Perceptions and Experiences of Inequity for Women of Refugee Background Having a Baby during the COVID-19 Pandemic in Melbourne, Australia

Listening to What Matters is an exploratory descriptive qualitative study that aimed to (1) understand how women of refugee background in Melbourne, Australia experienced access to health information and maternity and/or early parenting care during the COVID-19 pandemic and (2) whether pandemic health directives had an impact on structural inequities for women of refugee background who received maternity and/or early parenting care during the COVID-19 pandemic. Semi-structured interviews were conducted with 41 participants including 17 women of refugee background, who identified as belonging to the Karen, Assyrian Chaldean, Iraqi, Syrian, Afghan, Sudanese, or South Sudanese communities and 24 health and social care professionals who identified as providing pregnancy or early parenting care during the pandemic in the north western suburbs of Melbourne. Interviews with women were conducted in preferred languages by community researchers. Interviews with professionals were conducted in English by researchers. Reflexive thematic data analysis included constructivist positionality and a trauma and violence informed approach. The results reported in this paper include three themes, with four accompanying subthemes, as follows: theme (1), 'Structural inequities and the toll of the pandemic'; theme (2), 'Supportive infrastructure'; and theme (3), 'Cultural safety during the pandemic'. The results demonstrate that cumulative negative impacts such as unequal access to health information, family separation and isolation, inadequate household income, and mental and social health concerns had the potential to amplify pre-existing structural inequities for women of refugee background. Community engagement facilitated by bicultural workers, interpreters, and trusted care providers facilitated fast-paced, two-way communication that built capacity and health literacy for women who were unable to speak English and unfamiliar with the health care system and, improved experiences of care. More research is needed to understand how the intersectional cumulative impacts of structural inequities have affected maternal and neonatal health outcomes for women of refugee background during the pandemic, as well as any differences in maternal and neonatal health outcomes between Australian-born and refugee background women and babies.

Performance of different body weights in the Cockcroft-Gault equation in critically ill patients with and without augmented renal clearance: A multicenter cohort

STUDY OBJECTIVE

The primary objective was to evaluate the performance of the Cockcroft-Gault (CG) equation with different body weights (BWs) compared to a measured creatinine clearance (mCrCl) in an intensive care unit (ICU) population with and without augmented renal clearance (ARC).

DESIGN

Multicenter, retrospective cohort.

SETTING

Two ICUs in the United States and four ICUs from a previous international observational analysis.

PATIENTS

Adult ICU patients admitted from January 1, 2010 to July 30, 2020 with at least one mCrCl collected within the initial 10 days of hospitalization were eligible for inclusion.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was the performance of the CG equation in ARC (mCrCl≥130 ml/min/1.73 m2 ) and non-ARC (mCrCl<130 ml/min/1.73 m2 ) patients. Correlation was analyzed by Pearson's correlation coefficient, bias by mean difference, and accuracy by the percentage of patients within 30% of the mCrCl. A total of 383 patients were included, which provided 1708 mCrCl values. The majority were male (n = 239, 62%), median age of 55 years [IQR 40-65] with a surgical diagnosis (n = 239, 77%). ARC was identified in 229 (60%) patients. The ARC group had lower Scr values (0.6 [0.5-0.7] vs. 0.7 [0.6-0.9] mg/dl, p < 0.001) and higher mCrCl (172.8 (SD 39.1) vs. 89.9 mL/min/1.73 m2 (SD 25.4), p < 0.001) compared with the non-ARC group, respectively. Among non-ARC patients there was a moderate correlation (r = 0.33-0.39), moderate accuracy (range 48-58%), and low bias (range of -12.9 to 17.1) among the different BW estimations with the adjusted BW having the better performance. Among ARC patients there was low correlation (r = 0.24-0.28), low to moderate accuracy (range 38-70%), and high bias (range of -58.5 to -21.6).

CONCLUSIONS

The CG-adjusted BW had the best performance in the non-ARC patients, while CG performed poorly with any BW in ARC patients. Although the CG equation remains the standard equation for estimating CrCl in the ICU setting, a new, validated equation is needed for patients with ARC.

Changes in both top-down and bottom-up effective connectivity drive visual hallucinations in Parkinson's disease

Visual hallucinations are common in Parkinson's disease and are associated with a poorer quality of life and a higher risk of dementia. An important and influential model that is widely accepted as an explanation for the mechanism of visual hallucinations in Parkinson's disease and other Lewy body diseases is that these arise due to aberrant hierarchical processing, with impaired bottom-up integration of sensory information and overweighting of top-down perceptual priors within the visual system. This hypothesis has been driven by behavioural data and supported indirectly by observations derived from regional activation and correlational measures using neuroimaging. However, until now, there was no evidence from neuroimaging for differences in causal influences between brain regions measured in patients with Parkinson's hallucinations. This is in part because previous resting-state studies focused on functional connectivity, which is inherently undirected in nature and cannot test hypotheses about the directionality of connectivity. Spectral dynamic causal modelling is a Bayesian framework that allows the inference of effective connectivity-defined as the directed (causal) influence that one region exerts on another region-from resting-state functional MRI data. In the current study, we utilize spectral dynamic causal modelling to estimate effective connectivity within the resting-state visual network in our cohort of 15 Parkinson's disease visual hallucinators and 75 Parkinson's disease non-visual hallucinators. We find that visual hallucinators display decreased bottom-up effective connectivity from the lateral geniculate nucleus to primary visual cortex and increased top-down effective connectivity from the left prefrontal cortex to primary visual cortex and the medial thalamus, as compared with non-visual hallucinators. Importantly, we find that the pattern of effective connectivity is predictive of the presence of visual hallucinations and associated with their severity within the hallucinating group. This is the first study to provide evidence, using resting-state effective connectivity, to support a model of aberrant hierarchical predictive processing as the mechanism for visual hallucinations in Parkinson's disease.

A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction

OBJECTIVE

Scarcity of good quality electroencephalography (EEG) data is one of the roadblocks for accurate seizure prediction. This work proposes a deep convolutional generative adversarial network (DCGAN) to generate synthetic EEG data. Another objective of our study is to use transfer-learning (TL) for evaluating the performance of four well-known deep-learning (DL) models to predict epileptic seizure.

METHODS

We proposed an algorithm that generate synthetic data using DCGAN trained on real EEG data in a patient-specific manner. We validate quality of generated data using one-class SVM and a new proposal namely convolutional epileptic seizure predictor (CESP). We evaluate performance of VGG16, VGG19, ResNet50, and Inceptionv3 trained on augmented data using TL with average time of 10 min between true prediction and seizure onset samples.

RESULTS

The CESP model achieves sensitivity of 78.11% and 88.21%, and false prediction rate of 0.27/h and 0.14/h for training on synthesized and testing on real Epilepsyecosystem and CHB-MIT datasets, respectively. Using TL and augmented data, Inceptionv3 achieved highest accuracy with sensitivity of 90.03% and 0.03 FPR/h. With the proposed data augmentation method prediction results of CESP model and Inceptionv3 increased by 4-5% as compared to state-of-the-art augmentation techniques.

CONCLUSION

The performance of CESP shows that synthetic data acquired association between features and labels very well and by using the augmented data CESP predicted better than chance level for both datasets.

SIGNIFICANCE

The proposed DCGAN can be used to generate synthetic data to increase the prediction performance and to overcome good quality data scarcity issue.