JAMA Pediatrics

BackgroundAn upsurge in Streptococcus pyogenes infections 2022-2023 highlighted potential benefits of point-of-care tests (POCT) to support clinical pathways, prevent outbreaks, and optimise antibiotic use. ObjectivesWe conducted a pilot research study in a west London paediatric emergency department (ED) to determine whether a molecular POCT had potential to alter management in children who were also having a conventional throat swab taken for culture. MethodsChildren <16 years presenting to ED who had a throat swab requested by a clinician were invited to have a second swab taken for research purposes only. Clinical management was unaffected by the research swab result, which was processed using a molecular POCT that was not approved for use in the host NHS Trust. ResultsPrevalence of streptococcal infection was low during the study (May 2023-June 2025); swab positivity in symptomatic children was 12.8% (6/47). Overall, 38/49 (77.6%) participants who had throat swabs received antibiotics. Of those children recommended to receive antibiotics, 29/38 (76.3%) had a negative POCT. Mean time to reporting of positive throat swab culture results was 3.67 days (range 3-5 days) leading to occasional delay in treatment, although POCT identified positive results within minutes. ConclusionAntibiotic use was frequent and could be avoided or stopped by use of a rule out POCT in over three-quarters of children in the ED, if suspicion of S. pyogenes is the main driver for prescribing. POCT were easy to process and produced immediate results compared with culture, in theory enabling timely decision-making and avoiding treatment delay.

Progress in pharmacological treatment development for neurodevelopmental disorders is hindered by a misalignment between targeted mechanisms, outcome measures, and trial designs. This study was initiated as a post-trial access pathway for bumetanide and later expanded with treatment-naive participants. Within this framework, we implemented a parent-cocreated sensory outcome measure set (PROMset) in an unmasked, multiple-baseline single-case experimental design with randomized baseline periods of 2-12 weeks, followed by 6 months of bumetanide treatment (up to 1.5 mg twice daily). Participants (7-19 years) had atypical sensory reactivity and a diagnosis of ASD, ADHD, epilepsy, or TSC. The primary outcome was a PROMset comprising seven PROMIS item banks assessing anxiety, depressive symptoms, sleep disturbance, fatigue, sleep-related impairment, cognitive function, and peer relationships. Secondary outcomes included SSP, SRS-2, RBS-R, and ABC. Of 113 enrolled participants (mean age 13.2 [SD 2.7], 64% male), 102 completed the trial and 95 had analyzable PROMsets. At baseline, PROMset scores showed substantial impairment across domains (mean deviation =9.0 T-score points, p<.001) and correlated with sensory reactivity (SSP; r=-0.40, p<.001). Individual-level analyses showed improvement in 24-41% of participants per PROM domain, most frequently in anxiety and depressive symptoms (41% and 38%; mean across-case Cohen's d=-1). Overall, 83% improved on at least one domain. Group-level analyses showed improvement across all secondary outcomes (p<.001), with superiority over historic placebo for RBS-R and SSP. Integrating PROMsets with individualized trial designs can reveal clinically meaningful changes, supporting a more sensitive and patient-centered framework for treatment evaluation in heterogeneous populations.

Background Individuals experiencing or at risk of homelessness face substantial barriers to preventive eye care that are poorly addressed by standard service models. Interdisciplinary optometry-social work collaboration offers a rights-based approach to improving engagement and continuity of care. Methods A convergent mixed-methods study was conducted between February and August 2024 at a multidisciplinary community centre. Clients experiencing or at risk of homelessness received integrated optometry and social work assessment and were prioritised as high, medium, or low based on combined clinical and social risk. Social work follow-up was guided by the Triple Mandate and W-Questions framework. Quantitative data were summarised using mean (SD), median [IQR], or n (%). Qualitative case notes were analysed using content analysis with inductive coding and secondary review for consistency. Results A total of 165 clients had priority categories coded (high: 68; medium: 47; low: 154). Demographic data were available for 132 clients (60% male; mean age 49.5 years [SD 16]); 27% had not completed high school, 89% reported weekly income below AUD 1000, and 28% had vision impairment. Two hundred forty-five case-note entries were consolidated into 146 unique records. SMS (46%) and phone calls (38%) were the most documented contact methods, although only 21% of calls were answered; missed calls (13%) and disconnected numbers (7%) were common. Multi-modal contact was more frequently documented for higher-priority clients. Appointment assistance was the most recorded facilitator (71%), while rights-based supports, including interpreter and transport assistance, were infrequently documented (<=5%). Qualitative analysis identified unstable communication, reliance on informal supports, and service fragmentation as key influences on recall outcomes. Conclusion This study supports an interdisciplinary, rights-based optometry-social work model to address barriers to preventive eye care among people experiencing or at risk of homelessness. Embedding structured handovers and tiered recall processes within community-based services may strengthen continuity and accountability for high-priority clients. Future implementation should evaluate outcomes related to equity of reach, service integration, and sustained engagement in care.

AimAutistic children have a high but varied prevalence of internalizing and externalizing problems. This study aimed to identify the subtypes of internalizing and externalizing problems among autistic preschool children in Japan, examine their temporal stability, and investigate differences in participation in daily life and family outcomes across these subtypes. MethodsA prospective cohort study was conducted with 275 caregivers of autistic children aged 51-75 months. Internalizing and externalizing problems were assessed using the Strengths and Difficulties Questionnaire. ResultsLatent transition analysis identified five subtypes: Low-symptom, High-emotional, Externalizing, Comorbid, and Peer-difficulty groups. Membership in the High-emotional and Externalizing groups was relatively stable over time, whereas the Peer-difficulty group showed frequent transitions to subtypes with higher levels of internalizing or externalizing problems. Significant differences in participation in daily life and family outcomes were observed across subtypes, but these patterns were inconsistent with a simple gradient of symptom levels. ConclusionsThe novel findings that the temporal stability of subtype membership varied and that differences in participation in daily life and family outcomes were observed across the subtypes suggest that the heterogeneity of internalizing and externalizing problems may be associated with variations in childrens participation in daily life and family outcomes over time. Plain Language SummaryAutistic preschool children often experience emotional and behavioral difficulties, but the way these difficulties manifest varies widely across individuals. This study aimed to identify the patterns of these difficulties, examine how they change over time, and investigate how participation in daily life and family outcomes differ across autistic preschool children. We conducted a study with 275 caregivers of autistic children aged 4-6 years in Japan. From caregiver reports of childrens emotional and behavioral difficulties, five distinct patterns were identified: a group with mainly emotional difficulties, a group with mainly behavioral difficulties, a group with both types of difficulties, a group with relatively low levels of difficulties, and a group characterized primarily by peer-related difficulties. Our findings suggest that different patterns of emotional and behavioral difficulties are associated with differences in childrens participation in daily life and family outcomes. These differences could not be explained simply by the overall severity of difficulties but rather reflect distinct patterns based on the type of difficulty. The results indicate that autistic children face diverse difficulties that change over time.

ObjectiveThere is little longitudinal research investigating links between violence exposure and mental disorders among children in low- and middle-income countries (LMICs), despite high rates of violence. We examined cross-sectional and longitudinal violence-mental health associations among children in a large South African birth cohort, the Drakenstein Child Health Study, including direct clinical interviews capturing childrens mental disorders. MethodIn this birth cohort (N=974), we assessed lifetime violence exposure and four subtypes (witnessed community, community victimization, witnessed domestic, domestic victimization) at ages 4.5 and 8-years via caregiver reports. At 8-years, caregivers completed the Child Behaviour Checklist; and psychiatric disorders were assessed using the Mini-International Neuropsychiatric Interview for Children and Adolescents, a self-report measure. We tested for associations using linear/logistic regressions, adjusted for confounders. ResultsMost children (91%) had experienced violence by 8-years. Cross-sectionally, total violence exposure was associated with total (B =0.49 [95% CI 0.32, 0.66]), internalizing (0.32 [0.17, 0.47]), and externalizing problems (0.46 [0.31, 0.61]), and with increased odds of disorder at 8 years (aOR=1.09 [1.05, 1.13]). Longitudinally, total violence exposure up to 4.5-years was associated with total (B=0.27 [0.03, 0.52]), internalizing (0.24 [0.04. 0.44]), and externalizing scores (0.23 [0.008, 0.45]) at 8-years, but not with increased risk of psychiatric disorders. The strongest and most consistent associations were observed for domestic versus community violence subtypes. ConclusionOur strong cross-sectional but weaker longitudinal findings suggest that recent violence exposures may be more critical than early exposures for childrens mental health. Longitudinal exploration of other violence-affected LMIC populations is urgently needed.

Objective The decline of the perinatal demise rate is slowing and demises are often unexplained. Significant research has been done regarding diagnostic yield and genetic causes of demise, but little is known about how Geneticist involvement impacts outcomes. The goal of the study was to evaluate post-mortem genetic testing practices and effects of the geneticists involvement. Methods Retrospective data from 111 perinatal demise cases was examined, including rates of prenatal genetic counseling, post-delivery genetics consult, genetic testing, and autopsy investigation. Results In this cohort 54% received genetic testing and 25% received a genetics consult. When compared to those without, cases with genetic specialist involvement were associated with significant increases in testing uptake (p=0.007), diagnostic yield (p<0.001), and patient education (p<0.001). Second trimester stillbirths and those with fewer ultrasound (US) abnormalities were less likely to receive genetic testing (both p values <0.001) and consults (p<0.001, p=0.020). Conclusion Though it was not possible to avoid ascertainment bias, this data demonstrates that geneticist involvement correlates with a higher rate of testing, greater diagnostic yield, and more thorough counseling. These findings underscore the importance of integrating genetics providers into perinatal postmortem healthcare teams.

Abstract Importance Stigma and discrimination against transgender and gender-diverse people are prevalent across many settings and may contribute to substantial health disparities. Objective To synthesize global evidence on the prevalence of stigma, discrimination, and resilience among transgender (trans) and gender-diverse adults. Data Sources A systematic search was conducted in PubMed, Embase, CINAHL, Cochrane Central, LILACS, and PsycInfo for articles published between January 1, 2010 and January 2, 2023. This database search was supplemented by grey literature and secondary reference searches. Article Selection Studies were eligible if they presented primary quantitative data on prevalence of stigma, discrimination, and/or resilience among trans and gender-diverse adults (aged 18 and over), with no restrictions on study design, language, or geographic region. Data Extraction and Synthesis Two independent reviewers extracted data using standardized forms, with discrepancies resolved by consensus. The JBI Critical Appraisal Checklist for Prevalence Articles was used to assess risk of bias. Random effects meta-analysis was conducted for dichotomous prevalence measures using inverse variance weighting and logit transformation; non-dichotomous prevalence data were summarized descriptively. Main Outcomes and Measures Outcomes included prevalence estimates for various forms of stigma (anticipated, perceived, internalized, and experienced), discrimination in legal/institutional settings (housing, healthcare, employment, police/prison), and resilience. Results A total of 97 articles, with data from 72,158 unique trans and gender-diverse participants across 26 countries, met inclusion criteria. Studies showed moderate levels of anticipated stigma, perceived stigma, and internalized stigma. Meta-analyses of 36 studies provided pooled estimates of discrimination prevalence across multiple domains: 21.4% in housing (e.g., eviction, rental denial), 24.6% in healthcare (e.g., denial of care, mistreatment), 32.8% in employment (e.g., hiring bias, workplace harassment), and 39.1% in police/prison settings (e.g., profiling, mistreatment). High heterogeneity was observed across studies, reflecting regional and methodological differences. Resilience scores ranged from moderate to high, indicating variation within trans and gender-diverse communities. Conclusions and Relevance This systematic review and meta-analysis found that stigma and discrimination against trans and gender-diverse adults are pervasive globally. Variation in stigma and discrimination across settings and regions underscores the need for targeted interventions and policy reforms. Funding World Health Organization through a grant from the Elton John AIDS Foundation and the Bill and Melinda Gates Foundation.

Background: The instability-plasticity framework proposes that multimorbidity trajectories periodically enter instability phases that are vulnerable to escalation but also potentially modifiable through relational intervention. Whether such phases commonly resolve without acute care, or predominantly progress to hospitalisation, has not been quantified at scale. Objective: To quantify instability window outcomes across a longitudinal monitoring cohort; to test whether the characteristics distinguishing admitted from resolved windows reflect within-patient trajectory dynamics or between-patient severity; and to characterise which patient-reported and operator-rated signals reliably precede admission, using both a curated pilot sub-cohort and the full monitoring cohort with an explicit cross-cohort comparison. Methods: Two complementary analyses were conducted on data from the MonashWatch Patient Journey Record (PaJR) relational telehealth system. Instability windows were identified algorithmically (>=2 consecutive calls with Total_Alerts >=3) across the full longitudinal dataset (16,383 calls, 244 patients, 2.5 years) and classified by linkage to ED and hospital admission data. Window characteristics were compared at window, patient, and paired within-patient levels. Pre-admission signal cascades were analysed in two configurations: a curated pilot sub-cohort (64 patients, 280 calls, +/-10-day window, 103 admissions, December 2016-September 2017) and the full monitoring cohort (175 patients, 1,180 pre-admission calls, +/-14-day window, December 2016-July 2019). A three-way cross-cohort comparison decomposed differences between the two configurations into pipeline and population effects. Results: 621 instability windows were identified across 157 patients (64% of the monitored cohort). 67.3% resolved without hospital admission or ED attendance, a rate stable across alert thresholds 1-5. In paired within-patient analysis (n = 70), duration in days (p = 0.002) and multi-domain breadth (p < 0.001) distinguished admitted from resolved windows; alert intensity did not. In the pilot sub-cohort, patient-reported illness prognosis (Q21) was the dominant pre-admission signal (GEE beta = +0.058, AUC = 0.647, p-BH = 0.018). This finding did not replicate in the full cohort: Q21 was non-significant (GEE beta = -0.008, p = 0.154, AUC = 0.507). Cross-cohort analysis identified selective curation of the pilot sub-cohort as the primary explanation. In the full cohort, six signals escalated significantly before admission after Benjamini-Hochberg correction: total alerts, health impairment (Q26), red alerts, self-rated health (Q3), patient concerns (Q1), and operator concern (Q34). Health impairment achieved the highest individual AUC (0.605) and showed the longest pre-admission lead. No individual signal exceeded AUC 0.61. Conclusions: Two thirds of instability phases resolve without hospitalisation, providing direct empirical support for trajectory plasticity as a clinically frequent phenomenon. Within the same patient, persistence - in duration and in the consistency of high-severity multi-domain flagging across calls - distinguishes trajectories that tip into admission from those that resolve. The Q21 signal reversal between cohorts illustrates how selective curation can produce compelling but non-replicable findings in monitoring research. In the full population, objective alert signals and operator judgement, rather than patient illness prognosis, carry the pre-admission signal

Objective: Childhood poverty is a high-risk context that involves diverse adversities, making it difficult to understand how poverty confers later psychopathology risk and why some children remain resilient despite growing up in poverty. To address this heterogeneity, we quantified adversity-linked vulnerability as adversity-psychopathology coupling and tested whether childhood poverty amplifies this coupling and whether multilevel inhibitory-control profiles stratify vulnerability and resilience within poverty-exposed youth. Methods: We analyzed 10,112 youth (48.4% female; mean age = 9.92 years) from the Adolescent Brain Cognitive Development Study, linking baseline cumulative early-life adversity (ELA) to later behavioral problems across 4 waves. In the stop-signal task fMRI subsample of 7,401 youth, semi-supervised clustering of inhibitory-control activation identified neurofunctional subtypes within poverty-exposed youth. We also tested temperamental inhibitory control as an additional moderator. Results: Childhood poverty amplified the association between cumulative ELA and behavioral problems at baseline ({Delta}{beta} = 0.088; P < .001) and across follow-up waves. Two neurofunctional subtypes were identified within poverty-exposed youth: subtype-1 showed greater vulnerability than higher-income peers ({Delta}{beta} = 0.149; P < .001), whereas subtype-2 showed attenuated vulnerability and did not differ from higher-income peers ({Delta}{beta} = 0.049; P = .135); this pattern persisted longitudinally. Among poverty-exposed youth in subtype-2 with high temperamental inhibitory control, the association between cumulative ELA and later behavioral problems was no longer significant. Conclusions: Childhood poverty strengthened the translation of adversity burden into later behavioral problems, but inhibitory-control profiles differentiated higher- and lower-risk pathways within poverty, highlighting inhibitory control as a candidate target for prevention.

Background: The 2024-25 influenza season was the most severe in the United States (US) since 2017-18, with co-circulation of both influenza A virus subtypes (H1N1 and H3N2). Influenza vaccine effectiveness (VE) has varied by season, setting, and patient characteristics. Methods: Using electronic healthcare encounter data from eight US states, we evaluated influenza vaccine effectiveness (VE) against influenza-associated hospitalizations and emergency department or urgent care (ED/UC) encounters from October 2024-April 2025 among children aged 6 months-17 years and adults aged 18+ years. Using a test-negative, case-control design, we compared the odds of influenza vaccination between acute respiratory illness (ARI) encounters with a positive (cases) versus negative (controls) test for influenza by molecular assay, adjusting for confounders. Results: Analyses included 108,618 encounters (5,764 hospitalizations and 102,854 ED/UC encounters) among children and 309,483 encounters (76,072 hospitalizations and 233,411 ED/UC encounters) among adults. Among children across care settings, 17.0% (6,097/35,765) of cases versus 29.4% (21,449/72,853) of controls were vaccinated. Among adults, 28.2% (21,832/77,477) of cases versus 44.2% (102,560/232,006) of controls were vaccinated. VE was 51% (95% confidence interval [95% CI]: 41-60%) against influenza-associated hospitalizations and 54% (95% CI: 52-55%) against influenza-associated ED/UC encounters among children. VE was 43% (95% CI: 41-46%) against influenza-associated hospitalizations and 49% (95% CI: 47-50%) against influenza-associated ED/UC encounters among adults. Conclusions: Influenza vaccination provided protection against influenza-associated hospitalizations and ED/UC encounters among children and adults in the US during the severe 2024-25 influenza season. These findings support influenza vaccination as an important tool to reduce influenza-associated disease.

Introduction: Intermittent preventive treatment in pregnancy (IPTp) with sulfadoxine-pyrimethamine (SP) has become less effective at preventing malaria due to rising parasite resistance. IPTp with dihydroartemisinin-piperaquine (DP) alone or in combination with SP (DP+SP) dramatically lowers the risk of malaria in pregnancy compared to SP but is associated with lower birthweight and early life wasting. We estimated the effect of IPTp-DP, DP+SP, and SP on infant growth outcomes and assessed possible treatment mechanisms through a causal mediation analysis. Methods: We used infant follow-up data (N=761) from a trial (NCT04336189) that randomized pregnant women to receive monthly IPTp-DP, SP, or DP+SP. We compared weight-for-length (WLZ) and length-for-age (LAZ) z-scores between treatment arms. We assessed possible mediation through pregnancy, birth, and infancy factors using interventional indirect effect models. Results: Compared to IPTp-SP, IPTp-DP+SP decreased mean WLZ by 0.18 [95% confidence interval (CI) -0.03, 0.39] between 1-3 months and 0.28 (95% CI 0.07, 0.49) between 4-6 months, with the largest differences among primigravidae. Lower risk of active placental malaria in IPTp-DP+SP helped reduce differences in mean WLZ vs IPTp-SP (+0.06, 95% CI 0.02, 0.10). The IPTp-DP+SP arm had up to 0.28 lower mean LAZ between 7-13 months compared to IPTp-DP, particularly among children who were wasted between 0-6 months; low birthweight had a persistent, mediating effect on linear growth. Conclusion: Adverse birth outcomes contributed to early growth faltering among children born to mothers receiving IPTp-DP+SP vs IPTp-SP, but the prevention of placental malaria partially counteracted the negative effects of IPTp-DP+SP on ponderal growth.

Introduction Secondary use of electronic health records (EHRs) often requires transforming raw clinical information into research-grade data. A central step in this process is EHR phenotyping - the identification of patient cohorts defined by specific medical conditions. Although numerous approaches exist, from ICD-based heuristics to supervised learning and large language models (LLMs), the field lacks standardized benchmark datasets, limiting reproducibility and hindering fair comparison across methods. Methods We developed the MIMIC-IV Phenotype Atlas (MIPA) dataset, an adaptation of MIMIC-IV that provides expert-annotated discharge summaries across 16 phenotypes of varying prevalence and complexity. Two independent clinicians reviewed and labeled the discharge summaries, resolving disagreements by consensus. In parallel, we implemented a processing pipeline that extracts multimodal EHR features and generates training, validation, and testing datasets for supervised phenotyping. To illustrate MIPA's utility, we benchmarked four phenotyping methods : ICD-based classifiers, keyword-driven Term Frequency-Inverse Document Frequency (TF-IDF) classifiers, supervised machine learning (ML) models, and LLMs on the task. Results The final MIPA corpus consists of 1,388 expert-annotated discharge summaries. Annotation reliability was high (mean document-level kappa = 0.805, mean label-level kappa = 0.771), with 91% of disagreements resolved through consensus review. MIPA provides high-quality phenotype labels paired with structured EHR features and predefined train/validation/test splits for each phenotype. In the benchmarking case study, LLMs achieved the highest F1 scores in 13 of 16 phenotypes, particularly for conditions requiring contextual interpretation of clinical narrative, while supervised ML offered moderate improvements over rule-based baselines. Conclusion MIPA is the first publicly available benchmark dataset dedicated to EHR phenotyping, combining expert-curated annotations, broad phenotype coverage, and a reproducible processing pipeline. By enabling standardized comparison across ICD-based heuristics, ML models, and LLMs, MIPA provides a durable reference resource to advance methodological development in automated phenotyping.

BackgroundSouth Africas public healthcare system serves most of the population through approximately 3,900 primary healthcare clinics characterised by long waiting times and high volumes of repeat-prescription visits. No published pre-arrival digital triage system operates across all 11 official South African languages while aligning with the South African Triage Scale (SATS). This paper reports the design and preliminary safety validation of BIZUSIZO, a hybrid deterministic-AI WhatsApp triage system. MethodsBIZUSIZO delivers SATS-aligned triage via WhatsApp, combining AI-assisted free-text classification (Claude Haiku 4.5) with a Deterministic Clinical Safety Layer (DCSL) that overrides AI output for 53 clinical discriminator categories (14 RED, 19 ORANGE, 20 YELLOW) coded in all 11 official languages and independent of AI availability. A five-domain risk factor assessment can only upgrade triage level. One hundred and twenty clinical vignettes in patient language (English, isiZulu, isiXhosa, Afrikaans; 30 per language) were scored against a developer-assigned gold standard with independent blinded nurse review. A 121-vignette multilingual DCSL safety consistency check across all 11 languages and a 220-call post-hoc framing sensitivity evaluation (110 paired vignettes) were also conducted. ResultsUnder-triage was 3.3% (4/120; 95% CI: 0.9%-8.3%) with no RED under-triage; exact concordance was 80.0% (96/120) and quadratic weighted kappa 0.891 (95% CI: 0.827-0.932). One two-level under-triage was observed on a non-RED presentation (V072, isiXhosa burns vignette, ORANGEGREEN); one two-level over-triage was observed (V054, isiZulu deep laceration, YELLOWRED). In the framing sensitivity evaluation, AI-only classification achieved 50.9% RED invariance under adversarial framing; full-pipeline classification achieved 95.0% in four validated languages, with the DCSL rescuing 18 of 23 AI drift cases. ConclusionsA hybrid deterministic-AI triage system with DCSL-based emergency detection achieved zero RED under-triage and consistent RED detection across all 11 official languages. The 16.7% over-triage rate falls within published South African SATS ranges (13.1-49%). A single two-level under-triage event was observed on an isiXhosa burns vignette (ORANGEGREEN) and is discussed in Limitations. Findings are preliminary; prospective validation against independent nurse triage is the necessary next step.

BackgroundDrowning remains a major global public health challenge. This study examined whether the timing and trajectories of urbanisation--beyond the current built environment--are associated with subnational drowning mortality. MethodsWe linked satellite-derived measures of built-environment change (GHSL), population crowding (WorldPop), surface water exposure (JRC Global Surface Water), and infrastructure proxies (VIIRS/DMSP nighttime lights) to GBD 2021 drowning mortality estimates across 203 ADM1 regions in 12 countries (2006-2021; 3,248 region-year observations). Temporal predictors captured recent expansion, development "newness" ([&le;]10-year built share), acceleration/volatility, and a crowdingxgrowth interaction. We screened predictors using LASSO (10-fold cross-validation) and fitted mixed-effects models with region random intercepts. Distributed-lag models tested temporal precedence and development age, and income-stratified models assessed heterogeneity. ResultsAdding temporal predictors improved fit beyond contemporaneous built-environment measures ({Delta}AIC=177; {Delta}BIC=147). In adjusted models, crowdingxgrowth was strongly positively associated with drowning mortality, and a higher share of recent development was associated with higher mortality. Lag models showed a development age gradient: older built environment was most protective. Associations differed by income group, with several key coefficients reversing sign across strata. DiscussionDrowning mortality appears shaped by development histories as well as present-day conditions, with risk concentrated in rapidly changing, dense settings and the newest built environments. Cross-context heterogeneity suggests mechanisms and prevention priorities are unlikely to be uniform. ConclusionsDevelopment timing and trajectories help explain subnational drowning mortality beyond current built form alone. Prevention and planning should prioritise transition-period safety strategies in newly developing and rapidly densifying areas.

BackgroundIn England, the burden of respiratory infections varies by ethnicity, contributing to health inequalities, but the role of additional demographic factors remains underexplored. We quantified how differences in social mixing and demographic characteristics between ethnic groups cause inequalities in transmission dynamics. MethodsWe analysed the association between the ethnicity and the number of contacts of 12,484 participants in the 2024-2025 Reconnect social contact survey, using a negative binomial regression model. We simulated respiratory pathogen epidemics using a compartmental model stratified by age, ethnicity, and contact levels, at a national level and in major cities in England. FindingsAfter adjusting for demographic variables, participants of Black and Mixed ethnicities had more contacts than those of White ethnicity (rate ratios (RR): 1.18 [95% Credible Interval (CI): 1.11-1.26], and 1.31 [95% CI: 1.14-1.52]). Participants of Asian ethnicity had fewer contacts (RR: 0.85 [95% CI: 0.79-0.91]). In national-level simulations, individuals of White ethnicity had the lowest attack rates due to demographic differences and mixing patterns. Local demographic structures changed simulated dynamics: attack rates in individuals of Black and Mixed ethnicities were approximately double those of White ethnicity in Birmingham, but less than 60% higher in Liverpool. InterpretationDemographic characteristics and mixing patterns create inequalities in transmission dynamics between ethnicities, while local demographic characteristics and pathogen infectiousness change the expected relative burden. To ensure mitigation strategies are effective and equitable, their evaluation must explicitly account for inequalities arising from local context. FundingMedical Research Council, National Institute for Health and Care Research, Wellcome Trust Research in context Evidence before this studyWe searched PubMed for population-based studies quantifying differences in respiratory infections between ethnic groups, up to 1 April 2026, with no language restrictions. Keywords included: (respiratory pathogens OR influenza OR COVID-19) AND (ethnic* OR race) AND (inequ*) AND (compartmental model OR incidence rate ratio OR hazard ratio). We excluded studies that focused on non-respiratory pathogens (e.g. looking at consequences of COVID-19 on incidence of other pathogens). A population-based cohort study showed that influenza infection risk was higher in South Asian, Black, and Mixed ethnic groups compared to White ethnicity in England. Another population-based cohort study highlighted that during the first wave of COVID-19 in England, the South Asian, Black, and Mixed ethnic groups were more likely to test positive and to be hospitalised than the White ethnic group. Census data in England showed that the distributions of age, household size, household income and employment status differed between ethnic groups, and the recent Reconnect social contact surveys highlighted the impact of each demographic factor on the participants number of contacts. Added value of this studyOur study shows that social contact patterns, mixing, and demographic structure all lead to unequal infection risk between ethnic groups in respiratory pathogen epidemics. Using the largest available social contact survey in England, we show that both the average number of contacts and the proportion of high-contact individuals varied by ethnic group, even after adjusting for participants demographics. These differences, together with mixing patterns and age structure, led to lower expected incidence among individuals of White ethnicity than in all other ethnic groups in simulated outbreaks. The level of inequality between ethnic groups changed when we used different values of pathogen transmissibility. Finally, as ethnic composition and population structure differ between cities in England, our results show differences in expected inequalities at a local level. Implications of all the available evidenceInequalities in infection risk between ethnic groups are context- and pathogen-dependent. They arise from both local population structure and contact patterns. Detailed information on mixing between groups and population structure is needed to accurately measure group-specific infection risk. These findings indicate that public health interventions based only on national-level estimates conceal regional variation in risk and may ultimately increase inequalities. Public health interventions need to be tailored to local contexts to be equitable and effective. Finally, our findings provide a foundation for understanding the progression from infection-risk inequalities to disparities in disease presentation and clinical outcomes.

Background Childhood sensory and intellectual disabilities represent significant yet under-recognized barriers to learning and human capital development. This study analyzes prevalence and severity of these conditions among 149.3 million children aged 5-19 years across 25 countries in Latin America and the Caribbean (LAC) using Global Burden of Disease 2023 data. Methods We extracted GBD 2023 estimates for vision loss, hearing loss, and intellectual disability across 25 LAC countries, stratified by age, sex, and severity. Regional estimates were calculated using population-weighted averages. Severity distributions were compared with OECD countries to contextualize regional patterns. Results: These conditions are estimated to affected 9,282,921 children (6.22%; 95% UI: 5.89-6.54%). Hearing loss was predominant, affecting an estimated 5.42 million (3.63%, 3.41-3.86), with 87.6% mild-to-moderate. Intellectual disability estimated to affected 2.56 million (1.71%, 1.58-1.85), with 61.7% borderline-to-mild. Vision loss estimated to affected 1.30 million (0.87%, 0.79-0.96), with 89% that can be effectively addressed with spectacles. Prevalence increased with age across all conditions. Male predominance was consistent for intellectual disability (2.00% vs 1.42%). Annual economic cost totaled US$19.3-29.0 billion, while comprehensive interventions would require US$9.45-14.23 billion with benefit-cost ratios of 2:1 to 15:1. Conclusions The distribution of children across milder levels of difficulty underscores the opportunity for education and public health systems to provide timely and accessible support. With approximately 88% of sensory impairments addressable through established technologies, investments in inclusive services can yield strong social and economic returns.

Background Although Kenya's HIV programme has long prioritized high-burden counties for intensified paediatric interventions, a critical evidence gap remains in developing integrated analytic frameworks that can objectively predict and validate paediatric HIV burden using data-driven models. We therefore developed and tested a framework that combines machine-learning (ML) prediction with geostatistical hotspot analysis, where a hotspot denotes a statistically significant spatial cluster of elevated paediatric HIV cases to strengthen data-driven surveillance and resource targeting. Methods National HIV testing data for children aged 0-14 years were analysed together with indicators from the 2022 Kenya Demographic and Health Survey. Multiple supervised ML algorithms were trained to predict the number of children living with HIV (CLHIV) across Kenya's 47 counties. Model performance was evaluated using root-mean-square and mean-absolute error. The tuned Lasso-regression model demonstrated the best predictive accuracy and generated county-level estimates for October 2022 to June 2023. These predictions were subsequently assessed for spatial autocorrelation (Moran's I) and validated using Getis-Ord Gi* statistics. Findings The model predicted 3160 newly identified CLHIV during the study period, compared with 3092 cases reported nationally. To account for differences in county population size, paediatric HIV incidence was calculated as cases per 10,000 children aged 0-14 years using 2023 census projections as the denominator. Incidence-based choropleth maps revealed that the highest reported burden was concentrated in Isiolo (11.2 per 10,000) and western Kenya (Homa Bay 7.7, Kisumu 3.6, Siaya 3.5), while model predictions identified additional high-incidence counties in eastern and northern regions. Significant spatial clustering was confirmed for both reported (z = 3.23, Moran's I = 0.22, p = 0.001) and predicted (z = 4.92, Moran's I = 0.37, p < 0.001) distributions. Thirteen counties, predominantly in western Kenya, were identified as statistically significant hotspots. Interpretation This study presents a validated methodological framework integrating ML prediction with geostatistical analysis for paediatric HIV surveillance. By expressing model outputs as population-adjusted incidence, the framework enables equitable comparison of paediatric HIV burden across counties of differing size, strengthening the evidence base for geographic prioritization and resource allocation. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

PurposeDiagnostic yield from exome and genome sequencing varies widely across studies. It remains unclear how much of this variation reflects patient-level factors (e.g., sex, clinical features, race/ethnicity, genetic ancestry) versus site-level practices such as sequencing modality or variant interpretation workflows. We aimed to quantify the contributions of these factors to diagnostic outcomes across five U.S. clinical sequencing sites. MethodsWe performed a cross-sectional analysis of 3,008 prenatal, neonatal, and pediatric cases from the NHGRI Clinical Sequencing Evidence-Generating Research (CSER) consortium (2017-2023). Clinical indications spanned neurodevelopmental, neurological, immunological, metabolic, craniofacial, skeletal, cardiac, prenatal, and oncologic presentations. Genetic ancestry was inferred from sequencing data, and variants were interpreted using ACMG/AMP guidelines to classify DNA-based diagnoses. Generalized linear mixed models were used to estimate associations between diagnostic yield and fixed effects (sex, prenatal status, isolated cancer, number of clinical indications, sequencing modality, race/ethnicity, and genetic ancestry), while modeling study site as a random effect to quantify between-site variation. ResultsThe overall diagnostic yield was 19.0%. Multiple clinical indications (OR=1.47, 95% CI 1.20-1.80, p<0.001) were associated with higher diagnostic yield, and male sex (OR=0.80, 95% CI 0.66-0.96, p=0.017) and prenatal status (OR=0.63, 95% CI 0.44-0.90, p=0.012) were associated with lower yield. Sequencing modality, race/ethnicity, genetic ancestry, and isolated cancer were not statistically significantly associated with diagnostic outcomes.. A model without fixed effects attributed [~]10% of variance in diagnostic yield to between-site differences. After adjusting for covariates, site-level variance decreased to 5.7%, indicating consistent variation across sites not explained by measured patient factors. ConclusionAcross five sites, patient-level clinical features influenced diagnostic yield, but substantial site-level variation remained even after adjustment. Differences in variant interpretation, or case-classification practices may contribute to this residual variability. Further efforts to increase consistency in exome- and genome-sequencing diagnostic workflows may help reduce inter-site differences.

Background: Opioid-related mortality in Texas has escalated dramatically, increasingly driven by illicitly manufactured fentanyl. To address local surges in mortality, the Galveston County Health District deployed the Galveston County Opioid Defense Effort (GCODE) in July 2023, leveraging digitally integrated surveillance data from emergency medical services (EMS) and the Medical Examiner to provide targeted naloxone distribution in identified overdose hot spots. Methods: Using a segmented interrupted time series (ITS) design and Poisson regression with robust standard errors, we evaluated the population-level impact of GCODE on opioid-involved mortality through the end of 2025. Data were sourced from the Galveston Area Ambulance Authority (GAAA) and vital statistics (ICD-10 codes). We assessed mortality trajectory changes, the observed fatality ratio among EMS-detected opioid events (the Survival Gap), and demographic and geographic covariates. Results: The Poisson ITS model included 519 weekly observations (N = 14,827 tract-weeks across 101 census tracts). Pre-intervention, opioid mortality increased by 0.16% weekly (IRR = 1.0016; 95% CI: 1.000-1.003; p = 0.011). Following GCODE deployment, the mortality trajectory reversed to a sustained 0.55% weekly decrease (IRR = 0.9945; 95% CI: 0.990-0.999; p = 0.021). The observed fatality ratio among EMS-detected events declined from 7.59% (preintervention mean; SD = 0.111) to 1.71% (post-intervention; SD = 0.042; Chi^2 = 19.824; p = 0.0001). Opioid decedents were significantly younger than the general mortality population (OR = 0.945 per year of age; p < 0.001), and were descriptively more likely to lack documented race/ethnicity data (41.23% vs. 8.27% Unknown; p < 0.001), limiting equity analysis. Conclusions: The findings are consistent with GCODE having meaningfully reduced opioid mortality by substantially lowering event-level lethality. These results suggest that targeted, digitally coordinated harm reduction can decouple overdose incidence from fatal outcomes, with implications for harm reduction program design in structurally constrained environments.

BackgroundAccess to safe, equitable, and affordable surgical and anesthesia care is critical to reducing the burden of surgical diseases in Africa. To understand the state of access in South Sudan, we conducted a baseline assessment of surgical services in Central Equatoria State (CES) in May 2024. ObjectivesThis study aimed to survey public healthcare facilities in CES capable of providing essential surgical services. We used the capacity to perform cesarean section, laparotomy, and open fracture management--Bellwether procedures--as a proxy for assessing workforce, infrastructure, financing, information management, and service delivery. MethodsWe used a validated and contextualized Surgical Assessment Tool developed by the Harvard Program on Global Surgery and Social Change and the World Health Organization. Data were collected at the facility level and summarized descriptively using percentages, means (standard deviations), medians (minimum, maximum), and visualized in graphs, charts, and tables. ResultsAll three public health facilities assessed could perform Bellwether procedures for their catchment populations. However, workforce availability, financing, and surgical infrastructure were major constraints. The surgical workforce density was 2.27 surgical, anesthesia, and obstetric specialists per 100,000 population. Specialized procedures--such as repair of cleft lip and palate, clubfoot, and hydrocephalus shunt--were unavailable at all sites. None had magnetic resonance imaging (MRI) machines. The total average annual facility budget was $918,850, ranging from $3,960 to $800,000 at the teaching hospital--insufficient for proper operations. ConclusionWhile Bellwether procedures are routinely performed, access to quality and affordable care is compromised by deficits in workforce, financing, and infrastructure. We recommend that the Ministry of Health scale this survey nationally and develop a surgical policy and strategic plan focused on improving infrastructure, workforce, and financing for surgical and anesthesia care in South Sudan.