Swiss Medical Weekly

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.

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.

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.

OBJECTIVE: Anthropometric data are critical in paediatric care, routinely assessed during clinical visits, and available in electronic health records (EHRs). We describe the feasibility of extracting anthropometric data from heterogeneous EHR systems of Swiss childrens hospitals, evaluate their availability and quality, and assess the cohorts representativeness of the general population. METHODS: In this multicentre study (SwissPedGrowth), we retrospectively collected EHRs from patients <20 years who visited hospitals in Basel, Bern, Geneva, Lausanne, Luzern, St. Gallen, or Zurich between 2017-2023. Sociodemographic, administrative, and clinical information from EHRs were provided in a standardized way by a paediatric national data stream (SwissPedHealth), including the Swiss Neighbourhood Index of Socioeconomic Position (Swiss-SEP). We counted anthropometric recordings per visit to describe availability and used a self-developed and an existing (growthcleanr) algorithm to investigate data quality. To assess representativeness, we compared sociodemographic characteristics between SwissPedGrowth and the general paediatric population in Switzerland, computed standardized differences (effect size: 0.2 small, 0.5 medium, 0.8 large), and weighted the study population to reduce differences. RESULTS: We included 477,531 patients and 2,171,633 hospital visits; 54% boys, 71% Swiss, mean Swiss-SEP 65 (SD: 11), and median age at visit 6.3 [IQR: 2.3, 11.8] years. Height recordings were available for 20% of the visits, weights for 43%, and head circumferences for 5%, with better availability for inpatient stays than outpatient or emergency visits. Combining the self-developed and existing algorithm, 4% of heights and 3% of weights were flagged as outliers and 29% of heights and 31% of weights as carried forward from previous visits or same day duplicates. Sociodemographic differences between SwissPedGrowth and the general population were small or small-to-medium and disappeared after weighting. CONCLUSION: SwissPedGrowth demonstrates feasibility of extracting high-quality anthropometric data for paediatric growth research, but challenges regarding completeness and harmonization of EHR data across Swiss hospitals remain.

Cameroon introduced Human papilloma virus vaccine (HPVV) into the routine immunization schedule in October 2020. By the end of 2022, coverage remained low. To increase coverage, Cameroon switched to a country-wide, gender-neutral vaccination (GNV) approach in 2023, coupled with a revamped delivery strategy consisting of Community Dialogues (CDs) and Periodic Intensification of Routine Immunization (PIRIs) activities in selected health districts (HDs). We assessed the impact of these programmatic changes, notably the GNV approach, on HPVV coverage. This retrospective, cross-sectional study measured the effect of GNV and CDs + PIRIs on HPVV coverage among 9-year-old girls in Cameroon (2022-2023). Data on HPVV coverage from all 203 HDs were extracted from DHIS2, and coverage was calculated at the HD level, based on the estimated population eligible of 9-year-old girls. Descriptive statistics and multiple regression models were employed to assess the impact of GNV on vaccination coverage while adjusting for CDs + PIRIs and urban/rural status. In 2023, of the 203 HDs, 115 (56.7%) conducted GNV only, 74 (36.5%) implemented GNV & CDs + PIRIs, and 75.9% (154) were classified as rural. Among age-eligible girls, there was an overall increase in HPV vaccination coverage, with coverage rising 39.2 percentage points from 2022 to 2023. Following multiple linear regression, there was a significant increase in HPVV coverage in HDs with GNV & CDs + PIRIs compared to those with no GNV and no CDs + PIRIs ({beta}:55.5%, 95%CI: 38.7, 72.3, p=0.000). Furthermore, there was a significant increase in HPVV coverage in HDs with GNV only compared to those with no GNV or no CDs + PIRIs ({beta}:28.7%, 95%CI: 12.5, 45.0 p=0.001). Overall, the GNV approach increased HPVV coverage for girls significantly, particularly when implemented alongside CDs + PIRIs.

Dystonia treatment evaluation in cerebral palsy (CP) is limited by the lack of clinician-assessed scales linking dystonia severity to functional impact. We asked 7 pediatric movement disorder specialists to review videos of 27 children with CP while performing an upper extremity task and while walking. Experts rated arm and leg dystonia severity using the Global Dystonia Severity Rating Scale (GDRS) and task-specific functional impact on a five-point scale adapted from the Dyskinetic Cerebral Palsy Functional Impact Scale. Arm GDRS scores correlated with functional impact on the upper extremity task (linear regression R^2=0.48, p=0.0005). Leg GDRS scores correlated with gait impact (R^2=0.43, p=0.001). A four-point increase in total GDRS corresponded to a one-point worsening in combined functional impact. By demonstrating how expert-rated limb dystonia severity correlates with task-specific functional impact in children with CP, these results could help clinically identify functionally-meaningful differences in dystonia severity.

Lightweight epidemic calculators are widely used for teaching and rapid scenario exploration, yet many omit the methodological detail needed for scientific reuse. We present a browser-native SIR calculator that exposes forward Euler and classical fourth-order Runge--Kutta (RK4) integration alongside epidemiologically interpretable outputs and a population-conservation diagnostic. The implementation is anchored to analytical properties of the deterministic SIR system, including the epidemic threshold, the peak condition, and the final-size relation. Benchmark experiments show that RK4 is essentially step-size invariant over practical discretizations, whereas Euler at a coarse one-day step overestimates peak prevalence by 3.97% and final size by 0.66% relative to a fine-step RK4 reference. These results demonstrate that browser-based tools can support publication-quality computational narratives when solver choice, diagnostics, and assumptions are treated as first-class outputs.

Background: Understanding the underlying mechanisms for differences in vaccine uptake between migrants and non-migrants is crucial in order to design targeted interventions encouraging vaccination and to ensure vaccine-related equity. Therefore, this study examined to what extent migration-related disparities in COVID-19 vaccination were associated with psychological factors, based on the established 5C model of vaccine behaviour (Confidence, Complacency, Constraints, Calculation, Collective Responsibility). Methods: Data were obtained from the German study "Corona Monitoring Nationwide - Wave 2" (RKI-SOEP-2 study), which was carried out between November 2021 and March 2022. The association between COVID-19 vaccination and migration status, while considering the psychological factors, was investigated using multivariable binary logistic regressions. A decomposition analysis (Karlson-Holm-Breen method) was conducted to examine the extent to which migration-related disparities in vaccine uptake were associated with the psychological factors of the 5C framework. Results: Migrants were less likely to be vaccinated against COVID-19 compared to non-migrants, especially participants from the Middle East and North Africa (MENA) region. Our decomposition showed that almost two-thirds of the disparities in COVID-19 vaccine uptake between migrants and non-migrants were associated with the psychological factors (first-generation: 61.2%, second-generation: 64.2%). Confidence in safety of the vaccine was the most relevant factor in the 5C framework. Furthermore, the results highlighted the importance of a differentiated analysis regarding country of origin: While the 5C model accounted for only 19.4% of the difference between participants from the MENA region and non-migrants, the proportion for participants from Eastern Europe was 73.5%, suggesting that the underlying mechanisms for the lower uptake in the MENA group need further investigation. Conclusions: Overall, migration-related disparities in COVID-19 vaccination were significantly associated with differences in psychological factors of vaccine behaviour. To increase vaccine acceptance within the heterogeneous group of migrants in general, tailored and proactive health communication interventions are needed.

Background: Dengue is rapidly emerging in parts of Europe. How households value vector control attributes, and whether inferences depend on decision models or message framing, is unclear. Methods: We conducted a split-ballot online experiment among adults in Italy and France, as well as a hotspot subsample from Marche, Italy. National samples included 1,505 respondents in Italy and 1,501 in France; 183 respondents were recruited in Marche. Participants were randomised to a discrete choice experiment (random utility maximisation) or a regret-based choice experiment (random regret minimisation) and to one of three pre-task messages (control, loss aversion, community values). Each respondent completed 12 choice tasks comparing two dengue control programmes and an opt-out. We estimated mixed logit and mixed random-regret models with random parameters and treatment effects. Results: Across frameworks, nearby cases and high mosquito prevalence were the dominant drivers of programme uptake, whereas cost and operational burden were secondary. In pooled analyses, loss-aversion messaging increased the weight on high mosquito prevalence in both models (from 0.483 to 0.547 in the utility model; from 0.478 to 0.557 in the regret model). Cost effects were small nationally but larger in the hotspot subsample. Conclusions: Risk salience dominates preferences for dengue vector control in these European settings. Random utility and random regret models yield consistent rankings of attributes but differ in behavioural interpretation and some secondary effects; messaging effects were modest and context dependent.

In dense urban settings, delays between diagnostic sampling and effective isolation can sustain transmission during peak infectiousness. We define a waiting-window transmission externality arising when infectious individuals remain mobile while awaiting results, formalized as E = N{middle dot}P{middle dot}TR{middle dot}D, where N is daily testing volume, P test positivity, TR transmission during the waiting period, and D turnaround time. Using Monte Carlo simulation and a susceptible-infectious-recovered (SIR) framework, we quantify excess infections per 1,000 tests/day under multiple diagnostic workflows. A surge scenario incorporates positive coupling between TR and D ({rho} = 0.45), reflecting co-occurrence of laboratory saturation and elevated contacts during system stress. Under centralized 48-hour workflows, excess infections reach [~]80 at P = 10% and [~]401 at P = 50%, increasing to [~]628 under surge conditions. In contrast, near-patient rapid testing and home sampling reduce this to [~]5 and [~]25-26, respectively. Workflows that eliminate the waiting window--either through immediate isolation at sampling or through home-based PCR that returns results at the point of collection--effectively collapse the transmission term. These findings identify diagnostic delay as a modifiable driver of epidemic dynamics. Operational redesign of testing workflows, including decentralized sampling and home-based molecular diagnostics, offers a scalable pathway to improve epidemic controllability and reduce inequities in dense urban environments.

Background Social contact surveys, which measure who-contacts-whom, are widely used to inform infectious disease transmission models and estimate the reproduction number (R), a key metric for assessing epidemic risk. Despite their widespread use, sample size calculations are not routinely performed. Aims To assess the impact of sample size on estimates of R and determine a practical target sample size for social contact surveys used in epidemic modelling. Methods We conducted a review of social contact surveys (2008-2025) to characterise current practice. We characterised the impact of survey size on epidemic metrics using two social contact surveys, the UK Social Contact Survey and POLYMOD (Europe) and two methods. For each dataset and approach, we generated repeated subsamples and calculated the resulting reproduction numbers, characterised their distributions and measured uncertainty. Results We identified 107 unique social contact surveys from 57 studies. Sample sizes ranged from 30 to more than 10,000 participants, with a median of 1,438. One quarter of surveys contained fewer than 1,000 participants. From our simulations, we find that sample sizes below 200 individuals can result in highly variability reproduction numbers. Increasing sample size increases precision, and the most meaningful gains are up to 1,300 individuals. Increasing sample sizes over 3,000 individuals leads to smaller gains. Conclusions A minimum sample size of approximately 1,200-1,300 participants appears sufficient for general-purpose use. These findings support the inclusion of sample size considerations in the design, reporting and interpretation of social contact surveys used for epidemic intelligence and public health decision-making.

Methods: Using Global Burden of Disease (GBD) data, we analyzed prevalence, incidence, mortality, and disability-adjusted life years (DALYs) rates across global and 21 GBD regions from 1990-2023. Joinpoint regression identified temporal trends, age-period-cohort models analyzed effect contributions, Das Gupta decomposition quantified demographic and epidemiological impacts, inequality indices assessed health equity, and Bayesian models projected 2024-2038 trends. Results: In 2023, the global number of children and adolescents with asthma reached 131 million, with an age-standardized prevalence rate (ASPR) of 1,789.9 per 100,000. From 1990 to 2023, the global ASPR and age-standardized incidence rate (ASIR) of asthma in children and adolescents showed an upward trend, while the age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years (DALYs) rate (ASDR) exhibited a downward trend. Among the 0-14 age group, the disease burden was greater in males than in females, whereas in the 15-19 age group, males had a lower disease burden than females. Projections indicate that over the next 15 years, the overall disease burden will continue to decline; however, female mortality rates and DALYs rates are projected to show an upward trend. Conclusions: The increasing prevalence and incidence rates, coupled with declining mortality and DALYs rates of asthma among children and adolescents globally, underscore the necessity for targeted public health interventions. These findings provide crucial insights for early diagnosis, treatment optimization, and global health policy formulation.

BackgroundThe World Health Organization has developed several global template protocols for epidemiological investigations, including for household transmission investigations (HHTIs). These investigations facilitate rapid characterisation of novel or re-emerging respiratory pathogens and support evidence-based public health actions. Beyond technical readiness, community buy-in is central to the feasibility and acceptability of HHTIs. Research is needed to determine the perceived legitimacy among the community to inform local protocol adaptation and development of implementation plans that consider community attitudes and needs. MethodsIn 2025, we conducted a convenience survey of community members living in Victoria, Australia to explore: their understanding of emerging respiratory diseases; their willingness to take part in public health surveillance activities such as HHTIs; the acceptability of clinical and epidemiological data collection and respiratory/blood sample collection as main components of HHTIs, and; participant comfort towards including their companion animals in HHTIs. ResultsWe received 282 survey responses, of which 235 were included in the analysis dataset. Compared to the general Victorian population, our participants included a higher proportion of participants who reported being female, tertiary-educated, of Aboriginal and/or Torres Strait Islander heritage, born in Australia and speaking only English at home. Participants indicated overall high levels of comfort and acceptability towards participation in HHTIs, particularly in relation to clinical and epidemiological data collection, with lesser but still high levels of comfort with providing multiple respiratory specimens in a 14-day period. Participants were least comfortable with other specimens such as urine and blood. Involving companion animals in HHTIs was similarly acceptable as human-focused components. ConclusionsDespite our survey population being non-representative of the general Victorian population, our findings provide valuable descriptive insights into the acceptability of HHTIs in Victoria, Australia from which to benchmark future local and international surveys and community engagement activities.

In October 2025, mpox virus clade I infections have been detected among men who have sex with men (MSM) in the EU/EEA, suggesting local transmission in MSM sexual networks. Given the large outbreak of mpox among MSM in 2022 and the uncertain transmission parameters of clade I in the European context, clade I poses a public health concern to the EU/EEA. This work assesses the potential effect of increasing the mpox vaccine uptake among MSM via two contributions. First, building on the European MSM and Trans Persons Internet Survey 2024, we estimate the mpox vaccine uptake among MSM as well as the proportion who are unvaccinated but willing to get vaccinated for 28 countries in the EU/EEA. Specifically, we fit Bayesian mixed-effects models for the vaccine and recovery status of an individual depending on their number of sexual partners and country. Second, we develop a susceptible-infectious-recovered model on a sexual contact network to estimate the reduction of the reproduction number if vaccines are provided to MSM who are willing to get vaccinated. Our results suggest a substantial willingness for mpox vaccination among MSM if mpox cases increase and a large reduction of the effective reproduction number if this willingness is met. These findings highlight a large potential of increasing mpox vaccine uptake among MSM and preventing future mpox outbreaks in the EU/EEA.

Scorpionism is a public health concern in warm regions, particularly affecting children under 10 years old. Timely treatment with antivenom, provided free by the Brazilian Unified Health System, at strategic care points (PEs) is crucial to prevent avoidable deaths. Our study focused on the Sao Paulo state (SP), which has the largest population in Brazil. The objectives were to adapt a network analysis method suited to SPs context; to assess the efficiency of the SP PE network coverage, considering the 90-minute response time; and to determine the ideal number of vials to be stored at each PE. After adapting the healthcare network analysis, we applied spatial coverage models to evaluate the adequacy of PE response times. We also estimated the demand for antivenom vials at each PE based on Notifiable Diseases Information System data from 2021 to 2023, which is currently limited to the state level. We identified 12 areas lacking coverage, of which only one was suitable for a new PE. The estimated serum requirements aligned with SP's current distributions. However, the estimation carried out according to the PEs has the advantage of reducing the risk of antivenom shortages, especially in emergencies, thus ensuring timely care to prevent avoidable deaths. Our adapted method and PE serum estimates can enhance the scorpion sting care system by supporting geographic planning and optimizing resource allocation. Moreover, these findings and methodologies have potential applicability to other Brazilian regions and warm countries facing similar challenges, contributing to improved access and outcomes for scorpionism victims.

Sylvatic Yellow Fever (YF) is an infectious mosquito-borne disease with significant epidemiological relevance due to its widespread distribution and high lethality for human and non-human primates, particularly in tropical regions of the planet such as in Brazil. Identifying regions and periods of high environmental suitability for the occurrence of YF is essential for preventing or mitigating its burden, as it enables the efficient allocation of surveillance efforts, prevention, and implementation of control measures. Environmental modeling of YF occurrence has proven to be an effective approach toward this goal; however, its effectiveness strongly depends on the modeling framework's capabilities as well as the spatial and temporal precision of all associated data. We propose a fine-scale geospatial modeling of YF environmental suitability that is based on a generative machine-learning ensemble method built on a large set of high-resolution environmental covariates. First, we take the spatiotemporal statistical description of the environment of each of the 545 YF cases from 2019--2024 up to 30 m/monthly resolution at three buffer scales: 100 m, 500 m, and 1000 m ratios. Then, we perform a feature selection and train hundreds of One-Class Support Vector Machine submodels to form a robust ensemble model, whose predictions are projected to a 1x1 km resolution grid of Brazil under several metrics, exceeding seven million ensemble evaluations. The predictions ranked the Southern Brazil region with the highest mean suitability for YF, with a level of 0.64; Southeast comes next with 0.46, followed closely by Central-West region (0.44), North (0.39), and finally Northeast (0.28). The model exhibited high uncertainty for the North region, indicating that data collection efforts are much needed in this region. As for the environmental covariates, a feature analysis pointed out that Land use and cover accounts for the largest influence in the model output.

BackgroundThe rapid growth of the worlds urban population has contributed to the expansion of informal urban settlements in many cities across the world. In these settings, lack of safe sanitation combined with high population density and poverty contributes to heightened health risks for often vulnerable populations. The aim of this study was to evaluate the effect of a shared, onsite sanitation intervention on the nutritional status of children in Maputo, Mozambique. MethodsThe Maputo Sanitation (MapSan) trial was a controlled before-and-after study to evaluate the effect of a shared, onsite sanitation intervention on child health in Maputo, Mozambique. Here, we report the effects on childhood stunting, wasting and underweight, and height-for-age, weight-for-height and weight-for-age z-scores. Children were enrolled aged 1-48 months at baseline and outcomes were measured before and 12 and 24 months after the intervention, with concurrent measurement among children in a comparable control arm. The primary analysis was intention-to-treat. The trial was registered at ClinicalTrials.gov, number NCT02362932. ResultsWe enrolled 757 and 852 children in the intervention and control groups respectively. There was no evidence for an effect of the intervention on any outcome at 12 or 24 months of follow-up except for wasting where there was very weak evidence for an effect (adjusted prevalence ratio: 0.497; 95% CI: 0.22-1.11; p=0.09). In two exploratory analyses - one including only those children born into compounds post-intervention and a second excluding children in control compounds which had independently improved their sanitation facilities during follow-up - we found that stunting increased in the intervention group whilst wasting decreased. ConclusionsThis study contributes to the growing evidence on the role of sanitation in shaping child health outcomes in informal urban settlements. We found no evidence for an effect on stunting and weak evidence for an effect on wasting. More research is needed to understand how sanitation can reduce childhood undernutrition in complex urban environments.

Background: Since September 2024, France has implemented a national reform allowing prescription-free access (PFA) to sexually transmitted infection (STI) screening in medical biological laboratories (MBLs). This study aims to characterize the populations undergoing STI testing according to their access modality and evaluate the probability of test positivity in relation to testing pathway, sex, and age groups. Methods: We conducted a cross-sectional analysis of all individuals screened for Chlamydia trachomatis, Gonorrhoea, human immunodeficiency virus (HIV), hepatitis B virus (HBV), and syphilis by treponemal-specific immunoassay (TSI) in Cerballiance MBLs between Mars 2025 and February 2026. Multivariable logistic regression models stratified by sex and adjusted for age and region assessed associations between screening modality and STI positivity. Results: Among 1,008,737 individuals included, 27.8% were under PFA and 72.2 under prescription-based access (PBA). PFA users were more frequently male (47.4% vs. 36.3%, p<0.001) and aged 20-39 years (34.0%, p<0.001). Overall positivity rates differed by modality: PFA was associated with higher detection of Chlamydia (4.6% vs. 3.6%). PBA group showed more positive cases of syphilis (3.4% vs. 1.2%), HBV (1.3% vs. 0.4%), and HIV infections (0.3% vs. 0.2%, all p<0.001). Co-infection and gonorrhoea proportions did not significantly differ between modalities. Conclusions: PFA substantially increased STI screening uptake, particularly among young adults and men, and enhanced detection of bacterial STIs. PBA remains essential for diagnosing viral and chronic infections. These findings highlight the complementary roles of both access strategies and support PFA screening as an effective public health intervention to broaden STI detection and reduce transmission.

Electronic health records (EHRs) are a rich source of data which can be used to analyse health outcomes using computable phenotypes. With the approval of NHS England we used the OpenSAFELY secure analytics platform to design and assess phenotypes to classify three key respiratory viruses - respiratory syncytial virus (RSV), influenza, and COVID-19 - in English coded health data between September 2016 and August 2024. We compared specific and sensitive phenotypes to one another and to publicly available surveillance data. Cases from both phenotypes showed similar seasonal patterns to surveillance data. Sensitive phenotypes led to increased risk of misclassification than specific phenotypes for mild cases. For severe cases the risk of misclassification was higher in infants than for older adults, irrespective of the phenotype used. The phenotypes presented here offer a solution to classifying respiratory viruses from coded health records in the absence of testing information.