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01-08-2019 | Uitgave 4/2019

Journal of Behavioral Medicine 4/2019

Bolstering gun injury surveillance accuracy using capture–recapture methods

Tijdschrift:
Journal of Behavioral Medicine > Uitgave 4/2019
Auteurs:
Lori Ann Post, Zev Balsen, Richard Spano, Federico E. Vaca
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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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