Frontiers in epidemiology最新文献

{"title":"Estimation of populations at risk for severe influenza and vaccination coverage in Mexico, 2010-2021.","authors":"Arturo Galindo-Fraga, Gerardo Luna-Casas, Ricardo Gasca-Pineda, Gilberto Sánchez-González","doi":"10.3389/fepid.2026.1826780","DOIUrl":"10.3389/fepid.2026.1826780","url":null,"abstract":"<p><strong>Background: </strong>Influenza remains an important cause of morbidity among populations with underlying medical conditions associated with increased risk of severe disease. This study aimed to estimate the size of populations eligible for influenza vaccination according to the Mexican Universal Vaccination Program and to evaluate vaccination coverage gaps among populations at risk in Mexico between 2010 and 2021.</p><p><strong>Methods: </strong>A retrospective analytical study was conducted using epidemiological and administrative healthcare databases from Mexican public healthcare institutions. Population estimates were constructed using prevalence-based epidemiological projections, healthcare system records, and attended patient data from populations at risk. Descriptive analyses and time-series modeling were performed to evaluate vaccination coverage patterns and healthcare demand over time.</p><p><strong>Results: </strong>Across all analyzed populations at risk, the number of individuals receiving healthcare services was consistently lower than the number of notified cases and substantially lower than prevalence-based epidemiological estimates. Vaccination coverage varied considerably across populations at risk and remained incomplete throughout the study period. In 2021, although approximately 12.5 million influenza vaccine doses were administered among populations at risk, a substantial proportion of potentially eligible individuals remained unvaccinated. Forecasting analyses suggested a progressive increase in healthcare demand among populations at risk over time.</p><p><strong>Conclusions: </strong>Important gaps persist between the estimated population at risk, diagnosed individuals, healthcare utilization, and influenza vaccination coverage in Mexico. The analytical framework proposed in this study integrates epidemiological prevalence estimates, healthcare utilization patterns, and vaccination data to identify unmet vaccination needs and may support improved public health planning, prioritization strategies, and strengthening of influenza vaccination programs in Mexico and similar settings.</p>","PeriodicalId":73083,"journal":{"name":"Frontiers in epidemiology","volume":"6 ","pages":"1826780"},"PeriodicalIF":2.9,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bayesian spline-augmented piecewise exponential model with spatial frailty for under-five mortality in Nigeria.","authors":"Peter Enesi Omaku, Joseph Odunayo Braimah, Fabio Mathias Correa","doi":"10.3389/fepid.2026.1826352","DOIUrl":"10.3389/fepid.2026.1826352","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;The Cox proportional hazards (PH) model is widely used in time-to-event research, but its validity depends on the PH assumption, which can be violated in child mortality studies where hazards vary with age. Piecewise exponential models (PEMs) relax this assumption by partitioning follow-up time into intervals. However, standard formulations impose constant hazards within each interval and typically ignore spatial heterogeneity, limiting their usefulness for public health analyses in which geographical variation in risk is important. Existing extensions address non-proportional hazards or spatial dependence separately, but rarely combine a smooth baseline hazard, interval-level temporal heterogeneity, and structured spatial frailty within a single, computationally tractable Bayesian framework. This study proposes and applies such a framework to under-five mortality (U5M) in Nigeria.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We formulated a Bayesian spline-augmented PEM incorporating a cubic-spline-smoothed baseline hazard, interval-specific Gaussian random effects, and a spatial frailty term with an intrinsic conditional autoregressive (ICAR) prior, within the latent Gaussian modeling framework of integrated nested Laplace approximation (INLA), with survival reformulated as a Poisson likelihood. The model was applied to U5M data for 103,439 children from the 2024 Nigeria Demographic and Health Survey (NDHS). Five nested model specifications (basic PEM, spline-augmented, spline plus interval effects, spline plus spatial effects, and the full spline-interval-spatial model) were compared across three sample sizes using DIC, WAIC, conditional predictive ordinates, posterior predictive checks, and calibration metrics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The global test of the PH assumption was significant (χ&lt;sup&gt;2&lt;/sup&gt; = 898.66, &lt;i&gt;p&lt;/i&gt; &lt; 2 x 10&lt;sup&gt;-16&lt;/sup&gt;), with all individual covariates significant at &lt;i&gt;p&lt;/i&gt; &lt; 0.05. Across all three sample sizes, the full spline-interval-spatial model achieved the lowest DIC and WAIC, the lowest CPO, and the highest posterior predictive correlation and calibration R&lt;sup&gt;2&lt;/sup&gt; among the five candidates, with the largest single reduction in DIC (approximately 17,884 units at n = 104,557) attributable to the addition of interval-specific random effects. In the fitted model, twin birth (HR = 2.82; 95% CrI: 2.62-3.03), breastfeeding status (HR = 0.45; 95% CrI: 0.42-0.49), and term delivery (HR = 0.47; 95% CrI: 0.41-0.53) had the largest effects on U5M, with additional protective effects for longer birth intervals and higher maternal education, and elevated risk in the North West (HR = 1.39) and North East (HR = 1.43) relative to the North Central region. Posterior spatial frailty estimates showed positive residual clustering concentrated in the North West and North East zones.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;The results indicate that jointly modeling temporal and spatial heterogeneity yield","PeriodicalId":73083,"journal":{"name":"Frontiers in epidemiology","volume":"6 ","pages":"1826352"},"PeriodicalIF":2.9,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13422484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148633021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical review of Toscana virus: a Mediterranean Neuroinvasive Phlebovirus.","authors":"Ahmad Makky, Parham Elahi, Hadi Awali","doi":"10.3389/fepid.2026.1909843","DOIUrl":"10.3389/fepid.2026.1909843","url":null,"abstract":"<p><p>Toscana virus (TOSV) is a sandfly-transmitted phlebovirus of the family Phenuiviridae and a leading cause of summer meningitis and meningoencephalitis in the Mediterranean basin. It was first isolated in 1971 from Phlebotomus perniciosus in central Italy and circulates wherever its principal vectors are established. Seroprevalence surveys show that population exposure is common, whereas overt disease is comparatively infrequent. Among patients who become symptomatic, most present with neuroinvasive illness, which separates TOSV from related sandfly-borne phleboviruses that cause undifferentiated febrile disease. This review covers virology, pathogenesis, epidemiology, vector biology, clinical features, diagnosis, and management of TOSV infection, and it keeps a clear separation between established clinical knowledge and experimental research. The practical message has not changed in a decade: management is supportive, no licensed antiviral or vaccine exists, and the agents that attract research interest, including repurposed nucleoside analogues, venom-derived peptides, RNA-interference strategies, and candidate vaccines, remain preclinical. Given warmer Mediterranean summers and a documented rise in recognised cases, the most useful priorities are better clinician awareness, wider access to TOSV-specific molecular testing, and integrated human and entomological surveillance.</p>","PeriodicalId":73083,"journal":{"name":"Frontiers in epidemiology","volume":"6 ","pages":"1909843"},"PeriodicalIF":2.9,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13402448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148610757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating a new era in cardiovascular disease epidemiology: big data, artificial intelligence and the imperative of disability inclusion.","authors":"Theophilus I Emeto","doi":"10.3389/fepid.2026.1871078","DOIUrl":"10.3389/fepid.2026.1871078","url":null,"abstract":"<p><p>Cardiovascular disease (CVD) remains the leading cause of premature global mortality and one of the largest contributors to disability-adjusted life years lost. Over the past decade, the field has been transformed by the convergence of population biobanks, deep learning applied to imaging and electrocardiography, polygenic risk scores, wearable biosensors, and methodological advances in causal inference and target trial emulation. These innovations are reshaping precision public health for the general population. Yet the gains have not been equitably distributed. People with disability (PwD), comprising approximately 16 per cent of the global population and recognised by the United States National Institute on Minority Health and Health Disparities as a population experiencing health disparities are systematically under-represented in clinical trials, biobanks, electronic health records and the artificial-intelligence (AI) models trained upon them. Their cardiovascular health is therefore both worse and less precisely characterised than that of the general population. This article maps the key methodological vectors of change in CVD epidemiology, explains why each has so far failed to reach PwD, and presents a layered, defendable framework for disability-inclusive big-data and AI-enabled CVD research. It argues that disability inclusion is not a peripheral equity concern but a stress-test for the validity, generalisability and ethical legitimacy of the entire precision-cardiovascular enterprise.</p>","PeriodicalId":73083,"journal":{"name":"Frontiers in epidemiology","volume":"6 ","pages":"1871078"},"PeriodicalIF":2.9,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148581400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variability of obesity prevalence in US states: are obesity growth or deterioration rates constant for different income quantiles?","authors":"Yuval Arbel, Yifat Arbel, Oriya Kerner, Miryam Kerner","doi":"10.3389/fepid.2026.1821610","DOIUrl":"10.3389/fepid.2026.1821610","url":null,"abstract":"<p><strong>Background: </strong>Obesity remains a major public health concern in the United States, with evidence suggesting that its prevalence varies significantly across socioeconomic groups. This study investigates how annual changes in obesity rates differ by income quantiles across U.S. states from 2012 to 2020.</p><p><strong>Methods: </strong>An empirical model was developed incorporating income-quantile dummy variables and a linear time trend, allowing for differences in average annual changes in obesity prevalence across income groups. Annual change is defined as the year-to-year percentage change in age-adjusted obesity prevalence relative to the preceding year. The analysis draws on state-level data to evaluate baseline differences and subsequent yearly changes in obesity prevalence.</p><p><strong>Results: </strong>Obesity prevalence differs substantially across income groups in the baseline year, with lower-income groups exhibiting higher prevalence levels. All income groups experienced positive annual increases in obesity prevalence over the study period. Differences in annual changes among lower- and middle-income categories are relatively small in magnitude, while the highest income group (annual income above $75,000) exhibits a larger average annual increase relative to other groups. The estimated time trend is not statistically significant, indicating stable differences in annual changes across income groups over time.</p><p><strong>Conclusion: </strong>The findings highlight persistent socioeconomic differences in obesity prevalence across U.S. states. While lower-income groups bear a higher overall obesity burden, rising obesity prevalence is observed across the entire income distribution. These patterns suggest that public health responses may benefit from accounting for income-related differences in obesity prevalence, particularly through voluntary approaches such as information campaigns, expanded access to healthy foods and opportunities for physical activity, and initiatives related to the built environment.</p>","PeriodicalId":73083,"journal":{"name":"Frontiers in epidemiology","volume":"6 ","pages":"1821610"},"PeriodicalIF":2.9,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13388406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global information-seeking behavior of air pollution and cardiovascular disease: insights from google trends analysis.","authors":"Javeria Akhter, Talha Ali, Aasiya Shahbaz, Noor Ul Ain Shahzad, Jamil Nasrallah","doi":"10.3389/fepid.2026.1874654","DOIUrl":"10.3389/fepid.2026.1874654","url":null,"abstract":"<p><strong>Introduction: </strong>Air pollution (AP) contributes to over 4.2 million premature deaths annually, with approximately 20% linked to cardiovascular disease (CVD)-related deaths. Despite robust scientific evidence, information-seeking behavior remains poorly characterized. This study applies an infodemiological approach to assess global information-seeking behavior related to AP and its cardiovascular implications.</p><p><strong>Methods: </strong>A retrospective analysis was conducted using Google Trends data from June 2020 to June 2025. Five AP-related search terms (e.g., \"air pollution,\" \"PM₂.₅,\" \"air quality index,\" \"environmental pollution,\" and \"mask\") and five CVD-related search terms (e.g., \"cardiovascular disease\", \"heart disease,\" \"heart attack,\" \"chest pain,\" and \"high blood pressure\") were analyzed globally. Pearson correlation coefficients, partial correlation analysis controlling for pandemic-related confounding, and time-series approaches including ARIMAX modeling and Granger causality testing were applied to assess temporal and correlational patterns in information-seeking behavior.</p><p><strong>Results: </strong>Global information-seeking behavior showed marked variability across AP and CVD search terms. AP showed moderate positive correlations with \"CVD\", \"heart disease\", and \"high blood pressure\" (r = 0.275;0.295;0.386, <i>p</i> < 0.01). \"Environmental pollution\" showed a moderate correlation with \"heart disease\" (r = 0.417, <i>p</i> < 0.01) and \"blood pressure\" (r = 0.315, <i>p</i> < 0.01). Technical AP indicators showed weak or negative zero-order associations with most CVD terms; however, sensitivity analysis controlling for pandemic-related confounding revealed complete directional reversal for PM₂.₅, with partial correlations becoming significantly positive, indicating that original negative associations were pandemic-driven artefacts. Search volumes were highest for Chest Pain and High Blood Pressure, with low global engagement for PM₂.₅ and AQI. Twenty-seven countries, including Pakistan, India, and Australia, demonstrated concurrent AP and CVD information-seeking behavior.</p><p><strong>Conclusion: </strong>This analysis indicates moderate information-seeking behavior in the association between AP and CVD, with geographic variation. Findings highlight the need for targeted public health communication strategies that translate AP risk into accessible cardiovascular health messages.</p>","PeriodicalId":73083,"journal":{"name":"Frontiers in epidemiology","volume":"6 ","pages":"1874654"},"PeriodicalIF":2.9,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13388820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial modeling of HIV prevalence in Malawi using generalized additive models.","authors":"Zacharie Tsala Dimbuene, Crispin Mabika Mabika, Blandine Bawawana Bavwidinsi, Emmanuel Juakaly Wayisovia, Hugues Sampasa-Kanyinga","doi":"10.3389/fepid.2026.1867778","DOIUrl":"10.3389/fepid.2026.1867778","url":null,"abstract":"<p><strong>Introduction: </strong>Malawi has made substantial progress in HIV prevention and treatment, yet HIV prevalence remains unevenly distributed across the country. Sub-national estimates are needed to guide targeted interventions.</p><p><strong>Methods: </strong>We analyzed individual-level HIV biomarker data from the 2016 Malawi Demographic and Health Survey. A spatial modeling approach was applied to capture broad geographic patterns alongside sociodemographic determinants. High-resolution maps of predicted HIV prevalence were generated to visualize fine-scale differences across districts.</p><p><strong>Results: </strong>The analysis revealed persistent geographic disparities, with the highest prevalence concentrated in southern Malawi and more varied patterns in central and northern regions. Although geography contributed to explaining HIV variation, sociodemographic factors-including age, education, sex, and household characteristics-were the primary drivers in most districts. Geography emerged as the leading contributor in only 18% of areas.</p><p><strong>Discussion and conclusion: </strong>These findings provide policy-relevant, sub-national evidence to support more precise targeting of HIV prevention, testing, and treatment efforts. They underscore the importance of tailoring interventions to both geographic and sociodemographic contexts to accelerate progress toward epidemic control.</p>","PeriodicalId":73083,"journal":{"name":"Frontiers in epidemiology","volume":"6 ","pages":"1867778"},"PeriodicalIF":2.9,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13388554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pan-continental spillover risk: integrated spatiotemporal, transmissibility and surveillance analysis of avian influenza a(H5N1) in Africa.","authors":"Maladho Diaby, Alhassane Diallo, Castro G Hounmenou, Salifou Talassone Bangoura, Emile Faya Bongono, Kadio Jean-Jacques Olivier Kadio, Aly Badara Touré, Abdourahamane Barry, Saidouba Chérif Camara, Haby Diallo, Thibaut Armel Chérif Gnimadi, Sidikiba Sidibé, Alexendre Delamou, Alioune Camara, Alpha-Kabinet Kéita, Abdoulaye Touré","doi":"10.3389/fepid.2026.1813211","DOIUrl":"10.3389/fepid.2026.1813211","url":null,"abstract":"<p><strong>Background: </strong>The HPAI H5N1 panzootic represents a critical threat to human health in Africa, where traditional poultry systems and dense human-animal interfaces facilitate frequent zoonotic spillover. While sporadic human cases raise pandemic concerns, continent-wide integration of spatial dynamics, transmissibility indicators, and surveillance performance has been lacking. This study quantifies avian influenza transmission over two decades across Africa, identifies geographical hotspots, and evaluates the responsiveness of current surveillance systems.</p><p><strong>Methods: </strong>We analysed 8,037 avian influenza outbreak events and 369 laboratory-confirmed human cases, predominantly caused by HPAI H5N1 (2004-2025), using harmonised data from FAO (EMPRES-i+), WHO, and WOAH. A Bayesian Besag-York-Mollié (BYM) spatiotemporal model estimated residual transmission risks and Incidence Rate Ratios (IRR) by subtype. The basic reproduction number (R₀) was derived via an exponential growth model applied to human outbreak phases across infectious durations of 7-30 days. Surveillance responsiveness was assessed by quantifying notification delays between clinical observation and official reporting.</p><p><strong>Results: </strong>Risk of infection in animals: HPAI H5N1 was the dominant strain, representing 87.8% of animal cases, with Egypt acting as the primary epidemiological epicentre (66% of total records). The spatiotemporal model revealed that H5N1 is associated with a significantly higher risk of animal infection (IRR = 8.37; 95% CI: 6.65-10.53). Although 71% of outbreaks were reported within 5 days of detection, significant delays (≥15 days) occurred in 12% of cases, with notable regional disparities. Risk of infection in human: H5N1 was associated with a 67-fold increase in the incidence of human cases compared to other subtypes (IRR = 66.78; 95% CI: 25.29-176.37). Sensitivity analyses yielded R₀ estimates ranging from 1.05 (95% CI: 0.91-1.31) to 1.23 (95% CI: 0.60-2.33), indicating localised epidemic potential.</p><p><strong>Conclusion: </strong>Our findings highlight a persistent and geographically heterogeneous H5N1 reservoir in Africa with high zoonotic affinity. Although sustained human-to-human transmission remains limited, the identification of dual poultry-human hotspots and localised R0 peaks underscores the urgent need for geographically targeted One Health interventions. Strengthening real-time reporting systems and improving biosecurity in high-risk poultry value chains are critical to mitigating future pandemic threats on the continent.</p>","PeriodicalId":73083,"journal":{"name":"Frontiers in epidemiology","volume":"6 ","pages":"1813211"},"PeriodicalIF":2.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13368709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reimagining tuberculosis elimination in India: diagnostics, drug resistance, and digital health strategies.","authors":"Sneha Jha, Loka Bikash Chaliha, Kshipra Pandey, Ritu Patel","doi":"10.3389/fepid.2026.1868261","DOIUrl":"10.3389/fepid.2026.1868261","url":null,"abstract":"<p><p>Tuberculosis (TB) remains a major global public health challenge and one of the leading infectious causes of mortality worldwide, with India contributing approximately 26% of the global TB burden. Despite substantial progress under the National Tuberculosis Elimination Programme (NTEP), India's efforts to achieve the ambitious 2025 TB elimination target continue to face major challenges due to the COVID-19 pandemic, multidrug-resistant tuberculosis (MDR-TB), healthcare disparities, and limitations in surveillance and reporting systems. This review provides a comprehensive overview of the epidemiology of TB in India and critically evaluates the impact of COVID-19, drug resistance, healthcare accessibility, and emerging diagnostic technologies on TB control efforts. A structured literature search was conducted using PubMed, Scopus, and Google Scholar, along with reports from the World Health Organization and the Government of India. The reviewed evidence indicates that the COVID-19 pandemic significantly disrupted TB diagnosis, treatment, surveillance, and healthcare accessibility, leading to declines in case notification and the possible accumulation of undiagnosed TB cases. In addition, the increasing burden of MDR/RR-TB, fragmented private healthcare systems, underreporting, and unequal access to diagnostics and treatment continue to hinder progress toward TB elimination. Although advancements including molecular diagnostics, digital surveillance platforms such as Nikshay, AI-assisted screening, and patient-support programmes have strengthened TB control strategies, important challenges related to implementation, infrastructure, scalability, and antimicrobial stewardship remain unresolved. This review highlights the need for integrated and patient-centred TB control strategies that combine rapid diagnostics, strengthened surveillance, public-private healthcare integration, health systems strengthening, and interventions addressing broader socioeconomic determinants such as poverty, malnutrition, and healthcare inequity. Coordinated multisectoral approaches will be essential for accelerating TB elimination efforts in India in the post-COVID era.</p>","PeriodicalId":73083,"journal":{"name":"Frontiers in epidemiology","volume":"6 ","pages":"1868261"},"PeriodicalIF":2.9,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13357570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148439010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coronary heart disease and chronic obstructive pulmonary disease prevalence and temporal trends among United States adults: a national population-based study.","authors":"Muinat Abolore Idris, Valerie M Valenzuela, Juan Aguilera","doi":"10.3389/fepid.2026.1840095","DOIUrl":"10.3389/fepid.2026.1840095","url":null,"abstract":"<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD) are two independent leading causes of morbidity and death, with co-existence contributing substantially to morbidity and deaths among aging populations. Despite growing evidence on the impact of COPD-CVD co-existence, little is known about COPD-CHD co-morbidity, as other previous studies mostly examine CVD as a single aggregated health outcome, obscuring disease-specific trajectories.</p><p><strong>Objective: </strong>To assess 5-year national trends and sociodemographic disparities in the prevalence of coexisting CHD-COPD co-morbidity.</p><p><strong>Methods: </strong>Using the 2020-2024 National Health Interview Survey Integrated Public Use Microdata Series adults' dataset, we estimated the national prevalence of CHD-COPD co-morbidity. Weighted descriptive statistics, multivariate regression models, and sensitivity analysis were used to examine the associations.</p><p><strong>Results: </strong>CHD-COPD co-morbidity prevalence showed no statistically significant increase from 2020 to 2024; although the estimated linear trend was positive, it did not reach statistical significance (OR per 1-year increase = 1.04; 95% CI: 0.997-1.08; <i>p</i> = 0.072). Age, sex, body mass index, race/ethnicity, geographical region, smoking status, insurance status, income, and educational attainment were strong independent predictors of CHD-COPD co-morbidity. Older age (aOR: 1.004; 95% CI: 1.003-1.004, <i>p</i> < 0.0001) was independently associated with higher odds; current and former smokers had approximately 5-6 times higher odds of CHD-COPD co-morbidity.</p><p><strong>Conclusion: </strong>CHD-COPD co-morbidity is a rare but clinically significant condition, with a stable adjusted prevalence of approximately 0.35%-0.43% annually among United States adults from 2020 to 2024. Smoking, older age, male sex, lower income, and Southern residence were the strongest independent predictors of CHD-COPD co-morbidity. These findings underscore the need for integrated, sociodemographically targeted prevention and chronic-disease management strategies addressing shared cardiopulmonary risk factors, particularly tobacco use, across vulnerable populations.</p>","PeriodicalId":73083,"journal":{"name":"Frontiers in epidemiology","volume":"6 ","pages":"1840095"},"PeriodicalIF":2.9,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346163/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148426936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}