Frontiers in epidemiology最新文献

Introduction: Visceral leishmaniasis (VL), a neglected tropical disease that causes substantial morbidity and mortality, is a serious health problem in Ethiopia. Infections are caused by Leishmania (L.) donovani parasites. Most individuals remain asymptomatic, but some develop VL, which is generally fatal if not treated. We identified the area of Metema-Humera in Northwest Ethiopia as a setting in which we could follow migrant workers when they arrived in an endemic area. The demographic characteristics of this population and factors associated with their risk of asymptomatic infection are poorly characterised.

Methods: We divided our cohort into individuals who visited this area for the first time (first comers, FC) and those who had already been in this area (repeat comers, RC). We followed them from the beginning (Time 1, T1) to the end of the agricultural season (Time 2, T2), performing tests for sand fly bite exposure (anti-sand fly saliva antibody ELISA) and serology for Leishmania infection (rK39 rapid diagnostic test and the direct agglutination test) at each time point and collecting information on risk factors for infection.

Results: Our results show that most migrant workers come from non-endemic areas, are male, young (median age of 20 years) and are farmers or students. At T1, >80% of them had been already exposed to sand fly bites, as shown by the presence of anti-saliva antibodies. However, due to seasonality of sand flies there was no difference in exposure between FC and RC, or between T1 and T2. The serology data showed that at T1, but not at T2, a significantly higher proportion of RC were asymptomatic. Furthermore, 28.6% of FC became asymptomatic between T1 and T2. Over the duration of this study, one FC and one RC developed VL. In multivariable logistic regression of asymptomatic infection at T1, only age and the number of visits to Metema/Humera were significantly associated with asymptomatic infection.

Conclusion: A better understanding of the dynamics of parasite transmission and the risk factors associated with the development of asymptomatic infections and potentially VL will be essential for the development of new strategies to prevent leishmaniasis.

Objective: Leveraging the Manhattan Lupus Surveillance Program (MLSP), a population-based registry of cases of systemic lupus erythematosus (SLE) and related diseases, we investigated the proportion of SLE with concomitant rheumatic diseases, including Sjögren's disease (SjD), antiphospholipid syndrome (APLS), and fibromyalgia (FM), as well as the prevalence of autoantibodies in SLE by sex and race/ethnicity.

Methods: Prevalent SLE cases fulfilled one of three sets of classification criteria. Additional rheumatic diseases were defined using modified criteria based on data available in the MLSP: SjD (anti-SSA/Ro positive and evidence of keratoconjunctivitis sicca and/or xerostomia), APLS (antiphospholipid antibody positive and evidence of a blood clot), and FM (diagnosis in the chart).

Results: 1,342 patients fulfilled SLE classification criteria. Of these, SjD was identified in 147 (11.0%, 95% CI 9.2-12.7%) patients with women and non-Latino Asian patients being the most highly represented. APLS was diagnosed in 119 (8.9%, 95% CI 7.3-10.5%) patients with the highest frequency in Latino patients. FM was present in 120 (8.9%, 95% CI 7.3-10.5) patients with non-Latino White and Latino patients having the highest frequency. Anti-dsDNA antibodies were most prevalent in non-Latino Asian, Black, and Latino patients while anti-Sm antibodies showed the highest proportion in non-Latino Black and Asian patients. Anti-SSA/Ro and anti-SSB/La antibodies were most prevalent in non-Latino Asian patients and least prevalent in non-Latino White patients. Men were more likely to be anti-Sm positive.

Conclusion: Data from the MLSP revealed differences among patients classified as SLE in the prevalence of concomitant rheumatic diseases and autoantibody profiles by sex and race/ethnicity underscoring comorbidities associated with SLE.

In the medical domain, substantial effort has been invested in generating internally valid estimates in experimental as well as observational studies, but limited effort has been made in testing generalizability, or external validity. Testing the external validity of scientific findings is nevertheless crucial for the application of knowledge across populations. In particular, transporting estimates obtained from observational studies requires the combination of methods for causal inference and methods to transport the effect estimates in order to minimize biases inherent to observational studies and to account for differences between the study and target populations. In this paper, the conceptual framework and assumptions behind transporting results from a population-based study population to a target population is described in an observational setting. An applied example to life-course epidemiology, where internal validity was constructed for illustrative purposes, is shown by using the targeted maximum likelihood estimator.

The wide distribution of tularemia in the territory of Kazakhstan is associated with landscape and geographical characteristics. This is explained by a combination of natural factors: the presence of certain types of rodents-reservoirs and sources, ectoparasites-carriers of the causative agent of tularemia. The study of the current spatial and temporal characterization of tularemia in Kazakhstan from 2000 to 2020 will determine the epidemiological status of tularemia and improve the monitoring system in Kazakhstan. In this work we demonstrated the results of a retrospective survey of natural foci of tularemia: analysis of vector, small mammal and human data. The spatial and temporal characteristics of tularemia from 2000 to 2020 in the territory of Kazakhstan were studied in comparison with historical data, including the description of tularemia outbreaks, the clinical picture, and the source of infection, transmission factors, and geographical coordinates of outbreak registration. Sampling was carried out by trapping rodents on snap traps and collecting ticks by rodent combing and by "flagging" methods. For the last 20 years, 85 human cases of tularemia have been reported. During the period from 2000 to 2020, more than 600 strains of F. tularensis were isolated from field rodents and ticks in the natural foci of tularemia. MLVA typing of F. tularensis strains isolated from natural foci of tularemia in Kazakhstan over the past 20 years. The results of retrospective monitoring indicate that currently active foci of tularemia include the Aktobe, West Kazakhstan, Almaty, East Kazakhstan, and Pavlodar regions. Low-activity natural foci are located in the territory of the Akmola, Karaganda, North Kazakhstan, Kostanay, Atyrau, Zhambyl, and Kyzylorda regions. There are no active natural foci of tularemia in the Mangystau and Turkestan regions. The widespread occurrence of tularemia in the country is associated with landscape and geographical features that contribute to the circulation of the pathogen in the natural focus. An analysis of natural foci of tularemia showed that it is necessary to continue monitoring studies of carriers and vectors for the presence of the causative agent of the F. tularensis, in order to prevent mass cases of human disease.

In this paper, we introduce a novel model for parental decision-making about vaccinations against a childhood disease that spreads through a contact network. This model considers a bilayer network comprising two overlapping networks, which are either Erdős-Rényi (random) networks or Barabási-Albert networks. The model also employs a Bayesian aggregation rule for observational social learning on a social network. This new model encompasses other decision models, such as voting and DeGroot models, as special cases. Using our model, we demonstrate how certain levels of social learning about vaccination preferences can converge opinions, influencing vaccine uptake and ultimately disease spread. In addition, we explore how two different cultures of social learning affect the establishment of social norms of vaccination and the uptake of vaccines. In every scenario, the interplay between the dynamics of observational social learning and disease spread is influenced by the network's topology, along with vaccine safety and availability.