Epidemiology and Infection最新文献

A scoping review was conducted to map out sources, types, characteristics of evidence that substantiate the existence of a community dividend arising from testing and treating hepatitis C virus (HCV) infection in people living in detention - where community dividend is defined as the benefit of prison-related intervention for general population health. Joanna Briggs Institute methodology guidance was used. Literature search was done in EMBASE, Scopus, ASSIA, UWE library, CINAHL Plus, and Medline to find studies published in any country, any language between January 1991 and June 2022. PRISMA ScR flow chart mapped out the number of records identified, included, and reasons for exclusion. Data were extracted and charted in Excel. The findings were systematically reported by charting table headings then synthesized in the discussion. Quality assessment was carried out. The descriptive analysis demonstrated economic, clinical, and epidemiological domains to the community dividend in long-term health expenditure savings, reduction in HCV-related disease sequelae, increase in survival, improvement in quality of life, and reduction in infection transmission, most of which are realized in the community following release. Therefore, targeting marginalized populations affected by HCV could expedite the elimination effort, reduce inequalities, and have a positive impact on the wider population.

Our study aimed to develop and validate a nomogram to assess talaromycosis risk in hospitalized HIV-positive patients. Prediction models were built using data from a multicentre retrospective cohort study in China. On the basis of the inclusion and exclusion criteria, we collected data from 1564 hospitalized HIV-positive patients in four hospitals from 2010 to 2019. Inpatients were randomly assigned to the training or validation group at a 7:3 ratio. To identify the potential risk factors for talaromycosis in HIV-infected patients, univariate and multivariate logistic regression analyses were conducted. Through multivariate logistic regression, we determined ten variables that were independent risk factors for talaromycosis in HIV-infected individuals. A nomogram was developed following the findings of the multivariate logistic regression analysis. For user convenience, a web-based nomogram calculator was also created. The nomogram demonstrated excellent discrimination in both the training and validation groups [area under the ROC curve (AUC) = 0.883 vs. 0.889] and good calibration. The results of the clinical impact curve (CIC) analysis and decision curve analysis (DCA) confirmed the clinical utility of the model. Clinicians will benefit from this simple, practical, and quantitative strategy to predict talaromycosis risk in HIV-infected patients and can implement appropriate interventions accordingly.

Serogroup epidemiology of invasive meningococcal disease (IMD) is constantly evolving, varying by time and location. Surveillance reports have indicated a rise in meningococcal serogroup Y (MenY) in some regions in recent years. This systematic literature review explores the evolving epidemiology of MenY IMD globally based on review of recent articles and national surveillance reports published between 1 January 2010 and 25 March 2021. Generally, MenY incidence was low (<0.2/100,000) across all ages in most countries. The reported incidence was more frequent among infants, adolescents, and those aged ≥65 years. More than 10% of all IMD cases were MenY in some locations and time periods. Implementation of vaccination evolved over time as the rise in MenY IMD percentage occurred. Cases decreased in countries with quadrivalent vaccine programs (e.g., United Kingdom, the Netherlands, United States, and Australia), whereas the MenY burden increased and made up a large proportion of cases in areas without vaccine programs. Continuous monitoring of epidemiologic changes of IMD is essential to establish MenY burden and for implementation of prevention strategies.

Mali is a country where little information is known about the circulation of avian influenza viruses (AIVs) in poultry. Implementing risk-based surveillance strategies would allow early detection and rapid control of AIVs outbreaks in the country. In this study, we implemented a multi-criteria decision analysis (MCDA) method coupled with geographic information systems (GIS) to identify risk areas for AIVs occurrence in domestic poultry in Mali. Five risk factors associated with AIVs occurrence were identified from the literature, and their relative weights were determined using the analytic hierarchy process (AHP). Spatial data were collected for each risk factor and processed to produce risk maps for AIVs outbreaks using a weighted linear combination (WLC). We identified the southeast regions (Bamako and Sikasso) and the central region (Mopti) as areas with the highest risk of AIVs occurrence. Conversely, northern regions were considered low-risk areas. The risk areas agree with the location of HPAI outbreaks in Mali. This study provides the first risk map using the GIS-MCDA approach to identify risk areas for AIVs occurrence in Mali. It should provide a basis for designing risk-based and more cost-effective surveillance strategies for the early detection of avian influenza outbreaks in Mali.

Most infections with pandemic Vibrio cholerae are thought to result in subclinical disease and are not captured by surveillance. Previous estimates of the ratio of infections to clinical cases have varied widely (2 to 100 infections per case). Understanding cholera epidemiology and immunity relies on the ability to translate between numbers of clinical cases and the underlying number of infections in the population. We estimated the infection incidence during the first months of an outbreak in a cholera-naive population using a Bayesian vibriocidal antibody titer decay model combining measurements from a representative serosurvey and clinical surveillance data. 3,880 suspected cases were reported in Grande Saline, Haiti, between 20 October 2010 and 6 April 2011 (clinical attack rate 18.4%). We found that more than 52.6% (95% Credible Interval (CrI) 49.4-55.7) of the population ≥2 years showed serologic evidence of infection, with a lower infection rate among children aged 2-4 years (35.5%; 95%CrI 24.2-51.6) compared with people ≥5 years (53.1%; 95%CrI 49.4-56.4). This estimated infection rate, nearly three times the clinical attack rate, with underdetection mainly seen in those ≥5 years, has likely impacted subsequent outbreak dynamics. Our findings show how seroincidence estimates improve understanding of links between cholera burden, transmission dynamics and immunity.

West Nile virus (WNV) is a mosquito-borne pathogen that can infect humans, equids, and many bird species, posing a threat to their health. It consists of eight lineages, with Lineage 1 (L1) and Lineage 2 (L2) being the most prevalent and pathogenic. Italy is one of the hardest-hit European nations, with 330 neurological cases and 37 fatalities in humans in the 2021-2022 season, in which the L1 re-emerged after several years of low circulation. We assembled a database comprising all publicly available WNV genomes, along with 31 new Italian strains of WNV L1 sequenced in this study, to trace their evolutionary history using phylodynamics and phylogeography. Our analysis suggests that WNV L1 may have initially entered Italy from Northern Africa around 1985 and indicates a connection between European and Western Mediterranean countries, with two distinct strains circulating within Italy. Furthermore, we identified new genetic mutations that are typical of the Italian strains and that can be tested in future studies to assess their pathogenicity. Our research clarifies the dynamics of WNV L1 in Italy, provides a comprehensive dataset of genome sequences for future reference, and underscores the critical need for continuous and coordinated surveillance efforts between Europe and Africa.

Anthrax is a bacterial zoonotic disease caused by Bacillus anthracis. We qualitatively examined facilitators and barriers to responding to a potential anthrax outbreak using the capability, opportunity, motivation behaviour model (COM-B model) in the high-risk rural district of Namisindwa, in Eastern Uganda. We chose the COM-B model because it provides a systematic approach for selecting evidence-based techniques and approaches for promoting the behavioural prompt response to anthrax outbreaks. Unpacking these facilitators and barriers enables the leaders and community members to understand existing resources and gaps so that they can leverage them for future anthrax outbreaks.This was a qualitative cross-sectional study that was part of a bigger anthrax outbreak simulation study conducted in September 2023. We conducted 10 Key Informant interviews among key stakeholders. The interviews were audio recorded on Android-enabled phones and later transcribed verbatim. The transcripts were analyzed using a deductive thematic content approach through Nvivo 12.The facilitators were; knowledge of respondents about anthrax disease and anthrax outbreak response, experience and presence of surveillance guidelines, availability of resources, and presence of communication channels. The identified barriers were; porous boarders that facilitate unregulated animal trade across, lack of essential personal protective equipment, and lack of funds for surveillance and response activities.Generally, the district was partially ready for the next anthrax outbreak. The district was resourced in terms of human resources but lacked adequate funds for animal, environmental and human surveillance activities for anthrax and related response. The district technical staff had the knowledge required to respond to the anthrax outbreak but lacked adequate funds for animal, environmental and human surveillance for anthrax and related response. We think that our study findings are generalizable in similar settings and therefore call for the implementation of such periodic evaluations to help leverage the strong areas and improve other aspects. Anthrax is a growing threat in the region, and there should be proactive efforts in prevention, specifically, we recommend vaccination of livestock and further research for human vaccines.

Reducing antimicrobial use (AMU) in livestock may be one of the keys to limit the emergence of antimicrobial resistance (AMR) in bacterial populations, including zoonotic pathogens. This study assessed the temporal association between AMU in livestock and AMR among Campylobacter isolates from human infections in the Netherlands between 2004 - 2020. Moreover, the associations between AMU and AMR in livestock and between AMR in livestock and AMR in human isolates were assessed. AMU and AMR data per antimicrobial class (tetracyclines, macrolides and fluoroquinolones) for Campylobacter jejuni and Campylobacter coli from poultry, cattle, and human patients were retrieved from national surveillance programs. Associations were assessed using logistic regression and the Spearman correlation test. Overall, there was an increasing trend in AMR among human C. jejuni/coli isolates during the study period, which contrasted with a decreasing trend in livestock AMU. In addition, stable trends in AMR in broilers were observed. No significant associations were observed between AMU and AMR in domestically produced broilers. Moderate to strong positive correlations were found between the yearly prevalence of AMR in broiler and human isolates. Reducing AMU in Dutch livestock alone may therefore not be sufficient to tackle the growing problem of AMR in Campylobacter among human cases in the Netherlands. More insight is needed regarding the population genetics and the evolutionary processes involved in resistance and fitness among Campylobacter.

This study presents surveillance data from 1 July 2003 to 30 June 2023 for community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) notified in the Kimberley region of Western Australia (WA) and describes the region's changing CA-MRSA epidemiology over this period. A subset of CA-MRSA notifications from 1 July 2003 to 30 June 2015 were linked to inpatient and emergency department records. Episodes of care (EOC) during which a positive CA-MRSA specimen was collected within the first 48 hours of admission and emergency presentations (EP) during which a positive CA-MRSA specimen was collected on the same day as presentation were selected and analysed further. Notification rates of CA-MRSA in the Kimberley region of WA increased from 250 cases per 100,000 populations in 2003/2004 to 3,625 cases per 100,000 in 2022/2023, peaking at 6,255 cases per 100,000 in 2016/2017. Since 2010, there has been an increase in notifications of Panton-Valentine leucocidin positive (PVL⁺) CA-MRSA, predominantly due to the 'Queensland Clone'. PVL⁺ CA-MRSA infections disproportionately affect younger, Aboriginal people and are associated with an increasing burden on hospital services, particularly emergency departments. It is unclear from this study if PVL⁺ MRSA are associated with more severe skin and soft-tissue infections, and further investigation is needed.

Since the outbreak of the COVID-19 epidemic, it has posed a great crisis to the health and economy of the world. The objective is to provide a simple deep-learning approach for predicting, modelling, and evaluating the time evolutions of the COVID-19 epidemic. The Dove Swarm Search (DSS) algorithm is integrated with the echo state network (ESN) to optimize the weight. The ESN-DSS model is constructed to predict the evolution of the COVID-19 time series. Specifically, the self-driven ESN-DSS is created to form a closed feedback loop by replacing the input with the output. The prediction results, which involve COVID-19 temporal evolutions of multiple countries worldwide, indicate the excellent prediction performances of our model compared with several artificial intelligence prediction methods from the literature (e.g., recurrent neural network, long short-term memory, gated recurrent units, variational auto encoder) at the same time scale. Moreover, the model parameters of the self-driven ESN-DSS are determined which acts as a significant impact on the prediction performance. As a result, the network parameters are adjusted to improve the prediction accuracy. The prediction results can be used as proposals to help governments and medical institutions formulate pertinent precautionary measures to prevent further spread. In addition, this study is not only limited to COVID-19 time series forecasting but also applicable to other nonlinear time series prediction problems.