JMIR Public Health and Surveillance

Background. Public-health use of influenza surveillance - situational awareness, planning for clinical surge, and issuing advisories or warnings - needs more than an accurate point estimate of next week's count. What matters operationally is whether a rise is caught early, whether the direction of change is right, and whether an alert threshold is about to be crossed, all with honest uncertainty. For such use a forecast must also respect reporting delay, later data revision, and the calibration of its prediction intervals. Using Japan's sentinel surveillance, we evaluated short-term probabilistic forecasts that respect the data release history, and we measured what adding other surveillance series contributes, focusing on the properties that matter for public-health decisions. Methods. The target was the number of influenza reports per sentinel site in each of the 47 prefectures. For each forecast we predicted, as quantiles, the value 1-4 weeks after the most recent observed week. We compared three models: a ridge regression trained jointly across prefectures; a gradient-boosting quantile regression (GBQR) that learns from many surveillance series; and their equal-weight combination on the log scale. Six seasons were used to develop the models. The 2025/26 season was held out as a temporal evaluation period after the specification was fixed; because its epidemic profile had already been seen, it is not a fully independent prospective test. We scored forecasts with the weighted interval score (WIS) and checked prediction-interval coverage. For the Japanese target series we used the value first published for each week; some auxiliary series were taken as retrieved and therefore include later revisions. Results. In 2025/26, the combination's WIS relative to a persistence forecast was 0.841, 0.811, 0.794 and 0.818 at horizons of 1-4 observed weeks - that is, 16-21% lower than persistence. Coverage of the 80% prediction interval was 79.7%, 77.1%, 74.5% and 75.1%, and of the 95% interval 94.3%, 93.4%, 92.6% and 91.8%. Adding auxiliary series improved the GBQR over the development period. However, on the original scale the combination's edge over ridge regression was not statistically clear, and in some low-level rising phases the combination did worse than ridge. Conclusions. Over the period studied, models that share information across regions scored better than a persistence forecast. For public-health use, however, the operative questions are the real lead time once reporting delay is accounted for, whether rises from low levels and threshold exceedances are caught in time, and whether the upper prediction intervals are wide enough; on these, the present evidence is not yet sufficient. The 2025 change to the sentinel system also means that pre-change advisory and warning thresholds cannot be read mechanically. A prospective evaluation with strictly controlled data availability is needed before such forecasts are used to support advisories or planning.

Background: Clinical decision support (CDS) systems may improve chronic pain management by providing guideline-based recommendations within clinical workflows. However, low clinician use can limit their effectiveness. Understanding the association between CDS use and guideline-recommended care may help distinguish implementation challenges from limitations in CDS functionality. Objective: To estimate the association of OneSheet, a CDS system for chronic pain management, and guideline-recommended care when the system was used during patient encounters. Methods: We conducted a per-protocol secondary data analysis of data from a pragmatic randomized clinical trial conducted from October 2020 to May 2022 across 25 primary care clinics at 2 academic health systems in Indiana and North Carolina. The parent trial enrolled 137 primary care clinicians. This analysis included 145511 eligible chronic pain encounters and 15480 long term opioid therapy (LTOT) encounters that occurred after OneSheet implementation. OneSheet use at a patient encounter was the exposure of interest. Propensity score matching was used to create comparable groups of encounters with and without OneSheet use. Outcomes included documentation of pain-related goals, documentation of pain and function using the Pain, Enjoyment of Life and General Activity (PEG) scale, prescription drug monitoring (PDMP) review, urine drug screening (UDS) orders, and naloxone orders. Results: OneSheet was used in 959 of 145511 chronic pain encounters (0.7%) and 582 of 15480 LTOT encounters (4.0%). In matched analyses, OneSheet use was associated with increased documentation of pain-related goals (relative risk (RR)=11.70 (95% CI 4.25-32.50) and PEG score documentation (RR=9.70 (95% CI 4.14-22.31). Among LTOT encounters, OneSheet use was associated with increased UDS ordering (RR=1.52 (95% CI 1.40-1.65) and PDMP review (RR=1.06 (95% CI 1.03-1.09), but decreased naloxone ordering (RR=0.13 (95% CI, 0.02-0.90). Conclusions: OneSheet use was associated with several guideline-recommended chronic pain management actions, suggesting that they system may support care quality when clinicians use it. However, use was rare, indicating that increasing routine use through better workflow integration may be necessary to achieve population-level improvements in chronic pain care.

Infectious disease forecasting supports public health preparedness and response by informing resource allocation, intervention planning, and situational awareness. Infectious disease forecasting capabilities in the United States have grown over the past decade yet whether forecast quality has improved over this time remains unclear. Current evaluation metrics describe relative performance for a common set of forecast targets but cannot evaluate performance across different outcomes, seasons, and pathogens. We addressed this challenge by developing a novel skill score metric to assess forecasts based on performance relative to two benchmark models: a hindcast as an idealized upper bound of performance and a baseline model as a minimal performance standard. To study the impact of the baseline choice, we considered three types of baseline models with varying levels of complexity. Using this skill score framework, we retrospectively evaluated short-term influenza (influenza-like illness and hospitalization) and COVID-19 (case, death, and hospitalization) forecasts, focusing on how the performance of team and multi-model ensemble forecasts changed over time. The ensemble outperformed 99% of forecasts from a simplistic, naive baseline, and 84-91% of forecasts from more complex baseline models that incorporate recent data. We found consistent improvements in ensemble and team forecast skill over time for both influenza forecast targets, with 4-week horizon forecasts in the most recent year having higher skill scores than 1-week horizon forecasts in the first year of each challenge. Results were mixed for COVID-19 forecasts. This framework provides a comprehensive picture of progress in infectious disease forecasting over the past decade enabled by improved standardization, data, models, and collaboration.

Aims: To understand how users believe continuous glucose monitoring (CGM) changes their behaviour, which app features they credit, and whether endorsing more mechanisms of action (MoAs) is linked to reported diet and physical activity change. Methods: We surveyed 112 U.S. adults with type 1 or type 2 diabetes who had used a Dexcom or Abbott Libre CGM within 12 months. Participants rated 26 MoA statements mapped to the Capability, Opportunity, Motivation-Behaviour (COM-B) model, identified contributing app features, and reported CGM-attributed diet and physical activity change. Logistic regression examined MoA count and COM-B domains as predictors of behaviour change. Results: Participants (mean age 54.5 years; 60.7% female) reported dietary (82.1%) and physical activity (46.4%) change and endorsed a median of 18 MoAs, most often relating to knowledge, skills and behavioural consequences. Real-time glucose and graphs were attributed to many MoAs. Each additional MoA was associated with higher odds of dietary (OR 1.27, 95% CI 1.15-1.44) and physical activity change (OR 1.27, 95% CI 1.16-1.43). Motivation made the strongest distinct contribution among COM-B domains. Conclusions: Users perceive CGM as engaging many behavioural mechanisms; endorsing more was linked to behaviour change. Motivation may be a key target for CGM-based behavioural support.

Objective: To examine associations of health insurance status, household income, and cost-related healthcare barriers with receipt of a routine medical checkup among U.S. adults. Methods: This cross-sectional study analyzed 2024 Behavioral Risk Factor Surveillance System data. Survey-weighted descriptive estimates and multivariable logistic regression incorporated sampling weights, strata, and primary sampling units. The outcome was a routine medical checkup within the previous 12 months. Results: Among 321,592 complete cases, 78.0% of U.S. adults reported a recent routine checkup. Cost-related barriers (adjusted odds ratio [AOR], 0.56; 95% confidence interval [CI], 0.54-0.57) and uninsurance (AOR, 0.28; 95% CI, 0.27-0.29) were associated with lower checkup utilization. Lower household-income groups generally had lower adjusted odds than adults with incomes of $200,000 or more. Conclusions: Insurance, income, and reported cost barriers were independently associated with routine checkup utilization. Findings are descriptive and do not establish causal effects. Keywords: health insurance; household income; cost-related barriers; routine medical checkup; healthcare utilization; BRFSS

Importance: Conditions that are hard to diagnose, several more common in women, may be missing from electronic health records (EHRs), hiding patients from research cohorts and case finding. Objective: To measure how often the EHR contains a diagnosis code for chronic conditions that participants report, and to compare this with national survey benchmarks. Design, Setting, and Participants: This cross-sectional study used the All of Us Research Program Registered Tier Dataset v9. Participants were adults with EHR visits in both 12-month periods from July 2021 to June 2023 who completed a health history survey. We analyzed the data in October 2026. Exposures: Self-reported clinician diagnosis of 10 conditions with ongoing visits or treatment, as well as sex at birth, race and ethnicity, age, and years of EHR records. Main Outcomes and Measures: Share of participants reporting current care who had a qualifying diagnosis code, alone and relative to hypertension and type 2 diabetes, and the ratio of age-standardized coded shares to national survey prevalence. Results: Of 161,480 respondents, 114,610 (73,870 female [64.5%], median [IQR] age 63 [50-72] years) had at least 5 years of EHR records before the survey. Among those reporting current care, 84.6% (95% CI, 84.2%-85.0%) with hypertension and 87.0% (95% CI, 86.5%-87.6%) with type 2 diabetes had a code, compared with 41.3% (95% CI, 38.9%-43.8%) for endometriosis, 39.4% (95% CI, 34.3%-44.8%) for Lyme disease, and 24.4% (95% CI, 23.2%-25.6%) for chronic fatigue. Counting ICD-9-CM and other mapped codes, 55.3%, 51.5%, and 28.3%, respectively, had any qualifying record. Women had lower coded shares than men for hypertension, type 2 diabetes, obesity, depression, and anxiety (crude ratios 0.93-0.96, within-health-system ratios 0.98-1.02) and higher shares for migraine and fibromyalgia (within health systems, 1.11 [95% CI, 1.07-1.15] and 1.41 [95% CI, 1.29-1.57]). Age-standardized coded shares among active patients were 1.1 to 2.4 times national prevalence of the same self-reported diagnoses. Conclusions and Relevance: In this cross-sectional study, fewer than half of participants reporting ongoing care for chronic fatigue, Lyme disease, or endometriosis had a qualifying ICD-10-CM code. Linking each participant's report to their own record measured this gap directly.

Background: Since Michigan's legalization of recreational adult-use cannabis in December 2018, health care providers in the city of Kalamazoo, Michigan, have informally observed an increase in cannabinoid hyperemesis syndrome (CHS) presentations, although the extent of this increase has not been formally characterized locally. CHS is frequently underrecognized and misdiagnosed, often resulting in repeated emergency department (ED) visits and substantial diagnostic costs before a diagnosis is reached. Despite this clinical and financial burden, it is unclear whether consumers are adequately informed of this risk through point-of-sale or public health resources. Methods: A cross-sectional environmental scan was conducted to identify publicly available educational content about CHS on the websites of licensed cannabis retailers in Kalamazoo and relevant public health agencies. Provisioning centers and public health websites were screened using a structured coding rubric developed specifically for the environmental scan to assess the presence, depth, web page placement, and intended audience of CHS-related content. Additionally, the electronic medical records at a local teaching hospital in Kalamazoo were queried to identify ED visits with a primary diagnosis of CHS from January 1, 2010 to December 31, 2025. Results: Before the legalization of marijuana, the hospital recorded six ED encounters with the primary diagnosis of CHS between January 2010 and November 2018, and 274 ED encounters between December 2018 and December 2025 (mean age: 29 years; 157 females, 123 males). The incidence rate of CHS-related ED encounters was 57.2 times higher during the post-legalization period than during the pre-legalization period (95% CI, 26.0-157.3; exact Poisson rate-ratio test, p < 0.0001). Among the licensed Kalamazoo provisioning centers reviewed, only one mentioned CHS as a warning but did not describe symptoms or offer medical guidance. CHS content was not identified on the websites of the state or local health departments. Conclusion: Despite the growing clinical concern regarding CHS, consumer-facing educational resources remain scarce. Although this study cannot establish a causal relationship between the availability of CHS-related educational content and the observed increase in ED encounters following legalization, it identifies a substantial lack of CHS-specific warnings in the evaluated consumer-facing resources. Given the clinical and potential economic burden associated with delayed CHS recognition, we recommend that Michigans existing point-of-sale pamphlet requirement be amended to include CHS-specific warning language.

Objective: To estimate Long COVID undercoding in electronic health records (EHRs) against federal survey prevalence, and to evaluate a predictor of later coding that anyone can rebuild from counts a research enclave releases. Materials and Methods: Among 23,790 All of Us adults with COVID-19, we compared coded shares with Household Pulse Survey prevalence. Among 23,300 adults uncoded at day 90, we predicted a first U09.9 code by day 455. We fitted naive Bayes and linear discriminant models outside the enclave from released counts and compared them with an elastic net fitted inside to the same patients and features. Tests were a random 20% of patients (4,680, 50 coded) and 18 held-out EHR sites (5,760, 60 coded). Results: Under survey-comparability assumptions, about 1 in 9 adults who ever had Long COVID carried its code, and the women-to-men coding ratio was 0.79 (95% sensitivity interval, 0.65-0.95). The released models' areas under the receiver operating characteristic curve (AUROCs) were 0.68 (95% CI, 0.60-0.75) on the patient test and 0.64 (0.55-0.72) at held-out sites, 0.049 (0.001-0.104) above the elastic net. On the patient test, the elastic net had a higher area under the precision-recall curve (difference 0.062, 0.003-0.122). Released probabilities overestimated coding risk at held-out sites. Discussion: Fitting from released counts did not consistently lower discrimination. Coded outcomes inform disease discrimination only under stated coding assumptions. Conclusion: Long COVID is substantially and unevenly undercoded, and a model rebuilt from public counts predicts its later coding with moderate discrimination.

Background: Digital diabetes programs have expanded quickly, but evidence of durable long-term benefit remains limited. In addition, as diabetes treatment guidelines changed to include cardiorenal-protective antihyperglycemics as first-line therapy, the effects of their adoption on glycemic control and program engagement within these programs remain poorly understood. Objective: To characterize longitudinal trends in glycemic control and estimate the extent to which these trends are associated with time-invariant and time-varying domains, among adults with T2D participating in Teladoc Health Remote Diabetes Monitoring Program (RDMP). Methods: This was a retrospective, longitudinal cohort study of adults with T2D participating in the Teladoc RDMP from 2021-2025. Domains were split into time-invariant (i.e. demographics and baseline clinical characteristic) and time-varying covariates (e.g. antihyperglycemic treatment complexity and adherence, program utilization and engagement, and adoption of cardiorenal protective antihyperglycemics). Primary outcome was estimated A1c (eA1c), calculated from FBG measurements captured through cellular-enable smart glucose meters. Longitudinal mixed-effects models were used to estimate adjusted annual trends in eA1c over 5 years, and Gelbach decomposition to quantify statistical contributions across domains. Secondary analyses examined attainment of eA1c <7% using GEE and complementary decomposition methods. Results: A total of 56,754 members with mean age 64.2 (SD 10.4), and 49% female were included in the analysis. Mean eA1c declined by 0.06 percentage points per year (95% CI, -0.06 to -0.06) and remained significantly lower after full adjustment for demographic, antihyperglycemic treatment and cardiorenal-protective therapy adherence, and program engagement factors ({beta}=-.04 percentage points/year; 95% CI, -.04 to -.03), demonstrating durable glycemic improvement over the five-year study period. Gelbach decomposition attributed most of the change between the base and fully adjusted trends to cardiorenal-protective therapies (170.8%), with an opposing contribution from program engagement (-71.9%) and minimal contribution from treatment history and complexity (1.1%). Adjusted odds of attaining eA1c <7% increased 8.0% annually (OR 1.08; 95% CI 1.07-1.09), corresponding to a 1.21-percentage-point annual increase in probability; adjusted predicted glycemic control increased from 75.2% in 2021 to 80.1% in 2025. Conclusion: In this five-year cohort, members who remained in the RDMP sustained improvements in eA1c and became more likely to reach an eA1c < 7%. Cardiorenal protective antihyperglycemics made the largest measured contribution to these trends, while program engagement contributed in the opposite direction. These findings show durable glycemic control but does not establish causal effects. Future evaluations of digital diabetes programs should account for pharmacotherapy, durability of improvements, and measure outcomes across cardiometabolic health.

Introduction: Japan achieves high retention and viral suppression among people on antiretroviral therapy, yet treatment starts late. Most people obtain subsidised treatment through a physical disability certificate for HIV-related immune dysfunction, which requires two blood tests at least four weeks apart. We quantified the diagnosis-to-treatment cascade, identified who waits and tested whether the second test changes certification. Methods: Retrospective cohort of people newly diagnosed with HIV at 11 designated AIDS core hospitals in 2015 to 2024. We measured intervals from screening to hospital visit and to antiretroviral therapy, and emulated certification from the first test alone and from the two-test rule under every possible number of non-laboratory criteria (k = 0 to 8). Results: Of 1302 people (96% men; 68% with CD4 <350 cells/L or AIDS), 1036 (80%) followed the two-test pathway and 261 started treatment after one test, mostly because of AIDS or acute infection. People reached the hospital a median of 11 days after screening. Treatment then began a median of 70 days after the first visit in the two-test pathway and 11 days in the single-test pathway; 0.2% and 60.9% started within 14 days. Delay rose with CD4 count: 46 days when the mean count was [&le;]200 cells/L, 117 days when it was >500 with a laboratory criterion (16% of the two-test pathway) and 334 days when it was >500 without one (3%; adjusted hazard ratio 0.14, 95% confidence interval 0.09 to 0.23). Medians ranged from 35 to 96 days across facilities. The second test changed little: the first CD4 count agreed with the mean of two (intraclass correlation coefficient 0.959), HIV-RNA [&ge;]5000 copies/mL persisted in 97.6%, and certification status agreed in 99.0% for k [&ge;]1 (92.5% for k = 0), with discordance concentrated near 500 cells/L; the first test gave a milder grade in at most 2.9%. Conclusions: The statutory second test adds at least four weeks before antiretroviral therapy in Japan without changing who is certified, and delays most the people with the highest CD4 counts. Certification on a single test, with treatment separated from the subsidy procedure, would remove a delay that has no clinical or administrative justification.

Objective: We explored the implementation considerations of machine learning (ML) suicide risk identification in a health system that serves primarily American Indian patients in the Southwestern United States. Previous work developed and reported the performance of suicide risk models, but traditional performance metrics do not directly reflect clinical workflows. Materials and Methods: Using EHR data collected for Emergency Department (ED) visits between 10/01/2019 and 10/02/2021, we conducted Decision Curve Analysis (DCA) to identify the standardized net benefit (SNB) of all models previously evaluated and plotted the potential workload implications of the model with the greatest SNB. Finally, we evaluated suicide screening practices during the study period to inform implementation barriers. Results: Among 9,244 patients, 139 (1.5%) had a suicide attempt or death within 90 days of an included visit. The population-specific logistic regression model had the greatest SNB compared to other approaches. When operationalized, the 95th percentile cutoff did not substantially increase clinical workloads, while the 75th percentile cutoff substantially impacted workloads. The secondary analysis on suicide screening depicted variations in screening administration over time. Discussion: While DCA identified advantages of ML suicide risk identification over current standards, the complexities of suicide-related care limited its clinical interpretability. A stepped-care framework may mitigate potential workload burdens. Training initiatives and dynamic implementation strategies will support high-quality implementation of ML suicide risk identification. Conclusion: Our analyses support the feasibility and utility of augmenting existing suicide screening practices with ML suicide risk identification, and demonstrate the importance of nuanced analyses to inform implementation considerations.

Background: Antimicrobial resistance (AMR) is a growing global health threat, and India's high antibiotic consumption makes public knowledge, attitudes, and practices (KAP) a critical determinant of stewardship outcomes. Existing Indian KAP evidence is drawn from small, localised cohorts, leaving a gap in nationwide, digitally-sourced data. This study aimed to describe antibiotic-related knowledge, attitudes and self-reported practices among users of a digital healthcare platform in India, examine demographic and regional variation, and identify potential areas for future stewardship interventions. Methods: A cross-sectional survey was conducted among adult users of the Tata 1mg platform (December 2025-February 2026), with participants invited via an in-app banner. Knowledge and attitude were each scored on a 0-4 scale (4 items); practice was scored as a percentage of applicable items answered favourably, with scoring adjusted for branching logic based on antibiotic-access route. Of 10,726 respondents who started the survey, 4,883 completed it (45.5%). City of residence was mapped to one of six regions (India's five statutory Zonal Council zones plus a North-Eastern region) for 4,187 respondents. Associations with demographic characteristics were assessed using multivariable linear regression, and regional differences were assessed using one-way ANOVA. A parallel survey of 256 physicians was conducted to enable patient-provider comparison. Results: Mean scores were: knowledge 2.28 (SD 1.19), attitude 0.91 (SD 1.14), practice 44.9% (SD 33.4); attitude, not knowledge, was the weakest domain. Older age was associated with higher scores across all domains (practice: B=12.52, p<0.001 for age [&ge;]65 vs. 18-34); city tier was independently associated with lower knowledge and attitude (Tier 3 vs. Tier 1: p=0.010 and p<0.001, respectively). Regional differences were non-significant for knowledge and practice but significant for attitude (F=2.56, p=0.026, ^2=0.003), driven by lower scores in the North-Eastern region. In the parallel physician survey, 55.5% of respondents estimated that more than half of their patients self-medicated before consultation. Conclusion: In this platform-based sample, attitudes were the weakest domain, not knowledge, and 42.0% said their last antibiotic did not come on a doctor's recommendation. The findings may inform digital stewardship touchpoints, such as point-of-purchase guidance and prescription verification, in line with India's National Action Plan on Antimicrobial Resistance 2.0. As a cross-sectional survey of platform users, it cannot establish the relative contributions of knowledge, beliefs and access pathways to antibiotic use.

Background: Health Information Exchanges (HIEs) offer a potential resource for timely, population-level infectious disease surveillance by integrating electronic health record data across healthcare systems. Methods: We developed an end-to-end informatics pipeline for near-real-time community-level health surveillance based in a regional HIE serving Southeast Texas. We identified deidentified individual-level COVID-19, influenza, and RSV cases using diagnosis codes and laboratory results and assessed demographic and temporal trends from January 2022 through August 2026. Results: We identified 492,242 COVID-19 cases, 343,977 influenza cases, and 33,159 RSV cases during the study period. Surveillance data captured expected seasonal patterns, including recurring winter COVID-19 and influenza peaks and shifting RSV epidemiology, while enabling monitoring of laboratory test positivity metrics and hospitalization trends across age groups. Conclusions: HIE-based surveillance provides scalable, near-real-time monitoring of disease targets across large populations. HIE infrastructure can enhance public health situational awareness and complement traditional surveillance systems by harmonizing vital health data across systems and providers and delivering timely, standardized, and representative population health indicators.

Background: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have rapidly expanded from primarily glucose-lowering agents for type 2 diabetes (T2D) to therapies used across diabetes, obesity, and cardiometabolic disease. Prescribing has risen sharply since 2018, especially with the broader uptake of semaglutide-based products. Still, real-world initiation patterns remain incompletely characterized in older adults with varying levels of cognitive vulnerability and frailty. Methods: We analyzed treatment patterns of GLP-1RA use in a retrospective cohort of adults aged >=65 years with T2D in a regional healthcare system from 2018 to 2026. We describe the demographic and clinical characteristics of new initiators of GLP-1RAs compared with initiators of alternative therapies, including sodium-glucose cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase-4 inhibitors (DPP-4i). We analyzed a sub-cohort of patients to identify the prevalence of baseline cognitive concerns using a large language model (LLM). We used a LASSO logistic regression to identify important predictors of GLP-1RA initiation compared to other therapies. Results: Of 58,754 adults aged 65 years or older with T2D, 5,095 initiated GLP-1RA, 5,483 initiated a SGLT2 inhibitor, and 1,516 initiated a DPP-4 inhibitor. GLP-1RA initiators were younger (mean 75.2 [SD 4.9] vs 77.7 [6.0] and 79.5 [7.1] years) and less often aged 80 years or older (17.7% vs 35.0% and 47.6%). The largest difference across groups was adiposity: mean BMI was 33.8 (6.2) vs 29.7 (5.7) and 28.6 (5.6) kg/m2 (SMD 0.592), with a BMI of 30 kg/m2 or higher in 72.1% vs 41.9% and 34.0%. Glycemic control was similar (mean HbA1c 7.46% vs 7.38% and 7.70%; SMD 0.165). GLP-1RA initiators were younger; more likely to be overweight; had lower average comorbidity index; and less dementia, heart failure, and renal disease than those initiating alternative therapies. GLP-1RA initiators had fewer hospitalizations 1-year prior to drug initiation. In a LASSO model, BMI greater than 27 kg/m2 was the strongest predictor of GLP-1RA initiation (positive), followed by baseline congestive heart failure (negative) and baseline hospitalization (negative). Conclusion: In this cohort of older adults, those with T2D who initiated GLP-1RAs were younger and had less comorbidities at baseline compared to initiators of other T2D therapies. Overweight BMI was the strongest predictor of GLP-1RAs initiation, whereas baseline HbA1c was similar across groups, suggesting that the decision to start GLP1-RA is more influenced by adiposity than glycemic control. Our analysis of real-world prescribing patterns appear to be in alignment with guidelines for Type II diabetes treatment1, which recommend GLP-1RA treatment for glucose lowering and weight management and SGLT2 treatment in those with risk of cardiovascular disease or heart failure.

Background HPV-vaccination prevents HPV-related cancers, but uptake remains suboptimal. In the Netherlands, a catch-up campaign invited 18-27-year-olds for an additional cost-free opportunity to initiate or complete their HPV-vaccination in 2023-2024, which led to 24% of this invited population getting vaccinated. As young adults increasingly seek health information online, understanding online questions, concerns and misinformation in response to the campaign may help improve future vaccination efforts. Supplemental to overall evaluation of the vaccination campaign, we piloted online social listening processes to generate an insight report for Public Health Service (PHS) Amsterdam. Objective This study piloted online social listening processes at the PHS Amsterdam to identify actionable insights into questions, concerns and misinformation surrounding the HPV-vaccination catch-up campaign among young adults. Methods Upon inventory of potential platforms and data sources, a retrospective online social listening analysis was conducted across the platforms Instagram, Facebook, and X, collecting data from PHS, the National Public Health Institute for Public Health and the Environment (RIVM), the Dutch public broadcasting foundation (NOS), and LINDA.meiden sources from January 1, 2023, to June 1, 2024. Publicly available posts, comments, and engagement concerning HPV-vaccination were manually collected, and inductively coded. Themes were assessed for risk, based on expected potential harm and observed reach, and used to develop recommendations. Results A total of 821 posts and 5157 comments were collected and analysed. Ten recurring themes were identified across varying levels of risk. Two themes were classified as high risk: an information gap regarding the effectiveness of HPV-vaccination and concerns about the transparency on HPV-vaccine side effects. Three themes were classified as medium risk: questioning HPV-vaccine effectiveness under certain circumstances, reduced general vaccine trust following COVID-19, and accessibility issues related to vaccination locations and service times. Two themes were classified as neutral risk: questions regarding eligibility criteria for the campaign and calls for broader HIPV-prevention measures. Finally, three low risk themes concerned questions regarding HPV-vaccine brand specifics, campaign choices, and questions about scheduling or rescheduling HPV-appointments. Comments also referred to subsequent information sources, including reliable sources (public health institutes), false narratives and misinformation. Recommendations focused on addressing questions and information gaps (i.e. improving HPV risk perception and providing more detailed HPV-vaccine information), improving communication about HPV-vaccination, and reducing practical barriers to vaccination. Conclusions Manual social listening across selected online sources provided actionable insights into the public discourse during the HPV-vaccination catch-up campaign. Results mostly reflected questions, concerns and information gaps. Although some themes were critical of HPV-vaccination, false narratives and misinformation did not dominate the discourse. Recommendations aim to reduce practical barriers and address information needs. This pilot showed that online social listening can be effectively implemented even with limited resources.

Automated surveillance systems can prove to be extremely useful in detecting outbreaks of respiratory diseases that need further epidemiological investigation. In order for the system to be effective, careful attention has to be paid to the baseline used, absence of the observations, and the use of proper statistical methods. As a solution, the paper describes EpiAlert, a weekly surveillance system based on CDC FluView ILINet. The system is able to transform the number of ILI visits and the total number of visits into a ILI prevalence for the region, create the first reference from the three previous years using a +/- 2-week interval, transform the rate value, and apply two different detectors - CUSUM and EWMA. CUSUM is set at k=0.5 and h=5, whereas EWMA uses {lambda}=0.2 and H=2.7 values. Among the 55 districts, the system gives 45,138 pieces of information about the disease by transforming 44,297 pieces of data into alerts. It is found that there are 2,820 alerts in total (out of which 2,711 alerts used CUSUM, 402 used EWMA, and 293 used both detectors). Additionally, the experiments show that the sensitivity of the device depends greatly on the detector. The latest statistics can be tracked in the report for week 37 of 2026 that recently concluded in West.

High low-density lipoprotein cholesterol (LDL-C) and triglyceride (TG) levels are associated with heavy alcohol consumption, but the accuracy of LDL-C estimation equations according to drinking status remains unclear. We analyzed direct LDL-C and drinking-status data from 28,882 participants in the Korea National Health and Nutrition Examination Survey, classified as non/ex-drinkers, moderate drinkers, or binge drinkers. Mean absolute deviation (MAD) was calculated for the Friedewald, Martin-Hopkins, and Sampson equations relative to directly measured LDL-C, and 95% confidence intervals for the median absolute deviation (MeAD) were determined by TG level. Estimation error increased with heavier alcohol consumption and higher TG level. In binge drinkers, all three equations underestimated LDL-C, with the largest deviation at higher TG levels. The Sampson equation showed the lowest MAD and MeAD across drinking categories and TG strata and was most accurate at a TG level of 150-400 mg/dL, whereas the Martin-Hopkins equation was more accurate at TG levels of [&ge;]400 mg/dL. The Sampson or Martin-Hopkins equation estimates LDL-C more accurately than the Friedewald equation regardless of drinking status and should be preferred, with the choice further individualized by TG level in heavy drinkers.

Dynamic systems often evolve faster than reliable measurements can be collected. More frequent indirect observations may fill these gaps, but their noise and differing scales complicate estimation of the underlying state. We developed Bayesian Anchored Latent Learning (BALL) to combine sparse reference measurements with denser records on an interpretable scale. A bidirectional transformer teacher reconstructs trajectories from complete training histories, and a forward-only transformer student learns from those reconstructions and observed measurements. Across 30 simulations, BALL reduced composite trajectory error by 7% relative to the same transformer trained directly from measurements, achieving lower error across all datasets. In an applied example using psychiatric records from 1,609 patients, BALL improved the prediction of withheld questionnaire scores compared with direct transformer training at 21- and 28-day measurement schedules. Requests guided by estimated uncertainty further improved prediction. BALL connects retrospective learning with prospective state estimation and provides a framework for directing additional measurement.

Background Chronic diseases are the leading causes of illness, disability, and death worldwide, with a disproportionate burden in low- and middle-income countries. Hospitalization is a critical outcome in chronic disease management, reflecting disease severity, treatment failure, and healthcare utilization. However, data on hospitalization burden and its associated factors among chronic disease patients in Ethiopia, are limited. This study aimed to determine the rate of hospitalization and its associated factors among patients with chronic diseases attending a tertiary hospital in Wolaita Zone, South Ethiopia. Methods A facility-based cross-sectional study was conducted among 321 patients with chronic diseases attending the chronic follow-up clinic at Wolaita Sodo University Comprehensive Specialized Hospital from November to December 2022. Consecutive recruitment was used, with unique medical record numbers tracked to prevent double recruitment. Data were collected through structured questionnaires and medical record review. Negative binomial regression was used to identify factors associated with hospitalization rate, with incidence rate ratios (IRR) and 95% confidence intervals (CI) reported. A p-value < 0.05 was considered statistically significant. Results Of the 321 participants, 66% reported at least one hospitalization while on treatment, with a mean of 1.70 {+/-} 1.81 hospitalizations per patient. Drug discontinuation was the most frequently documented reason for hospitalization (39.5%), followed by disease complications (22.3%) and infections (11.7%). Multimorbidity was prevalent, with the majority (59.8%) had 2-3 conditions, and a mean of 2.40 {+/-} 1.02 chronic diseases per patient. Male sex was associated with a higher hospitalization rate, while regular physical exercise was associated with a lower number of hospitalizations. Conclusion This study found a high burden of hospitalization among chronic disease patients, with drug discontinuation being the leading documented reason. Male sex and regular physical exercise were significantly associated highlighting the need for medication adherence support programs and structured exercise programs integrated into routine chronic disease care, as well as gender-sensitive interventions.

Background: Digital systems are central to regulating access to care in Brazil's Unified Health System (SUS), but fragmented data can hinder coordination. Objective: This study examines the database structure of SUSfacilMG and identifies modernization priorities, using CORE Saude MG as a comparative reference. Methods: We assessed metadata from 1,586 SUSfacilMG tables, focusing on naming patterns, relationships, and repeated attributes. We then compared architectural features described for CORE Saude MG. Results: Approximately 40% of the assessed tables repeated descriptive attributes, including patient identifiers, suggesting substantial duplication. Metadata alone cannot establish operational failures, security incidents, or effects on care. Conclusion: A phased review of data models, interfaces, and governance is warranted. Standardized exchange, referential integrity, and controlled access should be evaluated alongside service continuity and data quality.