Little research has been done on post-COVID symptoms at 24 months postinfection and on the association these may have on health-related quality of life (HRQOL).

We assessed the prevalence and severity of post-COVID symptoms and quantified EuroQol 5 Dimension 5 Level (EQ-5D-5L), self-perceived health question (EuroQol Visual Analogue Scale (EQ-VAS)) and health utility scores (HUS) up to 24 months follow-up.

The longitudinal multiple cohort CORona Follow-Up (CORFU) study combines seven COVID-19 patient cohorts and a survey among the general public. The participants received questionnaires on several time points. Participants were stratified by: without a known SARS-CoV-2 infection (control group), proven SARS-CoV-2 infection but non-hospitalised, proven SARS-CoV-2 infection hospitalised to the ward, and proven SARS-CoV-2 infection hospitalised to the intensive care unit (ICU).

In this study, data of seven COVID-19 patient cohorts and a survey among the general public are included.

Former COVID-19 patients and controls participated in this cohort study.

Former COVID-19 patients and non-COVID-19 controls were sent questionnaires on symptoms associated with post-COVID condition. The CORFU questionnaire included 14 symptom questions on post-COVID condition using a five-level Likert-scale format. Furthermore, HRQOL was quantified using the EuroQol EQ-5D-5L questionnaire: EQ-VAS and the EQ-5D-5L utility score. The EQ-5D-5L questionnaire includes five domains that are scored on a five-point Likert scale: mobility, self-care, usual activities, pain/discomfort and anxiety/depression.

A total of 901 participants (and 434 controls) responded at 24 months follow-up. In all former COVID-19 patients, the presence of post-COVID condition at 24 months was observed in 62 (42.5%, 95% CI 34.3% to 50.9%) of the non-hospitalised patients, 333 (65.0%, 95% CI 60.7% to 69.2%) of the hospitalised ward patients and 156 (63.2%, 95% CI 56.8% to 69.2%) of the ICU patients, respectively (p

Many former COVID-19 patients experience post-COVID symptoms at 24 months follow-up, with the highest prevalence in hospitalised participants. Also, former patients reported a lower HRQOL.

The CORFU study was registered at clinicaltrials.gov (registration number NCT05240742).

Guideline-based strategies to prevent chronic kidney disease (CKD) progression and complications are available, yet their implementation in clinical practice is uncertain. We aimed to synthesise the available evidence on the concordance of CKD care with clinical guidelines to identify gaps and inform future CKD care.

Systematic review and meta-analysis.

We systematically searched MEDLINE (OVID), EMBASE (OVID) and CINAHL (EBSCOhost) (to 18 July 2025) for observational studies of adults with CKD reporting data on the quality of CKD care. We assessed data on quality indicators of CKD care across domains that related to patient monitoring (glomerular filtration rate and albuminuria), medications use (ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), statins) and treatment targets (blood pressure (BP) and HbA1c). Pooled estimates (95% CI) of the percentage of patients who met the quality indicators for CKD care were estimated using random effects model.

59 studies across 24 countries, including a total of 3 003 641 patients with CKD, were included. Across studies, 81.3% (95% CI: 75% to 87.6%) of patients received eGFR monitoring, 47.4% (95% CI: 40.0% to 54.7%) had albuminuria testing, and 90% (95% CI: 84.3% to 95.9%) had BP measured. ACEIs/ARBs were prescribed among 56.7% (95% CI: 51.5% to 62%), and statins among 56.6% (95% CI: 48.9% to 64.3%) of patients. BP (systolic BP ≤140/90 mm Hg) and HbA1c (

Current evidence shows substantial variation in CKD care quality globally. Guideline-concordant care varied according to quality measures and across patient groups, with gaps in indicators like albuminuria testing. These findings underscore the need for effective quality improvement strategies to address gaps in CKD care, including increased albuminuria testing for risk stratification, together with systematic measures for monitoring care quality.

CRD42023391749.

Loneliness and adverse childhood experiences (ACEs) constitute significant risk factors for mental disorders, with loneliness emerging as a serious global public health concern. Recent research highlights the role of loneliness as a potential link between early life adversities and current psychopathology. However, most studies have been conducted in high-income, highly individualistic countries. This cross-sectional study explores the interplay between loneliness, social network size, recalled ACEs and depressive symptoms in Ethiopia—a low-income and collectivistic cultural context.

The study included 125 psychiatric outpatients at Jimma University Medical Center in Southwest Ethiopia diagnosed with major depressive disorder, bipolar disorder or psychotic disorders, as well as 131 non-clinical participants. Trained interviewers administered the University of California, Los Angeles (UCLA) Loneliness Scale, the Childhood Trauma Questionnaire, the Social Network Index and the WHO-5 Well-Being Index. We used Mann-Whitney U tests, partial correlation and mediation analysis for data analysis.

We found mild-to-moderate correlations between loneliness and ACEs (clinical group: rho=0.29, p1b₁=0.07, 95% CI (0.02 to 0.13); non-clinical group: indirect effect a₁b₁=0.03, 95% CI (0.01 to 0.07)). In contrast, social network size was neither correlated with ACEs nor did it mediate the association between ACEs and depressive symptoms in either group.

This study replicates previous findings that loneliness—rather than social network size—is associated with ACEs and mediates their impact on depressive symptoms. These results support the transcultural and transdiagnostic relevance of loneliness as a universal psychological mechanism, independent of societal structure.

Sepsis is a major cause of death both globally and in the United States. Early identification and treatment of sepsis are crucial for improving patient outcomes. International guidelines recommend hospital sepsis screening programmes, which are commonly implemented in the electronic health record (EHR) as an interruptive sepsis screening alert based on systemic inflammatory response syndrome (SIRS) criteria. Despite widespread use, it is unknown whether these sepsis screening and alert tools improve the delivery of high-quality sepsis care.

The Sepsis Electronic Prompting for Timely Intervention and Care (SEPTIC) master protocol will study two distinct populations in separate trials: emergency department (ED) patients (SEPTIC-ED) and inpatients (SEPTIC-IP). The SEPTIC trials are pragmatic, multicentre, blinded, randomised controlled trials, with equal allocation to compare four SIRS-based sepsis screening alert groups: no alerts (control), nurse alerts only, prescribing clinician alerts only, or nurse and prescribing clinician alerts. Randomisation will be at the patient level. SEPTIC will be performed at eight acute-care hospitals in the greater New York City area and enrol patients at least 18 years old. The primary outcome is the percentage of patients with completion of a modified Surviving Sepsis Campaign (SSC) hour-1 bundle within 3 hours of the first SIRS alert. Secondary outcomes include time from first alert to completion of a modified SSC hour-1 bundle, time from first alert to individual bundle component order and completion, intensive care unit (ICU) transfer, hospital discharge disposition, inpatient mortality at 90 days, positive blood cultures (bacteraemia), adverse antibiotic events, sepsis diagnoses and septic shock diagnoses.

Ethics approval was obtained from the Columbia University Institutional Review Board (IRB) serving as a single IRB. Results will be disseminated in peer-reviewed journal(s), scientific meeting(s) and via social media.

ClinicalTrials.gov: NCT06117605 and NCT06117618.

To evaluate the burden of asthma in five European countries (5EU; France, Germany, Italy, Spain and United Kingdom [UK]).

A retrospective cross-sectional study was conducted based on the data from the 2018 National Health and Wellness Survey. Health-related quality of life (HRQoL), work productivity and activity impairment, and healthcare resource utilisation (HCRU) were compared between different groups: asthma versus non-asthma, mild/moderate/severe asthma versus non-asthma and moderate/severe asthma versus mild asthma.

Internet-based survey across Western Europe.

Adult patients (aged ≥18 years) with self-reported physician diagnosis of asthma and experienced asthma symptoms in the past 12 months.

Socio-demographic characteristics, asthma-related outcomes, HRQoL and productivity, HCRU and prevalence of asthma.

The prevalence of asthma in the 5EU was 6.7% (95% CI: 6.5% to 6.9%), with the UK reporting the highest rates (10.4%; 95% CI: 9.9% to 10.9%). About 52.0% of the respondents had mild asthma, 27.9% had moderate and 20.1% had severe asthma. The asthma group reported significantly poorer HRQoL, higher rates of overall work productivity impairment and activity impairment, and a greater number of visits to emergency room, healthcare provider and hospitalisations versus the non-asthma group (all p

Asthma prevalence and burden are still high in Western Europe, indicating the need for effective interventions that could lead to improved outcomes.

The COVID-19 pandemic highlighted the significance of mathematical modelling in decision-making and the limited capacity in many low-income and middle-income countries (LMICs). Thus, we studied how modelling supported policy decision-making processes in LMICs during the pandemic (details in a separate paper).

We found that strong researcher–policymaker relationships and co-creation facilitated knowledge translation, while scepticism, political pressures and demand for quick outputs were barriers. We also noted that routine use of modelled evidence for decision-making requires sustained funding, capacity building for policy-facing modelling, robust data infrastructure and dedicated knowledge translation mechanisms.

These lessons helped us co-create a framework and policy roadmap for improving the routine use of modelling evidence in public health decision-making. This communication paper describes the framework components and provides an implementation approach and evidence for the recommendations. The components include (1) funding, (2) capacity building, (3) data infrastructure, (4) knowledge translation platforms and (5) a culture of evidence use.

Our framework integrates the supply (modellers) and demand (policymakers) sides and contextual factors that enable change. It is designed to be generic and disease-agnostic for any policy decision-making that modelling could support. It is not a decision-making tool but a guiding framework to help build capacity for evidence-based policy decision-making. The target audience is modellers and policymakers, but it could include other partners and implementers in public health decision-making.

The framework was created through engagements with policymakers and researchers and reflects their real-life experiences during the COVID-19 pandemic. Its purpose is to guide stakeholders, especially in lower-resourced settings, in building modelling capacity, prioritising efforts and creating an enabling environment for using models as part of the evidence base to inform public health decision-making. To validate its robustness and impact, further work is needed to implement and evaluate this framework in diverse settings.