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We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries ( www.covidclinical.net ). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, p FDR  < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, p FDR  < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease.

IntroductionNon-pharmaceutical interventions (NPIs) such as mask-wearing and social distancing, implemented as public health measures to slow COVID-19 transmission, had a major impact on the epidemiology of viral infections. However, little is known about their influence on bacterial infections in children.MethodsWe performed a multicentre observational study including eight hospitals in three countries (Spain, UK and USA). All hospitalisations in children under the age of 18 from January 2019 to February 2023 were included. Electronic health record data were used to assess changes in hospitalisations for bacterial infections in three different periods based on NPI stringency, classified as pre-NPI (January 2019 to February 2020), full NPI (March 2020 to February 2021) and partial NPI (March 2021 to February 2023). The primary outcomes were the counts of hospitalisations for invasive, respiratory and skin-associated bacterial infections. To identify changes in the monthly counts of bacterial infections in a data-driven manner, we used a multivariable quasi-Poisson regression model adjusting for important covariates with adaptive lasso penalty. We then assessed the statistical significance of the identified changes and examined the temporal trend before and after each change point.ResultsWe found that of the 508 585 paediatric hospitalisations, 41 076 (8.1%) were associated with any bacterial infection. 14 656 (35.7%) were invasive bacterial infections, 6763 (16.5%) were respiratory tract-associated and 7757 (18.9%) were skin-associated. Counts of bacterial infections decreased during the full-NPI period (average count 93.7 infections/month) compared with the pre-NPI period (average count 104.8 infections/month) and increased during the partial NPI period (average count 112.4 infections/month). A quasi-Poisson regression model showed a significant decrease in respiratory tract-associated bacterial infections after the start of the COVID-19 pandemic and a subsequent significant increase after the gradual lifting of NPIs, peaking during the winter of 2022–2023. No significant changes were observed over time for skin-associated and invasive bacterial infections.ConclusionsThe implementation of COVID-19 NPIs was significantly associated with changes in hospitalisations for respiratory associated-bacterial infections, but not invasive and skin-associated bacterial infections. These findings suggest that the impact of NPIs has been greatest for respiratory infections and indicate the potential of targeted NPIs to reduce these infections among children in the future.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries ( www.covidclinical.net ). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Background As part of the German Medical Informatics Initiative, the MIRACUM project establishes data integration centers across ten German university hospitals. The embedded MIRACUM Use Case “Alerting in Care - IT Support for Patient Recruitment”, aims to support the recruitment into clinical trials by automatically querying the repositories for patients satisfying eligibility criteria and presenting them as screening candidates. The objective of this study is to investigate whether the developed recruitment tool has a positive effect on study recruitment within a multi-center environment by increasing the number of participants. Its secondary objective is the measurement of organizational burden and user satisfaction of the provided IT solution. Methods The study uses an Interrupted Time Series Design with a duration of 15 months. All trials start in the control phase of randomized length with regular recruitment and change to the intervention phase with additional IT support. The intervention consists of the application of a recruitment-support system which uses patient data collected in general care for screening according to specific criteria. The inclusion and exclusion criteria of all selected trials are translated into a machine-readable format using the OHDSI ATLAS tool. All patient data from the data integration centers is regularly checked against these criteria. The primary outcome is the number of participants recruited per trial and week standardized by the targeted number of participants per week and the expected recruitment duration of the specific trial. Secondary outcomes are usability, usefulness, and efficacy of the recruitment support. Sample size calculation based on simple parallel group assumption can demonstrate an effect size of d =0.57 on a significance level of 5% and a power of 80% with a total number of 100 trials (10 per site). Data describing the included trials and the recruitment process is collected at each site. The primary analysis will be conducted using linear mixed models with the actual recruitment number per week and trial standardized by the expected recruitment number per week and trial as the dependent variable. Discussion The application of an IT-supported recruitment solution developed in the MIRACUM consortium leads to an increased number of recruited participants in studies at German university hospitals. It supports employees engaged in the recruitment of trial participants and is easy to integrate in their daily work.

This study aimed to explore relations between Internet use, socio-economic status (SES), social support and subjective health. Participants were from representative samples between 15 and 80 years of age from seven different European countries. Two different survey datasets were used: (i) eHealth trends (eHT; N = 7934) and (ii) the European social survey (ESS2; N = 11248). Internet users who had used the Internet for health purposes were compared with Internet users who had not used it for health purposes. Structural equation modelling was used to assess the relationships between SES, Internet use, social support and subjective health. Use of other media was compared to Internet use in relation to social support and subjective health. Internet use was found to be more closely related to social support and subjective health than use of other media. Internet use was also found to be a plausible mediator between SES and subjective health, especially through interacting with social support.

There is a need for knowledge on activities that can reduce cognitive decline and dementia risk. Volunteering is a productive activity that entails social, physical, and cognitive functions. Therefore, volunteering could be a protective factor for cognitive loss. Thus, this review aims to examine the associations between volunteering and volunteers' cognition and to identify influencing variables. Six international literature databases were searched for relevant articles published between 2017 and 2021 (ALOIS, CENTRAL, CINAL, Embase, PsycINFO, PubMed). Quantitative studies of all study designs were included. The primary outcome was the volunteers' cognition measured by objective, internationally established psychometric function tests. Two authors independently assessed the eligibility and quality of the studies. A narrative synthesis was performed using all studies included in this review. The methodology was in line with the PRISMA guidelines. Fourteen studies met the inclusion criteria and were included. Seven of the included studies confirmed that volunteering positively affects the volunteers' cognitive function. Two other studies identified an association between volunteer activity and volunteers' cognition using cross-sectional measurements. In particular, women and people with a low level of education benefit from the positive effects and associations. The study quality of the included articles was moderate to weak. Our review suggests that volunteering can improve volunteers' cognition. Unfortunately, little attention is given to specific volunteer activities and the frequency of engagement. Additionally, more attention is needed on various risk factors of cognitive impairment.

Background Diabetes mellitus (DM) is the leading cause of end-stage renal disease. Little is known about practice patterns of anti-diabetic therapy in the presence of chronic kidney disease (CKD) and correlates with glycaemic control. We therefore aimed to analyze current antidiabetic treatment and correlates of metabolic control in a large contemporary prospective cohort of patients with diabetes and CKD. Methods The German Chronic Kidney Disease (GCKD) study enrolled 5217 patients aged 18–74 years with an estimated glomerular filtration rate (eGFR) between 30–60 mL/min/1.73 m 2 or proteinuria >0.5 g/d. The use of diet prescription, oral anti-diabetic medication, and insulin was assessed at baseline. HbA1c, measured centrally, was the main outcome measure. Results At baseline, DM was present in 1842 patients (35 %) and the median HbA1C was 7.0 % (25 th –75 th percentile: 6.8–7.9 %), equalling 53 mmol/mol (51, 63); 24.2 % of patients received dietary treatment only, 25.5 % oral antidiabetic drugs but not insulin, 8.4 % oral antidiabetic drugs with insulin, and 41.8 % insulin alone. Metformin was used by 18.8 %. Factors associated with an HbA1C level >7.0 % (53 mmol/mol) were higher BMI (OR = 1.04 per increase of 1 kg/m 2 , 95 % CI 1.02–1.06), hemoglobin (OR = 1.11 per increase of 1 g/dL, 95 % CI 1.04–1.18), treatment with insulin alone (OR = 5.63, 95 % CI 4.26–7.45) or in combination with oral antidiabetic agents (OR = 4.23, 95 % CI 2.77–6.46) but not monotherapy with metformin, DPP-4 inhibitors, or glinides. Conclusions Within the GCKD cohort of patients with CKD stage 3 or overt proteinuria, antidiabetic treatment patterns were highly variable with a remarkably high proportion of more than 50 % receiving insulin-based therapies. Metabolic control was overall satisfactory, but insulin use was associated with higher HbA1C levels.