Highlights A survey of a large cohort with predominantly mild course of COVID-19 was conducted. 6.9% reported regular job-related contact with children below ten years. 23.2% reported regular contact with their own children below ten years.
It is known that severe COVID-19 cases in small children are rare. If a childhood-related infection would be protective against severe course of COVID-19, it would be expected that adults with intensive and regular contact to small children also may have a mild course of COVID-19 more frequently. To test this hypothesis, a survey among 4,010 recovered COVID-19 patients was conducted in Germany. 1,186 complete answers were collected. 6.9% of these patients reported frequent and regular job-related contact to children below 10 years of age and 23.2% had own small children, which is higher than expected. In the relatively small subgroup with intensive care treatment (n=19), patients without contact to small children were overrepresented. These findings are not well explained by age, gender or BMI distribution of those patients and should be validated in other settings.
Secondary use, the reuse of medical patient data stored during routine care in the hospital’s electronic medical records (EMR) for research purpose is common, especially for registers and pragmatic trials. Often the medical data items are copied manually from the EMR into the used research database. This process is time consuming and error prone. In the “Safety of the Living Kidney Donor – The German National Register” (SOLKID-GNR), laboratory results gathered during control check-ups of the living donors before and after the transplantation are to be transferred from the EMR into the electronic data capture system REDCap of the register. In this work, we present our approach of realizing an automated transfer of time-dependent laboratory results from the EMR of the University Hospital of Münster to REDCap. A challenge lies in the multi-center structure of SOLKID-GNR. The participating transplant centers are using different EMR systems, which requires a flexible architecture design. In addition, we aimed to support reuse of the implementation for other research settings with other medical data items of interest.
Background Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages. Objective The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects. Methods We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. Results We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. Conclusions Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians.
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