Changes in the Demographic Distribution of Chicago Gun-Homicide Decedents From 2015-2021: Violent Death Surveillance Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Homicide is one of the 5 leading causes of death in the United States for persons aged 1 to 44 years. In 2019, 75% of US homicides were by gun. Chicago has a gun-homicide rate 4 times the national average, and 90% of all homicides are by gun. The public health approach to violence prevention calls for a 4-step process, beginning with defining and monitoring the problem. Insight into the characteristics of gun-homicide decedents can help frame next steps, including identifying risk and protective factors, developing prevention and intervention strategies, and scaling effective responses. Although much is known about gun homicide because it is a long-standing, entrenched public health problem, it is useful to monitor trends to update ongoing prevention efforts. OBJECTIVE: This study aimed to use public health surveillance data and methods to describe changes in the race/ethnicity, sex, and age of Chicago gun-homicide decedents from 2015-2021, in the context of year-to-year variation and an overall increase in the city's gun-homicide rate. METHODS: We calculated the distribution of gun-related homicide deaths by 6 race/ethnicity and sex groups (non-Hispanic Black female, non-Hispanic White female, Hispanic female, non-Hispanic Black male, non-Hispanic White male, and Hispanic male), age in years, and age by age group. We used counts, percentages, and rates per 100,000 persons to describe the distribution of deaths among these demographic groups. Comparisons of means and column proportions with tests of significance set at P≤.05 were used to describe changes in the distribution of gun-homicide decedents over time by race-ethnicity-sex and age groups. The comparison of mean age by race-ethnicity-sex group is done using 1-way ANOVA with significance set at P≤.05. RESULTS: The distribution of gun-homicide decedents in Chicago by race/ethnicity and sex groups had been relatively stable from 2015 to 2021 with 2 notable exceptions: a more than doubling of the proportion of gun-homicide decedents who were non-Hispanic Black female (3.6% in 2015 to 8.2% in 2021) and an increase of 3.27 years in the mean age of gun-homicide decedents. The increase in mean age coincided with a decrease in the proportion of non-Hispanic Black male gun-homicide decedents between the ages of 15-19 and 20-24 years and, conversely, an increase in the proportion of non-Hispanic Black male gun-homicide decedents aged 25-34 years. CONCLUSIONS: The annual gun-homicide rate in Chicago had been increasing since 2015 with year-to-year variation. Continued monitoring of trends in the demographic makeup of gun-homicide decedents is necessary to provide the most relevant and timely information to help shape violence prevention efforts. We detected several changes that suggest a need for increased outreach and engagement marketed toward non-Hispanic Black female and non-Hispanic Black male individuals between the ages of 25-34 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".