Modelling the relationship of driver license and offense history with fatal and serious injury (FSI) crash involvement.

INTRODUCTION: Previous research has indicated that increases in traffic offenses are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study. METHOD: A preliminary cluster analysis was performed to describe the offense data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables, the key traffic offenses that predict future FSI crash involvement were then identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the 'all driver' sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle license, a heavy vehicle license endorsement and/or a history of license bans. RESULTS: With the exception of the group of drivers who had a history of bans, offense history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offenses may be an important factor to consider when analyzing FSI crash involvement risk and the effects of road safety countermeasures. CONCLUSIONS: The results are helpful for identifying driver groups to target with further road safety countermeasures, and for showing that machine learning methods have an important role to play in research of this nature. Practical Application: This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offenses related to FSI crash involvement and repeat traffic offenders, who are at greater risk of FSI crash involvement, are recommended.

A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data.

BACKGROUND: Expenditure on driver-related behavioral interventions and road use policy is often justified by their impact on the frequency of fatal and serious injury crashes. Given the rarity of fatal and serious injury crashes, offense history, and crash history of drivers are sometimes used as an alternative measure of the impact of interventions and changes to policy. The primary purpose of this systematic review was to assess the rigor of statistical modeling used to predict fatal and serious crashes from offense history and crash history using a purpose-made quality assessment tool. A secondary purpose was to explore study outcomes. METHODS: Only studies that used observational data and presented a statistical model of crash prediction from offense history or crash history were included. A quality assessment tool was developed for the systematic evaluation of statistical quality indicators across studies. The search was conducted in June 2019. RESULTS: One thousand one hundred and five unique records were identified, 252 full texts were screened for inclusion, resulting in 20 studies being included in the review. The results indicate substantial and important limitations in the modeling methods used. Most studies demonstrated poor statistical rigor ranging from low to middle quality. There was a lack of confidence in published findings due to poor variable selection, poor adherence to statistical assumptions relating to multicollinearity, and lack of validation using new data. CONCLUSIONS: It was concluded that future research should consider machine learning to overcome correlations in the data, use rigorous vetting procedures to identify predictor variables, and validate statistical models using new data to improve utility and generalizability of models. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019137081.

Computer-based cognitive interventions for mild cognitive impairment and dementia in older adults: protocol for a systematic review of published studies and meta-analysis.

BACKGROUND: A growing number of older adults experience mild cognitive impairment (MCI) and dementia. Recent technological advances allow for traditional cognitive interventions to be administered via computers and other devices. The aim of the proposed systematic review and meta-analyses is to determine the efficacy of computerised cognitive interventions for MCI and dementia in older adults. METHODS: We will systematically search electronic databases and reference lists to identify randomised controlled trials. We will include studies that examine the use of computer-based cognitive interventions for adults aged over 60 with MCI or dementia. Primarily outcomes are global and domain-specific cognitive function. Secondary outcomes are attitudes (usability, understandability, acceptability of the intervention), mood and quality of life. Risk of bias will be assessed. Finally, the summary effect sizes will be reported. DISCUSSION: This systematic review will summarise existing high-quality primary studies on computerised-cognitive interventions for MCI and dementia. Results from this review will provide the basis for future research in developing computer-based interventions for this population. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016050236.

A systematic review of factors that influence the efficacy of cognitive remediation therapy in schizophrenia.

OBJECTIVE: Cognitive remediation therapy is a moderately effective intervention for ameliorating cognitive deficits in individuals with schizophrenia-related disorders. With reports of considerable variability in individual response to cognitive remediation therapy, we need to better understand factors that influence cognitive remediation therapy efficacy to realise its potential. A systematic review was conducted to identify and evaluate predictors of cognitive outcome. METHODS: An electronic database search was conducted identifying peer-reviewed articles examining predictors of cognitive response to cognitive remediation therapy. RESULTS: A total of 40 articles accounting for 1681 cognitive remediation therapy participants were included; 81 distinct predictors of cognitive response were identified. Data synthesis and discussion focused on 20 predictors examined a minimum three times in different studies. Few of the examined predictors of cognitive outcome following cognitive remediation therapy were significant when examined through systematic review. A strong trend was found for baseline cognition, with reasoning and problem solving and working memory being strongly predictive of within-domain improvement. Training task progress was the most notable cross-domain predictor of cognitive outcome. CONCLUSION: It remains unclear why a large proportion of participants fail to realise cognitive benefit from cognitive remediation therapy. However, when considering only those variables where a majority of articles reported a statistically significant association with cognitive response to cognitive remediation therapy, three stand out: premorbid IQ, baseline cognition and training task progress. Each of these relates in some way to an individual's capacity or potential for change. There is a need to consolidate investigation of potential predictors of response to cognitive remediation therapy, strengthening the evidence base through replication and collaboration.

Types of avoidance in hair-pulling disorder (trichotillomania): An exploratory and confirmatory analysis.

Hair-pulling disorder (HPD) or Trichotillomania is a complex disorder with frequent relapses. Avoidance has been highlighted as an important behavioural feature in HPD. To improve our understanding of avoidance, two studies were conducted to identify the types of avoidance that may be experienced by those who pull hair. Internet questionnaires were used to collect data. Data from study one was split into two subsets. An exploratory factor analysis was conducted to identify the different types of avoidance experienced by those reporting symptoms of hair pulling (subset one, n = 278), followed by a confirmatory factor analysis (subset two, n = 295). In study two a MANOVA was conducted (n = 300) to examine whether levels of avoidance differed between controls and those with hair pulling symptoms. Participants with hair pulling symptoms had greater levels of avoidance on each of the five types: 'Avoidance of non-social goals', 'Self-concealment', 'Behavioural social avoidance', 'Avoidance of relationship problem solving' and 'Avoidance of thinking about the future'. These data expand on the current literature, which has predominantly focused on experiential avoidance. Future research will need to validate these findings in a clinical group.

Systematic Review of Published Primary Studies of Neuropsychology and Neuroimaging in Trichotillomania.

OBJECTIVES: Existing models of trichotillomania (TTM; hair pulling disorder) rely heavily on a biological predisposition or biological pathogenesis of the disorder, but fail to capture the specific neuropsychological mechanisms involved. The present systematic review aims to scope existing neuropsychological studies of TTM to explore gaps in current models. METHODS: A systematic literature search was conducted to detect all published primary studies using neuropsychological and neuroimaging measures in a cohort of individuals experiencing TTM. Studies addressing neuropsychological function were divided into domains. Findings from imaging studies were considered within brain regions and across methodology. RESULTS: Thirty studies with a combined 591 participants with TTM, 372 healthy controls and 225 participants in other types of control group were included. Sixteen studies investigated neuropsychological parameters, and 14 studies pursued neuroimaging technologies. Available studies that used neuropsychological assessments and reported a statistically significant difference between those with TTM and controls ranged in effect size from 0.25 to 1.58. All domains except verbal ability and visual ability reported a deficit. In neuroimaging studies, several structural and functional brain changes were reported that might be of significance to TTM. Only tentative conclusions can be made due to the use of multiple methodologies across studies, a major limitation to meaningful interpretations. CONCLUSIONS: Positive neuropsychological and neuroimaging results require replication, preferably with multi-site studies using standardized methodology. Increased standardized testing and analyses across the literature, as a whole, would improve the utility and interpretability of knowledge in this field. (JINS, 2018, 24, 188-205).