varying cannabis law status, fixed effects for state, categorical year, time-varying state-level sociodemographic covariates, and patient covariates (age group [18-34 years, 35-64 years, and 65-75 years], sex, and race and ethnicity). FINDINGS: Between 2005 and 2019, 3 234 382-4 579 994 patients were included per year. Among patients without pain in 2005, 5·1% were female, mean age was 58·3 (SD 12·6) years, and 75·7%, 15·6%, and 3·6% were White, Black, and Hispanic or Latino, respectively. In 2019, 9·3% were female, mean age was 56·7 (SD 15·2) years, and 68·1%, 18·2%, and 6·5% were White, Black, and Hispanic or Latino, respectively. Among patients with pain in 2005, 7·1% were female, mean age was 57·2 (SD 11·4) years, and 74·0%, 17·8%, and 3·9% were White, Black, and Hispanic or Latino, respectively. In 2019, 12·4% were female, mean age was 57·2 (SD 13·8) years, and 65·3%, 21·9%, and 7·0% were White, Black, and Hispanic or Latino, respectively. Among patients with chronic pain, enacting MCL led to a 0·135% (95% CI 0·118-0·153) absolute increase in cannabis use disorder prevalence, with 8·4% of the total increase in MCL-enacting states attributable to MCL. Enacting RCL led to a 0·188% (0·160-0·217) absolute increase in cannabis use disorder prevalence, with 11·5% of the total increase in RCL-enacting states attributable to RCL. In patients without chronic pain, enacting MCL and RCL led to smaller absolute increases in cannabis use disorder prevalence (MCL: 0·037% [0·027-0·048], 5·7% attributable to MCL; RCL: 0·042% [0·023-0·060], 6·0% attributable to RCL). Overall, associations of MCL and RCL with cannabis use disorder were greater in patients with chronic pain than in patients without chronic pain. INTERPRETATION: Increasing cannabis use disorder prevalence among patients with chronic pain following state legalisation is a public health concern, especially among older age groups. Given cannabis commercialisation and widespread public beliefs about its efficacy, clinical monitoring of cannabis use and discussion of the risk of cannabis use disorder among patients with chronic pain is warranted. FUNDING: NIDA grant R01DA048860, New York State Psychiatric Institute, and the VA Centers of Excellence in Substance Addiction Treatment and Education.

BACKGROUND: Identifying community characteristics associated with firearm assault could facilitate prevention. We investigated the effect of community firearm dealer and alcohol outlet densities on individual risk of firearm assault injury. METHODS: In this density-sampled case-control study of Californians, January 2005-September 2015, cases comprised all residents with a fatal or nonfatal firearm assault injury. For each month, we sampled controls from the state population in a 4:1 ratio with cases. Exposures were monthly densities of county-level pawn and nonpawn firearm dealers and ZIP code-level off-premises alcohol outlets and bars and pubs (bars/pubs). We used case-control-weighted G-computation to estimate risk differences (RD) statewide and among younger Black men, comparing observed exposure densities to hypothetical interventions setting these densities to low. We estimated additive interactions between firearm and alcohol retailer density. Secondary analyses examined interventions targeted to high exposure density or outcome burden areas. RESULTS: There were 67,850 cases and 268,122 controls. Observed (vs. low) densities of pawn firearm dealers and off-premises alcohol outlets were individually associated with elevated monthly risk of firearm assault per 100,000 people (RD pawn dealers : 0.06, 95% CI: 0.05, 0.08; RD off-premises outlets : 0.01, 95% CI: 0.01, 0.03), but nonpawn firearm dealer and bar/pub density were not; models targeting only areas with the highest outcome burden were similar. Among younger Black men, estimates were larger. There was no interaction between firearm and alcohol retailer density. CONCLUSIONS: Our results are consistent with the hypothesis that limiting pawn firearm dealers and off-premises alcohol outlet densities can reduce interpersonal firearm violence.

In the United States, cannabis is increasingly used to manage chronic pain. Veterans Health Administration (VHA) patients are disproportionately affected by pain and may use cannabis for symptom management. Because cannabis use increases the risk of cannabis use disorders (CUDs), we examined time trends in CUD among VHA patients with and without chronic pain, and whether these trends differed by age. From VHA electronic health records from 2005 to 2019 (∼4.3-5.6 million patients yearly), we extracted diagnoses of CUD and chronic pain conditions ( International Classification of Diseases [ ICD ]- 9-CM , 2005-2014; ICD-10-CM , 2016-2019). Differential trends in CUD prevalence overall and age-stratified (

BACKGROUND AND AIMS: Low outcome prevalence, often observed with opioid-related outcomes, poses an underappreciated challenge to accurate predictive modeling. Outcome class imbalance, where non-events (i.e. negative class observations) outnumber events (i.e. positive class observations) by a moderate to extreme degree, can distort measures of predictive accuracy in misleading ways, and make the overall predictive accuracy and the discriminatory ability of a predictive model appear spuriously high. We conducted a simulation study to measure the impact of outcome class imbalance on predictive performance of a simple SuperLearner ensemble model and suggest strategies for reducing that impact. DESIGN, SETTING, PARTICIPANTS: Using a Monte Carlo design with 250 repetitions, we trained and evaluated these models on four simulated data sets with 100 000 observations each: one with perfect balance between events and non-events, and three where non-events outnumbered events by an approximate factor of 10:1, 100:1, and 1000:1, respectively. MEASUREMENTS: We evaluated the performance of these models using a comprehensive suite of measures, including measures that are more appropriate for imbalanced data. FINDINGS: Increasing imbalance tended to spuriously improve overall accuracy (using a high threshold to classify events vs non-events, overall accuracy improved from 0.45 with perfect balance to 0.99 with the most severe outcome class imbalance), but diminished predictive performance was evident using other metrics (corresponding positive predictive value decreased from 0.99 to 0.14). CONCLUSION: Increasing reliance on algorithmic risk scores in consequential decision-making processes raises critical fairness and ethical concerns. This paper provides broad guidance for analytic strategies that clinical investigators can use to remedy the impacts of outcome class imbalance on risk prediction tools.

ObjectiveIn the United States, adult cannabis use has increased over time, but less information is available on time trends in cannabis use disorder. The authors used Veterans Health Administration (VHA) data to examine change over time in cannabis use disorder diagnoses among veterans, an important population subgroup, and whether such trends differ by age group (

As opioid prescribing has declined, it is unclear how the landscape of prescription pain treatment across the U.S. has changed. We used nationally-representative data from the Medical Expenditure Health Survey, 2014 to 2018 to examine trends in prescriptions for opioid and non-opioid pain medications, including acetaminophen, non-steroidal anti-inflammatory drugs, gabapentinoids, and antidepressants among U.S. adults with self-reported pain. Overall, from 2014 to 2018, the percentage of participants receiving a prescription for opioids declined, (38.8% vs 32.8%), remained stable for non-steroidal anti-inflammatory drugs (26.8% vs 27.7%), and increased for acetaminophen (1.6% vs 2.3%), antidepressants (9.6% vs 12.0%) and gabapentinoids (13.2% vs 19.0%). In this period, the adjusted odds of receiving an opioid prescription decreased (aOR = .93, 95% CI = .90-.96), while the adjusted odds of receiving antidepressant, gabapentinoid and acetaminophen prescriptions increased (antidepressants: aOR = 1.08, 95% CI = 1.03-1.13 gabapentinoids: aOR = 1.11, 95% CI = 1.06-1.17; acetaminophen: aOR = 1.10, 95% CI: 1.02-1.20). Secondary analyses stratifiying within the 2014 to 2016 and 2016 to 2018 periods revealed particular increases in prescriptions for gabapentinoids (aOR = 1.13, 95% CI = 1.05-1.21) and antidepressants (aOR = 1.23, 95% CI = 1.12-1.35) since 2016. PERSPECTIVE: These data demonstrate that physicians are increasingly turning to CDC-recommended non-opioid medications for pain management, particularly antidepressants and gabapentinoids. However, evidence for these medications' efficacy in treating numerous common pain conditions, including low back pain, remains limited.

BackgroundSubstance use disorders (SUD) elevate the risk for COVID-19 hospitalization, but studies are inconsistent on the relationship of SUD to COVID-19 mortality.MethodsVeterans 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).ResultsAmong 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.ConclusionsIn 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.

Purpose We aimed to synthesize insights from systems science approaches applied to domestic and gender-based violence. Methods We conducted a systematic review of systems science studies (systems thinking, group model-building, agent-based modeling [ABM], system dynamics [SD] modeling, social network analysis [SNA], and network analysis [NA]) applied to domestic or gender-based violence, including victimization, perpetration, prevention, and community responses. We used blinded review to identify papers meeting our inclusion criteria (i.e., peer-reviewed journal article or published book chapter that described a systems science approach to domestic or gender-based violence, broadly defined) and assessed the quality and transparency of each study. Results Our search yielded 1,841 studies, and 74 studies met our inclusion criteria (45 SNA, 12 NA, 8 ABM, and 3 SD). Although research aims varied across study types, the included studies highlighted social network influences on risks for domestic violence, clustering of risk factors and violence experiences, and potential targets for intervention. We assessed the quality of the included studies as moderate, though only a minority adhered to best practices in model development and dissemination, including stakeholder engagement and sharing of model code. Conclusions Systems science approaches for the study of domestic and gender-based violence have shed light on the complex processes that characterize domestic violence and its broader context. Future research in this area should include greater dialogue between different types of systems science approaches, consideration of peer and family influences in the same models, and expanded use of best practices, including continued engagement of community stakeholders., Author(s): Melissa Tracy [sup.1] , Li Shen Chong [sup.2] , Kate Strully [sup.3] , Elana Gordis [sup.2] , Magdalena Cerdá [sup.4] , Brandon D. L. Marshall [sup.5] Author Affiliations: (1) [...]

BackgroundFirearm violence remains a persistent public health threat. Comparing the impact of targeted high-risk versus population-based approaches to prevention may point to efficient and efficacious interventions. We used agent-based modeling to conduct a hypothetical experiment contrasting the impact of high-risk (disqualification) and population-based (price increase) approaches on firearm homicide in New York City (NYC).MethodsWe simulated 800,000 agents reflecting a 15% sample of the adult population of NYC. Three groups were considered and disqualified from all firearm ownership for five years, grouped based on prevalence: low prevalence (psychiatric hospitalization, alcohol-related misdemeanor and felony convictions, 0.23%); moderate prevalence (drug misdemeanor convictions, domestic violence restraining orders, 1.03%); and high prevalence (all other felony/misdemeanor convictions, 2.30%). Population-level firearm ownership was impacted by increasing the price of firearms, assuming 1% price elasticity.ResultsIn this hypothetical scenario, to reduce firearm homicide by 5% in NYC, 25% of the moderate prevalence group, or 12% of the high prevalence group needed to be effectively disqualified; even when all of the low prevalence group was disqualified, homicide did not decrease by 5%. An 18% increase in price similarly reduced firearm homicide by 5.37% (95% CI 4.43-6.31%). Firearm homicide declined monotonically as the proportion of disqualified individuals increased and/or price increased. A combined intervention that both increased price and effectively disqualified "high-risk" groups achieved approximately double the reduction in homicide as any one intervention alone. Increasing illegal firearm ownership by 20%, a hypothetical response to price increases, did not meaningfully change results.ConclusionA key takeaway of our study is that adopting high-risk versus population-based approaches should not be an "either-or" question. When individual risk is variable and diffuse in the population, "high-risk approaches" to firearm violence need to focus on relatively prevalent groups and be highly efficacious in disarming people at elevated risk to achieve meaningful reductions in firearm homicide, though countering issues of social justice and stigma should be carefully considered. Similar reductions can be achieved with population-based approaches, such as price increases, albeit with fewer such countering issues.

BackgroundLimiting the incidence of opioid-naïve patients who transition to long-term opioid use (i.e., continual use for > 90 days) is a key strategy for reducing opioid-related harms.ObjectiveTo identify variables constructed from data routinely collected by prescription drug monitoring programs that are associated with opioid-naïve patients' likelihood of transitioning to long-term use after an initial opioid prescription.DesignStatewide cohort study using prescription drug monitoring program data PARTICIPANTS: All opioid-naïve patients in California (no opioid prescriptions within the prior 2 years) age ≥ 12 years prescribed an initial oral opioid analgesic from 2010 to 2017.Methods and main measuresMultiple logistic regression models using variables constructed from prescription drug monitoring program data through the day of each patient's initial opioid prescription, and, alternatively, data available up to 30 and 60 days after the initial prescription were constructed to identify probability of transition to long-term use. Model fit was determined by the area under the receiver operating characteristic curve (C-statistic).Key resultsAmong 30,569,125 episodes of patients receiving new opioid prescriptions, 1,809,750 (5.9%) resulted in long-term use. Variables with the highest adjusted odds ratios included concurrent benzodiazepine use, ≥ 2 unique prescribers, and receipt of non-pill, non-liquid formulations. C-statistics for the day 0, day 30, and day 60 models were 0.81, 0.88, and 0.94, respectively. Models assessing opioid dose using the number of pills prescribed had greater discriminative capacity than those using milligram morphine equivalents.ConclusionsData routinely collected by prescription drug monitoring programs can be used to identify patients who are likely to develop long-term use. Guidelines for new opioid prescriptions based on pill counts may be simpler and more clinically useful than guidelines based on days' supply or milligram morphine equivalents.

BackgroundTools are needed to aid clinicians in estimating their patients' risk of transitioning to long-term opioid use and to inform prescribing decisions.ObjectiveThe 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 designThis was a statewide population-based prognostic study.SubjectsOpioid-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).MeasuresA 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.ResultsDevelopment 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.ConclusionsA 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.

BackgroundAs of July 2018, 45 United States (US) states and the District of Columbia have enacted an overdose Good Samaritan law (GSL). These laws, which provide limited criminal immunity to individuals who request assistance during an overdose, may be of importance in the current wave of the overdose epidemic, which is driven primarily by illicit opioids including heroin and fentanyl. There are substantial differences in the structures of states' GSL laws which may impact their effectiveness. This study compared GSLs which have legal provisions protecting from arrest and laws which have more limited protections.MethodsUsing national county-level overdose mortality data from 3109 US counties, we examined the association of enactment of GSLs with protection from arrest and GSLs with more limited protections with subsequent overdose mortality between 2013 and 2018. Since GSLs are often enacted in conjunction with Naloxone Access Laws (NAL), we examined the effect of GSLs separately and in conjunction with NAL. We conducted these analyses using hierarchical Bayesian spatiotemporal Poisson models.ResultsGSLs with protections against arrest enactment in conjunction with a NAL were associated with 7% lower rates of all overdose deaths (rate ratio (RR): 0.93% Credible Interval (CI): 0.89-0.97), 10% lower rates in opioid overdose deaths (RR: 0.90; CI: 0.85-0.95) and 11% lower rates of heroin/synthetic overdose mortality (RR: 0.89; CI: 0.82-0.96) two years after enactment, compared to rates in states without these laws. Significant reductions in overdose mortality were not seen for GSLs with protections for charge or prosecution.ConclusionGSLs with more expansive legal protections combined with a NAL, were associated with lower rates of overdose deaths, although these risk reductions take time to manifest. Policy makers should consider enacting and implementing more expansive GSLs with arrest protections to increase the likelihood people will contact emergency services in the event of an overdose.

BackgroundThe rise in opioid-related mortality and opioid-related emergency department (ED) visits has stimulated research on whether broader economic declines, such as the Great Recession, affect opioid-related morbidity. We examine in New York City whether one measure of morbidity-opioid-related ED visits-responded acutely to the large negative "shock" of the Great Recession.MethodsData comprise outpatient "treat and release" opioid-related ED visits in New York City for the 72 months spanning January 2006 to December 2011, taken from the Statewide Emergency Department Database (n = 150,246). We modeled the monthly incidence of opioid-related ED visits using Autoregressive, Integrated, Moving Average (ARIMA) time-series methods to control for patterning in ED visits before examining its potential association with the economic shock of the Great Recession.ResultsNew York City shows a mean of 1761 outpatient ED visits per month for opioid dependence and abuse. Unexpectedly large drops in employment coincide with fewer than expected opioid dependence and abuse ED visits in that same month. The result (coefficient = 0.046, 95% Confidence Interval [CI]: 0.002, 0.090) represents a 0.8% drop in overall incidence of opioid dependence and abuse ED visits during the Great Recession. We, however, observe no association between the Great Recession and ED visits for prescription opioid overdose or heroin overdose, or with inpatient ED visits for opioid dependence and abuse.ConclusionsFindings, if replicated, indicate distinct short-term reductions in opioid-related morbidity following the Great Recession. This result diverges from previous findings of increased opioid use following extended economic downturns.

PurposeIndividuals with poor physical and mental health may face elevated risk for suicide, particularly suicide by firearm.MethodsThis retrospective cohort study used statewide, longitudinally linked emergency department (ED) patient record and mortality data to examine 12-month incidence of firearm suicide among ED patients presenting with a range of physical health problems. Participants included all residents presenting to a California ED in 2009-2013 with nonfatal visits for somatic diagnoses hypothesized to increase suicide risk, including myocardial infarction, congestive heart failure, cerebrovascular disease, chronic obstructive pulmonary disease, diabetes, cancer, back pain, headache, joint disorder, and injuries. For each patient diagnostic group, we calculated rates of firearm suicide per 100,000 person-years and standardized mortality ratios (SMRs) relative to the demographically matched California population.ResultsFirearm suicide rates per 100,000 person-years ranged from 9.6 (among patients presenting with unintentional injury) to 55.1 (patients with cancer diagnoses), with SMRs from 1.48 to 7.45 (all p

Background: Being bullied online is associated with being bullied in school. However, links between online bullying and violence-related experiences are minimally understood. We evaluated potential disparities in these associations to illuminate opportunities to reduce school-based violence. Methods: We used five cohorts of Youth Risk Behavior Survey national cross-sectional data (2011-2019, N[subscript total] = 73 074). We used survey-weighted logistic and multinomial models to examine links between online bullying and five school-based violence-related experiences: offline bullying, weapon carrying, avoiding school due to feeling unsafe, being threatened/injured with a weapon, and physical fighting. We examined interactions by sex, race/ethnicity, and sexual identity. Results: Being bullied online was positively associated with all offline violence-related behaviors. Groups with stronger associations between online bullying and physical fighting, including boys, adolescents whose sexual identity was gay/lesbian or unsure, and many adolescents of color (Black, Hispanic/Latino, and Asian/Pacific Islander adolescents), had stronger associations between online bullying and either weapon carrying or avoiding school. Conclusions: Online bullying is not an isolated harmful experience; many marginalized adolescents who experience online bullying are more likely to be targeted in school, feel unsafe, get in fights, and carry weapons. Reduction of online bullying should be prioritized as part of a comprehensive school-based violence prevention strategy.

BackgroundIn 2016, California updated its prescription drug monitoring program (PDMP), adding two key features: automated proactive reports to prescribers and mandatory registration for prescribers and pharmacists. The effects of these changes on prescribing patterns have not yet been examined. We aimed to evaluate the joint effect of these two PDMP features on county-level prescribing practices in California.MethodsUsing county-level quarterly data from 2012 to 2017, we estimated the absolute change associated with the implementation of these two PDMP features in seven prescribing indicators in California versus a control group comprising counties in Florida and Washington: opioid prescription rate per 1000 residents; patients' mean daily opioid dosage in milligrams of morphine equivalents[MME]; prescribers' mean daily MME prescribed; prescribers' mean number of opioid prescriptions per day; percentage of patients getting >90 MME/day; percentage of days with overlapping prescriptions for opioids and benzodiazepines; multiple opioid provider episodes per 100,000 residents.ResultsProactive reports and mandatory registration were associated with a 7.7 MME decrease in patients' mean daily opioid dose (95 %CI: -11.4, -2.9); a 1.8 decrease in the percentage of patients prescribed high-dose opioids (95 %CI: -2.3, -0.9); and a 6.3 MME decrease in prescribers' mean daily dose prescribed (95 %CI: -10.0, -1.3).ConclusionsCalifornia's implementation of these two PDMP features was associated with decreases in the total quantity of opioid MMEs prescribed, and indicators of patients prescribed high-dose opioids compared to states that had PDMP's without these features. Rates of opioid prescribing and other high-risk prescribing patterns remained unchanged.

BackgroundUnintentional injury, a leading cause of morbidity among adolescents, may also be a risk factor for deliberate self-harm. To inform clinical and public health prevention efforts in adolescent populations, we examined whether distinct subtypes of unintentional injury were differentially associated with deliberate self-harm.MethodsStatewide, all-payer, individually linkable administrative data on adolescent patients presenting to any California emergency department (ED) in 2010 (n = 490,071) were used to investigate longitudinal associations between subtypes of unintentional injury and deliberate self-harm. Adolescents aged 10-19 years presenting with unintentional drug poisoning, other poisoning, fall, suffocation, or cutting/piercing injuries formed the exposure groups; adolescents presenting with unintentional strike injuries formed the primary referent group. Study patients were followed back in time (2006-2009) to compare the groups' odds of a prior ED visit for deliberate self-harm, as well as forwards in time (2010-2015) to compare their risks of subsequent self-harm.ResultsUnintentional drug-poisoning injury was strongly associated with increased likelihood of ED visits for deliberate self-harm, assessed both retrospectively (adjusted OR = 4.52; 95% confidence interval [CI] = 3.08, 6.64) and prospectively (adjusted RR = 3.74; 95% CI = 3.03, 4.60). Positive associations with odds of prior self-harm and/or risk of subsequent self-harm were also observed for patients with unintentional non-drug poisoning, suffocation, and cutting/piercing injuries.ConclusionsCertain subtypes of unintentional injury, particularly drug poisoning, are strongly associated with risk for deliberate self-harm among adolescents, a finding with implications for targeting clinical assessment and intervention in emergency department settings. More research is needed to understand the mechanisms underlying these associations.

ObjectiveSelf-harm rates among U.S. adolescents have risen substantially. Health and social outcomes among contemporary self-harming youths are infrequently tracked and poorly understood. This study investigated long-term health service utilization (emergency department [ED] visits and inpatient admissions) and inpatient costs among a recent cohort of adolescents with deliberate self-harm.MethodsThis retrospective cohort study used statewide, all-payer, longitudinally linked discharge data from California. All residents ages 10-19 presenting to EDs in 2010 with deliberate self-harm (N=5,396) were compared with two control groups: a random sample of adolescent ED patients with other complaints, matched on sex, age, residential zip code, and month of index visit (general control patients, N=14,921), and matched ED patients with psychiatric complaints but no self-harm (psychiatric control patients, N=15,835). Outcomes included 5-year rates of ED visits, inpatient admissions, and inpatient costs, overall and for psychiatric and nonpsychiatric complaints separately.ResultsSelf-harm patients' ED use, inpatient admissions, and inpatient costs were significantly higher than those of general control patients (by 39%, 81%, and 21%, respectively), when the analysis controlled for confounding demographic and utilization characteristics. Associations mostly persisted, although smaller in magnitude, in comparisons between self-harm and psychiatric control patients. Psychiatric and nonpsychiatric complaints contributed to self-harming adolescents' excess health service utilization and costs.ConclusionsDeliberate self-harm among adolescents was found to be associated with long-lasting and costly patterns of health service utilization, often but not exclusively for psychiatric complaints. Future research should investigate the pathways underlying these associations and incorporate service utilization as a key patient outcome.

BackgroundPrescription drug monitoring programs (PDMPs) that collect and distribute information on dispensed controlled substances have been adopted by nearly all US states. We know little about program characteristics that modify PDMP impact on prescription opioid (PO) overdose deaths.MethodsWe measured associations between adoption of any PDMP and changes in fatal PO overdoses in 2002-2016 across 3109 counties in 49 states and D.C. We then measured changes related to the adoption of "proactive PDMPs," which report outlying prescribing/dispensing patterns and provide broader access to PDMP data by law enforcement. Comparisons were made within 3 time intervals that broadly represent the evolution of PDMPs (2002-2004, 2005-2009, and 2010-2016). We modeled overdoses using Bayesian space-time models.ResultsAdoption of electronic PDMP access was associated with 9% lower rates of fatal PO overdoses after three years (rate ratio [RR] = 0.91, 95% credible interval [CI]: 0.88-0.93) with well-supported effects for methadone (RR = 0.86,95% CI: 0.82-0.90) and other synthetic opioids (RR = 0.82, 95% CI: 0.77-0.86). Compared with states with no/weak PDMPs, proactive PDMPs were associated with fewer deaths attributed to natural/semi-synthetic opioids (2002-2004: RR = 0.72 [0.66-0.78]; 2005-2009: RR = 0.93 [0.90-0.97]; 2010-2016: 0.89 [0.86-0.92]) and methadone (2002-2004: RR = 0.77 [0.69-0.85]; 2010-2016: RR = 0.90 [0.86-0.94]). Unintended effects were observed for synthetic opioids other than methadone (2005-2009: RR = 1.29 [1.21-1.38]; 2010-2016: RR = 1.22 [1.16-1.29]).ConclusionsState adoption of PDMPs was associated with fewer PO deaths overall while proactive PDMPs alone were associated with fewer deaths related to natural/semisynthetic opioids and methadone, the specific targets of these programs. See video abstract at, http://links.lww.com/EDE/B619.

Objective: To describe trends in prevalent and incident stimulant prescribing between 2008 and 2017 in California. Method: Statewide trends were estimated by age and sex category for prevalent (any) and incident (no prescriptions in the preceding 2 years) stimulant prescribing while adjusting for area-level covariates. Results: Prevalent prescribing rates increased by 126%, while incident prescribing increased 23%. Patients aged 25 to 44 years experienced over 200% increases in prevalent prescribing and 34% to 55% increases in incident prescribing. Among patients older than 25, women had consistently higher prescribing rates than men. ZIP code tabulation areas with the largest minority populations had the lowest baseline prescribing rates but experienced the greatest annual prescription rate increases. Conclusion: Adult stimulant prescribing increased substantially for early working aged adults. Prescription rates were greater for women than men.

PurposeThe purpose of this study was to estimate the effect of firearm involvement during violent victimization on the level of distress experienced and daily functioning within sociodemographic subgroups.MethodsWe used cross-sectional data from the National Crime Victimization Survey (n = 5698) and Targeted Maximum Likelihood Estimation. Sociodemographic subgroups were defined by age, race, sex, and socioeconomic position. Outcomes included experiencing the victimization as severely distressing and problems in the workplace or at school, or with peers or family.ResultsAmong people victimized with a firearm, nearly 40% experienced the victimization as severely distressing and 28% reported daily functioning problems as a result of the victimization, compared with 25% and 27% of those victimized without a firearm. In most of the subgroups examined, a greater proportion of people described the event as severely distressing when a firearm was involved in the victimization, ranging up to 19 percentage points higher among women and among black respondents (95% CI for women = 10%-28%; for blacks = 6%-31%).ConclusionsOur findings suggest an almost universal negative response to firearm involvement during a violent victimization as compared with violent victimizations involving other or no weapons. These findings highlight the need for efforts by medical and mental health practitioners to address the potential sequelae of experiencing severe distress during a firearm victimization.

ImportanceAlcohol use is a risk factor for firearm-related violence, and firearm owners are more likely than others to report risky drinking behaviors.ObjectiveTo study the association between prior convictions for driving under the influence (DUI) and risk of subsequent arrest for violent crimes among handgun purchasers.DesignIn this retrospective, longitudinal cohort study, 79 678 individuals were followed up from their first handgun purchase in 2001 through 2013. The study cohort included all legally authorized handgun purchasers in California aged 21 to 49 years at the time of purchase in 2001. Individuals were identified using the California Department of Justice (CA DOJ) Dealer's Record of Sale (DROS) database, which retains information on all legal handgun transfers in the state.ExposuresThe primary exposure was DUI conviction prior to the first handgun purchase in 2001, as recorded in the CA DOJ Criminal History Information System.Main outcomes and measuresPrespecified outcomes included arrests for violent crimes listed in the Crime Index published by the Federal Bureau of Investigation (murder, rape, robbery, and aggravated assault), firearm-related violent crimes, and any violent crimes.ResultsOf the study population (N=79 678), 91.0% were males and 68.9% were white individuals; the median age was 34 (range, 21-49) years. The analytic sample for multivariable models included 78 878 purchasers after exclusions. Compared with purchasers who had no prior criminal history, those with prior DUI convictions and no other criminal history were at increased risk of arrest for a Crime Index-listed violent crime (adjusted hazard ratio [AHR], 2.6; 95% CI, 1.7-4.1), a firearm-related violent crime (AHR, 2.8; 95% CI, 1.3-6.4), and any violent crime (AHR, 3.3; 95% CI, 2.4-4.5). Among purchasers with a history of arrests or convictions for crimes other than DUI, associations specifically with DUI conviction remained.Conclusions and relevanceThis study's findings suggest that prior DUI convictions may be associated with the risk of subsequent violence, including firearm-related violence, among legal purchasers of handguns. Although the magnitude was diminished, the risk associated with DUI conviction remained elevated even among those with a history of arrests or convictions for crimes of other types.

BACKGROUND:Prescription drug monitoring programs (PDMP), by reducing access to prescribed opioids (POs), may contribute to a policy environment in which some people with opioid dependence are at increased risk for transitioning from POs to heroin/other illegal opioids. This study examines how PDMP adoption and changes in the characteristics of PDMPs over time contribute to changes in fatal heroin poisoning in counties within states from 2002 to 2016. METHODS:Latent transition analysis to classify PDMPs into latent classes (Cooperative, Proactive, and Weak) for each state and year, across three intervals (1999-2004, 2005-2009, 2010-2016). We examined the association between probability of PDMP latent class membership and the rate of county-level heroin poisoning death. RESULTS:After adjustment for potential county-level confounders and co-occurring policy changes, adoption of a PDMP was significantly associated with increased heroin poisoning rates (22% increase by third year post-adoption). Findings varied by PDMP type. From 2010-2016, states with Cooperative PDMPs (those more likely to share data with other states, to require more frequent reporting, and include more drug schedules) had 19% higher heroin poisoning rates than states with Weak PDMPs (adjusted rate ratio [ARR] = 1.19; 95% CI = 1.14, 1.25). States with Proactive PDMPs (those more likely to report outlying prescribing and dispensing and provide broader access to law enforcement) had 6% lower heroin poisoning rates than states with No/Weak PDMPs (ARR = 0.94; 95% CI = 0.90, 0.98). CONCLUSION:There is a consistent, positive association between state PDMP adoption and heroin poisoning mortality. However, this varies by PDMP type, with Proactive PDMPs associated with a small reduction in heroin poisoning deaths. This raises questions about the potential for PDMPs to support efforts to decrease heroin overdose risk, particularly by using proactive alerts to identify patients in need of treatment for opioid use disorder. Future research on mechanisms explaining the reduction in heroin poisonings after enactment of Proactive PDMPs is merited.

AimsAmid changing marijuana policies in the Southern Cone, we examined relationships between marijuana-related risk factors and marijuana use among adolescents in Argentina, Chile, and Uruguay from 2001 to 2016.MethodsUsing cross-sectional surveys from 8th, 10th, and 12th graders and weighted time-varying effect models, we estimated associations between perceived risk (no/low risk versus moderate/great risk) and perceived availability (easy/very easy versus difficult/very difficult/not able to obtain) of marijuana, and any past-month marijuana use.ResultsIn all countries, marijuana use increased over time and adolescents who perceived no/low risk and easy availability had higher odds of use. In Argentina, the bivariate risk/use association weakened from 2001 (OR = 15.24, 95%CI = 9.63, 24.12) to 2004 [OR = 3.86 (2.72, 5.48)] and strengthened until 2011 [OR = 8.22 (7.56, 10.30)]; the availability/use association strengthened from 2005 [OR = 5.32 (4.05, 6.98)] to 2009 [OR = 20.77 (15.57, 27.70)] and weakened until 2014 [OR = 11.00 (9.11, 13.27)]. In Chile, the risk/use association weakened from 2001 [OR = 7.22 (6.57, 7.95)] to 2015 [OR = 5.58 (4.82, 6.48)]; the availability/use association weakened from 2001 [OR = 5.92 (4.96, 7.06)] to 2015 [OR = 4.10 (3.15, 5.34)]. In Uruguay, the risk/use association weakened from 2003 [OR = 34.22 (22.76, 51.46)] to 2016 [OR = 6.23 (4.96, 7.83)]; the availability/use association weakened from 2005 [OR = 29.13 (13.39, 63.39) to 2007 [OR = 9.42 (3.85, 23.07)], and strengthened until 2016 [OR = 22.68 (12.03, 42.76)].ConclusionsOverall, the association between risk and use weakened in all countries, suggesting risk perceptions became a weaker determinant of marijuana use. Perceived availability remained strongly associated with use and may become an increasingly important driver of use (particularly in Uruguay and Argentina).

ObjectivesTo estimate associations between psychiatric disorders and gun carrying among adolescents and to estimate the total number of adolescents in the US who have psychiatric disorders and report carrying guns.Study designWe used cross-sectional data from the National Comorbidity Survey - Adolescent Supplement, a nationally representative sample of adolescents age 13-18 years (N = 10 123; response rate = 75.6%). Psychiatric disorders were assessed using the Composite International Diagnostic Interview. Gun carrying in the 30 days prior to the interview was assessed by self-report. We used multivariable Poisson regression to test for associations.ResultsThe analytic sample included 10 112 adolescents, 2.4% of whom reported carrying a gun in the prior 30 days. The prevalence of gun carrying was greater among adolescents with conduct disorder (adjusted prevalence ratio [APR] = 1.88, 95% CI 1.38, 2.57), drug use disorders (APR = 1.91, 95% CI 1.05, 3.45), and specific phobias (APR = 1.54, 95% CI 1.07, 2.22) compared with adolescents without these disorders. We estimated that 1.1% (95% CI 0.77, 1.48) of adolescents with a disorder associated with self- or other-directed violence also carry guns. Nationally, that is approximately 272 000 adolescents with both risk factors.ConclusionsSpecific psychiatric disorders are associated with an increased risk of gun carrying among adolescents, but the vast majority of adolescents with psychiatric disorders did not report gun carrying. Targeted efforts to assess access to and use of firearms in mental healthcare and other clinical settings are important, as are efforts to identify population approaches to prevention.

IntroductionWe sought to understand the association between youthful self-harm and subsequent chronic disease-related healthcare utilization and whether self-harm reflects unique vulnerability in comparison with severe psychiatric disorders.MethodsWe used a retrospective matched cohort design with statewide, all-payer, individually linked emergency department (ED) data from California, USA. Risk of future ED visits for common chronic conditions in adolescence (headaches, asthma, epilepsy, diabetes, and gastrointestinal disorders, assessed using ICD-9 diagnoses) were compared between three adolescent study groups presenting to an ED in 2010: self-harm patients (n = 5,484), patients with psychiatric complaints but no self-harm (n = 14,235), and patients with other complaints (n = 16,452). Cohort follow-up ended on Sept. 30, 2015. Analyses were adjusted for patients' prior histories of ED utilization for chronic conditions as well as patient- and area-level sociodemographic characteristics.ResultsRisk of subsequent ED visits was higher among self-harm patients compared to non-psychiatric control patients for subsequent epilepsy- (aRR = 1.77, 95% CI [1.42, 2.21]). Risk of subsequent ED visits was higher among psychiatric patients compared to non-psychiatric control patients for subsequent headache- (aRR = 1.31, 95% CI [1.21, 1.42]), and epilepsy-related problems (aRR = 1.85, 95% CI [1.55, 2.21]). Self-harm patients were at higher risk than psychiatric patients for subsequent gastrointestinal disorder (aRR = 1.76, 95% CI [1.03, 3.01]).ConclusionsFindings suggest that self-harm behavior and psychiatric disorders are associated with increased ED utilization for subsequent chronic disease-related ED utilization. Chronic disease among adolescent psychiatric patients should be attended to, potentially involving new models of clinical follow-up care.

Increasing alcohol outlet density is well-documented to be associated with increased alcohol use and problems, leading to the policy recommendation that limiting outlet density will decrease alcohol problems. Yet few studies of decreasing problematic outlets and outlet density have been conducted. We estimated the association between closing alcohol outlets and alcohol use and alcohol-related violence, using an agent-based model of the adult population in New York City. The model was calibrated according to the empirical distribution of the parameters across the city's population, including the density of on- and off-premise alcohol outlets. Interventions capped the alcohol outlet distribution at the 90th to the 50th percentiles of the New York City density, and closed 5% to 25% of outlets with the highest levels of violence. Capping density led to a lower population of light drinkers (42.2% at baseline vs. 38.1% at the 50th percentile), while heavy drinking increased slightly (12.0% at baseline vs. 12.5% at the 50th percentile). Alcohol-related homicides and nonfatal violence remained unchanged. Closing the most violent outlets was not associated with changes in alcohol use or related problems. Results suggest that focusing solely on closing alcohol outlets might not be an effective strategy to reduce alcohol-related problems.

BACKGROUND:Prescription drug monitoring program are designed to reduce harms from prescription opioids; however, little is known about what populations benefit the most from these programs. We investigated how the relation between implementation of online prescription drug monitoring programs and rates of hospitalizations related to prescription opioids and heroin overdose changed over time, and varied across county levels of poverty and unemployment, and levels of medical access to opioids. METHODS:Ecologic county-level, spatiotemporal study, including 990 counties within 16 states, in 2001-2014. We modeled overdose counts using Bayesian hierarchical Poisson models. We defined medical access to opioids as the county-level rate of hospital discharges for noncancer pain conditions. RESULTS:In 2010-2014, online prescription drug monitoring programs were associated with lower rates of prescription opioid-related hospitalizations (rate ratio 2014 = 0.74; 95% credible interval = 0.69, 0.80). The association between online prescription drug monitoring programs and heroin-related hospitalization was also negative but tended to increase in later years. Counties with lower rates of noncancer pain conditions experienced a lower decrease in prescription opioid overdose and a faster increase in heroin overdoses. No differences were observed across different county levels of poverty and unemployment. CONCLUSIONS:Areas with lower levels of noncancer pain conditions experienced the smallest decrease in prescription opioid overdose and the faster increase in heroin overdose following implementation of online prescription drug monitoring programs. Our results are consistent with the hypothesis that prescription drug monitoring programs are most effective in areas where people are likely to access opioids through medical providers.

BackgroundPrescription opioid overdose (POD) and heroin overdose (HOD) rates have quadrupled since 1999. Community-level socioeconomic characteristics are associated with opioid overdoses, but whether this varies by urbanicity is unknown.MethodsIn this serial cross-sectional study of zip codes in 17 states, 2002-2014 (n = 145,241 space-time units), we used hierarchical Bayesian Poisson space-time models to analyze the association between zip code-level socioeconomic features (poverty, unemployment, educational attainment, and income) and counts of POD or HOD hospital discharges. We tested multiplicative interactions between each socioeconomic feature and zip code urbanicity measured with Rural-Urban Commuting Area codes.ResultsPercent in poverty and of adults with ≤ high school education were associated with higher POD rates (Rate Ratio [RR], 5% poverty: 1.07 [95% credible interval: 1.06-1.07]; 5% low education: 1.02 [1.02-1.03]), while median household income was associated with lower rates (RR, $10,000: 0.88 [0.87-0.89]). Urbanicity modified the association between socioeconomic features and HOD. Poverty and unemployment were associated with increased HOD in metropolitan areas (RR, 5% poverty: 1.12 [1.11-1.13]; 5% unemployment: 1.04 [1.02-1.05]), and median household income was associated with decreased HOD (RR, $10,000: 0.88 [0.87-0.90]). In rural areas, low educational attainment alone was associated with HOD (RR, 5%: 1.09 [1.02-1.16]).ConclusionsRegardless of urbanicity, elevated rates of POD were found in more economically disadvantaged zip codes. Economic disadvantage played a larger role in HOD in urban than rural areas, suggesting rural HOD rates may have alternative drivers. Identifying social determinants of opioid overdoses is particularly important for creating effective population-level interventions.

Background and aimsPrescription drug monitoring programs (PDMP), defined as state-level databases used in the United States that collect prescribing information when controlled substances are dispensed, have varied substantially between states and over time. Little is known about the combinations of PDMP features that, collectively, may produce the greatest impact on prescribing and overdose. We aimed to (1) identify the types of PDMP models that have developed from 1999 to 2016, (2) estimate whether states have transitioned across PDMP models over time and (3) examine whether states have adopted different types of PDMP models in response to the burden of opioid overdose.MethodsA latent transition analysis of PDMP models based on an adaptation of nine PDMP characteristics classified by prescription opioid policy experts as potentially important determinants of prescribing practices and prescription opioid overdose events.ResultsWe divided the time-period into three intervals (1999-2004, 2005-09, 2010-16), and found three distinct PDMP classes in each interval. The classes in the first and second interval can be characterized as 'no/weak', 'proactive' and 'reactive' types of PDMPs, and in the third interval as 'weak', 'cooperative' and 'proactive'. The meaning of these classes changed over time: until 2009, states in the 'no/weak' class had no active PDMP, whereas states in the 'proactive' class were more likely to proactively provide unsolicited information to PDMP users, provide open access to law enforcement, and require more frequent data reporting than states in the 'reactive' class. In 2010-16, the 'weak' class resembled the 'reactive' class in previous intervals. States in the 'cooperative' class in 2010-16 were less likely than states in the 'proactive' class to provide unsolicited reports proactively or to provide open access to law enforcement; however, they were more likely than those in the 'proactive' class to share PDMP data with other states and to report more federal drug schedules.ConclusionsSince 1999, US states have tended to transition to more robust classes of prescription drug monitoring programs. Opioid overdose deaths in prior years predicted the state's prescription drug monitoring program class but did not predict transitions between prescription drug monitoring program classes over time.