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.

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 ([≤]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.

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.

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.

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.

Widespread screening for Adolescent Idiopathic Scoliosis (AIS) is critical for timely intervention but is currently constrained by the radiation risks of X-rays and the subjectivity of physical examinations. Here, we present PointScol, a radiation-free triage system leveraging 3D back surface point clouds. To reconcile the conflicting clinical demands for "zero-miss" screening and "fine-grained" severity assessment, we developed a two-stage deep learning framework. First, an automated segmentation module extracts the dorsal region of interest (ROI) to standardize input geometry. Second, the system employs a dual-branch diagnostic strategy: a binary classification network designed for maximal sensitivity to rule out health, and a 5-class grading network designed to stratify severity (0-10{degrees}, 11-20{degrees}, 21-30{degrees}, 31-40{degrees}, >40{degrees}). Validation on a multi-center dataset (n=128) confirmed the distinct utility of this hierarchical approach. For the scoliosis screening task using a 10{degrees} Cobb angle threshold, the binary classification model achieved a sensitivity of 100.00% in the external cohort, ensuring that no cases requiring further clinical attention were missed. While the 5-class grading task inherently faces greater complexity, it successfully achieved an overall accuracy of 84.48% and, crucially, demonstrated a high specificity of 98.42% for severe surgical cases (>40{degrees}). This performance profile establishes PointScol as a safe clinical filter: the binary module reliably excludes healthy individuals, while the 5-class module flags high-risk patients for prioritized intervention, collectively offering a non-invasive, resource-efficient paradigm for scoliosis management.

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.

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.

Background: Human-to-human transmission of pathogens fundamentally depends on interactions among infectious and susceptible individuals, yet traditional population-scale models often overlook the stochastic, behaviour-driven, and highly heterogeneous nature of these interactions. Methods: Here, we develop a large-scale actor-based model capturing early epidemic dynamics of a novel respiratory pathogen on dynamic contact networks. We build these networks upon explicitly integrating detailed demographic and residential registry data from the Netherlands. The model simulates the Dutch population characterised by age, residency and mobility patterns, with actors interacting stochastically across households, workplaces and schools. Results: We show how the geographic and demographic profiles of initial cases impact transmission trajectories, with densely populated municipalities in the country's western core acting as key hubs driving epidemic spread. The framework enables rigorous assessment of intervention strategies incorporating behavioural adaptations. As case studies, we quantify the effects of symptomatic self-isolation and travel restrictions to and from major urban centres, highlighting their potential to modulate epidemic outcomes. Conclusions: Our findings underscore the necessity of integrating fine-scale human-to-human contact realism and population scale in epidemic forecasting and control.

IntroductionAtmospheric conditions under climate change increase pressure on healthcare systems. Especially, the intensive care units (ICU) are vulnerable due to low buffer capacity and high utilization rates. MethodsDaily ICU cases from 2009 to 2023 were derived from the German statutory health insurance data of eleven regional AOK insurances. Cases were stratified by age and sex. Generalized additive models were used to investigate the associations between daily ICU cases and lagged atmospheric variables. Thirteen intensive care relevant diseases were analyzed using disease-specific predictor sets. Analyses were conducted for regions derived from a human-biometeorological characterization of Germany. Model performance was assessed using (weighted) explained deviance. ResultsOver the 15-year study period, 9,970,548 ICU patients were recorded (44% women), 74.3% aged [&ge;]60 years. Trauma was the most common ICU-related disease, followed by non-ST elevation myocardial infarction (NSTEMI), pneumonia and ischemic stroke. ICU demand was most sensitive (p [&le;] 0.05) to pressure-related factors, thermo-physiological parameters and ozone concentration. In terms of sex-age differences, atmospheric factors affected men more frequently, while women were more impacted by cold weather and particulate matter (PM10). Heat was more relevant for patients aged [&ge;]60 years. The NSTEMI model in Central Eastern Germany performed best (weighted explained deviance of 49.3%). In males [&ge;]60 years, heatwaves were associated with a reduced risk of ICU cases (Relative Risk = 0.94, 95%-Confidence Interval 0.89 to 0.99). ConclusionThe study identified key atmospheric factors for ICU, enabling the German healthcare system to prepare better for short-term impacts of meteorological and air quality factors. KEY MESSAGESWhat is already known on this topic: O_LIThe atmospheric changes have a direct impact on public health and the inpatient care, particularly in intensive care units. C_LIO_LIConsequently, there is a necessity to investigate the influence of atmospheric factors on intensive care in order to prepare the healthcare system for the new circumstances. C_LI What this study adds: O_LIThe study provides evidence that atmospheric factors influence the intensive care in Germany and describes age and sex-specific aspects. C_LIO_LIThe results offer valuable insights into how different atmospheric factors affect the demand for intensive care in hospitals. C_LI How this study might affect research, practice or policy: O_LIThe study enables the German healthcare system to better prepare for short-term effects of atmospheric factors, and structural or resource-related adjustments could be made in hospitals to anticipate for short-term fluctuations in intensive care demand. C_LI

Students who frequently miss school are at greater risk for academic difficulty. High levels of absenteeism as early as kindergarten have been associated with long-term consequences, such as low reading proficiency in Grade 3 and low academic achievement in Grade 5, both of which have been associated with lower rates of high school graduation and enrollment in post-secondary education. The prevalence of school absenteeism has increased significantly since the COVID-19 pandemic and there have been sustained shifts in student attendance rates from kindergarten to Grade 12 since 2020. The goals of this population-level, repeated cross-sectional cohort study were to compare rates of chronic absenteeism, defined as being absent from school at least 10% of the time, in kindergarten in Canada before and after the onset of the COVID-19 pandemic, and examine the association between childrens chronic absenteeism and their concurrent developmental vulnerability. A total of 513,159 kindergarten children participated in the study, with 284,712 (55.5%) being in the pre-COVID-19 cohort (2017-2020) and 228,447 (44.5%) in the post-COVID-19 cohort (2020-2023). Across Canada, rates of chronic absenteeism increased from pre- to post-COVID-19, from 17.7% to 41.3%, with differences by jurisdiction. The greatest increase was seen in Ontario, while the smallest increase was seen in British Columbia. Children attending kindergarten in the post-COVID-19 cohort were three times more likely to be chronically absent compared to their peers attending kindergarten before the onset of the pandemic. Despite this, chronic absenteeism in the post-COVID-19 period was associated with reduced odds of overall developmental vulnerability, a pattern that is likely attributable to shifts in the composition of chronically absent children. In the post-COVID-19 cohort, a greater percentage of children who were chronically absent resided in higher SES neighbourhoods compared to their chronically absent peers attending school before the onset of the pandemic. While increasing rates of school absenteeism should not be ignored, our results suggest that chronic absenteeism following COVID-19 might be more nuanced than before. The jurisdictional differences in rates of chronic absenteeism observed in this study could be due to the various public health measures put in place by the various provincial and territorial governments. It is also possible that the children from higher SES neighbourhoods missed more school after the onset of the COVID-19 pandemic because their parents had the capability to work from home, making it easier to keep their child(ren) home from school. The decreased association between chronic absenteeism and developmental vulnerability post-COVID-19 may reflect improved access to online resources, which enables students to stay on track academically from home. Gaining a better understanding of the reasons behind missing school and the relation between absenteeism and academic achievement at various developmental stages is crucial to support successful learning trajectories.

BackgroundTwo RSV immunisations products: a maternal vaccine, Abrysvo, and a long-acting monoclonal antibody, nirsevimab, both designed to prevent RSV illness in infants, have recently become available. Modelling evidence is required to inform how to optimally use these products in immunisation programs to reduce the burden of RSV in young children. MethodsWe extend a dynamic transmission model calibrated to RSV-hospitalisation data of children aged < 5 years in temperate Western Australia (WA) to simulate a range of potential RSV immunisation programs. Using our model, we estimate the impact of both single-product and hybrid RSV immunisation programs. The analysis considers timing of administration, coverage levels and targeting of high-risk groups. Impact on RSV burden is analysed in the context of the WA setting and the possible significant cost differences between the two products. ResultsAll programs analysed were effective in reducing RSV burden. Programs using nirsevimab for newborn infants at similar coverage levels to the Abrysvo programs, averted more RSV-hospitalisations annually. Seasonal programs that focused on protection during high RSV activity and programs targeting high-risk infants were the most efficient in reducing RSV burden. When dose cost is considered alongside program impact on RSV burden, we find evidence to support further economic analysis of hybrid programs as they could mitigate the cost differential between the two products while remaining highly effective in reducing RSV burden. ConclusionsOur study is the first to comprehensively analyse hybrid RSV immunisation programs that use Abrysvo and nirsevimab. RSV immunisation programs can substantially reduce the burden of RSV in young children. Our modelling analysis provides evidence on immunisation type, timing, coverage, high-risk groups and dosage cost that will support decision makers and can be used in economic evaluations.

BackgroundHelmet use is a proven safety measure that reduces the risk of head injury among cyclists and e-scooter riders. Despite legal requirements for pedal bikes and e-bikes in Australia, compliance varies, particularly among users of electric vehicles. The growing popularity of e-bikes and e-scooters in urban areas presents new public health challenges, yet observational data on helmet use, behavioural determinants, and the effectiveness of safety interventions remain limited. AimPhases 1 and 2 aim to assess helmet use among e-bike, pedal bike, and e-scooter riders in Canberra, and evaluate the impact of health-benefit and legal-penalty signage on compliance. MethodsThis study employs a multi-phase, quasi-experimental observational design across three urban bike paths in Canberra. Phase 1 (Baseline): Helmet use will be recorded via discreet video surveillance, capturing vehicle type, estimated age group, gender presentation, and weather conditions. Phase 2 (Intervention): Two sites will receive signage emphasising either safety benefits or legal penalties, while a third site serves as a control; post-intervention observations will assess changes in helmet compliance. Expected ResultsBaseline helmet use is expected to be higher among pedal bike riders than e-bike and e-scooter riders. Signage interventions are anticipated to increase compliance, with potential variation by message type, vehicle type, and rider demographics. Trial RegistrationAustralian and New Zealand Clinical Trials Registry (ANZCTR) [ACTRN12626000245392]

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

BackgroundHelmet use is a proven safety measure that reduces the risk of head injury among cyclists and e-scooter riders. Despite legal requirements for pedal bikes and e-bikes in Australia, compliance varies, particularly among users of electric vehicles. The growing popularity of e-bikes and e-scooters in urban areas presents new public health challenges, yet observational data on helmet use, behavioural determinants, and the effectiveness of safety interventions remain limited. AimPhase 4 of the Helmet Use in Canberra study aims to identify demographic and behavioural predictors of unsafe riding and to explore perceived barriers and facilitators to helmet use, including compliance with existing regulations. MethodsA cross-sectional survey will be administered to Canberra residents aged 18 years or older, both online and in-person. The survey will assess attitudes toward helmet use, perceptions of head injury risk, and the deterrent effect of fines. Data will capture demographic characteristics, vehicle type, riding behaviours under varying conditions, and opinions regarding mandatory helmet laws and signage interventions. Survey responses will be de-identified, securely stored, and analysed using descriptive statistics and ordinal logistic regression to evaluate factors influencing compliance. Survey findings will be triangulated with observational and hospital data from earlier study phases. Expected ResultsThe survey is anticipated to provide insights into public attitudes toward helmet use, the perceived effectiveness of fines as behavioural deterrents, and the acceptability of policy interventions. These findings will inform evidence-based strategies to improve helmet compliance and reduce head injuries among urban riders. Trial RegistrationAustralian and New Zealand Clinical Trials Registry (ANZCTR) [ACTRN12626000245392].

BackgroundHelmet use is a proven safety measure that reduces the risk of head injury among cyclists and e-scooter riders. Despite legal requirements for pedal bikes and e-bikes in Australia, compliance varies, particularly among users of electric vehicles. The growing popularity of e-bikes and e-scooters in urban areas presents new public health challenges, yet observational data on helmet use, behavioural determinants, and the effectiveness of safety interventions remain limited. AimPhase 3 of the Helmet Use in Canberra study aims to characterise head injury presentations associated with cycling and e-scooter use and examine their association with helmet use and injury severity. MethodsDe-identified emergency department records from The Canberra Hospital will be retro-spectively analysed for presentations involving cycling or e-scooter-related head injuries during the study period. Extracted variables will include age, sex, vehicle type, documented helmet use, injury diagnosis, severity indicators, and date/time of presentation. Descriptive statistics will summarise injury patterns, while regression analyses will evaluate associations between helmet use and injury severity, controlling for demographic and contextual factors. Sensitivity analyses will address missing helmet data and subgroup differences by vehicle type, age, and gender. Expected ResultsIt is hypothesised that lower helmet use will correlate with higher rates and greater severity of head injury presentations. Findings will provide a population-level perspective on helmet effectiveness, inform local injury prevention strategies, and guide public safety interventions. Trial RegistrationAustralian and New Zealand Clinical Trials Registry (ANZCTR) [ACTRN12626000245392]

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.

Work-related stress is a major public health issue affecting workers across various sectors. Individuals experiencing work-related stress are more likely to consume psychoactive substances, primarily alcohol, tobacco, and cannabis, as well as psychoactive medications, which may be used as coping mechanisms. Work-related stress is also associated with adverse outcomes such as burnout, depression, anxiety, and sleep disorders. In France, early childhood professionals, including "ATSEMs", "animateurs", and "agents dentretien", play a crucial role in the education, care, and well-being of children but are exposed to high levels of occupational stress due to the emotionally demanding nature of their work and the associated physical strain, making them vulnerable to substance use, burnout, depression, anxiety, and sleep disorders. This cross-sectional epidemiological study, conducted at a single time point, will be carried out among early childhood professionals working in schools for children in Orleans Metropole, Communaute de Communes des Terres du Val de Loire (CCTVL), and Fleury-les-Aubrais. Ethical approval for this study was obtained from the Ethics Committee of the Centre Hospitalier Universitaire dOrleans (assigned reference number is CERO 2511-02). The study aims to provide a better understanding of the relationship between work-related stress and the use of psychoactive substances and medications among early childhood professionals, as well as the association between work-related stress and burnout, depression, anxiety, and sleep disorders. Data will be collected anonymously using self-administered online questionnaires, accessed via a QR code printed on flyers distributed to participants. The same QR code will also provide access to an information sheet explaining that the study complies with ethical guidelines and that proceeding implies non-objection to participation. Based on calculations performed using BiostaTGV, a sample size of 265 participants is required. Statistical analysis will be conducted using SPSS software. Studying these associations is essential for informing the development of targeted interventions and prevention.

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.

BackgroundWe recently developed a general egg count framework to support cost-efficient survey design choices to inform soil-transmitted helminthiasis (STH) control programs. Yet, the interpretation and the application was not always intuitive for program managers. MethodsWe first adapted the existing framework to make the interpretation of risks of incorrect decision making more intuitive and to allow for prior information. Then, we assessed the impact of the allowable risk of incorrect decision-making and prior information on the required sample size. Finally, we determined the most cost-efficient survey design to inform the decisions (i) to switch to an event-based deworming program, and (ii) to declare STH eliminated as a public health problem (EPHP). Principal findingsThe required sample sizes increased when the allowable risk of incorrect decision reduced and when the mean prior approached the program prevalence threshold. For the decisions to switch to event-based deworming and to declare EPHP, we found that duplicate Kato-Katz thick smears on a single stool sample was the most cost-efficient survey design, particularly when particularly when accounting for the added benefits of the free internal quality control. The required sample size for these survey designs varied between program targets and STH species. When aiming to have one sample size that fits all STHs, we recommend sampling 6 schools and 56 children per school for decisions on switching to event-based control programs and 11 schools (74 children per school) for the decision to declare EPHP. Conclusions/significanceWe developed an intuitive sampling framework for setting-specific decision-making in STH control programs. We identified the most cost-efficient survey designs for critical program decisions, but these are based on subjective but reasonable choices regarding the risk of incorrect decision making. Reaching consensus within the STH community on acceptable levels of risk is crucial to further support evidence-based decision-making. Author summaryWe recently developed a general computer simulation framework to support cost-efficient survey design choices for the control of intestinal worms. However, its interpretation was not always intuitive and it did not allow incorporation of prior knowledge on the prevalence of infections that programs might have. In this study, we adapted our framework to make the risks of incorrect decision-making more intuitive to interpret and to incorporate prior information on worm prevalence. We then quantified how different risk tolerances and prior prevalence assumptions affected required survey designs. Using this framework, we then identified the most cost-efficient survey designs for two key program decisions: switching to event-based deworming and declaring elimination of intestinal worms as a public health problem. We found that lower tolerance for incorrect decisions and greater uncertainty around prior prevalence substantially increase required sample sizes. Across the different program decisions and worm species, examining duplicate Kato-Katz thick smears from a single stool sample was consistently the most cost-efficient design, with the added benefit of internal quality control. Our results provide practical guidance for designing surveys tailored to local settings and highlight the importance of reaching consensus on acceptable levels of decision-making risk to support evidence-based STH control.