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.     

Neurocognitive markers of suicidal ideation in patients with anxiety and mood disorders

Abstract

Objective: This study aims at identifying associations between cognitive function and suicidal ideation in the sample of patients with anxiety and mood disorders (AMD).

Methods: In sum, 186 (age = 39?±?12.3 years; 142 [76.3%] females) patients with AMD were enrolled in the study. Assessment included evaluation of socio-demographic information, medication use, anxiety and depression symptoms. Cognitive tests included measures of psychomotor performance and incidental learning using the Digit Symbol Test. Trail Making Tests respectively measured perceptual speed, task-switching and executive control. Additionally, 21 patients completed tests from the Cambridge Automated Neuropsychological Test Battery measuring set shifting (Interdimensional/extradimensional set-shift), executive planning (Stockings of Cambridge), and decision making (Cambridge Gamble Task [CGT]).

Results: Almost half (45.0%, n?=?86) of the study sample patients had experienced suicidal ideations. In multivariable regression analysis, suicidal ideation was associated with a greater overall proportion of bet and risk taking on the CGT task (β?=?0.726, p?=?.010 and β?=?0.634, p?=?.019), when controlling for socio-demographic characteristics, medication use, anxiety and depression symptoms.

Conclusions: Outpatients with AMD and suicidal ideation could be distinguished by the presence of cognitive deficits in the executive function domain, particularly in impulse-control and risk taking.     

新型冠状病毒肺炎出院患者生活质量及影响因素研究

目的:了解新型冠状病毒肺炎(COVID-19)患者出院后的生活质量及其影响因素,为优化早期干预方案,预防社区生活受限,制定相应社区康复措施提供依据。方法:选择2020年3—4月在武汉华润武钢总医院治愈出院的COVID-19患者57例,于2020年4—5月通过"问卷星"平台采用简明健康状况调查量表(SF-12V2)调查患者的生活质量;采用焦虑自评量表(SAS)调查患者的焦虑状态;采用抑郁自评量表(SDS)调查患者的抑郁状态;采用呼吸困难指数量表(mMRC)调查患者的呼吸困难程度。比较不同特征COVID-19患者生活质量的差异;分析患者生活质量与焦虑、抑郁和呼吸困难程度的相关性及其相关的影响因素。结果:共发放57份调查问卷,剔除重复及无效问卷3份,获得有效问卷54份,问卷有效率达94.74%。(1)COVID-19出院后患者生活质量情况:生理总评分和心理总评分分别为(37.02±12.32)分、(38.46±14.42)分;呼吸困难等级0~3级的分别为3例(5.56%)、45例(83.33%)、5例(9.26%)、1例(1.85%);有19例(35.19%)存在焦虑情绪(SAS≥50分)和抑郁情绪(SDS≥53分)。(2)不同特征COVID-19患者生活质量比较:不同疾病分型的患者在生理总评分方面差异有统计学意义(P<0.05)。(3)生活质量与焦虑、抑郁和呼吸困难程度的相关性分析:Pearson相关分析结果显示,SF-12V2生理总评分与焦虑程度(r=-0.34,P=0.011)和呼吸困难程度(r=-0.39,P=0.003)之间存在负相关性,SF-12V2心理总评分与焦虑程度(r=-0.46,P=0.001)和抑郁程度(r=-0.40,P=0.002)之间存在负相关性。(4)COVID-19患者生活质量的影响因素分析:多元线性回归分析显示,性别(β=8.27)、抑郁程度(β=-0.34)和疾病分型(β=-11.68)是患者SF-12V2生理总评分的重要决定因素(P<0.05);焦虑程度(β=-0.62)是患者SF-12V2心理总评分的重要决定因素(P<0.05)。结论:COVID-19出院患者存在呼吸困难、焦虑抑郁情绪和生活质量下降的问题;性别、疾病分型、抑郁程度和焦虑程度是COVID-19患者生活质量下降的重要因素。COVID-19患者(特别是女性患者和重型患者)出院后要尽早进行抑郁症和焦虑症的筛查和干预,减少患者负性情绪,鼓励患者适当参与康复训练,提高呼吸功能,从而促进生活质量提高。     

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.