Firearm-related deaths remain a pressing public health concern in the United States. In 2021, firearm homicides reached their highest level in approximately 3 decades (
1). Although firearm homicides have since decreased nationwide, provisional estimates of 2023 deaths remain elevated relative to prepandemic levels. Concerningly, firearm suicides have continued to increase each year and currently are at their highest level in more than 50 years (
1–4). Furthermore, firearm injuries are the leading cause of death among children and teens in the United States (
5). Although the reasons for these long-term trends are complex, the large number of firearm injuries has prompted growing attention to the problem.
The infrastructure for firearm injury data in the United States is limited, particularly for nonfatal injuries; this constrains our understanding of temporal patterns (
6), especially because most health care–based data sources on firearm injuries lack granular temporal information (
7). For example, neither the administrative claims–based data sets of the Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project (
8) nor the National Electronic Injury Surveillance System (
9) release information on the precise day or time of injury. Although this helps prevent the disclosure of protected information at the individual level, it precludes researchers from understanding when injuries are most likely to occur.
Prior research on the temporal dynamics of firearm injury and other violence outcomes has observed variation according to time of day (
10–12), day of the week (
10,
13–17), and holidays (
10,
13), with higher rates of firearm injury occurring generally at night, on weekends, and on holidays. Other research in criminology has highlighted similar trends across multiple types of violent incidents and crime (
18–20). However, definitions of the temporal variables (for example, “night”) have varied due to the lack of temporal granularity, limiting comparability across studies. These studies also have limitations in representativeness and timeliness. For example, several of these studies focused primarily on urban areas (
10,
11,
15–17), perhaps because these areas may have greater public availability of firearm injury data provided by law enforcement or media sources. However, these results may not be generalizable to rural areas (
21). Other studies utilizing health care–based data sources are limited by substantial lags in data availability and the potential for bias inherent in sampling-based methods (
6,
13,
14). Emergency medical services data exclude patients who arrive to a hospital by other means, which would result in undercounting and may introduce other systemic biases (
12).
In this study, we analyzed granular temporal patterns of emergency department (ED) visits for firearm injuries using data from a novel and large-scale syndromic surveillance data source, covering both urban and rural areas, to improve resource allocation. Specifically, we utilized data on the date and time of firearm-related ED visits in 30-minute intervals to understand patterns of firearm injury and interactions between the precise time of day, day of the week, month, and holiday. This information expands on previous research (
19) by including detailed data on arrival time as well as incorporating both urban and rural areas. The timeliness of syndromic surveillance data combined with the detail they contain on the timing of patient encounters may be valuable for informing resource allocation (such as health care facility staffing), prevention, and response efforts.
Discussion
Our findings highlight significant temporal clustering of firearm injury ED visits, emphasizing the importance of evidence-based resource allocation and the need for targeted interventions during peak times. Specifically, firearm injury ED visit rates were highest during evenings, weekends, summer months, and holidays, with important interactions between temporal patterns (for example, time of day and holiday status).
This study is the largest analysis to date of temporal patterns in firearm injury using ED data and the first to utilize a data source that is simultaneously timely, granular, and inclusive of data from both urban and rural areas. These findings support and expand on previous research demonstrating differences in firearm injury incidence according to time of day (
10–12), day of the week (
10,
14,
15), holiday status (
10,
13,
15), and time of year (
10). Because the FASTER program captures data from throughout each included state, this study is unique in its inclusion of areas other than major metropolitan centers. Rural areas of the United States have been relatively understudied in recent work (
10); although the current analysis did not stratify by urbanicity, the inclusion of data from rural areas improves generalizability and suggests opportunities for future research.
These insights can inform health care staffing and emergency preparedness, potentially reducing mortality rates associated with firearm injuries. Identifying high-burden times when firearm injuries present in ED settings is important for informing prevention and response efforts, particularly in the form of resource allocation in treatment settings, as delays in care are associated with increased mortality rates after severe gunshot wounds (
31). Given that a delay between an injury and accessing advanced trauma care is associated with increased mortality (
31,
32), our findings provide detailed information that can help inform staffing for trauma centers and the surrounding infrastructure (for example, prehospital emergency services). Furthermore, because a large proportion of firearm injuries are assaults (
33), services such as community violence intervention (CVI) programs or hospital-based violence intervention programs (HVIPs), which provide rapid intervention by a violence prevention specialist, could help deescalate conflicts and prevent retaliatory violence that may occur in a community in a subset of the injuries studied here (
34). The implementation of these and other CVI programs could also benefit from the detailed understanding of temporal patterns provided by this study by allocating prevention and response resources when they are most likely to be needed (
35).
Other research has also suggested the role of climate (
36–38) and weather (
15–17) in firearm injury specifically and interpersonal violence in general as modifiers of underlying temporal patterns. Higher rates of firearm injury have been observed in association with higher daily temperatures and long-term warming trends (
15,
37), and lower rates have been observed in association with precipitation (
16,
17). Increases in interpersonal violence have also been observed in association with deviations from historical climate patterns (
37). Together, the interplay of local factors such as weather, urbanicity, variations in holiday observances, and potentially other unmeasured confounders highlights the need for further research using data that offer both temporal and spatial granularity. Furthermore, the period that was examined included the onset of the COVID-19 pandemic. Previous research using both ED and emergency medical services data (
39,
40) has documented significant increases in firearm injuries during this period. Additional research is needed to fully understand how the temporal patterns described here may shift over time.
This study has several limitations. First, the data were limited to FASTER-funded jurisdictions, including 9 states and the District of Columbia, and are not nationally representative. Second, the syndrome definition used in this analysis might underestimate or overestimate ED visits related to firearm injuries because of variation in coding, reporting, and availability of visit-level data between facilities or over time. The syndrome definition also does not distinguish firearm injury intent, making it impossible to know the proportions of visits related to assaults, unintentional injuries, and self-directed injuries. Further research should explore intent-specific patterns of firearm injuries to refine prevention strategies. Third, temporal analysis of trends in ED visits for firearm injury offers only an approximation of temporal trends in the injuries themselves, as the time between injury and arrival to an ED may vary and could be influenced by unmeasured confounders, including community conditions (
41–43). Fourth, the period examined in the current study (approximately 5 years and 8 months) results in relatively few data points for holidays (for example, 6 instances of Independence Day) compared with nonholidays. This may distort patterns observed for individual holidays as well as comparisons among holidays. Fifth, some individual patient–level and facility-level information was incomplete or not available (for example, discharge disposition) and thus could not be analyzed for this study. In addition, FASTER data that are available to CDC do not currently contain identifiers denoting multivictim incidents, a particular type of incident that merits additional research. Finally, temporal patterns in health care utilization for other conditions could distort the measurement of firearm injury rates as a proportion of total ED visits. A sensitivity analysis using raw counts of ED visits for firearm injury yielded largely equivalent results, and examining data as rates can still help inform the relative need for certain types of clinical services among all ED care being sought.
Understanding the factors contributing to the temporal patterns of firearm injury presents a valuable opportunity for future prevention efforts, and implementation of policies, programs, and practices grounded in the best available evidence can bolster states’ and communities’ prevention efforts. For example, some prevention programs account for temporal variation in demand for services, and these data could be used to support such programs by identifying high-risk periods, monitoring progress or evaluating program effectiveness, or facilitating information sharing among programs and partners. CDC’s Community Violence Prevention Resource for Action (
44) highlights a number of strategies and approaches for preventing community violence, including firearm injury. Strategies like creating protective environments involve approaches such as safe and secure storage of firearms and remediation of vacant lots to reduce opportunities for violence. Strengthening youths’ and young adults’ skills through school-based skill building and job training and employment programs can reinforce positive interpersonal, emotional, and behavioral skills from a young age and promote healthy relationships and economic stability. Furthermore, strategies to intervene and lessen future harms include approaches like HVIPs and street outreach with changes in community norms. Specifically, implementing and staffing CVI programs or HVIPs, particularly during high-risk periods, such as those elucidated in this study, are concrete actions that hospitals can take that could help reduce firearm injury and mortality rates.
Temporal Relationships of Firearm Injury Emergency Department Visits
I read article by Rowh, et.al., with interest. Their article provides solid evidence of temporal relationships in firearm injury emergency department visits including certain days of the week such as Fridays and Saturdays, some holidays such as the Independence Day and New Year’s Eve, some months of the year such as June and July, and the time of day. This evidence could be of great use in developing preventive measures, emergency department and trauma staffing, and policing measures. Additional insight might be found with additional analyses such as age and gender demographics. For example, do those who identify as women who suffer firearm injury have different temporal patterns, or do those over the age of 60 have different temporal patterns? While this study was limited to jurisdictions who participate in the FASTER program, there are markedly different demographics within some of these jurisdictions. In Oregon, for example, one could analyze the data based on county population which has seven counties with populations over 200,000, three from 100,000 to 200,000 and 26 with less than 100,000. These additional insights might also prove to be quite helpful and point to differences that could help tailor programs to address these differences.