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Loss of consciousness and pre-injury psychological issues are associated with persistent post-concussional symptom reporting. Not receiving information about mild traumatic brain injuries in the emergency department may also negatively influence symptom reporting. Objectives: Debate regarding factors associated with persistent symptoms following mild traumatic brain injury continues. Nested within a trial aiming to change practice in emergency department management of mild traumatic brain injury, this study investigated the nature of persistent symptoms, work/ study outcomes, anxiety and quality of life and factors associated with persistent symptoms following injury, including the impact of receiving information about mild traumatic brain injuries in the emergency department. Methods: A total of 343 individuals with mild traumatic brain injury completed the Rivermead Post-Concussion Symptom Questionnaire, Hospital Anxiety Depression Scale-Anxiety Scale, and Quality of Life-Short Form an average 7 months post-injury. Results: Overall, 18.7% of participants reported 3 or more post-concussional symptoms, most commonly fatigue (17.2%) and forgetfulness (14.6%). Clinically significant anxiety was reported by 12.8%, and was significantly associated with symptom reporting, as were mental and physical quality of life scores. Significant predictors of post-concussional symptoms at follow-up were pre-injury psychological issues, experiencing loss of consciousness, and having no recall of receiving information about brain injury in the emergency department. Conclusion: This study confirms that loss of consciousness and pre-injury psychological issues are associated with persistent symptom reporting. Not receiving injury information in the emergency department may also negatively influence symptom reporting.

Background The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets. Methods A random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods. Results From the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4 to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series. Conclusions The choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.

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Background Interrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. However, examination of the performance of statistical methods for this design has received relatively little attention. Methods We simulated continuous data to compare the performance of a set of statistical methods under a range of scenarios which included different level and slope changes, varying lengths of series and magnitudes of lag-1 autocorrelation. We also examined the performance of the Durbin-Watson (DW) test for detecting autocorrelation. Results All methods yielded unbiased estimates of the level and slope changes over all scenarios. The magnitude of autocorrelation was underestimated by all methods, however, restricted maximum likelihood (REML) yielded the least biased estimates. Underestimation of autocorrelation led to standard errors that were too small and coverage less than the nominal 95%. All methods performed better with longer time series, except for ordinary least squares (OLS) in the presence of autocorrelation and Newey-West for high values of autocorrelation. The DW test for the presence of autocorrelation performed poorly except for long series and large autocorrelation. Conclusions From the methods evaluated, OLS was the preferred method in series with fewer than 12 points, while in longer series, REML was preferred. The DW test should not be relied upon to detect autocorrelation, except when the series is long. Care is needed when interpreting results from all methods, given confidence intervals will generally be too narrow. Further research is required to develop better performing methods for ITS, especially for short series.