Impact of 30-day prescribed opioid dose trajectory on fatal overdose risk: A population-based, statewide cohort study.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

Repeal of Comprehensive Background Check Policies and Firearm Homicide and Suicide.

BACKGROUND: In 2016, firearms killed 38,658 people in the United States. Federal law requires licensed gun dealers, but not private parties, to conduct background checks on prospective firearm purchasers with the goal of preventing prohibited persons from obtaining firearms. Our objective was to estimate the effect of the repeal of comprehensive background check laws-requiring a background check for all handgun sales, not just sales by licensed dealers-on firearm homicide and suicide rates in Indiana and Tennessee. METHODS: We compared age-adjusted firearm homicide and suicide rates, measured annually from 1981 to 2008 and 1994 to 2008 in Indiana and Tennessee, respectively, to rates in control groups constructed using the synthetic control method. RESULTS: The average rates of firearm homicide and suicide in Indiana and Tennessee following repeal were within the range of what could be expected, given natural variation (differences = 0.7 firearm homicides and 0.5 firearm suicides per 100,000 residents in Indiana and 0.4 firearm homicides and 0.3 firearm suicides per 100,000 residents in Tennessee). Sensitivity analyses resulted in similar findings. CONCLUSION: We found no evidence of an association between the repeal of comprehensive background check policies and firearm homicide and suicide rates in Indiana and Tennessee. In order to understand whether comprehensive background check policies reduce firearm deaths in the United States generally, more evidence on the impact of such policies from other states is needed. See video abstract at, http://links.lww.com/EDE/B353.

Prescription Drug Monitoring Program: Registration and Use by Prescribers and Pharmacists Before and After Legal Mandatory Registration, California, 2010-2017.

OBJECTIVES: To estimate the effect of California's prescription drug monitoring program's (PDMP) registration mandate on use of the PDMP. METHODS: We evaluated the effect of California's mandatory PDMP registration law by fitting time series models on the percentage of clinicians registered for California's PDMP and the percentage of clinicians who were active PDMP users (users who created ≥ 1 patient prescription reports in a given month) from 2010 through 2017. We also compared PDMP use among early PDMP adopters (clinicians who registered > 8 months before the mandatory registration deadline) versus late adopters (clinicians who registered ≤ 8 months before the deadline). RESULTS: Mandatory registration was associated with increases in active PDMP users: 53.5% increase for prescribers and 17.9% for pharmacists. Early adopters were 4 times more likely to be active PDMP users than were late adopters. CONCLUSIONS: Mandatory registration was associated with increases in PDMP registration and use, but most new registrants did not become active users. Public Health Implications. Mandatory PDMP registration increases PDMP use but does not result in widespread PDMP usage by all clinicians prescribing controlled substances.

Firearms, alcohol and crime: convictions for driving under the influence (DUI) and other alcohol-related crimes and risk for future criminal activity among authorised purchasers of handguns.

Firearm violence frequently involves alcohol, but there are no studies of misuse of alcohol and risk for future violence among firearm owners. We examined the association between prior convictions for alcohol-related crimes, chiefly driving under the influence (DUI), and risk of subsequent arrest among 4066 individuals who purchased handguns in California in 1977. During follow-up through 1991, 32.8% of those with prior alcohol-related convictions and 5.7% of those with no prior criminal history were arrested for a violent or firearm-related crime; 15.9% and 2.7%, respectively, were arrested for murder, rape, robbery or aggravated assault. Prior alcohol-related convictions were associated with a fourfold to fivefold increase in risk of incident arrest for a violent or firearm-related crime, a relative increase greater than that seen for age, sex or prior violence. Prior convictions for alcohol-related crime may be an important predictor of risk for future criminal activity among purchasers of firearms.

Invited Commentary: Agent-Based Models-Bias in the Face of Discovery.

Agent-based models (ABMs) have grown in popularity in epidemiologic applications, but the assumptions necessary for valid inference have only partially been articulated. In this issue, Murray et al. (Am J Epidemiol. 2017;186(2):131-142) provided a much-needed analysis of the consequence of some of these assumptions, comparing analysis using an ABM to a similar analysis using the parametric g-formula. In particular, their work focused on the biases that can arise in ABMs that use parameters drawn from distinct populations whose causal structures and baseline outcome risks differ. This demonstration of the quantitative issues that arise in transporting effects between populations has implications not only for ABMs but for all epidemiologic applications, because making use of epidemiologic results requires application beyond a study sample. Broadly, because health arises within complex, dynamic, and hierarchical systems, many research questions cannot be answered statistically without strong assumptions. It will require every tool in our store of methods to properly understand population dynamics if we wish to build an evidence base that is adequate for action. Murray et al.'s results provide insight into these assumptions that epidemiologists can use when selecting a modeling approach.

Individual, firearm, and purchasing characteristics associated with risk of firearm-related violent crime arrest: a nested case-control study.

BACKGROUND: Firearm purchasing records offer a potentially important administrative data source to identify individuals at elevated risk of perpetrating firearm violence. In this study, we describe individual, firearm, and transaction characteristics of purchasers in California who were arrested for a firearm-related violent crime (FRV) as compared to the general population of registered purchasers in the state. METHODS: Relying on a dataset of all individuals with transaction records in California (1996-2021), linked to criminal records (1980-2021), we enrolled a cohort of individuals for whom we could capture the legal firearm purchase history. We identified those arrested for FRV post purchase, and using incidence density sampling, gender-matched cases to ten purchasers (controls) who remained "at risk" at the time the case was arrested. We focused on the purchase closest in time prior to the arrest ("index" purchase). We implemented conditional logistic regression and included models with controls for individual- and community-level demographics, as well as interactions between firearm and purchasing characteristics and criminal history. RESULTS: The cohort included 1,212,144 individuals, of whom 6153 were arrested for FRV (0.5%). Cases were matched to 61,530 controls to form the study sample. The largest risk factor was a prior criminal history: purchasers had 5.84 times the risk of FRV if they had a prior arrest within three years of the index purchase (CI 5.44-6.27). Several transaction and firearm characteristics were also associated with FRV. For example, risk increased if the firearm was redeemed at a pawn shop (aIRR: 1.37, CI 1.05-1.77) and decreased if the transaction was a registered private party transfer (vs. retail purchase) (aIRR: 0.83, CI 0.76-0.90) or the firearm was a bolt action firearm (vs. semi-automatic) (aIRR: 0.64, CI 0.51-0.79). In the interaction models, most of the purchase and firearm features only remained significant among those with no criminal history. CONCLUSIONS: Given limited data on firearm transactions, there has been little research on whether the type of firearm an individual purchases or the nature of the purchase might serve as indicators of risk for FRV. We found several transaction and firearm features were associated with risk of FRV. Notably, these features provided little evidence of additional risk for those with a prior criminal record.

Optimism Bias Among Gun Owners: Associations With Firearm Injury Prevention Practices and Policy Support.

Optimism bias is common across health risk assessments, including firearm injury risk, and can have behavioral consequences. Using data from the 2018 California Safety and Wellbeing Survey, we examine whether optimism bias influences firearm injury prevention practices and policy support by comparing the characteristics, behaviors, and opinions of gun owners who believed having a gun at home is comparatively safer for themselves than for similar others (Optimism Bias group) with (1) those who unequivocally believe guns increase safety for themselves and others (Always Safer group), and (2) those who said they "don't know" or "it depends" in both the self and other scenarios (Uncertain group). Weighted multinomial logistic regression results indicated that gun owners in the Optimism Bias group were more often female, members of minoritized racial or ethnic groups, and new gun owners than the Always Safer and Uncertain groups; they also demonstrated greater support for 4 of 5 firearm injury prevention policies/interventions. Despite similar prevalence of owning a gun for protection, gun owners in the Optimism Bias group less often carried a loaded firearm or stored a gun in an unsecure way compared with the Always Safer group. Findings suggest that gun owners characterized by optimism bias, who acknowledged some risk associated with firearms, even if only or more so for others than for themselves, may represent a "movable middle" that is more receptive to firearm injury prevention efforts. Public health messages emphasizing other-oriented (vs. personal) risk and collective responsibility may be perceived as less threatening to the symbolic significance of guns to individual identity, thus enhancing effectiveness.