A discrete mixture regression for modeling the duration of non-hospitalization medical leave of motor accident victims. (December 2018)

• Record Type: Journal Article
• Authors: Bermúdez, Lluís; Karlis, Dimitris; Santolino, Miguel
• Is Part Of: Accident analysis and prevention. Volume 121(2018)
• Page Start: 157
• Page End: 165
• Publication Date: 2018-12
• Subjects: Motor accident -- Multiple negative binomial -- Multiple Poisson -- Work disability days; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2018.09.006
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Risk factors influencing the length of sick leave of motor victims are analyzed. The distribution of the disability exhibits regular spikes in multiples of weeks and months. A regression model based on finite mixtures of multiple discrete distributions is fitted. The model specification captured the spikes in the data very accurately. Males, drivers and the elderly face longer expected periods of temporary disability. Abstract: Studies analyzing the temporary repercussions of motor vehicle accidents are scarcer than those analyzing permanent injuries or mortality. A regression model to evaluate the risk factors affecting the duration of temporary disability after injury in such an accident is constructed using a motor insurance dataset. The length of non-hospitalization medical leave, measured in days, following a motor accident is used here as a measure of the severity of temporary disability. The probability function of the number of days of sick leave presents spikes in multiples of five (working week), seven (calendar week) and thirty (month), etc. To account for this, a regression model based on finite mixtures of multiple discrete distributions is proposed to fit the data properly. The model provides a very good fit when the multiples for the working week, week, fortnight and month are taken into account. Victim characteristics of gender and age and accident characteristics of the road user type, vehicle class and the severity of permanent injuries were found to be significant when accounting for the duration of temporary disability.