RE: “THE HIDDEN EPIDEMIC OF FIREARM INJURY: INCREASING FIREARM INJURY RATES DURING 2001–2013”
Bibliographic record
Abstract
In a recent issue of the Journal, Kalesan et al. (1) made the case for a hidden epidemic of firearm injury in the United States during the period from 2001 to 2013. They concluded that “[t]he epidemic of firearm violence, driven largely by nonfatal injuries, is an important public health problem” and referred to the increase in nonfatal injuries as a “public health emergency” (1, p. 552). Over the course of their 12-year study period, there was a 2.5% increase in the crude rate of deaths from firearms (the net result of a large reduction in the firearm homicide rate coupled with an increase in the firearm suicide rate) and a supposed 20.4% increase in the nonfatal injury rate (due entirely to the trend in assault-related injury). It is this unexpected increase in the nonfatal injury rate that undergirds their principal conclusions. As it turns out, however, the surveillance data from which they computed the trends in nonfatal firearm injuries are flawed, and the apparent upward trend is an artifact of these flaws. Well-supported adjustments to the apparent trend in nonfatal injuries resulting from firearm assaults eliminate the upward trend, as we demonstrated in a recent article (2). Kalesan et al. estimated trends in nonfatal injuries that were primarily based on a nationally representative survey of hospital emergency departments. The National Electronic Injury Surveillance System–All Injury Program is managed by the Consumer Product Safety Commission (3). Annual estimates from 2001 onward are publically available on a website maintained by the Centers for Disease Control and Prevention (the Web-Based Injury Statistics Query and Reporting System, or WISQARS) (4). A closely related source of data on nonfatal firearm injuries is the Firearms Injury Surveillance System (NEISS-FISS), which is based on a somewhat expanded sample and includes more detail; in particular, it distinguishes between unintentional injuries and injuries of undetermined intent (5). We used the NEISS-FISS sample; because of data availability and other considerations, our analysis was focused on the period of 2003–2012. When examining the data as reported by Centers for Disease Control and Prevention, we found that the estimated trends in gunshot injuries from firearm assaults were very similar to those reported by Kalesan et al.; there was a 49% increase in the number of such nonfatal injuries during a time when there was essentially no change in the count of firearm homicides. However, we discovered that the “epidemic” increase was an artifact of problems with the NEISS-FISS data. There were 2 such problems. The first was a strong downward trend in coders’ use of “undetermined intent” during the decade, which implied that a larger share of the assault cases were concealed by this coding practice in 2003 than in 2012. Second, there were 15 instances during that decade in which one hospital was replaced by another to represent particular primary sampling units. Presumably by chance, the replacement hospitals had orders of magnitude more gunshot cases in 2 of the primary sampling units, and all of the apparent increase in the nonfatal cases came out of the replacements. (We cannot say whether the original or the replacement hospital was in some sense more representative, but we can say that the replacements distorted the estimated national trend.) Simple adjustments for these 2 problems eliminated the upward trend. Our conclusion is that there was no significant increase in nonfatal firearm assaults during this period. In particular, as the gun homicide rate dropped, the nonfatal assault rate dropped in proportion. An important implication is that there was no improvement in case fatality rates. We note that recently (beginning in 2015), there has been a sharp increase in the rates of gun homicides. That is indeed a serious problem for both public health and public safety. Conflict of interest: none declared.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.059 | 0.052 |
| Insufficient payload (model declined to judge) | 0.012 | 0.014 |
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 source (direct Gemma or distilled Codex), 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".