RT Journal Article
SR Electronic
T1 Predictors of injury mortality: findings from a large national cohort in Thailand
JF BMJ Open
JO BMJ Open
FD British Medical Journal Publishing Group
SP e004668
DO 10.1136/bmjopen-2013-004668
VO 4
IS 6
A1 Vasoontara Yiengprugsawan
A1 Janneke Berecki-Gisolf
A1 Christopher Bain
A1 Roderick McClure
A1 Sam-ang Seubsman
A1 Adrian C Sleigh
YR 2014
UL http://bmjopen.bmj.com/content/4/6/e004668.abstract
AB Objective To present predictors of injury mortality by types of injury and by pre-existing attributes or other individual exposures identified at baseline. Design 5-year prospective longitudinal study. Setting Contemporary Thailand (2005–2010), a country undergoing epidemiological transition. Participants Data derived from a research cohort of 87 037 distance-learning students enrolled at Sukhothai Thammathirat Open University residing nationwide. Measures Cohort members completed a comprehensive baseline mail-out questionnaire in 2005 reporting geodemographic, behavioural, health and injury data. These responses were matched with national death records using the Thai Citizen ID number. Age–sex adjusted multinomial logistic regression was used to calculate ORs linking exposure variables collected at baseline to injury deaths over the next 5 years. Results Statistically significant predictors of injury mortality were being male (adjustedOR 3.87, 95% CI 2.39 to 6.26), residing in the southern areas (AOR 1.71, 95% CI 1.05 to 2.79), being a current smoker (1.56, 95% CI 1.03 to 2.37), history of drunk driving (AOR 1.49, 95% CI 1.01 to 2.20) and ever having been diagnosed for depression (AOR 1.91, 95% CI 1.00 to 3.69). Other covariates such as being young, having low social support and reporting road injury in the past year at baseline had moderately predictive AORs ranging from 1.4 to 1.6 but were not statistically significant. Conclusions We complemented national death registration with longitudinal data on individual, social and health attributes. This information is invaluable in yielding insight into certain risk traits such as being a young male, history of drunk driving and history of depression. Such information could be used to inform injury prevention policies and strategies.