Do social support and community engagement act as mechanisms in the association between neighbourhood income inequality and the mental health of mothers in Calgary,Canada? A mediation analysis

PurposeAccording to the social determinants of health framework, income inequality is a potential risk factor for adverse mental health. However, few studies have explored the mechanisms suspected to mediate this relationship. The current study addresses this gap through a mediation analysis to determine if social support and community engagement act as mediators linking neighbourhood income inequality to maternal anxiety and depressive symptoms within a cohort of new mothers living in the City of Calgary, Canada.MethodsData collected at three years postpartum from mothers belonging to the All Our Families (AOF) cohort were used in the current study. Maternal data were collected between 2012 and 2015 and linked to neighbourhood socioeconomic data from the 2006 Canadian Census. Income inequality was measured using Gini coefficients derived from 2006 after-tax census data. Generalized structural equation models were used to quantify the associations between income inequality and mental health symptoms, and to assess the potential direct and indirect mediating effects of maternal social support and community engagement.ResultsIncome inequality was not significantly associated with higher depressive symptoms (β = 0.32, 95%CI = −0.067, 0.70), anxiety symptoms (β = 0.11, 95%CI = −0.39, 0.60), or lower social support. Income inequality was not associated with community engagement. For the depression models, higher social support was significantly associated with lower depressive symptoms (β = −0.13, 95%CI = −0.15, −0.097), while community engagement was not significantly associated with depressive symptoms (β = 0.059, 95%CI = −0.15, 0.27). Similarly, for the anxiety models, lower anxiety symptoms were significantly associated with higher levels of social support (β = −0.17, 95%CI = −0.20, −0.13) but not with higher levels of community engagement (β = 0.14, 95%CI = −0.14, 0.41).ConclusionThe current study did not find clear evidence for social support or community engagement mediating the relationship between neighbourhood income inequality and maternal mental health. Future investigations should employ a broader longitudinal approach to capture changes in income inequality, potential mediators, and mental health symptomatology over time.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

EEG power spectral density under Propofol and its association with burst suppression,a marker of cerebral fragility

ObjectiveUnder General Anesthesia (GA), age and Burst Suppression (BS) are associated with cognitive postoperative complications, yet how these parameters are related to per-operative EEG and hypnotic doses is unclear. In this prospective study, we address this question comparing age and BS occurrences with a new score (BP_TIVA) based on Propofol doses, EEG and alpha-band power spectral densities, evaluated for SEF₉₅ = 8–13 Hz.Methods59 patients (55 [34–67] yr, 67% female) undergoing neuroradiology or orthopedic surgery were included. Total IntraVenous Anesthesia was used for Propofol and analgesics infusion. Cerebral activity was monitored from a frontal electrodes montage EEG.ResultsBP_TIVA was inversely correlated with age (Pearson r = −0.78, p < 0.001), and was significantly lower (p < 0.001) when BS occurred during the GA first minutes (induction). Additionally, the age-free BP_TIVA score was better associated with BS at induction than age (AUC = 0.94 versus 0.82, p < 0.05).ConclusionWe designed BP_TIVA score based on hypnotics and EEG. It was correlated with age yet was better associated to BS occurring during GA induction, the latter being a cerebral fragility sign.SignificanceThis advocate for an approach based on evaluating the cerebral physiological age (« brain age ») to predict postoperative cognitive evolution.     

Patient preferences on the use of technology in cancer surveillance after curative surgery: A cross-sectional analysis

Background

Advances in communication technology have enabled new methods of delivering test results to cancer survivors. We sought to determine patient preferences regarding the use of newer technology in delivering test results during cancer surveillance.

Improving burn surgery education for medical students in China

IntroductionIn China, although burn treatment develops rapidly, and ranks in the forefront of the world, there is a relative shortage of burn specialists, which limits the development of burn education. In traditional curriculum of surgery education, burn surgery education accounts for few proportions, which results in the indifference to the burn surgery among medical students. To date, few research reported the application of Clinical pathway- Problem based Learning (CP-PBL) in burn surgery education. The objective of the study is to explore the teaching effect of this novel teaching method in burn surgery education.MethodsA pilot study was performed. One hundred and six students were randomly divided into a Lecture based Learning (LBL) only group (control group) and a LBL combined Clinical pathway- Problem based Learning group (observation group). A set of test was designed as evaluation criteria based on questions of burn surgery in National Medical Licensing Examination (NMLE) from 2011 to 2018.ResultsThe students with Clinical pathway- Problem based Learning had better academic performances in profession theory. Type A2 and Type A3/A4 scores in the observation group were higher than those in the control group (p < 0.05). The scores of the observation group were higher than those of control group in the domains of understanding and application (p < 0.05). They also have higher favorable impressions of learning experience.ConclusionsMore active approaches yield more learning and are viewed more favorable, which provides a vital message for the evolution of curriculum in Chinese medical schools.