The COVID-19 pandemic has strained global health systems, exacerbating health disparities, especially among vulnerable groups. It has also worsened mental health, leading to increased rates of depression and anxiety. We study the impact of the COVID-19 pandemic on the prevalence of mental health episodes involving violence in Ontario, the largest province of Canada. We compare the dangerousness of mental health patients who needed hospitalization before and during/after the pandemic across different socio-demographic groups and geographic regions. This enables us to identify the vulnerable populations in this domain as well as the key factors associated with disparities among patients at risk of exhibiting aggression. We conducted a retrospective study from March 2017 to March 2023. The study involved 340,000+ observations from patients aged 15 and above admitted to mental health inpatient hospital wards in Ontario, Canada. We evaluated violent behavior using three mental health indicators, including the risk of harming others, hospital admissions due to threats or danger to others, and history of police intervention for violent behavior within the last 30 days. We also examined associated disparities across several social determinants of health through a combination of absolute rate analysis, logistic regression, stratified autoregressive integrated moving average models, and Oaxaca-Blinder decomposition. Our findings indicated a pre-existing and noteworthy increase in violent behavior among patients with mental health conditions after the onset of the pandemic. Males, young and middle-aged adults, unmarried individuals, and low-income demographics suffered from the widening gap. The disparities were most evident in urban areas, and less educated groups showed higher levels of violent behavior. Policy announcements, such as school closures, had a substantial impact on mental health disparities, resulting in lasting effects on mental well-being. The COVID-19 pandemic has worsened mental health disparities related to violence, necessitating targeted interventions and policies to improve mental health outcomes and reduce violence-related health inequities.

Vaccination against SARS-CoV-2 remains the most powerful strategy to fight against the Covid-19 pandemic around the word. Nevertheless, the level of complete vaccination (including all recommended boosters) is not optimal, even in the most vulnerable population, i.e. older adults living in nursing homes (NH) and long-term care units (LTCU). In this context, the understanding of the patients' reluctance to vaccination seems particularly important and our objective was to understand the acceptance or refusal mechanisms of the Covid-19 vaccine among people living in NH and LTCU. National French cross-sectional study conducted between June and September 202 including 101 health care professionals working in NH or LTCU and represents 10,152 residents living in the respondents' establishments. The Vaccovid-Senior cohort included 323 residents (89 ± 6.5 years, 69% women, 89.2% living in NH); 179 agreed to vaccination and 144 were not vaccinated. In multivariate analysis, the only modifiable characteristics associated with the acceptance was a regular influenza vaccination (OR = 29.43; 95CI [12.11-71.53]; p < 0.001), and with refusal was receiving information from Internet and social media (OR = 0.34; 95CI [0.13-0.88]; p = 0.026). Remarkably, the interaction analysis revealed that the residents who were usually vaccinated against influenza and able to understand the information given about Covid-19 were 17,5 times more likely to accept the Covid-19 vaccination (RR = 17,5; 95CI [4,5-68,0]) compared to residents with polypharmacy but without regular influenza vaccination. Vaccine acceptance can be improved by repeated efforts to listen to and explain the vaccine to the older population, their families, and the care teams. Isolating these situations of refusal allowed us to foresee avenues of improvement such as the communication of information on the vaccine, the nudge strategy and training to give information adapted to the level of understanding.

To understand a treatment's potential impact at the individual level, it is crucial to explore whether the effect differs across patient subgroups and covariate values. Meta-analysis provides an important tool for detecting treatment-covariate interactions, as it can improve power compared to a single study. However, aggregation bias can occur when estimating individual-level treatment-covariate interactions in meta-analysis, due to trial-level confounding. This refers to when the association between the covariate and treatment effect across trials (at the aggregate level) differs from that observed within trials (at the individual level). It is, thus, recommended that heterogeneity in the treatment effect at the individual level should be disentangled from that at the trial level, ideally using an individual participant data (IPD) meta-analysis. Here, we explain this issue and provide new intuition about how trial-level confounding is impacted by differences in within-trial distributions of covariates and how this corresponds to asymmetry in subgroup-specific funnel plots in the case of categorical covariates. We then propose a sensitivity analysis to assess the robustness of interaction estimates to potential trial-level confounding. We illustrate these concepts using simulated and real data from an IPD meta-analysis of trials conducted on the TICO/ACTIV-3 platform, which assessed passive immunotherapy treatments for inpatients with COVID-19.

Sharing, harmonizing, and analyzing participant-level data is of central importance in the rapid research response to emerging pathogens. Individual participant data meta-analyses (IPD-MAs), which synthesize participant-level data from related primary studies, have several advantages over pooling study-level effect estimates in a traditional meta-analysis. IPD-MAs enable researchers to more effectively separate spurious heterogeneity related to differences in measurement from clinically relevant heterogeneity from differences in underlying risk or distribution of factors that modify disease progression. This tutorial describes the steps needed to conduct an IPD-MA of an emerging pathogen and how IPD-MAs of emerging pathogens differ from those of well-studied exposures and outcomes. We discuss key statistical issues, including participant- and study-level missingness and complex measurement error, and present recommendations. We review how IPD-MAs conducted during the COVID-19 response addressed these statistical challenges when harmonizing and analyzing participant-level data related to an emerging pathogen. The guidance presented here is based on lessons learned in our conduct of IPD-MAs in the research response to emerging pathogens, including Zika virus and COVID-19.

This is a protocol for a Cochrane Review (intervention). The objectives are as follows: To assess the benefits and harms of high-flow nasal cannula (HFNC) use, versus conventional oxygen therapy or other non-invasive ventilation, for respiratory support in children for indications other than acute bronchiolitis.

Neurally Adjusted Ventilatory Assist (NAVA) is a patient-triggered ventilatory mode requiring intact respiratory drive. We compared 2 intravenous morphine infusion doses in infants with acute viral bronchiolitis receiving NAVA. The objectives were, to evaluate (1) impact on neural respiratory drive, measured by electrical activity of the diaphragm (EA_di) and estimated airway occlusion pressure (P_0.1) and (2) tolerability, via the COMFORT behavioral scale and physiological variables.

Randomized, nonblinded, crossover (morphine dose), feasibility trial. Infants likely requiring mechanical ventilation for >48 h at enrollment received intravenous morphine infusion rates of 5 μg/kg/h (low dose) and 20 μg/kg/h (standard dose) on alternate days, via a randomized crossover design. On each day, a combination of 16 ventilatory settings were used, incorporating differing NAVA, PEEP and pressure support levels.

Thirteen participants received the intervention. The primary outcome, peak EA_di did not differ between morphine groups (P = .27), with an overall average of 12.2 ± 7.0 μV. The mean P_0.1 was higher during low-dose morphine (1.14 vs 0.89 cm H₂O, mean difference .25 cm H₂O, 95% CI 0.12-0.39, P = .002). The low-dose group exhibited higher mean heart rates (123 vs 113 beats/min, mean difference 9.9 beats/min, 95% CI 5.2-14.6, P < .001), systolic blood pressures (90 vs 86 mm Hg, mean difference 3.9 mm Hg, 95% CI 0.4-7.5, P < .001), and COMFORT-B scores, (13.7 vs 13.2, mean difference 0.5, 95% CI 0.1-1.0, P = .03). Low-dose morphine was associated with lower transcutaneous CO₂ levels (37 vs 43 mm Hg, mean difference 6 mm Hg, 95% CI 3.9-8.4 mm Hg, P < .001), despite no difference in between-group breathing frequency (P = .11), tidal volume (P = .16) or minute ventilation (P = .33).

Intravenous morphine at 20 μg/kg/h did not blunt neural respiratory drive during NAVA and appeared more favorable than 5 μg/kg/h in terms of tolerability. This may inform a larger trial evaluating NAVA in bronchiolitis.

The exhaled breath of infected, mechanically ventilated patients poses an infection risk to health care workers. Proper expiratory gas filtration with heat and moisture exchanger filters (HMEF) or filters could reduce that phenomenon. Current laboratory means of assessing the filtration efficiency are limited to the use of monodisperse aerosols at a single humidity level and flow. This study aims to examine the filtration efficiency of various devices under simulated clinical conditions, namely against a broad range of particle sizes containing viruses at different levels of gas humidity and flow.

A wind tunnel was adapted to evaluate the filtration efficiency of 4 devices (HME, HMEF, filters, and HEPA-HMEF) against viral aerosols. Bacteriophages PhiX174 and MS2 were used as a proxy for human viruses.

In general, particulate filtration was significantly increased under dry versus humid conditions and with low versus high flows (P < .05). The HEPA filter significantly outperformed all other devices under all tested conditions in filtration efficiency. Both HMEF and filter showed approximately a 1% decrease in absolute differences compared with the reference method (∼99% vs 99.99%). This difference could represent an emission of as many as 10² SARS-CoV-2 copies per hour by an ICU patient, which is enough to spread the infection.

Accurate testing of filtration function has long gone unexamined, and in preparation for the next respiratory pandemic, better evaluation of devices that filter potentially dangerous pathogens is vital for health care professionals and systems. Standard filtration testing should be adapted to mimic the clinical usage of HMEs and filters.

Following the lifting of China's stringent lockdown policy on December 7, 2022, COVID-19 cases surged in a pattern, creating unprecedented strain on healthcare systems. The Omicron variant, characterized by high transmissibility and rapid spread, led to a sharp rise in infections. Understanding its clinical impact-particularly on lung involvement percentage-is crucial for optimizing patient care under such outbreak conditions. This study aimed to assess the extent of lung involvement percentage during the outbreak and its major associations.

The hospital's daily computed tomography examination volume was quantified using artificial intelligence-based pulmonary inflammation analysis software and used as an indicator of epidemic intensity. Associations between lung involvement percentage and age, sex, and daily case counts were evaluated using GEE Logistic Regression, complemented by machine learning models. Model interpretation was performed using SHapley Additive exPlanations.

GEE Logistic regression demonstrated that age was strongly associated with lung involvement (OR 1.0813, 95% CI 1.0703-1.0925, p < 0.0001), while daily case counts also showed a small but significant independent association (OR 1.0033, 95% CI 1.0018-1.0047, p < 0.0001). Sex exhibited only minimal association (OR 0.8098, 95% CI 0.6983-0.9391, p = 0.0053). Complementary machine learning analyses, including gradient boosting, identified age as the dominant contributor, followed by daily case counts with a small effect and sex with minimal contribution. SHAP analysis provided interpretable insights into how each feature influenced model predictions at both global and individual levels.

During the Omicron surge, greater age and higher daily case counts were associated with higher lung involvement percentage. These associations highlight the relevance of demographic and epidemic factors in characterizing pulmonary findings during large-scale outbreaks.

Childhood obesity remains a major public health concern, exacerbated by the COVID-19 pandemic. This study examines temporal and spatial patterns in childhood obesity and overweight in Aragón, Spain, before (2018-2019) and after (2021) pandemic lockdowns.

We conducted a retrospective descriptive analysis using anthropometric data from children aged 0-14 years attending public health centres. Data were extracted from electronic medical records and classified according to national diagnostic standards. Sociodemographic indicators, including parental education and income, were derived from official datasets and geoprocessed to the Basic Health Zone (BHZ) level. Principal Component Analysis (PCA) reduced socioeconomic dimensions, whilst Geographically Weighted Regression (GWR) examined spatial associations.

Obesity prevalence increased from 3.8% in 2019 to 5.5% in 2021, and overweight from 14.5 to 15.8% (p < 0.001). Boys were consistently more affected than girls, though this gap narrowed post-pandemic. The largest increases were amongst children aged >6 years. Urban-rural typology alone did not fully explain the patterns; instead, intermediate-population BHZs and rural areas near urban centres exhibited higher prevalence. GWR analyses revealed spatial variability in the relationship between obesity and socioeconomic indicators, particularly in Zaragoza province, with income and education levels significantly associated with prevalence.

The COVID-19 pandemic reversed prior improvements in childhood obesity trends in Aragón. Socioeconomic inequalities, especially related to parental education and income, strongly influence obesity distribution at the local level. Geographically targeted policies are needed to reduce disparities and prevent long-term health consequences in children.

Recent studies show that the prevalence of many adolescents' mental health problems worsened during the pandemic, and improved recently, but is still higher than before the pandemic. The assessment of post-pandemic gender-specific mental health issues and their relation to global crises are of current interest.

A repeated cross-sectional design screened self-reported mental health in terms of anxiety, depression, and general mental disorders of 11-19 years aged students in South Tyrol, North Italy four times between 2021 and 2025 using standardized instruments. The study was representative for age and gender, screening was done for the whole population, not regarding the same participants in all four surveys.

A total of 1,471 questionnaires were evaluated in 2025, nearly 11% of the participants were screened positively for depression and 28% for anxiety. While percentages were the same in all 4 years for anxiety, we found a decreasing trend for depression. About 40% of females in 2025 were screened positively for at least one mental health problem, for males the percentage was about 27%. The difference was significant. About 45% of the positively screened cases were screened positively by more than one instrument, while the percentage was 35% for males. The most important, non-gender-dependent associated factors were school stress, crises-related stress, and problematic Internet use. Low/medium health literacy and single parenthood were predictors of mental health problems among girls. Less sleep and less physical activity were significant factors.

Gender specific mental health problems in adolescents are still high post-pandemically. Mental health interventions, focusing on gender-specific requirements, are needed in the light of new global crises. Educational settings should integrate preventive strategies and health literacy programs based on existing experiences and continually collected new data.