Frontiers in epidemiology最新文献

Purpose: Glomerular filtration rate (GFR) is used for evaluating kidney function. Creatinine and cystatin C levels are the two endogenous substances used to estimate GFR (eGFR_CR and eGFR_CYS). The agreement between these two is reflected by the eGFR_CYS/eGFR_CR ratio. An eGFR_CYS/eGFR_CR ratio <0.70 has been strongly associated with mortality and morbidity. An explanation is a selective decrease in the filtration of substances of different masses, and this condition is referred to as "Shrunken pore syndrome" (SPS). We aim to investigate the prevalence of SPS and its association with morbidity and mortality in a well-characterized population-based cohort.

Methods: The study population consisted of 5,061 individuals from the Malmö Diet and Cancer cardiovascular cohort (MDC-CC) with baseline examinations between 1991 and 1994 and a median follow-up of 25.3 years (IQR = 5.7). The eGFR_CYS/eGFR_CR ratio was categorized into four groups and used to estimate a generalized propensity score for SPS to adjust for confounding factors. Individuals were matched to create a quartet (one from each eGFR_CYS/eGFR_CR ratio category) with similar scores. We related the eGFR_CYS/eGFR_CR ratio to all-cause mortality, incident cardiovascular disease, incident kidney disease, and incident diabetes using Cox proportional hazards models with shared frailty.

Results: SPS was detected in 405 individuals (8.0%). The hazard ratio (HR) for all-cause mortality was 1.6 [95% confidence interval (CI) 1.3-2.0] when comparing individuals with SPS to the reference group (eGFR_CYS/eGFR_CR ratio ≥ 1.0). For incident kidney disease, the association seems to stem from a low eGFR_CYS rather than the eGFR_CYS/eGFR_CR ratio. For the other two outcomes, robust and statistically significant associations could not be found.

Conclusion: SPS was prevalent among middle-aged, generally healthy, individuals and led to markedly higher mortality during follow-up.

Introduction: This study aimed to investigate the excess mortality observed in China between 2020 and 2023 and its underlying causes, including the COVID-19 pandemic, sex ratio, and aging trends.

Methods: We collected data regarding number of deaths and mortality rates of the years 2015-2019 from the China Statistical Yearbook of Population and Employment, and analyzed the data by age and sex. We created a standardized sex and age structure dataset and compared the excess mortality rates of different sexes and age groups for the years 2020 to 2023.

Results: The sex-and age-specific number of deaths in all three years decreased compared to the number of deaths in the standardized sex and age structure dataset. The most significant decline was observed in 2021, whereas the number of deaths in 2022 and 2023 increased compared to that in 2021. It has been found that excess mortality is generally less prevalent among older age groups, and excess mortality rate tends to be lower among younger age groups. Additionally, in 2021, 2022, and 2023, the excess mortality rate was greater for men than for women, whereas it was greater for women in 2020. These differences can be attributed to various factors.

Discussion: This is the first study to examine excess mortality in China during the COVID-19 pandemic using age- and sex-standardized data. These findings underscore the need for a deeper exploration of the effects of sex and age on health outcomes.

Background: In December 2019, the world experienced one of the significant health crises of the 21st century with the emergence and rapid spread of the potentially fatal 2019 coronavirus (COVID-19). In this context, sentinel surveillance of SARS-CoV-2 variants was conducted in Conakry. Here we report the first data on reproduction numbers and risk factors during the Omicron post-epidemic period in Guinea.

Methods: A sentinel syndromic and genomic surveillance study was conducted on suspected patients from October 2022 to July 2024 at healthcare facilities in Conakry. Individual data and nasopharyngeal swabs were collected and sent to the Centre de Recherche et de Formation en Infectiologie de Guinée (CERFIG) laboratory for screening and sequencing by next-generation sequencing (NGS). The effective reproduction number (Rt) were estimated using EpiEstim to assess the transmission potential of the Omicron variant. Generalized linear models based on the binomial distribution were employed to analyze factors associated with SARS-CoV-2 positivity, following the identification of primary risk factors using Bayesian model averaging and the Data balancing algorithm using propensity score matching.

Results: Data from 1174 patients with suspected cases with a median age of 31 years (IQR: 20-51), were analyzed. The overall COVID-19 positivity rate was 11.8%. The global effective reproduction number (Rt) was 2.08 [95% CI: 0.35-5.81]. Only ageusia [AOR = 2.0; 95% CI (1.1-3.6)] was independently associated with SARS-CoV-2 test positivity.

Conclusion: SARS-CoV-2 is still circulating in Guinea, with a high positivity rate and a high number of effective reproductions in this post-epidemic period in our country. The associated factors and the circulation of variants with a diversity of circulating strains suggest the need to strengthen genomic and epidemiological surveillance, with the support of all those involved in the response to COVID-19, to ensure continuity of alerts and decision-making for public health.

Background: Implementation research (IR) studies and clinical trials have yielded conflicting results on improving treatment outcomes, especially among adolescent patients. To address this, we performed a Bayesian random-effects meta-analysis to evaluate the effectiveness of current IR interventions in improving retention in care and reducing viral suppression among HIV-infected adolescents.

Method: A comprehensive search was conducted from 1 January 2000 to 31 December 2020. A Bayesian random-effects meta-analysis was performed using historical evidence from adolescent interventions conducted outside sub-Saharan Africa (SSA) and from adult-derived interventions within SSA. A Bayesian Copas random-effects model was used to account for publication bias and study variations. Power priors were used to weight the contribution of historical data to the analysis. Bayesian meta-analysis was particularly suited for this study since it allowed us to directly include prior assessments from several intervention studies into the pooled intervention data.

Results: The pooled results from the 12 studies across eight African countries, involving 19,223 adolescent patients, showed significantly superior retention effects in adolescent-specialized interventions compared to standard care settings [odds ratio (OR) = 3.87; 95% credible interval (CrI): 0.94-10.82]. When 100% of data from eight observational studies on adolescent treatment outcomes were added to the analysis, the resulting OR was 3.02 (95% CrI: 1.01, 6.92). However, inclusion of 100% of historical data from adult randomised control trials (RCTs) reduced the retention effect to OR = 1.24 (95% CrI: 1.03-1.48). Regardless of whether adolescent historical data or adult RCT data were used, the associated posterior probability of benefiting from the intervention remained almost 1. There was no difference between standard care and specialized adolescent care in terms of virological suppression (OR = 1.27; 95% CrI: 0.57-2.32). However, specialized adolescent intervention achieved a superior overall retention rate of 59.7% compared to 52.1% under standard care.

Conclusion: Current adolescent-specific interventions are effective in improving retention rates in HIV care. Evidence from adult interventions showed a reduced retention effect, suggesting that while adult-promising interventions may improve adolescent treatment outcomes, they may require modifications.

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.