Identifying long-term and imminent suicide predictors in a general population and a clinical sample with machine learning

Background: Machine learning (ML) is increasingly used to predict suicide deaths but their value for suicide prevention has not been established. Our first objective was to identify risk and protective factors in a general population. Our second objective was to identify factors indicating imminent suicide risk. Methods: We used survival and ML models to identify lifetime predictors using the Cohort of Norway (n=173,275) and hospital diagnoses in a Saskatoon clinical sample (n=12,614). The mean follow-up times were 17 years and 3 years for the Cohort of Norway and Saskatoon respectively. People in the clinical sample had a longitudinal record of hospital visits grouped in six-month intervals. We developed models in a training set and these models predicted survival probabilities in held-out test data. Results: In the general population, we found that a higher proportion of low-income residents in a county, mood symptoms, and daily smoking increased the risk of dying from suicide in both genders. In the clinical sample, the only predictors identified were male gender and older age. Conclusion: Suicide prevention probably requires individual actions with governmental incentives. The prediction of imminent suicide remains highly challenging, but machine learning can identify early prevention targets

Resilience, stress, and coping among Canadian medical students

Background: Numerous studies have established that medical school is a stressful place but coping styles and resilience have not been adequately addressed as protective factors.Method: Using a cross-sectional design, 155 students were surveyed using the Connor-Davidson Resilience Scale, Perceived Stress Scale, and the Canadian Community Health Survey Coping Scale.  Mean scores were compared by gender and between our sample and normative scores using t-tests.  Multivariate linear regression was performed to examine whether stress levels were related to coping and resilience.Results:  Medical students had higher perceived stress, negative coping, and lower resilience than age and gender-matched peers in the general population.   Male medical students had higher positive coping scores than general population peers and higher resilience, and lower perceived stress than female medical students. Coping scores did not vary by gender in our sample.  The multivariate model showed that resilience and negative, but not positive coping, predicted stress.Conclusions: Medical students are neither more resilient nor better equipped with coping skills than peers in the population.  Greater emphasis on self-care among medical trainees is recommended.  Emphasizing the importance of self-care during medical training, whether by formal incorporation into the curriculum or informal mentorship, deserves further study

Affective instability and impulsivity predict nonsuicidal self-injury in the general population : a longitudinal analysis

Background Impulsivity and affective instability are related traits known to be associated with nonsuicidal self-injury, although few longitudinal studies have examined this relationship. The purpose of this study was to determine if impulsivity and affective instability predict future nonsuicidal self-injury in the general population while accounting for the overlap between these traits. Methods Logistic regression analyses were conducted on data from 2344 participants who completed an 18-month follow-up of the 2000 British National Psychiatric Morbidity Survey. Affective instability and impulsivity were assessed at baseline with the Structured Clinical Interview for DSM-IV Axis II Personality Disorders. Nonsuicidal self-injury was assessed at baseline and follow-up during semi-structured interviews. Results Affective instability and impulsivity predicted the onset of nonsuicidal self-injury during the follow-up period. Affective instability, but not impulsivity, predicted the continuation of nonsuicidal self-injury during the follow-up period. Affective instability accounted for part of the relationship between impulsivity and nonsuicidal self-injury. Conclusions Affective instability and impulsivity are important predictors of nonsuicidal self-injury in the general population. It may be more useful to target affective instability over impulsivity for the treatment of nonsuicidal self-injury

Medication Gaps and Antipsychotic Polypharmacy in Previously Hospitalized Schizophrenia Patients: An Electronic Cohort Study in Three Canadian Provinces

Background: Real world evidence about antipsychotics focuses on rehospitalization. Modeling the time course of pharmacotherapy would show patients\u27 adherence to medications and physicians\u27 adherence to medication guidelines. We aimed to calculate the cumulative time spent in second generation antipsychotics (SGAs), gaps, antipsychotic polypharmacy, and clozapine in discharged schizophrenia patients. Methods: Hospitalization and pharmacy dispensing data from 2008–2018 in Manitoba, Saskatchewan, and British Columbia were linked and an electronic cohort (N = 2,997) was created (mean follow-up: 49 months, SD = 38). Cohort members were required to have a minimum of 6 weeks medicated with aripiprazole, olanzapine, paliperidone, quetiapine, risperidone, or ziprasidone. Results: The multistate model predicted that schizophrenia patients accumulated 44 months in SGA monotherapy, 4 months in polypharmacy, 11 months in medication gaps and 17 days in clozapine over a 5-year period. The majority of transitions were between SGA and medication gap. Accumulated time in medication gaps was seven times as much as in clozapine. Each 10% delay in SGA initiation post-discharge was associated with a 2, 1, and 6% higher risk for polypharmacy (95% CI: 1.01–1.02), gap (95% CI: 1.01–1.01), and clozapine (95% CI: 1.04–1.08), respectively. Interpretation: Schizophrenia patients accumulated more time unmedicated and in polypharmacy compared to clozapine. Either treatment guidelines for schizophrenia are not followed, or real-world challenges hamper their implementation

Sunshine, Sea, and Season of Birth: MS Incidence in Wales

Maternal sun exposure in gestation and throughout the lifetime is necessary for vitamin D synthesis, and living near the sea is a population level index of seafood consumption. The aim of this study was to estimate the incidence rate of multiple sclerosis (MS) in Wales and examine its association with sun exposure, coastal living, and latitude. The study used a database of MS hospital visits and admissions in Wales between 2002 and 2013. For the 1,909 lower layer super output areas (LSOAs) in Wales, coastal status, population, longitude/latitude, and average sunshine hours per day were obtained. Age-specific and age-standardised MS incidence were calculated and modelled using Poisson regression. The distribution of births by month was compared between MS cases and the combined England and Wales population. There were 3,557 new MS cases between 2002 and 2013, with an average annual incidence of 8.14 (95% CI: 7.69-8.59) among males and 12.97 (95% CI: 12.44-13.50) among females per 100,000 population. The female-to-male ratio was 1.86:1. For both sexes combined, the average annual incidence rate was 9.10 (95% CI: 8.80-9.40). All figures are age-standardized to the 1976 European standard population. Compared to the combined England and Wales population, more people with MS were born in April, observed-to-expected ratio: 1.21 (95% CI: 1.08-1.36). MS incidence varied directly with latitude and inversely with sunshine hours. Proximity to the coast was associated with lower MS incidence only in easterly areas. This study shows that MS incidence rate in Wales is comparable to the rate in Scotland and is associated with environmental factors that probably represent levels of vitamin D

Can mental health related hospital visits be relied upon for suicide prevention?

Introduction Even among people with mental disorders, relatively few die of suicide. However, a large proportion of people dying from suicide have seen a physician in the year before death. This raises the question whether focusing on hospital visits for suicide-related outcomes is a viable suicide prevention strategy. Objectives and Approach Our objective was to examine whether a hospital visit for a mental disorder or prior suicide attempt preceded suicide death. We requested Saskatchewan’s provincial coroner for records of people dying of suicide in the Saskatoon Health Region catchment area for the years 2012 to 2016. The coroner’s list was linked with hospital and community mental health databases. Patient charts and medical abstracts in both settings were reviewed for risk factors. Results There were 143 suicide deaths in the time period and the yearly incidence was higher in Saskatoon as compared with the national average. Only 38 percent were seen previously in any Saskatoon hospital for a mental disorder (11 percent for a self-harm diagnosis). The chart review confirmed several known psychological and social risk factors. Having a history of depression or psychosis and alcohol and/or drug use were common. Many decedents also had disadvantaged socio-economic backgrounds characterized by vulnerable housing and being on social assistance. Conclusion/Implications With only 38 percent of decedents being seen in hospital, community-based mental health care and data are important for suicide prevention. Suicide prevention efforts can be aided by facilitating the linkage of community and medical records to better track patients as they move between care settings

Synthetic polymer-based membrane for lithium Ion batteries

Efficient energy storage systems are increasingly needed due to advances in portable electronics and transport vehicles, lithium-ion batteries standing out among the most suitable energy storage systems for a large variety of applications. In lithium-ion batteries, the porous separator membrane plays a relevant role as it is placed between the electrodes and serves as a charge transfer medium and affects the cycle behavior. Typically, porous separators membranes are comprised of a synthetic polymeric matrix embedded in the electrolyte solution. The present chapter focus on recent advances in synthetic polymers for porous separation membranes, as well as on the techniques for membrane preparation and physicochemical characterization. The main challenges to improve synthetic polymer performance for battery separator membrane applications are also discussed.Portuguese Foundation for Science and Technology (FCT) in the framework of the Strategic Funding UID/FIS/04650/2019, UID/QUI/50006/2019, UID/QUI/0686/2016 and UID/EMS/00151/2019. The authors thank FEDER funds through the COMPETE 2020 Programme and National Funds through FCT under the project PTDC/FIS-MAC/28157/2017, Grants 38 SFRH/BPD/117838/2016 (JNP). and SFRH/BPD/112547/2015 (C.M.C). Financial support from the Spanish Ministry of Economy and Competitiveness (MINECO) through the project MAT2016-76039-C4-3-R (AEI/FEDER, UE) (including the FEDER financial support) and from the Basque Government Industry and Education Departments under the ELKARTEK, HAZITEK and PIBA (PIBA-2018-06

Fear of Infection and the Common Good: COVID-19 and the First Italian Lockdown

In the first quarter of 2020, Italy became one of the earliest hotspots of COVID-19 infection, and the government imposed a lockdown. During the lockdown, an online survey of 2053 adults was conducted that asked about health behaviors and about the psychological and overall impact of COVID-19. The present study is a secondary analysis of that data. We hypothesized that self-control, higher socio-economic status, existing health conditions, and fear of infection were all inversely related to actions (or intentions) that violated the lockdown (i.e., infractions). Using partial least squares structural equation modeling (PLS-SEM), we found that only the fear of infection significantly dissuaded people from violating lockdown rules. Since it is not practical or ethical to sow a fear of infection, our study indicates that enacting rules and enforcing them firmly and fairly are important tools for containing the infection. This may become more important as vaccines become more widely available and people lose their fear of infection

The UK Research Excellence Framework and the Matthew effect: Insights from machine learning.

With the high cost of the research assessment exercises in the UK, many have called for simpler and less time-consuming alternatives. In this work, we gathered publicly available REF data, combined them with library-subscribed data, and used machine learning to examine whether the overall result of the Research Excellence Framework 2014 could be replicated. A Bayesian additive regression tree model predicting university grade point average (GPA) from an initial set of 18 candidate explanatory variables was developed. One hundred and nine universities were randomly divided into a training set (n = 79) and test set (n = 30). The model "learned" associations between GPA and the other variables in the training set and was made to predict the GPA of universities in the test set. GPA could be predicted from just three variables: the number of Web of Science documents, entry tariff, and percentage of students coming from state schools (r-squared = .88). Implications of this finding are discussed and proposals are given