Improving resolution of public health surveillance for human Salmonella enterica serovar Typhimurium infection: 3 years of prospective multiple-locus variable-number tandem-repeat analysis (MLVA)

<p>Abstract</p> <p>Background</p> <p>Prospective typing of <it>Salmonella enterica </it>serovar Typhimurium (STM) by multiple-locus variable-number tandem-repeat analysis (MLVA) can assist in identifying clusters of STM cases that might otherwise have gone unrecognised, as well as sources of sporadic and outbreak cases. This paper describes the dynamics of human STM infection in a prospective study of STM MLVA typing for public health surveillance.</p> <p>Methods</p> <p>During a three-year period between August 2007 and September 2010 all confirmed STM isolates were fingerprinted using MLVA as part of the New South Wales (NSW) state public health surveillance program.</p> <p>Results</p> <p>A total of 4,920 STM isolates were typed and a subset of 4,377 human isolates was included in the analysis. The STM spectrum was dominated by a small number of phage types, including DT170 (44.6% of all isolates), DT135 (13.9%), DT9 (10.8%), DT44 (4.5%) and DT126 (4.5%). There was a difference in the discriminatory power of MLVA types within endemic phage types: Simpson's index of diversity ranged from 0.109 and 0.113 for DTs 9 and 135 to 0.172 and 0.269 for DTs 170 and 44, respectively. 66 distinct STM clusters were observed ranging in size from 5 to 180 cases and in duration from 4 weeks to 25 weeks. 43 clusters had novel MLVA types and 23 represented recurrences of previously recorded MLVA types. The diversity of the STM population remained relatively constant over time. The gradual increase in the number of STM cases during the study was not related to significant changes in the number of clusters or their size. 667 different MLVA types or patterns were observed.</p> <p>Conclusions</p> <p>Prospective MLVA typing of STM allows the detection of community outbreaks and demonstrates the sustained level of STM diversity that accompanies the increasing incidence of human STM infections. The monitoring of novel and persistent MLVA types offers a new benchmark for STM surveillance.</p> <p>A part of this study was presented at the MEEGID × (Molecular Epidemiology and Evolutionary Genetics of Infectious Diseases) Conference, 3-5 November 2010, Amsterdam, The Netherlands</p

Genomic variation of Salmonella Typhimurium and dynamics of epidemics

Non-typhoidal salmonellosis (NTS) is responsible of a large proportion of foodborne gastroenteritis worldwide. Molecular identification of Salmonella with assessment of exposure and epidemiological analysis of outbreak-linked cases are main approaches to control NTS. This study focussed on Salmonella Typhimurium (STM) as the most common causative agent of foodborne NTS in Australia. The aims of this thesis were to examine temporal dynamics of STM in New South Wales; to analyse the discrimination power of evolving typing methods for STM and the understanding of within- and between-host variations and adaptations in STM genomes. We examined 11,799 STM isolates between 2009 and 2016. Our findings suggest that multi-locus variable sequence typing (MLST) can be successfully applied for molecular serotyping of Salmonella isolates circulating in NSW. However, its approach lacks discriminatory power for public health surveillance. In contrast, multi-locus variable number tandem repeat analysis (MLVA) identified major clades associated with extensive epidemics over time. A small number of MLVA profiles have been associated with clusters, masking the diversity of profiles and reducing investigations on transmission networks. The sequencing of STM isolates, confirmed the high-resolution and discriminatory power of whole genome sequencing (WGS) elucidating transmission pathways. A relatively constant core genome for STM population over time was revealed, translated in stable diversity with predominance of endemic STM MLVA profiles. A chronic model of salmonellosis in mice showed the adaptive evolution of STM in association within its host. It involved limited number of mutations, without compromising the ability of STM to maintain the infection. The temporal relation between the incidence of STM infections in NSW and the corresponding increase of particular STM clades was unveiled. The comparative genomic analysis performed on STM clades identified genomic polymorphisms within the successful clades. These observations emphasize the stability of accessory genomes, but require further in vivo validation. Our results and analyses have offered evidence to guide the interpretation of STM public health laboratory surveillance and the translation of WGS into more effective control of foodborne diseases

Comportamiento del efecto clúster hospital y los factores asociados a la mortalidad a largo plazo, después de un ingreso por exacerbación en epoc

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina Preventiva y Salud Pública y Microbiología. Fecha de lectura: 23-09-2020INTRODUCCIÓN: Resultado del análisis exhaustivo de cohortes multicéntricas, históricas y periódicas de casos, usando aproximaciones multivariables multinivel, hemos abordado el tema de la variabilidad de los datos y encontrado, la presencia de un claro efecto clúster de hospital, que reduce drásticamente la variabilidad de los desenlaces encontrados (duración del ingreso, mortalidad y reingresos a 90 días(1)) en los datos crudos. Reconociendo la advertencia de Juan Merlo en su trabajo (1-3), referida a que la OR promedio es solo una aproximación inexacta y quizá no represente completamente la variabilidad geográfica real en áreas sanitarias, postulamos como hipótesis que el efecto clúster hospital, se mantiene a largo plazo sobre la mortalidad y que este efecto, en parte, se debe a factores asociados al contexto territorial y ambiental del Área de Salud, como la calidad del aire respirado. METODOLOGIA Con el objetivo de demostrar el efecto diferencial del clúster hospital, particularmente en relación con la mortalidad a largo plazo, en el paciente con EPOC, se plantea un estudio descriptivo observacional, con seguimiento prospectivo de mortalidad a largo plazo, para una cohorte de pacientes con EPOC, identificados durante un ingreso hospitalario por exacerbación de su enfermedad. La Tabla de datos contiene información disociada y mortalidad a largo plazo de 10.449 casos procedentes de 142 hospitales públicos españoles, a la que se han asociado datos agregados por localidad, de los registros diarios de emisiones obtenidos entre 2008 y 2011 (Período de reclutamiento de la cohorte) por las diferentes estaciones. La mortalidad a corto plazo (a 90 días del ingreso), fue informada por los responsables locales de investigación de la red de hospitales participantes, y contrastada con la información obtenida de los registros oficiales del índice nacional de defunciones (INDEF) desde octubre de 2008 a diciembre de 2015. Todas las variables fueron evaluadas respecto de la significancia (valor P) en la diferencia de su distribución por mortalidad intrahospitalaria, a 90 días, al año y a los 5 años, usando como estadísticos el chi-cuadrado de independencia y log-Rank test. Se construyó un modelo de supervivencia de riesgos proporcionales (Cox), y un modelo en regresión logística de mortalidad, calculando los coeficientes estandarizados y la curva ROC. RESULTADOS La media de seguimiento fue de 304·5 días posteriores al ingreso hospitalario, con un máximo de 7 años. Casi la mitad de la mortalidad total de la cohorte se produjo dentro de los 90 días posteriores al ingreso hospitalario a partir del cual fueron reclutados. La ponderación del efecto de cada uno de las variables finalmente retenidas por los modelos explicativos, a través de los coeficientes estandarizados obtenidos en la regresión, enfatiza el peso del perfil clínico grave (dimensión paciente), seguido de cerca por la exposición de micro partículas (dimensión local territorio) y las características del hospital (dimensión local hospital). El modelo obtenido logró discriminar la mortalidad a largo plazo, con un área de 0·71 y un IC 95% entre 0·69-0·72. CONCLUSIONES: Además de los determinantes clínicos de enfermedad, otros factores del contexto espacio/temporal externo al individuo, sumados a las condiciones de salud y atención sanitaria recibida, afectan la supervivencia/mortalidad a largo plazo y configuran lo que hemos llamado en nuestros trabajos previos efecto clúster hospitalEste trabajo ha sido financiado principalmente por fondos destinados al Grupo de investigación de la Red temática Enfermedades Respiratorias del Consorcio CIBER M. P, en el Hospital Universitario 12 de Octubre. También obtuvo financiación de ayudas a los proyectos FIS número PS 09/01763, PS 09/01787 y PS 09/00629 (Instituto de Salud Carlos III, Secretaría de Estado de Investigación, Desarrollo e Innovación y FEDER/FSE

Biosurveillance of emerging biothreats using scalable genotype clustering

Developments in molecular fingerprinting of pathogens with epidemic potential have offered new opportunities for improving detection and monitoring of biothreats. However, the lack of scalable definitions for infectious disease clustering presents a barrier for effective use and evaluation of new data types for early warning systems. A novel working definition of an outbreak based on temporal and spatial clustering of molecular genotypes is introduced in this paper. It provides an unambiguous way of clustering of causative pathogens and is adjustable to local disease prevalence and availability of public health resources. The performance of this definition in prospective surveillance is assessed in the context of community outbreaks of food-borne salmonellosis. Molecular fingerprinting augmented with the scalable clustering allows the detection of more than 50% of the potential outbreaks before they reach the midpoint of the cluster duration. Clustering in time by imposing restrictions on intervals between collection dates results in a smaller number of outbreaks but does not significantly affect the timeliness of detection. Clustering in space and time by imposing restrictions on the spatial and temporal distance between cases results in a further reduction in the number of outbreaks and decreases the overall efficiency of prospective detection. Innovative bacterial genotyping technologies can enhance early warning systems for public health by aiding the detection of moderate and small epidemics.8 page(s