EClinicalMedicine

BackgroundHow post-COVID-19 condition (PCC) differs from post-acute infection syndromes (PAIS) caused by other respiratory viruses remains uncertain. Comparing these conditions may clarify whether post-acute symptoms reflect specific consequences of SARS-CoV-2 infection or broader post-viral mechanisms. MethodsWe conducted a systematic review and meta-analysis of cohort studies comparing persistent symptoms or conditions in adults after SARS-CoV-2 infection with those following other acute respiratory viral infections. PubMed, Embase, and Scopus were searched. Random-effects models were used to estimate pooled risks. ResultsAmong 9,371 records screened, 22 studies were included and 14 contributed to the meta-analysis. Increased risk after SARS-CoV-2 infection was observed for pulmonary embolism, abnormal breathing, fatigue, hemorrhagic stroke, memory loss/brain fog, and palpitations; heart rate abnormalities showed borderline significance. For most other outcomes pooled estimates were inconclusive. ConclusionsOnly a subset of outcomes appears more frequent after SARS-CoV-2 infection, suggesting many symptoms attributed to PCC may reflect broader post-viral syndromes.

PURPOSE: Cancer outcomes in sub-Saharan Africa are driven by delayed diagnosis and treatment initiation. We evaluated the magnitude and determinants of diagnostic and treatment delays among cancer patients in Kinshasa, Democratic Republic of the Congo (DRC). METHODS: We conducted a hospital-based cross-sectional study of 460 adults with confirmed cancer at Nganda Hospital Center in Kinshasa, DRC. Two outcomes were assessed: delay from symptom onset to diagnosis and delay from diagnosis to treatment initiation. Log-normal regression models were fitted for each outcome to estimate adjusted geometric mean ratios (aGMRs) and 95% confidence intervals (CIs). Covariates included demographic, socioeconomic, clinical, behavioral, and stigma-related factors. RESULTS: The median age was 55 years, and 76.2% of participants were women. Overall, 55.0% of participants experienced symptom-to-diagnosis delays >6 months, and 49.4% experienced diagnosis-to-treatment delays >3 months. Older age was associated with longer diagnostic delay (aGMR 1.55, 95% CI 1.03-2.31) and treatment delay (1.51, 1.07-2.14). Unemployment was strongly associated with both diagnostic delay (1.68, 1.15-2.47) and treatment delay (2.27, 1.54-3.33), as was hepatitis B co-infection (1.88, 1.06-3.34 and 2.42, 1.15-5.11, respectively). Longer diagnostic delay was additionally associated with informal trading (1.99, 1.21-3.28), taxi or motorbike transport (1.92, 1.25-2.94), and smoking history (2.25, 1.03-4.91), while high cancer-stereotype stigma was associated with longer treatment delay (1.56, 1.04-2.34). CONCLUSION: Substantial delays exist across the DRC cancer care continuum, driven by socioeconomic vulnerability, transport barriers, hepatitis B co-infection, and cancer-related stigma. These findings highlight the need for integrated interventions to improve timely diagnosis and treatment initiation, including strengthening financial protection, decentralizing cancer services, and reducing stigma in cancer care.

ImportanceMedicare-Medicaid dual eligible beneficiaries experience pronounced disparities in stroke recovery. However, it remains unclear whether inpatient rehabilitation services and outcomes are comparable between dual-eligible beneficiaries enrolled in Medicare fee-for-service (FFS) versus Medicare Advantage (MA) plans. ObjectiveTo compare rehabilitation therapy utilization and associated outcomes among dual-eligible beneficiaries enrolled in FFS versus MA plans with stroke. DesignRetrospective cohort study. SettingInpatient Rehabilitation Facilities (IRF). ParticipantsMedicare beneficiaries admitted to IRF with stroke (n=125,782) between 2017 and 2019. ExposureDual-eligible beneficiaries enrolled in FFS versus MA plans. Main Outcome MeasuresTotal number of minutes of physical and occupational therapy provided within the first 2 weeks of IRF stay, self-care and mobility change scores, and 30-day all-cause hospital readmission. ResultsFor the first 2 weeks of therapy utilization, we did not find significant differences between the four groups. Using the non-dual FFS beneficiaries and low category of change as a reference, we found significantly lower likelihood of achieving high change in self-care scores for the dual FFS (OR=0.73, 95% CI=0.69-0.76), and dual MA (OR=0.93, 95% CI=0.88-0.98). However, non-dual MA patients had a higher likelihood of changes in self-care scores (OR=1.17, 95% CI=1.13-1.22). Similar trends were found for the mobility change scores, compared to non-dual FFS: dual FFS (OR=0.72, 95% CI=0.68-0.75), and dual MA (OR=0.91, 95% CI=0.86-0.96) and non-dual MA (OR=1.16, 95% CI=1.12-1.20). For 30-day readmission risk, dual FFS showed a higher likelihood of readmission (OR=1.19, 95% CI=1.08-1.31), while non-dual MA had a significantly lower likelihood (OR=0.77, 95% CI=0.71-0.83). Conclusions and RelevanceAlthough no differences in rehabilitation therapy utilization for stroke among dual-eligible beneficiaries, they had poorer functional recovery and higher 30-day readmission risk irrespective of FFS vs MA. Whereas non-dual-eligible MA beneficiaries experienced favorable outcomes. These findings underscore the importance of addressing post-IRF discharge needs among disadvantaged populations.

Background: Long COVID presents with one or multiple symptoms or diagnosable conditions after SARS-CoV-2 infection. To study whether use of the antiviral remdesivir in persons hospitalized with acute COVID-19 is associated with reduced Long COVID, we created a computational phenotype for Long COVID. Methods: In electronic health records (EHR) from a multistate healthcare system (US), hospital admissions from 5/1/20 - 9/30/22 were reviewed. The study group was hospitalized with acute COVID-19 and the control group was hospitalized for other reasons without prior SARS-CoV-2 infection. The populations were balanced with overlap weights based on a high-dimensional propensity score of pre-specified variables and the top 100 comorbidities differing between the groups. Hazard ratios (HR) were calculated for the combined primary outcome: U09.9 (Post-Covid Conditions) or any incident secondary outcome from 90 to 365 days after admission. Secondary outcomes included 27 individual incident diagnoses, corrected for multiplicity with Holm-Bonferroni. Results: Admissions included 45,540 with, and 409,186 without COVID-19 during the study period, evaluable for the primary outcome. After weighting, standardized difference was < 0.01 for all measured confounders including demographic and clinical features. In the COVID+ and non-COVID groups 38.0% and 29.3% met the combined primary outcome, respectively. Weighted HR (95%CI) for the primary outcome was 1.37 (1.35, 1.40), p < 0.0001. All secondary outcomes were associated with the COVID+ group, when adjusted for multiplicity. Incident diagnoses with strong associations (HR > 2) included thromboembolism, hair loss, diabetes mellitus, obesity, and hypoxia. Anosmia/dysgeusia was associated with COVID, but wide confidence intervals reflected few charted diagnoses. Conclusions: Manifestations of Long COVID at population scale are detectable as part of routine symptoms and clinical diagnoses in the EHR after admissions for COVID-19, compared with all other hospital admissions. This a prior computational phenotype for Long COVID will be used to assess whether remdesivir use is associated with decreased Long COVID.

Background: Adults with severe mental health conditions (often referred to as severe mental illness, SMI) experience 15 to 20 year mortality gap relative to the general population, with lung cancer a significant contributor. National cancer policy targets earlier diagnosis but does not explicitly address how pathways function for this group. Aims: This study aimed to describe lung cancer risk, prevalence, screening eligibility, referral activity and diagnostic pathway performance for adults with SMI in South East London (SEL), and to examine where along the pathway inequalities arise. Methods: Co-designed with experts with lived experience and voluntary sector, this exploratory mixed-methods service evaluation combined quantitative analysis of routinely collected data from the Quality Outcomes Framework (QOF), SMI Register and Cancer Waiting Times Record (April 2023-March 2024) with semi-structured qualitative interviews (n=11 clinical staff) and focus groups (n=6 adults with lived experience of SMI). Quantitative and qualitative data were analysed using descriptive statistics and framework-based thematic analysis respectively, and findings were integrated using a joint display approach, organised by the Consolidated Framework for Implementation Research (CFIR). Results: Lung cancer prevalence was approximately double among adults with SMI (0.17% vs 0.09% in the general population). Despite Urgent Suspected Cancer (USC) referral rates being more than twice as high in the SMI population (63 vs 28 per 100,000), fewer cancers were detected via planned general practice (GP) routes (11% vs 20%), the 28-day Faster Diagnosis Standard was not met for any SMI patient diagnosed with lung cancer during the study period; overall FDS performance was 76% in the SMI population compared with 84% in the general population; and appointment non-attendance was more than double that in the general population (6% vs 3%). Qualitative findings identified individual, service and system-level mechanisms, including stigma, diagnostic overshadowing, fragmented coordination, and rigid pathway protocols, that compound disadvantage across lung cancer pathway stages. Conclusions: Inequality in lung cancer outcomes for adults with SMI accumulates across the pathway rather than arising at a single point of failure. Addressing this requires proportionate adaptations within existing cancer pathways, alongside routine reporting of cancer outcomes stratified by SMI population. Keywords: severe mental health conditions, lung cancer, health inequalities, cancer screening, diagnostic pathway, mixed methods

BackgroundForensic autopsy cohorts can help estimate the burden of clinically unrecognised cancer that is not captured by routine incidence statistics. We report incidental non-breast malignancies identified as secondary findings in the Sisyphus Study, a prospective forensic autopsy cohort originally established to investigate silent breast cancer prevalence. MethodsThis was a descriptive secondary analysis of 291 consecutive medicolegal autopsies performed in Lisbon, Portugal, between July 2016 and December 2019 (74 male and 217 female decedents). Key exclusions relevant to the present analysis were age below 40 years, major breast-region injury, and known or clinically evident cancer. An incidental cancer was defined as a histologically confirmed malignancy identified at autopsy in an individual without a prior clinical cancer diagnosis. ResultsFifteen incidental non-breast malignancies were identified among 291 decedents, yielding an overall prevalence of 5.15%. Prevalence was 6.76% in males (5/74) and 4.61% in females (10/217). Male findings comprised two colorectal adenocarcinomas, one pancreatic metastatic adenocarcinoma, one gastric adenocarcinoma, and one splenic lymphoma. Female findings comprised six colorectal adenocarcinomas, two lung adenocarcinomas, one perforated gastric adenocarcinoma, and one ovarian metastatic adenocarcinoma. Colorectal malignancies accounted for 8 of 15 cases (53.3%). Metastatic disease was documented in at least five cases, and perforation was present in two gastrointestinal tumours. None of the affected individuals had a prior cancer diagnosis during life. ConclusionsThis cohort demonstrates a measurable burden of clinically silent non-breast cancer, including advanced and potentially fatal disease. Forensic autopsy surveillance may complement conventional cancer surveillance by identifying malignancies that remain invisible to clinical registries. The predominance of colorectal cancer in this series is consistent with missed opportunities for earlier detection, although individual screening histories were unavailable.

BackgroundTo date, accessible diagnostic tools to identify whether a patients pneumonia is a bacterial, or viral infection, are not accurate or timely enough to prevent preemptive antibiotic administration. Relying on single biomarkers or clinical presentations has been insufficient. We aimed to incorporate a wide range of novel biomarkers and clinical presentations in a multivariable model and validate its capacity to differentiate cases of bacterial and viral pneumonia. MethodsData from 457 children aged 2-59 months, admitted to Kilifi County Referral Hospital, Kenya, with bacterial (n = 229) and viral (n = 228) infections, were used to develop and validate a predictive multivariable Poisson regression model to differentiate pneumonia etiology. The Receiver Operating Characteristic curve was used to assess biomarker performance and validate the model internally. ResultsSixty-three percent (63%) of the children presented with severe pneumonia. 72% with viral pneumonia had severe pneumonia, compared to 54% with bacterial pneumonia who had severe pneumonia. In crude analyses, chest-wall indrawing, cough, convulsions, crackles, angiotensinogen, and Serpin Family A Member 1 were significantly associated with pneumonia etiology, controlling for age. However, only chest-wall indrawing remained significant in multivariable analyses after controlling for age. The model demonstrated fair, but inadequate, discrimination, with an Area Under the Curve of 0.61. ConclusionAmong the children admitted to hospital with WHO defined pneumonia, a wide range of biomarkers and clinical presentations still failed to distinguish bacterial from viral pneumonia.

INTRODUCTION Severe acute brain injury (stroke, traumatic brain injury or hypoxic-ischemic encephalopathy; SABI) is increasingly recognized as a chronic condition with care and communication needs beyond the initial hospitalization. This study aimed to characterize post-acute care patterns among SABI survivors, focusing on healthcare utilization and outpatient communication. METHODS Data were collected from a prospective cohort of hospitalized SABI patients using surveys, chart reviews, and the ED Information Exchange database. Socioeconomic disadvantage was assessed using the Area Deprivation Index (ADI), and qualitative analysis of outpatient notes examined conversations around palliative care needs and goals-of-care. RESULTS Two-thirds of patients (140/222) survived until discharge, primarily to nursing facilities (39%) or inpatient rehabilitation (38%). Among 109 with one-year follow-up, there were 89 hospitalizations, 104 ED visits, and 28 deaths. Patients from the most disadvantaged neighborhoods had significantly higher odds of rehospitalization or ED use within 30 days (OR 3.37, p=0.036). ADI was not linked to one-year utilization. seen outpatient by primary care (40%), neurology/neurosurgery (57%), and palliative care (1%), but conversations rarely revisited prognosis or goals-of-care. CONCLUSIONS Our findings highlight the need for improved long-term care planning and communication, particularly for socioeconomically disadvantaged survivors of SABI.

BackgroundOutcome after stroke varies according to stroke subtype by location, but healthcare systems data studies do not include subtyping information. We linked natural language processing (NLP) of brain imaging reports to routinely collected data to estimate risk of death and other outcomes after stroke subtypes in a nationwide dataset. MethodsWe applied a previously validated NLP algorithm to all CT and MRI head scan reports in Scotland between 2010 and 2018. We linked the reports to hospital readmissions, prescriptions and death data to identify and characterize people with stroke, and to categorize into deep and cortical ischemic stroke, deep and lobar intracerebral hemorrhage (ICH), subarachnoid hemorrhage, and subdural hemorrhage. We used a matched cohort design, and age- and sex-matched four controls per case who never had a stroke. By subtype, we estimated rehospitalization with stroke, myocardial infarction (MI), cancer, dementia, epilepsy and death, accounting for confounders and competing risk of death. ResultsFrom 785,331 people with a head scan, we identified 64,219 with clinical stroke phenotypes (mean age 73.4yrs, 49.5% male), and subtyped 12,616 with deep ischemic stroke; 14,103 with cortical ischemic stroke; 1,814 with deep ICH; and 1,456 with lobar ICH. There was higher absolute rate of 1-year hospital readmission for lobar compared with deep ICH (4.9% [95%CI 3.9% - 6.1%] vs 3.4% [2.6% - 4.3%]), higher risk of dementia beyond 6 months after lobar ICH compared to controls than for other stroke subtypes (aHR 3.5 [2.3-5.3]); and higher risk of MI within 6 months of cortical ischemic stroke than for other stroke subtypes (aHR 4.6 [3.4-6.3]). ConclusionsNLP of free-text reports linked to coded data successfully subtyped stroke at scale, and we estimated risk of clinically relevant outcomes. Future work should use free text to enable large-scale audit and epidemiology of people with stroke.

BackgroundThe 2024-25 influenza season was the most severe in the United States (US) since 2017-18, with co-circulation of both influenza A virus subtypes (H1N1 and H3N2). Influenza vaccine effectiveness (VE) has varied by season, setting, and patient characteristics. MethodsUsing electronic healthcare encounter data from eight US states, we evaluated influenza vaccine effectiveness (VE) against influenza-associated hospitalizations and emergency department or urgent care (ED/UC) encounters from October 2024-April 2025 among children aged 6 months-17 years and adults aged [&ge;]18 years. Using a test-negative, case-control design, we compared the odds of influenza vaccination between acute respiratory illness (ARI) encounters with a positive (cases) versus negative (controls) test for influenza by molecular assay, adjusting for confounders. ResultsAnalyses included 108,618 encounters (5,764 hospitalizations and 102,854 ED/UC encounters) among children and 309,483 encounters (76,072 hospitalizations and 233,411 ED/UC encounters) among adults. Among children across care settings, 17.0% (6,097/35,765) of cases versus 29.4% (21,449/72,853) of controls were vaccinated. Among adults, 28.2% (21,832/77,477) of cases versus 44.2% (102,560/232,006) of controls were vaccinated. VE was 51% (95% confidence interval [95% CI]: 41-60%) against influenza-associated hospitalizations and 54% (95% CI: 52-55%) against influenza-associated ED/UC encounters among children. VE was 43% (95% CI: 41-46%) against influenza-associated hospitalizations and 49% (95% CI: 47-50%) against influenza-associated ED/UC encounters among adults. ConclusionsInfluenza vaccination provided protection against influenza-associated hospitalizations and ED/UC encounters among children and adults in the US during the severe 2024-25 influenza season. These findings support influenza vaccination as an important tool to reduce influenza-associated disease. Brief SummaryDuring the 2024-25 influenza season, influenza vaccination provided protection against influenza-associated hospitalizations (43-51%) and emergency department or urgent care encounters (49-54%) among children and adults in the United States.

ObjectiveBiologically defined sepsis subtypes have been identified in low- and middle-income countries (LMICs), but limited access to molecular diagnostics challenges broader evaluation and implementation in resource-limited settings. We assessed whether models including bedside clinical and rapid microbiologic data could accurately stratify Ugandan adults with sepsis by molecular subtype. DesignSecondary analysis of two prospective observational sepsis cohorts, testing bedside-adaptable classifier models against transcriptomic and proteomic subtype assignments. SettingEntebbe Regional Referral Hospital (urban) and Tororo General Hospital (rural), Uganda. PatientsAdults ([&ge;]18 years) hospitalized with sepsis, with available transcriptomic (N=355) and/or proteomic (N=495) profiling enabling subtype assignment. InterventionsNone. Measurements and Main ResultsUsing data from two prospective cohorts (RESERVE-U-2-TOR and RESERVE-U-1-EBB), we evaluated bedside-adaptable models against Uganda-derived molecular sepsis subtypes, and, secondarily, against molecular subtypes and axes derived in high-income countries. In RESERVE-U-2-TOR, clinical models including demographics and bedside physiological variables demonstrated moderate discrimination for transcriptomic and proteomic subtype assignment (AUROC 0.75 [95% CI, 0.69-0.81] and 0.73 [0.66-0.80], respectively) with strong calibration (Integrated Calibration Index [Eavg] [&le;]0.015 for both models). Adding rapid diagnostic results for HIV, malaria, and tuberculosis produced similar performance (AUROC 0.76 and 0.74; Eavg [&le;]0.016). In RESERVE-U-1-EBB, discrimination for clinical and clinico-microbiological models was more variable (AUROC range 0.63-0.75) while calibration remained acceptable (Eavg [&le;]0.053). Performance was similar when models were evaluated against molecular sepsis frameworks derived in high-income countries, with acceptable calibration and moderate discrimination. ConclusionsBedside-adaptable clinical models, with or without rapid microbiologic testing, demonstrated acceptable calibration but only modest discrimination for molecular sepsis subtype assignment in Uganda. Expanding laboratory capacity and access to scalable, low-cost molecular biomarker assays will be necessary to advance precision sepsis care in LMIC settings. Key PointsO_ST_ABSQuestionC_ST_ABSAmong adults hospitalized with sepsis in a resource-limited setting, can bedside clinical variables, alone or combined with rapid pathogen diagnostics, accurately stratify molecular sepsis subtype assignments? FindingsIn two prospective Ugandan sepsis cohorts, bedside clinical and clinico-microbiologic models showed robust calibration but only modest discrimination for classifying Uganda-derived transcriptomic and proteomic subtypes. Models also achieved moderate performance for stratifying high-income-country-derived transcriptomic subtypes and immune dysfunction axes, suggesting bedside variables reflect illness severity but incompletely capture underlying molecular signatures. MeaningBedside-adaptable models can support reasonably calibrated risk estimation for molecular sepsis stratification in resource-limited settings but lack sufficient discriminatory power to serve as stand-alone tools. These findings support efforts to improve acute-care laboratory capacity and access to scalable molecular biomarker panels, with the goal of enabling precision sepsis care in low- and middle-income countries.

Objectives: Among individuals attending stroke rehabilitation, we aimed to determine the proportion who participated in cardiorespiratory exercise, identify patient characteristics predicting participation, and describe exercise characteristics. Design, setting, and participants: This was an observational cohort study involving all patients admitted to four stroke rehabilitation centres in Ontario, Canada, during March or October 2019, or over 12 months starting in 2021. Main measures: Patient characteristics extracted during chart review included age, sex, marital status, employment status, date of stroke, time post-stroke at admission, length of stay for rehabilitation, past medical history that could affect exercise participation, Functional Independence Measure, Functional Ambulation Category, mobility aid use, Chedoke-McMaster Stroke Assessment, Montreal Cognitive Assessment, National Institutes of Health Stroke Scale, and details describing cardiorespiratory exercise completed. Results: 40.1% of stroke patients participated in cardiorespiratory exercise, with 26.4% having it included in their treatment plan. Diagnosed cardiac disease (OR=0.74), poor left ventricular function (OR=0.09), history of mental health conditions (OR=0.69), lower functional ambulation ability (OR=0.74), and wheelchair use at rehabilitation admission (OR=0.46) were associated with lower odds of participating in cardiorespiratory exercise after stroke (p-values<0.05). Use of a walker or rollator at rehabilitation admission (OR=3.22), having a cardiorespiratory exercise goal (OR=2.13), and longer lengths of stay (OR=1.01) were associated with higher odds of participating in cardiorespiratory exercise after stroke (p-values<0.05). Only 1.5% of patients (N=9/601) who participated in cardiorespiratory exercise completed it with recommended intensity and duration. Conclusion: Improving participation in cardiorespiratory exercise during stroke rehabilitation may require addressing cardiovascular, mental health, and mobility-related barriers.

Background: People affected by dementia experience intersecting care inequalities. We explored relationships between ethnicity and health and social care resource use among people with dementia in an ethnically diverse urban region. Methods: We conducted a retrospective observational cohort study using Discover-NOW, including patients with dementia between 1.4.2015 and 1.4.2025. We calculated ethnic density as the percentage of the Middle Layer Super Output Area (SOA) population self-identifying with the same ethnic group. Regression models, clustered by Local SOA, tested whether ethnic density moderated relationships between ethnicity and primary care, outpatient, inpatient, emergency and social care service use, controlling for sociodemographic characteristics, deprivation, comorbidities and time of diagnosis. Findings: We included 30,704 people with dementia. People from Black and Mixed ethnic groups used more primary care, and those from Asian ethnic groups less primary and secondary care, than White ethnic groups. Rates of local authority social care packages were similar across ethnic groups. High ethnic density predicted fewer GP consultations in Black ethnic groups, but more in South Asian groups. Interpretation: Among Black ethnic groups, primary care use was relatively high, especially in areas of low ethnic density, perhaps reflecting greater needs among communities at risk of racism and isolation. The trend towards increased primary care use among South Asian people in areas of higher ethnic density may reflect communities mitigating help-seeking hesitancy related to cultural and language barriers. Greater care integration could reduce care inequalities among minority ethnic communities who may experience fewer barriers to social relative to health care.

BackgroundThe emergency department (ED) serves as a critical entry point into hospital care and a sentinel indicator of health system performance. In-hospital mortality within 48 hours of ED admission represents acute care failures that are often preventable yet remain poorly characterized in sub-Saharan African (SSA) settings. This study aimed to identify the demographic, clinical, and hospital-related determinants of in-hospital mortality within 48 hours of admission to the Emergency and Urgent Care Department at the University Teaching Hospital (UTH), Lusaka, Zambia. MethodsA retrospective cross-sectional study was conducted using 385 patient records from UTHs Emergency and Urgent Care Department for the year 2021. Data were extracted from the District Health Information System 2 (DHIS2) using simple random sampling. Descriptive statistics, univariate, and multivariable logistic regression analyses were performed using STATA 16.1 MP. Variables with p<0.20 in univariate analysis were retained for adjusted modelling. Multicollinearity was assessed via variance inflation factors (VIF <5). Model fit was evaluated using the Hosmer-Lemeshow goodness-of-fit test and receiver operating characteristic (ROC) analysis. ResultsOf 385 patients, 175 (45.5%) died within 48 hours of admission. Patients who died were older (median age 45 vs. 37.5 years, p<0.001). In multivariable analysis, three variables were independently associated with 48-hour mortality: pulse rate (aOR = 0.98, 95% CI: 0.95-1.00, p = 0.036), Glasgow Coma Scale (GCS) score (aOR = 0.75, 95% CI: 0.63-0.90, p = 0.002), and out-of-hours admission between 00:00-07:59 (aOR = 11.44, 95% CI: 1.19-109.96, p = 0.035). Age was a significant predictor in univariate analysis but not in the adjusted model, indicating confounding. The model demonstrated good discriminatory ability (AUC = 0.81). ConclusionsReduced pulse rate, lower GCS score at admission, and out-of-hours presentation are independent determinants of 48-hour in-hospital mortality at UTH. These findings underscore the need for enhanced vital sign monitoring protocols, targeted staffing during overnight hours, and improved risk stratification tools in resource-constrained emergency care settings. The wide confidence interval for the time-of-admission finding warrants cautious interpretation and validation in future prospective studies.

Background The ageing of incarcerated populations is accelerating across high-income countries, yet dementia remains absent from routine correctional mental health statistics. We investigated whether correctional data systems in Japan, the United States, the United Kingdom, and Australia are structurally capable of detecting dementia in their prison populations. Methods We conducted a cross-national descriptive analysis of publicly available, aggregate-level correctional data. Japanese data comprised all newly admitted sentenced prisoners from 2006 to 2024 (approximately 390,000 individuals) from the Ministry of Justice Correctional Statistics Annual, including mental disorder classifications and CAPAS-derived work aptitude scores (used as a proxy for cognitive functioning; not clinical IQ measurements). US data were drawn from the Bureau of Justice Statistics Survey of Prison Inmates (2016). UK data were obtained from the Ministry of Justice Offender Management Statistics Quarterly (2015-2025). Australian data were sourced from the Australian Institute of Health and Welfare National Prisoner Health Data Collection (2022, n = 371). All analyses were descriptive; no inferential statistics were conducted. Findings Three distinct mechanisms rendered dementia statistically invisible across all four countries. First, in the United States and Australia, reliance on self-report instruments produced a paradox in which self-reported mental disorder prevalence declined with age: among US state prisoners, reported prevalence fell from 44.9% in the 35-44 age group to 31.9% among those aged 65 and older - the opposite of community epidemiological patterns. Second, in Japan - the only country with systematic cognitive assessment at prison admission - 35.0% of female theft offenders had work aptitude scores below 70, yet the classification system contains no dementia category; 43-52% of all detected mental disorders were absorbed into a residual "other" category even after a 2023 classification revision that added four new diagnostic categories but not dementia. Third, the United Kingdom lacks routine mental health prevalence data collection in prisons altogether. None of the four countries includes dementia as a standard correctional classification category. Interpretation Correctional mental health statistics across four high-income countries are structurally incapable of detecting dementia - not through clinical ignorance but by design: systems built for younger populations that have not been updated as prison demographics have changed. Japan's ageing female theft offender profile (39.4% aged 60 or older, 35.0% with low cognitive scores) represents a potential sentinel population for undetected cognitive impairment. Targeted interventions - cognitive screening at admission in the United States and Australia, introduction of a dementia classification category in Japan, and routine mental health data publication in the United Kingdom - are feasible with existing infrastructure. As prison populations continue to age, the statistical invisibility of dementia constitutes an escalating failure of health surveillance with direct consequences for clinical care, sentencing, and human rights.

IntroductionModeling when influenza epidemics typically occur can help countries optimize surveillance, time clinical and public health interventions, and reduce the burden of influenza. MethodsWe used influenza virus detections reported during 2011-2024 by 180 countries to the Global Influenza Surveillance and Response System, excluding COVID-19 pandemic impacted years (2020-2023). We analyzed data by calendar year (week 1-52) or shifted year (week 30-29) time windows, based on when most influenza detections occurred in each country. For countries with sufficient data, we computed generalized additive models (GAMs) of each countrys weekly influenza-positive tests to smooth and impute time series distributions. From these GAMs, we calculated each countrys normalized weekly influenza burden. Country-specific normalized time series were grouped using hierarchical k-means clustering reducing the Euclidean distance between time series within clusters. We calculated cluster-specific GAMs to estimate average seasonal timing. Countries without sufficient data were assigned to a cluster based on population-weighted latitudinal distance to a clusters mean latitude. ResultsWe identified five clusters, or epidemic zones, from 111 countries with sufficient data. The influenza burden in epidemic zones A and B was consistent with a northern hemisphere pattern, with most influenza detections occurring during October-April (A) and September-March (B), while epidemic zones D and E were characterized by southern hemisphere-like seasonal timing, with most influenza burden occurring during May-November. Epidemic zone C had most influenza burden occurring during September-March; most countries assigned to this cluster were in the tropics. ConclusionEpidemic zones may serve as a useful tool to strengthen and optimize influenza surveillance for global health decision-making (e.g., during vaccine strain composition discussions) and to guide country preparedness efforts for seasonal influenza epidemics, including the timing of enhanced surveillance, as well as the procurement and delivery of vaccines and antivirals. What is already known on this topicPrevious initiatives to provide a framework for describing global influenza patterns and to support national and regional prevention and control strategies have classified countries into influenza transmission zones, based primarily on geographic proximity. Many countries have improved their surveillance systems for respiratory viruses, providing an opportunity to re-assess patterns of influenza dynamics using analytic approaches focused on influenza detection data and maximizing global coverage. What this study addsOptimal clustering of influenza surveillance data from 111 countries and proximal assignment by latitude of countries lacking sufficient data grouped countries into five epidemic zones. How this study might affect research, practice or policyBy providing improved resolution on the temporal and geographical dynamics of influenza activity, this study offers an evidence base to aid national and global decision-making on enhanced surveillance strategies, vaccine strain selection, seasonal epidemic preparedness, and resource allocation, thereby strengthening efforts to prevent and control influenza worldwide. This data-driven framework for characterizing global patterns of influenza virus circulation can be leveraged to reexamine patterns as new data become available.

BackgroundApproximately 300 million people worldwide live with a rare disease, and the majority of rare diseases manifest in childhood. For families, the period before diagnosis is often protracted and distressing, marked by repeated consultations, inconclusive investigations, conflicting medical opinions, and the absence of a recognisable name for their childs condition. While the clinical and epidemiological dimensions of the diagnostic odyssey have been documented, the narrative and experiential dimensions of how parents live through and make sense of prolonged diagnostic uncertainty remain underexplored, particularly in low- and middle-income country contexts. AimTo explore the narrative experiences of parents navigating diagnostic uncertainty for children with rare diseases in Brazil. MethodsA narrative inquiry informed by the three-dimensional narrative space framework of Clandinin and Connelly was conducted. Sixteen parents (twelve mothers and four fathers) of children who had experienced a diagnostic delay of at least two years were recruited from two rare disease referral centres in Sao Paulo and Belo Horizonte. Data were collected through two narrative interviews per participant, supplemented by participant-produced timelines and family photographs. Analysis followed a narrative analytical approach attending to temporality, sociality, and place. FindingsThree narrative threads were woven across the parents stories: (a) "Living in the space before the name," capturing the disorienting experience of caring for a child whose suffering could not be categorised, explained, or predicted; (b) "Fighting to be believed," describing the relentless advocacy required to sustain medical attention in a system that struggled to accommodate conditions falling outside familiar diagnostic categories; and (c) "Rewriting the story," illuminating how the eventual arrival (or non-arrival) of a diagnosis reshaped parents understanding of their child, their family, and themselves. ConclusionDiagnostic uncertainty for parents of children with rare diseases is not a passive waiting period but an active, effortful, and identity-transforming experience. The findings highlight the need for healthcare systems to provide structured psychosocial support during the pre-diagnostic period and for clinicians to develop communication practices that acknowledge, rather than dismiss, the legitimacy of undiagnosed suffering.

BackgroundConventional evaluations of digital health interventions typically assess mean treatment effects, potentially masking heterogeneous impacts across the functional recovery distribution. Patients at the lower and upper tails of recovery trajectories may respond differently to AI-enhanced telerehabilitation, yet standard regression approaches cannot capture these distributional nuances. ObjectiveThis study applied Recentered Influence Function (RIF) quantile regression with Oaxaca-Blinder decomposition to examine how AI-enhanced telerehabilitation differentially affects functional recovery outcomes across the entire distribution, and to decompose observed disparities into explained (composition) and unexplained (structure) components. MethodsWe analyzed data from 486 post-stroke patients across three rehabilitation centres in Singapore (January 2023-December 2025). Patients received either AI-enhanced telerehabilitation (n=241) incorporating natural language processing-based progress monitoring and adaptive exercise prescription, or standard care (n=245). RIF-quantile regressions were estimated at the 10th, 25th, 50th, 75th, and 90th quantiles of the Functional Independence Measure (FIM) score distribution. Oaxaca-Blinder decomposition at each quantile partitioned group differences into composition effects (attributable to differences in observable characteristics) and structure effects (attributable to differential returns to those characteristics). ResultsThe AI-enhanced telerehabilitation group demonstrated significantly greater FIM improvements across all quantiles, with the largest effects at the 10th quantile ({beta} = 12.74, 95% CI: 8.92-16.56, p < 0.001) and 25th quantile ({beta} = 9.83, 95% CI: 6.71-12.95, p < 0.001), diminishing at the 90th quantile ({beta} = 3.21, 95% CI: 0.88-5.54, p = 0.007). RIF decomposition revealed that at the 10th quantile, 68.3% of the treatment-control gap was attributable to structure effects, indicating that AI-enhanced telerehabilitation fundamentally altered recovery mechanisms for lower-performing patients rather than merely leveraging differences in patient characteristics. ConclusionsAI-enhanced telerehabilitation produces its most pronounced benefits among patients at the lower end of the functional recovery distribution, suggesting a potential mechanism for reducing outcome inequality in stroke rehabilitation. RIF-quantile regression decomposition offers a methodologically rigorous framework for understanding distributional treatment effects that are invisible to conventional mean-focused analyses.

Background: The Functional Outcome in Patients with Primary Intracerebral Hemorrhage (FUNC) score was initially validated for prediction of functional independence on the Glasgow Outcome Scale (GOS) 90 days after intracerebral hemorrhage (ICH), but recovery often extends beyond three months. Aims: Our objective was to extend the FUNC score for prediction of 12-month functional independence to strengthen its utility for family counseling and research methodology. Methods: We conducted a single-center prospective cohort study enrolling adult patients with primary ICH between February 2009 and January 2018. We calculated FUNC scores at admission and assessed GOS 12 months after ICH. The primary outcome was 12-month functional independence, defined as a GOS score [&ge;]4. We calculated the area under the receiver operating characteristic curve (AUC) of the FUNC score using logistic regression, handling missing GOS with multiple imputation by chained equations. We evaluated score calibration using a calibration curve and the Brier score, and we assessed clinical utility using decision curve analysis. We explored the statistical efficiency gains of using FUNC-based sliding dichotomy thresholds for favorable outcome definitions by running simulations of a clinical trial with 1:1 randomization. We ran 5000 simulations for each sample size (100 to 1000, in increments of 10) and treatment effect (odds ratio of 1.5, 2.0 and 2.5) combination and calculated efficiency gains for each respective treatment effect as the percentage reduction in sample size required to have 80% power using sliding versus fixed dichotomy thresholds. Results: A total of 535 patients were included (median [IQR] age 68 [54-79], 237 [44%] female, median [IQR] NIHSS 16 [6-25], median [IQR] FUNC 8 [6-9]). Overall, 99 of 445 (22%) patients with known 12-month GOS achieved functional independence. The FUNC score had an AUC of 0.79 (95%-CI: 0.75-0.84) for 12-month functional independence. The calibration plot was reasonable, with modest evidence of overestimation at low predicted probabilities, and the Brier score was 0.15. A net benefit was observed across 5-50% threshold probabilities. Sliding dichotomy had an efficiency gain of 27% for a treatment effect of OR=2.0, and a gain of 22% for a treatment effect of OR=2.5. The efficiency gain for a treatment effect of OR=1.5 could not be calculated because the fixed dichotomy did not reach 80% power despite a sample size of 1000 patients. Conclusions: The FUNC score's predictive performance for 12-month functional independence was comparable to its originally validated 3-month discrimination. Following external validation across centers, the FUNC score may be leveraged to counsel families on global measures of long-term functional independence and to implement sliding dichotomy methodology in ICH research.

BackgroundIn England, individuals with chronic liver disease (CLD) are among those with the lowest seasonal influenza vaccine uptake despite being at elevated risk of severe influenza. We examined the relationship between CLD severity and aetiology, and influenza vaccine uptake in England. MethodsA retrospective cohort study of adults (18-115 years) using Clinical Practice Research Datalink Aurum primary care data was conducted for five seasons (2019/20-2023/24). Poisson regression was used to estimate rates of uptake by CLD severity (clinical diagnoses categorised as low, moderate, or severe) and aetiology (alcohol-related, viral-related, and diagnoses in the Green Book guidelines). FindingsThere were 182,174-277,470 with CLD per cohort. Among those who were additionally age-eligible for vaccination, uptake was 71{middle dot}1-79{middle dot}7% compared to 30{middle dot}9-40{middle dot}5% in those not additionally age-eligible. Among individuals below age eligibility without other comorbidities, severity was associated with higher uptake (incidence rate ratio [IRR] moderate 1{middle dot}80, 95% CI 1{middle dot}69-1{middle dot}90; severe 1{middle dot}95, 95% CI 1{middle dot}84-2{middle dot}08 in 2023/24); there was no effect in those with at least one additional comorbidity (moderate 1{middle dot}05, 95% CI 0{middle dot}99-1{middle dot}10; severe 1{middle dot}05, 95% CI 1{middle dot}01-1{middle dot}09). Alcohol- and viral-related aetiology were also associated with increased uptake in those not additionally age-eligible. Among individuals meeting age eligibility without additional comorbidities, severity was associated with a reduced uptake (moderate 0{middle dot}81, 95% CI 0{middle dot}73-0{middle dot}90; severe 0{middle dot}79, 95% CI 0{middle dot}74-0{middle dot}85), with attenuation in those with additional comorbidities (moderate 0{middle dot}99, 95% CI 0{middle dot}94-1{middle dot}04; severe 0{middle dot}91, 95% CI 0{middle dot}89-0{middle dot}94). InterpretationCLD severity and aetiology were important determinants of uptake in the absence of additional indications for influenza vaccination. Future research should prioritise understanding facilitators and barriers to vaccine uptake in individuals with CLD, particularly for those at highest risk of severe infection. FundingNIHR Health Protection Research Unit in Vaccines and Immunisation (NIHR200929/NIHR207408). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed up to June 2025 using the terms "chronic liver disease", "cirrhosis", "hepatitis", "influenza vaccination", "seasonal influenza", and "vaccine uptake". Previous research, including national data from England, has shown that people with chronic liver disease tend to have lower seasonal influenza vaccine uptake than individuals with other medical comorbidities which qualify for vaccination such as diabetes, chronic kidney disease or immunosuppression. The reasons for low influenza vaccine uptake in people with chronic liver disease are not well understood, and it is therefore difficult for vaccination providers, principally primary care services in England, to tailor interventions aimed to increase uptake. Qualitative research involving individuals aged less than 65 years living in England with clinical risk comorbidities, most commonly diabetes, found that chronic disease management pathways inconsistently provided information about the importance of influenza vaccination as part of chronic disease management. Individuals with long-term conditions reported low perceived risk of influenza infection and limited awareness of vaccine benefits as important reasons for non-uptake. We hypothesised that the severity and aetiology of chronic liver disease may be important determinants of uptake. Added value of this studyWe conducted a population-based study to examine how chronic liver disease severity and aetiology influence seasonal influenza vaccine uptake in adults in England. Using primary care electronic health record data from five consecutive influenza seasons (2019/20-2023/24), we found that more severe chronic liver disease was associated with a substantial increase in vaccine uptake in those without additional indications for seasonal influenza vaccination (age-based eligibility or other qualifying clinical risk comorbidities). Alcohol- and viral-related aetiology were also associated with increased uptake in those who were not additionally age-eligible for vaccination. In contrast, severity, alcohol- and viral-related underlying aetiology were associated with a modest reduction in uptake for individuals with chronic liver disease who also qualified for vaccination due to age. Implications of all the available evidenceDespite clear clinical vulnerability to infection and a substantially elevated risk of morbidity and mortality following infection, a large proportion of adults with chronic liver disease, particularly those aged under 65 years, remain unvaccinated against seasonal influenza each year. This study suggests that chronic liver disease severity and underlying aetiology are important determinants of uptake in individuals not meeting age-based vaccine eligibility, particularly in those without additional clinical risk comorbidities. This could be because of differing perceptions of influenza risk, or due to varying degrees of interaction with healthcare specialists as part of chronic disease management. In individuals who met age-based vaccination eligibility, the negative effect of severity on influenza vaccine uptake may reflect greater barriers to accessing vaccination services by those with more complex health needs, or competing medical priorities for long-term condition management during consultations. To inform targeted vaccination strategies, future research should aim to understand the specific facilitators and barriers to influenza vaccination experienced by individuals with chronic liver disease. This should include perspectives of individuals with different disease severity, across different age groups, in those with and without additional co-morbidities.