“…Operational and safety incidents were extracted from the Incident Information Management System—a state-wide incident reporting/monitoring system accessed by a wide range of rail professional staff employed by rail service providers in NSW to report incidents. Incidents deemed to be significant enough for inclusion were based on a detailed taxonomy, developed by coding incidents using conventional content analysis 23 , 24 . Figure 2 Timeline depicting the health assessment data extraction window in relation to the operational and safety incidents.…”
The rail industry in Australia screens workers for probable obstructive sleep apnea (OSA) due to known safety risks. However, existing criteria to trigger screening only identify a small proportion of workers with OSA. The current study sought to examine the relationship between OSA risk and rail incidents in real-world data from Australian train drivers, and conducted a proof of concept analysis to determine whether more conservative screening criteria are justified. Health assessment (2016–2018) and subsequent rail incident data (2016–2020) were collected from two passenger rail service providers. Predictors included OSA status (confirmed no OSA with a sleep study, controlled OSA, unknown OSA [no recorded sleep assessment data] and confirmed OSA with no indication of treatment); OSA risk according to the current Standard, and OSA risk according to more conservative clinical markers (BMI threshold and cardiometabolic burden). Coded rail safety incidents involving the train driver were included. Data were analysed using zero-inflated negative binomial models to account for over-dispersion with high 0 counts, and rail safety incidents are reported using Incidence Risk Ratios (IRRs). A total of 751 train drivers, typically middle-aged, overweight to obese and mostly men, were included in analyses. There were 43 (5.7%) drivers with confirmed OSA, 62 (8.2%) with controlled OSA, 13 (1.7%) with confirmed no OSA and 633 (84.4%) drivers with unknown OSA. Of the 633 train drivers with unknown OSA status, 21 (3.3%) met ‘at risk’ criteria for OSA according to the Standard, and incidents were 61% greater (IRR: 1.61, 95% Confidence Interval (CI) 1.02–2.56) in the years following their health assessment compared to drivers who did not meet ‘at risk’ criteria. A more conservative OSA risk status using lower BMI threshold and cardiometabolic burden identified an additional 30 ‘at risk’ train drivers who had 46% greater incidents compared to drivers who did not meet risk criteria (IRR (95% CI) 1.46 (1.00–2.13)). Our more conservative OSA risk criteria identified more workers, with greater prospective incidents. These findings suggest that existing validated tools could be considered in future iterations of the Standard in order to more sensitively screen for OSA.
“…Operational and safety incidents were extracted from the Incident Information Management System—a state-wide incident reporting/monitoring system accessed by a wide range of rail professional staff employed by rail service providers in NSW to report incidents. Incidents deemed to be significant enough for inclusion were based on a detailed taxonomy, developed by coding incidents using conventional content analysis 23 , 24 . Figure 2 Timeline depicting the health assessment data extraction window in relation to the operational and safety incidents.…”
The rail industry in Australia screens workers for probable obstructive sleep apnea (OSA) due to known safety risks. However, existing criteria to trigger screening only identify a small proportion of workers with OSA. The current study sought to examine the relationship between OSA risk and rail incidents in real-world data from Australian train drivers, and conducted a proof of concept analysis to determine whether more conservative screening criteria are justified. Health assessment (2016–2018) and subsequent rail incident data (2016–2020) were collected from two passenger rail service providers. Predictors included OSA status (confirmed no OSA with a sleep study, controlled OSA, unknown OSA [no recorded sleep assessment data] and confirmed OSA with no indication of treatment); OSA risk according to the current Standard, and OSA risk according to more conservative clinical markers (BMI threshold and cardiometabolic burden). Coded rail safety incidents involving the train driver were included. Data were analysed using zero-inflated negative binomial models to account for over-dispersion with high 0 counts, and rail safety incidents are reported using Incidence Risk Ratios (IRRs). A total of 751 train drivers, typically middle-aged, overweight to obese and mostly men, were included in analyses. There were 43 (5.7%) drivers with confirmed OSA, 62 (8.2%) with controlled OSA, 13 (1.7%) with confirmed no OSA and 633 (84.4%) drivers with unknown OSA. Of the 633 train drivers with unknown OSA status, 21 (3.3%) met ‘at risk’ criteria for OSA according to the Standard, and incidents were 61% greater (IRR: 1.61, 95% Confidence Interval (CI) 1.02–2.56) in the years following their health assessment compared to drivers who did not meet ‘at risk’ criteria. A more conservative OSA risk status using lower BMI threshold and cardiometabolic burden identified an additional 30 ‘at risk’ train drivers who had 46% greater incidents compared to drivers who did not meet risk criteria (IRR (95% CI) 1.46 (1.00–2.13)). Our more conservative OSA risk criteria identified more workers, with greater prospective incidents. These findings suggest that existing validated tools could be considered in future iterations of the Standard in order to more sensitively screen for OSA.
“…In addition to demographics, age (in years) and gender (1 = male, 2 = female, 3 = other, 4 = prefer not to say), and a series of questions relating to signals passed at danger (SPADs-an incident where the train exceeds the limit of its movement authority and attracts collision risk) 32,33 , respondents completed questions relating to work, sleep, wellbeing, and the impact of these factors on their driving performance.…”
Section: Methodsmentioning
confidence: 99%
“…Factors impacting on train driving. The perceived extent to which a series of factors negatively impacted respondents' personal driving performance was assessed using an adapted version 32 of the 56-item scale developed from the RSSB incident factor framework 36 . There were ten items relating to individual fatigue, health and wellbeing.…”
Train drivers work long hours on 24 h schedules and many factors impact their fatigue risk at work, creating a clear imperative for good rostering practice. Adopting a systems approach, this study investigated the relationship between multiple interrelated factors (train drivers’ schedule, sleep, wellbeing, and fatigue) and the perceived influence of these factors on train driving performance and safety using an online survey distributed in Australia and New Zealand. In addition to demographics and work schedule, passenger and freight train drivers (n = 751) answered questions about: (1) Sleep duration; (2) wellbeing, including physical and mental health, the extent to which shiftwork causes sleep, social, domestic, and work problems, and (3) the extent to which ten fatigue, health and wellbeing factors in the work and home environment negatively impact their driving performance. The key factor that emerged from analyses, with the largest and most consistent negative effects (and controlling for other factors) was schedule irregularity. Approved rosters were ranked as having the most important impact on day-to-day driving performance, followed by physical and mental health, and outside work factors. Results also suggested that schedule irregularity may amplify the negative impacts of the roster, impaired physical and mental health, and outside work factors on driving performance. As shift variability and schedule irregularity are often poorly represented in existing industry guidance, these results provide evidence for increased reflection on current fatigue management guidelines for train drivers and suggest a need for greater focus on schedule irregularity through the lens of a systems approach.
“…Visibility of signals is one factor that is often considered within rail. Another example of a factor is signal placement, with a focus on whether it might be confusing for the train driver to know which signal is for him/her (Wilson and Norris, 2005;Hamilton and Clarke, 2005;Lawton and Ward, 2005;Naweed et al, 2019;Stanton and Walker, 2011). Route knowledge can help to prevent mistakes as a result of problems in signal visibility or interpretation (Punzet et al, 2018;Naweed et al, 2013).…”
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