Frontiers in epidemiology最新文献

Background: Gastric cancer (GC) is a major public health issue and a leading cause of cancer-related mortality in Ecuador. Despite national cancer control efforts, the burden remains high, with variations by gender and age. This study aimed to quantify the burden of GC in Ecuador from 2010 to 2021 using Disability-Adjusted Life Years (DALYs), providing insights for public health strategies.

Methods: A cross-sectional study was conducted using hospital discharge and mortality data from the National Institute of Statistics and Census (INEC). The study included all Ecuadorian individuals diagnosed with GC (ICD-10: C16) from 2010 to 2021. The burden of disease was estimated using DALYs, which combined Years of Life Lost (YLL) and Years Lived with Disability (YLD). Data were stratified by gender and age groups. Analyses were performed using Microsoft Excel and the DALY calculator in R v4.2.1.

Results: Between 2010 and 2021, GC accounted for 802,135 DALYs in Ecuador, with an annual average of 66,845 DALYs. Males accounted for 57.2% of the total burden. The highest impact was observed in individuals aged 65-69 years. A progressive increase in disease burden was identified, particularly among older age groups.

Conclusions: The findings highlight the need for targeted interventions, including early detection programs, risk reduction strategies, and improved healthcare access. Strengthening public health policies is crucial to mitigating the rising burden of GC in Ecuador.

Introduction: Tuberculosis(TB) is still a serious problem with a remarkable global impacts particularly within developing countries such as Libya. According to World Health Organization (WHO) global report, the country is considered a moderate TB burden with incidence of 40 per 100,000 in 2011. Geographic epidemiology has been considered an important tool in preventing TB in large countries. In this study, we intended to identify the geographic and spatiotemporal patterns of the TB incidence rate in Libya between 2015 and 2024.

Methods: A cross-sectional retrospective analytical study was conducted within ten years on the data reported through the National TB surveillance system. The data on all TB cases reported from 2015 to 2024 by municipality and region was abstracted. Choropleth maps were drawn showing the TB case notification rates (CNR) per 100,000. Local Moran's I was performed to identify the spatial variations of the disease and temporal and Spatiotemporal analyses were employed in all instances.

Results: During the entire study period, 26,478 TB cases were reported from all 22 municipalities in Libya with an annual rate of 40.29/100,000 (95% CI: (40.229 ± 9.01). The highest incidence was reported in 2015 and the lowest one in 2024. Males were significantly reported more than females among notified TB cases, (P < 0.001). The highest CNR was reported in the Eastern region followed by Western and Southern regions. The geospatial distribution of reported cases of TB varied greatly within the provinces and during the study period. There was evident variability throughout the country and over time. High-rate and low-rate clusters were predominantly distributed in the periods. High clusters were concentrated northeast and northwest, though low-level clusters were mainly located in the middle and the southern region of the country.

Conclusion: The results of this study provided clear insights into the geographic and spatiotemporal mapping of TB in Libya. There was an overall decreasing trend in TB CNR from 2015 to 2024 parallel with high-risk and low-risk areas. This information should allow the decision-making personnel to implement proper policies to combat TB at national and regional levels.

Background: Missed opportunities for key vaccinations continue to exacerbate disease outbreaks. Accurately monitoring immunisation coverage is fundamental to identifying gaps in vaccine delivery and informing timely action. This study assesses the agreement between routine and survey-based coverage estimates for the second dose of the measles vaccine (MCV2) in Western Kenya.

Methods: This study utilised model-based geostatistics estimates MCV2 coverage from the 2022 Kenya Demographic and Health Survey (DHS), monthly immunisation data from routine health information systems (2019-2022) imputed for missingness and population data from WorldPop for 2019 across 62 Western Kenyan subnational areas (sub-counties). Routine MCV2 coverage was computed using MCV2 doses as a numerator and two separate denominators: (i) Pentavalent 1 doses to account for children already receiving prior vaccines at health facilities (service-based coverage) and (ii) surviving infants to account for all eligible children (population-based coverage). Concordance was assessed using the 95% confidence intervals (CIs) of survey-modelled estimates, intra-class correlation coefficient (ICC), and Bland-Altman (BA) plots.

Results: Survey-modelled estimates differed substantially in 55 (89%) and 39 (63%) sub-counties compared to population and service-based coverage estimates respectively. The different approaches showed poor congruence in survey-modelled vs. population-based coverage estimates (ICC: 0.10, p = 0.229) and survey-modelled vs. service-based coverage estimates (ICC: 0.42, p = <0.001); there was moderate congruence of population vs. service-based coverage estimates (ICC: 0.65, p = <0.001). Survey-modelled vs. population-based coverage estimates showed the highest bias in BA plots of 18.80 percent points (p.p) compared to 11.02 p.p. and 7.79 p.p. between survey-modelled vs. service-based coverage and population vs. service-based coverage estimates, respectively.

Conclusions: Substantial discrepancies among survey-modelled, routine population, and service-based coverage estimates expose important variations in each approaches' results. While all approaches offer distinct insights, improving survey models, routine data quality and refining estimates of population catchment is imperative for reliable fine-scale vaccine delivery monitoring.

The recent analysis by Mora and colleagues revealed that over 277 diseases can worsen due to climatic hazards resulting from greenhouse gas emissions. Specifically, more than 58% of known human diseases can be aggravated by climate change. Furthermore, there are over 1,000 pathways through which various climatic hazards have contributed to disease outbreaks, primarily due to the diversity of pathogens. This analysis also urges immediate action to address the root of the problem-reducing greenhouse gas (GHG) emissions. Numerous climatic hazards affect the incidence of human pathogenic diseases. Unfortunately, due to the complexity and multifaceted nature of the problem, there cannot be a single comprehensive solution to minimize climate-driven outbreaks. This study seeks to identify outbreaks of specific diseases categorized as epidemics, whose incidence is strongly correlated with global warming. The focus of this analysis is on (1) organizations responding to climate-related diseases to decelerate the incidence rates; (2) to call for a new disciplines in epidemiology that focuses exclusively on climate change-related prediction for future pandemics; (3) looking at the problem from the patient's point of view-how do non-medical/health professionals contribute to minimizing the spread of climate-related diseases?; (4) to analyze outbreaks vs. urbanization/pollution/increase in population density and public health policies; also (5) to verify the vaccination coverage vs. case reduction rate.

Introduction: Human behavior shapes the transmission of infectious diseases and determines the effectiveness of public health measures designed to mitigate transmission. To accurately reflect these dynamics, epidemiological simulation models should endogenously account for both disease transmission and behavioral dynamics. Traditional agent-based models (ABMs) often rely on simplified rules to represent behavior, limiting their ability to capture complex decision-making processes and cognitive dynamics.

Methods: Reinforcement Learning (RL) provides a framework for modeling how agents adapt their behavior based on experience and feedback. However, implementing cognitively plausible RL in ABMs is challenging due to high-dimensional state spaces. We propose a novel framework based on Adaptive Control of Thought-Rational (ACT-R) principles and Instance-Based Learning (IBL), which enables agents to dynamically adapt their behavior using nonparametric RL without requiring extensive training on large datasets.

Results: To demonstrate this framework, we model mask-wearing behavior during the COVID-19 pandemic, highlighting how individual decisions and social network structures influence disease transmission. Simulations reveal that local social cues drive tightly clustered masking behavior (slope = 0.54, Pearson r = 0.76), while reliance on global cues alone produces weakly disassortative patterns (slope = 0.05, Pearson r = 0.09), underscoring the role of local information in coordinating public health compliance.

Discussion: Our results show that this framework provides a scalable and cognitively interpretable approach to integrating adaptive decision-making into epidemiological simulations, offering actionable insights for public health policy.

Introduction: In low- and middle-income countries, self-reported data on chronic cardiometabolic conditions such as high blood pressure and diabetes are commonly used in large-scale epidemiologic studies because implementing objective measures is challenging in these contexts. However, existing evidence suggests that the sensitivity of such measures may be low, and performance may differ by factors such as age, education, or income. We sought to confirm these prior findings and assess bias due to the use of self-reported data in hypothetical epidemiologic studies considering high blood pressure and diabetes as exposures, outcomes, and confounders.

Methods: We used data from the Longitudinal Aging Study in India (analytic N = 55,392) to assess the performance of self-reported data on high blood pressure and diabetes compared with objective measures, overall and stratified by basic demographic factors. We then compared regression coefficients from models considering self-reported and objective high blood pressure and diabetes as exposures, outcomes, and confounders. In all models, we examined whether the mode of data collection (self-report or objective) for other key variables in the model affected results.

Results: The overall sensitivity of self-reported high blood pressure and diabetes was 0.514 and 0.570, respectively; specificity for the two conditions was 0.922 and 0.984. Sensitivity of both conditions increased with age, and was higher among women, those in urban settings, and those with higher educational attainment. Across almost all models considering high blood pressure and diabetes as either exposures or outcomes anti-conservative bias was observed when using self-reported vs. objective measures, regardless of the mode of data collection for other key variables. When high blood pressure and diabetes were considered as confounders, differences between using self-report and objective measures were minimal.

Discussion: Anti-conservative bias due to the use of self-reported measures of chronic cardiometabolic conditions in surveys conducted in low- and middle-income contexts may be common. Future studies may seek to quantify the magnitude of anticipated bias in existing data resources and use quantitative bias analysis to formally estimate the potential implications of misclassification.

Background: The escalation of the conflict in Sudan has created a major humanitarian challenge for neighboring countries, especially in the Eastern regions of Chad. This humanitarian setting's health needs are unique in that they are more vulnerable to both outbreak-prone disease and a lack of essential services. To address these challenges, the World Health Organization has supported implementing the Early Warning Alert and Response System (EWARS) Mobile. The purpose of this study was to evaluate the application of the EWARS Mobile epidemiological surveillance tool in Sudanese children's refugees in Eastern Chad.

Methods: This was a retrospective and population-based surveillance study that provided an overview of the pattern of cases and deaths in time and space related to potential outbreaks.

Results: In total, 1,645 alerts were reported among children in vulnerable provinces of Quaddai, Sila, and Wadi Fira. There were 41,738 alerted cases and 236 deaths, for a 0.56% projected fatality rate. The EWARS Mobile successfully reported alerted increases in cases of acute flaccid paralysis (AFP), acute jaundice syndrome (AJS), acute respiratory infection (ARI), acute watery diarrhea in children (AWD), measles, meningitis, diphtheria, neonatal tetanus (NT), dengue, dysentery, and atypical events in vulnerable children in time and space. Case reporting, alert recording, and weekly reporting were completed successfully at all levels (camps, district, zone, and province) (≥80% completion rate). In contrast, the timeliness of alert reporting, weekly reporting, and probable outbreaks did not perform well across levels (≥80% timeliness rate). Epidemic curves indicated multiple probable outbreak types, characterized by a point source (AJS and AWD under 5 years), common source (AWD in 5 years and above), propagated source (ARI and dysentery), and intermittent source (AFP, measles, meningitis, diphtheria, NT, and unusual events). The sensitivity and positive predictive value were estimated at 81% (79%-83%) and 72.0% (68%-75%), respectively.

Conclusions: The EWARS Mobile is a practical solution for Eastern Chad provinces to implement throughout the pre-epidemic and outbreak periods in vulnerable children in this severe humanitarian crisis. However, efforts should be made to improve timeliness indicators at all subnational levels and incorporate alarm indicators.

This paper presents the first systematic molecular epidemiology study of the HIV epidemic in Belarus, an Eastern European country that, like much of Eastern Europe and including the Post-Soviet region, has been largely understudied in relation to HIV epidemics. HIV sequences collected nationwide between January 2018 and May 2022 were analyzed using phylogenetic and phylodynamic methods. The findings reveal two distinct epidemic waves spanning 1997-2005 and 2009-2018, each driven by different dominant modes of transmission. The study also identifies potential introductions and intra-country transmission routes, emphasizing the pivotal role of the capital city and eastern industrial hubs within Belarus in shaping the epidemic's trajectory. This work addresses an important gap in understanding HIV dynamics in Eastern Europe.

Introduction: Respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and acute respiratory failure contribute significantly to the global health burden, particularly when co-occurring with chronic systemic conditions. Understanding these interrelationships is essential for designing resilient and integrated healthcare systems, especially in the context of pandemic stress.

Methods: We analyzed over 82 million de-identified healthcare claims from the Comprehensive Health Care Information System (CHIS), spanning 2020 to 2024. A disease co-occurrence matrix was constructed by identifying overlapping ICD-10 codes across individual patient timelines. Pairwise associations were quantified using Spearman's rank-order correlation. The resulting associations were visualized as an undirected disease network.

Results: COPD (J44.9) and asthma (J45.909) emerged as central nodes in the multimorbidity network, showing strong associations with metabolic (E11.9-Type 2 diabetes, E78.5-hyperlipidemia), cardiovascular (I10-hypertension), and mental health disorders (F32.9-depression, F41.9-anxiety). A significant reduction in chronic disease management services was observed in 2022, corresponding with the peak impact of the COVID-19 pandemic, followed by a partial rebound in 2023.

Discussion: The findings reveal the integrative role of respiratory diseases within broader patterns of multimorbidity, reinforcing the need for cross-disciplinary management approaches. The observed pandemic-related disruption in chronic care delivery highlights systemic vulnerabilities. Future preparedness strategies should integrate multimorbidity frameworks and ensure continuity of care for both respiratory and systemic conditions.

Importance: Medicaid, as the largest U.S. insurer, can reduce cardiometabolic multimorbidity.

Objective: Assess patterns and trends in cardiometabolic multimorbidity among Medicaid-enrolled adults.

Design: Analysis of 2018-2022 National Health Interview Survey data, a nationally representative cross-sectional survey.

Conditions studied: Hypertension, hyperlipidemia, coronary heart disease, angina, heart attack, stroke, diabetes, and obesity.

Setting: U.S., 2018-2022.

Participants: 11,090 adults (19-64 years) with Medicaid coverage.

Main outcomes: Proportion with one or multiple cardiometabolic conditions.

Findings: (a) 29.3% had one cardiometabolic condition; 29.7% had multimorbidity: 14.5% with 2, 8.0% with 3, and 7.1% with 4+ conditions. (b) Obesity, hypertension, and hyperlipidemia were the most common conditions either individually or together. (c) Obesity was more common in women than men, and women were more likely to have a single condition while men were more likely to have multimorbidity; these differences between men and women were larger in younger adults (<41 years) than older adults. (d) There was higher multimorbidity among older, non-working, and less educated Medicaid enrollees. (e) Prevalence of multimorbidity over time did not change but there was a decrease in the proportion of enrollees with no conditions which was offset by an increase in enrollees with a single condition.

Conclusion: 29.7% of Medicaid-insured adults had cardiometabolic multimorbidity, and another 29.3% were at risk for it. Potential cuts to Medicaid coverage may exacerbate the burden of cardiometabolic multimorbidity in Medicaid enrollees.