Frontiers in epidemiology最新文献

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.

Malaria control efforts in Kenya face persistent challenges due to fragmented health information systems, despite notable digital innovations. This mini review evaluates implementations in western Kenya, contrasting successes like Siaya County's Electronic Community Health Information System (eCHIS), developed through collaborations between the Ministry of Health, local agencies, and frontline health workers, which reduces reporting delays through real-time mobile data collection, with ongoing struggles including paper-based records in health facilities and unreliable rural internet. We document how analytical methods, when properly supported, can transform surveillance. Methods such as spatiotemporal models using climate and case data can improve outbreak predictions, while machine learning techniques can optimize insecticide-treated bed net distributions by pinpointing high-risk households. However, these analytical tools remain underutilized due to data fragmentation and limited technical capacity. Key implementation challenges emerged, including device charging difficulties for community health workers, inconsistent data standards between systems, and privacy concerns under Kenya's new Digital Health Act that policymakers are currently addressing through revised guidelines. Key recommendations from this review include the expansion of digital health platforms with co-design input from end-users, improved data quality through standardized reporting mechanisms enforced by county health leadership, and the incorporation of predictive modeling to identify high-risk areas and optimize intervention timing. Investing in robust health information infrastructure will not only strengthen malaria control efforts in Kenya but also serve as a model for other malaria-endemic regions. Digital tools show tremendous potential when paired with sustained training, community engagement, and realistic maintenance solutions supported by public-private partnerships.

Background: Two critical factors in the success of the response to a threatening epidemic outbreak are the degree of responsibility of the main political actors involved in the response and the population compliance to the proposed measures. The Behavioural epidemiology literature has focused on the latter factor but largely disregarded the former. The multiple failures in COVID-19 control and the lack of consensus that still surround the main response options (i.e., the elimination-suppression-mitigation trichotomy) highlight the importance of considering the political layer in preparedness activities.

Methods: We integrate a simple transmission model into a game-theoretic framework for the interaction between the main political actors involved in the response, namely a government, its opposition and lobbies. The aim is to provide a conceptual framework allowing one to identify the political factors promoting a timely and effective response.

Results: Low degrees of responsibility (i.e., prioritizing consensus over health protection) of political agents can delay or de-potentiate the response until when epidemic growth eventually overtakes the agents' payoffs, thereby forcing them to switch towards the higher degree of responsibility needed to promote an adequate response. When both the government and the opposition are only "partly" responsible, a stall in the response decision-making process likely arises, further delaying the response. Policy and epidemiological parameters amplifying the response delay are ranked by a sensitivity analysis.

Conclusions: Promoting a high degree of responsibility of political actors and lobbies during emergency situations should be a key target of preparedness. Therefore, future pandemic plans should also include, beyond technical indications, ethical statements "guiding" political entities to cooperation.

Background: Although tuberculosis mortality has dramatically decreased over the last decade, tuberculosis remains the world's biggest cause of death. Conflict affected nations hold vast majority of malnourished people globally, where many people die each year of tuberculosis. With regard to the global burden of tuberculosis, Ethiopia ranks third in the African continent and seventh overall. But in the research arena, the severity of the issue is not as well understood. Therefore, the current study aimed to assess undernutrition and the determinant factors among adult TB-patients receiving treatment in public health facilities in conflict affected zones of Southern.

Methods: A multicenter facility-based cross-sectional study was conducted from 27/08/2023-28/ 09/2023 among 414 randomly selected adult (age ≥18 years) TB-patients receiving treatment at public health facilities in conflict affected zones of Southern Ethiopia. An interviewer-administered questionnaire and anthropometric measurements were used to collect data from study participants after written informed consent provision. By using SPSS Version 25, bivariate and multivariable logistic regression models were employed to determine the factors related to nutritional status.

Results: Overall, 33.3% of study participants had undernutrition, with a [95% CI (28.8%-38.1%)]. Factors such as cigarette smoking [AOR = 2.02, 95% CI; 1.22, 3.34] chat chewing [AOR = 2.50, 95% CI; 1.59, 3.93] regular cheka drinking [AOR = 1.82; 95% CI, 1.22-2.71] and household food insecurity [AOR = 1.78, 95% CI; 1.19, 2.66] had significant association with undernutrition.

Conclusions: The results of this study show that undernutrition affects one in three adult TB patients. Lifestyle factors such as smoking and chewing, and dietary factors like cheka eating and household food security had significant association with undernutrition. In order to improve the quality of life for TB patients, it is imperative that all stakeholders should prioritize addressing the lifestyle and nutritional aspects that are essential to the effectiveness of TB control and prevention initiatives.

Background: Evidence is limited on the impact of temperature variability (TV) on health in low-and-middle-income countries (LMICs), such as South Africa. This study examined the association between TV and cardiovascular disease (CVD) and respiratory disease (RD) mortality in five South African cities.

Methods: Daily mortality and meteorological data in five South African cities (Bloemfontein, Cape Town, Durban, Johannesburg, and Gqeberha) were collected from Statistics South Africa and the South African Weather Service for the period 2006-2016. TV was calculated as the standard deviation of the daily minimum and maximum temperatures over the exposure period. City-specific risks were estimated using quasi-Poisson regression models combined with distributed lag nonlinear models, adjusting for potential confounders. A meta-analysis was then conducted to pool the overall estimates across cities. Additionally, stratified analyses by age group and sex were performed to assess effect modification.

Results: A total of 213,875 cardiovascular and 114,887 respiratory deaths were recorded in the five cities during the study period. The risks with increasing TV were higher for RD mortality as compared to CVD mortality. The pooled estimates showed the highest and significant increase in RD mortality of 1.21(95% CI: 1.04;1.38) per an increase in TV at 0-2 days from the 25th to the 50th percentile for all ages combined. The elderly appeared more vulnerable to RD mortality than <65 years age group, with significant mortality risks per increase in TV at 0-2 days (RR = 1.18, 95% CI: 1.04; 1.32),0-3 days (RR = 1.16, 95% CI: 1.04; 1.28) and at 0-7 days (RR = 1.12, 95% CI: 1.02; 1.22) from the 50th to the 75th percentile. A stratified analysis showed the elderly and women as more vulnerable. The pooled results across the five cities suggested no statistically significant TV effect on CVD mortality.

Conclusion: This study found a short-term association between temperature variability and respiratory mortality, especially among elderly individuals and women, in five South African cities. No significant effect was observed for cardiovascular mortality. The findings support targeted public health strategies that account for temperature-related risks in vulnerable populations.