The Effect of Less-Lethal Weapons on Injuries in Police Use-of-Force Events (2024)

Objectives. We investigated the effect of the use of less-lethal weapons, conductive energy devices (CEDs), and oleoresin capsicum (OC) spray on the prevalence and incidence of injuries to police officers and civilians in encounters involving the use of force.

Methods. We analyzed data from 12 police departments that documented injuries to officers and civilians in 24 380 cases. We examined monthly injury rates for 2 police departments before and after their adoption of CEDs.

Results. Odds of injury to civilians and officers were significantly lower when police used CED weapons, after control for differences in case attributes and departmental policies restricting use of these weapons. Monthly incidence of injury in 2 police departments declined significantly, by 25% to 62%, after adoption of CED devices.

Conclusions. Injuries sustained during police use-of-force events affect thousands of police officers and civilians in the United States each year. Incidence of these injuries can be reduced dramatically when law enforcement agencies responsibly employ less-lethal weapons in lieu of physical force.

Measures

All departments provided an indicator of whether there was an injury in each use-of-force case or a brief narrative describing the nature of the injury. Injury narratives were examined as a validity check on the injury indicators in each agency dataset. For example, a few departments counted skin irritation from pepper spray and CED dart punctures as injuries. We, therefore, recoded these cases as noninjuries unless CED dart punctures occurred in unapproved targets, such as the groin or face.

Eleven of the 12 departments provided varying details on the level of suspect resistance (e.g., passive resistance, active resistance, aggressive resistance, aggravated resistance). To provide a consistent measurement scheme, all resistance indicators were categorized into a dichotomous measure that recorded “resistance”; defensive (muscle tensing, fleeing on foot, grasping a fixed object) or greater (fighting an officer) was categorized as physical resistance. Passive (sitting or lying down) or verbal (refusing to comply with directives) resistance was coded as no physical resistance.

Records varied between departments on the types of physical force that was used (e.g., firm grips, takedowns, punches, elbow strikes, kicks, use of impact weapons like batons and flashlights). We created dichotomous measures of the types of physical force used by the police that included the use of any physical force (any use of hands, fists, feet, or impact weapons like batons and flashlights), the use of a chemical agent (such as OC), or CED. Less than 1% of these cases also included the discharge of a firearm. Removing these cases had no material effect on our results.

Ten out of 12 police departments provided information about suspect demographic characteristics; 8 departments provided data on suspect age, race, and gender, and 3 on only race and gender. Race was measured according to White versus non-White status. As part of a data-sharing agreement with police departments, we did not obtain information on officer demographic characteristics.

We also included a measure of how these police departments regulated the use of CEDs and OC spray among officers. Each agency was asked to respond to 5 hypothetical scenarios involving use of force by police and to indicate whether CEDs or OC spray would be authorized for use in each situation under the department's existing use-of-force policy. The scenarios ranged from passively resistant suspects (goes limp, sits down) to assaultive suspects (swings at officer's head with a closed fist). For the purpose of this study, we collapsed the departments' responses into a dichotomous variable that was coded 1 for a more restrictive OC spray or CED policy (e.g., weapons could be used against suspects exhibiting defensive or greater resistance) or 0 for a less restrictive policy (e.g., weapons could be used against passively or verbally resistant suspects).

Statistical Methods

We carried out 2 sets of analyses on injuries to officers and suspects, a set of cross-sectional estimates for injury outcomes across all 12 departments, and time-series estimates of the effect of CED adoption on the monthly incidence of injuries in 2 departments (Austin, TX, and Orlando, FL) for which we had data on periods before and after these departments adopted these weapons. Models were estimated using Stata version 10.0.⁴⁷

In the first set of analyses, we assessed the cross-sectional relationship between individual-, situation-, and agency-level variables of use-of-force cases on the odds of suspect or officer injury, using multilevel regression models.⁴⁸ We specified a multilevel logistic regression model of injury to suspects and officers (separately) according to the following form:

where η_ij represents the odds of experiencing an injury during a use-of-force event or the log

for individual event i in agency j, where xi represents the vector of individual case attributes (race, age, gender, physical force, OC use, etc.), with a group-level (random effect) parameter (γ_j) that allows the effects of individual case features to shift up or down according to each police department (j). To examine whether the department policies were associated with the probability of injury independent of individual case features, we extended equation 1 and included parameters measuring departmental policies—restricting the use of OC or CEDs to defensive resistance or higher—in place of the group-level parameter:

The error structure was specified as an exchangeable covariance matrix, thus allowing a shared variance among departmental policies but a common pairwise covariance with individual case features. The group-level intercept term was substituted to improve the numerical stability in the model optimization.

We also estimated a multilevel regression model of individual case features including dummy variables (fixed-effect terms) for each police agency, thereby removing the average between-agency differences in prevalence of suspect or officer injury. This model controlled for the average between-agency differences, rather than assuming they were randomly distributed, and was capable of unbiased estimates of the covariates, assuming no important omitted variable bias.

In the second set of analyses, we assessed the effect of adopting CEDs on the monthly incidence of injuries to suspects and officers by estimating cross-sectional time-series models for Orlando and Austin, where we had monthly data on use-of-force events for periods before and after adoption of CEDs. We modeled injury incidence according to a Poisson distribution with the incidence of injuries (λ_t) per use-of-force case as a function of the adoption of CEDs. We specify the injury incidence during a given month (t) according to the following form:

In equation 4, incidence of injuries in a given agency is indexed by month t. CED_t denotes a dummy variable indicating the month that CEDs became fully deployed. We included the natural log of the number of use-of-force events on the right-hand side of the regression equation and constrain the parameter to equal 1 so that the count of injuries is equivalent to a rate of injuries per event in a given month (t). To control for monthly trends in the use of force, we included 4 natural cubic spline parameters (NS_k).

The time-series model proposes a simple counterfactual: that the incidence of injuries (per month) in each agency after CEDs become fully adopted is proportional to what the incidence would have been had CEDs not been adopted. Tests for over-dispersion (excessive variation) indicated that no substantial improvement in fit occurred using a negative binomial model version of the Poisson model. We also substituted month and year fixed-effects parameters for the natural cubic spline parameters and found substantively similar results.

TABLE 1

Descriptive Statistics of Overall Sample of Use-of-Force Cases (N = 24 380) and Prevalence of Suspect and Officer Injury, by Demographic and Situational Variables: United States, 1998–2007

Cross-Sectional Models

Table 2 presents the multilevel models for suspect and officer injury outcomes. The first column shows the results for models of suspect and officer injury that included dummy variables for race (White = 1), gender (male = 1), use of physical force (yes = 1), CED use (CED = 1), and chemical spray use (OC = 1). The average differences between departments were allowed to vary randomly around the group mean. We found that the use of OC or CEDs reduced the odds of suspect injury by 69% (odds ratio [OR] = 0.31; 95% confidence interval [CI] = 0.28, 0.33) and 65% (OR = 0.35; 95% CI = 0.32, 0.38), respectively, after control for other case attributes. For officer injury outcomes, the odds of injury increased slightly if an officer used OC spray (OR = 1.42; 95% CI = 1.29, 1.58) than if he or she did not. There was no relationship between CED use and officer injury.

Column 2 includes the baseline covariates, along with suspect resistance and age variables, for the departments that had complete data on these factors. The results from these models are substantively the same as those in column 1 and indicate that OC or CED use reduces the odds of suspect injury. Suspects who exhibited defensive or greater resistance had 27% greater odds of injury than did suspects who resisted passively (OR = 1.27; 95% CI = 1.16, 1.40). Suspect resistance also increased the odds of officer injury by 72% (OR = 1.72; 95% CI = 1.51, 1.95).

Column 3 includes measures of departmental restrictions on OC spray or CED use, thus allowing an examination of whether the effects of individual-level case features on injury outcomes were conditional on departmental differences in these policies. Neither department-level differences in policies restricting the use of OC or CEDs were associated with odds of suspect or officer injuries, nor did they have a material effect on the other associations between individual use-of-force case features and injury outcomes.

Column 4 includes dummy variables (fixed-effects) for each agency to control for average departmental differences in the prevalence of injuries. Including agency parameters did not materially change the substantive conclusions regarding the covariates of suspect or officer injuries. The odds of suspect injury decreased by 67% (OR = 0.33; 95% CI = 0.30, 0.37) with use of OC and 59% (OR = 0.41; 95% CI = 0.37, 0.46) with use of a CED. By contrast, the odds of officer injury increased slightly if an officer used OC spray (OR = 1.23; 95% CI = 1.10, 1.40); there was no relation between CED use and officer injury.

Time-Series Models

The cross-sectional models control for observed differences between departments but do not explain the specific agency-level effect of deployment of less-lethal weapons on incidence of injury. This raises the question of whether agency-level differences are merely proxies for omitted variables. Because we do not know the level of potential omitted variable bias between departments, we focused a subsequent analysis on changes in the monthly injury incidence for Orlando and Austin associated with their adoption of CED technologies. Orlando data were aggregated to a 108-month period (1998–2006), with the major deployment of CEDs starting in the 62nd month (February 2003). Austin data were aggregated over a 60-month period (2002–2006), with the major deployment of CEDs occurring in the 31st month (July 2004), when all officers were issued CEDs and trained in their use. In Austin, CEDs were phased in during the 18 months before the 31st month, so the estimates we present of the impact of CEDs for this city are conservative. Neither department had a change in its use-of-force policy during the observed time periods.

Figure 1 displays the estimated monthly incidence of suspect injury, by city and month, after CEDs were fully deployed. There was an increase in use-of-force cases after CEDs were deployed in Orlando and a decline after full deployment of CEDs in Austin, but in both cities there was a significant drop in the monthly predicted incidence of injuries.

Table 3 presents the results from each model estimating the effect of adopting CEDs on the monthly incidence of suspect and officer injuries. These results are presented in terms of incidence ratios or the expected average monthly incidence in the postadoption period relative to the prior period. The results show a substantial reduction in incidence of injury to suspects and officers in both cities after the introduction of CEDs. For Orlando, the average monthly incidence of suspect injury decreased by 53% after adoption of CEDs (incidence ratio [IR] = 0.47; 95% CI = 0.37, 0.59). The incidence of officer injury dropped by 62% after introduction of CEDs (IR = 0.38; 95% CI = 0.23, 0.62). The results for Austin indicate that full-scale deployment of CED devices was associated with a 30% reduction in monthly incidence of suspect injury (IR = 0.70; 95% CI = 0.55, 0.88). For police officers, the monthly incidence of injury dropped by 25% (IR = 0.75; 95% CI = 0.55, 1.02) after the full deployment of CEDs. These models adjusted for the monthly total number of use-of-force events and time trends in each city, suggesting these associations are not driven by general or seasonal changes in the application of force by officers.

To test the sensitivity of these results to the chosen intervention month, we replicated the analysis, substituting the indicator of CED adoption with a measure of the number of CEDs used in each month. For both cities, an additional 10 uses of CEDs in a given month was associated with an estimated 9.8% to 9.9% reduction in the average injury incidence for both suspects and officers.

This work was supported by the National Institute on Justice (grant 2005-IJ-CX-0056).

We thank the anonymous reviewers and Greg Ridgeway for the constructive comments they provided.

Note. The points of view presented here are those of the authors and do not reflect the official positions of the National Institute of Justice or the US Department of Justice.

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