To Treat or to Prevent?: Reducing the Population Burden of Violence-related Post-traumatic Stress Disorder.

Interpersonal violence is one of the core public health issues in US cities today. Homicide is one of the leading causes of death for individuals under the age of 45 years,1 and most of the individuals who do survive assaultive violence experience long-term psychological and physical trauma.2 The problem of violence is especially concentrated in urban areas: in 2012, more than half of all homicides in the US occurred in cities with more than 100,000 residents, and more than a quarter occurred in cities with more 500,000 residents.2 One of the central consequences of violence is violence-related post-traumatic stress disorder (PTSD).3 Assault, mugging, and rape are among the traumatic events with the strongest association with PTSD.4–6 In violent urban neighborhoods, violence exposure can arise from direct risk of victimization, as well as continuous witnessing of victimization of friends, family, and neighbors. Hence, addressing violence-related PTSD is critical to developing a comprehensive urban mental health agenda. Only 34.4% of persons with PTSD receive mental health treatment.7 Among those who receive treatment, it takes an average of 12 years after first onset of symptoms to access treatment.8 The persistent problem of urban violence, and low treatment access, suggest that primary prevention of the causes of violence-related PTSD may be needed to reduce this problem in urban areas. However, the allocation of resources to primary prevention versus medical treatment of violence-related PTSD requires resource trade-offs, and the potential benefits of this trade-off remain unknown. The question, then, is to what extent should resources be allocated to primary prevention of the causes of violence-related PTSD in urban areas, and to what extent should they be allocated to medical treatment of the disorder? Building on data from evidence-based interventions, we contrast the impact of two interventions on the prevalence of violence-related PTSD: (1) a population-level violence preventive intervention, and (2) an individual-level treatment intervention. For violence prevention, we focused on hot-spot policing, which concentrates police crime prevention interventions at crime hot spots.9–11 This type of policing is based on the findings that small places, or “hot spots” within neighborhoods, generate half of all criminal events,12 and that crime can be reduced more efficiently if limited police resources are focused on a small number of high-activity areas of crime.11,13 A meta-analysis found that hot-spot policing reduced citizen calls for service in treatment compared with control sites, and had unintended crime prevention benefits in neighboring areas.11 For medical treatment, we focused on cognitive behavioral therapy (CBT). CBT is a psychotherapeutic approach that can include confrontation of memories or triggers related to the traumatic event (i.e., exposure therapy), as well as developing skills to manage anxiety and challenge distorted cognitions (i.e., cognitive restructuring).14 Meta-analyses of randomized trials found that improvement of PTSD symptoms was greater for treatment than for waitlist or supportive control participants.14,15 We used data from empirically guided research on these interventions and agent-based modeling16 to simulate a series of experiments designed to answer two major questions: (1) what is the comparative impact of hot-spot policing versus CBT on population-level rates of violence-related PTSD? and (2) what is the optimal combination of hot-spot policing and CBT to achieve the greatest reductions in violence-related PTSD? Agent-based modeling consists of computer simulations used to examine the interactions among people, places, and time. It is a type of complex systems approach that is particularly well suited for problems shaped by interactions between individuals and feedback loops in causal mechanisms, such as violence and PTSD. Furthermore, it is well-suited for the evaluation of interventions where the exposure or treatment assignment of one individual is dependent on the outcome of others in the population (i.e., interference)—such is the case with, for example, hot-spots policing.17 By simulating sets of alternative interventions in the same agent population, agent-based modeling can be interpreted as counterfactual outcomes.17 It can thus help us refine hypotheses about the sets of interventions that might be most effective in promoting population health.