Plos One
-
Conflict and humanitarian crises increase the risk of both intimate partner violence and non-partner sexual violence against women and girls. We measured the prevalence and risk factors of different forms of violence against women and girls in South Sudan, which has suffered decades of conflict, most recently in 2013. ⋯ Women and girls in South Sudan suffer among the highest levels of physical and sexual violence in the world. Although the prevalence of sexual assault by non-partners is four times the global average, women are still at greatest risk of physical and sexual assault from intimate partners. Conflict-related and intimate partner violence reinforce each other and are upheld by restrictive gender norms and marital practices. Expansion of comprehensive services, including health and psycho-social support for survivors is urgently needed. Moreover, policies and laws to prevent violence against women and provide survivors with access to justice should be given high priority within the ongoing peacebuilding process in South Sudan.
-
Frequent emergency department users are patients cumulating at least four visits per year. Few studies have focused on persistent frequent users, who maintain their frequent user status for multiple consecutive years. This study targets an adult population with chronic conditions, and its aims are: 1) to estimate the prevalence of persistent frequent ED use; 2) to identify factors associated with persistent frequent ED use (frequent use for three consecutive years) and compare their importance with those associated with occasional frequent ED use (frequent use during the year following the index date); and 3) to compare characteristics of "persistent frequent users" to "occasional frequent users" and to "users other than persistent frequent users". ⋯ Persistent frequent users are a subpopulation of frequent users with whom they share characteristics, such as physical and mental comorbidities, though the former are poorer and younger. More research is needed in order to better understand what factors can contribute to persistent frequent use.
-
Delirium is a common and serious acute neuropsychiatric syndrome which is often missed in routine clinical care. Inattention is the core cognitive feature. Diagnostic test accuracy (including cut-points) of a smartphone Delirium App (DelApp) for assessing attention deficits was assessed in older hospital inpatients. ⋯ Patients with delirium (with or without pre-existing cognitive impairment) perform poorly on the DelApp compared to patients with dementia and those without cognitive impairment. A cut-point of ≤8/10 is suggested as having optimal sensitivity and specificity. The DelApp is a promising tool for assessment of attention deficits associated with delirium in older hospitalised adults, many of whom have prior cognitive impairment, and should be further validated in representative patient cohorts.
-
To circumvent the limited availability of RNA extraction reagents, we aimed to develop a protocol for direct RT-qPCR to detect SARS-CoV-2 in nasopharyngeal swabs without RNA extraction. Nasopharyngeal specimens positive for SARS-CoV-2 and other coronaviruses collected in universal viral transport (UVT) medium were pre-processed by several commercial and laboratory-developed methods and tested by RT-qPCR assays without RNA extraction using different RT-qPCR master mixes. The results were compared to that of standard approach that involves RNA extraction. ⋯ Also, the RT-qPCR CT values obtained by the two methods were positively correlated (Pearson correlation coefficient r = 0.6971, p = 0.0013). The rate of PCR inhibition by the direct approach was 8% compared to 9% by the standard approach. Our simple approach to detect SARS-CoV-2 RNA by direct RT-qPCR may help laboratories continue testing for the virus despite reagent shortages or expand their testing capacity in resource limited settings.
-
Observational Study
Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter.
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. ⋯ Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.