JMIR Public Health and Surveillance

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

Objectives: In Latin America, up-to-date information to monitor UNAIDS 95-95-95 HIV targets in key populations, such as men who have sex with men, is limited. Elsewhere, structural homophobia restricts access to ART. Conceptual frameworks suggest that intersecting forms of violence and discrimination may negatively influence HIV care outcomes through psychosocial and structural pathways, although empirical evidence remains limited. The study aimed to assess whether sexual orientation outness and recent homophobic violence are associated with not being on ART among Latin American MSM living with HIV. Methods: This cross-sectional study is a secondary analysis of data from LAMIS-2018, including 7,609 MSM aged 18+ with an HIV diagnosis [&ge;]1 year prior from 18 Latin American countries. Participants self-reported ART status, sociodemographic characteristics, homophobic violence, and sexual orientation outness. Bivariate and multivariate logistic regressions identified those factors associated with not being on ART. Results: Nine percent of MSM with HIV were not on ART, 18% reported low sexual orientation outness, and 27% experienced homophobic violence, especially in Andean and Central American countries. Not being on ART was associated with recent homophobic violence (aPR=1.25), low outness (aPR=1.22), unemployment (aPR=1.27), and residence in the Andean subregion (aPR=1.87), Mexico (aPR=1.28), or the Southern Cone (aPR=1.45) versus Brazil. Protective factors included being older (25-39: aPR=0.72; >39: aPR=0.49), living in large cities (aPR=0.72), having a stable partner (aPR=0.78), and university education (aPR=0.74). Conclusions: Recent homophobic violence and low sexual orientation outness were associated with not being on ART among MSM in Latin America. While access varies across countries, structural factors such as stigma and violence may limit engagement in care. Addressing these barriers alongside strengthening health systems may be key to improving ART uptake and advancing progress toward the 95-95-95 targets.

Aims The oral glucose tolerance test (OGTT) is effective for detecting post-load dysglycemia, but it is burdensome and therefore not routinely used. Continuous glucose monitoring (CGM) offers a convenient way to capture real-world glucose patterns, yet it remains unclear whether CGM-derived metrics reflect OGTT-defined dysglycemia. We therefore aimed to evaluate CGM-derived and clinical metrics for predicting OGTT 2-hour glucose, classifying OGTT-defined dysglycemia, and assessing day-to-day repeatability. Methods We analyzed a cohort with paired free-living CGM and OGTT. Multiple CGM-derived metrics and clinical measures were compared for prediction of OGTT 2-hour glucose, classification of OGTT-defined dysglycemia, and day-to-day stability. Predictive performance was assessed primarily by leave-one-out (LOO) R^2, and day-to-day repeatability by intraclass correlation coefficients (ICC). Results The glycemic persistence index (GPI), a metric integrating the magnitude and duration of glycemic elevation, was the strongest single predictor of OGTT 2-hour glucose (LOO R^2 = 0.439). GPI also showed strong day-to-day repeatability (ICC = 0.665) and ranked first on a combined prediction-stability score. For classification of OGTT-defined dysglycemia, HbA1c had a slightly higher AUC than GPI, but GPI plus HbA1c performed best overall, indicating complementary information. Conclusions GPI was a strong predictor of OGTT 2-hour glucose and showed a favorable balance between predictive performance and day-to-day stability, supporting its potential utility as a CGM-derived marker of dysglycemia.

PurposeThis study evaluates the Visual Hemofilter, a novel decision-support and information transfer tool designed to assist with regional citrate anticoagulation (RCA) in hemofiltration. By representing hemofilter parameters and patient blood constituents as animated icons, the tool aims to improve clinicians interpretation of blood gas results and RCA reference tables. We hypothesized that the Visual Hemofilter would enhance clinical decision-making by enabling faster and more accurate therapy adjustments, increasing clinicians confidence in their decisions, and reducing cognitive workload compared to conventional methods. MethodsWe conducted a prospective, randomized, computer-based simulation study across four intensive care units at the University Hospital Zurich. Twenty-six critical care professionals participated, each managing regional citrate anticoagulation (RCA) scenarios using either the Visual Hemofilter or conventional methods involving blood gas analysis and reference tables. Following each scenario, participants made therapy adjustments and rated their decision confidence and cognitive workload. ResultsUse of the Visual Hemofilter significantly improved decision accuracy (odds ratio [OR] 3.96; 95% CI 2.03-7.73; p < 0.0001) and reduced decision time by an average of 33 seconds (mean difference -33.3 seconds; 95% CI -39.4 to -27.2; p < 0.0001). Participants also reported greater confidence in their decisions (OR 5.41; 95% CI 2.49-11.77; p < 0.0001) and experienced lower cognitive workload (mean difference -15.05 points on the NASA-TLX scale (National Aeronautics and Space Administration-Task Load Index); 95% CI -18.99 to -11.13; p < 0.0001). ConclusionsThe Visual Hemofilter enhances clinical decision-making in RCA by increasing accuracy and speed, boosting decision confidence, and reducing cognitive workload. This technology has the potential to reduce errors and better support critical care professionals in managing complex treatment scenarios.

Background: China has implemented policies to strengthen its pharmacist workforce since the 2009 healthcare reform, yet a comprehensive evaluation of their long-term systemic effects is lacking. Objective: To systematically analyze the evolution of Chinas pharmacist workforce in healthcare institutions from 2007 to 2023 across four dimensions: quantity, quality, structure, and distribution, providing an empirical foundation for policy optimization. Methods: A retrospective analysis was conducted using longitudinal data from the China Health Statistics Yearbooks. Trends were delineated via descriptive statistics. Equity and spatial evolution were assessed using the Gini coefficient, Theil index decomposition, and spatial autocorrelation analyses (Global Morans I and hotspot analysis). Results: From 2007 to 2023, the total number of pharmacists increased from 357,700 to 569,500 (average annual growth: 2.2%). This growth lagged behind physicians (4.6%) and nurses (7.4%), causing the pharmacist-to-physician ratio to decline from 1:5.15 to 1:8.39. The workforce showed trends of feminization (female proportion rose from 59.7% to 70.8%) and aging. While quality improved, 51.1% still held an associate degree or below, and only 6.6% held senior titles. Equity analysis revealed the provincial Gini coefficient improved from 0.145 to 0.093. Theil index decomposition confirmed intra-provincial disparities as the primary inequality driver. Spatial analysis showed a non-significant global Morans I by 2023 (0.154, P*>0.05), down from 0.254 (P<0.01) in 2007. Hotspot analysis confirmed this transition, revealing a contraction of high-confidence clusters and a trend toward balanced distribution. Conclusions: China has made measurable progress in expanding pharmacist workforce size and improving inter-provincial equity since 2007. However, persistent structural challenges remain: relative workforce contraction compared to other health professions, an aging demographic, a shortage of senior talent, and significant intra-provincial inequity. Future policies must prioritize optimizing workforce structure and enhancing clinical service capabilities to catalyze a shift toward patient-centered pharmaceutical care.

Background: Mobile phone-based tools are increasingly used to collect data on non-communicable disease (NCD) risk factors, particularly in low-resource settings where traditional data collection systems face operational and infrastructural constraints. This study examined stakeholder perspectives on the use of enhanced mobile phone-based capabilities to support the collection of public health surveillance data on NCD risk factors in low-resource settings. Methods: An exploratory qualitative study was conducted between November 2022 and July 2023. Twenty in-depth interviews were conducted with public health specialists, ethicists, NCD researchers, health informaticians, and policy makers in Uganda. Thematic analysis was used to interpret the results. Results: Four themes emerged from the data, including benefits of using mobile phone capabilities for NCD risk factor data collection; ethical, legal, and social implications; perceived challenges of using such mobile phone capabilities; and proposed solutions to improve the utility of phone-based capabilities in data collection on NCD risk factors. Participants recognized the potential of mobile technologies to improve data collection efficiency and expand access to hard-to-reach populations. However, concerns emerged regarding inadequate informed consent, risks to privacy and confidentiality, unclear data ownership, and vulnerabilities created by inconsistent enforcement of data protection laws. Social concerns included low digital literacy, unequal access to mobile devices, and fear of stigmatization. Participants emphasized the need for transparent communication, robust data governance, and community engagement. Conclusion: Mobile phone-based systems can strengthen the collection of NCD risk factor data in low-resource settings; however, their benefits depend on addressing key ethical, legal, and social challenges. To ensure responsible deployment, digital health initiatives must prioritize participant autonomy, data protection, equity, and trust building. Integrating contextualized ethical, legal, and social considerations into design and policy frameworks will be essential to leveraging mobile technologies in ways that support inclusive and effective NCD prevention and control.

Background Depressive symptoms among reproductive-aged women represent a major public health concern in low- and middle-income countries, yet systematic screening remains limited. In most population survey datasets, the low prevalence of depression results in severe class imbalance, which challenges conventional machine learning models. Therefore, we develop and evaluate a bagging-based ensemble machine learning framework to predict depressive symptoms among reproductive-aged women using highly imbalanced Bangladesh demographic and health survey (BDHS) 2022 data. Methods The sample comprised women aged 15-49 years drawn from BDHS 2022 data. Depressive symptoms were defined using the Patient Health Questionnaire (PHQ-9 [&ge;]10). Candidate predictors were drawn from sociodemographic, reproductive, nutritional, psychosocial, healthcare access, and environmental domains. Feature selection was performed using Elastic Net (EN), Random Forest (RF), and XGBoost model. Five classifiers (EN, RF, Support Vector Machine (SVM), K-nearest neighbors (KNN), and Gradient Boosting Machine (GBM)) were trained using both oversampling-based approaches and the proposed ensemble framework. Model performance was evaluated on an independent test set using accuracy, sensitivity, specificity, F1-score, and the normalized Matthews correlation coefficient (normMCC). Results Approximately 4.8% of women were identified with depressive symptoms. The proposed bagging ensemble framework consistently achieved more balanced predictive performance than oversampling-based models. Average normMCC improved from 0.540 (oversampling) to 0.557 (ensemble). RF and GBM ensembles demonstrated notable improvements in identifying depressive cases, while the EN ensemble achieved the highest overall performance and sensitivity. Threshold optimization yielded stable normMCC across models, indicating robust trade-offs between sensitivity and specificity. Conclusions Bagging-based ensemble learning provides a more robust and balanced approach than synthetic oversampling for predicting depressive symptoms in highly imbalanced population survey data. This approach has important implications for improving early identification and population-level mental health surveillance in resource-constrained settings.

ObjectiveThis study aimed to assess the effectiveness of SMS and voice message reminders in reducing the dropout rate in Lome-Togo, in 2026. MethodsWe conducted a cross-sectional study between October 2025 and March 2026 in the Grand Lome region. The intervention consisted of an integrated digital system used by health facilities to send automated SMS. Categorical variables were described in terms of frequency and proportion; Fishers exact test was used to compare proportions. Quantitative variables were described by their means accompanied by their standard deviation; the Wilcoxon rank-sum test was used to compare means. The significance level for statistical tests was set at 5%. ResultsA total of 30 health facilities were included. Seventy percent (70.0%) of the health facilities used messages associated with calls. Ninety percent (90.0%) of participants found the reminders useful, and 60.0% reported an improvement in Expanded Program on Immunization services related to their use. Among participants who received a reminder, 51.0% kept their vaccination appointments. The Penta 1/3 dropout rate decreased from 3.2% before the intervention to 1.3% (p < 0.001). Among the 323 parents of children included, only 20.74% reported receiving a reminder by phone. Sixty-point-five percent (60.5%) preferred to receive both text messages and voice calls. ConclusionThis study demonstrates the operational feasibility of an SMS/call-based reminder system in reducing dropout rate for childhood vaccination in Togo.

Women living with HIV face about a six-fold higher risk of cervical cancer, yet screening uptake remains low in many sub-Saharan African settings. We explored factors influencing repeated decisions to decline cervical cancer screening during routine HIV care among women living with HIV at a large HIV clinic in Jos, Nigeria. Between September and December 2024, we conducted an exploratory qualitative study at the AIDS Prevention Initiative in Nigeria Clinic in Jos, Nigeria. We purposively recruited 27 women living with HIV aged 21 to 65 years who had never undergone cervical cancer screening and had repeatedly declined screening offers during routine HIV care, including at the current clinic visit. Semi-structured in-depth interviews were conducted in English or Hausa, audio-recorded, transcribed verbatim, and translated into English where needed. Data were analyzed thematically using theory-informed coding based on the Health Belief Model and Social Ecological Model. Among 27 women living with HIV who had repeatedly declined screening, perceived susceptibility was often low or uncertain despite recognition of cervical cancer severity. Perceived benefits were acknowledged but were frequently outweighed by overlapping barriers, including knowledge gaps and misinformation, indirect and downstream costs, emotional barriers, logistical constraints, clinic-flow and service-delivery barriers, and anticipated stigma. Education, reminders, and supportive clinic processes acted as cues to action, and most participants expressed willingness to screen in future. Among women living with HIV at this clinic who repeatedly declined screening when it was offered, perceived benefits were often outweighed by multilevel barriers. Screening programs may integrate fear-reduction and stigma-sensitive counseling with practical service delivery improvements, including shorter waiting times, reduced indirect costs, predictable and streamlined clinic flow, and consistent provider invitations and reminders, while addressing misinformation through community-embedded, culturally tailored messaging. These strategies may improve screening uptake and support more equitable cervical cancer prevention for women living with HIV in similar HIV-care settings.

Introduction: Modeling when influenza epidemics typically occur can help countries optimize surveillance, time clinical and public health interventions, and reduce the burden of influenza. Methods: We used influenza virus detections reported during 2011-2024 by 180 countries to the Global Influenza Surveillance and Response System, excluding COVID-19 pandemic impacted years (2020-2023). We analyzed data by calendar year (week 1-52) or shifted year (week 30-29) time windows, based on when most influenza detections occurred in each country. For countries with sufficient data, we computed generalized additive models (GAMs) of each country's weekly influenza-positive tests to smooth and impute time series distributions. From these GAMs, we calculated each country's normalized weekly influenza burden. Country-specific normalized time series were grouped using hierarchical k-means clustering reducing the Euclidean distance between time series within clusters. We calculated cluster-specific GAMs to estimate average seasonal timing. Countries without sufficient data were assigned to a cluster based on population-weighted latitudinal distance to a cluster's mean latitude. Results: We identified five clusters, or epidemic zones, from 111 countries with sufficient data. The influenza burden in epidemic zones A and B was consistent with a northern hemisphere pattern, with most influenza detections occurring during October-April (A) and September-March (B), while epidemic zones D and E were characterized by southern hemisphere-like seasonal timing, with most influenza burden occurring during May-November. Epidemic zone C had most influenza burden occurring during September-March; most countries assigned to this cluster were in the tropics. Conclusion: Epidemic zones may serve as a useful tool to strengthen and optimize influenza surveillance for global health decision-making (e.g., during vaccine strain composition discussions) and to guide country preparedness efforts for seasonal influenza epidemics, including the timing of enhanced surveillance, as well as the procurement and delivery of vaccines and antivirals.

With Japans rapidly aging population, demand for home healthcare is projected to increase by 62% by 2040. This study quantitatively evaluated accessibility to 24-hour home healthcare and regional disparities across all 335 secondary medical areas (SMAs) in Japan using the Enhanced Two-Step Floating Catchment Area (E2SFCA) method. We conducted a nationwide cross-sectional study analyzing approximately 430,000 population points at 500-meter mesh resolution. The E2SFCA integrated demand (age-adjusted population), supply (24-hour home care support clinics and hospitals), and transportation (road networks). Accessibility scores (ASs) and Gini coefficients were calculated for each SMA. Wards hierarchical cluster analysis classified regional types, and multiple regression based on the Penchansky and Thomas five-dimensional access framework identified factors associated with the median AS (ASM) and Gini coefficient. The median ASM was 45.71 (0.00-153.49), and the median Gini coefficient was 0.33 (0.06-0.93). Cluster analysis identified six types ranked by descending ASM, from C1 (high access, equitable; n = 48) to C6 (access desert; n = 23). C6 had a median ASM of 0.00 and Gini coefficient of 0.74, indicating virtually no access within a 30-minute catchment. Home-visit standardized claim ratios, used as external validation, declined monotonically from C1 (125.6) to C6 (17.6). For ASM, 24-hour visiting nursing stations ({beta} = +0.369) and clinic physicians ({beta} = +0.342) showed the strongest positive associations, with non-residential area negatively associated ({beta} = -0.273). For the Gini coefficient, non-residential area showed the strongest positive association ({beta} = +0.523). Taxable income per taxpayer was not significantly associated with either outcome. Non-residential area was associated with both lower accessibility and greater intra-regional inequality, suggesting that geographic constraints may limit the effectiveness of resource investment alone. Uniform nationwide implementation of policies shifting care from long-term care beds to home healthcare may not be feasible; region-specific approaches considering geographic characteristics are necessary.

IntroductionOver one billion people worldwide have hypertension. In Zimbabwe, prevalence is an estimated 38%, surpassing the global average of 34%, and >50% of hypertensives are undiagnosed. The Community BP groups (Com-BP) study examined whether community groups of people living with hypertension, provided with BP machines and led by trained Facilitators could improve awareness, screening and support for those diagnosed with hypertension, to help blood pressure (BP) control. We present findings from the quantitative evaluation of the Com-BP pilot intervention. MethodsThe acceptability of the Com-BP intervention, its potential effectiveness in improving knowledge, attitudes and practices (KAP) and in reducing BP among hypertensive adults in Zimbabwe, was evaluated. Cross-sectional surveys using standardised questionnaires, and BP and Body Mass Index (BMI) assessments, were done at the start and end of the pilot intervention. Statistical evidence of difference between baseline and follow-up was examined using Wilcoxon signed-rank test for continuous data and McNemars test for categorical data. ResultsFourteen groups (seven urban and seven rural) were formed and 151 participants joined over a median of 5months. Retention in the groups was 97.9% (137/140 recruited at baseline), with approximately equal numbers from the urban and rural sites. Median age at baseline was 54 years (IQR 45-66y; min-max 30-92y) and the majority (79%, n=108) were female. Most participants (82.5%, n=113) rated their experience of the group sessions as excellent. The proportions of participants with changes in KAP from baseline to endline were as follows: 45.3% (n=62) to 81.0% (n=111) (p=0.004) able to identify at least two pre-disposing factors for hypertension; 65.0% (n=89) to 77.4% (n=106) (p=0.02) reporting [&ge;]1day of vigorous physical activity/week; 28.5% (n=39) to 13.9% (n=19) (p=0.001) reporting salt added to meals at the table. There was no statistical evidence of any difference in medication adherence, p=0.06. The proportion of participants with uncontrolled hypertension was 58.1% (n=79) at baseline and reduced to 31.8% (n=43) at follow-up (p<0.001). DiscussionCommunity groups for improving awareness, detection and support are acceptable and led to improvements in self-reported KAP and prevalence of uncontrolled BP. Further research on the sustainability and impact of the intervention is required.

Target trial emulation (TTE) offers a principled way to estimate treatment effects using real-world observational data, but analyses of time-varying treatment strategies remain vulnerable to immortal time bias. The clone-censor-weight (CCW) approach is increasingly used to address this problem, yet key aspects of its causal interpretation and implementation remain unclear. In this work, we emulate a target trial using electronic health records (EHRs) to compare completion of a 3-dose 9-valent human papillomavirus vaccination (HPV) series within 12 months versus remaining partially vaccinated among vaccine initiators. We link CCW to the classic potential outcome framework in causal inference, evaluate the role of different weighting mechanisms, and account for within-subject correlation induced by cloning using cluster-robust variance estimation. Our study provides practical guidance for applying CCW in real-world comparative effectiveness studies to address immortal time bias and supports more rigorous and interpretable treatment effect estimation in TTE.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continuously evolved since its emergence in the human population in 2019. As of 1st August 2025, more than 1,700 Omicron subvariants have been designated by the Pango nomenclature system. The Pango nomenclature system designates a new lineage based on genetic and epidemiological information of SARS-CoV-2 strains. However, there is a possibility that strains that have similar genetic backgrounds and the same phenotype are given different Pango lineage names. In this paper, we propose a new algorithm, called FindPart-w, which can identify groups of viral lineages that share the same relative effective reproduction numbers. We introduced a new lineage replacement model, called the constrained RelRe model, which constrains groups of lineages to have the same relative effective reproduction numbers. The FindPart-w algorithm searches the equality constraints that minimise the Akaike Information Criterion of constrained RelRe models. Using hypothetical observation count data created by simulation, we found that the FindPart-w algorithm can identify groups of lineages having the same relative effective reproduction number in a practical computational time. Applying FindPart-w to actual real-world data of time-stamped lineage counts from the United States, we found that the Pango lineage nomenclature system may have given different lineage names to SARS-CoV-2 strains even if they have the same relative effective reproduction number and similar genetic backgrounds. In conclusion, this study showed that viruses that had the same relative effective reproduction number were identifiable from temporal count data of viral sequences. These findings will contribute to the future development of lineage designation systems that consider both genetic backgrounds and transmissibilities of lineages.

Understanding the dynamics of HIV epidemics is important to control them effectively. Classical methods that mainly rely on occurrence data are limited by the fact that an unknown part of the epidemic eludes sampling. Since the early 2000s, phylodynamic methods have enabled the estimation of key epidemiological parameters from virus genetic sequence data. These methods have the advantage of being less sensitive to partial sampling and to provide insights about epidemic history that even predates the first samples. In this study, we analysed 2,205 HIV sequences from the French ANRS PRIMO C06 cohort. We identified and were able to reconstruct the temporal dynamics of two large clades that represent the HIV-1 epidemics in the country. Using Bayesian phylodynamic inference models, we found that the first clade, from subtype B, originated in the end of 1970s, grew rapidly during the 80s before decreasing from 2000 to 2015 and stagnating since then. The second clade, from circulating recombinant form CRF02_AG, emerged and spread in the 80s, grew again in the early 2000s, before declining slightly. We also estimated key epidemiological parameters associated with each clade. Finally, using numerical simulations, we investigated prospective scenarios and assessed the possibility to meet the 2030 UNAIDS targets. This is one of the rare studies to analyse the HIV epidemic in France using molecular epidemiology methods. It highlights the value of routine HIV sequence data for studying past epidemic trends or designing public health policies.

Non-pharmaceutical interventions (NPIs) have been important for controlling SARS-CoV-2 transmission, particularly before and during initial vaccine rollout. During the pandemic, the US Centers for Disease Control and Prevention issued isolation and masking guidance in case of COVID-19-like illness, a positive SARS-CoV-2 test, or known exposure to SARS-CoV-2. However, the impact of this guidance on mitigating transmission in office workplaces is unclear. We used a network-based mathematical model to estimate the impact of this guidance on SARS-CoV-2 transmission among office workers and their communities. The model represented social contacts in the home, office, and community. We used data from the CorporateMix study to parametrize social contacts among office workers and calibrated the model to represent the COVID-19 epidemic in Georgia, USA from January 2021 through August 2022. In the reference scenario (58% adherence to guidance among office workers and the broader population), workplace transmission accounted for a small fraction of total infections. Reducing adherence among office workers to 0% increased workplace transmissions by 27.1% and increasing adherence to 75% reduced workplace transmission by 7.0%. Increasing adherence to 75% among office workers had minimal impact on symptomatic cases and deaths; increasing it among the broader population was more effective in reducing office worker cases and deaths. In our model, moderate adherence to recommended NPIs in workplaces was effective in reducing transmission, but increasing adherence had limited benefit given workplaces that have low contact intensity and hybrid work arrangements. These results underscore the public health benefits of community-wide adoption of recommended NPIs.

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.

BackgroundType 2 diabetes mellitus (T2DM) is a leading global public health challenge. Machine learning (ML) combined with Explainable AI (XAI) is increasingly applied to T2DM risk prediction, but the field lacks a quantitative overview of methodological trends and integration gaps. MethodsWe present a structured synthesis and critical analysis of the XAI literature on T2DM risk prediction, combining (i) quantitative bibliometric analysis of a two-database corpus (N = 2,048 documents from Scopus and PubMed/MEDLINE, deduplicated via a transparent three-tier pipeline) and (ii) an in-depth selective review of 15 highly cited papers. Reporting follows PRISMA 2020, adapted for metadata-based synthesis; analyses include keyword frequency, rule-based thematic clustering, and publication trend analysis. ResultsThe field grew rapidly, from 36 documents (2020) to 866 (2025). SHAP and LIME dominate XAI methods; XGBoost and Random Forest dominate ML models. Critically, KG/GNN terms appeared in only 17 documents ([~]0.83%) compared with 906 for XAI methods, a 53.3:1 disparity. This gap is consistent across both databases, which share 33.2% of their records, ruling out a single-database artifact. The selective review confirmed that none of the 15 highly cited papers combined all three components, ML, XAI, and KG, in T2DM risk prediction. ConclusionsThe XAI for T2DM risk prediction field exhibits a clinical interpretability gap: statistical explanations are rarely linked to structured clinical pathways. We propose a three-layer conceptual framework (Predictive [-&gt;] Explainability [-&gt;] Knowledge) that integrates KG as a supplementary semantic layer, with potential applications in clinical decision support and population-level screening. The framework does not perform true causal inference but structures explanations around established pathophysiological knowledge. This study contributes a transferable methodology and a quantified research gap to guide future work integrating ML, XAI, and structured medical knowledge.

Ambulatory Care Sensitive Conditions (ACSCs) are conditions for which effective and timely primary health care (PHC) can prevent hospitalizations. They are widely used as a proxy indicator of access to and quality of PHC. Despite their relevance, evidence from Central America remains scarce. This study aimed to quantify the burden, describe the epidemiological profile, and assess temporal trends of ACSCs hospitalizations in Honduras from 2014 to 2024. We conducted a retrospective observational study using national administrative hospital discharge data from all Ministry of Health hospitals. ACSCs were defined using a standardized list of 20 diagnostic groups based on ICD-10 codes. We estimated percentages and sex-age-standardized hospitalization rates per 10,000 inhabitants. Clinical indicators included length of stay (LOS) and in-hospital fatality rates. Temporal trends were evaluated using joinpoint regression models to estimate annual percent changes (APC). Analyses included stratification by age, sex, and disease category. A total of 4,023,944 hospitalizations were analyzed, of which 547,486 (13.6%) were classified as ACSCs. The overall sex-age-standardized rate was 54.1 per 10,000 inhabitants. ACSCs' standardized rates increased between 2014 and 2018 (APC: 2.7%; 95% CI: -2.4; 15.2), declined sharply between 2018 and 2021 (APC: -17.8%; 95% CI: -30.6; -10.3), and increased again between 2021 and 2024 (APC: 15.9%; 95% CI: 4.6; 37.6). Despite this rebound, rates remained below pre-pandemic levels. ACSCs were concentrated among children under 5 years (27.7%) and adults aged 60 years and older (29.9%). Noncommunicable diseases accounted for 56.8% of cases, with diabetes mellitus as the leading cause. Compared with non-ACSCs hospitalizations, ACSCs were associated with longer LOS (4.9 vs. 3.9 days; p <0.001) and higher in-hospital fatality rates (2.4% vs. 1.7%; p <0.001). ACSCs hospitalizations constitute a substantial burden in Honduras and reflect persistent gaps in PHC performance. Strengthening PHC resilience and capacity, particularly for chronic disease management and vulnerable populations, is essential to reduce avoidable hospitalizations and improve health system efficiency and equity.

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