The emerging infrastructure of US firearms injury data.

For every fatal shooting in the United States, detailed information from reports of coroners or medical examiners, police departments, and other sources is recorded in the National Violent Death Reporting System. There is no such system in place for nonfatal shootings, which far outnumber fatalities. Hospital data systems are in place that could, with some improvements, provide access to reliable local, state and national estimates of firearm injuries. Such estimates are possible because most firearms injuries are treated in hospitals, and hospitals routinely assign "external cause of injury" codes to all injury encounters. Federal health agencies supervise a number of data systems that centralize hospital data. Challenges currently being addressed are public access, timeliness, and accuracy of coding of intent. (Hospitals misclassify many firearm assaults as accidents.) Law enforcement agencies provide detailed data on shootings in criminal circumstances, including shootings that are not treated in a hospital. The FBI's Uniform Crime Reports (UCR) system aggregates data from agencies. The FBI instituted a radical reform of this system beginning in 2021, resulting in a sharp agency participation drop that prevents valid national estimates. The reform requires agencies to report incident-level data instead of summary counts, which is all that was required for the previous 90 years. There are ongoing efforts to increase participation in the new system and restore its former status as the leading source of national crime estimates. In the meantime, data on nonfatal gunshot cases are available from a number of police departments. We discuss additional reforms needed to generate timely, accurate, publicly accessible data from hospitals and police.

Measuring gun violence in police data sources: transitioning to NIBRS.

BACKGROUND: The majority of gun violence in the United States does not result in physical injury and therefore cannot be completely measured using hospital data. To measure the full scope of gun violence, the nation's crime reporting systems that collect police reports of crimes committed with a firearm are vital. However, crime data reporting conventions may underestimate gun violence in the U.S. This paper compares crime data sources to assess underestimation of gun violence. FINDINGS: The Federal Bureau of Investigation's Summary Reporting System (SRS) and National Incident Based Reporting System (NIBRS) measures of gun violence were compared in 2019 for states comprehensively reporting data to both systems. Gun violence is underestimated in the SRS compared to NIBRS. Within the sample, 18.8% more aggravated assaults with a firearm are recorded and 2.1% more robberies with a firearm are recorded in NIBRS. The proportion of assaults and robberies committed with a firearm measured in both sources did not differ. If the additional gun violence events recorded in the NIBRS sample are consistent with national crime reporting, the number of additional gun violence events per year captured using NIBRS totals approximately 65,071 additional events, or an additional 178 gun violence events per day. Of the additional gun violence events, approximately 31% are due to omitted crime categories, with the remaining variation driven mostly by aggravated assaults with a firearm. CONCLUSIONS: Police data are important data sources for estimating the full scope of gun violence. Comparisons between police data sources suggest that the proportion of crimes committed with a firearm is unchanged. Due to crime reporting conventions, however, the number of gun violence events may be substantially understated. Despite advantages in measuring gun violence, agency participation in NIBRS is alarmingly low and jeopardizes accurate and reliable national crime data.

Estimating Nonfatal Gunshot Injury Locations With Natural Language Processing and Machine Learning Models.

Importance: Nonfatal gunshot injuries are the most common firearm injury, but where they frequently occur remains unclear owing to data limitations. Natural language processing can be applied to medical text narratives of gunshot injury records to classify injury location and inform prevention efforts. Objective: To examine the performance of natural language processing (NLP) and machine learning models to predict nonfatal gunshot injury locations and generate new national estimates of the locations in which these injuries occur. Design, Setting, and Participants: Cross-sectional study of data from the National Electronic Injury Surveillance System Firearm Injury Surveillance Study on nonfatal gunshot injuries that occurred in the US between January 1, 1993, and December 31, 2015. The unweighted sample included 59 025 gunshot injuries that were initially treated at emergency departments. Data were analyzed from June 1, 2019 to July 24, 2020. Main Outcomes and Measures: The primary outcomes were classification of injury location and subsequent estimation of nonfatal gunshot injury location. The NLP was used to generate 6 sets of predictors, and 4 machine learning models were trained to classify the missing locations: multinomial support vector machines, lasso regression, XgBoost gradient descent, and feed-forward neural networks. For each of the 6 sets of NLP predictors, 70% of records with locations were randomly sampled to form the training set and the remaining 30% of records composed the test set. The best-performing model was validated by comparing the predicted locations were with those from existing national estimates of nonfatal gunshot injuries stratified by location and intent. Results: The unweighted sample included 59 025 nonfatal gunshot injuries; patients with these injuries were predominantly male (n = 52 630, [89.2%]), of Black race/ethnicity (n = 29 304 [49.6%]), and young (15-24 years; n = 27 037 [45.8%]). In total, 54 089 nonfatal gunshot injuries that were weighted to approximate national estimates were included in the analysis. Existing national estimates suggest that the most prevalent nonfatal gunshot injury location is the home (n = 14 764 [23.4%]), followed by the street or highway (n = 14 402 [22.9%]), and other public places (n = 7276 [11.6%]). After implementation of NLP classification, the most frequent gunshot injury location was the street or highway (n = 27 200 [46.1%]), followed by the home (n = 23 738 [37.7%]), and other public places (n = 10 439 [15.1%]). Conclusions and Relevance: The findings of this study suggest that NLP and machine learning models may be useful for classifying gunshot injury location and that most nonfatal gunshot injuries occur in the street or highway rather than in the home; these findings can inform future firearm injury prevention efforts.

Sources of guns to dangerous people: what we learn by asking them.

Gun violence exacts a lethal toll on public health. This paper focuses on reducing access to firearms by dangerous offenders, contributing original empirical data on the gun transactions that arm offenders in Chicago. Conducted in the fall of 2013, analysis of an open-ended survey of 99 inmates of Cook County Jail focuses on a subset of violence-prone individuals with the goal of improving law enforcement actions. Among our principal findings: *Our respondents (adult offenders living in Chicago or nearby) obtain most of their guns from their social network of personal connections. Rarely is the proximate source either direct purchase from a gun store, or theft. *Only about 60% of guns in the possession of respondents were obtained by purchase or trade. Other common arrangements include sharing guns and holding guns for others. *About one in seven respondents report selling guns, but in only a few cases as a regular source of income. *Gangs continue to play some role in Chicago in organizing gun buys and in distributing guns to members as needed. *The Chicago Police Department has a considerable effect on the workings of the underground gun market through deterrence. Transactions with strangers and less-trusted associates are limited by concerns over arrest risk (if the buyer should happen to be an undercover officer or a snitch), and about being caught with a "dirty" gun (one that has been fired in a crime).

Early experience with sirolimus in lung transplant recipients with chronic allograft rejection.

BACKGROUND: Chronic lung allograft rejection, commonly manifest as obliterative bronchiolitis (OB/BOS), hinders long-term survival after lung transplantation (LT). OB/BOS is traditionally treated with augmented immunosuppression and results in short-term stabilization in pulmonary function for most patients. However, peribronchiolar fibroproliferation and airway obstruction usually recur despite initial improvements seen with increases in immunosuppression. In this observational, uncontrolled study, the effect of sirolimus, a novel immunosuppressant with anti-proliferative activity, was assessed in LT patients with OB/BOS. METHODS: Between June 1999 to November 2000, LT recipients with newly diagnosed or progressive OB/BOS received sirolimus in combination with a calcineurin inhibitor (CI) and prednisone. Pulmonary function, laboratory data and adverse effects were monitored for the first 24 weeks of therapy. RESULTS: Sirolimus was utilized in 12 LT recipients with OB/BOS. After drug initiation, 58% of patients required a reduction in CI dose to maintain appropriate CI trough concentrations. Despite CI dose reduction, serum creatinine rose in 75% of patients. Unexpected adverse effects included anemia of chronic disease (100%), edema (50%) and malignancy (17%). For the group, the rate of change in FEV(1) and FEF(25%-75%) was unchanged with sirolimus, but individual responses varied. CONCLUSIONS: For the group, the decline in pulmonary function was not affected by the addition of sirolimus. However, among individuals with rapidly declining pulmonary mechanics, sirolimus resulted in stabilization or improvement in pulmonary function. Significant adverse effects resulted from combination sirolimus plus CI therapy. Until optimal dosing strategies and a more complete adverse effect profile are established, combination therapy should be utilized cautiously in these patients.