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.     

Primary and recurrent regional metastases for lateralized oral cavity squamous cell carcinoma

ObjectivesMap regional lymph node metastases for lateralized oral cavity squamous cell carcinoma (OCSCC) and evaluate factors associated with regional metastases and recurrence.Materials and methodsRetrospective cohort study of 715 patients with lateralized OCSCC surgically treated in 1997–2011. Analysis was performed using log-rank, Kaplan-Meier, and multivariable logistic and Cox regression.ResultsRegional metastases were identified in ipsilateral levels IIA (24%), IB (18%), III (13%), V (9%), IV (7%), IA (2%) and IIB (1%) and the contralateral neck (3%). Lymphovascular invasion (LVI) (Hazard Ratio [HR] 2.2, 95% Confidence Interval [CI] 1.2–3.9) and T category (T3 vs. T1: HR 4.1, 95% CI 1.9–9.3; T4 vs. T1: HR 2.3, 95% CI 1.2–4.3) were associated with regional metastases. Most (71%) isolated regional metastatic recurrences were in undissected levels of the neck, including 58% in levels IV and V. Tumors of the hard palate (HR 4.3, 95% CI 1.2–16.1), upper alveolus (HR 3.2, 95% CI 1.0–4.7) or with LVI (HR 2.0, 95% CI 1.0–3.9) were associated with isolated regional recurrence. For upper alveolar/hard palate tumors, depth of invasion (DOI) ≥4 mm (P = .003) and LVI (P = .04) were associated with regional metastases.ConclusionsFor lateralized OCSCC, elective neck dissection of level IIB or the contralateral neck may rarely be needed, but additional surgical or radiation treatment of levels IV and V may be considered based on patient risk factors, including T category 3–4 or LVI. For upper alveolar/hard palate tumors, DOI ≥4 mm is an appropriate threshold for elective neck dissection.     

医院信息化系统在武汉新冠肺炎防疫救治过程中暴露出的问题及其引发的思考

目的针对武汉地区医院信息化系统在新型冠状病毒肺炎(简称新冠肺炎)防疫救治过程中暴露出的问题,提出应对突发公共卫生事件时医院信息化的完善方法。方法充分分析医院信息化系统在新冠肺炎防疫救治中所暴露出的各个医院信息化系统结构不同、各个医院之间无法实施信息共享、门诊病房之间系统相互独立、数据信息更新不及时等九大问题,并提出医院信息化基础布局的建立、区域链及大数据临床中的应用等多项措施。结果医院信息化能力的提高有助于降低医护人员工作强度,提高患者的安全。结论新冠肺炎疫情的发生给我国医院信息化系统带来了严峻的挑战,但同时也给我们带来了更大的机遇。我国在未来针对突发公共卫生事件时,医院信息化将为医护人员、患者提供更有利的支持。     

Association of regional socioeconomic deprivation and rurality with global developmental delay in early childhood: Data from mandatory school entry examinations in Germany

BackgroundFrom birth to young adulthood, health and development of young people are strongly linked to their living situation, including their family’s socioeconomic position (SEP) and living environment. The impact of regional characteristics on development in early childhood beyond family SEP has been rarely investigated. This study aimed to identify regional predictors of global developmental delay at school entry taking family SEP into consideration.MethodWe used representative, population-based data from mandatory school entry examinations of the German federal state of Brandenburg in 2018/2019 with n=22,801 preschool children. By applying binary multilevel models, we hierarchically analyzed the effect of regional deprivation defined by the German Index of Socioeconomic Deprivation (GISD) and rurality operationalized as inverted population density of the children’s school district on global developmental delay (GDD) while adjusting for family SEP (low, medium and high).ResultsFamily SEP was significantly and strongly linked to GDD. Children with the highest family SEP showed a lower odds for GDD compared to a medium SEP (female: OR=4.26, male: OR=3.46) and low SEP (female: OR=16.58, male: OR=12.79). Furthermore, we discovered a smaller, but additional and independent effect of regional socioeconomic deprivation on GDD, with a higher odds for children from a more deprived school district (female: OR=1.35, male: OR=1.20). However, rurality did not show a significant link to GDD in preschool children beyond family SEP and regional deprivation.ConclusionFamily SEP and regional deprivation are risk factors for child development and of particular interest to promote health of children in early childhood and over the life course.     

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.     

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.