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1.
J Trauma Acute Care Surg ; 92(1): 82-87, 2022 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-34284466

RESUMO

BACKGROUND: Current data on the epidemiology of firearm injury in the United States are incomplete. Common sources include hospital, law enforcement, consumer, and public health databases, but each database has limitations that exclude injury subgroups. By integrating hospital (inpatient and outpatient) and law enforcement databases, we hypothesized that a more accurate depiction of the totality of firearm injury in our region could be achieved. METHODS: We constructed a collaborative firearm injury database consisting of all patients admitted as inpatients to the regional level 1 trauma hospital (inpatient registry), patients treated and released from the emergency department (ED), and subjects encountering local law enforcement as a result of firearm injury in Jefferson County, Kentucky. Injuries recorded from January 1, 2016, to December 31, 2020, were analyzed. Outcomes, demographics, and injury detection rates from individual databases were compared with those of the combined collaborative database and compared using χ2 testing across databases. RESULTS: The inpatient registry (n = 1,441) and ED database (n = 1,109) were combined, resulting in 2,550 incidents in the hospital database. The law enforcement database consisted of 2,665 patient incidents, with 2,008 incidents in common with the hospital database and 657 unique incidents. The merged collaborative database consisted of 3,207 incidents. In comparison with the collaborative database, the inpatient, total hospital (inpatient and ED), and law enforcement databases failed to include 55%, 20%, and 17% of all injuries, respectively. The hospital captured nearly 94% of survivors but less than 40% of nonsurvivors. Law enforcement captured 93% of nonsurvivors but missed 20% of survivors. Mortality (11-26%) and injury incidence were markedly different across the databases. DISCUSSION: The utilization of trauma registry or law enforcement databases alone do not accurately reflect the epidemiology of firearm injury and may misrepresent areas in need of greater injury prevention efforts. LEVEL OF EVIDENCE: Epidemiological, level IV.


Assuntos
Bases de Dados Factuais , Armas de Fogo/legislação & jurisprudência , Sistemas de Informação Hospitalar/estatística & dados numéricos , Aplicação da Lei/métodos , Saúde Pública , Sistema de Registros , Ferimentos por Arma de Fogo , Adulto , Confiabilidade dos Dados , Bases de Dados Factuais/normas , Bases de Dados Factuais/estatística & dados numéricos , Serviço Hospitalar de Emergência/estatística & dados numéricos , Feminino , Humanos , Incidência , Armazenamento e Recuperação da Informação/métodos , Armazenamento e Recuperação da Informação/estatística & dados numéricos , Masculino , Avaliação das Necessidades , Saúde Pública/métodos , Saúde Pública/normas , Saúde Pública/estatística & dados numéricos , Sistema de Registros/normas , Sistema de Registros/estatística & dados numéricos , Estados Unidos/epidemiologia , Ferimentos por Arma de Fogo/diagnóstico , Ferimentos por Arma de Fogo/epidemiologia , Ferimentos por Arma de Fogo/prevenção & controle
2.
J Trauma Acute Care Surg ; 89(2): 371-376, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32345906

RESUMO

BACKGROUND: Recidivism is a key outcome measure for injury prevention programs. Firearm injury recidivism rates are difficult to determine because of poor longitudinal follow-up and incomplete, disparate databases. Reported recidivism rates from trauma registries are 2% to 3%. We created a collaborative database merging law enforcement, emergency department, and inpatient trauma registry data to more accurately determine rates of recidivism in patients presenting to our trauma center following firearm injury. METHODS: A collaborative database for Jefferson County, Kentucky, was constructed to include violent firearm injuries encountered by the trauma center or law enforcement from 2008 to 2019. Iterative deterministic data linkage was used to create the database and eliminate redundancies. From patients with at least one hospital encounter, raw recidivism rates were calculated by dividing the number of patients injured at least twice by the total number of patients. Cox proportional hazard models were used to evaluate risk factors for recidivism. The cumulative incidence of recidivism over time was estimated using a Kaplan-Meier survival model. RESULTS: There were 2, 363 assault-type firearm injuries with at least 1 hospital encounter, approximately 9% of which did not survive their initial encounter. The collaborative database demonstrated raw recidivism rates for assault-type firearm injuries of 9.5% compared with 2.5% from the trauma registry alone. Risk factors were young age, male sex, and African American race. The predicted incidence of recidivism was 3.6%, 5.6%, 11.4%, and 15.8% at 1, 2, 5, and 10 years, respectively. CONCLUSION: Both hospital and law enforcement data are critical for determining reinjury rates in patients treated at trauma centers. Recidivism rates following violent firearm injury are four times higher using a collaborative database compared with the inpatient trauma registry alone. Predicted incidence of recidivism at 10 years was at least 16% for all patients, with high-risk subgroups experiencing rates as high as 26%. LEVEL OF EVIDENCE: Epidemiological, level III.


Assuntos
Bases de Dados Factuais , Sistema de Registros , Ferimentos por Arma de Fogo/epidemiologia , Adulto , Negro ou Afro-Americano/estatística & dados numéricos , Distribuição por Idade , Serviço Hospitalar de Emergência , Humanos , Incidência , Estimativa de Kaplan-Meier , Kentucky/epidemiologia , Aplicação da Lei , Recidiva , Estudos Retrospectivos , Fatores de Risco , Distribuição por Sexo , Ferimentos por Arma de Fogo/etnologia , Adulto Jovem
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