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Journal Article

Citation

Yiengprugsawan V, Berecki-Gisolf J, Bain C, McClure RJ, Seubsman SA, Sleigh AC. BMJ Open 2014; 4(6): e004668.

Affiliation

National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia.

Copyright

(Copyright © 2014, BMJ Publishing Group)

DOI

10.1136/bmjopen-2013-004668

PMID

24902726

Abstract

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.


Language: en

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