Citation

Bermúdez L, Karlis D, Santolino M. Accid. Anal. Prev. 2018; 121: 157-165.

Affiliation

University of Barcelona, Riskcenter-IREA, Spain. Electronic address: msantolino@ub.edu.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.aap.2018.09.006

PMID

30248531

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.

Copyright © 2018 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Motor accident; Multiple Poisson; Multiple negative binomial; Work disability days