What are the Social Determinants of Health that Arrestees Face?

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Public health and public safety data sharing in Camden, New Jersey, reveals social determinants of health for four groups of high-utilizing patients and repeat offenders.

There are individuals that frequent both the healthcare and criminal justice systems, and new research suggests that cross-sector data sharing and integration may help improve understanding about the social determinants of health that arrestees face, according to Dr. Catherine R. Counts, a health services researcher with Seattle Medic One in the Department of Emergency Medicine at the University of Washington School of Medicine.

According to the U.S. Centers for Disease Control and Prevention, the “conditions in the places where people live, learn, work and play affect a wide range of health risks and outcomes” are known as the social determinants of health.

Counts reviewed a recently published comprehensive evaluation of high-utilizing patients and repeat offenders by Camden Administrative Records Integrated for Service Excellence (ARISE), a partnership between the Camden Coalition of Healthcare Providers and the Camden County Police Department, for PoliceOne.com.

Camden ARISE looked at arrest data and hospital claims data within Camden, New Jersey, from 2010 to 2014 to uncover the driving forces behind the crossover. Researchers hypothesized that “there is a relationship between the factors that contribute to both negative public safety outcomes and negative public health outcomes.”

Among the findings, Camden ARISE indicated that “research and practice are now moving away from a sole focus on criminogenic risk to a growing emphasis on services and interventions tailored to an individual’s crime- producing risk factors and responsivity to treatment,” according to the report Counts reviewed. What follows are the key findings and four distinct groups that comprise those that are both high-utilizing patients and repeat offenders.

3 Key Findings From the Arrestees’ Social Determinants of Health Data Evaluation

#1 Pareto’s Principle applies to arrests.

The Camden Coalition previously gained notoriety for demonstrating Pareto’s Principle can be applied to healthcare; that a small number of patients make up a large portion of healthcare utilization. The ARISE data shows similar trends with arrests, 5 percent of those over 18 that were arrested made up 25 percent of all arrests.

#2 There is a correlation between frequent hospital use and arrests.

During the five-year study period, two of every three arrestees made at least one trip to the emergency department, and nearly 55 percent of arrestees made five or more trips over that same period. Looking at the data from the perspective of the hospital, 32 percent of patients with six or more emergency department (ED) visits would be arrested during the same period.

Both entities focus their energy at the encounter level, whereas the individuals bouncing between the healthcare and criminal justice systems are living outside of the stability of those systems. This means that upon discharge or release, they are unable to sustain the status quo and relapse into one of the two systems.

#3 Super-users of both systems share unique characteristics.

There were 226 individuals who fell into the top 5 percent for both emergency department visits and arrests over the five-year study period. Ninety-five percent of the nearly 3,700 arrests of these super-users were for nonviolent offences. The median number of ED visits during the five-year study period for this group was 25, and they were four times more likely to be admitted to the hospital than all arrestees.

4 Groups of High-Utilizing Patients and Repeat Offenders

Perhaps more important than realizing there are distinct sub-groups within super-utilizers was the assertion that no one-size-fits-all solution would work across all four categories. Caring for such a medically and socially complicated population must be done in a way that mirrors their complexity.

  • Nonviolent, medically complex drug offenders – Mostly young men, these individuals were often arrested for disorderly conduct or drug possession, but never for an act of violence. At least 50 percent had suffered at least one drug overdose and most had a history of behavioral health issues or a serious mental illness.
  • Nonviolent offenders often arrested for petty crimes with complex behavioral health histories – These individuals were typically middle-aged men with a high prevalence of housing instability. While they frequented the emergency departments, they were the category least likely to require hospital admission.
  • Assault victims who would go on to become the assailant – These women were typically under 40 and suffered from addiction and other mental health issues. They were the group most likely to be arrested for violent crime and rarely arrested for drug-related offences. While they may perpetrate violence, individuals within this category were often admitted to the hospital for assault-related injuries suggesting they were often victims as well.
  • Young men arrested for a variety of drug-related offences including some proportion of violent crime arrests – They were the least likely to require hospitalization during the study period and less likely to suffer from mental illness or any form of substance abuse history.
About the Author

Catherine R. Counts is a health services researcher with Seattle Medic One in the Department of Emergency Medicine at the University of Washington School of Medicine. She received both her PhD and MHA from Tulane University School of Public Health and Tropical Medicine.

Dr. Counts has research interests in domestic healthcare policy, quality, patient safety, organizational theory and culture and prehospital emergency medicine. She is a member of the National Association of EMS Physicians and AcademyHealth. In her free time she trains Bruno, her USAR canine.

Explore our previous coverage of using public health data to reduce crime:

How Cities Can Use ER Data to Establish Violence Interventions

NSF Research Grant to Predict Crime, Social Harms

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