UK Biobank is a well-characterised cohort of over 500 000 participants including genetics, environmental data and imaging. An online mental health questionnaire was designed for UK Biobank participants to expand its potential.

Describe the development, implementation and results of this questionnaire.

An expert working group designed the questionnaire, using established measures where possible, and consulting a patient group. Operational criteria were agreed for defining likely disorder and risk states, including lifetime depression, mania/hypomania, generalised anxiety disorder, unusual experiences and self-harm, and current post-traumatic stress and hazardous/harmful alcohol use.

A total of 157 366 completed online questionnaires were available by August 2017. Participants were aged 45–82 (53% were ≥65 years) and 57% women. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status. Lifetime depression was a common finding, with 24% (37 434) of participants meeting criteria and current hazardous/harmful alcohol use criteria were met by 21% (32 602), whereas other criteria were met by less than 8% of the participants. There was extensive comorbidity among the syndromes. Mental disorders were associated with a high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation.

The UK Biobank questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed because of selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health.

Studies suggest that alcohol consumption and alcohol use disorders have distinct genetic backgrounds.

We examined whether polygenic risk scores (PRS) for consumption and problem subscales of the Alcohol Use Disorders Identification Test (AUDIT-C, AUDIT-P) in the UK Biobank (UKB; N = 121 630) correlate with alcohol outcomes in four independent samples: an ascertained cohort, the Collaborative Study on the Genetics of Alcoholism (COGA; N = 6850), and population-based cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC; N = 5911), Generation Scotland (GS; N = 17 461), and an independent subset of UKB (N = 245 947). Regression models and survival analyses tested whether the PRS were associated with the alcohol-related outcomes.

In COGA, AUDIT-P PRS was associated with alcohol dependence, AUD symptom count, maximum drinks (R2 = 0.47–0.68%, p = 2.0 × 10−8–1.0 × 10−10), and increased likelihood of onset of alcohol dependence (hazard ratio = 1.15, p = 4.7 × 10−8); AUDIT-C PRS was not an independent predictor of any phenotype. In ALSPAC, the AUDIT-C PRS was associated with alcohol dependence (R2 = 0.96%, p = 4.8 × 10−6). In GS, AUDIT-C PRS was a better predictor of weekly alcohol use (R2 = 0.27%, p = 5.5 × 10−11), while AUDIT-P PRS was more associated with problem drinking (R2 = 0.40%, p = 9.0 × 10−7). Lastly, AUDIT-P PRS was associated with ICD-based alcohol-related disorders in the UKB subset (R2 = 0.18%, p < 2.0 × 10−16).

AUDIT-P PRS was associated with a range of alcohol-related phenotypes across population-based and ascertained cohorts, while AUDIT-C PRS showed less utility in the ascertained cohort. We show that AUDIT-P is genetically correlated with both use and misuse and demonstrate the influence of ascertainment schemes on PRS analyses.

To determine whether HCP were at higher risk than adults working in nonhealthcare roles (non-HCP).

Prospective cohort study.

Acute-care hospitals and other businesses in Toronto, Ontario, Canada.

Adults aged 18–69 years were enrolled for 1 or more of the 2010/2011, 2011/2012, and 2012/2013 influenza seasons. Swabs collected during acute respiratory illnesses were tested for influenza and pre- and postseason blood samples were tested for influenza-specific immune response.

The adjusted odds of influenza were similar for HCP and non-HCP (odds ratio [OR], 1.29; 95% confidence interval [CI], 0.63–2.63). Older adults and those vaccinated against influenza had lower odds, and those who shared their workspace and who used corrective eyewear had higher odds of influenza.

HCP and other working adults are at similar risk of influenza infection.

Major depressive disorder and neuroticism (Neu) share a large genetic basis. We sought to determine whether this shared basis could be decomposed to identify genetic factors that are specific to depression.

We analysed summary statistics from genome-wide association studies (GWAS) of depression (from the Psychiatric Genomics Consortium, 23andMe and UK Biobank) and compared them with GWAS of Neu (from UK Biobank). First, we used a pairwise GWAS analysis to classify variants as associated with only depression, with only Neu or with both. Second, we estimated partial genetic correlations to test whether the depression's genetic link with other phenotypes was explained by shared overlap with Neu.

We found evidence that most genomic regions (25/37) associated with depression are likely to be shared with Neu. The overlapping common genetic variance of depression and Neu was genetically correlated primarily with psychiatric disorders. We found that the genetic contributions to depression, that were not shared with Neu, were positively correlated with metabolic phenotypes and cardiovascular disease, and negatively correlated with the personality trait conscientiousness. After removing shared genetic overlap with Neu, depression still had a specific association with schizophrenia, bipolar disorder, coronary artery disease and age of first birth. Independent of depression, Neu had specific genetic correlates in ulcerative colitis, pubertal growth, anorexia and education.

Our findings demonstrate that, while genetic risk factors for depression are largely shared with Neu, there are also non-Neu-related features of depression that may be useful for further patient or phenotypic stratification.

While echocardiographic parameters are used to quantify ventricular function in infants with single ventricle physiology, there are few data comparing these to invasive measurements. This study correlates echocardiographic measures of diastolic function with ventricular end-diastolic pressure in infants with single ventricle physiology prior to superior cavopulmonary anastomosis.

Data from 173 patients enrolled in the Pediatric Heart Network Infant Single Ventricle enalapril trial were analysed. Those with mixed ventricular types (n = 17) and one outlier (end-diastolic pressure = 32 mmHg) were excluded from the analysis, leaving a total sample size of 155 patients. Echocardiographic measurements were correlated to end-diastolic pressure using Spearman’s test.

Median age at echocardiogram was 4.6 (range 2.5–7.4) months. Median ventricular end-diastolic pressure was 7 (range 3–19) mmHg. Median time difference between the echocardiogram and catheterisation was 0 days (range −35 to 59 days). Examining the entire cohort of 155 patients, no echocardiographic diastolic function variable correlated with ventricular end-diastolic pressure. When the analysis was limited to the 86 patients who had similar sedation for both studies, the systolic:diastolic duration ratio had a significant but weak negative correlation with end-diastolic pressure (r = −0.3, p = 0.004). The remaining echocardiographic variables did not correlate with ventricular end-diastolic pressure.

In this cohort of infants with single ventricle physiology prior to superior cavopulmonary anastomosis, most conventional echocardiographic measures of diastolic function did not correlate with ventricular end-diastolic pressure at cardiac catheterisation. These limitations should be factored into the interpretation of quantitative echo data in this patient population.

Substantial clinical heterogeneity of major depressive disorder (MDD) suggests it may group together individuals with diverse aetiologies. Identifying distinct subtypes should lead to more effective diagnosis and treatment, while providing more useful targets for further research. Genetic and clinical overlap between MDD and schizophrenia (SCZ) suggests an MDD subtype may share underlying mechanisms with SCZ.

The present study investigated whether a neurobiologically distinct subtype of MDD could be identified by SCZ polygenic risk score (PRS). We explored interactive effects between SCZ PRS and MDD case/control status on a range of cortical, subcortical and white matter metrics among 2370 male and 2574 female UK Biobank participants.

There was a significant SCZ PRS by MDD interaction for rostral anterior cingulate cortex (RACC) thickness (β = 0.191, q = 0.043). This was driven by a positive association between SCZ PRS and RACC thickness among MDD cases (β = 0.098, p = 0.026), compared to a negative association among controls (β = −0.087, p = 0.002). MDD cases with low SCZ PRS showed thinner RACC, although the opposite difference for high-SCZ-PRS cases was not significant. There were nominal interactions for other brain metrics, but none remained significant after correcting for multiple comparisons.

Our significant results indicate that MDD case-control differences in RACC thickness vary as a function of SCZ PRS. Although this was not the case for most other brain measures assessed, our specific findings still provide some further evidence that MDD in the presence of high genetic risk for SCZ is subtly neurobiologically distinct from MDD in general.

Healthcare workers (HCWs) are at risk of acquiring and transmitting respiratory viruses while working in healthcare settings.

To investigate the incidence of and factors associated with HCWs working during an acute respiratory illness (ARI).

HCWs from 9 Canadian hospitals were prospectively enrolled in active surveillance for ARI during the 2010–2011 to 2013–2014 influenza seasons. Daily illness diaries during ARI episodes collected information on symptoms and work attendance.

At least 1 ARI episode was reported by 50.4% of participants each study season. Overall, 94.6% of ill individuals reported working at least 1 day while symptomatic, resulting in an estimated 1.9 days of working while symptomatic and 0.5 days of absence during an ARI per participant season. In multivariable analysis, the adjusted relative risk of working while symptomatic was higher for physicians and lower for nurses relative to other HCWs. Participants were more likely to work if symptoms were less severe and on the illness onset date compared to subsequent days. The most cited reason for working while symptomatic was that symptoms were mild and the HCW felt well enough to work (67%). Participants were more likely to state that they could not afford to stay home if they did not have paid sick leave and were younger.

HCWs worked during most episodes of ARI, most often because their symptoms were mild. Further data are needed to understand how best to balance the costs and risks of absenteeism versus those associated with working while ill.

To identify, in caregivers of patients with Alzheimer’s disease (AD) dementia, factors associated with subjective (personal, physical, emotional, and social) and objective (informal caregiver time and costs) caregiver burden.

Prospective longitudinal European observational study: post-hoc analysis.

Clinic.

Community-dwelling patients in France and Germany aged ≥ 55 years (n = 969) with probable AD and their informal caregivers.

Mini-Mental State Examination (MMSE), Alzheimer’s Disease Cooperative Study—Activities of Daily Living (ADCS-ADL), 12-item Neuropsychiatric Inventory (NPI-12), Zarit Burden Interview (ZBI), informal caregiver basic and instrumental ADL hours (Resource Utilization in Dementia instrument), and informal caregiver costs. Mixed-effect models of repeated measures (MMRM) were run, including baseline and time-dependent covariates (change from baseline [CFB] to 18 months in MMSE, ADCS-ADL, and NPI-12 scores) associated with CFB in ZBI score/informal caregiver time over 36 months (analyzed using linear regression models) and informal caregiver costs over 36 months (analyzed using generalized linear models).

Greater decline in patient function (ADCS-ADL) over 18 months was associated with increased subjective caregiver burden (ZBI), hours, and costs over 36 months. Increased behavioral problems (NPI-12) over 18 months also negatively impacted ZBI. Cognitive decline (MMSE) over 18 months did not affect change in caregiver burden.

Long-term informal caregiver burden was driven by worsening functional abilities and behavioral symptoms but not cognitive decline, over 18 months in community-dwelling patients with AD dementia. Identifying the drivers of caregiver burden could highlight areas in which interventions may benefit both caregivers and patients.

Process mining, a branch of data science, aims at deriving an understanding of process behaviors from data collected during executions of the process. In this study, we apply process mining techniques to examine retrieval and transport of road trauma patients in Queensland. Specifically, we use multiple datasets collected from ground and air ambulance, emergency department, and hospital admissions to investigate the various patient pathways and transport modalities from accident to definitive care.

The project aims to answer the question, “Are we providing the right level of care to patients?” We focus on (i) automatically discovering, from historical records, the different care and transport processes, and (ii) identifying and quantifying factors influencing deviance from standard processes, e.g. mechanisms of injury and geospatial (crash and trauma facility) considerations.

We adapted the Cross-Industry Standard Process for Data Mining methodology to Queensland Ambulance Service, Retrieval Services Queensland (aero-medical), and Queensland Health (emergency department and hospital admissions) data. Data linkage and “case” definition emerged as particular challenges. We developed detailed data models, conduct a data quality assessment, and preliminary process mining analyses.

Preliminary results only with full results are presented at the conference. A collection of process models, which revealed multiple transport pathways, were automatically discovered from pilot data. Conformance checking showed some variations from expected processing. Systematic analysis of data quality allowed us to distinguish between systemic and occasional quality issues, and anticipate and explain certain observable features in process mining analyses. Results will be validated with domain experts to ensure insights are accurate and actionable.

Preliminary analysis unearthed challenging data quality issues that impact the use of historical retrieval data for secondary analysis. The automatically discovered process models will facilitate comparison of actual behavior with existing guidelines.