Frontiers in epidemiology最新文献

Dementia is a leading cause of death and disability with over 60% of cases residing in low- and middle-income countries (LMICs). Therefore, new strategies to mitigate risk are urgently needed. However, despite the high burden of disease associated with dementia in LMICs, research into dementia risk profiling and risk prediction modelling is limited. Further, dementia risk prediction models developed in high income countries generally do not transport well to LMICs suggesting that context-specific models are instead needed. New prediction models have been developed, in China and Mexico only, with varying predictive accuracy. However, none has been externally validated or incorporated variables that may be important for predicting dementia risk in LMIC settings such as socio-economic status, literacy, healthcare access, nutrition, stress, pollutants, and occupational hazards. Since there is not yet any curative treatment for dementia, developing a context-specific dementia prediction model is urgently needed for planning early interventions for vulnerable groups, particularly for resource constrained LMIC settings.

Traditional methods for measuring chemical exposure have challenges in terms of obtaining sufficient data; therefore, improved methods for better assessing occupational exposure are needed. One possible approach to mitigate these challenges is to use self-monitoring methods such as sensors, diaries, or biomarkers. In the present study, a self-monitored method for measuring soot exposure, which included real-time air monitoring, a work diary, and the collection of urine samples, was evaluated. To validate the method, exposure measurements during the workday and diary entries were compared with velocities calculated from GPS tracking and the expected polycyclic aromatic hydrocarbon (PAH) metabolite patterns in urine. The method was applied with chimney sweeps, an occupational group at a high risk of many severe health outcomes and for whom effective control measures for reducing exposure are needed. In the study, 20 chimney sweeps followed a self-monitoring protocol for 8 consecutive workdays. Personal exposure to soot was measured as black carbon (BC) using micro-aethalometers. A diary was used to record the work tasks performed, and urine samples were collected and analysed for PAH metabolites. From the expected 160 full day measurements, 146 (91%) BC measurements and 149 (93%) diaries were collected. From the expected 320 urine samples, 304 (95%) were collected. The tasks noted in the diaries overlapped with information obtained from the GPS tracking of the chimney sweeps, which covered 96% of the measurement time. The PAH metabolites in urine increased during the work week. Factors believed to have positively influenced the sample collection and task documentation were the highly motivated participants and the continuous presence of trained occupational hygiene professionals during the planning of the study and throughout the measurement stage, during which they were available to inform, instruct, and address questions. In conclusion, the self-monitored protocol used in this study with chimney sweeps is a valuable and valid method that can be used to collect larger numbers of samples. This is especially valuable for occupations in which the employees are working independently and the exposure is difficult to monitor with traditional occupational hygiene methods.

The natural foci of Crimean-Congo haemorrhagic fever (CCHF) in Kazakhstan are geographically located in the southern regions of the country (Kyzylorda, Turkestan and Zhambyl regions), where the infection of ticks with the CCHF virus predominantly reside, tick species composition and the number of vectors are monitored annually. The objective of our research was to investigate the genetic variants of the CCHF virus in the southern endemic regions, as well as to monitor the spread of the CCHF virus in the western regions of the country (Aktobe, Atyrau and Mangystau regions). In total, 974 (216 pools) ticks from the western regions and 3527 (583 pools) ticks from the southern regions collected during 2021-2022 were investigated. The presence of CCHF virus was detected by real-time reverse transcription PCR (qRT- PCR) in 1 pool out of 799 pools (0.12%) with Hyalomma scupense ticks captured in the CCHF-endemic Kyzylorda region. In the western regions, CCHF virus was not detected in ticks. The sequencing of incomplete fragments of the S, M and L segments of the CCHF virus in the detected virus was identified as genotype Asia - I. Phylogenetic analysis showed that the isolate obtained in this study is grouped with the isolate from a patient with CCHF, which we reported in 2015 (KX129738 Genbank). Our findings highlight the importance of including sequencing in the annual monitoring system for better understanding the evolution of the CCHF virus in the study areas of our country.

Introduction: Global inequality in clinical research capacity and service delivery can be indicated simply by the proportion of clinical trials that a country or region has registered in clinical trial registry databases. The proportion of clinical trials registered in Africa is very low at 0.02%, even though the region accounts for approximately 15% of the world's population. Despite the economic challenges in most African countries, they have shown potential for growth and change in recent years.

Methods: We conducted desk reviews on the interventional clinical trials done in Kenya, Ethiopia, and Nigeria between 2015 to May 2023. The search was done in clinical trials repositories, and journal repositories. The search focused on intervention clinical trials. Data was extracted by screening through the publications and clinical trial platforms. The data extracted from the publications included the type of clinical trial, clinical trial phase, diseases, etc. The data extracted from the reports included: challenges in conducting clinical trials, capacity-building efforts, and the impact of the clinical trial.

Results: The number of clinical trial studies identified in Kenya was 113 (28 were on infant clinical trials). The study identified 97 clinical trials in Nigeria, of which 11 studies were on infant clinical trials. In Ethiopia, there were 28 clinical trials and only five were on infant clinical trials. The landscape review also expanded to capacity and gaps in clinical trials in the three countries. The largest proportion of clinical trials carried out in Kenya was on injury, occupational disease, and poisoning, 30.5% (n = 18) and the smallest proportion was on kidney disease, neonatal disease, obstetrics, and gynecology. Most Infant clinical trials were carried out in the area of infections and infestations 33.3% (n = 7). Most of the challenges faced by clinical trials in the three countries include a lack of infrastructure, a lack of human resources, and a lack of financial resources.

Implications: There is a need to map clinical trials done by African researchers based in Africa to exclude the trials done by non-African researchers based in Africa. Opportunities for clinical trials should be supported and challenges addressed.

Survival analysis (also referred to as time-to-event analysis) is the study of the time elapsed from a starting date to some event of interest. In practice, these analyses can be challenging and, if methodological errors are to be avoided, require the application of appropriate techniques. By using simulations and real-life data based on the French national registry of patients with primary immunodeficiencies (CEREDIH), we sought to highlight the basic elements that need to be handled correctly when performing the initial steps in a survival analysis. We focused on non-parametric methods to deal with right censoring, left truncation, competing risks, and recurrent events. Our simulations show that ignoring these aspects induces a bias in the results; we then explain how to analyze the data correctly in these situations using non-parametric methods. Rare disease registries are extremely valuable in medical research. We discuss the application of appropriate methods for the analysis of time-to-event from the CEREDIH registry. The objective of this tutorial article is to provide clinicians and healthcare professionals with better knowledge of the issues facing them when analyzing time-to-event data.

Objectives: Field Epidemiology Training Programs (FETPs) are competency-based training programs that play a critical role in strengthening global health security and enhancing the epidemiological capabilities of public health professionals. This scoping review examined available published literature on the evaluations of FETPs globally.

Methods: A literature review was conducted to evaluate FETPs globally. Keywords specific to the evaluation of FETPs were utilized to search the PubMed, Scopus, and Web Science databases. After applying the inclusion and exclusion criteria, 12 relevant studies from an initial pool of 60 were included in this study. Data extraction included key details, and a qualitative synthesis organized diverse findings using a narrative approach to draw appropriate conclusions and generate recommendations.

Results: The review covered findings from 12 studies covering all three FETP modalities and spanning countries in various regions. Evaluations explored gained skills, engagement in FETP activities, and improvements in field epidemiological functions. Gained skills and knowledge, engagement in FETP activities, and improvements in field epidemiological functions were evident, with specific expectations for each FETP tier. Positive outcomes were consistent across studies, revealing improvements in surveillance activities, outbreak response, data management, and other system functions.

Conclusion: This review confirmed the positive impact of FETPs on trainees and graduates, which emphasized competency enhancements across different modalities. Various strategies are recommended to improve the evaluation of FETPs. For effective evaluation, it is necessary to develop robust evaluation tools and establish standardized metrics to compare FETPs across regions or countries.

Background: Needlestick and sharp object injuries affect healthcare workers. However, there are limitations in the evidence available for informed decision-making by stakeholders, as individual research shows inconsistent results. Therefore, this study aims to assess the pooled prevalence of needlestick and sharp object injuries and their associated factors.

Methods: MEDLINE/PubMed, EMBASE, Web of Science, Google Scholar, and other databases were searched from 5 September 2023 to 10 October 2023 using the following search terms: "Prevalence" OR "Burden" OR "Magnitude" AND "Associated factors" OR "related factors" OR "Risk factors" OR "determinants" OR "Predictors" AND "Needle stick Injury" OR "Sharp Injury" OR "Health care Workers" OR " Health Care Personnel" OR "Nurses" OR "Professional" AND "Ethiopia".

Results: The pooled prevalence of needle sticks and sharp objects injury was 40.5 (95% CI: 35.0, 45.9). Needle-stick (AOR, 2.3; 95% CI: 1.6, 3.3, P < 0.001], absence of routine precaution [AOR, 2.3; 95% CI: 1.1, 4.5, P < 0.01] and lack of training (AOR = 2.4; 95% CI: 1.4, 4.1, p < 0.001) had increased odds of needle-sticks and sharp objects injury.

Conclusion: Forty percent of healthcare workers in Ethiopia have experienced needlestick and sharp object injuries. The identified factors included recapping, absence of routine precautions, and lack of training.

Systematic review registration: PROSPERO, identifier (CRD42023462311).

During the COVID-19 pandemic, several forecasting models were released to predict the spread of the virus along variables vital for public health policymaking. Of these, the susceptible-infected-recovered (SIR) compartmental model was the most common. In this paper, we investigated the forecasting performance of The University of Texas COVID-19 Modeling Consortium SIR model. We considered the following daily outcomes: hospitalizations, ICU patients, and deaths. We evaluated the overall forecasting performance, highlighted some stark forecast biases, and considered forecast errors conditional on different pandemic regimes. We found that this model tends to overforecast over the longer horizons and when there is a surge in viral spread. We bolstered these findings by linking them to faults with the SIR framework itself.