Single-year change in views of democracy and society and support for political violence in the USA: findings from a 2023 nationally representative survey.

BACKGROUND: A 2022 survey in the USA found concerningly high prevalences of support for and personal willingness to engage in political violence, of beliefs associated with such violence, and of belief that civil war was likely in the near future. It is important to determine the durability of those findings. METHODS: Wave 2 of a nationally representative cohort survey was conducted May 18-June 8, 2023; the sample comprised all respondents to 2022's Wave 1. Outcomes are expressed as weighted proportions; changes from 2022 to 2023 are for respondents who participated in both surveys, based on aggregated individual change scores. RESULTS: The completion rate was 84.2%; there were 9385 respondents. After weighting, 50.7% (95% confidence interval (CI) 49.4%, 52.1%) were female; weighted mean (SD) age was 48.5 (25.9) years. About 1 in 20 respondents (5.7%, 95% CI 5.1%, 6.4%) agreed strongly/very strongly that "in the next few years, there will be civil war in the United States," a 7.7% decrease. In 2023, fewer respondents considered violence to be usually/always justified to advance at least 1 of 17 specific political objectives [25.3% (95% CI 24.7%, 26.5%), a 6.8% decrease]. However, more respondents thought it very/extremely likely that within the next few years, in a situation where they consider political violence justified, "I will be armed with a gun" [9.0% (95% CI 8.3%, 9.8%), a 2.2% increase] and "I will shoot someone with a gun" [1.8% (95% CI 1.4%, 2.2%), a 0.6% increase]. Among respondents who considered violence usually/always justified to advance at least 1 political objective, about 1 in 20 also thought it very/extremely likely that they would threaten someone with a gun (5.4%, 95% CI 4.0%, 7.0%) or shoot someone (5.7%, 95% CI 4.3%, 7.1%) to advance such an objective. CONCLUSIONS: In this cohort, support for political violence declined from 2022 to 2023, but predictions of firearm use in political violence increased. These findings can help guide prevention efforts, which are urgently needed.

Comparison of record linkage software for deduplicating patient identities in California's Prescription Drug Monitoring Program.

BACKGROUND: To help prevent overdose deaths involving prescription drugs, accurate linkage of prescription drug monitoring program (PDMP) records for individual patients is essential. OBJECTIVES: To compare the accuracy of the linkage program used by California's PDMP against various record linkage programs with respect to accuracy in deduplicating patient identities in the PDMP, with implications for identifying high-risk opioid use and outlier behaviors. RESEARCH DESIGN: We evaluated California's program, Link Plus, LinkSolv, and The Link King on 557 861 PDMP identity records with addresses in two 3-digit zip code areas for patients who filled a controlled substance prescription in 2013. Manual review was performed on a stratified sample of 720 paired records identified as matches by at least one program. MEASURES: We estimated sensitivity and positive predictive value, and computed PDMP patient alerts for the patient entities identified by each program. RESULTS: Sensitivity was 95% for LinkSolv and The Link King, 84% for Link Plus, and 73% for California's program; positive predictive value was ≥93% for all programs. The number of patient entities prompting a PDMP alert was similar among the programs for all alerts except multiple provider episodes (obtaining prescriptions from ≥6 prescribers or ≥6 pharmacies in the last 6 months), which were 10.9%, 26.6%, and 16.9% greater using The Link King, Link Plus, and LinkSolv, respectively, compared to California's program. CONCLUSIONS: PDMPs should assess the accuracy of record linkage algorithms and the impacts of these algorithms on patient safety alerts and develop national best practices for PDMP record linkage.

Importance of categories of crime for predicting future violent crime among handgun purchasers in California.

BACKGROUND: Prohibiting the purchase and possession of firearms by those at risk of violence is an established approach to preventing firearm violence. Prior studies of legal purchasers have focused on convictions for specific crimes, such as violent misdemeanors and driving under the influence (DUI). We broaden that line of inquiry by investigating and comparing the associations between prior arrests for most categories of crime and subsequent arrest for violent offenses among legal handgun purchasers in California. METHODS: In this longitudinal cohort study of 79,678 legal handgun purchasers in California in 2001, we group arrest charges prior to their first purchases in 2001 according to categories defined by the Uniform Crime Report (UCR) Handbook. We use a gradient boosting machine to identify categories of offenses that are most important for predicting arrest for violent crime following firearm purchase. For each category identified, we then estimate the difference in risk of subsequent arrest for a violent offense using survival regression models. RESULTS: We identified eight crime categories with high predictive importance: simple assaults, aggravated assaults, vehicle violations, weapon, other crimes, theft, drug abuse, and DUI. Compared to purchasers with no prior arrests, those with a prior arrest for any one of the eight important categories and no other categories were found to be at increased risk of arrest for a Crime Index-listed violent crime (murder, rape, robbery, aggravated assault), with the greatest estimated risk corresponding to the simple assault UCR category (adjusted hazard ratio 4.0; 95% CI 2.8-5.9). Simple assault was also associated with the greatest risk for subsequent arrest for firearm violence (adjusted hazard ratio 4.6; 95% CI 2.4-9.0) and any violent offense (adjusted hazard ratio 3.7; 95% CI 2.7-5.0). CONCLUSION: The findings of this study suggest that prior arrests for a broad array of crimes, both violent and non-violent, are associated with risk of subsequent violent crimes, including Crime Index-listed violent crimes and firearm violence, among legal purchasers of firearms. Current policies aimed at restricting access to firearms for individuals at increased risk of violence should be re-examined considering these findings.

Views of democracy and society and support for political violence in the USA: findings from a nationally representative survey.

BACKGROUND: Current conditions in the USA suggest an increasing risk for political violence. Little is known about the prevalence of beliefs that might lead to political violence, about support for and personal willingness to engage in political violence, and about how those measures vary with individual characteristics, lethality of violence, political objectives that violence might advance, or specific populations as targets. METHODS: This cross-sectional US nationally representative survey was conducted on May 13 to June 2, 2022, of adult members of the Ipsos KnowledgePanel. Outcomes are weighted, population-representative proportions of respondents endorsing selected beliefs about American democracy and society and violence to advance political objectives. RESULTS: The analytic sample included 8620 respondents; 50.5% (95% confidence interval (CI) 49.3%, 51.7%) were female; and weighted mean (± standard deviation) age was 48.4 (± 18.0) years. Nearly 1 in 5 (18.9%, 95% CI 18.0%, 19.9%) agreed strongly or very strongly that "having a strong leader for America is more important than having a democracy"; 16.2% (95% CI 15.3%, 17.1%) agreed strongly or very strongly that "in America, native-born white people are being replaced by immigrants," and 13.7% (95% CI 12.9%, 14.6%) agreed strongly or very strongly that "in the next few years, there will be civil war in the United States." One-third of respondents (32.8%, 95% CI 31.7%, 33.9%) considered violence to be usually or always justified to advance at least 1 of 17 specific political objectives. Among all respondents, 7.7% (95% CI 7.0%, 8.4%) thought it very or extremely likely that within the next few years, in a situation where they believe political violence is justified, "I will be armed with a gun"; 1.1% (95% CI 0.9%, 1.4%) thought it very or extremely likely that "I will shoot someone with a gun." Support for political violence and for the use of firearms in such violence frequently declined with increasing age, education, and income. CONCLUSIONS: Small but concerning proportions of the population consider violence, including lethal violence, to be usually or always justified to advance political objectives. Prevention efforts should proceed urgently based on the best evidence available.

Gun violence restraining orders in California, 2016-2018: case details and respondent mortality.

BACKGROUND: Gun violence restraining orders (GVROs), implemented in California in 2016, temporarily prohibit individuals at high risk of violence from purchasing or possessing firearms and ammunition. We sought to describe the circumstances giving rise to GVROs issued 2016-2018, provide details about the GVRO process and quantify mortality outcomes for individuals subject to these orders ('respondents'). METHODS: For this cross-sectional description of GVRO respondents, 2016-2018, we abstracted case details from court files and used LexisNexis to link respondents to mortality data through August 2020. RESULTS: We abstracted information for 201 respondents with accessible court records. Respondents were mostly white (61.2%) and men (93.5%). Fifty-four per cent of cases involved potential harm to others alone, 15.3% involved potential harm to self alone and 25.2% involved both. Mass shooting threats occurred in 28.7% of cases. Ninety-six and one half per cent of petitioners were law enforcement officers and one-in-three cases resulted in arrest on order service. One-year orders after a hearing (following 21-day emergency/temporary orders) were issued in 53.5% of cases. Most (84.2%) respondents owned at least one firearm, and firearms were removed in 55.9% of cases. Of the 379 respondents matched by LexisNexis, 7 (1.8%) died after the GVRO was issued: one from a self-inflicted firearm injury that was itself the reason for the GVRO and the others from causes unrelated to violence. CONCLUSIONS: GVROs were used most often by law enforcement officers to prevent firearm assault/homicide and post-GVRO firearm fatalities among respondents were rare. Future studies should investigate additional respondent outcomes and potential sources of heterogeneity.

The impact of California wildfires on patient access to prescription opioids.

BACKGROUND: Patients on long-term opioid therapy are particularly vulnerable to disruptions in medication access, especially during traumatic and chaotic events such as wildfires and other natural disasters. OBJECTIVES: To determine whether past highly destructive California wildfires were associated with disrupted access to prescription opioids for patients receiving long-term, and therefore physically dependent on, opioid medications. METHODS: Using California prescription drug monitoring program data, this retrospective study selected patients with long-term prescription opioid use episodes residing in ZIP code tabulation areas impacted by either the Camp Fire or Tubbs Fire. Autoregressive integrated moving average time series models were fit to pre-fire data to forecast post-fire expected values and then compared with observed post-fire data, specifically for weekly proportions of long-term episodes with early fills, late fills, changes in patients' prescriber and pharmacy, and fills within a different ZIP code tabulation area than the patient's residence. RESULTS: After the Camp Fire, there were significant spikes in the proportions of early fills (peak at 56% of total, week 1 after fire), late fills (peak at 29%, week 6), and immediate significant increases in prescriber (peak at 37%, week 3) and pharmacy changes (peak at 71%, week 1) in high-impact ZIP code tabulation areas. Low-impact ZIP code tabulation areas experienced no similar disruptions. Disruptions due to the Tubbs Fire were far less severe. CONCLUSION: Access to prescription opioids was greatly disrupted for patients living in areas most impacted by the Camp Fire. Future research should explore effectiveness of current state and federal controlled substance prescribing policies to determine what improvements are needed to minimize disruptions in medication access due to wildfires and other natural disasters.

Unemployment and Crime in US Cities During the Coronavirus Pandemic.

Unemployment and violence both increased during the coronavirus pandemic in the United States (US), but no studies to our knowledge have examined their association. Using data for 16 US cities from January 2018 to July 2020, we estimated the association between acute changes in unemployment during the coronavirus pandemic and violent and acquisitive crime. We used negative binomial regression models and parametric g-computation to estimate average differences in crime incidents if the highest and lowest levels of unemployment observed in each city had been sustained across the exposure period (March-July 2020), compared with observed unemployment in each city-month. During the pandemic, the percentage of the adult population who were unemployed was 8.1 percentage points higher than expected, on average. Increases in unemployment were associated with increases in firearm violence and homicide. For example, we estimated an average increase of 3.3 firearm violence incidents (95% CI: - 0.2, 6.7) and 2.0 homicides (95% CI: - 0.2, 3.9) per city-month from March to July 2020 if all cities experienced their highest versus observed level of unemployment. There was no association between unemployment and aggravated assault or any acquisitive crime. Findings suggest that the sharp rise in unemployment during the pandemic may have contributed to increases in firearm violence and homicide, but not other crime. Additional research is needed on mechanisms of association, generalizability, and modifying factors.

G-Computation and Agent-Based Modeling for Social Epidemiology: Can Population Interventions Prevent Posttraumatic Stress Disorder?

Agent-based modeling and g-computation can both be used to estimate impacts of intervening on complex systems. We explored each modeling approach within an applied example: interventions to reduce posttraumatic stress disorder (PTSD). We used data from a cohort of 2,282 adults representative of the adult population of the New York City metropolitan area from 2002-2006, of whom 16.3% developed PTSD over their lifetimes. We built 4 models: g-computation, an agent-based model (ABM) with no between-agent interactions, an ABM with violent-interaction dynamics, and an ABM with neighborhood dynamics. Three interventions were tested: 1) reducing violent victimization by 37.2% (real-world reduction); 2) reducing violent victimization by100%; and 3) supplementing the income of 20% of lower-income participants. The g-computation model estimated population-level PTSD risk reductions of 0.12% (95% confidence interval (CI): -0.16, 0.29), 0.28% (95% CI: -0.30, 0.70), and 1.55% (95% CI: 0.40, 2.12), respectively. The ABM with no interactions replicated the findings from g-computation. Introduction of interaction dynamics modestly decreased estimated intervention effects (income-supplement risk reduction dropped to 1.47%), whereas introduction of neighborhood dynamics modestly increased effectiveness (income-supplement risk reduction increased to 1.58%). Compared with g-computation, agent-based modeling permitted deeper exploration of complex systems dynamics at the cost of further assumptions.

SuperMICE: An Ensemble Machine Learning Approach to Multiple Imputation by Chained Equations.

Researchers often face the problem of how to address missing data. Multiple imputation is a popular approach, with multiple imputation by chained equations (MICE) being among the most common and flexible methods for execution. MICE iteratively fits a predictive model for each variable with missing values, conditional on other variables in the data. In theory, any imputation model can be used to predict the missing values. However, if the predictive models are incorrectly specified, they may produce biased estimates of the imputed data, yielding inconsistent parameter estimates and invalid inference. Given the set of modeling choices that must be made in conducting multiple imputation, in this paper we propose a data-adaptive approach to model selection. Specifically, we adapt MICE to incorporate an ensemble algorithm, Super Learner, to predict the conditional mean for each missing value, and we also incorporate a local kernel-based estimate of variance. We present a set of simulations indicating that this approach produces final parameter estimates with lower bias and better coverage than other commonly used imputation methods. These results suggest that using a flexible machine learning imputation approach can be useful in settings where data are missing at random, especially when the relationships among the variables are complex.

Neighborhood Racial and Economic Segregation and Disparities in Violence During the COVID-19 Pandemic.

Objectives. To describe associations between neighborhood racial and economic segregation and violence during the COVID-19 pandemic. Methods. For 13 US cities, we obtained zip code-level data on 5 violence outcomes from March through July 2018 through 2020. Using negative binomial regressions and marginal contrasts, we estimated differences between quintiles of racial, economic, and racialized economic segregation using the Index of Concentration at the Extremes as a measure of neighborhood privilege (1) in 2020 and (2) relative to 2018 through 2019 (difference-in-differences). Results. In 2020, violence was higher in less-privileged neighborhoods than in the most privileged. For example, if all zip codes were in the least privileged versus most privileged quintile of racialized economic segregation, we estimated 146.2 additional aggravated assaults (95% confidence interval = 112.4, 205.8) per zip code on average across cities. Differences over time in less-privileged zip codes were greater than differences over time in the most privileged for firearm violence, aggravated assault, and homicide. Conclusions. Marginalized communities endure endemically high levels of violence. The events of 2020 exacerbated disparities in several forms of violence. Public Health Implications. To reduce violence and related disparities, immediate and long-term investments in low-income neighborhoods of color are warranted. (Am J Public Health. 2022;112(1):144-153. https://doi.org/10.2105/AJPH.2021.306540).

Physical Distancing, Violence, and Crime in US Cities during the Coronavirus Pandemic.

Violent crime increased and most property crime decreased in many United States (US) cities during the coronavirus pandemic. Using negative binomial regressions, we examined the association between physical distancing (a central coronavirus containment strategy) and crime within 16 large cities (in 12 US states and the District of Columbia) through July 2020. Physical distancing was measured with aggregated smartphone data and defined as the average change in the percentage of the population staying completely at home. Outcome data were obtained from the Gun Violence Archive and city open data portals. In multivariable models, increases in the percentage of the population staying home were associated with decreases in reported incidents of aggravated assault, interpersonal firearm violence, theft, rape, and robbery, and increases in arson, burglary, and motor vehicle theft. Results suggest that changes in the frequency of interpersonal interactions affected crime during the coronavirus pandemic. More research is needed on the specificity of these assocations and their underlying mechanisms.

A Risk Prediction Model for Long-term Prescription Opioid Use.

BACKGROUND: Tools are needed to aid clinicians in estimating their patients' risk of transitioning to long-term opioid use and to inform prescribing decisions. OBJECTIVE: The 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 DESIGN: This was a statewide population-based prognostic study. SUBJECTS: Opioid-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). MEASURES: A 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. RESULTS: Development 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. CONCLUSIONS: A 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.

Impacts of prescription drug monitoring program policy changes and county opioid safety coalitions on prescribing and overdose outcomes in California, 2015-2018.

In 2015, California received funding to implement the Prescription Drug Overdose Prevention Initiative, a 4-year program to reduce deaths involving prescription opioids by 1) leveraging improvements to California's prescription drug monitoring program (PDMP) (i.e., mandatory PDMP registration for prescribers and pharmacists), and 2) supporting county opioid safety coalitions. We used statewide data from 2011 to 2018 to evaluate the Initiative's impact on opioid prescribing and overdose rates. Prescribing data were obtained from California's PDMP; fatal and non-fatal overdose data were obtained from the California Department of Public Health. Outcomes were monthly opioid prescribing rates and opioid overdose rates, modeled using generalized linear mixed models. Exposures were mandatory PDMP registration, presence of county coalitions, and Initiative support for county coalitions. Mandatory PDMP registration was associated with a 25% decrease (95%CI, 0.71-0.79) in opioid prescribing rates after 24 months. Having a county coalition was associated with a 2% decrease (95%CI, 0.96-0.99) in the opioid prescribing rate; receiving Initiative support was associated with an additional 2% decrease (95%CI, 0.97-0.98). Mandatory PDMP registration and county coalitions were associated with a 35% decrease (95%CI, 0.43-0.97) and a 21% decrease (95% CI, 0.70-0.90), respectively in prescription opioid overdose deaths. Both interventions were also associated with significantly fewer deaths involving any opioid but had no significant association with non-fatal overdose rates. Findings add to the knowledge available to guide policy to prevent high-risk prescribing and opioid overdoses. While further study is needed, coalitions and mandatory PDMP registration may be important components in such efforts.

Alcohol and drug offenses and suicide risk among men who purchased a handgun in California: A cohort study.

Firearm access is a risk factor for firearm suicide; substance use may confer additional risk. In this retrospective cohort study, we estimated the associations between prior alcohol and drug charges at the time of handgun purchase and subsequent suicide among men in California. The sample comprised all men who legally purchased a handgun in California in 2001 and who were age ≥ 21 at the time of acquisition (N = 101,377), identified in the California Department of Justice (CA DOJ) Dealer's Record of Sale database. Exposures included alcohol and drug criminal charges and convictions accrued January 1, 1990 until the first ('index') handgun acquisition in 2001, recorded in the CA DOJ Criminal History Information System. Outcomes included suicide and firearm suicide occurring after the index purchase and before January 1, 2016. A total of 1907 purchasers had alcohol charges, 1248 had drug charges, and 304 had both; 594 purchasers died by suicide (516 by firearm suicide). Compared with those with neither alcohol nor drug charges, those with alcohol charges had 2.20 times the hazard of suicide (95% confidence interval [CI], 1.39-3.46) and 2.22 times the hazard of firearm suicide (95% CI, 1.36-3.62). Risk was most elevated among those with more recent charges and those with 2 or more charges, and in the time period closest to the purchase. The associations for drug charges and the combination of alcohol and drug charges were not distinguishable from the null. Firearm owners with alcohol offenses may benefit from intervention to reduce firearm access and alcohol use.

Firearm purchasing and firearm violence during the coronavirus pandemic in the United States: a cross-sectional study.

BACKGROUND: Firearm violence is a significant public health problem in the United States. A surge in firearm purchasing following the onset of the coronavirus pandemic may have contributed to an increase in firearm violence. We sought to estimate the state-level association between firearm purchasing and interpersonal firearm violence during the pandemic. METHODS: Cross-sectional study of the 48 contiguous states and the District of Columbia from January 2018 through July 2020. Data were obtained from the National Instant Criminal Background Check System (a proxy for firearm purchasing) and the Gun Violence Archive. Using negative binomial regression models, we estimated the association between cumulative excess firearm purchases in March through July 2020 (measured as the difference between observed rates and those expected from autoregressive integrated moving average models) and injuries (including nonfatal and fatal) from intentional, interpersonal firearm violence (non-domestic and domestic violence). RESULTS: We estimated that there were 4.3 million excess firearm purchases nationally from March through July 2020 and a total of 4075 more firearm injuries than expected from April through July. We found no relationship between state-level excess purchasing and non-domestic firearm violence, e.g., each excess purchase per 100 population was associated with a rate ratio (RR) of firearm injury from non-domestic violence of 0.76 (95% CI: 0.50-1.02) in April; 0.99 (95% CI: 0.72-1.25) in May; 1.10 (95% CI: 0.93-1.32) in June; and 0.98 (95% CI: 0.85-1.12) in July. Excess firearm purchasing within states was associated with an increase in firearm injuries from domestic violence in April (RR: 2.60; 95% CI: 1.32-5.93) and May (RR: 1.79; 95% CI: 1.19-2.91), though estimates were sensitive to model specification. CONCLUSIONS: Nationwide, firearm purchasing and firearm violence increased substantially during the first months of the coronavirus pandemic. At the state level, the magnitude of the increase in purchasing was not associated with the magnitude of the increase in firearm violence. Increases in purchasing may have contributed to additional firearm injuries from domestic violence in April and May. Results suggest much of the rise in firearm violence during our study period was attributable to other factors, indicating a need for additional research.

Using Prescription Drug Monitoring Program Data to Assess Likelihood of Incident Long-Term Opioid Use: a Statewide Cohort Study.

BACKGROUND: Limiting 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. OBJECTIVE: To 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. DESIGN: Statewide 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 MEASURES: Multiple 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 RESULTS: Among 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. CONCLUSIONS: Data 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.

Criminal charge history, handgun purchasing, and demographic characteristics of legal handgun purchasers in California.

BACKGROUND: The prevalence and characteristics of handgun purchasers' criminal charge histories have never been described for a large population of firearm owners, but such information is critical to understanding risk factors for subsequent violence in this population. We sought to characterize legal handgun purchasers in California and compare this group to the state population, to quantify the proportion with a criminal charge history at purchase, and to identify modifiable factors associated with of having such a history. METHODS: This cross-sectional study of all 79,927 legal handgun purchasers aged 21-49 years in California in 2001 used log-linear generalized additive models to identify factors associated with having a criminal charge history at purchase. Subjects are from a longitudinal study of incident criminal activity among handgun purchasers. RESULTS: The majority (91.03%) of purchasers were male; whites were overrepresented and Hispanics were underrepresented relative to their population size. At the time of purchase, 16.68%  had a criminal charge history and 10.71% had a criminal conviction. Among men with such a history, 31.28% had been charged with a violent crime and 16.54% had been charged with a firearm-related crime. The strongest factor associated with having a criminal charge history was redeeming a pawned handgun (prevalence ratio: 1.82; 95% confidence interval: 1.71, 1.93). CONCLUSIONS: Despite California's stringent firearm purchase laws, more than 1-in-6 handgun purchasers had a criminal charge history at purchase. This proportion may be higher in states with less restrictive firearm purchasing eligibility criteria.

Changes in opioid prescribing after implementation of mandatory registration and proactive reports within California's prescription drug monitoring program.

BACKGROUND: In 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. METHODS: Using 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. RESULTS: Proactive 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). CONCLUSIONS: California'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.

Risk factors for assaultive reinjury and death following a nonfatal firearm assault injury: A population-based retrospective cohort study.

Individuals with a firearm injury are at high risk of subsequent firearm victimization, but characteristics associated with sustaining recurrent firearm injuries are not well understood. In this retrospective cohort study, we sought to quantify the hazards of sustaining subsequent assaultive firearm injuries among people with an initial firearm assault injury and to identify characteristics associated with recurrent victimization. Using hospital discharge, emergency department, and mortality records, we identified and followed all individuals aged ≥15 years with a nonfatal firearm assault injury resulting in an emergency department visit or hospital admission in California, 2005-2013. We model transitions from one injury to the next and from injury to death, accounting for event history, covariates, and competing risks using multistate models. 29,156 people had an index nonfatal firearm assault injury. Among individuals with 1 such injury, 3.1% had additional nonfatal firearm assault injuries and 1.0% subsequently died from firearm homicide. Among individuals with 2+ nonfatal firearm assaults, 2.0% died from firearm homicide. The estimated transition probability for 1 to 2+ nonfatal injuries reached 10% by 8.5 years post-index injury. The rate of subsequent nonfatal firearm assault injury was highest among men (hazard ratio [HR]: 3.87; 95% confidence interval [CI]: 2.63-5.69) and Blacks (vs. whites) (HR: 2.69; 95% CI: 1.99-3.64). Identification of additional risk markers will require more detailed individual-level data; nonetheless, this study supports the generalizability of findings from smaller studies, provides broad guidance for allocating scarce resources, and suggests that interventions on root causes of violence disparities may have downstream effects on recurrence.