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From Regression Analysis to Deep Learning: Development of Improved Proxy Measures of State-Level Household Gun Ownership

In the absence of direct measurements of state-level household gun ownership (GO), the quality and accuracy of proxy measures for this variable are essential for firearm-related research and policy development. In this work, we develop two highly accurate proxy measures of GO using traditional regre...

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Detalles Bibliográficos
Autores principales: Gomez, David Benjamin, Xu, Zhaoyi, Saleh, Joseph Homer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733878/
https://www.ncbi.nlm.nih.gov/pubmed/33336203
http://dx.doi.org/10.1016/j.patter.2020.100154
Descripción
Sumario:In the absence of direct measurements of state-level household gun ownership (GO), the quality and accuracy of proxy measures for this variable are essential for firearm-related research and policy development. In this work, we develop two highly accurate proxy measures of GO using traditional regression analysis and deep learning, the former accounting for non-linearities in the covariates (portion of suicides committed with a firearm [FS/S] and hunting license rates) and their statistical interactions. We subject the proxies to extensive model diagnostics and validation. Both our regression-based and deep-learning proxy measures provide highly accurate models of GO with training R(2) of 96% and 98%, respectively, along with other desirable qualities—stark improvements over the prevalent FS/S proxy (R(2) = 0.68). Model diagnostics reveal this widely used FS/S proxy is highly biased and inadequate; we recommend that it no longer be used to represent state-level household gun ownership in firearm-related studies.