Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid ...methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.
The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.
After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).
The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.
This observational study aimed to investigate temporal trends in transport-related injuries in New Zealand by mode of transport and explore whether specific population groups and localities have a ...relatively higher incidence of injury. These trends provide insight into changes in injury patterns from road trauma.
A retrospective study of hospitalised road trauma in New Zealand was conducted between 1 July 2017 to 30 June 2021. Data were obtained from the National Minimum Dataset of hospital admissions, and the New Zealand Trauma Registry (NZTR). Road trauma was identified using ICD-10 coding, and major trauma using Abbreviated Injury Scale (AIS) coding. Analysis included road trauma by mode, ethnicity, rurality and population rates. Statistical analysis included Interrupted Time Series (ITS) analysis to account for the impact of COVID-19 on road trauma.
Over the 4-year period there were 20,607 incidents of transport-related injury that resulted in admission to a New Zealand hospital. Of these, 14.5% (2,992) involved injuries that were classified as major trauma. Car occupants accounted for 62% of hospitalisations, followed by motorcyclists (23%), pedestrians (9%) and pedal cyclists (4%). Temporal trends showed no reduction in injuries from cars, pedal cyclists and pedestrian injuries, but an increase in motorcycling injuries. Māori had an age-standardised incidence rate almost 3.5 times higher than the rate for Asian peoples.
The increases in motorcycling injuries and no changes in pedestrian and cycling injuries, as well as demographic variation, highlight the need to focus on vulnerable road users. Effective and targeted initiatives on vulnerable road users will support objectives to reduce deaths and serious injury on New Zealand roads. Enhanced exposure data is needed for vulnerable road users to account for mobility changes over time. Linked data across population-based datasets is an important asset that enhances our understanding of road traffic injuries and allows evidence-based countermeasures to be developed.
Distal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, ...guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The "Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)" study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data.
Adult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS.
The PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture.
The Personality Inventory for DSM-5 (PID-5) is a self-report measure of personality pathology designed to measure pathological personality traits outlined in the DSM-5 alternative model of ...personality disorders. Within the extensive literature exploring the relationship between personality and disordered eating, there are few that explore the relationship between the PID-5 and disordered eating behaviours in a non-clinical sample of males and females: restrictive eating, binge eating, purging, chewing and spitting, excessive exercising and muscle building.
An online survey assessed disordered eating, PID-5 traits and general psychopathology and was completed by 394 female and 167 male participants aged 16-30. Simultaneous equations path models were systematically generated for each disordered eating behaviour to identify how the PID-5 scales, body dissatisfaction and age predicted behaviour.
The results indicated that each of the six disordered behaviours were associated with a unique pattern of maladaptive personality traits. The statistical models differed between males and females indicating possible differences in how dimensional personality pathology and disordered eating relate.
It was concluded that understanding disordered eating behaviour in the context of personality pathology may assist formulating potentially risky behaviour.
Reference data for dental age estimate is sparse in New Zealand (NZ), with only two contemporary studies. Te Moananui et al. (J For Sci. 53(2), 2008) presented modified Demirjian percentile curves to ...estimate dental age of Pasifika, Māori, and European males and females (n = 1383), while Timmins et al. (Forensic Sci Med Pathol. 8:101-8, 2012) found the Demirjian method (1973) was valid for a smaller sample (n = 200) of unknown ancestry. The study presented here sought to validate the Demirjian and the Te Moananui methods for a sample of the NZ population of unknown ancestry and a subgroup of known ancestry i.e., Pasifika, Māori and European, for males and females. The Demirjian method (1976) was applied to the current study's sample consisting of 3523 individuals aged 4 to < 20 years. The seven left mandibular teeth (third molar excluded) and tooth scores were summed for each individual, with the Te Moananui methods applied to this subgroup. The results revealed these methods to be less than ideal for estimating dental age of the NZ sample, for both males and females. The probit regression form of Transition Analysis (TA) was employed to calculate the mean age entering each tooth stage, for the seven teeth, to reduce age mimicry that is commonly associated with traditional regression analysis. TA results revealed Pasifika and Māori individuals to be more advanced than Caucasian individuals. The sex groups were also compared to the mean ages presented by Demirjian and Levesque with mixed results (J Dent Res. 59(7):1110-22, 1980), highlighting the need for more research in this area.
There is a wealth of research that has highlighted the relationship between personality and eating disorders. It has been suggested that understanding how subclinical disordered eating behaviours are ...uniquely associated with personality can help to improve the conceptualization of individuals with eating disorders. This study aimed to explore how the facets of the Five‐Factor Model (FFM) predicted restrictive eating, binge eating, purging, chewing and spitting, excessive exercising and muscle building among males and females. An online survey assessing disordered eating behaviours, FFM and general psychopathology was completed by 394 females and 167 males aged between 16 and 30 years. Simultaneous equations path models were systematically generated for each disordered eating behaviour to identify how the FFM facets, body dissatisfaction and age predicted behaviour. The results indicated that each of the six disordered behaviours were predicted by a unique pattern of thinking, feeling and behaving. Considerable differences between males and females were found for each path model, suggesting differences between males and females in the personality traits that drive disordered eating behaviours. It was concluded that it is important to take personality into account when treating males and females who engage in disordered eating behaviours.
Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and ...treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study.
A multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and Nutrition Examination Study (2009-2010) (N = 3,922). A Self-organised Mapping (SOM) ML algorithm, combined with hierarchical clustering, was performed to create participant clusters based on 68 medical symptoms. Binary logistic regression, controlling for sociodemographic confounders, was used to then identify the key clusters of participants with higher levels of depression (PHQ-9≥10, n = 377). Finally, a Multiple Additive Regression Tree boosted ML algorithm was run to identify the important medical symptoms for each key cluster within 17 broad categories: heart, liver, thyroid, respiratory, diabetes, arthritis, fractures and osteoporosis, skeletal pain, blood pressure, blood transfusion, cholesterol, vision, hearing, psoriasis, weight, bowels and urinary.
Five clusters of participants, based on medical symptoms, were identified to have significantly increased rates of depression compared to the cluster with the lowest rate: odds ratios ranged from 2.24 (95% CI 1.56, 3.24) to 6.33 (95% CI 1.67, 24.02). The ML boosted regression algorithm identified three key medical condition categories as being significantly more common in these clusters: bowel, pain and urinary symptoms. Bowel-related symptoms was found to dominate the relative importance of symptoms within the five key clusters.
This methodology shows promise for the identification of conditions in general populations and supports the current focus on the potential importance of bowel symptoms and the gut in mental health research.
Trampolines are an important cause of childhood injury and focus of injury prevention. Understanding and prevention of trampoline park injury is constrained by inadequate exposure data to estimate ...the at-risk population. This study aimed to measure trampoline park injury incidence and time trends using industry data.
Cross-sectional study to retrospectively analyze reported injuries and exposure in 18 trampoline parks operating in Australia and the Middle East, from 2017 to 2019. Exposure was derived from ticket sales and expressed as jumper hours. Exposure-adjusted incidence was measured using marginalized 0-inflated Poisson modeling and time trends using Joinpoint regression.
There were 13 256 injured trampoline park users reported from 8 387 178 jumper hours; 11% sustained significant injury. Overall, trampoline park injuries occurred at a rate of 1.14 injuries per 1000 jumper hours (95% confidence intervals 1.00 to 1.28), with rates highest for high-performance (2.11/1000 jumper hours, 1.66 to 2.56) and inflatable bag or foam pit (1.91/1000 jumper hours, 1.35 to 2.50) jumping. Significant injuries occurred at a rate of 0.11 injuries per 1000 jumper hours (0.10 to 0.13), with rates highest for high-performance (0.29/1000 jumper hours, 0.23 to 0.36), and parkour (0.22/1000 jumper hours, 0.15 to 0.28) jumping. Overall, injury rates decreased by 0.72%/month (-1.05 to -0.40) over the study period.
Trampoline park injuries occur in important numbers with sometimes serious consequences. However, within these safety standard-compliant parks, exposure-adjusted estimates show injuries to be uncommon and injury rates to be declining. Further reductions are required, especially severe injuries, and this study can enhance injury prevention initiatives.
This study aimed to characterise recovery from pain and mental health symptoms, and identify whether treatment use facilitates recovery.
Victorian State Trauma Registry and Victorian Orthopaedic ...Trauma Outcomes Registry participants without neurotrauma who had transport injury claims with the Transport Accident Commission from 2007 to 2014 were included (n = 5908). Latent transition analysis of pain Numeric Rating Scale, SF-12, and EQ-5D-3L pain and mental health items from 6 to 12 months, and 12 to 24 months post-injury were used to identify symptom transitions.
Four transition groups were identified: transition to low problems by 12-months; transition to low problems at 24-months; stable low problems; and no transition from problems. Group-based trajectory modelling of pain and mental health treatments found three treatment trajectories: low/no treatment, a moderate treatment that declined to low treatment 3-12 months post-injury, and increasing treatment over time. Predictors of pain and mental health recovery transitions, identified using multinomial logistic regression, were primarily found to be non-modifiable socioeconomic and health-related characteristics (e.g., higher education, working pre-injury, and not having comorbidities), and low treatment trajectories.
Targeted and collaborative rehabilitation should be considered for people at risk of persistent pain or mental health symptoms to optimise their recovery, particularly patients with socioeconomic disadvantage.
IMPLICATIONS FOR REHABILITATION
Two-thirds of people experience pain and/or mental health within the first 24-months after hospitalization for road trauma, of whom only 6-7% recover by 12-months, and a further 6% recover by 24-months post-injury.
There were three main trajectories of administrative records of treatments received in the first two years after injury: 76 and 83% had low treatment, 18 and 12% had moderate then declining treatment levels, and 6 and 5% had stable high treatment for pain or mental health, respectively.
People who recovered from pain or mental health symptoms generally had lower treatment and higher socioeconomic position, highlighting that coordinated rehabilitation care should be prioritized for people living with socioeconomic disadvantage.
Abstract Background Type 2 diabetes and depression are commonly comorbid high-prevalence chronic disorders. Diet is a key diabetes risk factor and recent research has highlighted the relevance of ...diet as a possible risk for factor common mental disorders. This study aimed to investigate the interrelationship among dietary patterns, diabetes and depression. Methods Data were integrated from the National Health and Nutrition Examination Study (2009–2010) for adults aged 18+ ( n =4588, Mean age=43 yr). Depressive symptoms were measured by the Patient Health Questionnaire-9 and diabetes status determined via self-report, usage of diabetic medication and/or fasting glucose levels ≥126 mg/dL and a glycated hemoglobin level ≥6.5% (48 mmol/mol). A 24-h dietary recall interview was given to determine intakes. Multiple logistic regression was employed, with depression the outcome, and dietary patterns and diabetes the predictors. Covariates included gender, age, marital status, education, race, adult food insecurity level, ratio of family income to poverty, and serum C-reactive protein. Results Exploratory factor analysis revealed five dietary patterns (healthy; unhealthy; sweets; ‘Mexican’ style; breakfast) explaining 39.8% of the total variance. The healthy dietary pattern was associated with reduced odds of depression for those with diabetes (OR 0.68, 95% CI 0.52, 0.88, p =0.006) and those without diabetes (OR 0.79, 95% CI 0.64, 0.97, p =0.029) (interaction p =0.048). The relationship between the sweets dietary pattern and depression was fully explained by diabetes status. Conclusion In this study, a healthy dietary pattern was associated with a reduced likelihood of depressive symptoms, especially for those with Type 2 diabetes.
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