Temporal trends of transport-related injuries on New Zealand roads

AIM

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Identifying overcontrol and undercontrol personality types among young people using the five factor model, and the relationship with disordered eating behaviour, anxiety and depression

BACKGROUND

Overcontrol and undercontrol personality types have been associated with an increase in eating pathology, depression and anxiety. The aim of the research was to explore whether latent overcontrol and undercontrol personality types could be identified using cluster analysis of the facets of the five factor model (FFM). We further aimed to understand how these personality types were associated with eating pathology, depressed mood and anxiety.

METHODS

A total of 561 participants (394 women and 167 men), aged 16-30 years in Australia completed a survey designed to assess disordered eating, FFM personality traits, anxiety, depression and stress. A systematic four-step process using hierarchical, k-means, and random forest cluster analyses were used to identify a meaningful 3-cluster solution.

RESULTS

The results revealed a cluster solution that represented overcontrol, undercontrol and resilient personality types, and highlighted facets of the FFM that were associated with each type. Both overcontrol and undercontrol personality types were associated with increased clinical symptoms compared to the resilient types.

CONCLUSIONS

It was concluded that FFM facets may potentially be more meaningful than broad domains in identifying personality types, and that both overcontrol and undercontrol personality types are likely associated with increased clinical symptoms.

Trampoline Park Injury Trends

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Impact of an injury hospital admission on childhood academic performance: a Welsh population-based data linkage study

BACKGROUND

While injuries can impact on children's educational achievements (with threats to their development and employment prospects), these risks are poorly quantified. This population-based longitudinal study investigated the impact of an injury-related hospital admission on Welsh children's academic performance.

METHODS

The Secure Anonymised Information Linkage databank, 55 587 children residing in Wales from 2006 to 2016 who had an injury hospital admission (58.2% males; 16.8% born in most deprived Wales area; 80.1% one injury hospital admission) were linked to data from the Wales Electronic Cohort for Children. The primary outcome was the Core Subject Indicator reflecting educational achievement at key stages 2 (school years 3-6), 3 (school years 7-9) and 4 (school years 10-11). Covariates in models included demographic, birth, injury and school characteristics.

RESULTS

Educational achievement of children was negatively associated with: pedestrian injuries (adjusted risk ratio, (95% CIs)) (0.87, (0.83 to 0.92)), cyclist (0.96, (0.94 to 0.99)), high fall (0.96, (0.94 to 0.97)), fire/flames/smoke (0.85, (0.73 to 0.99)), cutting/piercing object (0.96, (0.93 to 0.99)), intentional self-harm (0.86, (0.82 to 0.91)), minor traumatic brain injury (0.92, (0.86 to 0.99)), contusion/open wound (0.93, (0.91 to 0.95)), fracture of vertebral column (0.78, (0.64 to 0.95)), fracture of femur (0.88, (0.84 to 0.93)), internal abdomen/pelvic haemorrhage (0.82, (0.69 to 0.97)), superficial injury (0.94, (0.92 to 0.97)), young maternal age (<18 years: 0.91, (0.88 to 0.94); 19-24 years: 0.94, (0.93 to 0.96)); area based socioeconomic status (0.98, (0.97 to 0.98)); moving to a more deprived area (0.95, (0.93 to 0.97)); requiring special educational needs (0.46, (0.44 to 0.47)). Positive associations were: being female (1.04, (1.03 to 1.06)); larger pupil school sizes and maternal age 30+ years.

CONCLUSION

This study highlights the importance on a child's education of preventing injuries and implementing intervention programmes that support injured children. Greater attention is needed on equity-focused educational support and social policies addressing needs of children at risk of underachievement, including those from families experiencing poverty. VIBES-JUNIOR STUDY PROTOCOL: http://dx.doi.org/10.1136/bmjopen-2018-024755.

Understanding suicidality in Pacific adolescents in New Zealand using network analysis

INTRODUCTION

Pacific adolescents in New Zealand (NZ) are three to four times more likely than NZ European adolescents to report suicide attempts and have higher rates of suicidal plans. Suicidal thoughts, plans, and attempts, termed suicidality in this study, result from a complex dynamic interplay of factors, which emerging methodologies like network analysis aim to capture.

METHODS

This study used cross-sectional network analysis to model the relationships between suicidality, self-harm, and individual depression symptoms, whilst conditioning on a multi-dimensional set of variables relevant to suicidality. A series of network models were fitted to data from a community sample of New Zealand-born Pacific adolescents (n = 550; 51% male; Mean age (SD) = 17 (0.35)).

RESULTS

Self-harm and the depression symptom measuring pessimism had the strongest associations with suicidality, followed by symptoms related to having a negative self-image about looks and sadness. Nonsymptom risk factors for self-harm and suicidality differed markedly.

CONCLUSIONS

Depression symptoms varied widely in terms of their contribution to suicidality, highlighting the valuable information gained from analysing depression at the symptom-item level. Reducing the sources of pessimism and building self-esteem presented as potential targets for alleviating suicidality amongst Pacific adolescents in New Zealand. Suicide prevention strategies need to include risk factors for self-harm.

Family violence homicide rates: a state-wide comparison of three data sources in Victoria, Australia

BACKGROUND

Family violence homicide (FVH) is a major public health and social problem in Australia. FVH trend rates are key outcomes that determine the effectiveness of current management practices and policy directions. Data source-related methodological problems affect FVH research and policy and the reliable measurement of homicide trends.

OBJECTIVE

This study aimed to determine data reliability and temporal trends of Victorian FVH rates and sex and relationship patterns.

METHOD

FVH rates per 100,000 persons in Victoria were compared between the National Coronial Information System (NCIS), Coroners Court of Victoria (CCoV) Homicide Register, and the National Homicide Monitoring Program (NHMP). Trends for 2001-2017 were analysed using Joinpoint regression. Crude rates were determined by sex and relationship categories using annual frequencies and Australian Bureau of Statistics population estimates.

RESULTS

NCIS closed FVH cases totalled 360, and an apparent downward trend in the FVH rate was identified. However, CCoV and NHMP rates trended upwards. While NCIS and CCoV were case-based, NHMP was incident-based, contributing to rate variations. The NCIS-derived trend was particularly impacted by unavailable case data, potential coding errors and entry backlog. Neither CCoV nor NHMP provided victim-age in their public domain data to enable age-adjusted rate comparison.

CONCLUSION

Current datasets have limitations for FVH trend determination; most notably lag times for NCIS data.

IMPLICATIONS

This study identified an indicative upward trend in FVH rates in Victoria, suggesting insufficiency of current management and policy settings for its prevention and control.

The effects of diet and beauty products on the uptake and storage of 14C in hair and nails: ramifications for the application of bomb pulse dating to forensic anthropological casework

Radiocarbon dating is a useful tool in the examination of unknown human remains. Recent studies have shown that the analysis of hair and nail samples can provide a highly accurate estimation of the year of death (YOD). However, little research has examined factors that may influence the uptake and storage of 14C in these tissues, such as diet, or the use of beauty products. This study measured the level of 14C in human hair and nail samples collected from living individuals to determine whether diet, and the use of hair dye or nail polish, has a significant impact on the estimation of YOD. The results of this study showed that diet did not appear to impact the radiocarbon content in human hair and nail, and thus should not be considered a limitation when analysing samples obtained from unidentified human remains. The use of nail polish, and in the majority of cases, hair dye, did not significantly impact the 14C concentration in nails and hair. While the results of this study are preliminary, they suggest that in most cases, both hair and nail can be successfully analysed using radiocarbon dating to estimate an individual's YOD. However, best practice should involve the analysis of multiple tissue types, to minimise any error that may be introduced as a result of the decedent's use of beauty products.

Dimensional personality pathology and disordered eating in young adults: measuring the DSM-5 alternative model using the PID-5

Introduction

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.

Methods

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.

Results

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.

Discussion

It was concluded that understanding disordered eating behaviour in the context of personality pathology may assist formulating potentially risky behaviour.

Using network analyses to characterise Australian and Canadian frequent attenders to the emergency department

OBJECTIVE

To explore and compare the characteristics of frequent attenders to the ED at an Australian and a Canadian tertiary hospitals by utilising a network analysis approach.

METHODS

We conducted a retrospective population-based study using administrative data over the 2018 and 2019 calendar years. Participants were from a tertiary hospital in Melbourne, Australia, and Toronto, Canada. Frequent attenders were defined as patients with four or more visits in 12 months. Characteristics of younger (18-39 years), middle-aged (40-69 years) and older (70 years and older) frequent attenders were described using descriptive statistics and network analyses.

RESULTS

Younger frequent attenders were characterised by mental illness and substance use, while older frequent attenders had high rates of physical (including chronic) diseases. Middle-aged frequent attenders were characterised by a combination of mental and physical illnesses. These findings were observed at both hospitals. Across all age groups, the network analyses between the Melbourne and Toronto hospitals were different. Among older frequent attender visits, more diagnoses were associated with high triage acuity at the Toronto hospital than at the Melbourne hospital. Some associations were similar at both sites, for example, the negative correlation between high triage acuity and joint pain.

CONCLUSION

Younger, middle-aged and older frequent attenders have distinct characteristics, made readily apparent by using network analyses. Future interventions to reduce ED visits should consider the heterogeneity of frequent attenders who have needs specific to their age, presenting problems and jurisdiction.

One size does not fit all: Exploring how the five-factor model facets predict disordered eating behaviours among adolescent and young adult males and females

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.

Long-term outcomes of major trauma patients with concussion

INTRODUCTION

Concussion may be sustained in the setting of injuries to multiple body regions and persistent effects of concussion may impact recovery. This project aimed to evaluate the association between concussion and 6-month and 12-month functional outcomes in survivors after major trauma.

METHODS

This was a registry-based cohort study that included adult patients with major trauma who presented to hospital between 01 Jan 2008 and 31 Dec 2017 and survived to hospital discharge. We excluded patients presenting with a Glasgow Coma Scale score <13 and those diagnosed with other intracranial injuries. Additionally, from the non-concussed group, patients with fractured skull and/or face were excluded, with the assumption that such patients may have had undiagnosed concussion. A good recovery was considered for Glasgow Outcome Scale-Extended (GOS-E) scores of 7 or 8. In addition, we assessed for patient reported anxiety and/or depression measured using the 3-level EuroQol 5 dimensions questionnaire. A modified mixed effects Poisson models with random intercepts for participant was used to assess the association between concussion and outcome.

RESULTS

There were 28,161 eligible patients and 12,822 met inclusion criteria. Concussion was diagnosed in 1860 patients (14.5%; 95%CI: 13.9-15.1). There was no association between concussion and good recovery at 12 months (aRR 1.05 (95%CI: 0.99-1.11). There was no association between concussion and anxiety and/or depression at 12 months (aRR 1.03; 95%CI: 0.99-1.07).

CONCLUSIONS

Concussion was sustained among 14.5% of included patients in the setting of major trauma but not associated with longer-term adverse outcomes using GOS-E. Concussed patients did not report differential rates of anxiety and/or depression.

Pain and mental health symptom patterns and treatment trajectories following road trauma: a registry-based cohort study

PURPOSE

This study aimed to characterise recovery from pain and mental health symptoms, and identify whether treatment use facilitates recovery.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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 REHABILITATIONTwo-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.

Risk factors for depression in Pacific adolescents in New Zealand: A network analysis

BACKGROUND

Network analysis provides opportunities to gain a greater understanding of the complex interplay of risk factors for depression and heterogeneous symptom presentations. This study used network analysis to discover risk factors associated with both depression severity and depression symptoms amongst Pacific adolescents in New Zealand.

METHODS

Mixed graphical models with regularization were fitted to data from a community sample of New Zealand born, Pacific adolescents, (n = 561; 51% male; Mean age (SD) = 17 (0.35)) and associations between a wide range of potentially explanatory variables and depression severity and depression symptoms investigated. The associations identified were then tested for reliability, using resampling techniques and sensitivity analysis.

RESULTS

In the networks, the explanatory variables associated with both depression severity and depression symptoms were those related to quality of the relationships with mother or friends, school connectedness, and self-assessed weight, but the symptoms they were associated with varied substantially. In the depression severity networks, impulsivity appeared to be a bridging node connecting depression severity with delinquency and negative peer influence.

LIMITATIONS

The data were analysed cross-sectionally, so causal inferences about the directions of relationships could not be inferred and most of the data were self-reported.

CONCLUSIONS

The results illustrate the varied way that adolescent depression can manifest itself in terms of symptoms and suggest specific items on the depression inventory that might be suitable targets for prevention strategies and interventions, based on the risk factor - depression symptom profiles of individuals or groups.

A registry-based observational cohort study examining patterns of pain and mental health symptoms and their impact on work or other activities after injury

Purpose/Objective Research: This study aimed to examine patterns of pain and mental health after injury, and the patient characteristics associated with reductions in those symptoms.

RESEARCH METHOD/DESIGN

This registry-based observational cohort study included all people ≥ 16 years old hospitalized for unintentional injuries from 2007 to 2014 who were included in the Victorian State Trauma Registry or Victorian Orthopaedic Trauma Outcomes Registry, survived to 12-months postinjury and did not have severe brain injury or spinal cord injury (N = 31,073). Symptoms and related impacts were measured with pain Numerical Rating Scale, EuroQol Five Dimensions Three Level questionnaire (EQ-5D-3L), and 12-item Short Form Health Survey (SF-12) pain and mental health items at 6-, 12-, and 24-months postinjury. Symptom patterns over time, and their predictors, were examined using Latent Class and Transition Analyses and multinomial logistic regression.

RESULTS

Four classes were identified: (1) Low pain and mental health problems (49-54%); (2) mental health problems only (11-12%); (3) pain problems only (18-23%); and (4) pain and mental health problems (16-17%). Most people stayed within the same class over time, or transitioned to fewer problems. People who transitioned to lower problems had higher socioeconomic status (e.g., higher education level, higher neighborhood-level advantage, and employment), better preinjury health (e.g., no disability or substance use condition) and noncompensable injuries.

CONCLUSION/IMPLICATIONS

Reduced pain and mental health symptoms and related impairments were primarily associated with nonmodifiable biological, social, or economic characteristics. People with persistent symptoms were often already living with social disadvantage preinjury, and may have benefited from risk screening and proactive interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Predictors of health-related quality of life following injury in childhood and adolescence: a pooled analysis

BACKGROUND

Injury is a leading contributor to the global disease burden in children and places children at risk for adverse and lasting impacts on their health-related quality of life (HRQoL) and development. This study aimed to identify key predictors of HRQoL following injury in childhood and adolescence.

METHODS

Data from 2259 injury survivors (<18 years when injured) were pooled from four longitudinal cohort studies (Australia, Canada, UK, USA) from the paediatric Validating Injury Burden Estimates Study (VIBES-Junior). Outcomes were the Paediatric Quality of Life Inventory (PedsQL) total, physical, psychosocial functioning scores at 1, 3-4, 6, 12, 24 months postinjury.

RESULTS

Mean PedsQL total score increased with higher socioeconomic status and decreased with increasing age. It was lower for transport-related incidents, ≥1 comorbidities, intentional injuries, spinal cord injury, vertebral column fracture, moderate/severe traumatic brain injury and fracture of patella/tibia/fibula/ankle. Mean PedsQL physical score was lower for females, fracture of femur, fracture of pelvis and burns. Mean PedsQL psychosocial score was lower for asphyxiation/non-fatal submersion and muscle/tendon/dislocation injuries.

CONCLUSIONS

Postinjury HRQoL was associated with survivors' socioeconomic status, intent, mechanism of injury and comorbidity status. Patterns of physical and psychosocial functioning postinjury differed according to sex and nature of injury sustained. The findings improve understanding of the long-term individual and societal impacts of injury in the early part of life and guide the prioritisation of prevention efforts, inform health and social service planning to help reduce injury burden, and help guide future Global Burden of Disease estimates.

Orofacial injuries in child family homicide: a population study

Child family violence homicide (FVH) is a significant public health problem in Australia and globally. Population-wide studies of orofacial injuries in child FVH are uncommon despite their recognized importance. This whole population descriptive study of orofacial injuries in child FVH in Victoria, Australia aims to implement a novel methodological approach to provide an overview of child FVH and describe frequency and patterns of abusive orofacial injuries. Closed cases of child FVH aged 0-17 years, January 2000-December 2018, were identified from screening all Victorian assault deaths for eligible offender relationships. Significant associations of clinical/demographic characteristics were explored using two-step clustering and the Spearman correlation coefficient. Of 895 closed homicide cases, 358 were FV-related. Of the 53 child FVH, 40 were eligible for injury analysis with 36 of these cases (90%) having orofacial injuries. Among these 36 cases, 72% were aged 0-4 years, males predominated (64%) and the injury mechanism was blunt force for 56%. The discrete orofacial injury frequency was associated with the non-orofacial injury frequency (rho: 0.362, 2-tailed p < 0.03). A three-cluster statistical solution was identified, each represented by an injury mechanism. The largest cluster identified a pattern of blunt force trauma in 0-4 years with drug presence, high average non-orofacial injury numbers and parent-offenders. A novel methodological approach was implemented to comprehensively describe the frequency, nature, patterns and risk indicators of orofacial injuries in child FVH. It explored associations between a wide range of clinical and demographic characteristics, which might have otherwise been missed in summary description. These methods will potentially underpin future comparative studies of intentional-unintentional child injuries and fatal-nonfatal child abuse. The study narrows a significant research gap regarding patterns of inflicted injuries, and demographic and clinical indicators in child FVH potentially informing future systematic classification processes, risk assessment tools and pathways to FV intervention.

Driver, Collision and Meteorological Characteristics of Motor Vehicle Collisions among Road Trauma Survivors

Road trauma remains a significant public health problem. We aimed to identify sub-groups of motor vehicle collisions in Victoria, Australia, and the association between collision characteristics and outcomes up to 24 months post-injury. Data were extracted from the Victorian State Trauma Registry for injured drivers aged ≥16 years, from 2010 to 2016, with a compensation claim who survived ≥12 months post-injury. People with intentional or severe head injury were excluded, resulting in 2735 cases. Latent class analysis was used to identify collision classes for driver fault and blood alcohol concentration (BAC), day and time of collision, weather conditions, single vs. multi-vehicle and regional vs. metropolitan injury location. Five classes were identified: (1) daytime multi-vehicle collisions, no other at fault; (2) daytime single-vehicle predominantly weekday collisions; (3) evening single-vehicle collisions, no other at fault, 36% with BAC ≥ 0.05; (4) sunrise or sunset weekday collisions; and (5) dusk and evening multi-vehicle in metropolitan areas with BAC < 0.05. Mixed linear and logistic regression analyses examined associations between collision class and return to work, health (EQ-5D-3L summary score) and independent function Glasgow Outcome Scale - Extended at 6, 12 and 24 months. After adjusting for demographic, health and injury characteristics, collision class was not associated with outcomes. Rather, risk of poor outcomes was associated with age, sex and socioeconomic disadvantage, education, pre-injury health and injury severity. People at risk of poor recovery may be identified from factors available during the hospital admission and may benefit from clinical assessment and targeted referrals and treatments.

Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol

Background

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.

Methods and design

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.

Discussion

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.

Health status after penetrating major trauma in Victoria, Australia: a registry-based cohort study

PURPOSE

As few studies have examined long-term health after penetrating injury, this population-based registry study sought to assess health outcomes up to 24 months post-injury.

METHODS

Major trauma patients with penetrating trauma (2009-2017) were included from the Victorian State Trauma Registry (N = 1,067; 102 died, 208 were lost to follow-up). The EQ-5D-3L was used to measure health status at 6, 12 and 24-months. Mixed linear and logistic regressions were used to examine predictors of summary scores, and problems versus no problems on each health dimension.

RESULTS

Average health status summary scores were 0.70 (sd = 0.26) at 6 and 12 months, and 0.72 (sd = 0.26) at 24 months post-injury. Prevalence of problems was consistent over time: mobility (24-26%), self-care (17-20%), usual activities (47-50%), pain/discomfort (44-49%), and anxiety/depression (54-56%). Lower health status and reporting problems was associated with middle-older age, female sex, unemployment; pre-injury disability, comorbid conditions; and assault and firearm injury versus cutting/piercing.

CONCLUSION

Problems with usual activities, pain/discomfort and anxiety or depression are common after penetrating major trauma. Risk factor screening in hospital could be used to identify people at risk of poor health outcomes, and to link people at risk with services in hospital or early post-discharge to improve their longer-term health outcomes.

Impact of exposure to mine fire emitted PM2.5 on ambulance attendances: A time series analysis from the Hazelwood Health Study

BACKGROUND

For six weeks from February 9, 2014, smoke and ash from a fire in the Morwell open cut brown coal mine adjacent to the Hazelwood power station covered parts of the Latrobe Valley, in south eastern Australia.

AIMS

To evaluate the health impact of the mine fire, daily counts of ambulance attendances from July 2010 to March 2015 were analysed.

METHODS

Time series models were used to evaluate the relative risk of ambulance attendances during the mine fire, in comparison with the remainder of the analysis period, and to also assess the risk of ambulance attendances associated with lagged effects of exposure to mine fire-related PM_2.5 levels. The models controlled for factors likely to influence ambulance attendances including seasonality, long-term temporal trends, day of the week, daily maximum temperature and public holidays.

RESULTS

A 10 μg/m³ increase in fire-related PM_2.5 was found to be associated with a 42% (95%CI: 14-76%) increase in ambulance attendances for respiratory conditions and a 7% (0-14%) increase in all ambulance attendances over a 20-day lag period. A smaller effect associated with exposure to fire-related PM_2.5 was identified when assuming shorter lag effects. Similar results were identified when assessing whether ambulance attendances increased during the 30-day mine fire period. There was a 15% (8-21%) increased risk of ambulance attendances for all conditions and a 47% (19-81%) increased risk for respiratory conditions during the mine fire period.

CONCLUSIONS

Exposure to smoke and ash from a fire in an open cut brown coal mine was associated with increased ambulance attendances, particularly for respiratory conditions. These findings guide the development and implementation of effective and timely strategies and health service planning to respond and mitigate health risks that arise in affected communities during future major air pollution episodes.

A novel way to quantify schizophrenia symptoms in clinical trials

BACKGROUND

A major problem in quantifying symptoms of schizophrenia is establishing a reliable distinction between enduring and dynamic aspects of psychopathology. This is critical for accurate diagnosis, monitoring and evaluating treatment effects in both clinical practice and trials.

MATERIALS AND METHODS

We applied Generalizability Theory, a robust novel method to distinguish between dynamic and stable aspects of schizophrenia symptoms in the widely used Positive and Negative Symptom Scale (PANSS) using a longitudinal measurement design. The sample included 107 patients with chronic schizophrenia assessed using the PANSS at five time points over a 24-week period during a multi-site clinical trial of N-Acetylcysteine as an add-on to maintenance medication for the treatment of chronic schizophrenia.

RESULTS

The original PANSS and its three subscales demonstrated good reliability and generalizability of scores (G = 0.77-0.93) across sample population and occasions making them suitable for assessment of psychosis risks and long-lasting change following a treatment, while subscales of the five-factor models appeared less reliable. The most enduring symptoms represented by the PANSS were poor attention, delusions, blunted affect and poor rapport. More dynamic symptoms with 40%-50% of variance explained by patient transient state including grandiosity, preoccupation, somatic concerns, guilt feeling and hallucinatory behaviour.

CONCLUSIONS

Identified dynamic symptoms are more amendable to change and should be the primary target of interventions aiming at effectively treating schizophrenia. Separating out the dynamic symptoms would increase assay sensitivity in trials, reduce the signal to noise ratio and increase the potential to detect the effects of novel therapies in clinical trials.

Population study of orofacial injuries in adult family violence homicides in Victoria, Australia

BACKGROUND

This study describes the prevalence and orofacial injury patterns associated with adult family violence (FV) homicides in Victoria, Australia. It follows a methods study for case selection of all FV homicides and injury measurement. Comprehensive analysis of orofacial injuries in FV homicides and their clinico-demographic context will inform future research on clinical FV indicators and sentinel injuries, and potentially lead to premorbid intervention in health services.

METHODS

All closed cases of FV homicides aged ≥18 years, January 2006 to December 2018, were identified by screening Victorian fatal assaults, based on victim-offender relationship. Primary data such as post-mortem computed tomography scans and photographs were assessed. Socio-demographic, clinical, interpersonal and incident parameters were descriptively analysed and statistically compared across FV homicides with and without facial injuries using cluster analysis and nonparametric tests.

RESULTS

Of 170 adult homicides screened for eligibility, 151 were included for final analysis. Over the 12-year period, 78.1% of all Victorian adult FV homicides had orofacial injuries. Significant cluster patterns of injury mechanism, victim-offender relationship and drug/alcohol impairment were identified in all homicides. Non-facial injuries were significantly higher in facial vs. non-facial injury homicides. Facial abrasion and incised wounds were the most common injury types.

CONCLUSIONS

This is the first forensic-epidemiologic study evaluating the empirical evidence concerning orofacial injuries associated with population-wide adult Victorian FV homicides. The high level of orofacial injuries in this population during the study period may inform clinical practice and policy in FV intervention in Victoria and globally.

Coal-mine fire-related fine particulate matter and medical-service utilization in Australia: a time-series analysis from the Hazelwood Health Study

Background

This study assessed the association between coal-mine-fire-related fine particulate matter (PM2.5) and medical-service utilization, following a 6-week coal-mine fire in Australia, in 2014. Areas in the immediate vicinity of the mine experienced hourly mine-fire-related PM2.5 concentrations of up to 3700 μg/m3.

Methods

Data on medical-service utilization were collected from the Medicare Benefits Schedule—a national database of payment for medical services. PM2.5 concentrations were modelled using atmospheric chemical transport modelling. Quasi-Poisson interrupted distributed lag time-series analysis examined the association between daily mine-fire-related PM2.5 concentrations and medical-service utilization, including General Practitioner (GP) consultations and respiratory, cardiovascular and mental health services. Confounders included seasonality, long-term trend, day of the week, maximum daily temperature and public holidays. Gender and age stratification were conducted.

Results

A 10-μg/m3 increase in PM2.5 was associated with an increased relative risk of service usage for all long and short GP consultations [11% (95% confidence interval: 7 to 15%)] and respiratory services [22% (4 to 43%)] in both men and women. Sex stratification found an increased relative risk in mental health consultations in men [32% (2 to 72%)] but not women. No associations were found for cardiovascular services in men or women.

Conclusions

Coal-mine-fire-related PM2.5 exposure was associated with increased use of medical services for GP consultations and respiratory services in men and women and mental health consultations in men. These findings can inform the development of future public-health-policy responses in the event of major air-pollution episodes.

Fine particulate matter exposure and medication dispensing during and after a coal mine fire: A time series analysis from the Hazelwood Health Study

Limited research has examined the impacts of coal mine fire smoke on human health. The aim of this study was to assess the association between prolonged smoke PM_2.5 exposure from a brown coal mine fire that burned over a seven week period in 2014 and medications dispensed across five localities in South-eastern Victoria, Australia. Spatially resolved PM_2.5 concentrations were retrospectively estimated using a dispersion model coupled with a chemical transport model. Data on medications dispensed were collected from the national Pharmaceutical Benefits Schedule database for 2013-2016. Poisson distributed lag time series analysis was used to examine associations between daily mine fire-related PM_2.5 concentrations and daily counts of medications dispensed for respiratory, cardiovascular or psychiatric conditions. Factors controlled for included: seasonality, long-term trend, day of the week, maximum ambient temperature and public holidays. Positive associations were found between mine fire-related PM_2.5 and increased risks of medications dispensed for respiratory, cardiovascular and psychiatric conditions, over a lag range of 3-7 days. A 10 μg/m³ increase in coal mine fire-related PM_2.5 was associated with a 25% (95%CI 19-32%) increase in respiratory medications, a 10% (95%CI 7-13%) increase in cardiovascular medications and a 12% (95%CI 8-16%) increase in psychiatric medications dispensed. These findings have the potential to better prepare for and develop more appropriate public health responses in the event of future coal mine fires.

Validating injury burden estimates using population birth cohorts and longitudinal cohort studies of injury outcomes: the VIBES-Junior study protocol

INTRODUCTION

Traumatic injury is a leading contributor to the global disease burden in children and adolescents, but methods used to estimate burden do not account for differences in patterns of injury and recovery between children and adults. A lack of empirical data on postinjury disability in children has limited capacity to derive valid disability weights and describe the long-term individual and societal impacts of injury in the early part of life. The aim of this study is to establish valid estimates of the burden of non-fatal injury in children and adolescents.

METHODS AND ANALYSIS

Five longitudinal studies of paediatric injury survivors <18 years at the time of injury (Australia, Canada, UK and USA) and two whole-of-population linked administrative data paediatric studies (Australia and Wales) will be analysed over a 3-year period commencing 2018. Meta-analysis of deidentified patient-level data (n≈2,600) from five injury-specific longitudinal studies (Victorian State Trauma Registry; Victorian Orthopaedic Trauma Outcomes Registry; UK Burden of Injury; British Columbia Children's Hospital Longitudinal Injury Outcomes; Children's Health After Injury) and >1 million children from two whole-of-population cohorts (South Australian Early Childhood Data Project and Wales Electronic Cohort for Children). Systematic analysis of pooled injury-specific cohort data using a variety of statistical techniques, and parallel analysis of whole-of-population cohorts, will be used to develop estimated disability weights for years lost due to disability, establish appropriate injury classifications and explore factors influencing recovery.

ETHICS AND DISSEMINATION

The project was approved by the Monash University Human Research Ethics Committee project number 12 311. Results of this study will be submitted for publication in internationally peer-reviewed journals. The findings from this project have the capacity to improve the validity of paediatric injury burden measurements in future local and global burden of disease studies.

Getting RID of the blues: Formulating a Risk Index for Depression (RID) using structural equation modeling

OBJECTIVE

While risk factors for depression are increasingly known, there is no widely utilised depression risk index. Our objective was to develop a method for a flexible, modular, Risk Index for Depression using structural equation models of key determinants identified from previous published research that blended machine-learning with traditional statistical techniques.

METHODS

Demographic, clinical and laboratory variables from the National Health and Nutrition Examination Study (2009-2010, N = 5546) were utilised. Data were split 50:50 into training:validation datasets. Generalised structural equation models, using logistic regression, were developed with a binary outcome depression measure (Patient Health Questionnaire-9 score ⩾ 10) and previously identified determinants of depression: demographics, lifestyle-environs, diet, biomarkers and somatic symptoms. Indicative goodness-of-fit statistics and Areas Under the Receiver Operator Characteristic Curves were calculated and probit regression checked model consistency.

RESULTS

The generalised structural equation model was built from a systematic process. Relative importance of the depression determinants were diet (odds ratio: 4.09; 95% confidence interval: [2.01, 8.35]), lifestyle-environs (odds ratio: 2.15; 95% CI: [1.57, 2.94]), somatic symptoms (odds ratio: 2.10; 95% CI: [1.58, 2.80]), demographics (odds ratio:1.46; 95% CI: [0.72, 2.95]) and biomarkers (odds ratio:1.39; 95% CI: [1.00, 1.93]). The relationships between demographics and lifestyle-environs and depression indicated a potential indirect path via somatic symptoms and biomarkers. The path from diet was direct to depression. The Areas under the Receiver Operator Characteristic Curves were good (logistic:training = 0.850, validation = 0.813; probit:training = 0.849, validation = 0.809).

CONCLUSION

The novel Risk Index for Depression modular methodology developed has the flexibility to add/remove direct/indirect risk determinants paths to depression using a structural equation model on datasets that take account of a wide range of known risks. Risk Index for Depression shows promise for future clinical use by providing indications of main determinant(s) associated with a patient's predisposition to depression and has the ability to be translated for the development of risk indices for other affective disorders.

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM)

BACKGROUND

Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM).

METHODS

Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders.

RESULTS

The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, P<0.001) and GLUMM7-1 (OR: 7.88, P<0.001) with depression was found, with significant interactions with those married/living with partner (P=0.001).

CONCLUSION

Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

The association between dietary patterns, diabetes and depression

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=43yr). 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 ≥126mg/dL and a glycated hemoglobin level ≥6.5% (48mmol/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.