generalised linear mixed model
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AbstractDepression is a common non-motor feature of Parkinson’s disease (PD) which confers significant morbidity and is challenging to treat. The thalamus is a key component in the basal ganglia-thalamocortical network critical to the pathogenesis of PD and depression but the precise thalamic subnuclei involved in PD depression have not been identified. We performed structural and diffusion-weighted imaging (DWI) on 76 participants with PD to evaluate the relationship between PD depression and grey and white matter thalamic subnuclear changes. We used a thalamic segmentation method to divide the thalamus into its 50 constituent subnuclei (25 each hemisphere). Fixel-based analysis was used to calculate mean fibre cross-section (FC) for white matter tracts connected to each subnucleus. We assessed volume and FC at baseline and 14–20 months follow-up. A generalised linear mixed model was used to evaluate the relationship between depression, subnuclei volume and mean FC for each thalamic subnucleus. We found that depression scores in PD were associated with lower right pulvinar anterior (PuA) subnucleus volume. Antidepressant use was associated with higher right PuA volume suggesting a possible protective effect of treatment. After follow-up, depression scores were associated with reduced white matter tract macrostructure across almost all tracts connected to thalamic subnuclei. In conclusion, our work implicates the right PuA as a relevant neural structure in PD depression and future work should evaluate its potential as a therapeutic target for PD depression.

IntroductionEarly intervention (EI) endorses family-centred and participation-focused services, but there remain insufficient options for systematically enacting this service approach. The Young Children’s Participation and Environment Measure electronic patient-reported outcome (YC-PEM e-PRO) is an evidence-based measure for caregivers that enables family-centred services in EI. The Parent-Reported Outcomes for Strengthening Partnership within the Early Intervention Care Team (PROSPECT) is a community-based pragmatic trial examining the effectiveness of implementing the YC-PEM e-PRO measure and decision support tool as an option for use within routine EI care, on service quality and child outcomes (aim 1). Following trial completion, we will characterise stakeholder perspectives of facilitators and barriers to its implementation across multiple EI programmes (aim 2).Methods and analysisThis study employs a hybrid type 1 effectiveness-implementation study design. For aim 1, we aim to enrol 223 caregivers of children with or at risk for developmental disabilities or delays aged 0–3 years old that have accessed EI services for three or more months from one EI programme in the Denver Metro catchment of Colorado. Participants will be invited to enrol for 12 months, beginning at the time of their child’s annual evaluation of progress. Participants will be randomised using a cluster-randomised design at the EI service coordinator level. Both groups will complete baseline testing and follow-up assessment at 1, 6 and 12 months. A generalised linear mixed model will be fitted for each outcome of interest, with group, time and their interactions as primary fixed effects, and adjusting for child age and condition severity as secondary fixed effects. For aim 2, we will conduct focus groups with EI stakeholders (families in the intervention group, service coordinators and other service providers in the EI programme, and programme leadership) which will be analysed thematically to explain aim 1 results and identify supports and remaining barriers to its broader implementation in multiple EI programmes.Ethics and disseminationThis study has been approved by the institutional review boards at the University of Illinois at Chicago (2020-0555) and University of Colorado (20-2380). An active dissemination plan will ensure that findings have maximum reach for research and practice.Trial registration numberNCT04562038.

Abstract BackgroundInformation on site utilisation and movement is essential for managing species' resource requirements. Collecting these data requires frequent location sampling of multiple individuals, which can be challenging for small-bodied animals due to the often-large size of animal-borne satellite-based telemetry devices. We show how coded VHF-radio nano-transmitters and an array of passive receivers to collect location data at sufficient frequency and accuracy can be utilised to test whether the highly mobile and small-bodied Gouldian finch adheres to optimal foraging theory within a landscape of high spatiotemporal heterogeneity.MethodsCoded VHF-radio nano-transmitters (0.23 g wt.) were attached to 38 Gouldian finches (Erythrura gouldiae; < 12g bd. wt). Tagged birds were then detected by an array of 6 passive VHF-radio receivers (~ 530 m detection range) spread over 120 km2. We tracked the tagged birds during two sessions corresponding with high and low abundance of their primary food resource (Sorghum spp. seed). The mean and total duration that an individual spent within any receiver detection field (residence time) were used as dependants in a generalised linear mixed model approach within a Bayesian framework to assess the influence of tracking session, life stage, receiver site, and release site. Network analysis revealed the importance of specific areas for the finches within the receiver array using the proportional time a bird spent within any receiver's detection field and the movement between adjacent detection fields. ResultsThe daily mean and total duration that a tagged individual spent within a receiver detection field decreased by ~50% between periods of high and low seed availability, while the extent of movements across the landscape increased with a decline in seed resources. These data support the hypothesis that the Gouldian finch adheres to optimal foraging theory to survive periods of food scarcity. ConclusionsThe data collected via passive telemetry technology support the hypothesis that the Gouldian finch adheres to optimal foraging theory and uses alternative behavioural strategies to survive periods of food scarcity. Specifically, we found that Gouldian finches reduced the time spent foraging within a patch, increased the frequency of transit flights among patches, and expanded the size of their activity space as grass resources declined.

Background: School absenteeism has been studied in detail in relation to health risk behaviours using cross sectional studies.Aim: The aim of this longitudinal study was to examine the association amongst alcohol, drug use and high-school learners’ absenteeism.Setting: This study was set in the Western Cape.Methods: Data were collected at three separate time points from 2950, 2675 and 2230 grade 8 learners aged 13–18 years old on school absenteeism, alcohol and drug use and sociodemographic characteristics. Associations between school absenteeism, alcohol and cannabis and sociodemographic factors use were examined using descriptive and chi-square analyses. Binary logistic regression was performed using generalised linear mixed model analyses.Results: Results revealed that 9.3% of the learners were absent for 2 weeks in the 15 weeks of the school year. Alcohol consumption (X2 = 34.1, p 0.001; odds ratio [OR]: 1.64 (1.38–1.94), p 0.001) and smoking cannabis (X2 = 49.9, p 0.001; OR: 2.01 (1.65–2.45), p 0.001) were associated with school absenteeism at bivariate and multivariate analyses. Furthermore, alcohol (OR: 1.42 (1.06–1.89), p 0.05) and cannabis (OR: 1.57 (1.11–2.22), p 0.05) use remained robust in predicting learners school absenteeism after adjusting for age, sex and socioeconomic status.Conclusion: These findings suggest that alcohol consumption and smoking cannabis are contemporary factors associated with school absenteeism. Therefore, interventions to ensure learners’ consistent attendance to school should integrate prevention of alcohol and cannabis use.

Taxonomic bias is a known issue within the field of biology, causing scientific knowledge to be unevenly distributed across species. However, a systematic quantification of the research interest that the scientific community has allocated to individual species remains a big data problem. Scalable approaches are needed to integrate biodiversity datasets and bibliometric methods across large numbers of species. The outputs of these analyses are important for identifying understudied species and directing future research to fill these gaps.In this study, we used the species h-index to quantity the research interest in 7,521 species of mammals. We tested factors potentially driving species h-index, by using a Bayesian phylogenetic generalised linear mixed model (GLMM). We found that a third of the mammals had a species h-index of zero, while a select few had inflated research interest. Further, mammals with higher species h-index had larger body masses, were found in temperate latitudes, had more humans uses, including domestication, and were in lower risk IUCN Red List categories. These results surprisingly suggested that critically endangered mammals are understudied. A higher interest in domesticated species suggested that human use rather than conservation drives mammalian scientific literature.Our study has demonstrated a scalable workflow and systematically identified understudied species of mammals, as well as identified the likely drivers of this taxonomic bias in the literature. This case study can become a benchmark for future research that asks similar biological and meta-research questions for other taxa.

Voluntary assisted dying (VAD) is when a terminally ill person with decision-making capacity consensually ends their life with assistance from an authorised professional. Many countries have legalised VAD, and health professionals’ roles within VAD frameworks are varied. Health professionals must be well informed of their legal obligations to ensure they practice within the legal boundaries, and those professionals with objections toward VAD should ensure that their eligible patients have equitable access. Given the current landscape of VAD, it is important to understand different health professionals’ attitudes toward VAD and what may underpin these attitudes. We explored (a) Australian health professionals’ attitudes toward VAD; (b) the psychological components that underpin those attitudes; (c) health professionals’ level of knowledge about VAD; (d) health professionals’ most common beliefs, emotions, and experiences related to VAD. A cross-sectional correlational survey design was used. A total of 182 Australian health professionals participated in the online survey based on a tripartite model of attitudes. We conducted a binomial logistic regression through a Generalised Linear Mixed Model and found polarised attitudes toward VAD between health professionals. Attitudes were accounted for by beliefs, emotions, education, and strength of religious beliefs. Knowledge of VAD was low, but not associated with overall attitude in our model. We highlight the importance of reflexive practice to help health professionals identify their values and feelings related to VAD, and to understand how these may affect their clinical practice. Low knowledge of VAD suggests that legislative and procedural training should be mandatory.

Governments around the world have implemented non-pharmaceutical interventions (NPIs), e.g. physical distancing and travel restrictions, to limit the transmission of COVID-19. While lockdowns and physical distancing have proven effective for reducing COVID-19 transmission, there is still limited understanding of the degree to which these interventions impact disease transmission, and how they are reflected in measures of human behaviour. Further, there is a lack of understanding about how new sources of data can be used to monitor NPIs, where these data have the potential to augment existing disease surveillance and modelling efforts. In this study, we assess the relationship between indicators of human mobility, NPIs, and estimates of Rt, a real-time measure of the intensity of COVID-19 transmission in subnational districts of Ghana using a multilevel generalised linear mixed model. We demonstrate a relationship between reductions in human mobility and decreases in Rt during the early stages of the COVID-19 epidemic in Ghana, and show how reductions in human mobility relate to increasing stringency of NPIs. We demonstrate the utility of combining local disease surveillance data with large scale human mobility data to augment existing surveillance capacity to estimate and monitor the effect of NPI policies.

Prescribed burns produce smoke pollution, but little is known about the spatial and temporal pattern because smoke plumes are usually small and poorly captured by State air-quality networks. Here, we sampled smoke around 18 forested prescribed burns in the Sydney region of eastern Australia using up to 11 Nova SDS011 particulate sensors and developed a Generalised Linear Mixed Model to predict hourly PM2.5 concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM2.5 tended to show hourly exceedances (indicating poor air quality) up to ~2 km from the fire but only in the downwind direction. In the evening, this zone expanded to up to 5 km and included upwind areas. PM2.5 concentrations were higher in still, cool weather and with an unstable atmosphere. PM2.5 concentrations were also higher in larger fires. The statistical model confirmed these results, identifying the effects of distance, period of the day, wind angle, fire size, temperature and C-Haines (atmospheric instability). The model correctly identified 78% of hourly exceedance and 72% of non-exceedance values in retained test data. Applying the statistical model predicts that prescribed burns of 1000 ha can be expected to cause air quality exceedances over an area of ~3500 ha. Cool weather that reduces the risk of fire escape, has the highest potential for polluting nearby communities, and fires that burn into the night are particularly bad.

BackgroundThe associated mortality with COVID-19 has improved compared with the early pandemic period. The effect of hospital COVID-19 patient prevalence on COVID-19 mortality has not been well studied.MethodsWe analysed data for adults with confirmed SARS-CoV-2 infection admitted to 62 hospitals within a multistate health system over 12 months. Mortality was evaluated based on patient demographic and clinical risk factors, COVID-19 hospital prevalence and calendar time period of the admission, using a generalised linear mixed model with site of care as the random effect.Results38 104 patients with COVID-19 were hospitalised, and during their encounters, the prevalence of COVID-19 averaged 16% of the total hospitalised population. Between March–April 2020 and January–February 2021, COVID-19 mortality declined from 19% to 12% (p<0.001). In the adjusted multivariable analysis, mid and high COVID-19 inpatient prevalence were associated with a 25% and 41% increase in the odds (absolute contribution to probability of death of 2%–3%) of COVID-19 mortality compared with patients with COVID-19 in facilities with low prevalence (<10%), respectively (high prevalence >25%: adjusted OR (AOR) 1.41, 95% CI 1.23 to 1.61; mid-prevalence (10%–25%): AOR 1.25, 95% CI 1.13 to 1.38). Mid and high COVID-19 prevalence accounted for 76% of patient encounters.ConclusionsAlthough inpatient mortality for patients with COVID-19 has sharply declined compared with earlier in the pandemic, higher COVID-19 hospital prevalence remained a common risk factor for COVID-19 mortality. Hospital leaders need to reconsider how we provide support to care for patients in times of increased volume and complexity, such as those experienced during COVID-19 surges.

ObjectivesPrevious studies have investigated the association between socioeconomic characteristics and fractures among children, producing different results. In a population-based study, we previously found an increased risk of fractures among children living in an urban municipality compared with rural municipalities. This study aimed to evaluate the importance of socioeconomic variables for the incidence of fractures among 0–17 year olds.Setting, design and outcome measureWe present a longitudinal, observational study of a population 0–17 years of age. Data from an injury database were linked with additional socioeconomic data for the population at risk. These were 55 758 individuals residing within the primary catchment area of a regional hospital in northern Sweden. Using the number of fractures as the outcome, we fitted a generalised linear mixed model for a Poisson response with socioeconomic variables at the family level as independent variables while controlling for age, sex and place of residence.ResultsWe found a significant association between higher levels of family income and the risk of fracture, rate ratio 1.40 (1.28–1.52) p<0.001 when comparing the highest income quintile to the lowest as well as the number of siblings and the risk of fracture. Children with one or two siblings had a rate ratio of 1.28 (1.19–1.38) p<0.001 when compared with children with no siblings. Parents’ educational level and having a single parent showed no significant association with fractures. The previously observed association between municipalities and fracture risk was less pronounced when taking family-level socioeconomic variables into account.ConclusionOur results indicate that children from families with higher income and with siblings are at greater risk of sustaining fractures.