Dataset Information

Biomarkers of autonomic regulation for predicting psychological distress and functional recovery following road traffic injuries: protocol for a prospective cohort study.

ABSTRACT:

Introduction

Psychological distress is a prevalent condition often overlooked following a motor vehicle crash (MVC), particularly when injuries are not severe. The aim of this study is to examine whether biomarkers of autonomic regulation alone or in combination with other factors assessed shortly after MVC could predict risk of elevated psychological distress and poor functional recovery in the long term, and clarify links between mental and physical health consequences of traffic injury.

Methods and analysis

This is a controlled longitudinal cohort study, with follow-up occurring at 3, 6 and 12 months. Participants include up to 120 mild to moderately injured MVC survivors who consecutively present to the emergency departments of two hospitals in Sydney and who agree to participate, and a group of up to 120 non-MVC controls, recruited with matched demographic characteristics, for comparison. WHO International Classification of Functioning is used as the framework for study assessment. The primary outcomes are the development of psychological distress (depressive mood and anxiety, post-traumatic stress symptoms, driving phobia, adjustment disorder) and biomarkers of autonomic regulation. Secondary outcomes include indicators of physical health (presence of pain/fatigue, physical functioning) and functional recovery (quality of life, return to function, participation) as well as measures of emotional and cognitive functioning. For each outcome, risk will be described by the frequency of occurrence over the 12 months, and pathways determined via latent class mixture growth modelling. Regression models will be used to identify best predictors/biomarkers and to study associations between mental and physical health.

Ethics and dissemination

Ethical approvals were obtained from the Sydney Local Health District and the research sites Ethics Committees. Study findings will be disseminated to health professionals, related policy makers and the community through peer-reviewed journals, conference presentations and health forums.

Trial registration number

ACTRN12616001445460.

SUBMITTER: Pozzato I

PROVIDER: S-EPMC6500247 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Biomarkers of autonomic regulation for predicting psychological distress and functional recovery following road traffic injuries: protocol for a prospective cohort study.

Pozzato Ilaria I Craig Ashley A Gopinath Bamini B Tran Yvonne Y Dinh Michael M Gillett Mark M Cameron Ian I

BMJ open 20190404 4

<h4>Introduction</h4>Psychological distress is a prevalent condition often overlooked following a motor vehicle crash (MVC), particularly when injuries are not severe. The aim of this study is to examine whether biomarkers of autonomic regulation alone or in combination with other factors assessed shortly after MVC could predict risk of elevated psychological distress and poor functional recovery in the long term, and clarify links between mental and physical health consequences of traffic injur ...[more]

PMID: 30948569

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Project description:ObjectivesEvery day, an average of 3,400 deaths and tens of millions of injuries occur as a result of traffic accidents. This study aims to model and validate road traffic injury (RTI) times series, specifically considering gender.Study designTime trend studies of monthly road traffic injuries (RTI) in Iran from March 2005 to February 2016, as well as those of males and females from March 2009 to February 2016 were performed.MethodsThe seasonal auto-regressive integrated moving average method (SARIMA) was employed to predict RTI time series. The final model was selected from various SARIMA models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). To examine whether the residuals were white noise, the Ljung-Box (LB) test and residuals plots were used for un-correlation, and the zero mean and stationarity, respectively. Additionally, smoothing methods were utilized to validate the SARIMA models for fitting and out-of-range prediction of the time series models under study. The sample auto-correlation function (ACF) and the partial autocorrelation function (PACF) with 20 lags were employed to determine the order of models and to ascertain if the residuals of the model were uncorrelated.ResultsBased on the obtained results, SARIMA (2,1,0)(0,1,1)12, SARIMA (0,1,1)(0,1,1)12, SARIMA (1,1,1)(0,0,1)12, and SARIMA (2,0,0)(1,0,0)12 were chosen for the time series including incidence rates of total road traffic injuries (IRTI), IRTI of males, females, and males-to-females, respectively. The AIC values were -87.57, 413.38, -732.91, and -85.32, respectively. The LB test for the residuals of the time series models of (0.539) IRTI, (0.3) IRTI of males, (0.23) females, and (0.237) males-to-females indicated that residuals were uncorrelated. Furthermore, prediction values for the next 24 months (2016 to 2018) showed no decline in the incidence rate of male and female traffic injuries. Results of the predictions using exponential smoothing methods indicated out-of-range prediction validity of the SARIMA models.ConclusionThis study exemplified the high efficiency of SARIMA models in predicting road traffic injuries (RTIs). Based on observations, the IRTI mean in Iran was 35.57 in 2016. The predicted values of the IRTI for 2016-2018 by the SARIMA model showed no decreasing trend. During the studied period, the observed values of IRTI for males were two to three times the female values. Thus, prediction of RTI can provide a useful tool for traffic safety policymaking by simulating interrupted time series when applying new traffic enforcement interventions and regulations in the future. Additionally, IRTI analysis of males and females showed that men had a non-increasing trend but higher incidence of traffic injuries, whereas the IRTI for women revealed an increasing trend from 2009 to 2012 with a lower incidence of injuries. This growth could be attributed to the impact of increased outdoor activities of women and the increased number of issued driving licenses in the period of 2009-2012.

Dataset Information

Biomarkers of autonomic regulation for predicting psychological distress and functional recovery following road traffic injuries: protocol for a prospective cohort study.

Introduction

Methods and analysis

Ethics and dissemination

Trial registration number

Publications

Biomarkers of autonomic regulation for predicting psychological distress and functional recovery following road traffic injuries: protocol for a prospective cohort study.

Similar Datasets

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