ABSTRACT
De-identified wound data from 197 homicidal gunshot postmortems were obtained between 2000 and 2008. Forensic ballistics data were only available for cases between 2004 and 2008. Males represent 91% of gunshot victims and were struck in the thorax/abdomen with an average of 2.3 bullets. The type of firearms involved were semi-automatic pistols in the predominant caliber 9-mm Luger and assault rifles in caliber 5.56 × 45 mm and caliber 7.62 × 39 mm Soviet, using full metal jacket bullets. The majority of shootings occurred at ranges of 1 m or greater. The most common bullet path was front to back in 66% of cases. Entry wounds occurred more often on the left side of the thorax, abdomen, and back. The most common critical organs/tissues to sustain bullet trauma in descending order were as follows: heart, lungs, liver, aorta, spleen, kidneys, and vena cava. Ribs were struck by most bullets that entered the thorax.
Subject(s)
Abdominal Injuries/pathology , Homicide/statistics & numerical data , Thoracic Injuries/pathology , Wounds, Gunshot/pathology , Abdominal Injuries/mortality , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , Child , Crime Victims/statistics & numerical data , Female , Firearms/statistics & numerical data , Forensic Ballistics , Humans , Israel/epidemiology , Male , Middle Aged , Sex Distribution , Thoracic Injuries/mortality , Wounds, Gunshot/mortality , Young AdultABSTRACT
Firearms identification imaging systems help solve crimes by comparing newly acquired images of cartridge casings or bullets to a database of images obtained from past crime scenes. We formulate an optimization problem that bases its matching decisions not only on the similarity between pairs of images, but also on the time and spatial location of each new acquisition and each database entry. The objective is to maximize the detection probability subject to a constraint on the false positive rate. We use data on all cartridge casings matches detected in Israel during 2006-2008 to estimate most of the model parameters. We estimate matching accuracy from two different studies and predict that the optimal use of extraneous information would increase the detection probability from 0.931 to 0.987 and from 0.707 to 0.844, respectively. These improvements are achieved by favoring pairs of images that are closer together in space and time.
