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Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).
Methods
A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.
Results
In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.
Conclusions
Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS.
Methods
Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale – Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample).
Results
Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms.
Conclusions
These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.
In times of repeated disaster events, including natural disasters and pandemics, public health workers must recover rapidly to respond to subsequent events. Understanding predictors of time to recovery and developing predictive models of time to recovery can aid planning and management.
Methods:
We examined 681 public health workers (21-72 y, M(standard deviation [SD]) = 48.25(10.15); 79% female) 1 mo before (T1) and 9 mo after (T2) the 2005 hurricane season. Demographics, trauma history, social support, time to recover from previous hurricane season, and predisaster work productivity were assessed at T1. T2 assessed previous disaster work, initial emotional response, and personal hurricane injury/damage. The primary outcome was time to recover from the most recent hurricane event.
Results:
Multivariate analyses found that less support (T1; odds ratio [OR] = .74[95% confidence interval [CI] = .60-.92]), longer previous recovery time (T1; OR = 5.22[95%CI = 3.01-9.08]), lower predisaster work productivity (T1; OR = 1.98[95%CI = 1.08-3.61]), disaster-related personal injury/damage (T2; OR = 3.08[95%CI = 1.70-5.58]), and initial emotional response (T2; OR = 1.71[95%CI = 1.34-2.19]) were associated with longer recovery time (T2).
Conclusions:
Recovery time was adversely affected in disaster responders with a history of longer recovery time, personal injury/damage, lower work productivity following prior hurricanes, and initial emotional response, whereas responders with social support had shorter recovery time. Predictors of recovery time should be a focus for disaster preparedness planners.
Background: Despite a higher prevalence of traumatic spinal cord injury (TSCI) amongst Canadian Indigenous peoples, there is a paucity of studies focused on Indigenous TSCI. We present the first Canada-wide study comparing TSCI amongst Canadian Indigenous and non-Indigenous peoples. Methods: This study is a retrospective analysis of prospectively-collected TSCI data from the Rick Hansen Spinal Cord Injury Registry (RHSCIR) from 2004-2019. We divided participants into Indigenous and non-Indigenous cohorts and compared them with respect to demographics, injury mechanism, level, severity, and outcomes. Results: Compared with non-Indigenous patients, Indigenous patients were younger, more female, less likely to have higher education, and less likely to be employed. The mechanism of injury was more likely due to assault or transportation-related trauma in the Indigenous group. The length of stay for Indigenous patients was longer. Indigenous patients were more likely to be discharged to a rural setting, less likely to be discharged home, and more likely to be unemployed following injury. Conclusions: Our results suggest that more resources need to be dedicated for transitioning Indigenous patients sustaining a TSCI to community living and for supporting these patients in their home communities. A focus on resources and infrastructure for Indigenous patients by engagement with Indigenous communities is needed.
Background: This is a population-based retrospective study of cardiac and neurological complications of COVID-19 among Ontario Chinese and South Asians. Methods: From January 1, 2020 to September 30, 2020 using the last name algorithm to identify Ontario Chinese and South Asians who were tested positive by PCR for COVID-19, their demographics, cardiac, and neurological complications including hospitalization and emergency visit rates were analyzed compared to the general population. Results: Chinese (N = 1,186) with COVID-19 were found to be older (mean age 50.7 years) compared to the general population (N = 42,547) (mean age 47.6 years) (p < 0.001), while South Asians (N = 3,459) were younger (age of 42.1 years) (p < 0.001). For neurological complications, the 30-day crude rate for Chinese was 160/10,000 (p < 0.001); South Asians was 40/10,000 (p = 0.526), and general population was 48/10,000. The 30-day all-cause mortality rate was significantly higher for Chinese at 8.1% vs 5.0% for the general population (p < 0.001), while it was lower in South Asians at 2.1% (p < 0.001). Conclusions: Chinese and South Asians in Ontario with COVID-19 during the first wave of the pandemic were found to have a significant difference in their demographics, cardiac, and neurological outcomes.
This paper studies a generalization of the Gerber-Shiu expected discounted penalty function [Gerber and Shiu (1998). On the time value of ruin. North American Actuarial Journal 2(1): 48–72] in the context of the perturbed compound Poisson insurance risk model, where the moments of the total discounted claims and the discounted small fluctuations (arising from the Brownian motion) until ruin are also included. In particular, the latter quantity is represented by a stochastic integral and has never been analyzed in the literature to the best of our knowledge. Recursive integro-differential equations satisfied by our generalized Gerber-Shiu function are derived, and these are transformed to defective renewal equations where the components are identified. Explicit solutions are given when the individual claim amounts are distributed as a combination of exponentials. Numerical illustrations are provided, including the computation of the covariance between discounted claims and discounted perturbation until ruin.
Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.
Methods
This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.
Results
32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.
Conclusions
Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
Tropical forest ecosystems are rich in epiphytes that make up a significant portion of the overall plant diversity. However, epiphytic plants are often understudied due to inaccessibility and the lack of basic ecological information poses challenges to their conservation, particularly in a time of rapid global change. The mule-ear orchid, Trichocentrum undulatum (Orchidaceae), is a large flowering epiphyte found in southern Florida (USA), the Greater, and Lesser Antilles including Cuba. The plant is Florida state-listed as endangered with only one remaining small and declining population in a coastal mangrove forest due to historical extraction and habitat destruction. Currently, there is no systematic understanding of the species’ habitat requirements. To fill this void, we compared the habitat and microhabitat of the species on its northern distribution edge (southern Florida) and the core range (in Cuba). The Florida population has only one host species, Conocarpus erectus, found in one habitat type. This is in sharp contrast to the 92 documented hosts and 5 habitats across 8 provinces in Cuba. Based on our findings from Cuba, we suggest conservation and restoration options in Florida by proposing potential suitable host plants and habitats. Proactive restoration of this species will help to ease the threat from sea-level rise to the species by securing and expanding range margins.
The distinct turbulence dynamics and transport modulated by a common seagrass species were investigated experimentally using a flexible surrogate canopy in a refractive-index-matching environment that enabled full optical access. The surrogate seagrass replicated the dynamic behaviour and morphological properties of its natural counterpart. The flows studied were subcritical with Froude numbers $Fr<0.26$ and concerned five Reynolds numbers $Re\in [3.4\times 10^{4}, 1.1\times 10^{5}]$ and Cauchy numbers $Ca\in [120, 1200]$. Complementary rigid canopy experiments were also included to aid comparative insight. The flow was quantified in wall-normal planes in a developed region using high-frame-rate particle image velocimetry. Results show that the deflection and coordinated waving motion of the blades redistributed the Reynolds stresses above and below the canopy top. Critically, in-canopy turbulence associated with the seagrass lacked periodic stem wake vortex shedding present in the rigid canopy, yet the flexible canopy induced vortex shedding from the blade tips. Inspection of spatial and temporal characteristics of coherent flow structures using spectral proper orthogonal decomposition reveals that Kelvin–Helmholtz-type vortices are the dominant flow structures associated with the waving motion of the seagrass and that modulated the local flow exchange in both rigid and flexible canopies. A barrier-like effect produced by the blade deflections blocked large-scale turbulence transport, thereby reducing vortex penetration into the canopy. In addition, we uncovered a transition from sweep-dominated to ejection-dominated behaviour in the surrogate seagrass. We hypothesise that the vortices created during the upward blade motion period play a major role in the sweep-to-ejection-dominated transition. Conditionally averaged quadrant analysis on the downward and upward blade motion supports this contention.
Poor mental health is a state of psychological distress that is influenced by lifestyle factors such as sleep, diet, and physical activity. Compulsivity is a transdiagnostic phenotype cutting across a range of mental illnesses including obsessive–compulsive disorder, substance-related and addictive disorders, and is also influenced by lifestyle. Yet, how lifestyle relates to compulsivity is presently unknown, but important to understand to gain insights into individual differences in mental health. We assessed (a) the relationships between compulsivity and diet quality, sleep quality, and physical activity, and (b) whether psychological distress statistically contributes to these relationships.
Methods
We collected harmonized data on compulsivity, psychological distress, and lifestyle from two independent samples (Australian n = 880 and US n = 829). We used mediation analyses to investigate bidirectional relationships between compulsivity and lifestyle factors, and the role of psychological distress.
Results
Higher compulsivity was significantly related to poorer diet and sleep. Psychological distress statistically mediated the relationship between poorer sleep quality and higher compulsivity, and partially statistically mediated the relationship between poorer diet and higher compulsivity.
Conclusions
Lifestyle interventions in compulsivity may target psychological distress in the first instance, followed by sleep and diet quality. As psychological distress links aspects of lifestyle and compulsivity, focusing on mitigating and managing distress may offer a useful therapeutic approach to improve physical and mental health. Future research may focus on the specific sleep and diet patterns which may alter compulsivity over time to inform lifestyle targets for prevention and treatment of functionally impairing compulsive behaviors.
The early identification and prediction of hand-foot-and-mouth disease (HFMD) play an important role in the disease prevention and control. However, suitable models are different in regions due to the differences in geography, social economy factors. We collected data associated with daily reported HFMD cases and weather factors of Zibo city in 2010~2019 and used the generalised additive model (GAM) to evaluate the effects of weather factors on HFMD cases. Then, GAM, support vectors regression (SVR) and random forest regression (RFR) models are used to compare predictive results. The annual average incidence was 129.72/100 000 from 2010 to 2019. Its distribution showed a unimodal trend, with incidence increasing from March, peaking from May to September. Our study revealed the nonlinear relationship between temperature, rainfall and relative humidity and HFMD cases and based on the predictive result, the performances of three models constructed ranked in descending order are: SVR > GAM> RFR, and SVR has the smallest prediction errors. These findings provide quantitative evidence for the prediction of HFMD for special high-risk regions and can help public health agencies implement prevention and control measures in advance.
Understanding the size of oil droplets released from a jet in crossflow is crucial for estimating the trajectory of hydrocarbons and the rates of oil biodegradation/dissolution in the water column. We present experimental results of an oil jet with a jet-to-crossflow velocity ratio of 9.3. The oil was released from a vertical pipe 25 mm in diameter with a Reynolds number of 25 000. We measured the size of oil droplets near the top and bottom boundaries of the plume using shadowgraph cameras and we also filmed the whole plume. In parallel, we developed a multifluid large eddy simulation model to simulate the plume and coupled it with our VDROP population balance model to compute the local droplet size. We accounted for the slip velocity of oil droplets in the momentum equation and in the volume fraction equation of oil through the local, mass-weighted average droplet rise velocity. The top and bottom boundaries of the plume were captured well in the simulation. Larger droplets shaped the upper boundary of the plume, and the mean droplet size increased with elevation across the plume, most likely due to the individual rise velocity of droplets. At the same elevation across the plume, the droplet size was smaller at the centre axis as compared with the side boundaries of the plume due to the formation of the counter-rotating vortex pair, which induced upward velocity at the centre axis and downward velocity near the sides of the plume.
Mars exploration motivates the search for extraterrestrial life, the development of space technologies, and the design of human missions and habitations. Here, we seek new insights and pose unresolved questions relating to the natural history of Mars, habitability, robotic and human exploration, planetary protection, and the impacts on human society. Key observations and findings include:
– high escape rates of early Mars' atmosphere, including loss of water, impact present-day habitability;
– putative fossils on Mars will likely be ambiguous biomarkers for life;
– microbial contamination resulting from human habitation is unavoidable; and
– based on Mars' current planetary protection category, robotic payload(s) should characterize the local martian environment for any life-forms prior to human habitation.
Some of the outstanding questions are:
– which interpretation of the hemispheric dichotomy of the planet is correct;
– to what degree did deep-penetrating faults transport subsurface liquids to Mars' surface;
– in what abundance are carbonates formed by atmospheric processes;
– what properties of martian meteorites could be used to constrain their source locations;
– the origin(s) of organic macromolecules;
– was/is Mars inhabited;
– how can missions designed to uncover microbial activity in the subsurface eliminate potential false positives caused by microbial contaminants from Earth;
– how can we ensure that humans and microbes form a stable and benign biosphere; and
– should humans relate to putative extraterrestrial life from a biocentric viewpoint (preservation of all biology), or anthropocentric viewpoint of expanding habitation of space?
Studies of Mars' evolution can shed light on the habitability of extrasolar planets. In addition, Mars exploration can drive future policy developments and confirm (or put into question) the feasibility and/or extent of human habitability of space.
Falls and fall-related injuries among older adults represent a substantial health burden. Approximately 30% of older adults experience at least one fall each year, and half of these individuals fall recurrently [1, 2]. Fall-related non-fatal injuries are associated with increased morbidity, decreased functioning, and increased health care resource utilization [3, 4]. Fall-related injuries such as fracture account for 10-15% of emergency department presentations of those aged 65 years and older [5, 6]. With the number of adults aged 65 and older expected to increase to 1 in 5 by 2050, the economic burden imposed by falls is expected to increase proportionally [7].
Background: We describe an infant with a diagnosis of GM3 synthase deficiency, presenting with severe neuroirritability from birth. He required multiple admissions due to extreme agitation and caregiver burnout. Multiple pharmacological agents were tried, and the effect of each medication was modest and short-lasting at best. The literature on the management of neuroirritability in children with progressive genetic and metabolic conditions is sparse, and a neuroirritability management protocol has yet to be developed at our institution. Methods: We searched for relevant primary research and articles on PubMed. We reviewed the evidence of each pharmacological agent and added non-pharmacological strategies. We developed management guidelines for neuroirritability at our hospital. This protocol was reviewed by several pediatric neurologists and pediatric palliative care specialists at the Stollery and SickKids Hospitals. Results: We present the Pediatric Neuroirritability Management Protocol for the Stollery Children’s Hospital. Conclusions: Further study is required to assess whether this protocol can be adapted to treat irritability in the context of other neurological conditions such as hypoxic-ischemic encephalopathy and non-accidental injury. In addition, we will expand our guidelines to include other symptoms such as spasticity, dystonia, and autonomic dysfunction.