Healthcare workers (HCWs) are on the frontline of combating COVID-19, hence are at elevated risk of contracting an infection with SARS-CoV-2. The present study aims to measure the impact of SARS-CoV-2 on HCWs in central sub-Saharan Africa.

A cross-sectional serological study was conducted at six urban and five rural hospitals during the first pandemic wave in the South Kivu province, Democratic Republic of the Congo (DRC).

Serum specimens from 1029 HCWs employed during the first pandemic wave were collected between August and October 2020, and data on demographics and work-related factors were recorded during structured interviews.

The presence of IgG antibodies against SARS-CoV-2 was examined by ELISA. Positive specimens were further tested using a micro-neutralisation assay. Factors driving SARS-CoV-2 seropositivity were assessed by multivariable analysis.

Overall SARS-CoV-2 seroprevalence was high among HCWs (33.1%), and significantly higher in urban (41.5%) compared with rural (19.8%) hospitals. Having had presented with COVID-19-like symptoms before was a strong predictor of seropositivity (31.5%). Personal protective equipment (PPE, 88.1% and 11.9%) and alcohol-based hand sanitizer (71.1% and 28.9%) were more often available, and hand hygiene was more often reported after patient contact (63.0% and 37.0%) in urban compared with rural hospitals, respectively. This may suggest that higher exposure during non-work times in high incidence urban areas counteracts higher work protection levels of HCWs.

High SARS-CoV-2 seropositivity indicates widespread transmission of the virus in this region of DRC. Given the absence of publicly reported cases during the same time period at the rural sites, serological studies are very relevant in revealing infection dynamics especially in regions with low diagnostic capacities. This, and discrepancies in the application of PPE between urban and rural sites, should be considered in future pandemic response programmes.

Stroke is a major cause of death and disability worldwide, frequently resulting in persistent cognitive deficits among survivors. These deficits negatively impact recovery and therapy engagement, and their treatment is consistently rated as high priority by stakeholders and clinicians. Although clinical guidelines endorse cognitive screening for poststroke management, there is currently no gold-standard approach for identifying cognitive deficits after stroke, and clinical stroke services lack the capacity for long-term cognitive monitoring and care. Currently, available assessment tools are either not stroke-specific, not in-depth or lack scalability, leading to heterogeneity in patient assessments.

To address these challenges, a cost-effective, scalable and comprehensive screening tool is needed to provide a stroke-specific assessment of cognition. The current study presents such a novel digital tool, the Imperial Comprehensive Cognitive Assessment in Cerebrovascular Disease (IC3), designed to detect both domain-general and domain-specific cognitive deficits in patients after stroke with minimal input from a health professional. To ensure its reliability, we will use multiple validation approaches, and aim to recruit a large normative sample of age-matched, gender-matched and education-matched UK-based controls. Moreover, the IC3 assessment will be integrated within a larger prospective observational longitudinal clinical trial, where poststroke cognition will be examined in tandem with brain imaging and blood biomarkers to identify novel multimodal biomarkers of recovery after stroke. This study will enable deeper cognitive phenotyping of patients at a large scale, while identifying those with highest risk of progressive cognitive decline, as well as those with greatest potential for recovery.

This study has been approved by South West—Frenchay Research Ethics Committee (IRAS 299333) and authorised by the UK’s Health Research Authority. Results from the study will be disseminated at conferences and within peer-reviewed journals.

NCT05885295. Stage: Pre-results.

Healthcare data analytics is a methodological approach to the systematic analysis of health data, and it provides opportunities for healthcare professionals to improve health system management, patient engagement, budgeting, planning and performing evidence-based decision-making. Literature suggests that certain skills and/or competencies for health professionals working with big data in health care would be required. A review of the skills and competencies in health data analytics required by health professionals is needed to support the development or re-engineering of curriculum for health professionals to ensure they develop the abilities to make evidence-based decisions that ultimately can lead to the effective and efficient functioning of a healthcare system.

Using Arksey and O’Malley’s framework, this study will review literature published in English from January 2012 to December 2022. The database search includes Academic Search Complete, CINAHL, and MEDLINE via EBSCOhost, PubMed, Science Direct, Scopus, and Taylor and Francis. The reference lists of key studies will be searched to identify additional appropriate studies to include. The review will be conducted using an inclusion and exclusion criteria. Iterative processes will be involved at the various stages of search strategy piloting, screening and data extraction. Articles will be reviewed through a two-step process (title and abstract, and full-text review) by at least two reviewers. Data will be described quantitatively and/or qualitatively and presented in diagrams and tables.

Ethical clearance has been received, and strict protocol measures will be followed to ensure the data reported is of quality and relevant to the review purpose. The results will be disseminated through a peer-reviewed scientific journal, presentation at national and/or international conferences, and other platforms such as social media (eg, LinkedIn, Twitter), and relevant stakeholders.

Despite significant advances in managing acute stroke and reducing stroke mortality, preventing complications like post-stroke epilepsy (PSE) has seen limited progress. PSE research has been scattered worldwide with varying methodologies and data reporting. To address this, we established the International Post-stroke Epilepsy Research Consortium (IPSERC) to integrate global PSE research efforts. This protocol outlines an individual patient data meta-analysis (IPD-MA) to determine outcomes in patients with post-stroke seizures (PSS) and develop/validate PSE prediction models, comparing them with existing models. This protocol informs about creating the International Post-stroke Epilepsy Research Repository (IPSERR) to support future collaborative research.

We utilised a comprehensive search strategy and searched MEDLINE, Embase, PsycInfo, Cochrane, and Web of Science databases until 30 January 2023. We extracted observational studies of stroke patients aged ≥18 years, presenting early or late PSS with data on patient outcome measures, and conducted the risk of bias assessment. We did not apply any restriction based on the date or language of publication. We will invite these study authors and the IPSERC collaborators to contribute IPD to IPSERR. We will review the IPD lodged within IPSERR to identify patients who developed epileptic seizures and those who did not. We will merge the IPD files of individual data and standardise the variables where possible for consistency. We will conduct an IPD-MA to estimate the prognostic value of clinical characteristics in predicting PSE.

Ethics approval is not required for this study. The results will be published in peer-reviewed journals. This study will contribute to IPSERR, which will be available to researchers for future PSE research projects. It will also serve as a platform to anchor future clinical trials.

NCT06108102