Dataset Information

Predictors of injury mortality: findings from a large national cohort in Thailand.

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.

SUBMITTER: Yiengprugsawan V

PROVIDER: S-EPMC4054638 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Publications

Predictors of injury mortality: findings from a large national cohort in Thailand.

Yiengprugsawan Vasoontara V Berecki-Gisolf Janneke J Bain Christopher C McClure Roderick R Seubsman Sam-Ang SA Sleigh Adrian C AC

BMJ open 20140605 6

<h4>Objective</h4>To present predictors of injury mortality by types of injury and by pre-existing attributes or other individual exposures identified at baseline.<h4>Design</h4>5-year prospective longitudinal study.<h4>Setting</h4>Contemporary Thailand (2005-2010), a country undergoing epidemiological transition.<h4>Participants</h4>Data derived from a research cohort of 87 037 distance-learning students enrolled at Sukhothai Thammathirat Open University residing nationwide.<h4>Measures</h4>Coh ...[more]

PMID: 24902726

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Project description:BackgroundThere is not enough evidence regarding how information obtained from general health check-ups can predict individual mortality based on long-term follow-ups and large sample sizes. This study evaluated the applicability of various health information and measurements, consisting of self-reported data, anthropometric measurements and laboratory test results, in predicting individual mortality.MethodsThe National Health Screening Cohort included 514,866 participants (aged 40-79 years) who were randomly selected from the overall database of the national health screening program in 2002-2003. Death was determined from causes of death statistics provided by Statistics Korea. We assessed variables that were collected at baseline and repeatedly measured for two consecutive years using traditional and time-variant Cox proportional hazards models in addition to random forest and boosting algorithms to identify predictors of 10-year all-cause mortality. Participants' age at enrollment, lifestyle factors, anthropometric measurements and laboratory test results were included in the prediction models. We used c-statistics to assess the discriminatory ability of the models, their external validity and the ratio of expected to observed numbers to evaluate model calibration. Eligibility of Medicaid and household income levels were used as inequality indexes.ResultsAfter the follow-up by 2013, 38,031 deaths were identified. The risk score based on the selected health information and measurements achieved a higher discriminatory ability for mortality prediction (c-statistics = 0.832, 0.841, 0.893, and 0.712 for Cox model, time-variant Cox model, random forest and boosting, respectively) than that of the previous studies. The results were externally validated using the community-based cohort data (c-statistics = 0.814).ConclusionsIndividuals' health information and measurements based on health screening can provide early indicators of their 10-year death risk, which can be useful for health monitoring and related policy decisions.

Dataset Information

Predictors of injury mortality: findings from a large national cohort in Thailand.

Objective

Design

Setting

Participants

Measures

Results

Conclusions

Publications

Predictors of injury mortality: findings from a large national cohort in Thailand.

Similar Datasets

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