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Gepubliceerd in: Journal of Behavioral Medicine 4/2019

01-08-2019

Bolstering gun injury surveillance accuracy using capture–recapture methods

Auteurs: Lori Ann Post, Zev Balsen, Richard Spano, Federico E. Vaca

Gepubliceerd in: Journal of Behavioral Medicine | Uitgave 4/2019

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Abstract

Using a single source of data, such as police records, or combining data from multiple sources results in an undercount of gun-related injuries. To improve gun-related injury surveillance accuracy by using capture–recapture methods, data were culled from law enforcement, emergency departments, emergency medical services, media, and medical examiner records. The data overlap was operationalized using capture–recapture to generate estimates of uncounted gun incidents. Dependencies between data sources were controlled using log-linear modeling for accurate estimates. New Haven, Connecticut. The study population included subjects injuried/killed from a gun projectile. Incidence was measured using capture–recapture. 49 gun injuries occurred within the defined geography. No single source recorded more than 43 gun-related injuries/deaths. Log-linear modeling estimated the actual number of injuries to be 49.1 (95% CI 49–49.9). Capture–recapture may be less useful in large metropolitan areas that cross state geographical boundaries because of how government agency data are aggregated within each state. No single data source achieves complete gun-related case ascertainment. Log-linear and capture–recapture methods significantly improve gun-related injury estimates.
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Metagegevens
Titel
Bolstering gun injury surveillance accuracy using capture–recapture methods
Auteurs
Lori Ann Post
Zev Balsen
Richard Spano
Federico E. Vaca
Publicatiedatum
01-08-2019
Uitgeverij
Springer US
Gepubliceerd in
Journal of Behavioral Medicine / Uitgave 4/2019
Print ISSN: 0160-7715
Elektronisch ISSN: 1573-3521
DOI
https://doi.org/10.1007/s10865-019-00017-4

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