Publications by authors named "Aaron B Shev"

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.

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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 ( group) with (1) those who unequivocally believe guns increase safety for themselves and others ( group), and (2) those who said they "don't know" or "it depends" in both the self and other scenarios ( group). Weighted multinomial logistic regression results indicated that gun owners in the group were more often female, members of minoritized racial or ethnic groups, and new gun owners than the and groups; they also demonstrated greater support for 4 of 5 firearm injury prevention policies/interventions.

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Article Synopsis
  • A 2022 survey indicated alarming levels of support for political violence in the US, prompting further investigation into the persistence of these attitudes in 2023.
  • A new survey in 2023 found that while support for justifying political violence decreased, there was an increase in beliefs regarding the likelihood of using firearms in such situations.
  • Overall, the findings suggest a decline in support for political violence, yet some respondents still anticipate the possibility of armed conflict in the context of political disagreements.
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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.

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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.

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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.

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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.

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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.

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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.

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To describe associations between neighborhood racial and economic segregation and violence during the COVID-19 pandemic. 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).

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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.

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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.

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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.

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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.

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Article Synopsis
  • - The study investigated how agent-based modeling (ABM) and g-computation can estimate the impacts of interventions aimed at reducing PTSD, using data from a diverse group of 2,282 adults in New York City from 2002-2006, where 16.3% developed PTSD.
  • - Four models were created: g-computation, ABM without interactions, ABM with violent interactions, and ABM with neighborhood dynamics, to evaluate the outcomes of three intervention strategies such as reducing violent victimization and providing income supplements.
  • - Results showed that while g-computation gave a slight reduction in PTSD risk, using ABM offered a more nuanced view of system dynamics but required additional assumptions, with minor variations in intervention
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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.

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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.

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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.

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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.

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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.

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To describe trends in prevalent and incident stimulant prescribing between 2008 and 2017 in California. 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. Prevalent prescribing rates increased by 126%, while incident prescribing increased 23%.

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Importance: Alcohol use is a risk factor for firearm-related violence, and firearm owners are more likely than others to report risky drinking behaviors.

Objective: To study the association between prior convictions for driving under the influence (DUI) and risk of subsequent arrest for violent crimes among handgun purchasers.

Design: In this retrospective, longitudinal cohort study, 79 678 individuals were followed up from their first handgun purchase in 2001 through 2013.

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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).

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