Swiss Medical Weekly

We describe a fast, noninvasive, low-cost survey method designed to understand the mode of transmission of an emerging pathogen. It is inspired from the standard household prevalence survey consisting in sampling households and counting the total number of people infected in each household, but refines it with the aim of improving diagnosis and estimating more parameters of the model of intra-household transmission. The survey was carried out in May-June 2020, during part of the first national French lockdown and received responses from more than 6,000 households involving a total of 20,000 people. We explain how we conceived the questionnaire, how we disseminated it, to the public through an open website hosted by CNRS, marketed through media and social media, and to a socially representative panel hosted by two survey institutes (BVA, Bilendi). We used the data obtained from the representative panel to correct for sampling biases in the CNRS survey using a classical raking procedure. Our results indicate that raking correctly canceled statistical biases between the two populations. We obtain the empirical distribution in households of the number and nature of symptoms. The main factors affecting the presence of symptoms are age, gender, body mass index (BMI), household size, but not necessarily in the expected direction. Our study shows that combining self-reporting and representative surveys allows investigators to obtain information on prevalence and household transmission mechanisms on emerging diseases at low cost.

IntroductionIn-hospital cardiac arrest (IHCA) in the pediatric population is associated with poor survival and neurological outcomes. We aimed to develop and internally validate a risk score to predict survival to discharge following pediatric IHCA. MethodsWe included pediatric IHCA patients in the Get With The Guidelines-Resuscitation(R) registry between 2005 and 2021. We used logistic regression (LR), classification and regression trees (CART), and artificial neural networks (ANN) to develop models using 70% of the data and validate them using the remaining 30% of the data. Discrimination was based on the area under the receiver operating characteristic curve (AUC), and predictive accuracy on percent survival in each risk group. ResultsWe included 6141 patients with a mean age of 4.8 years, of whom 41.3% (n = 2535) were infants < 1 year of age and 39.1% of whom survived to hospital discharge. We developed separate models for infants and older children. The most important independent pre-arrest predictors were age, illness category, acyanotic cardiac malformation, cyanotic cardiac malformation, hepatic insufficiency, hypotension/hypoperfusion, metabolic/electrolyte abnormality, metastatic/hematologic malignancy, renal insufficiency, congenital malformation, septicemia, hypotension, trauma, and pediatric cerebral performance score on admission. All three approaches showed good classification accuracy in the derivation sample (AUC for LR: 0.70, 0.71, AUC for CART: 0.68, 0.70, AUC for ANN: 0.76, 0.74 for infants and older children respectively) and used almost the same number of variables. Logistic regression and CART models were the most useful as they identified patients with the lowest survival, showed good discrimination, and could be used to develop a simple point score and decision trees that can be implemented in the clinical or research setting. In infants, the average probability of survival was 10%, 36%, and 60% whereas in older children it was 6.2%, 31.1%, and 62.3% in the low, moderate, and high survival categories in the LR model. ConclusionPediatric patients experiencing IHCA can be classified into low, moderate, and high survival categories using a simple risk score and easily identified pre-arrest variables. These risk scores can support clinical decisions, facilitate research, and help monitor the quality of medical services.

Automated outbreak detection can enhance infectious disease surveillance by enabling early identification of outbreaks and supporting timely public health measures. However, information on its current use by national public health institutes (NPHI) remains limited. This paper provides an updated and extended overview of automated outbreak detection usage in the European Union (EU) and United Kingdom (UK). Key findings were gathered through the Joint Action United4Surveillance via an online survey of 21 countries, an in-presence workshop, and online meetings with NPHI, focusing on three objectives: assessing current demand for automated outbreak detection, examining the availability of necessary prerequisites within existing surveillance systems, and identifying challenges and requirements for implementation. Findings indicate that seven countries currently have automated outbreak detection systems (AODS) in place. While many countries have sufficient surveillance data and a clear demand for automated outbreak detection, adoption is often limited by constrained funding and lack of IT resources. While the specific methods in existing AODS differ, overall demands and outputs are similar, suggesting a single tool could serve multiple countries. Capacity building as part of EU-funded Joint Actions can bridge these gaps by developing sustainable tools and fostering cross-country collaboration.

The COVID-19 pandemic exposed major vulnerabilities of hospital capacity and management worldwide, particularly in intensive care units (ICUs) and emergency rooms (ER), imposing prompt adaptation and resource reallocation. Although SARS-CoV-2 is no longer endangering healthcare systems, winter seasons continue to bring recurrent overload of critical care services, primarily due to respiratory infections. In France e.g., this pattern led to the reactivation of the national emergency response plan during the 2024-2025 seasonal influenza peak, highlighting the continuous need for improved predictive tools. However, forecasting hospitalization surges at a local scale remains a methodological challenge because the (very) low incidence numbers are subject to strong stochasticity and therefore require additional input of information and dedicated approaches. This study investigates the potential for early forecasting of respiratory infection peaks by analyzing ER visit trends. By clustering all-cause ER visits during the 2023-2025 winter seasons from the Nimes University Hospital (France), we identified a strong temporal correlation between early pediatric hospitalizations ([&le;]5 years old) and the following weeks adult hospitalization incidence for respiratory infections. The results suggest that tracking hospital admissions of pediatric ER visits, even without hospital care needs, can serve as a valuable early warning signal for upcoming peaks in respiratory-related hospitalizations. This predictive approach could improve hospital preparedness and resource management during seasonal influenza outbreaks. Author summaryThe epidemics of respiratory viruses present a significant challenge to hospitals in the temperate zone on an annual basis. Frequently, the hospital overload is mitigated by the late reactive allocation of human and material resources that are, hence, suboptimal. This study proposes a statistical framework to assist hospitals in anticipating bed requirements during seasonal influenza waves, despite high noise at the local level, by enhancing hospitalization forecasting with emergency room (ER) visit data. The prediction of the adult epidemic peak is possible through the analysis of the respiratory pediatric ER visits, which facilitates hospital management.

AimBesides registries, healthcare databases can provide useful information for assessing major congenital malformations (MCMs) frequency and investigating their risk factors, particularly medications exposures. This study aimed to assess the validity of MCMs identification based on French national, comprehensive healthcare databases. MethodsUsing information on hospital discharge diagnoses, medical procedures (e.g. surgery) and death causes from the EPI-MERES register nested in the French National Health Data System, 72 specific MCMs grouped in 11 organ groups were assessed among all births occurred after 22 weeks of amenorrhea in France between 2010 and 2023. MCMs prevalence rates were estimated and compared to those from EUROCAT, and associations with prenatal exposure to valproate were assessed. ResultsAmong 10.5 million births, 213,153 live born infants with at least one MCM, i.e. 203.0 cases per 10,000 births, were identified. MCMs prevalence rates among live births were close to those reported in EUROCAT overall (difference: -1.76 per 10,000 births [-1%]), for each organ group (differences ranging from -9.10 [-13%] to +3.44 [+16%] per 10,000 births), and for the 72 specific MCMs (median prevalence difference: 1%). Prenatal exposure to valproate was significantly associated with increased risks of any MCM (adjusted odds ratio (aOR) 2.82, 95% CI [2.33-3.41]) and of 15 specific MCMs including spina bifida (aOR 17.88 [7.88-40.53]). ConclusionThis study supports the validity of MCMs identification based on data of the EPI-MERES register. The EPI-MERES register provides a highly powerful, reactive and operational tool complementing MCMs registries for improving real-life knowledge on drug teratogenicity. Plain language summaryMajor congenital malformations are serious structural abnormalities present at birth that can have lasting consequences on childrens health. Better understanding their risk factors, particularly medication exposures during pregnancy, is crucial. Population-based registries are today the primary source of information on malformations, but healthcare databases could offer a faster and broader alternative. This study tested whether the EPI-MERES register, built upon the French National Health Data System (SNDS), could accurately identify 72 specific malformations across 10.5 million births between 2010 and 2023. Prevalence estimates closely matched those from the European EUROCAT registry, confirming good data accuracy. As expected, valproate (a known teratogen) was associated with an increased risk of various malformations, including spina bifida, EPI-MERES thus constitutes a promising tool for studying medication risks during pregnancy.

We present results from the second season of Influcast, a multi-model collaborative forecasting hub focused on influenza in Italy. During the 2024/25 winter season, Influcast collected one- to four-week-ahead probabilistic forecasts of influenza-like illness (ILI) incidence alongside influenza A and B ILI+ incidence signals. New ILI+ targets were constructed integrating syndromic surveillance data with virological detections collected weekly by the Italian National Institute of Health. Forecasts were submitted by six independent models (including compartmental, metapopulation, and statistical approaches) and combined into an ensemble. Ensemble forecasts for ILI+ consistently outperformed both the baseline (a naive persistence model) and most individual models in terms of Weighted Interval Score (WIS), Absolute Error (AE), and prediction coverage. Importantly, ensemble ILI+ forecasts achieved significantly lower WIS and AE ratios (i.e., ratio between the ensemble and the baseline models) and improved calibration compared to ILI forecasts. Our findings support the integration of virological surveillance data in forecasting target definition to improve the reliability of epidemic forecasts and strengthen their utility for situational awareness, communication, and targeted intervention.

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative approaches beyond traditional epidemiological models. This study introduces an advanced method for automated model discovery using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, leveraging a dataset from the COVID-19 outbreak in Thuringia, Germany, encompassing over 400,000 patient records and vaccination data. By analysing this dataset, we develop a flexible, data-driven model that captures many aspects of the complex dynamics of the pandemics spread. Our approach incorporates external factors and interventions into the mathematical framework, leading to more accurate modelling of the pandemics behaviour. The fixed coefficient values of the differential equation as globally determined by the SINDy were not found to be accurate for locally modelling the measured data. We therefore refined our technique based on the differential equations as found by SINDy, by investigating three modifications that account for recent local data. In a first approach, we re-optimized the coefficient values using seven days of past data, without changing the globally determined differential equation. In a second approach, we allowed a temporal dependence of the coefficient values fitted using all previous data in combination with regularization. As a last method, we kept the coefficients fixed to the original values but augmented the differential equation with a small neural network, locally optimized to the data of the past week. Our findings reveal the critical role of vaccination and public health measures in the pandemics trajectory. The proposed model offers a robust tool for policymakers and health professionals to mitigate future outbreaks, providing insights into the efficacy of intervention strategies and vaccination campaigns. This study advances the understanding of COVID-19 dynamics and lays the groundwork for future research in epidemic modelling, emphasising the importance of adaptive, data-informed approaches in public health planning.

AimsWe aimed to examine public perceptions of sharing various types of health data relevant for AI development, including electronic health records, audio recordings of consultations, medical images, and genetic information, with actors from either the public or the private sectors. MethodsWe analysed data from 38,740 participants of the Health in Central Denmark survey conducted in 2024. Participants were asked whether they would share different types of health data with an AI solution in healthcare. Each participant was randomised to either of two versions of the scenario and question where the AI application was developed in the public or private sector. Descriptive results (proportions and percentages) were weighted to represent the background population of approx. 1 million people in the Central Denmark Region. The association between randomization group (data recipient) and data sharing attitude ("Yes", "No", "Dont know") was analysed using multinomial logistic regression with "Dont know" as reference category. ResultsParticipants were most willing to share medical images (46%), followed by text from patient journals (39%), genetic information (35%), and audio recordings (27%). There were 12-16% higher proportions of willingness to share with public institutions than with private institutions. A high level of uncertainty was observed for all data types (29-36%) regardless of data recipient. Odds ratios ranged from 1.37 to 1.78 for responding "Yes", and from 0.51 to 0.67 for responding "No" to sharing data with public institutions compared to private institutions. ConclusionsPublic acceptance of health data sharing for AI depends on both the perceived sensitivity of the data and the institutional context of use. Strong public governance, transparent safeguards, and clear communication about data use may be important for maintaining trust and enabling responsible development of AI in healthcare.

Background: The increasing availability of routinely collected health data offers new opportunities for population-level research, yet access to comprehensive, linked, and standardised datasets remains limited. We describe EST-Health-30, a large-scale, population-representative health data resource from Estonia. Methods: EST-Health-30 comprises a random 30% sample of the Estonian population (~500,000 individuals), with longitudinal data from 2012 to 2024 and annual updates planned through 2026. Individual-level records are linked across five nationwide databases, including electronic health records, health insurance claims, prescription data, cancer registry, and cause of death records. A privacy-preserving hashing approach ensures consistent cohort inclusion over time while maintaining pseudonymisation. All data are harmonised to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (version 5.4) using international standard vocabularies. Data quality was assessed using established OMOP-based validation frameworks. Results: The dataset contains rich multimodal information on diagnoses, procedures, laboratory measurements, prescriptions, free-text clinical notes, healthcare utilisation, and costs, with high population coverage and longitudinal depth. Data quality assessment showed high completeness and consistency, with 99.2% of applicable checks passing. The age-sex distribution closely reflects the national population, supporting representativeness, though coverage is marginally below the target 30% (29.2%), primarily attributable to recent immigrants without health system contact. The dataset enables construction of detailed clinical cohorts, analysis of disease trajectories, and evaluation of healthcare utilisation and outcomes across the life course. Conclusions: EST-Health-30 is a comprehensive, standardised, and population-representative real-world data resource that supports epidemiological, clinical, and methodological research. Its alignment with the OMOP CDM facilitates reproducible analytics and participation in international federated research networks, while secure access infrastructure ensures compliance with data protection regulations.

BackgroundInitial reports from the Netherlands indicate a decline in routine childhood vaccination uptake during and after the COVID-19 pandemic, with emerging evidence of reduced parental vaccine confidence. This study aimed to evaluate the long-term impact of the COVID-19 pandemic on routine childhood vaccination uptake. MethodsWe conducted a retrospective nationwide cohort study including all children born in the Netherlands in 2016-2024. First-dose DTaP-IPV vaccination status by age six months was obtained from the national immunisation register. National trends in vaccination uptake across pre-pandemic, pandemic, and post-pandemic periods were assessed using interrupted time series analyses. To further assess the independent effect of the pandemic, a matched-sibling analysis compared vaccination uptake within families before, during and after the pandemic. ResultsInterrupted time series analyses showed significant immediate decreases in vaccination uptake both at the start and end of the pandemic, accompanied by a continuing downward trend during the pandemic (OR 0.984, 95%CI 0.982-0.985) that further declined after its end (OR 0.995, 95%CI 0.994-0.997). In the matched-sibling analysis children eligible during and after the pandemic had lower odds of being vaccinated (pandemic: OR 0.66, 95%CI 0.55-0.80; post-pandemic: OR 0.20, 95%CI 0.17-0.25) compared to their pre-pandemic siblings. Also, later birth order was associated with lower odds compared to first-born siblings (second-born: OR 0.42, 95%CI 0.37-0.48). ConclusionsBoth analyses indicate a negative impact of the COVID-19 pandemic on parental vaccination decisions, which may reflect lingering pandemic effects or new post-pandemic factors, highlighting the need for further research into the drivers of vaccination uptake changes in the post-pandemic era.

PurposeTo develop and validate a prediction model for sleep apnea syndrome (SAS) treated with continuous positive airway pressure (CPAP) in the general population. MethodsUsing claims and health checkup data held by JMDC Inc., linked to personal health records (Pep Up), we developed and internally validated a prediction model for SAS treated with CPAP, defined as a diagnosis of SAS and reimbursement records of CPAP. Every three months from January 1, 2022 to July 1, 2024 (i.e., 11 timepoints), we identified eligible individuals with available data both 1 year before and 1 year after that timepoint to define the presence/absence of SAS treated with CPAP, as well as 279 predictor variables. We developed a LightGBM model for the training and tuning datasets and evaluated its performance on the validation dataset. ResultsAmong 18,692,873 observations (mean age 44.8{+/-}11.3 years, women 37.5%) obtained from 1,858,566 people, 300,868 (1.6%) had SAS treated with CPAP. The area under the receiver operating characteristic curve was 0.898 (95% confidence interval 0.895-0.901). The positive predictive values among people with the top 1% and 10% prediction scores were 28.3% and 10.3%, respectively. According to the SHapley Additive exPlanations plot, male sex was the most important predictor, followed by age, body mass index, and waist circumference. We also demonstrated that personal health records significantly improved the predictive performance. ConclusionWe developed a prediction model to identify people at high risk of SAS and encourage them to undergo polysomnography or related tests.

AIMExecutive functions (EF) are advanced cognitive processes that play an essential role in daily functioning and may be of increased importance in cerebral palsy (CP), given the complexity of primary and associated impairments. This study aims to synthesize existing evidence on the relation between EF and domains of the International Classification of Functioning, Disability and Health (ICF) in individuals with CP, and to quantify the magnitude of these associations through meta-analysis. METHODA systematic literature search was conducted in eight electronic databases up to 14 July 2025, examining associations between EF and ICF domains in CP. EF outcomes were classified into inhibitory control, working memory, cognitive flexibility, higher-order EF, and EF composite scores. Outcome measures were mapped onto ICF domains: Body Functions and Structures, Activity, Participation, and Contextual factors, using the CP Core Sets. Correlation coefficients were transformed to Fishers z and entered into three-level meta-analyses to estimate pooled effect sizes. Single moderator analyses examined CP subtype, EF domain, EF assessment type, and mean age. Risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool. RESULTSFrom 4637 identified records, 38 studies were included, comprising a total sample of 1633 participants with CP. There was substantial heterogeneity in CP subtype, participant age, and EF conceptualization, while the ICF Contextual factors domain was underrepresented. A medium-to-large association was found between EF and functioning across all ICF domains combined (r=0.26, p<0.001). Domain-specific analyses showed a medium association of EF with Body Functions and Structures (r=0.21, p<0.01), a medium-to-large association with Activity (r=0.38, p<0.001) and Participation (r=0.26, p<0.01). CP subtype and mean age significantly moderated the overall EF-functioning association, with mixed CP and younger age associated with stronger effects. INTERPRETATIONEF are meaningfully associated with multiple domains of functioning in individuals with CP. These findings support the relevance of routine EF assessment and suggest that EF are an important cognitive correlate to consider when addressing broader aspects of daily functioning. WHAT THIS PAPER ADDSO_LIExecutive functions (EF) showed medium-to-large associations with all ICF domains in people with cerebral palsy (CP) C_LIO_LIThe strongest and most consistent associations were found between EF and ICF Activity C_LIO_LIOverall associations highlight the relevance of EF as a meaningful intervention target in CP C_LI

BackgroundDystonia is a debilitating movement disorder that is difficult to assess when co-existing with spasticity, as is typical in cerebral palsy (CP). Querying caregivers about their childrens movements is known to increase clinical dystonia identification. However, beyond identification, determining whether dystonia is the predominant vs. accompanying movement feature in a child with CP can guide clinical decision making, particularly regarding surgical candidacy. ObjectiveTo determine whether caregivers movement descriptions differed between children with predominant dystonia, predominant spasticity with accompanying dystonia, and predominant spasticity without dystonia. MethodsIn this cross-sectional study, we used conventional content analysis to codify caregivers descriptions of triggered involuntary movements in children with CP seen in a tertiary care CP center between 4/2023 and 12/2024. Movement feature frequencies were compared across tone types using Chi-square tests with Bonferroni corrections for multiple comparisons. ResultsOf 180 children with CP (mean age 9.2, 47.8% male), caregivers of children with predominant dystonia (50/180, 27.8%) more frequently described movements triggered by negative emotions (p<0.002) and affecting their back, trunk, and whole body (p<0.04). Caregivers of children with predominant spasticity with dystonia (99/180, 55.0%) more frequently described movements affecting a single limb (p<0.04). Caregivers of children without dystonia (31/180, 17.2%) described movements as being slight or small (p<0.008). These differences persisted even for caregivers unaware their child had dystonia (77/149, 51.6%). ConclusionsCaregivers movement descriptions differ between children with different combinations of dystonia and spasticity, which may help inform clinical management and guide communication with families about dystonia.

Objective Cerebral palsy (CP) affects approximately 1 million Americans and 18 million individuals worldwide, yet contemporary US epidemiologic data remains limited. We aimed to use Cerebral Palsy Research Network (CPRN) clinical registry to describe demographics and clinical characteristics of individuals with CP across the US and determine associations with gross motor function and genetic etiology. Methods Registry subjects were included if they had clinician-confirmed CP and prospectively entered data for Gross Motor Function Classification System (GMFCS) Level, gestational age, genetic etiology, CP distribution, and tone/movement types. Logistic regression was used to determine which of these variables plus race, sex, ethnicity, and age were associated with GMFCS level and genetic etiology. Results A total of 9,756 children and adults with CP from 22 CPRN sites met inclusion criteria. Participants were predominantly White (73.0%), male (57.3%), non-Hispanic (87.8%), and younger than 18 years (73.7%). Most were classified as GMFCS levels I-III (55.6%), born preterm (52.8%), had spasticity (83.8%), and had quadriplegia (41.9%); 12.2% were identified as having a genetic etiology. Tone/movement types, CP distribution, and gestational age were significantly associated with both GMFCS level and genetic etiology (p<0.001). Compared to White individuals, Black individuals were more likely to have greater gross motor impairment (p<0.001). Conclusion In this large US cohort, clinical and demographic factors, including race, were associated with gross motor function and genetic etiology in CP. These findings highlight persistent disparities and demonstrate the value of a national clinical registry for informing prognostication, quality improvement efforts, and targeted genetic testing strategies.

Despite the evolution of epidemiological analysis and modeling tools, difficulties still remain, especially in developing countries, regarding the availability and use of these tools. Often expensive, requiring high technical expertise, demanding constant connectivity of several or sometimes even significant resources, these tools, although efficient, present a major gap with the operational realities of health districts. It is in this context that we introduce Episia, an open-source Python library designed and conceived to provide a framework to facilitate epidemiological analysis and modeling. It integrates a suite of compartmental epidemic models (SIR, SEIR, SEIRD) with a sensitivity analysis using the Monte Carlo method, a complete biostatistics suite validated against the OpenEpi reference standard, as well as a native DHIS2 client for automated data ingestion. Developed in Burkina Faso, it is optimized and aims not only to address these health challenges encountered in Africa but also remains a versatile tool for global health informatics.

BackgroundAlthough the impact of COVID-19 vaccination is widely documented in the general population, the evidence on its effectiveness in children under 5 years of age is still limited. In this context, the continuation of vaccination programs in this age group has been debated globally. Consequently, we estimated the effectiveness of the 3-dose series of BNT162b2 (Pfizer-BioNTech) in children aged 6 months to 4 years and the complete 2-dose series of CoronaVac (Sinovac) in children aged 3 to 4 in reducing the risk of hospitalizations due to COVID-19-attributed severe acute respiratory infection (SARI) in Brazil. MethodsWe conducted a retrospective cohort study in 24 Brazilian municipalities, using surveillance data. We evaluated vaccine effectiveness in reducing the incidence rate of COVID-19-attributed SARI hospitalizations from July 2023 to December 2024. Covariate adjustments, defined a priori based on a conceptual model represented by a directed acyclic graph (DAG), were implemented using random-effects Poisson regression models. We also analyzed alternative vaccination schedules and obtained age-specific estimates of effectiveness. ResultsThe cohort comprised 37.6 million person-months of follow-up and 1,384 COVID-19-attributed SARI hospitalizations, including 27 associated deaths. The 3-dose series of BNT162b2 vaccine had an effectiveness of 97% (IRR 0.03, 95%CI 0.01-0.10) in the group aged 6 months to 4 years, with no significant differences among age-specific estimates. No deaths occurred among children who completed the 3-dose series, whereas four deaths were observed among those with fewer doses. The effectiveness of CoronaVac was small and not statistically significant (IRR 0.96, 95%CI 0.57-1.62). However, no deaths were recorded among children who received any number of CoronaVac doses, and no COVID-19-attributed SARI hospitalizations were observed among those who received a third dose of this vaccine. ConclusionsThe 3-dose series of the mRNA vaccine (BNT162b2) had high and consistent effectiveness in protecting against severe COVID-19 in children aged 6 months to 4 years. These findings support the maintenance of routine COVID-19 vaccination in this age group.

BackgroundSerotype 3 (S3) has remained a major cause of invasive pneumococcal disease (IPD) despite its inclusion in 13-valent pneumococcal conjugate vaccine (PCV). In October 2023, a 15-valent PCV (PCV15) including S3 was introduced into the Catalan universal childhood immunization program. MethodsWe conducted a retrospective pre-post surveillance study to compare pediatric IPD incidence in Catalonia during a pre-PCV15 period (October 1, 2022-September 30, 2023) and two post-PCV15 periods (October 1, 2023-September 30, 2024, and October 1, 2024-September 30, 2025). All IPD episodes in children <18 years attended in 34 hospitals were included. IPD was defined as detection of S. pneumoniae in a sterile site by culture or PCR. Results323 IPD episodes were identified in 319 children (mean age, 4.5 years). Overall IPD incidence declined from 13.0 to 9.4 episodes per 100,000 children in the first post-PCV15 period compared with the pre-PCV15 period (28% reduction; p=0.02), but returned to baseline in the second post-PCV15 period. S3-IPD incidence decreased significantly from 4.1 to 1.6 episodes per 100,000 (60% reduction; p=0.001) in the first post-PCV15 period and remained lower in the second period: 2.3 episodes per 100,000 (42% reduction compared with baseline; p=0.04). In contrast, IPD incidence caused by PCV7 serotypes increased from 0.3 in the pre-PCV15 and first post-PCV15 period to 2.7 episodes per 100,000 in the second post-PCV15 period (690% increase; p<0.001). ConclusionPCV15 introduction was associated with a sustained reduction in S3-IPD over two years. However, a marked increase in PCV7 serotypes offset overall gains in IPD incidence. SUMMARYPCV15 introduction in Catalonia achieved sustained reduction in serotype 3 invasive pneumococcal disease over two years, but a marked increase in PCV7 serotypes offset the overall disease reduction in the second post-vaccination year.

During the COVID-19 pandemic, reported incidence data played a central role in public health surveillance and in tracking epidemic dynamics, although they provide limited insight into the behavioral, immunological, and socioeconomic drivers of transmission.Population-based seroprevalence studies with linked survey data offer a rich but untapped source of individual-level information that can complement routine surveillance. In this study, we investigate whether aggregated seroprevalence cohort data can be leveraged to predict local COVID-19 incidence and to identify interpretable predictors associated with transmission dynamics. Using data from the Multilocal SeroPrevalence (MuSPAD) study in Germany (2020--2022), we trained multiple machine learning models, including least absolute shrinkage and selection operator (LASSO), vector autoregressive models (VAR), multilayer perceptrons (MLPs), and long short-term memory neural networks (LSTMs), to predict location-specific seven-day incidence rates. Feature importance was assessed using regression coefficients where applicable and model-agnostic explainability methods, including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). Across model classes, cohort-derived features enabled accurate prediction of local incidence, with time-aware models achieving the strongest performance. Consistent predictors included prior infection and testing history, employment-related changes, vaccination status, and mask-wearing behavior, highlighting the importance of behavioral and reporting-related signals. While differential privacy introduced modest degradation in predictive performance under strict privacy budgets, SHAP-based explanations remained stable, and LIME-based explanations were more sensitive to privacy-induced noise. These results demonstrate that aggregated cohort data encode meaningful and interpretable signals of population-level transmission dynamics. Population-based serosurveys therefore provide a complementary source of information for predicting local COVID-19 incidence and identifying key drivers of transmission beyond routine surveillance data. Our findings show that integrating interpretable machine learning with privacy-aware analysis enables actionable insights from sensitive cohort data, supporting their use in digital epidemiology and informing data-driven public health decision-making.

Widespread screening for Adolescent Idiopathic Scoliosis (AIS) is critical for early intervention, yet it is currently bottlenecked by the inherent limitations of traditional methods. Radiographic diagnosis poses cumulative radiation risks, while manual physical examinations are highly subjective and time-consuming. Recent non-invasive 2D computer vision approaches suffer from an unavoidable "dimensionality gap," failing to capture critical depth and rotational information, which frequently leads to diagnostic misjudgments. To address these challenges, we present PointScol, a high-sensitivity, radiation-free triage system leveraging direct geometric processing of 3D back surface point clouds. Our framework employs a sequential pipeline: first, an automated segmentation module rigorously standardizes the input geometry by isolating the dorsal region of interest; subsequently, a diagnostic classification module evaluates the spinal deformity. Validation on a multi-center dataset (n=128) demonstrated that for the primary screening task (10{degrees} Cobb angle threshold), PointScol achieved 100.00% sensitivity in the external cohort, acting as a reliable gatekeeper to safely rule out healthy individuals without missing any cases requiring referral. Building upon the robust accuracy established at this 10{degrees} baseline, an extended 5-class grading module provides further diagnostic value. Rather than functioning as a rigid predictive task, this multi-class stratification acts as an advanced clinical assistant, offering nuanced severity insights to guide referral urgency and optimize medical resource allocation for high-risk patients. Collectively, this sequential design establishes PointScol as a safe and highly efficient clinical filter: it reliably prevents unnecessary radiation exposure for healthy adolescents while ensuring prioritized interventions for those most in need.

The 1.8 million residents of Region Hovedstaden (Denmarks Capital Region) currently lack a secure, standardized pathway for integrating continuous wearable health data into Sundhed.dk, the national electronic health record. Consumer wearables such as Apple Watch, Oura Ring, and Garmin generate longitudinal physiological data relevant to chronic disease management, yet existing workflows rely on manual, non-standardized exports incompatible with FHIR DK v6.0.2 profiles and GDPR Article 25 privacy-by-design requirements. This paper presents a conceptual five-layer microservice architecture for secure wearable data sharing, employing MitID national authentication, National Service Infrastructure (NSI) integration, and Zero Trust security controls. Requirements were derived from a mixed-methods study including surveys of 47 Danish stakeholders and systematic benchmarking of existing platforms. Results show 51.1% conditional willingness to share wearable data under secure conditions, with audit transparency and non-medical misuse identified as central trust factors. Fourteen MoSCoW-prioritized requirements (F1-F7, NF1-NF7) are mapped to architecture components, providing a traceable blueprint for closing the interoperability gap in Danish public healthcare.