This cross-sectional study assesses the association of state firearm laws with county-level firearm-related suicide rates in the US.
Key Points
Question
Are any state gun laws associated with reduced county-level rates of firearm-related suicides?
Findings
In this cross-sectional study of 10 893 observations drawn from 637 US counties, 5 laws were associated with lower firearm-related suicide rates; these laws (1) allow law enforcement to petition a court for extreme risk protection orders; (2) require a minimum age of 21 years for handgun purchase; (3) prohibit firearm purchase by individuals convicted of a violent misdemeanor; (4) require a permit for handgun purchase; and (5) promote the prevention of child access to firearms.
Meaning
These findings account for county- and state-level factors that may influence suicide rates, and future research should employ multilevel modeling to confirm these findings.
Abstract
Importance
Although studies on the potential benefits of firearm laws for the rates of firearm-related suicide have been performed at the state level, suicides are not evenly distributed across each state, and a more granular analysis at the county level would be better equipped to capture variation within states.
Objective
To examine the association between state firearm laws and county-level firearm-related suicide deaths among all 50 states from 2000 to 2023.
Design, Setting, and Participants
This cross-sectional study assessed data obtained from the Centers for Disease Control and Prevention WONDER database on annual county-level firearm-related suicide rates from 637 counties across all 50 US states from January 1, 2000, to December 31, 2023. The analysis used a panel design with a difference-in-differences, fixed-effects model. The model accounted for heteroskedasticity and adjusted for a range of sociodemographic covariates. Data were analyzed from February 1 to March 15, 2026.
Exposures
Eight firearm laws potentially associated with suicide risk were included: a minimum age of 21 years to buy a handgun, allowing law enforcement to petition courts for extreme risk protection orders, waiting periods for firearm purchases, handgun permit requirements, concealed carry permit requirements, violent misdemeanor prohibitions, “may issue” laws that give law enforcement discretion in approving applications for concealed carry permits, and laws to prevent child access to firearms.
Main Outcome and Measures
The primary outcome was a change in annual county-specific firearm-related and non–firearm-related suicide rates.
Results
The overall population of the 637 counties in the sample for 2023 was 250 million, representing 74.2% of the total US population. Among 10 893 county-year observations, the percentage change in firearm-related suicide rates was associated with the implementation of 5 state firearm laws, including extreme risk protection orders for law enforcement (−9.1%; 95% CI, −14.5% to −3.8%), violent misdemeanor prohibitions (−13.5%; 95% CI,–21.5% to –5.5%), minimum age of 21 years for purchasing a handgun (−9.2%; 95% CI, −11.6% to −6.9%), a permit requirement for purchasing a handgun (−6.7%; 95% CI, −10.6% to −2.8%), and child access protection laws (−8.3%; 95% CI, −11.0% to −5.7%).
Conclusions and Relevance
Findings of this cross-sectional study of county-level firearm-related suicide rates in all 50 states during a 24-year period account for county- and state-level factors that may influence suicide rates. Future research should use multilevel modeling to confirm these findings.
Introduction
From 2000 to 2023, the average county-level firearm-related suicide rate in the US increased from 6.5 per 100 000 population to 9.4 per 100 000 population, a 44% increase.1 In 2024, 56.5% of all suicides in the US were committed with a firearm, with firearm-related suicides making up nearly two-thirds of all firearm-related fatalities.1 Firearm-related suicide is widely considered a public health crisis in the US.2 To address this crisis, many states have enacted laws to reduce firearm-related suicides. However, the empirical evidence on the effectiveness of these laws has been mixed,3,4,5,6,7,8,9,10,11,12,13 in part due to limitations in study designs.
Nationally representative studies of the potential effects of firearm laws on firearm-related suicide rates have frequently been at the state level.4,5,6,7,8,9 Most recently, Goh et al4 used a difference-in-differences fixed effects panel model to look at the effects of 6 state-level laws on state-level firearm suicide-related rates across all 50 states during a 49-year period. A county-level analysis would add to the existing literature because suicides are not evenly distributed across a state, and there is in fact a large amount of variation in the suicide rate within a state that a more granular analysis at the county level would be more equipped to capture. Furthermore, many county-level covariates that are associated with suicides, such as socioeconomic status, cannot be controlled for in a state-level study, and there is often significant variation among such covariates among counties within a single state.
While there have been several county-level studies on firearm-related suicide rates, many do not focus on state laws as an explanatory variable.14,15,16,17 The studies that explore the relationship between state laws and county-level firearm-related suicide rates tend to focus on a specific state, set of states, or region10,11,12 and/or on analyzing the implications of 1 specific law.10,11,12,13 We are not aware of any prior county-level panel study that explores the implications of multiple state firearm laws for firearm-related suicide rates and that includes hundreds of counties across all 50 states.
In this cross-sectional study, we used difference-in-differences fixed effects applied to county-level firearm-related suicide rates. We controlled for state- and county-level characteristics and included fixed effects for both year and county. Our model also allowed us to cluster counties within states while accounting for heteroskedasticity. By adopting this nationally representative panel model, our study aimed to strengthen the empirical basis for understanding the association of state firearm laws with county-level firearm-related suicide rates.
Methods
Study Design
We conducted this study using a difference-in-differences panel model with annual county-level data to examine the association between state firearm laws and firearm-related suicide deaths among 637 counties in all 50 states from January 1, 2000, to December 31, 2023. The analysis consisted of a linear regression with fixed effects for year and counties and a state-level clustering of residuals to account for correlated shocks within counties in the same state. We used indicator variables for the presence or absence of a particular law in each state, along with a vector of state- and county-level control variables. We did not consider municipal laws because 45 of the 50 states have preemption laws in place that preclude municipalities from enacting firearm ordinances that are stricter than the provisions of state law.18 We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) and Reporting of Studies Conducted Using Observational Routinely-Collected Data (RECORD) guidelines. This study was deemed by the Tufts Social, Behavioral, and Educational Research Institutional Review Board not to be human participants research and did not require informed consent.
Sample
The sample consisted of 637 counties across all 50 US states during the 24-year period from 2000 to 2023, yielding a total of 10 893 observations. Each observation consisted of a year-county combination, the county firearm-related suicide rate, a set of state- and county-level control variables for that year, and the status of the corresponding state’s firearm laws.
Data and Measures
Outcome Variables
The primary outcome variables were logged annual county-specific rates of firearm-related, non–firearm-related, and total suicides. County data on firearm-related and total suicide rates were obtained from the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) database,1 and the non–firearm-related suicide rates were obtained by calculating the difference between the two. A detailed description of the characteristics and background of CDC WONDER, data quality, validation checks, and cleaning used in this study is provided in the eMethods in Supplement 1.
We included in our analysis only observations with 10 or more reported deaths, as CDC WONDER only reports data from counties in which there were 10 or more total suicides within a given year. We included only counties for which we had at least 18 years of reported suicide data. Because the difference-in-differences analysis compares trends in suicide rates over time, a large number of missing annual observations would result in unstable estimates of these trends. To avoid this problem, we decided to include only counties for which we had data for 75% of the 24 years in the study period, which is 18 years. This resulted in a final sample size of 637 counties with a mean (SD) of 23.1 (1.2) years of data for each county (10 893 county-year observations).
Primary Variables
We used dichotomous indicator variables (0 or 1) to indicate whether a state had a particular firearm law. State firearm laws were obtained from the Tufts Clinical and Translational Science Institute State Firearm Law Database,19 which provides the status of 72 different gun provisions in each state from 1976 to the present.
We selected for analysis state firearm laws that have a strong theoretical probability of influencing firearm-related suicide rates. These included: (1) a minimum age requirement of 21 years to buy a handgun (henceforth referred to as a handgun minimum age law); (2) laws that allow law enforcement to petition for the temporary removal of an individual’s firearms if they are found to be a threat to themselves or others (henceforth referred to as an extreme risk protection order [ERPO]); (3) waiting periods for firearm purchases; (4) permit required to purchase a handgun (henceforth referred to as a handgun permit); (5) permit required for concealed carry (henceforth referred to as concealed carry permit [note that this is the opposite of permitless carry]); (6) “may issue” laws that give law enforcement discretion in approving applications for concealed carry permits; (7) prohibition of firearm purchase and possession by individuals convicted of a violent misdemeanor (henceforth referred to as a violent misdemeanor law); and (8) child access protection laws that make gun owners liable for damages if a minor younger than 18 years accesses the gun and uses it. The status of the laws in 2023 and the year of implementation or repeal are shown in eTable 1 in Supplement 1.
Most of the laws we examined could not be enforced immediately on passage, requiring either systemic procedural change or the establishment of new state institutions to oversee them (as is the case with ERPOs and permitting systems). Thus, we lagged the laws by 2 years. We conducted a sensitivity analysis to check whether the results were robust to lags ranging from 1 to 3 years.
Control Variables
We controlled for factors that have been found in previous research to be associated with firearm-related suicide rates and that could also plausibly relate to the likelihood of enactment of state firearm laws. These included: (1) percentage of veterans in the total county population, (2) state-level household gun ownership, (3) percentage institutionalized of the total county population, (4) county property crime rate per 100 000 population, (5) state per capita alcohol consumption, (6) county unemployment rate, (7) educational level (percentage of county population older than 25 years who have graduated high school), (8) county-level percentage non-Hispanic White population, and (9) total county population.
County-level control variables such as percentage of veterans, percentage of institutionalized persons, unemployment rate, educational level, percentage of non-Hispanic White institutionalized population, unemployment rate, high school graduation rate, and percentage of non-Hispanic White population were obtained from the US Census using American Community Survey 1-year data.20 Since data for the years 2001 to 2009 were not available, we used linear interpolation to estimate these values. County-level data regarding property crime rates were obtained from the Uniform Crime Reports of the US Federal Bureau of Investigation 21 and interpolated for the years 2000 to 2002. We obtained data on per capital alcohol consumption from the National Institute on Alcohol Abuse and Alcoholism22 for the years until 2023; for 2023, we obtained data on alcohol consumption by state from WiseVoter.23 State-level data on household gun ownership were measured by a validated proxy based on the percentage of suicides committed with a firearm and the hunting license rate.24
Statistical Analysis
Data were analyzed from February 1 to March 15, 2026. We used a 2-way fixed-effect, difference-in-differences linear regression model that included fixed effects for year and county and an indicator variable for the presence or absence of a particular law in that year and county. We also clustered counties by state to avoid artificially low SEs. We used the formal model
| 1n(fsct) = α + β1(Lc[t −2]) + β2(Cs) + β3(Cc) + yt + zc + ε(s) |
where 1n(fsct) is the natural log of the firearm-related suicide rate in county c at time t, α is the intercept, Lc[t −2] is a dummy variable for the presence or absence of a particular law in county c at time t2, Cs is a vector of state-level control variables, Cc is a vector of county-level control variables, yt is year fixed effects, zc is county fixed effects, and ε(s) is the error term when we cluster counties within their states.
Our model compared changes in firearm-related suicide rates in counties before and after the implementation of a state law with changes in the firearm-related suicide rate for counties in states that did not implement the law (ie, control counties) during the same time period. By including fixed effects for year and county, we were able to control for latent variables influencing time-specific trends in firearm-related suicides and county-specific trends in firearm-related suicides. The outcome variables were log-transformed, and the control variables were standardized so that the regression coefficients could be interpreted as a percentage change in the outcome variables associated with each 1-SD increase in the value of the control variable.
Our data are heteroskedastic because of sizable population differences across counties, so the variance cannot be equally distributed across our observations. If unaccounted for, the heteroskedasticity in our model could have produced artificially low SEs.24 This was addressed by clustering counties within states, which produced SEs that are robust to heteroskedasticity. If we had not clustered by state, the model would have assumed that errors in counties within the same state were independent, which, much like ignoring heteroskedasticity, would have led to an artificially low SE.25
An assumption of the model was that there was no correlation between error terms from year to year (serial autocorrelation), an assumption violated based on a Durbin-Watson statistic of 1.3.26 The presence of unaccounted-for serial autocorrelation may result in biased SEs.25 To address the effects of serial autocorrelation on the SE of our treatment variables, we ran a Prais-Winsten regression as part of our sensitivity analysis. We implemented this in Stata, release 19 (StataCorp LLC), using the prais command and adding in rhotype(dw), which accounted for autocorrelation in the model.26 After the Prais-Winsten transformation, the final model showed a Durbin-Watson statistic of 1.8, which is sufficiently close to 2 to indicate that the serial autocorrelation was addressed.
As a falsification test, we used a negative control outcome, examining the association between the implementation of each type of law and the non–firearm-related suicide rate. If these laws were found to be associated with both lower firearm-related and non–firearm-related suicide rates, it would call into question the validity of the observed association between the laws and firearm-related suicide rates.
Results
The overall population of the 637 US counties (10 893 county-year observations) in our sample for 2023 was 250 million, representing 74.2% of the total US population. We found a wide variation in county-level firearm-related suicide rates. For example, in 2023, the mean county suicide rate was 17.3 per 100 000 population with a median of 16.4 per 100 000 population, ranging from a low of 5.3 per 100 000 population in Essex County, New Jersey, to a high of 48.5 per 100 000 population in Nye County, Nevada. The Figure displays a heat map showing variation in county-specific firearm-related suicide rates for the year 2023.
Figure. Heat Map of US Firearm Suicide Rates by County, 2023.
Alaska and Hawaii are not represented because the firearm suicide rate per 100 000 was available for only 3 counties in each state (Alaska: Anchorage County, 14.0; Fairbanks North Star County, 25.3; Matanuska-Susitna County, 14.8; Hawaii: Hawaii County, 5.8; Honolulu County, 2.4; Maui County, 6.7).
Six of the 8 laws were associated with lower firearm-related suicide rates (Table 1 and Table 2). All 6 laws passed a falsification test with a 2-year lag (ensuring that they were associated with firearm-related suicide rates, not non–firearm-related suicide rates). A sensitivity analysis of these 6 laws was conducted to determine whether our results were robust for a 1-year and a 3-year lag. Five of the 6 laws passed this sensitivity analysis: ERPO (−9.1%; 95% CI, −14.5% to −3.8%), violent misdemeanor (−13.5%; 95% CI, −21.5% to −5.5%), handgun minimum age (−9.2%; 95% CI, −11.6% to −6.9%), handgun permit (−6.7%; 95% CI, −10.6% to −2.8%), and child access protection (−8.3%; 95% CI, −11.0% to −5.7%).
Table 1. Regression Results: Falsification Test Analysis of State Firearm Laws.
| Law | Change at a 2-y lag, % (95% CI) | ||
|---|---|---|---|
| Total suicide rate | Firearm-related suicide rate | Non–firearm suicide-related rate | |
| Handgun minimum age | −4.1 (−6.7 to −1.5) | −9.2 (−11.6 to −6.9) | −0.3 (−3.2 to 2.6) |
| Violent misdemeanor | −7.4 (−10.5 to −4.3) | −13.5 (−21.5 to −5.5) | −0.6 (−3.4 to 2.3) |
| ERPO | −5.8 (−10.3 to −1.4) | −9.1 (−14.5 to −3.8) | −0.4 (−5.0 to 4.1) |
| Handgun permit | −3.4 (−7.1 to 0.3) | −6.7 (−10.6 to −2.8) | −2.5 (−5.1 to 0.1) |
| Waiting period | −6.9 (−10.6 to −3.3) | −8.9 (−12.4 to −5.3) | −2.1 (−5.9 to 1.8) |
| Concealed carry permit | −2.9 (−6.6 to 0.7) | −2.3 (−7.0 to 2.4) | 0.3 (−4.7 to 5.3) |
| “May issue” discretionary law | −2.2 (−5.4 to 1.0) | −3.4 (−7.3 to 0.5) | −2.2 (−7.4 to 2.9) |
| Child access protection | −5.1 (−8.0 to −2.1) | −8.3 (−11.0 to −5.7) | −2.7 (−7.3 to 1.9) |
Abbreviation: ERPO, extreme risk protection order.
Table 2. Regression Results: Lag Sensitivity Analysis of State Firearm Laws.
| Law | Change in firearm suicide rate, % (95% CI) | ||
|---|---|---|---|
| 1-y Lag | 2-y Lag | 3-y Lag | |
| Handgun minimum age | −8.0 (−11.0 to −5.0) | −9.2 (−11.6 to −6.9) | −7.4 (−11.7 to −3.1) |
| Violent misdemeanor | −13.2 (−20.5 to −5.8) | −13.5 (−21.5 to −5.5) | −19.5 (−23.5 to −15.5) |
| ERPO | −8.2 (−13.3 to −3.1) | −9.1 (−14.5 to −3.8) | −9.5 (−15.1 to −3.9) |
| Handgun permit | −4.9 (−9.1 to −0.7) | −6.7 (−10.6 to −2.8) | −6.9 (−11.4 to −2.3) |
| Waiting period | −6.2 (−10.0 to −2.4) | −8.9 (−12.4 to −5.3) | −1.2 (−4.6 to 2.2) |
| Child access protection | −5.3 (−8.4 to −2.1) | −8.3 (−11.0 to −5.7) | −3.5 (−6.5 to −0.5) |
Abbreviation: ERPO, extreme risk protection order.
To examine the possible joint associations of the laws, we summed the number of the 5 laws to test whether the presence of multiple laws was associated with a steady decrease in firearm-related suicides (Table 3). We found that the presence of 1 or 2 of these 5 laws had a small but significant inverse association with firearm-related suicide rates (−4.7% [95% CI, −7.4% to −1.9%]), the presence of 3 to 4 of these laws had an increasing inverse association with the firearm-related suicide rate (−12.4% [95% CI, −21.4% to −3.4%]), and the presence of all 5 laws had an even greater inverse association with the firearm-related suicide rate (−29.1% [95% CI, −38.2% to −20.0%]).
Table 3. Regression Results: Percentage Change in Firearm Suicide Rates Associated With Control Variables and Presence of Increasing Number of Firearm Laws.
| Control variable | Change in firearm-related suicide rate, % (95% CI) |
|---|---|
| County-level factors | |
| Veterans in population | 0.6 (−2.4 to 3.6) |
| Institutionalized non-Hispanic White population | 0.5 (−0.6 to 1.5) |
| Unemployment rate | 0.1 (−1.7 to 1.9) |
| Property crime rate | 0.2 (−0.7 to 1.0) |
| Non-Hispanic White population | 6.6 (−4.2 to 17.4) |
| Educational attainment (high school graduates aged >25 y) | −11.1 (−20.6 to −1.6) |
| Total population | −4.0 (−15.5 to 7.6) |
| State-level factors | |
| Household gun ownership | 13.2 (9.4 to 17.1) |
| Per capita alcohol consumption | 3.4 (0.3 to 6.4) |
| No. of laws present lagged at 2 ya | |
| 0 | 1 [Reference] |
| 1 or 2 | −4.7 (−7.4 to −1.9) |
| 3 or 4 | −12.4 (−21.4 to −3.4) |
| 5 | −29.1 (−38.2 to −20.0) |
Laws are (1) age 21 handgun permit; (2) violent misdemeanor; (3) gun violence restraining order; (4) handgun permit; and (5) child access protection law.
Last, we used the Prais-Winsten regression to conduct a sensitivity analysis of changes associated with these 5 laws at a 2-year lag to ensure they remained significant after accounting for serial autocorrelation in the error terms (Table 4). By running this regression, we confirmed that our results remained significant and essentially unchanged after accounting for serial autocorrelation.
Table 4. Prais-Winsten Regression Results for Percentage Change in Firearm Suicide Rates Associated With 5 State Firearm Laws.
| Control variable | Change in firearm-related suicide rate lagged at 2 y, % (95% CI) |
|---|---|
| Individual law | |
| Handgun minimum age | −8.6 (−10.6 to −6.5) |
| Violent misdemeanor | −10.7 (−17.8 to −3.6) |
| ERPO | −8.1 (−13.6 to −2.7) |
| Handgun permit | −7.0 (−10.6 to −3.4) |
| Child access protection | −8.7 (−11.1 to −6.3) |
| No. of laws present | |
| 0 | 1 [Reference] |
| 1 or 2 | −4.6 (−7.4 to −1.7) |
| 3 or 4 | −11.8 (−20.3 to −3.2) |
| 5 | −27.8 (−36.4 to −19.3) |
Abbreviation: ERPO, extreme risk protection order.
Regarding the control variables, the percentage of persons with a high school degree at the county-level was inversely associated with the firearm-related suicide rate (−11.1% [95% CI, −20.6% to −1.6%]), and household gun ownership (13.2% [95% CI, 9.4%-17.1%]) and per capita alcohol consumption (3.4% [95% CI, 0.3%-6.4%]) at the state level were directly associated with the firearm-related suicide rate (Table 4). eTable 2 in Supplement 1 provides estimated intervals for the year 2023 to give an idea of how precisely model-estimated results can be applied to individual counties.
Discussion
In this analysis of 24 years of state-level and county-level data, we found that 5 firearm laws (ERPO, handgun minimum age, violent misdemeanor, handgun permit, and child access protection laws) were each independently associated with significantly lower firearm-related suicide rates. These results were consistent through a lag of 3 years after controlling for a broad range of county- and state-level demographic, economic, and criminological factors and after accounting for heteroskedasticity in the model. Importantly, these associations were specific to firearm-related suicides and not observed for non–firearm-related suicides.
A state that enacted anywhere between 1 or all 5 of these laws experienced reductions in firearm-related suicide rates; however, the reductions were substantially larger in states that enacted more laws, with a monotonic pattern of increasing reductions as the number of enacted laws increased. This finding is consistent with prior research suggesting that state firearm laws may work together synergistically.3
These findings are consistent with those of Goh et al,4 who reported that state laws requiring a permit to purchase a handgun were associated with significantly lower rates of firearm-related suicide but not non–firearm-related suicide. Our findings support these previous findings by examining the association at a more granular level and in a multilevel model, thus being able to account for both county-level and state-level factors that might influence suicide rates.
Limitations
This study has limitations. First, the cross-sectional design limited our ability to conclude whether the observed changes in firearm-related suicide in states with certain firearm laws were caused by those laws. In particular, states with lower suicide rates to begin with may be more likely to enact strong firearm laws. We attempted to address this using a difference-in-differences design that examines changes in suicide rates rather than the absolute level of the suicide rates. However, we cannot rule out the possibility of reverse causality. It should also be noted that if states enact firearm laws in response to increasing suicide rates, then this would bias the results toward the null because law enactment would be associated with rising suicide rates.
Second, because we included only counties with 10 or more reported suicides in a year, smaller rural counties tended to be excluded. Therefore, these results may not be generalizable to rural counties. Nevertheless, our sample of counties accounts for 74.2% of the US population.
Third, since states tend to enact multiple firearm laws together, it is difficult to determine whether an observed association between a particular law and the firearm-related suicide rate is attributable to that law or to a different law that happens to have been enacted at the same time. For example, among the 6 states that had a violent misdemeanor law in place in 2023, all 6 also had minimum age of 21 years for handgun sales. It is therefore difficult, if not impossible, to disentangle the differential effects of these 2 laws. We were unable to test the contribution of each individual law to an association while specifying the absence of the other laws, as the sample sizes for some of the combinations of laws were too small to derive meaningful estimates.
Fourth, the staggered nature of firearm law implementation made it difficult to assess the parallel trends assumption. While there are procedures that can be used for staggered designs,27 they require highly balanced panel data without many missing values. There were too many gaps in our data to implement these procedures.
Finally, we were also unable to examine suicide attempts as an outcome due to a lack of county-level historical data. That said, the case-fatality rates for firearm-related suicides are approximately 90%.28
Conclusions
Our results suggest that a combination of state laws consisting of violent misdemeanor prohibitions, ERPOs, a minimum age of 21 years for handgun purchase, a handgun permit requirement, and a child access protection law was associated with significantly lower firearm-related suicide rates. These findings account for county- and state-level factors that may influence suicide rates and strengthen previous research.
eMethods. Detailed Description of the 2 Major Databases Used in the Study and Linkage
eTable 1. Status of Firearm Suicide Rates and State Firearm Laws in 2023
eTable 2. 95% Prediction Intervals for County Firearm Suicide Rates for 2023
eReferences.
Data Sharing Statement
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eMethods. Detailed Description of the 2 Major Databases Used in the Study and Linkage
eTable 1. Status of Firearm Suicide Rates and State Firearm Laws in 2023
eTable 2. 95% Prediction Intervals for County Firearm Suicide Rates for 2023
eReferences.
Data Sharing Statement
