Eurosurveillance

Background Differential contributions to transmission across age groups have been reported for many respiratory infections, including SARS-CoV-2. They are crucial for estimating the impact of age-specific interventions. Disentangling these age-dependent contributions remains challenging, as they may reflect differences in contact rates, biological susceptibility, or infectiousness. Aim We aim to jointly estimate age-specific per-contact infectiousness and susceptibility and their effect on the impact of age-specific interventions. Methods The age-specific infectiousness and susceptibility were jointly estimated in a Bayesian framework by combining contact data with transmission pair data (who-infected-whom). We applied this approach to 197,840 self-reported household transmission pairs collected in the Netherlands during the COVID-19 pandemic. Using these estimates, we projected the expected impact of school closure and work-from-home measures during the early stages of an epidemic in the absence of other interventions. Results Both infectiousness and susceptibility to SARS-CoV-2 infection were lowest in children aged 0-9 years and highest in adults over 30 years old, with 2- to 4.5-fold differences between these groups. Projected impacts of age-specific interventions indicated that school closures would reduce the reproduction number by 8% or 29% when age-specific susceptibility and infectiousness were or were not considered, respectively. Conversely, working-from-home policies would lead to reductions of 41% with and 20% without age-specific infectiousness and susceptibility. Conclusion Our method enables robust estimation of age-specific infectiousness and susceptibility. Accounting for these age heterogeneities is essential for projecting the impact of age-targeted interventions. Our approach is adaptable to other respiratory infections and can guide more tailored public health responses.

Introduction: Respiratory infections are a leading cause of morbidity. Newly available vaccines to prevent respiratory syncytial virus (RSV) disease and encouraging clinical progress on vaccines for human metapneumovirus (hMPV) and parainfluenza (PIV) could reduce the disease burden beyond existing influenza and SARS-CoV-2 immunisation programs. However, evidence on the contribution of these viruses to respiratory disease burden across the lifespan remains limited. Methods: We reviewed studies from 01/2002-11/2025 reporting age-stratified, medically attended cases of influenza, and at least one of RSV, hMPV, or PIV, in high-income countries, excluding periods substantially overlapping with the COVID-19 pandemic. Using only studies that tested for all four viruses, we estimated the age-specific proportion of cases that were non-influenza (total across RSV, hMPV and PIV) compared to influenza using a mixed-effects logistic regression model. Results: Following exclusions and screening, 61 studies were included in the primary analysis comprising >500,000 detections of the four viruses. We found that a substantial proportion of medically attended respiratory illness in infants and young children was due to PIV, hMPV and RSV, rather than influenza, with a non-influenza virus proportion of 90.2% (95% CI 85.9-93.2%) in young infants aged 0-6 months. The converse was true for school-aged children, with a non-influenza virus proportion of 34.8% (95% CI 26.5-44.2%) in children aged 5-18 years. In adults aged 65+ years, non-influenza causes of medically attended disease were common at 60.2% (95% CI 50.0-69.5%). Restricting to studies reporting hospitalised cases (n=19) produced broadly similar age-specific trends in relative virus burden contributions. Discussion: We highlight the significant burden of medically attended illness due to PIV, hMPV and RSV across ages, particularly in infant and preschool-aged children and older adults, supporting the need for effective vaccines targeting this burden.

Background: Mass gathering events (MGEs) are associated with several public health challenges and may cause a strain on healthcare services. Literature findings on the impact of MGEs on emergency departments (EDs) are heterogeneous. Objectives: To examine shifts in ED attendance characteristics during a major sporting tournament, namely the UEFA European Football Championship 2024 held in Germany. Methods: We conducted a retrospective observational study using ED data from the Emergency Department Data Registry. We compared baseline ED attendance characteristics between the tournament and the reference period, defined as two weeks before and two weeks after the tournament, and between Germany game days and non-Germany game days. Hourly attendance patterns were analysed for all Germany games using a reference range. Results: We included data from 41 EDs, totalling 253,493 attendances during the study period. A 1.57% increase in attendance was observed during the tournament compared to the reference period, with baseline characteristics remaining similar. The median daily attendance within all EDs was slightly lower on Germany game days (4066) compared to non-Germany game days (4128). Modest changes were observed in the hourly attendance on Germany game days, most notable during the last Germany game where a decrease in attendance below the reference range extended over three hours. Conclusions: The observed shifts in ED attendance were minimal, suggesting that no major changes of public health relevance occurred in ED attendance during the tournament. We highlight the utility of using ED data for monitoring and for enhancing the understanding of the public health risks and challenges associated with MGEs.

Pooled testing programs were introduced during the COVID-19 pandemic to expand surveillance capacity while preserving testing resources, but evidence on their epidemiological impact in schools under real-world conditions remains limited. We analyzed data from the pooled testing program implemented in public primary schools of the canton of Basel-Landschaft, Switzerland, during the Fall-Winter 2021 Delta wave. We used an agent-based transmission model informed by pooled and individual testing results, school characteristics, contact networks, and community incidence. The model was fitted to pooled positivity ratios in four clusters of administrative areas with similar epidemic trajectories. We compared pooled testing with alternative protocols in terms of school transmission, testing volume, and student-days lost. During the study period, pooled testing was offered to 21'187 students across 62 public primary schools, with high and stable participation across clusters (mean 71-79%). The fitted model reproduced observed pool positivity trends well. Compared with pooled testing, reactive class closure, reactive screening, and symptomatic testing were associated with higher in-school transmission, with excess ranging from 50% to 87%, 63% to 104%, and 72% to 133% across clusters. Weekly individual screening achieved similar reductions in transmission but required 15-25 times more tests. Relaxing class closure after depooling substantially reduced student-days lost without increasing transmission. Under real-world conditions, pooled testing provided an effective and resource-efficient strategy to reduce SARS-CoV-2 transmission in primary schools. Combining early detection of asymptomatic infections with low testing demands, pooled testing offers a scalable approach to school surveillance and control for pandemic response in educational settings.

Introduction Care home (CH) influenza vaccination of staff improves resident health, yet uptake remains low at just over 11% (England, 2025/2026). We report an economic evaluation (EE) of "FluCare", an intervention to increase staff influenza vaccination through: vaccination clinics at CHs; promotional materials; and CH financial incentives. Method Seventy-five CHs were randomised to FluCare or control. A cost-consequence analysis took the influenza vaccination programme funder perspective, but also extended to the National Health Service (NHS) and CH perspective. Costs included: influenza vaccination; administration fee; FluCare components; CH resident NHS utilisation. Outcomes were: staff influenza vaccination rates; staff sickness; and resident mortality. Sensitivity analyses excluded intervention CHs that did not host vaccination clinics. Results Compared to control CHs, adjusted analysis found intervention homes with a mean absolute increase in vaccination rates of 1.8% (95% CI: -6.0%, 10.8%; p=0.572) at an increased cost of {pound}451 (95% CI: {pound}239, {pound}675; p<0.001) to the vaccination programme funders: {pound}249 per additional percentage point (PAPP) per CH. Vaccination clinics were delivered late in the influenza season, with 80% taking place from February 2023. Including only intervention CHs that hosted staff flu vaccination clinics (23/35), increases the mean difference to 10.1% (95% CI: 0.9%, 21.9%; p=0.018) and costs to {pound}805 (95% CI: {pound}603, {pound}1,079; p<0.001): {pound}79 PAPP per CH. Differences between trial arms in other costs and outcomes were marginal and generally non-significant. Conclusions FluCare delivered little improvement when staff flu vaccination clinics did not occur and had little impact on other costs/outcomes. Cost-effectiveness depends on willingness-to-pay for increased staff vaccination, but cost PAPP per CH improved from {pound}249 to {pound}79 when only CHs hosting clinics were considered. Late implementation, likely reduced impact by limiting clinic delivery, as reflected in sensitivity analysis. Future evaluations should implement FluCare earlier in the season.

Background Healthcare-associated infections pose a major burden to neonatal health worldwide and remain difficult to track in low-resource hospitals because patient movement data and pathogen genomic data are rarely integrated into actionable transmission models. Existing approaches are often restricted to specific settings, highly structured electronic health records (EHRs), or analyses focused on either patient movements or pathogen characteristics alone. To address this gap, we developed PathoPath, an open-source integrative modelling platform, and evaluated its utility in a high burden paediatric hospital in Dhaka, Bangladesh. Methods PathoPath is an open-source R package that combines electronic health records with whole genome sequencing data to generate contact networks from direct and indirect contacts using minimal structured inputs. We retrospectively applied PathoPath to 373 cases of Klebsiella pneumoniae species complex (KpSC) infection identified in 2021 at the largest paediatric referral hospital in Dhaka, Bangladesh. Ward level patient movement trajectories were used to reconstruct contact networks, and genomic data from isolates from children <60 days were integrated to identify probable dissemination of bacterial clones and antimicrobial resistance plasmids. Findings PathoPath identified 750 direct contacts among 317 patients, forming 25 connected components, with the largest including 93 patients. KpSC infections were identified across 21 of 37 wards, with the neonatal intensive care unit accounting for 77.9% of all cases. Integration of genomic and network data distinguished sustained clustering of ST147 from multiple probable inter-clonal dissemination events involving IncFII plasmids carrying blaNDM-5 and/or blaOXA-181 within ST16. Four dominant sequence types accounted for 65.6% of sequenced isolates, and carbapenemase genes were detected in 95.8%. Interpretation PathoPath reconstructs hospital-wide contact networks and integrates them with pathogen genomics to map probable dissemination of pathogens and antimicrobial resistance using minimal structured clinical data. It could support more targeted infection prevention and control in hospitals where granular digital records are not available.

Background Large-for-gestational-age (LGA) and macrosomic newborns are at increased risk of adverse perinatal outcomes, including death, yet the burden of neonatal mortality associated with these conditions in low- and middle-income countries (LMICs), where ongoing nutritional and epidemiological transitions suggest their prevalence will rise, remains poorly quantified. In this study, we quantify the neonatal mortality risk associated with LGA and macrosomia from 16 subnational birth cohorts in low- and middle-income countries between 2000 and 2017. Methods and findings This is an individual-participant meta-analysis to estimate neonatal mortality rates (NMRs) and relative risks among LGA infants (>90th and >97th percentile birth weight-for-gestational-age using INTERGROWTH-21st) versus appropriate-for-gestational-age (AGA, 10th-90th percentile) infants. Macrosomic ([&ge;]4000 g and [&ge;]4500 g) neonates were compared with those weighing 2500 g-3999g. Missing birth weights were imputed using recalibration and multiple imputation methods. We used random effects meta-analysis to pool relative risks. Median prevalences of LGA >90th and >97th percentile were 5.3% (interquartile range 3.6-8.2) and 2.6% (IQR 1.3-4.5), respectively; macrosomia ([&ge;]4000 g and [&ge;]4500 g) prevalences were 1.0% (IQR 0.3-3.1) and 0.06% (IQR 0.0, 0.30), respectively. Mortality was highest among preterm plus LGA infants (61.3 per 1000). LGA infants in the >90th percentile had over twofold increased mortality compared with appropriate-for-gestational-age infants (RR: 2.46; 95% CI: 1.86-3.25), while >97th percentile infants had a higher risk (RR: 3.77; 95% CI: 2.50-5.69). Term LGA >97th percentile infants also showed elevated mortality (RR: 3.14; 95% CI: 1.58-6.22). For LGA >97th percentile, the risk was higher in the early neonatal period (RR: 2.71; 95% CI: 1.92-3.82) than late (RR: 1.69; 95% CI: 1.22-2.34). There was no overall association between macrosomia ([&ge;]4000 g) and neonatal mortality. Population attributable fractions were 7.2% for LGA >90th percentile and 0.4% for macrosomia ([&ge;]4000 g). Conclusions Neonatal mortality risks were elevated among LGA infants in low- and middle-income countries, particularly at extreme values (>97th percentile) and during the early neonatal period. Macrosomia showed weaker, less robust associations. Although LGA prevalence is currently low ([~]5%) and contributes less to neonatal mortality than small newborns, ongoing nutritional and epidemiological transitions suggest increasing prevalence. This highlights the need for strengthened surveillance, monitoring, and improved delivery planning to ensure that no population is left behind.

Background: Whole-population linked administrative data platforms provide an opportunity to generate evidence on early life multidimensional disadvantage to inform resourcing and service provision to families with complex needs. Methods: We used individual-level de-identified data from nine administrative data sources included in the Better Evidence Better Outcomes Linked Data (BEBOLD) platform. The population included all children born in South Australia between 2004-2011 (n=143,083), and their parents. We described the prevalence and distribution of multiple disadvantages affecting children from the 12 months before birth to age 5. Eleven domains of parental disadvantage were created: economic, education, access to services, mental health, substance misuse, smoking during pregnancy, domestic and family violence, health, child protection contact, justice system contact, and death. We investigated the concordance of our measure with an area-level socioeconomic measure used in government reporting. Results: One in two children (48%) were exposed to at least one disadvantage domain, and one in seven (14%) were exposed to three or more domains before age five. Economic disadvantage was most prevalent, affecting one in four (27%) children, of which 75% were exposed to additional forms of disadvantage. Substance misuse, domestic and family violence, and justice system contact were the least likely domains to occur in isolation. Only 54.4% who experienced five or more disadvantage domains were classified in the area-level socioeconomic measure's 'most disadvantaged' quintile. Conclusion: Early life exposure to parental disadvantage can be highly multidimensional. Measurement across different systems is important for informing coordinated service provision for families with complex needs.

The 2026 Bundibugyo Ebola outbreak in eastern Democratic Republic of the Congo (DRC) has already generated international spread to Uganda, raising concerns about further regional and international dissemination. Using International Air Transport Association origin-destination passenger flows, we assessed relative exposure to Ebola virus disease importation into Europe under six outbreak expansion scenarios reflecting plausible pathways of geographical spread, including cross-border transmission and amplification in highly connected regional capitals. Relative exposure patterns remained largely unchanged under localized transmission in eastern DRC and border-spillover scenarios. Expansion into South Sudan generated a first structural increase in importation pressure to Europe through the connectivity associated with Juba, while hypothetical amplification in Kampala, Kigali, and Kinshasa substantially increased importation pressure and reshaped exposure patterns across Europe. Across all scenarios, France, Italy, and the United Kingdom remained among the most exposed countries. Mobility-informed scenario analyses support preparedness as the geography of the outbreak evolves.

During the 2026 Bundibugyo virus disease outbreak in the Democratic Republic of the Congo and Uganda, we projected potential airline-mediated importation risk using contemporary airline network and an externally calibrated Ebola importation hazard. Effective-distance analyses identified major international hub countries, including Belgium, France, South Africa, Kenya, and the United Arab Emirates, as higher-probability gateways within 30 days. These early projections provide a reproducible framework for real-time international situational awareness, while emphasizing that importation risk does not imply local transmission risk.

Objective: The study aimed to describe the challenges, best practices, and lessons learned during meningitis vaccination campaigns conducted in the context of COVID-19 in Cameroon in 2020. Results: During the prevention campaigns, 3,460 individuals were selected. All were tested before the campaign (100%). Eight cases were positive, representing a positivity rate of 0.23% (8/3,460). The campaign was carried out using a fixed strategy in health facilities and prisons and a fixed-temporary strategy in communities. Most health areas received sufficient quantities of COVID-19 equipment for some items and insufficient quantities for others. No screening was done during or after the campaign. The main difficulties encountered were compliance with social distancing and the continuous wearing of gowns. The challenges faced were the screening of actors and the use of personal protective equipment. Lessons learned: aspects related to COVID-19 impacted the speed of the campaign. Vaccination coverage ranged from 91% to 140% in prisons on the one hand, and from 35% to 112% in the health areas surrounding prisons on the other. The campaign in the context of COVID-19 was effective. Compliance with barrier measures was not optimal due to difficulties encountered with aspects such as social distancing, continuous wearing of gowns, screening of participants during and after the campaign, and insufficient personal protective equipment.

Introduction Education is a core social determinant of health for children and adolescents. Unfortunately, academic achievement, health, and wellbeing of adolescents have decreased in many developed countries in the past decade. The purpose of the Wellbeing and Education linkages in school-aged children (WELL-ED) study is to examine associations of school absences and academic achievement with use of school-based and community-based health and social welfare services. In addition, we will assess user experiences and multi-sector services pathways of school-aged children for a better understanding of how the service system could respond to the needs of children. Methods and analysis WELL-ED is a large population-based study that combines register data on school absences and educational support from municipalities with register data on healthcare and social service use collected from wellbeing services counties in Finland. The study cohort includes all children who attended mandatory education in public schools in Southern Finland in school year 2023-2024. A smaller cohort of adolescents in school year 8 was invited to complete a user experience survey. The primary outcomes of this study are related to equity of service use. Ethics and dissemination The Regional Committee on Medical Research Ethics of the Helsinki and Uusimaa Hospital District (2803/2024) has approved the WELL-ED study protocol. For the survey, adolescents in year 8 and parents of adolescents younger than 15 provided informed consent. Results will be published in peer-reviewed journals, summaries will be sent to participating municipalities and wellbeing services counties and press releases will be written on key findings.

An ongoing outbreak of Bundibugyo virus disease (BVD) in the Democratic Republic of the Congo was deemed a public health emergency of international concern in May 2026. To prevent cross-border importation, many countries, including the United States, Canada, India, Thailand, and Kenya have already proposed containment strategies, and others are likely to follow suit. How well (or poorly) are screening and quarantine containment measures are likely to work? We leverage established epidemiological theory and develop a mathematical model of traveler screening and post-arrival quarantine for BVD to answer this question. We find that traveler screening via symptom screening or molecular testing will miss the majority of infected travelers, and should be complemented by post-arrival quarantine and monitoring of sufficient duration to detect those with long incubation periods. Our findings underscore the limitations of border screening and the importance of complementary measures like post-arrival quarantine to prevent local importation of BVD.

Background Despite existing road safety regulations, commercial motorcycle riders commonly referred to as "Boda Bodas" in Uganda continue to experience high rates of injuries due to road traffic accidents resulting from unsafe riding behaviours, contributing significantly to morbidity and mortality among both riders and passengers. Safe riding behaviours are less well documented, as well as factors associated with the observance of those behaviours. This study aimed to determine factors associated with safe riding behaviors for both boda-boda riders and their passengers in Kampala Central Division. Methods A cross-sectional survey study design was conducted using a convergent parallel mixed-methods design guided by the PRECEDE model. Quantitative data were collected from 424 riders through structured questionnaires administered by trained research assistants. Binary Logistic regression was used to determine the independent predictors of safe road riding behaviors, and Adjusted Odds ratios (AORs) have been reported. Data were analyzed using descriptive and inferential statistics, with a p-value <0.05 considered statistically significant. Qualitative data were collected simultaneously with quantitative data through in-depth semi-structured interviews with 10 passengers to capture perceptions of rider behaviors and safety practices. Thematic analysis was applied, and results were triangulated to highlight convergences and divergences between quantitative and qualitative findings, providing a comprehensive understanding of safety determinants for both riders and passengers. Results Of the 424 riders (mean rider age was 29.56 {+/-} 5.71), overall, 276 (65.1%) of riders exhibited unsafe riding behaviors. In the bivariate analysis with Logistic regression, predisposing factors (education, marital status, religion, and willingness to obey traffic regulations), and reinforcing factors (family encouragement) were significantly associated with safe riding behaviors. However, in the adjusted model, secondary (AOR=0.50; 95% CI:0.30-0.85) and post-secondary education (AOR=0.57; 95% CI:0.33-0.98), being married (AOR=0.56; 95% CI:0.34-0.91), Christian religion (AOR=2.98; 95% CI:1.63-5.47), willingness to obey traffic regulations (AOR=0.41; 95% CI:0.24-0.70), union advocacy (AOR=1.76; 95% CI:1.03-3.01), and well-maintained roads (AOR=1.65; 95% CI:1.07-2.55) were significant predictors of safe riding behaviors. Qualitative interviews further highlighted barriers to safety, including a lack of helmets, over-speeding, disregard for traffic regulations, and poor road infrastructure. Conclusions Rider and passenger safety is still low, interdependent, and influenced by multiple factors. Integrated interventions focusing on education, stronger families, religious affiliations, union safety advocacy, and stricter enforcement of traffic regulations are vital for enhancing safety for both riders and passengers.

Background Pediatric immunizations for Respiratory Syncytial Virus (RSV), including monoclonal antibodies for infants and vaccines for pregnant people, have become broadly available and can prevent severe RSV outcomes in infants. However, quantifying the impact of RSV immunization in prevention of severe pediatric illness at the population-level is limited by lack of RSV case surveillance data. The Massachusetts Department of Public Health (DPH) conducted a modeling analysis using routine public health surveillance data to estimate the state-level impact of new RSV immunization products on Emergency Department (ED) visits and hospitalizations in Massachusetts for highest risk pediatric groups. Methods A scenario projection tool, called R.Scenario.Vax, was utilized to simulate RSV-associated ED hospital encounters by age group in the context of newly available immunizations. ED visit and hospitalization data from the National Syndromic Surveillance Program (NSSP) during the time period 10/08/2017--10/19/2024 were analyzed, scaled to account for changes in RSV testing practices over time and missing encounter volume in historic data, and utilized to inform model fit of a "typical" RSV season. RSV immunization data from the Massachusetts Immunization Information System (MIIS) for the 2023--2024 and 2024--2025 RSV seasons informed high and moderate pediatric RSV immunization coverage scenarios and their impact was compared to a counterfactual reference scenario of no new immunizations. Median projections were quantitatively and qualitatively compared to observed 2024--2025 season data. Percent reduction in hospital encounters and encounters averted per 10,000 population were calculated for each scenario as compared to the reference. Results Projections for the youngest at-risk age groups showed significantly lower RSV-associated ED visits and hospitalizations during the 2024--2025 season for both high and moderate immunization coverage scenarios. Median projections for infants under 6 months old in the highest coverage scenario, wherein nearly all infants were immunized, showed 72.6% lower ED visits and 73.4% lower hospitalizations when compared to the reference scenario, equating to 262 ED visits and 85 hospitalizations averted per 10,000 population. Conclusions Our results support the use of modeling methods for public health insights and suggest that RSV immunizations for infant populations result in significantly lower RSV-related ED encounters in Massachusetts.

To understand the utility of healthcare facility-level wastewater surveillance (WWS) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is important to correlate wastewater SARS-CoV-2 RNA detection with the number of clinical infections. WWS for SARS-CoV-2 was performed at three skilled nursing facilities (SNFs) over 25 weeks. Electronegative membrane filtration (enMF) and Nanotrap(R) Magnetic Virus Particles (NP) virus concentration methods were compared. Extracts were tested by droplet digital polymerase chain reaction. Spearman's correlations ({rho}) between wastewater virus RNA concentrations and infection counts were calculated. From split wastewater samples, enMF recovered higher SARS-CoV-2 RNA concentrations than NP. Combining data from all facilities, the median concentrations were 53.0 versus 38.6 gc/100 mL for enMF and NP, respectively (p=0.001). Using enMF, correlations were moderate to strong at SNF A ({rho} ranged 0.67 to 0.86, all p-values <0.001). Weak to moderate correlations can be explained by the sampled manhole not representing the entire facility (SNF B, {rho} ranged 0.47 to 0.72, p-values ranged <0.001 to 0.12) and longitudinal data gaps from summer heat and equipment maintenance (SNF C, {rho} ranged 0.14 to 0.59, p-values ranged 0.52 to <0.01). WWS can be a valuable tool for tracking dynamics of SARS-CoV-2 infections in healthcare facilities.

Background: In the absence of high-resolution response data, exposure-response modelling often relies on aggregated low-frequency exposure data, leading to loss of high-resolution information. Mixed Data Sampling (MIDAS) from econometrics offers an alternative but is limited due to its inability to make high-resolution predictions, inflexible likelihoods and penalised nonlinear functions, and limited visualization options. We propose a mixed-frequency Distributed Lag Non-linear Model (mf-DLNM) which can eliminate the need to aggregate exposure data in environmental epidemiology and provide high resolution predictions for time series studies. Methods: We evaluated the inference and predictive performance of the mf-DLNM. To evaluate its ability to estimate exposure-response relationships, we applied mf-DLNM and same-frequency (sf)-DLNM using data from the West Midlands, UK. Additionally, we compared the predictive performance of mf-DLNM with sf-DLNM and MIDAS across nine regions of England. As MIDAS cannot predict at the resolution of the predictor (daily), we compared the predictive performance of mf-DLNM and MIDAS at weekly resolution. To test the model's ability to predict high temporal resolution risk (daily), we compared sf-DLNM (with access to daily mortality counts) with mf-DLNM (with access only to weekly mortality counts). Results: In the West Midlands example, mf-DLNM performed comparably to sf-DLNM in estimating daily risk of temperature on respiratory mortality. Furthermore, mf-DLNM and MIDAS exhibited similar performance for weekly predictions. For high-resolution predictions, mf-DLNM and sf-DLNM showed nearly similar performance, despite mf-DLNM having access only to low-resolution response data. Conclusion: This mixed-frequency approach in environmental epidemiology overcomes the limitations of predicting health risks using aggregated exposure data and provides estimates of high-resolution outcomes in the absence of high-frequency health outcome datasets.

Abstract Background. Area-level cancer disparities are routinely estimated from public county data in which rates based on small counts (fewer than 16 cases or deaths) are suppressed. Analysts typically drop suppressed counties (complete-case analysis). Because suppression depends on case counts tied to population size and demographic composition, this missingness may be informative, but its effect on the disparity estimate has not, to our knowledge, been quantified. Methods. In a cross-sectional ecological study of 3,143 U.S. counties (analytic sample 3,018 with computable exposure) using one frozen public release of NCI State Cancer Profiles incidence and mortality data and ACS 2018-2022 5-year data, we estimated the most- versus least-deprived ICE(race+income) quintile rate ratio (RR) and rate difference for female breast, stomach, and cervix cancers under four suppression-handling methods: complete-case, available-case, bounding, and model-based small-area estimation. We characterized which counties were erased, and, following the ADEMP framework, ran a Monte Carlo simulation (1,000 replicates per cell; Monte Carlo standard error of bias approximately 0.0025) calibrated to the release to measure bias against a known truth. Analyses were pre-registered. Results. The suppressed fraction rose with rarity: 7.4% of counties for breast, 61.3% for stomach, and 75.7% for cervix incidence. Suppression was concentrated in the most-deprived quintile (cervix, 81.8% suppressed vs 63.8% least-deprived) and overwhelmingly removed rural rather than minority residents (cervix: 81% of the rural but 9% of the minority population erased). For breast (little suppression) the RR was 0.87 (95% CI 0.85-0.89) and identical across methods; for cervix incidence the complete-case RR (1.56) exceeded the model-based estimate (1.50), and for cervix mortality (91% suppressed) complete-case (1.86) exceeded model-based (1.56) by 16% with a wide bounding interval (1.88-2.62). In calibrated simulation, population-weighted complete-case bias was small (less than 2%) at the observed deprivation-county-size correlation and grew with rarity, threshold, and unweighted aggregation; its direction was conditional, becoming positive (over-estimation) as deprived counties became smaller. Conclusions. Complete-case handling of suppressed counties over-estimates rare-cancer area disparities relative to methods that retain them, while silently erasing most of the rural and most-deprived communities the estimate is meant to represent. The effect is negligible for common cancers and grows with rarity. Public-data disparity analyses should report the suppressed fraction and use bounded or model-based estimates by default. Keywords: cancer disparities; small-count suppression; Index of Concentration at the Extremes; informative missingness; small-area estimation; rural health.

Seroprevalence surveys reveal the extent of humoral immunity against pathogens such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and under some circumstances represent cumulative incidence of prior infection. However, antibody waning - or seroreversion - biases these estimates by reducing assay sensitivity in a time-varying manner. Because assay sensitivity decays over time, naively using serosurveys can substantially bias estimates of SARS-CoV-2 cumulative incidence and fatality rates. The Bayesian assay-specific, time-varying sensitivity adjustment developed in this paper can reliably correct for this bias and account for the delay between infection and serosurvey. In seroprevalence studies conducted in the United States in 2020, adjusting for time-varying sensitivity increased cumulative incidence by up to 1.4-fold, with an adjustment of 1.08 for a national study. Our estimates contrast with a previously published 2-fold adjustment that did not account for assay design. This suggests that previous analyses overestimated cumulative incidence by applying seroreversion corrections that did not account for assay-specific effects, or underestimated cumulative incidence by not applying seroreversion corrections. These biases imply fatality rate underestimation and overestimation, respectively. Our model provides a framework for design-specific time-varying sensitivity corrections in seroprevalence surveys for other pathogens.

Vaccines to prevent infant group B streptococcus (GBS) disease are advancing, with licensure likely based on safety and immunologic endpoints rather than clinical efficacy data. This approach requires robust, generalisable serological thresholds of risk reduction (SToRRs). We combined data from six case-control studies in Europe and Africa to define SToRRs for early-onset (EOD) and late-onset (LOD) GBS disease. Across diverse epidemiological and healthcare settings, anti-capsular polysaccharide IgG concentrations were consistently higher in infants who remained disease free than in those who developed disease. Higher antibody concentrations were required to reduce the risk of EOD than LOD, and higher concentrations were required for serotype Ia than for serotype III. This study provides a quantitative framework to support correlates-based evaluation and potential licensure of maternal GBS vaccines.