JMIR Public Health and Surveillance

Cancer prevalence is distributed unevenly across regions and caused by the interaction of multiple risk factors. Previous studies focused on the use of global modeling techniques to predict cancer at the county level that overlooks important spatial differences. This study aims to develop geographically weighted machine learning models to predict cancer prevalence at the census tract level in the United States and identify local determinants of cancer burden. First, a scoping review was conducted to find a list of measurable drivers of cancer in the United States. Using this list, the data of these variables for 84415 census tracts were obtained from the Center for Disease Control and Prevention PLACES dataset and other publicly accessible resources. Then, several predictive models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), as well as Random Forest, XGBoost, and Deep Neural Network and their geographically weighted counterparts, were developed and compared using the Coefficient of Determination, Root Mean Square Error, and Absolute Error. Results presented that geographically weighted models outperformed other methods, and geographically weighted XGBoost achieved the strongest and most consistent overall performance with pseudo-R2 ranging between 0.89 and 0.98. Feature importance analysis of this model illustrated that most important cancer drivers changed location by location. Aged people, racial composition, preventative behaviors, and metabolic conditions such as diabetes, hypertension, and high cholesterol were determined as influential predictors, although their relative importance varied across regions. These findings revealed the value of localized models at a small geographic scale to identify regional cancer risk patterns and help the allocation of proper resources to hotspot areas. Keywords: Cancer prevalence, Census tracts, geographically weighted machine learning models, Deep neural network, XGBoost, Random Forest, Ordinary Least Squares, risk factor, determinant

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

Diabetes distress (DD) has a major impact on both diabetes control and emotional well-being but is mis- and underdiagnosed and undertreated. Treatment often starts with behavioral health (BH) through counseling or medications; in some patients, Certified Diabetes Care and Education Specialists (CDCES) are proven to better manage treatment burden and address distress. We performed an observational study comparing outcomes for people with high emotional distress (PHQ9 or GAD7 > 10) and high A1c 8.0 at baseline with CDCES support, BH treatment, both or neither. Results demonstrate both CDCES and behavioral health treatment were most effective (A1c -1.2%, PHQ -3.1) but received in only 4.3% of patients, while 50% of patients received BH treatment alone. Conclusion is that these services remain valuable in routine practice but are underutilized. Workflows that assist in CDCES treatment for patients at risk could improve outcomes.

Indiana still faces significant health challenges, ranking among the least healthy U.S. states due to high obesity rates, mental health issues, and other chronic conditions. These disparities are closely linked to inequities in healthcare access, which are largely shaped by social determinants of health. Using data from the Social Vulnerability Index and County Health Rankings and Roadmaps, this study analyzes trends in obesity, mental health, and premature death across Indiana counties before, during, and after the COVID-19 pandemic. Descriptive statistics, correlation analyses, and Negative Binomial regression models were used to evaluate county-level disparities. In 2018, higher rates of uninsured, obese, and physically inactive populations were associated with increased premature death. In 2020, diabetes, smoking, and alcohol consumption were significant factors. By 2022, unemployment, education, obesity, insurance, exercise access, and mental health provider availability were associated with premature death. Findings indicate that socially vulnerable counties experienced amplified health impacts, with obesity rising most sharply where exercise infrastructure was limited and poor mental health days increasing across all counties. These results highlight persistent service gaps and the critical need for targeted investments in recreational infrastructure and mental healthcare. Future research should examine policy influences and causal relationships to inform equity-focused interventions.

Background: Hepatitis B virus (HBV) seroprevalence reaches 10--12% among out-of-school youths in urban Southwest Nigeria, yet fewer than three in ten in this demographic have ever undergone HBV testing. E-health self-testing applications offer an accessible alternative delivery modality for hepatitis screening, but direct evidence comparing their acceptance against traditional laboratory-based delivery in informal commercial settings is lacking. Objective: To determine whether e-health app-guided hepatitis self-testing achieves equivalent disease detection and superior acceptance outcomes compared with traditional laboratory-based screening among out-of-school youths in a high-footfall commercial site in Ibadan, Nigeria. Methods: A cross-sectional parallel-arm pilot study recruited 120 out-of-school youths at commercial premises on Iwo Road, Ibadan (test group: n=60, e-health app-guided hepatitis B and C self-testing; control group: n=60, traditional laboratory-based testing using identical rapid diagnostic kits). Pre- and post-test structured questionnaires assessed technology readiness, behavioural intention, perceived ease of use, and adoption barriers. The Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) provided the analytical framework. Group comparisons used chi-square and Fisher's exact tests (=0.05). Results: HBV seroprevalence was 10.0% in the test group and 11.7% in the control group (X2=0.087, p=0.769), confirming statistically equivalent detection across modalities. Hepatitis C virus was undetected in both groups. The test group demonstrated greater willingness to use e-health testing over traditional laboratory services (81.6% vs 70.0%), higher comfort with mobile health technology (75.0% vs 71.7%), and equivalent recommendation intent (93.3% vs 95.0%). Prior e-health service use was significantly higher in the test group (28.3% vs 13.3%; X2=4.09, p=0.043). Anticipated ease of use (75.0% "very comfortable") substantially exceeded experienced ease post-use (41.7% "very or extremely easy"). Privacy concern was the leading adoption barrier in both groups (30.0% vs 36.7%), outranking cost (25.0% vs 15.0%). Conclusions: E-health self-testing achieves non-inferior hepatitis B detection compared with traditional laboratory delivery while generating comparable acceptance outcomes. The gap between anticipated and experienced ease of use identifies usability as the primary design target. Privacy-centred tool design, not cost reduction alone, is the critical condition for scaling hepatitis e-health self-testing among out-of-school youths in urban Nigeria.

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.

ABSTRACT Background: To examine trends in Indonesia's diabetes care cascade from 2013 to 2023, identify key determinants, and assess progress toward global targets of 80% diagnosis and 80% glycemic control among those diagnosed. Methods: We analyzed nationally representative data from Indonesia's Health Surveys in 2013, 2018, and 2023. Diabetes was defined using fasting plasma glucose and oral glucose tolerance tests. We estimated diagnosis, treatment, and control rates and examined sociodemographic predictors of cascade progression using survey-weighted logistic regression models. Results: Between 2013 and 2023, the prevalence of diabetes among adults aged [&ge;]15 years remained stable, ranging from 10.7% to 11.8%. Diagnosis increased from 15.1% (95% CI: 13.4-16.7) to 20.7% (18.5-22.9), treatment nearly doubled from 10.5% (9.1-11.9) to 19.0% (16.9-21.2), and control rose modestly from 4.6% (3.6-5.6) to 6.5% (5.2-7.8). Older age, urban residence, higher socioeconomic status, and insurance coverage were associated with greater progression through the cascade. Wealth-related inequalities persisted in 2023: one-third of cases were diagnosed among the richest (35.3% [29.0-41.7]) versus only 11.0% (7.9-14.2%) among the poorest. Compared with the lowest quintile, wealthier individuals had higher odds of diagnosis (AOR 3.55 [2.11-5.98] for diagnosis, 3.59 [2.01-6.41] for treatment, and 2.24 [1.12-4.51] for control). Conclusions: Indonesia achieved meaningful improvements in the diabetes care cascade over the past decade, yet remains far below global 80/80 targets, with nearly 80% of cases undiagnosed and control below 10%. Persistent wealth-based inequities highlight that near-universal insurance coverage has not been translated into equitable care access, underscoring the need for equity-focused screening and primary care strengthening. Keywords: Diabetes, Care Cascade, Health Services, Indonesia

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.

Context: Most individuals in the United States have commercial health insurance, yet costs for tuberculosis (TB) care have focused on the public sector. Objective: To quantify 12 month all cause healthcare costs and identify predictors of expenditure among commercially insured persons with TB disease in the United States. Design/Setting: Retrospective cohort study using Merative (TM) MarketScan (R) Commercial Claims Database (2013 to 2018). Participants: Adults 18 years old with TB disease Main Outcome Measure: Total 12 month all cause healthcare costs (outpatient, inpatient, pharmacy) were calculated from the date of diagnosis. Adjusted cost ratios (aCR) were estimated using a Gamma generalized linear model. Results: We included 303 individuals diagnosed with TB disease, median age 46 years, 158 (52%) male, 16 (5%) with HIV, 12 (4%) with hepatitis B (HBV), and 13 (4%) with a drug use disorder. Mean total 12-month costs were $32,404 (median $8,075; SD $78,829). Median 12-month costs were substantially higher among persons with any comorbidity (HIV, HBV, hepatitis C (HCV), alcohol use disorder, drug use disorder, or Charlson score >0) compared to those without ($11,930 [IQR $4,194 to $36,073] vs $3,385 [IQR $1,506 to $8,609]; p<0.001). HIV coinfection and drug use disorder were the strongest independent predictors. HIV coinfection was associated with 4.7 fold higher costs (aCR 4.70, p<.001), driven predominantly by pharmacy expenditure (aCR 16.4). Drug use disorder was associated with 3.2 fold higher costs (aCR 2.62, p=.03). Comorbidity burden was a continuous independent predictor (aCR 1.36 per Charlson point, p<.001). Conclusions: Healthcare costs are high among persons with TB who have commercial insurance, and are further increased with comorbidities including HIV coinfection and drug use disorder. Improved screening, care coordination and management of TB and high risk comorbidities could yield significant cost savings.

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.

Importance: Evidence on GLP-1/GIP therapies is largely derived from trials enrolling selected populations or medical records that miss utilization outside healthcare channels. No nationally representative cohort has characterized real-world uptake, indications, and access. Objective: To characterize GLP-1/GIP prevalence, indication, clinical profile, and access. Design: Prospective cohort study with three GLP-1/GIP surveillance waves (March 2024, December 2024, October 2025). Setting: The Understanding America Study, an address-based, nationally representative panel of approximately 15,000 US adults aged 18+ years initiated in 2014. Participants: UAS participants responding to at least one surveillance wave (n=9150). Exposures: GLP-1/GIP use status (never vs any use, comprising current and former use), self-reported primary indication (diabetes, weight loss, or other), and access pathway (traditional vs non-traditional). Main Outcomes and Measures: Survey-weighted prevalence of GLP-1/GIP use, overall and by indication and access pathway; sociodemographic, cardiometabolic, treatment, and access characteristics; and smartwatch-derived resting heart rate, heart rate variability, maximum activity heart rate, step count, and sleep duration and variability. Results: Among n=9150 adults (1274 with any use; 60.9% female; median age 53 years), weighted prevalence increased 46%, from 8.2% (March 2024) to 12.0% (October 2025) representing 32 million. Weight-loss indications grew, reaching nearly half of use (4.1% to 5.6%); diabetes-indicated use was stable (5.3% to 5.4%). Users carried high cardiometabolic burden (obesity, 68.2%; diabetes, 53.6%) but diverged by indication: diabetes-indicated users were older (median, 59 vs 49 years), whereas weight-loss-indicated users were more often female (69.9% vs 51.3%) and healthier. One in three users (~9 million) had non-traditional access, especially in weight-loss-indicated users, of whom 33% had no conventional prescription; 41% used compounding, online, or foreign pharmacies; and, 43% lacked coverage. Non-traditional users were five times as likely to report an unlisted, likely compounded formulation (19.8% vs 4.1%). All p<0.05. Conclusions and Relevance: Real-world GLP-1/GIP use has grown rapidly and diversified substantially in indication, access, and population profile. One in 3 users obtained treatment through nontraditional channels largely invisible to claims data, raising long-term safety, efficacy, and coverage questions. GLIMMER provides a public, nationally representative longitudinal evidence base for future payer and provider decisions.

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: Evaluation of public health surveillance systems is a programmatic obligation but has largely been conducted as a periodic, externally commissioned activity requiring dedicated resources and additional data collection. India's Integrated Disease Surveillance Program (IDSP) generates continuous outbreak data through weekly reports but lacks a routine, embedded performance evaluation mechanism. This study assessed the quality of IDSP outbreak detection and response across multiple surveillance attributes and developed a weighted composite performance scoring framework using only routine program data. Methods: A cross-sectional evaluation study was conducted across 38 districts of Bihar using secondary data from IDSP Central Surveillance Unit weekly outbreak reports for 2016 - 2018 (n=559 outbreaks). Six surveillance quality attributes were assessed - timeliness, completeness, representativeness, relative sensitivity, acceptability and flexibility. A weighted composite performance scoring scale was developed using expert opinion-derived attribute weightages (n=25 experts). District-level scores were computed and scaled to 100. Results: Timeliness was the poorest-performing attribute, with fewer than 15% of outbreaks notified within 48 hours across all three years. Private sector participation was entirely absent - the acceptability score was 0 across all 38 districts for all three years. Completeness was the strongest attribute, exceeding 95% in all years. The mean composite score remained consistently low (23 - 27 out of 100) with widening inter-district disparity over time. Four districts (10.5%) scored 0 in all three years. Conclusions: This study presents a dynamic, routine-data-based composite performance evaluation framework for IDSP outbreak detection and response. The modular, configurable framework functions at any administrative level (from block to national) and is compatible with digital health information platforms, enabling continuous, embedded performance monitoring without additional data collection. The framework has been registered as an Intellectual Property with the Government of India. Keywords: Disease surveillance; IDSP; IDSR; performance evaluation; composite score; outbreak detection; timeliness; completeness; relative sensitivity; digital health

During the 2022 global mpox outbreak, gay, bisexual and other men who have sex with men (gbMSM) were disproportionately affected,s however, the true number of cases is likely higher than reported due in part to the stigma associated with mpox. This stigma may have played an even more influential role on health-seeking behaviour in politically conservative regions, such as the Canadian Prairies. The shifting epidemiology may have also led to misdiagnoses during initial months of the outbreaks. Seroprevalence studies can help assess these gaps in surveillance and population-level immunity. To address this, we conducted a cross-sectional seroprevalence study among 275 individuals who identified as potentially eligible to receive the mpox vaccine in the Canadian provinces of Manitoba and Alberta from June 2024 to December 2025 based on provincial vaccination criteria. This eligibility included, but was not exclusively limited to, gbMSM, gender diverse individuals, and sex workers. Dried blood spots were analyzed using the Meso Scale Discovery Orthopoxvirus assay targeting five orthologous mpox and vaccinia virus antigens. In total, 14.5% of individuals self-reported receiving the mpox vaccine and of those without reported Orthopoxvirus vaccination, 5.9% were seroreactive to multiple mpox antigens. These results suggest the potential for undetected mpox circulation within Manitoba and Alberta. While primarily men who have sex with men were eligible for the mpox vaccine, an expansive range of gender and sexual orientation identities were classified as seroreactive, suggesting a misalignment between those who were seroreactive and vaccine eligibility criteria. This emphasizes how broader mpox vaccine eligibility, not restricted by specific gender and sexuality requirements, may better encompass those at risk of infection.