There is little previous research on firearm background check applications and denials despite the potential for such research to significantly benefit policy and practice. The U.S. firearm background check system is complex with federal, state, and local laws creating a patchwork system intended to increase public safety and reduce mortality. State characteristics may play an important role in changes in application and denial rates. We examined the relationship between application and denial rates and state characteristics such as poverty, race, gender, existing firearm ownership, and population density. Multi‐level longitudinal modeling was used in an ecological study design with the state as the unit of analysis spanning a time period between 2005 and 2010, inclusive. Results indicated that application and denial rates significantly increased over time. Population density and poverty level were negatively related to application and denial rates. Firearm ownership, male population size, and percent of residents that were white were positively related to application and denial rates. Percent of African–American residents was negatively related to application rate. The importance of understanding the factors that predict firearm prevalence and the need for additional research on the denial of firearms is discussed. Copyright © 2016 John Wiley & Sons, Ltd.

The aim of the present study was to determine whether latent class analysis (LCA) could obtain a measure of the aggregate firearm transfer law environment. LCA, analysis of variance, and multinomial logistic regression were used to analyze statelevel firearm transfer laws. Results indicated that a three-class solution fit the data better than a two-or four-class solution. These classes were associated with the two covariates in patterns consistent with hypotheses. Results suggest that LCA is a useful technique for classifying states based on the restrictiveness of firearm transfer laws. This classification may be useful in intervention and prevention planning.