Frontiers in epidemiology最新文献

Background: Evidence-based Directed Acyclic Graphs (DAGs) are effective tools to comprehensively visualize complex causal and biasing pathways in pharmacoepidemiologic research in rheumatology. This paper outlines the process of developing and implementing a DAG, using a cohort study evaluating the impact of targeted synthetic disease-modifying antirheumatic drugs (tsDMARDs) on congenital anomalies as a case example. We include a discussion of how factors would be operationalized into variables in administrative data within the case example.

Methods: DAG Development involved: 1) identifying exposure and outcome, 2) identifying factors affecting the exposure, 3) identifying factors affecting the outcome, 4) identifying factors affecting both the exposure and outcome, 5) ascertaining relationships between factors, and lastly, 6) finalizing the DAG in DAGitty v3.1.

Results: The final DAG for our case example on evaluating the association between tsDMARDs and congenital anomalies consisted of 21 nodes (points in the diagram representing factors such as exposures, outcomes, confounders, or mediators): 1 affecting the exposure, 12 affecting the outcome, 7 on the biasing pathways, and 1 mediator (maternal infection) on the exposure-outcome pathway. One minimally sufficient adjustment set was identified to inform confounder adjustment in a multivariable model, consisting of: concomitant conventional synthetic DMARDs, rheumatic disease activity, and maternal demographics (i.e., age, place of residence, race/ethnicity). Implications for implementing this DAG in a study using administrative health data include comprehensively revealing confounders to be adjusted for.

Conclusions: Our systematic approach to developing a DAG is particularly valuable for improving study designs in the growing field of perinatal pharmacoepidemiology in rheumatology, where there is a critical need for robust perinatal data on novel arthritis medications.

The narrative review aimed to explore how the sociocultural belief systems influence the health-seeking behavior of individuals living with HIV (late ART initiation and treatment discontinuation) and the subsequent impact on SDG Target 3.3. We searched PubMed, using a search strategy using keywords such as "HIV management barriers," "SDG Target 3.3," and "sociocultural beliefs", and it was adapted on Google Scholar, and AJOL between 1st may to 30th June 2025. Findings demonstrate that pluralistic health-seeking behavior, such as sequential use of biomedical care, religious healing, and traditional medicine, persists amongst individuals living with HIV. This is informed by society, religious, and traditional healers. The pluralistic health-seeking behavior is practiced based on what the individual perceives as the causes of HIV, the influence of religion and faith leaders, and traditional claims of HIV cure. Although pluralistic health-seeking behavior may offer emotional support, they associated with delayed initiation, disruptions, and adherence to ART, inadequate retention in care, and lower likelihood of long-term viral suppression, weakening the HIV care continuum. Although emerging research has explored the potential role of traditional medicine in HIV management, there is a lack of evidence to support its use as a standalone treatment. The findings of this review, emphasizes a need for a structured collaborative care models. Formal engagement and dialogue amongst traditional, religious leaders, and PHC practitioners', development of referral linkages and integration of culturally sensitive HIV education within existing health systems at a policy level should be explored.

Objective: This study aimed to explore the association between sleep duration and prevalence ofcoronary artery diseases (CAD) among adults in Chongqing, China, and discuss implications for clinical practice and public health policy.

Methods: Baseline variables were collected from 2,320 adults who participated in community medical examinations in Chongqing, China, between August 2018 and October 2020. Sleep duration was self-reported and categorized into short (<6 h/day), normal (6-8 h/day), and long (>8 h/day). Multivariate logistic regression was used to examine associations between sleep duration and CAD, adjusting for demographic and clinical confounders.

Results: Short sleep (<6 h/day; OR = 1.595, 95% CI = 1.230-2.067) and long sleep (>8 h/day; OR = 2.284, 95% CI = 1.456-3.583) were significantly associated with increased odds of CAD compared to normal sleep duration (6-8 h/day), even after adjusting for confounders. Long sleep duration demonstrated a notably stronger association with CAD risk.

Conclusion: Both short and long sleep durations are significant risk factors for coronary artery diseases, with longer sleep duration showing a stronger association. Public health initiatives and clinical practices should integrate sleep duration assessments to identify at-risk populations and implement targeted interventions.

Introduction: The COVID-19 pandemic posed significant challenges for public health systems, especially in Africa, where data scarcity, inadequate healthcare infrastructure, and regional disparities hindered effective forecasting and response efforts. Conventional forecasting methods have faced challenges in adequately addressing the complexity and detail necessary for effective policy interventions at various administrative levels. This study examines the challenge of producing accurate and coherent forecasts of COVID-19 cases within the hierarchical structure of Africa, which includes the continental, regional, and national levels.

Methods: To establish a comprehensive forecasting model that uses hierarchical time series forecasting through a bottom-up reconciliation approach augmented by machine learning algorithms. We employ extreme gradient boosting (XGBoost) and random forest models, subsequently improving predictive accuracy via a weighted average ensemble method. We produce forecasts at the national level and then aggregate them to ensure consistency across all hierarchical levels. The models are evaluated in comparison to conventional methods such as ARIMA and exponential smoothing.

Results: Empirical findings indicate that XGBoost is the best among all the single forecast models used in this study, combining forecasts from the XGBoost with the random forest and assigning more weights to the XGBoost surpasses all other models in the area of mean absolute error, root mean square error, and mean absolute scale error. Results further revealed that Southern Africa, despite its low population density, reported the highest number of cases, indicating underlying health vulnerabilities and socioeconomic factors. In summary, the bottom-up HTSF method, when combined with machine learning, serves as an effective tool for forecasting in environments with limited data availability.

Discussion: It is advisable to apply similar models to other infectious diseases and to expand their use to guide health interventions, resource allocation, and early warning systems in future pandemics.

[This retracts the article DOI: 10.3389/fepid.2024.1492579.].

Background: Despite advances in treatment, mortality in patients with chronic limb-threatening ischemia (CLTI) is high. The aim of our study was to evaluate 5-year all-cause mortality and factors associated with it in endovascularly treated (EVT) patients with foot ischemic ulcers.

Methods: We reviewed all patients who had undergone EVT for lower extremity peripheral artery disease between January 2016 and December 2018. Adjustments in multivariate analyses were performed for age, hypertension, diabetes mellitus, sex, smoking, dyslipidemia, chronic obstructive pulmonary disease, malignancy, atrial fibrillation, heart failure with reduced ejection fraction, coronary artery disease, postprocedural ipsilateral amputation, ipsilateral reintervention, number of endovascularly treated regions, fibrinogen and creatinine.

Results: Four hundred and fifty-one patients (155 women, 296 men) with a mean age of 70.4 ± 9.60 years were included in the analysis. The 5-year all-cause mortality was 60.5%. In multivariate analysis mortality risk was higher in women (HR 1.42; 95% CI 1.09-1.86; p = 0.010), and after EVT in two or more anatomical regions (HR 1.37; 95% CI 1.05-1.79; p = 0.022). The mortality risk was positively associated with creatinine (HR 1.003; 95% CI 1.002-1.004; p < 0.001), and fibrinogen (HR 1.19; 95% CI 1.11-1.29; p < 0.001). Ipsilateral reintervention (HR 0.67; 95%CI 0.47-0.94; p = 0.021) and ipsilateral amputation after EVT (HR 0.71; 95% CI 0.51-0.98; p = 0.037) were associated with lower all-cause mortality risk.

Conclusions: Female sex, treatment in two or more anatomical regions, creatinine and fibrinogen were associated with higher 5-year mortality risk. Lower 5-year all-cause mortality risk was observed in patients with ipsilateral reintervention and ipsilateral amputation after EVT.

This perspective piece explores the potential to implement wastewater surveillance on military vessels to improve disease monitoring and prevention. We examine five key topics: (1) recent studies of wastewater surveillance on military bases and training centers; (2) best practices for confined populations (e.g., colleges, prisons, hospitals, and low-income and middle-income countries) and their transferability to military settings; (3) current technologies enabling deployed personnel to conduct wastewater surveillance without advanced microbiological training; (4) key questions the military should address to prevent future outbreaks on vessels; and (5) unique ethical considerations surrounding implementation. This work aims to inform military decision-makers considering the adoption of wastewater surveillance programs.

Background: Parotitis is an inflammation of the parotid glands. It can be acute or chronic based on etiological factors such as bacterial and viral infections, autoimmune and metabolic disorders. The prevalence and characteristics of parotitis among the Saudi population are unknown. Therefore, this study aimed to explore the frequency, demographic patterns, and temporal trends of parotitis in Saudi Arabia.

Methodology: This was a multicenter, retrospective cohort study using electronic health record data from five tertiary medical centers (Riyadh, Jeddah, Dammam, Madinah, and Taif) of the Ministry of National Guard Health Affairs (NGHA) between 2015 and 2023. Data from clinically diagnosed patients with parotitis were collected, including demographics, patient type, body mass index (BMI), and region. Statistical analysis was conducted using R (version 4.3.2). Categorical variables were expressed as counts (%) and continuous variables as mean (SD) or median (IQR), as appropriate. Several statistical tests were performed, including annual counts and proportions for temporal trends, and join-point regression to estimate data-driven change points. Statistical significance was estimated at a P-value of less than 0.05.

Results: A total of 1,340 cases of parotitis were recorded between March 2015 and March 2023. The average age at diagnosis was 27.2 years. Males accounted for 54.6% of this cohort, 36.67% of the patients were underweight, and 19.2% were obese. Additionally, 49% of the cases were inpatients, and the majority (66.1%) resided in Riyadh. Within the designated timeframe (2015-2023), no significant changes in parotitis occurrence were observed, especially during the COVID-19 pandemic, with a higher frequency among patients aged 1-20 years.

Conclusion: This exploratory study characterized parotitis cases among Saudi patients. The high frequency of parotitis diagnosis among children and adolescents compared to adults, along with other demographic characteristics, highlights the need to understand the underlying factors that could improve clinical awareness, documentation, and prevention strategies.

Coxiella burnetii is a zoonotic pathogen that causes Q fever in humans. There are many known reservoirs of C. burnetii, including cattle, sheep, and goats with an expanding list of potential reservoirs including birds, reptiles, ticks and additional mammalian species, such as swine. Feral swine are a highly invasive species in the United States with significant populations and a broad geographic distribution. The role of feral swine in the transmission and spread of C. burnetii is poorly understood, although a recent report identified overlap between feral swine seroprevalence and human Q fever incidence in Texas. California accounts for a large proportion of human Q fever cases in the United States and in this study we characterized the seroprevalence of C. burnetii in feral swine populations in the state. Feral swine showed seropositivity rates up to 1.64% indicating some level of exposure and the possibility that they may serve as a reservoir for disease transmission and spread. Overlap with human Q fever incidence was identified in the central region of California. Although this study does not directly link feral swine to human infection, it identified spatial overlap between feral swine seroprevalence and human Q fever incidence in the state of California, possibly due to the presence of ruminants as the principal reservoirs of C. burnetii. The environmental stability and low infectious dose of C. burnetii, coupled with the geographic overlap between feral swine seroprevalence and human Q fever incidence suggests that feral swine may contribute to zoonotic disease transmission and spread.

Recently, it has been shown that the transition rates of the illness-death model (IDM) for chronic conditions are related to the age-specific prevalence by a partial differential equation (PDE). Given mortality, the PDE could be used to estimate incidence rates from cross-sectional data. The aim of this article is to extend the IDM and introduce a novel method to estimate the age-specific incidence rate together with the two mortality rates from aggregated current status (ACS) data. By ACS data we mean counts of people in the four states of the extended IDM at different points in time. ACS data stem from epidemiological studies where only current disease status and vital status data need to be collected without following-up people (as, for example, in cohort studies). To demonstrate feasibility of the method, we use a simulation study from the context of diabetes in Germany. Two estimation methods are introduced, a least squares estimator and a maximum likelihood estimator. We find a good agreement between the estimates and the input parameters used to set up the simulation.