JMIR Public Health and Surveillance

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

Objective: In this study, we utilized a large-scale clinical database to evaluate the relationship between polypharmacy and adverse outcomes among type 2 diabetes patients in rural Montana to inform strategies that improve adherence, reduce preventable complications, and promote equitable diabetes care in underserved regions. Research Design and Methods: 591 patients from the Big Sky Care Connect Database (BSCC) with type 2 diabetes and medication history were stratified into 3 cohorts based on prescribed number of medications: (1-4 medications, non-polypharmic), (5-9 medications, polypharmic), and ([&ge;]10 medications, hyperpolypharmic). Each cohort was examined for Major Adverse Cardiovascular Events (MACE) and Diabetes Complication Severity Index (DCSI). Descriptive statistics, multivariate logistic regressions, linear regression, and Poisson regression analyses were performed. Results: Medication count was associated with male gender ({beta} = -2.1341, p < 0.001). Both medication count (IRR 1.06 per additional medication, p < 0.001) and age (IRR 1.03 per year, p < 0.001) were significant predictors of MACE. Neuropathy and nephropathy prevalence was statistically significant (p < 0.001) across patient cohorts and increased with medication count.

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.

Background: Older in-patients have a higher prevalence of diabetes and cognitive impairment. Cognitive impairment can make blood glucose management more challenging, since patients might not remember to measure blood glucose or report symptoms. Investigating the accuracy of continuous glucose monitoring (CGM) compared to usual care will inform clinical interpretations in this vulnerable population. Aim: To compare CGM derived glucose metrics and point-of-care tests (POCT) in older in-patients with T2DM and cognitive impairment and to investigate CGM accuracy compared to POCT in the hospital settings with the same population. Methods: Thirty-two older people with comorbid T2DM and cognitive impairment were recruited within a tertiary care hospital in the UK. All participants were naive to CGM and were asked to wear blinded Dexcom G7 sensors for up to 10 days. All participants received usual care in their hospital stay including the use of POCT. Key accuracy metrics comprised the mean absolute relative difference (MARD), median absolute relative difference (median ARD), and Clarke Error Grid (CEG), correlation (R2) analysis. In addition, the percentage of CGM readings falling within +/-20% of reference glucose values when the reference was >5.6 mmol/L, or within +/-1.1 mmol/L when the reference was <=5.6 mmol/L (+/-20%/1.1 mmol/L) was calculated to assess analytical and clinical accuracy. Results: Thirty participants completed the study. CGM derived mean glucose for time in range (TIR= 4-10 mmol/mol) was 36.23% (min= 0%, max= 90%), time above range (TAR >= 10 mmol/mol) was 62.87% and time below range (TBR <= 3.9 mmol/mol) was 1.03%. Mean TIR based on available POCT readings was 40.84%, TAR was 57.24% and TBR 1.81%, showing similar readings as CGM derived glucose metrics. Comparison of the two resulted in a MARD of 17.4%, and median ARD of 12.2% and the outcome of +/-20%/1.1 mmol/L analysis was 72.3%. CEG analysis revealed that 99.3% of the data points fell within the clinically acceptable zones (Zone A and Zone B), and there was a strong correlation (R2=0.82) between CGM and POCT. CGM captured more hypoglycaemic readings in our participants. Conclusion: Our study suggests that CGM and POCT derived glucose metrics are largely similar for in-patients with diabetes and cognitive impairment. CGM remains as a safe and clinically acceptable tool, and able to capture more nocturnal hypoglycaemia compared to POCT in a subgroup of patients. These initial findings show that CGM might be a viable alternative for people with comorbid T2DM and cognitive impairment.

Background Six years after its emergence, SARS-CoV-2 continues to have a substantial burden. The impact of vaccination and the optimal timing of its rollout remain uncertain given existing population immunity and variability in outbreak timing between summer and winter. Methods The US Scenario Modeling Hub convened its 19th round of ensemble projections for COVID-19 hospitalizations and deaths in the United States, where eight teams projected trajectories in each US state and nationally from April 2025 to April 2026 under five scenarios regarding vaccine recommendations and timing. Recommendations had two eligibility scenarios (high-risk individuals only and all-eligible) and two timing scenarios (classic start: mid-August, earlier start: late June). These were crossed to create four scenarios and were compared against a counterfactual scenario with no vaccination. Findings Compared to no vaccination, our ensemble projections estimated 90,000 (95% PI 53,000-126,000) hospitalizations averted in the high-risk and classic timing scenario across the US. Expanding to all-eligible age-groups averted an additional 26,000 (95% PI 14,000-39,000) hospitalizations, which when coupled with the early vaccination timing, was projected to further reduce national hospitalizations by 15,000 (95% PI -3,000-33,000). The majority of teams projected both summer and winter waves. Implications We project COVID-19 will cause significant hospitalizations and deaths in the US in the 2025-26 season and estimate significant benefits from a broad all-eligible vaccination recommendation. The results also suggest an additional benefit is likely to be gained from an earlier vaccination campaign. Funding Centers for Disease Control and Prevention; National Institute of Health (US), National Science Foundation (US)

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.

AimWe examined associations between smoking and HbA1c among U.S. adults, and whether these associations vary by diabetes status. MethodsWe analyzed NHANES data from 2015-2018 for adults aged [&ge;]20 years. Smoking was assessed by self-report and serum cotinine. Survey-weighted multivariable linear regression was used to evaluate the association between smoking and HbA1c in the full population (N=9,214) and in adults without diabetes (N=7,328), adjusting for demographics, blood pressure, waist circumference, lipids, and C-reactive protein. ResultsAfter adjustment for cardiometabolic covariates, there was no significant association between smoking and HbA1c in the full population (former: {beta}=0.029%, p=0.30; current: {beta}=0.053%, p=0.13). Among adults without diabetes, former smoking was not associated with HbA1c, whereas current smoking remained significantly associated (former: {beta}=-0.001%, p=0.923; current: {beta}=0.067%, p<0.001). These findings were similar when cotinine was used as the exposure measure, with active smoking ([&ge;]3.0 ng/mL) associated with higher HbA1c among non-diabetic adults (p<0.001), but not in the full population. ConclusionsAmong adults without diabetes, current but not former smoking was associated with higher HbA1c. The absence of an association in former smokers suggests that this effect may attenuate following cessation. These findings support early cessation interventions and may inform cessation counseling and diabetes screening.

ObjectivesTo develop and evaluate predictive models for unused outpatient appointments (missed or cancelled) using a large national electronic health record (EHR) repository in the United States. DesignRetrospective observational study using machine learning and statistical modeling. SettingA U.S. national electronic health record repository (Cerner Real World Database) covering healthcare encounters from 2010 to 2025. ParticipantsAdult patients aged [&ge;]18 years with routine outpatient encounters recorded in the database. One outpatient appointment with a known status was randomly selected per patient, resulting in a final analytic sample of 5,699,861 encounters. Primary and Secondary Outcome MeasuresThe primary outcome was whether the index outpatient appointment was attended or unused (missed or cancelled). Model performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. MethodsPredictors included patient characteristics (demographics and insurance type), appointment characteristics (day, time, season, and urbanicity), prior cancellation rate, and time gap between the index appointment and the previous visit. We compared the predictive performance of two machine learning models (random forest classifier and extreme gradient boosting (XGBoost)) with logistic regression. An explainable AI analysis of feature impact was performed on the final XGBoost model. ResultsAmong 5,699,861 outpatient encounters, 3,650,715 (64.0%) were attended and 2,049,146 (36.0%) were unused. XGBoost achieved the best predictive performance on the test dataset (AUC = 0.95), followed by random forest (AUC = 0.92) and logistic regression (AUC = 0.89). Feature impact score analysis revealed highly non-linear associations between predictors and the risk of unused appointments at the individual level. ConclusionsUnused outpatient appointments can be accurately predicted using routinely available EHR data. Integrating predictive models into scheduling workflows may improve healthcare efficiency and optimize appointment management. Article SummaryStrengths and limitations of this study O_LIThis study used one of the largest national electronic health record datasets to develop predictive models for unused outpatient appointments. C_LIO_LIMultiple modeling approaches, including logistic regression and machine learning methods (random forest and XGBoost), were compared to evaluate predictive performance. C_LIO_LIAn explainable artificial intelligence method was applied to quantify feature impact and improve model interpretability. C_LIO_LIThe retrospective design and reliance on routinely collected EHR data may introduce data quality limitations and unmeasured confounding. C_LIO_LIThe database did not distinguish clearly between cancelled appointments and no-shows. C_LI

Background: People with HIV (PWH) have increased risk for cardiovascular diseases and other age-related comorbidities. These risks can be reduced through moderate to vigorous physical activity (MVPA), but MVPA can be difficult to sustain over time. Purpose: We tested tailored text messages added to motivational interviewing (MI) to sustain MVPA among PWH. Messages were created based on Two Minds Theory and matched to daily survey responses about exercise barriers. Methods: 118 PWH ages > 50 were initially randomized to high-intensity interval training or continuous moderate-intensity exercise. After 16 weeks, 92 participants were re-randomized to receive either tailored messages plus MI, or educational control messages, for 12 weeks. Both groups completed daily barrier surveys and wore an ActiGraph monitor for 1 week/month. Results: PWH still receiving messages at 28 weeks maintained their MVPA, ending at M = 48.8 minutes per day (SD = 45.8, n = 22/29), compared to a decrease among PWH in the educational-control group, ending at M = 40.7 (SD = 24.6, n = 25/32), p = .01 for the group-by-time interaction. Findings were similar using both actigraphy and self-reported MVPA, and were robust to attrition based on intent-to-treat analysis. PWH in the tailored-messaging group also reported higher exercise self-efficacy and better perceived health over time, relative to those in the educational-control group. Conclusions: An automated tailored-messaging intervention led to sustained MVPA. Tailored messages were superior to non-tailored educational messages, and may help PWH maintain their long-term health. Exploratory analyses suggested these effects were additive to motivational interviewing.

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.

Introduction. Empirical evidence linking specific national structural policies to the provision of key HIV services in low- and middle-income settings remains scarce. This study addresses the research gap by quantifying the within-country relationships between six national structural policy indicators and the presence of the HIV prevention service component targeted at sex workers in Southeast Asia. Methods. We constructed a balanced panel dataset covering eight Southeast Asian countries from 2018 to 2025 from the UNAIDS Global AIDS Monitoring (GAM) framework. We used Fixed-Effects (FE) and Random-Effects (RE) models to analyse the relationships, with the FE model selected as the more statistically appropriate estimator. We enhanced robustness by using clustered standard errors and one-period lagged explanatory variables. Results. The primary finding from the FE model indicated a statistically significant and positive contemporaneous association between the existence of legal or administrative barriers to social protection (barriers_spi,t) and the presence of HIV prevention services for sex workers ({beta} = 0.8531; p < 0.001). However, the robustness check revealed a statistically significant negative association between the two when using the lagged barrier variable (barriers_spi,t-1), suggesting a decline in HIV prevention service availability over time ({beta} = -0.3540; p < 0.05). We did not find any other policy variable's coefficient to be statistically significant in the FE models. Conclusions. While the immediate recognition (contemporaneous effect) of structural barriers to access social protection may occur alongside prioritised HIV prevention service provision, the sustained presence of these impediments acts as a long-term constraint that undermines the effectiveness and sustainability of targeted HIV programmes. National HIV programmes must urgently prioritise the removal of structural barriers to ensure long-term service stability for key populations.

BackgroundIn long-term care facilities (LTCFs), close-contact identification often relies on staff recall and monitoring records because residents may be unable to self-report reliably. How these different record-generation processes relate to proximity-based sensor measurements in routine LTCF workflow remain unclear, and how such differences may influence contact-based decision-making in outbreak response is not well understood. MethodsWe conducted a five-day observational study in a Japanese LTCF using ultra-wideband (UWB) indoor positioning. Twenty-seven participants wore UWB tags, including 16 residents and 11 staff members; 10 staff members completed questionnaires. We compared UWB-derived proximity with questionnaire-derived contacts from staff self-report and monitoring-based proxy records, and assessed directional discrepancies under multiple distance-time thresholds. ResultsQuestionnaire-based records and UWB-derived proximity showed different patterns of discrepancy across contact types. Within this facility, resident-related monitoring-based proxy records showed relatively small directional discrepancies, whereas staff self-reports tended to identify additional resident-staff contacts under the baseline threshold ([&le;]1.0 m for [&ge;]15 min). Several alternative thresholds were associated with discrepancies closer to zero than the baseline, although the apparent ranking varied by summary metric. ConclusionsIn this single-facility observational study, different contact-list generation processes were associated with different patterns of discrepancy relative to a proximity-based operational measure. These findings support interpretation in terms of workflow-specific contact-list generation rather than a single universally optimal threshold and may help inform facility-level review of contact identification practices in LTCFs. These findings support aligning contact identification strategies with facility-specific workflows to improve the feasibility and effectiveness of IPC practices in LTCFs.

Background: Safety is a critical concern in behavioral health crisis units (BHCUs), where environmental risks (e.g., ligature points) can lead to injury to self or others. However, limited research has examined how perceived safety influences facility selection among patients and care partners, or how these perceptions align with AI-driven safety risk assessments in such environments. Method: To address these gaps, a nationwide discrete choice online survey was conducted using image-based scenarios of BHCU environments, where participants selected preferred facilities based on a range of attributes, including environmental safety risks (e.g., ligature points). Additionally, participants identified safety risks in survey images, which were compared with outputs from an AI-driven tool developed and trained to detect environmental risks by experts. Quantitative analysis using conditional logit models examined the influence of attributes on facility choice, while spatial comparisons of annotated images and heatmaps assessed participant and AI-identified risk alignments. Results: Findings revealed that the higher frequency of safety risks in images significantly reduced the likelihood of facility selection (p < .001, OR {approx} 1.28), highlighting the importance of perceived safety in user decision-making. While there was notable alignment between heatmaps generated by participants and AI, key differences emerged, suggesting that participant safety perception was influenced by features not fully captured by AI, such as the type of materials or unknown, out-of-label safety risks in facility images. Conclusions: Despite these limitations, results highlighted the value of integrating AI-driven assistive tools for non-expert user safety risk assessment to support decision-making for safer BHCU environments.

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.

The global ambition to end the human immunodeficiency virus (HIV) epidemic requires understanding which system-level policy levers, enacted under the framework of Universal Health Coverage (UHC), are most effective in achieving both transmission reduction and diagnostic coverage. This study addresses an important evidence gap by quantifying the within-country association between measurable UHC policy indicators and the estimated rate of new HIV infections across nine Southeast Asian countries between 2013 and 2022. Employing a Fixed-Effects panel data methodology, the analysis controls for time-invariant national heterogeneity, ensuring reliable estimates of policy impact. We found that marginal changes in total current health expenditure (CHE) as a percentage of gross domestic product (GDP) were not statistically significantly associated with changes in HIV incidence. However, increases in the UHC Infectious Disease Service Coverage Index were statistically significantly associated with concurrent reductions in HIV incidence (p < 0.001), suggesting the efficacy of targeted service implementation as the principal driver of curbing new HIV infections. In addition, the UHC Reproductive, Maternal, Newborn, and Child Health Service Coverage Index exhibited a statistically significant positive association with changes in HIV incidence (p < 0.01), which is interpreted as a vital surveillance artefact resulting from expanded detection and reporting of previously undiagnosed HIV cases. Furthermore, out-of-pocket (OOP) health expenditure as a percentage of CHE showed a counter-intuitive negative association with changes in HIV incidence (p < 0.01), suggesting this metric primarily shows ongoing indirect cost burdens on the established patient cohort, or, alternatively, presents a diagnostic access barrier that results in lower case finding. These findings suggest that policymakers should prioritise investment in targeted infectious disease service efficacy over aggregate fiscal commitment and utilise integrated sexual health platforms for strengthened HIV surveillance and case identification.

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.