Long COVID-19 is a term used to describe the symptomatic sequelae that develop after suffering from COVID-19. Very few studies have investigated the impact of COVID-19 sequelae on employment status. The aim of this research was to characterise sequelae of COVID-19 in a population of workers who tested positive for COVID-19, with a follow-up within one year of the acute illness, and to analyse the possible association between this and changes in the workers’ occupational status. In this retrospective cohort study, a questionnaire was administered to 155 workers; descriptive, univariate (chi-square tests), and multivariate (logistic regression model) analyses were carried out. The mean age was 46.48 years (SD ± 7.302); 76 participants were males (49.7%), and 33 participants reported being current smokers (21.3%). Overall, 19.0% of patients reported not feeling fully recovered at follow-up, and 13.7% reported a change in their job status after COVID-19. A change in occupational status was associated with being a smoker (OR 4.106, CI [1.406–11.990], p = 0.010); hospital stay was associated with age > 46 years in a statistically significant way (p = 0.025) and with not feeling fully recovered at follow-up (p = 0.003). A persistent worsening in anxiety was more common in women (p = 0.028). This study identifies smoking as a risk factor for workers not able to resume their job; furthermore, occupational physicians should monitor mental health more closely after COVID-19, particularly in female workers.

To synthesize evidence from meta-analyses of randomized controlled trials (RCTs) evaluating interventions aimed at improving polypharmacy management in older adults, with a focus on key clinical outcomes including mortality, hospitalizations, and falls. Umbrella review. We conducted an umbrella review of systematic reviews with meta-analyses of RCTs assessing interventions to optimize polypharmacy in elderly populations across healthcare settings. Eligible interventions included medication reviews, deprescribing strategies, and educational or behavioral approaches targeting medication appropriateness, reduction, or adherence. Searches were performed in Pubmed, Cochrane and Web of Science up to September 2024. Study selection, data extraction, and quality assessment, performed using AMSTAR2 and GRADE, were independently conducted by multiple reviewers. The protocol was prospectively registered on the Open Science Framework, and reporting followed the PRIOR checklist. Six articles including 21 meta-analyses, encompassing 110 primary RCTs, were included. Medication reviews focused on deprescribing were associated with reduced mortality in community settings but not in hospitals. Behavioral and mixed educational-behavioral interventions appeared most effective in reducing hospitalizations, particularly in community populations. Medication reviews in hospital settings showed only modest effects, and no intervention significantly reduced fall risk. The overall certainty of evidence was low due to heterogeneity, small-study effects, and publication bias. Evidence on interventions to manage polypharmacy in older adults remains limited and inconsistent. Tailored, patient-centered strategies may offer greater benefit, but higher-quality and standardized trials are needed to strengthen the evidence base and guide public health strategies in aging populations.

Genetic and genomic tests are the cornerstone of personalized preventive approaches. Inconsistency in evaluating their clinical utility is often cited as a reason for their limited implementation in clinical practice. Previous reviews have primarily focused on theoretical frameworks used for clinical utility evaluations of genetic tests, rather than actual assessments and examined dimensions, rather than specific indicators within these dimensions. We aimed to review the dimensions and the specific indicators measured in published assessment reports of genetic or genomic tests. We conducted a scoping review of assessment reports of genetic and genomic tests used for prevention, searching through PubMed, Web of Science, Scopus, the websites of 20 different organizations, Google, and Google Scholar. From the included assessments, we extracted the reported indicators of clinical utility, compiling a list of disease-specific indicators that detailed their numerator, denominator, and calculation methods. We analyzed the extracted indicators by stratifying them according to ten comprehensive dimensions of clinical utility, the assessment framework used, and the type of indicator (categorized as quantitative, qualitative, reference, or no evidence reported). From these indicators, we then distilled a list of general indicators. We reviewed 3054 unique references and 12,000 results from gray literature searches, ultimately selecting 57 assessment reports. The reference frameworks used were health technology assessment (HTA) (42%), Evaluation of Genomic Applications in Practice and Prevention (EGAPP) (25%), ACCE (21%), and others (12%). We identified 951 disease-specific indicators. The dimensions most frequently evaluated (ie, had at least one indicator) were analytic validity (60%), clinical validity (79%), clinical efficacy (79%), and economic impact (58%). Only 12 assessments compared health outcomes between tested and untested groups, and fewer than 15% of the assessments addressed equity, acceptability, legitimacy, and personal value. Our study illustrates that, although dimensions such as equity and acceptability, are significantly emphasized in traditional evaluation frameworks, these are often not considered in the assessments. Additionally, our study has underscored a significant dearth of reported primary evidence concerning the clinical efficacy of these tests. Genetic and genomic tests analyze a person's genes to predict health risks and guide healthcare decisions, potentially identifying who might benefit from certain treatments or check-ups. However, determining whether these tests are genuinely useful for wide use in health services is complex, because there is no standard way to define "clinical utility" of a genetic test. To understand how these tests are evaluated, we reviewed 57 evaluation reports from high-income countries, most of which focused on cancer-related genetic tests. We found that many evaluations looked mainly at how well a test predicted a condition (validity) and considered some form of effectiveness, yet often failed to measure whether the test truly improved patient health outcomes, such as lowering death rates or enhancing the quality of life. Moreover, factors like patient acceptance, equity, and personal relevance (eg, reducing anxiety) were frequently overlooked. Without including these broader considerations, evaluations risk missing critical evidence that would indicate whether a test is helpful, fair, and worth using. From over 900 unique indicators used to measure clinical utility, we created a simpler list of about 150 general indicators that can guide future evaluations. This consolidated list can help test developers decide which factors to investigate, evaluators determine what to measure, and policymakers identify what might be missing before deciding if a test should be adopted in healthcare. By highlighting the gaps-areas that should be assessed but currently are not-our study encourages a more comprehensive approach to evaluating genetic tests. If we fail to consider issues like equity, patient preferences, and proven health benefits, we risk investing in tests that may do little good or even harm patients. Ultimately, recognizing these shortcomings can lead to better-informed decisions, ensuring that genetic testing is used in ways that truly benefit patients and deliver safer, more personalized, and fairer healthcare for everyone.

Body mass index (BMI) has been associated with gastric cancer (GC), though results are conflicting regarding the GC subsites of cardia and non-cardia. This study aims to evaluate the associations between BMI and GC risk, focusing on these distinct anatomical subsites. We pooled data from seven case-control studies from the Stomach Cancer Pooling (StoP) Project. Pooled odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) of GC risk across BMI categories (normal weight, overweight, obesity) were calculated by pooling study-specific ORs through random-effects meta-analytic models. The dose-response relationship between BMI and the risk of GC cancer was assessed by using a one-stage mixed-effects logistic regression model. Results were stratified according to cardia and non-cardia GC. The analysis comprised 1478 GC cases, including 511 cardia and 967 non-cardia cases, and 6671 controls. There was an increased risk of cardia GC among obese patients (OR 1.57, 95% CI 1.20-2.06), while no association was found for non-cardia GC (OR 0.82, 95% CI 0.66-1.01). Restricting the analysis to population-based studies, the association for cardia GC became stronger for obese (OR 1.65, 95% CI 1.09-2.48) and overweight (OR 1.62, 95% CI 1.10-2.39) patients. The dose-response meta-analysis showed an increased risk of cardia GC with increasing BMI values, ranging from a null effect at a BMI of 21.75 to an OR of 2.06 (95% CI 1.22-3.48) for a BMI of ≥40. Our results indicate an association between higher BMI categories and the risk of cardia GC, whereas no association was found with non-cardia GC.

During the COVID-19 pandemic, many athletes from several sporting disciplines were infected with the SARS-CoV-2. The aim of this systematic review is to summarize the current scientific evidence on the psychological sequelae and mental health of elite athletes who have been infected by the virus. The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement; three databases were searched: PubMed, ISI Web of Knowledge, and Scopus. The initial search resulted in 2420 studies; after duplicate removal and screening by title and abstract, 41 articles were screened by full-text. A total of four eligible articles were included in the review. All included articles measured depression and anxiety in athletes who had suffered from COVID-19, while in three papers levels of stress were measured. Overall, the only two questionnaires used in more than one study were the DASS-21 and the APSQ. In our systematic review, we highlighted that mental and psychological health in elite athletes has the same importance as physical health. This statement suggests that these examinations should be introduced and performed during the competitive sports’ medical examinations conducted at the start of the sporting season, which currently consists only of the examination of physical parameters. Due to lack of studies on the topic, the results of our review show that mental health in athletes with a history of SARS-CoV-2 infection is an issue that requires more investigation, considering the evidence of clinical consequences. The importance of post-infection psychological sequelae is significant in assessing possible repercussions on the athletes’ sporting performance.

Importance Differences in treatment effects between men and women may be important across diverse interventions and diseases. Objective We aimed to evaluate claims of sex-based differences in treatment effects across published meta-analyses. Data Sources PubMed (searched up to January 17, 2024). Study Selection Published meta-analyses of randomized controlled trials (RCTs) that had any mention of sex (male/female) subgroup or related analysis in their Data Extraction and Synthesis We determined how many meta-analyses had made claims of sex-based differences in treatment effects. These meta-analyses were examined in depth to determine whether the claims reflected sex-treatment interactions with statistical support or fallacious claims and categorized the frequency of different fallacies. For claims with statistical support, we examined whether they were considered and discussed in UpToDate. Whenever possible, we re-analyzed the p-value for sex-treatment interaction. Main Outcomes and Measures Number of claims with statistical support and fallacious claims; clinical implications of subgroup differences. Results 216 meta-analysis articles fulfilled the eligibility criteria. Of them, 99 stated in the abstract that that there was no sex-based difference, and 20 mentioned a sex-based subgroup analysis without reporting results in the 97 meta-analyses made 115 claims of sex-based differences. Of them, 27 claims across 21 articles had statistical support at p<0.05. 4/27 claims were mentioned in UpToDate, but none led to different recommendations for men and women. 35 articles had 39 fallacious claims where the sex-treatment interaction was not statistically significant (significant effects in one sex (29 claims in 25 articles), larger effects in one sex (7 claims in 7 articles), other (3 claims in 3 articles)). Another 44 articles made claims based on potentially fallacious methods (39 based on meta-regression of percentage of one group and 5 providing the results of only one group), but proper data were unavailable to assess statistical significance. Conclusions and relevance Few meta-analyses of RCTs make claims of sex-based differences in treatment effects and most of these claims lack formal statistical support. Statistically significant and clinically actionable sex-treatment interactions may be rare.