International Journal of Hygiene and Environmental Health

Introduction: Pesticide exposure has been associated with poorer mental health, yet little is known about whether seasonal fluctuations in agricultural pesticide use are associated with cyclical changes in mood among people living in agricultural communities. We evaluated associations between 10 peak and off-peak pesticide spray seasons and ecological momentary assessment (EMA)-derived mood in a floricultural region in Ecuador. Methods: 496 participants completed repeated EMA surveys across 10 assessment administrations over 2.5 years (2022-2024), including one single-assessment administration and nine 10-day bursts (Nobservations=14,332). Mood outcomes included negative affect (sadness, anxiety, stress, anger, tiredness, difficulty concentrating; overall mean=2.79, Standard Deviation [SD]=1.32), positive affect (happiness, motivation; overall mean=4.21, SD=1.60), and behavioral dysregulation (aggression and impulsivity, overall mean=1.86, SD=1.29). Generalized linear mixed models estimated pooled, between-person and within-person associations between peak pesticide spray seasons and mood, adjusting for demographic and longitudinal factors Effect modification by sex, floricultural occupation, and cohabitation with a floricultural worker was assessed. Results: Peak spray seasons were associated with higher negative affect ({beta}=0.11 [0.06, 0.16]) and behavioral dysregulation ({beta}=0.10 [0.05, 0.15]) but not positive affect ({beta}=0.01 [-0.05, 0.07]). Associations were driven by within-person changes. Sex modified associations with negative affect, which were stronger in males than females. Floricultural occupation modified associations, with poorer mood, greater behavioral dysregulation and lower positive affect among floricultural workers than non-workers. Discussion: Peak pesticide spray seasons were associated with worsening of negative affect and behavioral dysregulation. EMA may provide a sensitive approach for detecting short-term neurobehavioral changes associated with seasonal pesticide exposure.

Objective Reducing household air pollution exposure during gestation and infancy may have longer-term benefits for child linear growth. We examined whether a liquefied petroleum gas (LPG) cookstove and fuel intervention delivered during gestation and infancy could improve height-for-age Z scores (HAZ) and reduce the risk of stunting at ages 2-5 years in India, Rwanda, and Guatemala. Materials and method The Household Air Pollution Intervention Network (HAPIN) was a randomized controlled trial that enrolled ~3200 pregnant women across Guatemala, India, Peru, and Rwanda. Women in the intervention group received an LPG cookstove and free fuel during pregnancy and until the infant reached 12 months of age. Following the HAPIN trial, we continued following children in Guatemala, India, and Rwanda (N=2169) and measured child height at ages 2, 3, 4, and 5 years (HAPIN 2.0). As prespecified in the study protocol, we used mixed-effects models, as well as linear and log-binomial regression, within the intention to treat (ITT) framework, to evaluate the interventions effect on HAZ and stunting. Subgroup analyses were performed to assess effect modification by child sex, maternal height, and intervention country. Results The intervention reduced gestational exposures to particulate matter (PM2.5: 80.7 vs 117.5 {micro}g/m3), carbon monoxide (CO: 1.3 vs 2.0 ppm), and black carbon (BC: 8.6 vs 12.1 {micro}g/m3) and infancy exposures (PM2.5: 36.9 vs 113.2 {micro}g/m3; CO: 0.60 vs 2.2 ppm). Post-trial PM2.5 exposure were similar between arms: 99.9 vs 98.7 {micro}g/m3. We found no statistically significant effect of the intervention on repeated measures of HAZ ({beta} = -0.06, 95% CI: -0.16, 0.03 p=0.204), annual HAZ, stunting, nor severe stunting at any age during the follow-up. There was no statistically significant effect modification by subgroups. Conclusion Despite substantial reductions in air pollution exposures during the trial, the LPG intervention did not improve growth or reduce stunting risk from ages 2 through 5 years in this multi-country cohort. These findings, consistent with prior results of the lack of effects on growth through 12 months, provide additional evidence that a clean cooking intervention alone may not be sufficient to address childhood stunting in low-resource settings. Future exposure-response analyses will supplement these ITT analyses.

Background: Previous studies indicated that regional fluctuations in COVID-19 impact were associated with socioeconomic, demographic and environmental factors early in the pandemic. It is unclear whether factors associated with regional COVID-19 mortality and SARS-CoV-2 infections changed over the course of the pandemic. Methods: We evaluated the association of ecological factors with municipality-specific COVID-19 mortality in the national population of Austria between January 2020 and June 2023. Analyses were conducted separately per calendar year and pooled across years via random-effects meta-analysis. Outcomes were analysed using univariable and multivariable zero-inflated negative binomial (ZINB) regression, including annually averaged environmental exposome exposure (including air pollution and UV radiation), socioeconomic, demographic and infection dissemination predictors with significance threshold set to p<0.005. Secondary analysis modelled SARS-CoV-2 infections using of negative-binomial model (NB). Two additional approaches for smaller predictor set selection were applied as sensitivity analyses. Results: Within the entire observation period 18,430 COVID-19 deaths were reported. In univariable analyses (pooled RR (99.5% CI)), COVID-19 mortality was significantly associated with the proportion of commuters (0.79 (0.73-0.86)), residents aged 75-84 (1.18 (1.01-1.37)) and [&ge;]85 years (1.29 (1.04-1.60)), residents with at most compulsory education (1.15 (1.02-1.29)), employed residents aged 15-64 (0.89 (0.85-0.93)), and homeowners (0.91 (0.83-0.99)), as well as with precipitation (0.94 (0.89-0.99)) and the average time with a mean temperature of 5-15 C (0.95 (0.90-0.99)). In multivariable analyses the share of population aged 85 or older (1.30 (1.09-1.54)) and home ownership (0.92 (0.87-0.98)) were independently significant. Mortality predictor estimates were relatively stable across pandemic years. Across 5,963,882 documented SARS-CoV-2 infections share of employed in 15- to 64-year-olds (1.01 (1.01-1.02)), part-time employment rate (1.01 (1.00-1.02)) and district level nursing care allowance recipiency (0.97 (0.95-0.98)) were associated with detected infections. Overnight stays, population 85 or older, income, compulsory education or less and air pollution all had multiple significant estimates with opposite directionality, leading to non-significant overall associations. Sensitivity analyses outcomes mirrored main analyses outcomes. Conclusions: Throughout the pandemic in Austria, demographic and socioeconomic factors were associated with COVID-19 mortality and infections, but many associations, especially for infections, were unstable over time. These findings underscore the importance of structural vulnerabilities in epidemic impact.

Tick-borne disease risk reflects the combined influence of vector abundance, pathogen prevalence, human exposure, and seasonal activity, yet these components are rarely integrated at the county level. We developed species-specific ecological risk scores for three medically important tick species (Acari: Ixodidae): Amblyomma americanum (L.), Dermacentor variabilis (Say), and Ixodes scapularis Say, across all 102 Illinois counties using data from 2019 to 2022. County-level predicted tick abundance and pathogen prevalence were scaled and combined using equal weights to generate primary vector-level risk scores. Robustness to the equal-weight assumption was evaluated by systematically varying the relative weights assigned to abundance and pathogen prevalence, while a separate sensitivity analysis incorporated reported tick-bite incidence where available. Associations with human disease were evaluated using negative binomial regression of tick-borne disease case counts on the corresponding risk scores, with county population included as an offset. Monthly phenology indicators were constructed by integrating human disease cases, reported tick bites, and sampled tick abundance and were evaluated in relation to temperature and precipitation. Seasonal risk was estimated by combining county-level vector risk with species-specific monthly phenology, generating monthly risk surfaces that were deployed through an interactive R Shiny application. Higher risk scores were significantly associated with greater disease case rates for each tick species. County rankings were highly stable under moderate changes in abundance and prevalence weighting. Seasonal activity differed among species, with A. americanum and D. variabilis showing concentrated warm-season risk and I. scapularis exhibiting a broader seasonal profile. These county-level seasonal risk maps identify where and when relative tick-borne disease risk is elevated in Illinois, supporting targeted prevention, surveillance, and clinical awareness.

Introduction Despite the significant burden of Shigellosis, routine surveillance systems in resource-limited settings lack predictive capabilities to guide proactive public health responses. Consequently, we developed and evaluated the accuracy, uncertainty and computational cost of multi-horizon forecasting models for Shigella incidence among children <5 years in rural western Kenya using 16-year longitudinal incidence time-series. Methods We estimated monthly Shigella incidence using data from three pediatric enteric studies (2008-2024), supplemented with Kenya Health Information System during inter-study gaps. Incidence estimates incorporated sampling, healthcare-seeking, stool-testing, and pathogen-specific weights, with bootstrap-derived 95% confidence intervals. Predictors included Google Trends health-related indicators, climate, temporal trends, and seasonality. Forecasts at 1-, 3-, and 6-month horizons were evaluated using rolling-origin validation. Following horizon-specific predictor selection using Boruta, 14 state-space, machine-learning, and hybrid models were compared using point-forecast and probabilistic metrics, bootstrap confidence intervals, and Model Confidence Sets. Adaptive conformal prediction recalibrated prediction intervals, while Shapley Additive exPlanations assessed predictor contributions. Results Mean monthly Shigella incidence was 2.51 per 100 child-years (standard deviation=1.85). Shigella dynamics in Kenya can be forecast beyond seasonal persistence alone for up to six months reducing absolute errors by ~ 33%. However, optimal model architectures and predictor sets shift systematically across forecasting horizons. Temporal trend and recent incidence were the dominant predictors, while predictor importance shifted with forecast horizon toward syndromic, pharmaceutical, seasonal, and lagged environmental signals. Extreme Gradient Boosting performed best at 1 month (Root mean squared error [RMSE]=1.86), remained competitive at 3 months (RMSE=1.89), whereas exponential smoothing (ETS) performed best at 6 months (RMSE=2.05). Forecast accuracy declined with increasing horizon. ETS and TBATS provided the best-calibrated prediction intervals, whereas machine-learning models generally under-covered. Model Confidence Sets demonstrated horizon-dependent model distinguishability. Conclusion Horizon-specific model selection balancing predictive accuracy, uncertainty calibration, and computational feasibility could strengthen operational early warning systems for shigellosis.

Introduction: Raised blood pressure is an important cardiovascular risk factor in Cambodia; however, national averages may obscure substantial local geographic heterogeneity. We assessed the spatial distribution of raised blood pressure and its demographic, metabolic, behavioural, built-environment, and environmental correlates using the 2023 Cambodia STEPwise approach to noncommunicable disease (NCD) risk factor surveillance (STEPS) survey. Methods: We analysed 289 georeferenced primary sampling units (PSUs) from the 2023 Cambodia STEPS Survey. PSU-level case counts and numbers of adults with valid blood pressure data were modelled using a frequentist binomial generalised additive model with a two-dimensional Gaussian-process smooth. An explanatory model included selected PSU-level and contextual covariates; a separate mapping model used spatially continuous predictors to generate predictions on a 5-km grid. Design-based national and residence-specific prevalence estimates were calculated separately using the survey design and Step-2 weights. Results: Among 4,225 adults with valid blood pressure data, unweighted prevalence of raised blood pressure was 28.6%, compared with 18.8% after Step-2 weighting; weighted prevalence was 17.5% in rural areas and 21.7% in urban/suburban areas. Mean observed unweighted PSU-level prevalence was 29.0%. In the adjusted spatial explanatory model, the proportion aged 60-69 years ({beta}=1.969, SE=0.327; p<0.001) and overweight/obesity prevalence ({beta}=0.996, SE=0.265; p<0.001) were positive ecological correlates. The spatial model had lower Akaike information criterion and residual deviance than its non-spatial counterpart, although Bayesian information criterion was higher. Prediction uncertainty was greater in some sparsely sampled areas. Conclusions: Raised blood pressure showed significant geographic heterogeneity across Cambodia. Older age composition and overweight/obesity prevalence were positive PSU-level correlates, not individual-level causal effects. Predicted prevalence should be interpreted alongside uncertainty and should complement, rather than replace, design-based STEPS prevalence estimates for geographically informed NCD planning.

Vegetation plays a critical role in shaping fine-scale tick density by structuring local microclimates, habitat resources, and host interactions that largely determine tick survival and activity, with implications for tick-borne disease transmission. Tick burden in the United States continues to increase, with annual reported tick-borne disease cases more than doubling from 2004 through 2019. These trends call for a broader scope of tick-mitigation strategies that go beyond traditional prevention measures that focus on personal protection; and there is an opportunity to inform habitat-level interventions. Here, we quantify fine-scale vegetation structure across 48 subplots at tick surveillance sites in Western Massachusetts to assess local habitat features associated with Ixodes scapularis density and potential for human-tick contact risk. In assessing understory and overstory vegetation attributes, this study identified life stage-specific differences in predicted tick density, with higher adult densities in areas with deeper leaf litter and presence of invasive plant species and nymphs associated with high presence of ferns. Both adult and nymphal Ix. scapularis were predicted at higher densities in relatively low basal area forests (18.9 to 26.9 m2/hectare). This study design provides a simple, low-cost framework for organizations looking to incorporate tick-informed forest measurement ecology into their public health tick surveillance protocols with minimal training.

Total serum perfluorooctane sulfonate (PFOS) concentration and its isomer composition describe different aspects of internal exposure, but their respective associations with depressive symptoms remain uncertain. We examined adults aged 18-25 years in NHANES August 2021-August 2023 using survey-weighted models of the Patient Health Questionnaire-9 (PHQ-9). Total measured PFOS was the sum of linear PFOS and pooled monomethyl-branched PFOS; composition was their log-ratio. The analytic cohort included 169 participants, with 147 complete cases for core adjustment. Median total PFOS was 1.70 ng/mL, the median linear fraction was 72.2%, and the weighted prevalence of PHQ-9 [&ge;]10 was 22.6%. In the joint adjusted model, a doubling of total PFOS at fixed composition corresponded to -0.49 PHQ-9 points (95% CI, -1.65 to 0.68); a doubling of the linear-to-branched ratio at fixed total corresponded to 0.21 points (-1.94 to 2.36). Algebraic rescaling across the reported interquartile ranges yielded differences of -0.58 points (-1.95 to 0.81) for total PFOS and 0.13 points (-1.16 to 1.41) for composition. The inverse continuous-score association weakened with adjustment, whereas the screening odds ratio primarily lost precision. Excluding three observations with an isomer below detection shifted both joint coefficients. The data therefore do not establish an independent composition association or equivalent isomer effects. Experimental evidence supports candidate oxidative, inflammatory, and synaptic pathways, but differential protein binding and elimination prevent a serum composition ratio from being interpreted directly as brain dose or toxicity. Larger longitudinal analyses with isomer-resolved exposure and toxicokinetic measurements are needed to test these pathways.

Background: Privacy curtains represent a uniquely difficult infection control surface because they are frequently touched by patients, visitors, and healthcare workers, yet are difficult to disinfect using routine environmental cleaning protocols. We sought to quantify the performance of a novel automated aerosolized hydrogen peroxide system against pathogens on privacy curtains. Methods: This study was performed in an enclosed office space ({approx} 65 m^3), approximating a patient exam room. Four Bacillus atrophaeus (ATCC 9372) biological indicators (BIs) were affixed to a standard polyester privacy curtain. A single one-minute fogging phase was followed by a ten-minute contact/aeration phase (total = 11 min). Four corresponding chemical indicators measured peak airborne aerosolized hydrogen peroxide concentration, and two control BIs were placed outside of the room. Results: All four BIs demonstrated complete inactivation (no growth after seven-day incubation), representing [&ge;] 4-log10 spore reduction. Peak dosages reached 42 ppm nearest the aerosolized hydrogen peroxide jet outlet, with the lowest value at the near point on the back side of the curtain reaching 27 ppm (exceeding the target dosage of 25 ppm). Control exposures failed to achieve sterilization. Conclusions: A next-generation aerosolized hydrogen peroxide dry-fogging disinfection system achieved full inactivation of resistant spores on both sides of privacy curtains within a short 11-minute automated cycle.

Community-led urban greening may contribute to neighborhood safety, but its relationship with crime remains uncertain. We examined whether Keep Indianapolis Beautiful Project GreenSpaces in Indianapolis were associated with changes in violence and total crime. We used a matched-pair difference-in-differences design comparing funded sites with matched unfunded applicant sites. The primary analysis included 29 matched pairs with two years of pre- and post-grant follow-up. Monthly Part I violent offenses and total crime within 600-m buffers were modeled using Poisson pseudo-maximum likelihood. In the first post-grant year, the violent-crime estimate was lower but imprecise (IRR, 0.903; 95% CI, 0.805-1.014). In the second post-grant year, the relative rate was lower (IRR, 0.830; 95% CI, 0.718-0.961). Total crime estimates showed no clear post-grant change. Additional analyses indicated some sensitivity to spatial scale, but no sustained divergence in pre-grant crime patterns. Analyses of short-term crime dependence showed clear month-to-month persistence in violent crime within sites but limited evidence of stable spatial dependence across nearby sites. Therefore, within the two-year post-grant period examined, Project GreenSpaces was associated most consistently with lower relative violent-crime rates in the second year, while no corresponding decrease was observed for total recorded crime. Longer follow-up is needed to determine whether these patterns persist or change as projects mature.

Background Despite substantial improvements in maternal and child health over the past two decades, preventable maternal and neonatal morbidity and mortality remain major public health concerns in rural Bangladesh. Community-based interventions involving Community Volunteers (CVs), Community Groups (CGs), and Community Support Groups (CSGs) have been introduced to strengthen maternal and child health (MCH) services through health promotion, community engagement, and improved linkage with primary healthcare facilities. This study explored the roles, challenges, and opportunities of these community-based platforms in improving maternal and child health services. Methods A qualitative study was conducted in Kalaroa Upazila of Satkhira District, Bangladesh, between July and October 2020. Data were collected through focus group discussions and in-depth interviews with mothers, Community Volunteers, Community Group members, and Community Support Group members. Participants were purposively selected to capture diverse experiences from intervention and comparison areas. Interviews were conducted using semi-structured guides, audio-recorded with informed consent, transcribed verbatim, translated into English, and analyzed thematically using an inductive approach. Results Participants from intervention areas demonstrated greater knowledge of birth preparedness, antenatal care, pregnancy danger signs, institutional delivery, postnatal care, and newborn care than those from comparison areas. Community Volunteers, CGs, and CSGs played important roles in identifying pregnant women, conducting household counselling, promoting skilled maternal healthcare, facilitating referrals, and strengthening community participation. Participants perceived that these activities increased maternal awareness, improved health-seeking behaviour, and enhanced utilization of maternal and newborn health services. However, persistent barriers, including socio-cultural beliefs, financial constraints, transportation difficulties, shortages of skilled healthcare providers, and inadequate logistical support for community volunteers, continued to limit equitable access to quality services. Participants recommended strengthening community awareness programmes, improving community clinic readiness, enhancing referral systems, and providing regular training and supportive supervision for community-based health workers. Conclusions Community-based maternal health interventions involving Community Volunteers, Community Groups, and Community Support Groups contribute to improved maternal knowledge, health-seeking behaviour, and utilization of essential maternal and newborn health services in rural Bangladesh. Sustaining these gains will require stronger primary healthcare services, continued community engagement, and targeted strategies to address persistent health system and socio-cultural barriers. These findings provide important evidence for strengthening community-based maternal and child health programmes in Bangladesh and similar resource-constrained settings.

Background Urban inequities are often masked by the misleading notion of an urban advantage, despite persistent unequal access to quality healthcare services. Moreover, negative perceptions about the urban poor and slum-dwellers persist, despite their substantial role in driving and holding up the urban economy. It is thus essential to work with the media to bring visibility to these realities, raise public awareness, and inform more equitable health policies. In low- and middle-income countries (LMICs), evidence remains scarce on how health-related information from media outlets influences policy decisions. This study aimed to understand how journalists get information, engage with stakeholders in the health systems, and contribute to influencing urban health discourse in Bangladesh and Nepal. Methods We conducted a cross-country exploratory qualitative study comprising 30 key informant interviews from Bangladesh and Nepal, including 17 journalists, 10 health policymakers, and 3 public health advocates. Participants were purposively selected to ensure diversity across media types, gender, and professional roles. Interviews were conducted between September 2024 and March 2025. Data were transcribed, translated, and analyzed thematically using NVivo software. Cross-country comparative analysis was carried out to identify common themes and contextual differences. Findings Across both countries, participants relied on multiple and diverse information sources. Government data were often prioritized as a primary source of information, although they were shaped by political influences and accessibility constraints. Research evidence was valued but underutilized due to difficulties in interpreting scientific articles. Urban health issues received limited priority in health news reporting due to journalists constraints in covering multiple issues and financial constraints for in-depth coverage. Additional challenges included inadequate health and urban health-related training, limited fellowship opportunities for investigative reporting, and restricted access to data, while health policymakers and public health advocates frequently questioned the credibility and use of media-reported information. Media engagement in policymaking largely remained indirect and issue-focused or event-driven. However, consistent media coverage raised public awareness and generated public pressure and, at times, indirectly influenced health policy decisions such as immediate inspection after reporting on pesticides, installation of oxygen plants during the pandemic, dengue surveillance, and tobacco control measures and actions. Conclusion In Bangladesh and Nepal, news media play a significant but indirect role in shaping urban health policy decisions. Enhancing journalists capacity, improving access to credible information and data, and continuous engagement of stakeholders could strengthen evidence-informed urban health policymaking. Furthermore, establishing greater independence of journalists and government and community recognition of their responsible health reporting could help and motivate the media to contribute more meaningfully to public debate and policy reform processes.

Background Influenza vaccines have seen many changes over recent years in terms of both policy recommendations and product types. Immune responses and vaccine effectiveness vary by vaccine strain composition and product type. This study aimed to explore this variation on a regional level through examining spatial and temporal autocorrelations of vaccine uptake of different product types within Michigan from 2017 to 2021. Methods Influenza vaccine administration data from the 2017/18 to 2021/22 seasons were obtained from the Michigan Care Improvement Registry. Population size estimates for 2017 and 2019 were obtained from the Michigan Department of Health and Human Services. Age-standardized and age-stratified vaccination rates were computed. Disparate county-level vaccine uptake clusters were constructed using spatial autocorrelation indicators and spatiotemporal clusters were identified using scan statistics. Linear mixed effect models were used to study the impact of social vulnerability and urbanicity level on seasonal influenza vaccination rate. Results Vaccination incidence rates for adjuvanted, recombinant, and nasal sprays vaccines and for any vaccines increased significantly through the study period. County-level spatiotemporal clusters of type-specific uptake rates were identified. Most notably, we identified an emerging cluster with high uptake of adjuvanted vaccine radiating from Ottawa County, located in suburban Grand Rapids, and the disappearance of high uptake cluster for high-dose vaccine uptake in the area. Vaccination rates by any product type were found to vary seasonally and showed weak negative association with more rural regions. The overall vaccination rates appear to be lower among counties with higher social vulnerability in certain urbanicity levels, but social vulnerability and urbanicity were not significantly associated with overall vaccination rate during the study period. Conclusions Vaccine type-specific uptake was spatially and temporally diverse in Michigan between 2017 and 2022. Mid and Northern Michigan had consistently relatively lower vaccination rate compared to other regions in Michigan.

Background. Nigeria's reported Lassa fever case fatality ratio (CFR) rose from 18.9% at week 23 of 2025 to 25.0% in 2026, prompting concerns over failing clinical care. Because surveillance artefacts can also alter CFRs, candidate mechanisms that published data can resolve were identified and evaluated. Methods. This ecological study extracted weekly state-level counts from 474 Nigeria Centre for Disease Control and Prevention situation reports (2017-2026). Per a pre-specified plan, years were compared at phase-matched points, year-on-year CFR changes were decomposed into within-state and between-state components, and tipping-point ascertainment shifts calculated. Weekly incident and cumulative published totals were compared post hoc. Findings. Of 11 candidate mechanisms, 2 could be evaluated fully and 5 in part. On weekly incident counts, 2026 did not differ from 2025 at the phase-matched midpoint (+0.3 percentage points, 95% CI -5.0 to 7.6); within-state (-1.1, 95% CI -4.7 to 3.0) and between-state (+0.5, -2.0 to 2.9) components spanned zero. Conversely, the published cumulative CFR was 5.4 to 6.2 percentage points higher because 70 deaths were added retrospectively by week 36 outside weekly reports. Reconciling cumulative figures would require 279 missed survivors (6% of suspected cases not confirmed). Interpretation. The 2026 CFR rise came from deaths added to cumulative totals outside the weekly counts, and published data cannot attribute it to care. Endemic zoonotic CFRs cannot serve as clinical outcome measures unless testing volumes, deaths added late, and post-mortem sampling are published alongside case counts. Funding. US NSF-NIH-NIFA EEID; UK BBSRC; Birgitta Sintring Fellowship (Uppsala University).

To deal with the COVID-19 pandemic, Denmark implemented an aggressive program aimed at controlling the pandemic which included both testing and contact tracing programs. The large amount of data collected in Denmark on the pandemic and its integrated nature made it possible to investigate the effectiveness of the testing and contact tracing effort. The results of that investigation and a discussion of the effectiveness and limitations of the different control efforts are reported herein, as are lessons learned concerning possible improvements for the future. The analysis is limited to the period from the start of the COVID-19 pandemic in Denmark (late Feb. 2020) into May 2021, at which time the impact of vaccination intervention was already playing a role with ~25% of the Danish population at least partially vaccinated. From the available data sets we estimate the national Infection Detection Efficiency (IDE) starting around July 1, 2020, the time at which the national infection detection program started to be implemented in a more robust form. The results rely on daily COVID-19 hospital admissions, PCR test results, and anti-body prevalence values for the Danish population. We find that for the long-term average, the combined Danish national testing and contact tracing program identified on average about 55 to 60% of the infected individuals during the period from Sept. 1, 2020, into early May 2021. However, higher infection detection efficiencies were achieved in localized areas for short time intervals. Our investigations further indicate that the Danish testing and contact tracing program, which was among the most ambitious in the world, could have had a better balance between voluntary probing on the one hand and focused contact tracing and targeted testing on the other hand. As typically implemented, it proved to be largely ineffective in controlling the pandemic. However, in at least one case when it was implemented in an enhanced and focused mode, it was able to bring a large local outbreak that dominated the country's cases under control. We have compiled a list of lessons learned regarding the use of contact tracing procedures to control potential future pandemics before vaccinations become available.

Importance: Coccidioidomycosis, also known as Valley fever, is a serious fungal disease caused by inhaling Coccidioides spores aerosolized from the soil. Despite rising public health concern, the distribution and incidence of coccidioidomycosis in the US remain poorly characterized as not all states are required to report cases, and case under-ascertainment is presumed to be high, particularly outside of the endemic region. Objective: To estimate monthly, county-level coccidioidomycosis incidence across the US while accounting for widespread under-ascertainment. Design, Setting, and Participants: We integrated multisource data, including human cases diagnosed in electronic health records, serological testing of dogs, and ecological and sociodemographic predictors, in a machine learning model estimating monthly, county-level coccidioidomycosis incidence. The model was trained on surveillance data from Arizona and California from 2013 to 2023, then applied to predict incidence in states across the contiguous US over the same time period. Main Outcomes and Measures: Incidence of coccidioidomycosis at the monthly county level. Results: The model predicted coccidioidomycosis incidence from 2013 to 2023 for 3,108 counties from all states in the contiguous US. The model identified 60 counties in non-reporting states where predicted incidence was [&ge;]2 cases per 100,000 population (considered moderate to high incidence). In states with mandatory reporting outside of Arizona and California, the model predicted county-level incidence rates that were 28% higher (95% bootstrap interval, 0.1%-69.8%) than those captured in national surveillance, equating to a total of 608 potentially missed cases per year (95% bootstrap interval, 340-964). Conclusions and Relevance: In this cross-sectional study, the model quantified case under-ascertainment for coccidioidomycosis relative to Arizona and California, and highlighted the potential for alternative data sources to fill in gaps in mandatory reporting. Estimating incidence while accounting for under-ascertainment and non-reporting may help efforts to improve public and physician awareness and to detect emergence in new regions.

Wastewater-based epidemiology uses both targeted and non-targeted bioanalytical approaches to detect and track disease-causing microbes in wastewater. Targeted approaches such as quantitative polymerase chain reaction (qPCR) remain widely used while sequencing-based approaches have slowly gained popularity. However, concordance between established targeted molecular assays and sequencing approaches remains incompletely characterized. To assess the validity of sequencing as an alternative to qPCR, we compared outcomes of both methods targeting five respiratory viruses in wastewater samples collected across 16 Texas cities over 4 years (May 2022-July 2026) of continuous sampling. We found positive correlations between qPCR and sequencing measurements, with both methods independently correlating with statewide clinical emergency department visits. qPCR positivity also increased with genomic sequencing coverage, and sequencing demonstrated greater potential than qPCR as an early respiratory virus outbreak detection method. Our findings support metagenomic sequencing as a sensitive and highly specific alternative to targeted qPCR for wastewater surveillance, and reinforce its utility towards long-term longitudinal monitoring of respiratory viruses.

Wolbachia-based interventions were less effective in achieving introgression in Rio de Janeiro than other settings. Reduced introgression occurred in locations where dengue was historically high, raising concerns of inequity. We examined factors affecting Wolbachia introgression in Rio through trajectory modeling, spatiotemporal Bayesian models, and Ae. aegypti abundance estimation. During the intervention (2017-2020), Wolbachia introgression was 26% (median; IQR,14-44%), with four distinct trajectories. Despite receiving more releases, densely populated areas with low socioeconomic status had lower introgression. In spatiotemporally explicit multivariable regressions, higher Ae. aegypti abundance, temperature, population density and lower socioeconomic status were associated with reduced introgression. Improved characterization of Ae. aegypti abundance and other structural/environmental predictors of introgression could be used to calibrate release doses to ensure equitable benefits from Wolbachia interventions.

Globally, measles is re-emerging in countries that have previously achieved elimination, including the United States. As immunization rates decline, characterizing the dynamics and evolving spatial pattern of measles re-emergence is critical to inform the public health response. We analyze annual county-level measles cases and vaccine coverage data in the United States (US) from 2011-2025 and find a shift in measles epidemiology in recent years indicative of re-emergence. While outbreak risk is spatially concentrated, we find that outbreaks are increasingly spreading throughout the broader population and across counties, but targeted interventions are predicted to prevent re-emergence nationwide. During the first decade of this period (2011-2020), outbreaks were only weakly associated with county-level vaccine coverage and predominantly isolated to single counties; however, recently (2021-2025), vaccine coverage has become strongly associated with outbreak size and spread across neighboring counties has increased. These findings suggest that measles transmission is increasingly sustained across the broader population rather than only in socially clustered communities. We developed a spatial statistical model to predict outbreak risk and size in all US counties with validation over 2021-2025; the model predicted counties with [&ge;]10 cases with a sensitivity of 95% (range across 2021-2025: 75-100%) and specificity of 83% (64-98%). Mechanistic models based on county vaccine coverage alone had worse performance, particularly in predicting larger outbreaks. Using the spatial statistical model, we find that prioritizing interventions in the areas of greatest model-predicted risk (e.g., top decile of US counties) was more impactful than state-level interventions (e.g., top five states) or targeting based on low vaccine coverage alone. Achieving even modest (5 percentage point) increases in vaccine coverage in counties in the top decile of model-predicted risk reduced long-term measles risk nationwide by over 90%. Together, these findings demonstrate that predictive risk modeling to inform spatially targeted interventions can likely mitigate the re-emergence of vaccine-preventable diseases.

Purpose Prescribing to lactating women is constrained by limited evidence. Published compatibility classifications provide clinical guidance and a discrete research comparator. How often prescribing diverges from that guidance, and what characteristics are associated with divergence, is unknown at national scale. Methods Retrospective analysis of prescriptions filled during lactation, identified in the 2012-2014 MarketScan Commercial Claims Database. Prescriptions were classified as concordant (Hale's lactation risk category L1-L3) or discordant (L4-L5). Multivariable logistic regression identified factors associated with concordance. Results Of 96,484 new prescriptions, 95,710 (99.2%) were concordant and 774 (0.8%) were discordant. Discordant prescriptions were filled later after delivery (median 59 vs 33 days) and had a day supply three times longer (30 vs 10 days). In multivariable analysis (N=28,732), concordant prescriptions were associated with maternal age under 35 (adjusted odds ratio (aOR)=1.34, 95% confidence interval (CI): 1.02-1.75) and chronic-condition medications (aOR=10.94, 95% CI: 6.47-18.50), and were less likely within 14 days of delivery (aOR=0.53, 95% CI: 0.38-0.73) and with each additional day of supply (aOR=0.96, 95% CI: 0.96-0.96). Compared with family practice, concordance did not differ for obstetric, internal medicine, or emergency medicine prescriptions, but prescriptions from other provider types had lower odds of concordance (aOR=0.57, 95% CI: 0.41-0.79). Conclusions Prescribing during lactation was overwhelmingly concordant with published compatibility guidance; the rare divergence was patterned by prescription and prescriber characteristics. Because the categories are an expert classification rather than consensus guidance, divergence should not be read as inappropriate prescribing.