Predicting the 1 % who account for 63 % of violent crime
This 2013 study from Sweden is followed by a current report from Harvard Medical School of work toward accurately predicting people with a propensity to become violent. This is a study of soldiers in the U.S. Military, the model developed may someday lead to a similar program that predicts those most likely to commit a violent act in the general population. Again, notice that a very small percentage of individuals who commit violent crime are diagnosed with a mental illness - just 4% in this study of 2.5 million people.
The majority of all violent crime in Sweden is committed by a small number of people. They are ~
Of the 2.5 million individuals included in the study,
One finding the Gothenburg researchers present is that "acts of insanity" that receive a great deal of mass media coverage, committed by someone with a severe psychiatric disorder, are not responsible for the majority of violent crimes.
According to the researchers, the study's results are important to crime prevention efforts. "This helps us identify which individuals and groups in need of special attention and extra resources for intervention. A discussion on the efficacy of punishment (prison sentences) for this group is needed as well, and we would like to initiate a debate on what kind of criminological and medical action that could be meaningful to invest in," says Örjan Falk.
Studies like this one are often used as arguments for more stringent sentences and US principles like "three strikes and you're out." What are your views on this?
"Just locking those who commit three or more violent crimes away for life is of course a compelling idea from a societal protective point of view, but could result in some undesirable consequences such as an escalation of serious violence in connection with police intervention and stronger motives for perpetrators of repeat violence to threaten and attack witnesses to avoid life sentences. It is also a fact that a large number of violent crimes are committed inside the penal system."
"And from a moral standpoint it would mean that we give up on these, in many ways, broken individuals who most likely would be helped by intensive psychiatric treatments or other kind of interventions. There are also other plausible alternatives to prison for those who persistently relapse into violent crime, such as highly intensive monitoring, electronic monitoring and of course the continuing development of specially targeted treatment programs. This would initially entail a higher cost to society, but over a longer period of time would reduce the total number of violent crimes and thereby reduce a large part of the suffering and costs that result from violent crimes," says Örjan Falk.
"I first and foremost advocate a greater focus on children and adolescents who exhibit signs of developing violent behavior and who are at the risk of later becoming repeat offenders of violent crime."
Predicting which soldiers will commit severe, violent crimes
A report out of Harvard Medical School shows that a machine learning model using Department of Defense and Army administrative records was able to identify in advance the 5 percent of US Army soldiers serving from 2004 to 2009 who later committed more than one-third of all major Army workplace violent crimes over that time period.
Workplace violence perpetrated by military personnel is a major concern of the U.S. Department of Defense (DoD). Although programs have been implemented to teach violence prevention strategies to all military personnel, such programs are much less intensive than others developed in settings for people judged to be at high risk of violent behavior.
But what is the best way to predict who is at high risk for committing violent acts?
A new report published online in Psychological Medicine suggests that big data predictive analytic methods might help provide an answer. The report describes research funded by the DoD and conducted in collaboration with the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS), a multicomponent epidemiological-neurobiological study of Army suicides and related behavioral health outcomes.
The report describes the development of a machine learning model based on an analysis of administrative data available for all 975,057 Regular U.S. Army soldiers on active duty from 2004 to 2009. The model was constructed to predict which soldiers would subsequently commit a severe physical violent crime.
Hundreds of potential predictors were examined using the extensive administrative records available for all soldiers. The 5 percent of soldiers classified by the final model as having the highest predicted risk accounted for 36.2 percent of all major physical violent crimes committed by men and 33.1 percent by women over the six years of study. When the model was applied to a more recent cohort from 2011 to 2013, the 5 percent of soldiers with highest predicted risk accounted for 50.5 percent of all major physical violent crimes.
"These numbers are striking,"said Ronald Kessler, the McNeil Family Professor of Health Care Policy at HMS and principal investigator on the project. "They show us that predictive analytic models can pinpoint the soldiers at highest violence risk for preventive interventions. Targeting such interventions might be the best way to bring down the violent crime rate in the Army."
"The fact that the model identifies such a high proportion of violent crimes is especially exciting because the variables used in the model are routinely collected administrative data the Army can use to identify high-risk soldiers without carrying out expensive one-on-one clinical assessments," said Anthony Rosellini, a postdoctoral fellow at HMS and the lead author of the paper.
John Monahan, the John S. Shannon Distinguished Professor of Law at the University of Virginia School of Law, another study author, cautioned that "it is important to recognize that severe violent crimes are uncommon even in this high-risk group. This means that implementing intensive high-risk preventive interventions would make sense only if the interventions are shown to be highly efficient--something that has not yet been demonstrated."
Related stories:
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One percent of population responsible for 63% of violent crime
The majority of all violent crime in Sweden is committed by a small number of people. They are ~
- almost all male (92%)
- who early in life develops violent criminality,
- substance abuse problems,
- often diagnosed with personality disorders and
- commit large number non-violent crimes.
Of the 2.5 million individuals included in the study,
- 4 percent were convicted of at least one violent crime, 93,642 individuals in total. Of these convicted at least once,
- 26 percent were re-convicted three or more times, thus resulting in 1 percent of the population (23,342 individuals) accounting for 63 percent of all violent crime convictions during the study period.
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| The Murderer Next Door: Why the Mind Is Designed to Kill by David M. Buss Buy new or used from Powell's Books |
According to the researchers, the study's results are important to crime prevention efforts. "This helps us identify which individuals and groups in need of special attention and extra resources for intervention. A discussion on the efficacy of punishment (prison sentences) for this group is needed as well, and we would like to initiate a debate on what kind of criminological and medical action that could be meaningful to invest in," says Örjan Falk.
Studies like this one are often used as arguments for more stringent sentences and US principles like "three strikes and you're out." What are your views on this?
"Just locking those who commit three or more violent crimes away for life is of course a compelling idea from a societal protective point of view, but could result in some undesirable consequences such as an escalation of serious violence in connection with police intervention and stronger motives for perpetrators of repeat violence to threaten and attack witnesses to avoid life sentences. It is also a fact that a large number of violent crimes are committed inside the penal system."
"And from a moral standpoint it would mean that we give up on these, in many ways, broken individuals who most likely would be helped by intensive psychiatric treatments or other kind of interventions. There are also other plausible alternatives to prison for those who persistently relapse into violent crime, such as highly intensive monitoring, electronic monitoring and of course the continuing development of specially targeted treatment programs. This would initially entail a higher cost to society, but over a longer period of time would reduce the total number of violent crimes and thereby reduce a large part of the suffering and costs that result from violent crimes," says Örjan Falk.
"I first and foremost advocate a greater focus on children and adolescents who exhibit signs of developing violent behavior and who are at the risk of later becoming repeat offenders of violent crime."
* * * * *
Predicting which soldiers will commit severe, violent crimes
Soldiers at high risk for perpetrating severe violent crimes
can be identified using big data predictive analytics
A report out of Harvard Medical School shows that a machine learning model using Department of Defense and Army administrative records was able to identify in advance the 5 percent of US Army soldiers serving from 2004 to 2009 who later committed more than one-third of all major Army workplace violent crimes over that time period.
Workplace violence perpetrated by military personnel is a major concern of the U.S. Department of Defense (DoD). Although programs have been implemented to teach violence prevention strategies to all military personnel, such programs are much less intensive than others developed in settings for people judged to be at high risk of violent behavior.
But what is the best way to predict who is at high risk for committing violent acts?
A new report published online in Psychological Medicine suggests that big data predictive analytic methods might help provide an answer. The report describes research funded by the DoD and conducted in collaboration with the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS), a multicomponent epidemiological-neurobiological study of Army suicides and related behavioral health outcomes.
The report describes the development of a machine learning model based on an analysis of administrative data available for all 975,057 Regular U.S. Army soldiers on active duty from 2004 to 2009. The model was constructed to predict which soldiers would subsequently commit a severe physical violent crime.
Hundreds of potential predictors were examined using the extensive administrative records available for all soldiers. The 5 percent of soldiers classified by the final model as having the highest predicted risk accounted for 36.2 percent of all major physical violent crimes committed by men and 33.1 percent by women over the six years of study. When the model was applied to a more recent cohort from 2011 to 2013, the 5 percent of soldiers with highest predicted risk accounted for 50.5 percent of all major physical violent crimes.
"These numbers are striking,"said Ronald Kessler, the McNeil Family Professor of Health Care Policy at HMS and principal investigator on the project. "They show us that predictive analytic models can pinpoint the soldiers at highest violence risk for preventive interventions. Targeting such interventions might be the best way to bring down the violent crime rate in the Army."
"The fact that the model identifies such a high proportion of violent crimes is especially exciting because the variables used in the model are routinely collected administrative data the Army can use to identify high-risk soldiers without carrying out expensive one-on-one clinical assessments," said Anthony Rosellini, a postdoctoral fellow at HMS and the lead author of the paper.
John Monahan, the John S. Shannon Distinguished Professor of Law at the University of Virginia School of Law, another study author, cautioned that "it is important to recognize that severe violent crimes are uncommon even in this high-risk group. This means that implementing intensive high-risk preventive interventions would make sense only if the interventions are shown to be highly efficient--something that has not yet been demonstrated."
Related stories:
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- How Visiting High Crime Neighborhood Effects People
- Inside the Minds of Murderers
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- Materials provided by University of Gothenburg. Örjan Falk, Märta Wallinius, Sebastian Lundström, Thomas Frisell, Henrik Anckarsäter, Nóra Kerekes. The 1 % of the population accountable for 63 % of all violent crime convictions. Social Psychiatry and Psychiatric Epidemiology, 2013.
- Materials provided by Harvard Medical School. A. J. Rosellini, J. Monahan, A. E. Street, S. G. Heeringa, E. D. Hill, M. Petukhova, B. Y. Reis, N. A. Sampson, P. Bliese, M. Schoenbaum, M. B. Stein, R. J. Ursano, R. C. Kessler. Predicting non-familial major physical violent crime perpetration in the US Army from administrative data. Psychological Medicine, 2015.
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