Inferring source attribution from a multiyear multisource data set of <i>Salmonella</i> in Minnesota

Ahlstrom, Christina A.; Muellner, Petra; Spencer, Simon E. F.; Hong, Samuel L.; Saupe, Amy; Rovira, Albert; Hedberg, Craig W.; Pérez, Andrés; Muellner, Ulrich; Álvarez, Julio

doi:10.1111/zph.12351

Cited by 7 publications

(6 citation statements)

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“…Confidence intervals for the Simpson's indexes were estimated through 1,000 bootstrap replicates. Additionally, a rarefaction analysis to estimate the resistotype richness controlling for the different sample size in each host species was carried out as previously described (Ahlstrom et al, ). For serotypes in which at least 15 isolates from each host were recovered, the proportional similarity index (PSI), as described by Muellner et al (), was used to compare resistotypes in each host.…”

Section: Methodsmentioning

confidence: 99%

Identifying emerging trends in antimicrobial resistance using Salmonella surveillance data in poultry in Spain

Álvarez

López

Muellner

et al. 2019

Transbound Emerg Dis

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Despite of controls and preventive measures implemented along the food chain, infection with non‐typhoidal Salmonella (NTS) remains one of the major causes of foodborne disease worldwide. Poultry is considered one of the major sources of NTS. This has led to the implementation of monitoring and control programmes in many countries (including Spain) to ensure that in poultry flocks infection is kept to a minimum and to allow the identification and monitoring of circulating NTS strains and their antimicrobial resistance (AMR) phenotypes. Here, we investigated the information from the monitoring programme for AMR in Salmonella from poultry in Spain in 2011–2017 to assess the diversity in phenotypic resistance and to evaluate the programme's ability to detect multi‐resistance patterns and emerging strains in the animal reservoir. Data on serotype and AMR to nine antimicrobials obtained from 3,047 NTS isolates from laying hens (n = 1,060), broiler (n = 765) and turkey (n = 1,222) recovered during controls performed by the official veterinary services and food business operators were analysed using univariate and multivariate methods in order to describe host and serotype‐specific profiles. Diversity and prevalence of phenotypic resistance to all but one of the antimicrobials (colistin) were higher in NTS from broiler and turkey compared with laying hen isolates. Certain combinations of serotype and AMR pattern (resistotype) were particularly linked with certain hosts (e.g. susceptible Enteritidis with laying hens, multi‐drug resistant (MDR) Derby in turkey, MDR Kentucky in turkey and broiler). The widespread presence of certain serotype‐resistotype combinations in certain hosts/years suggested the possible expansion of MDR strains in the animal reservoir. This study demonstrates the usefulness of the analysis of data from monitoring programmes at the isolate level to detect emerging threats and suggests aspects that should be subjected to further research to identify the forces driving the expansion/dominance of certain strains in the food chain.

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Section: Methodsmentioning

confidence: 99%

Identifying emerging trends in antimicrobial resistance using Salmonella surveillance data in poultry in Spain

Álvarez

López

Muellner

et al. 2019

Transbound Emerg Dis

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“…Ultimately, this systematic review will provide rational basis for source attribution studies at both regional and country levels. 28 Additionally, this study will inform policy maker actions in order to strengthen national foodborne disease surveillance and to improve food safety and public health in MENA.…”

Section: Discussionmentioning

confidence: 99%

EntericSalmonellain humans and food in the Middle East and North Africa: protocol of a systematic review

Chaabna

Alali

2017

BMJ Open

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IntroductionNon-typhoidal Salmonella is considered one of the leading causes of foodborne disease worldwide. This protocol provides methods that will be used to synthesise available epidemiological data on non-typhoidal enteric Salmonella in humans and food in Middle East and North Africa (MENA) region and to characterise the morbidity of human salmonellosis in this region.Methods and analysisA systematic review will be conducted based on the Cochrane Collaboration handbook and will be reported following the items outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We will search PubMed, Embase, CAB Direct and Global Health Library (WHO) databases in order to identify relevant reports. Additionally, the literature search will be supplemented by checking references of the included reports and the identified reviews. Furthermore, we will hand-search conference proceedings and Ministry of Health's website of each country of the MENA region. We will use comprehensive search criteria with no time and no language restrictions. We will extract data on report and study characteristics, biological assay characteristics, individuals’ demographic characteristics and on primary and secondary outcomes of interest. If appropriate, meta-analysis will be conducted in order to estimate pooled prevalence measures using DerSimonian and Laird random-effects models. We will conduct meta-regression analysis to explore the effect of study-level characteristics as potential sources of heterogeneity.Ethics and disseminationThe results of the systematic review will be disseminated in a peer-reviewed journal and presented at relevant conferences.Trial registration numberThe trial registration number is CRD42016046360.

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“…The (modified) Dutch and Hald models have been extensively used for source attribution of major (bacterial) foodborne pathogens. Studies have focused on Salmonella (Hald et al, 2004, 2007; Mullner et al, 2009a; Pires and Hald, 2010; Guo et al, 2011; Wahlstrom et al, 2011; David et al, 2013a, b; Mughini-Gras et al, 2014a,b,c, 2016; Mughini-Gras and van Pelt, 2014; de Knegt et al, 2015, 2016; Vieira et al, 2016; Ahlstrom et al, 2017; Mikkela et al, 2019) and Campylobacter (Mullner et al, 2009a, b; Ranta et al, 2011; Boysen et al, 2014; Liao et al, 2019), and to a lesser extent on L. monocytogenes (Little et al, 2010; Nielsen et al, 2017), and Shiga -toxin producing E. coli (STEC) (Mughini-Gras et al, 2018b).…”

Section: Approaches Based On Microbial Subtypingmentioning

confidence: 99%

Critical Orientation in the Jungle of Currently Available Methods and Types of Data for Source Attribution of Foodborne Diseases

et al. 2019

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With increased interest in source attribution of foodborne pathogens, there is a need to sort and assess the applicability of currently available methods. Herewith we reviewed the most frequently applied methods for source attribution of foodborne diseases, discussing their main strengths and weaknesses to be considered when choosing the most appropriate methods based on the type, quality, and quantity of data available, the research questions to be addressed, and the (epidemiological and microbiological) characteristics of the pathogens in question. A variety of source attribution approaches have been applied in recent years. These methods can be defined as top–down, bottom–up, or combined. Top–down approaches assign the human cases back to their sources of infection based on epidemiological (e.g., outbreak data analysis, case-control/cohort studies, etc.), microbiological (i.e., microbial subtyping), or combined (e.g., the so-called ‘source-assigned case-control study’ design) methods. Methods based on microbial subtyping are further differentiable according to the modeling framework adopted as frequency-matching (e.g., the Dutch and Danish models) or population genetics (e.g., Asymmetric Island Models and STRUCTURE) models, relying on the modeling of either phenotyping or genotyping data of pathogen strains from human cases and putative sources. Conversely, bottom–up approaches like comparative exposure assessment start from the level of contamination (prevalence and concentration) of a given pathogen in each source, and then go upwards in the transmission chain incorporating factors related to human exposure to these sources and dose-response relationships. Other approaches are intervention studies, including ‘natural experiments,’ and expert elicitations. A number of methodological challenges concerning all these approaches are discussed. In absence of an universally agreed upon ‘gold’ standard, i.e., a single method that satisfies all situations and needs for all pathogens, combining different approaches or applying them in a comparative fashion seems to be a promising way forward.

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Inferring source attribution from a multiyear multisource data set of Salmonella in Minnesota

Abstract: . (2017) Inferring source attribution from a multi-year multi-source dataset of Salmonella in Minnesota. Zoonoses and Public Health. Permanent WRAP URL:

Cited by 7 publications

References 31 publications

Identifying emerging trends in antimicrobial resistance using Salmonella surveillance data in poultry in Spain

Identifying emerging trends in antimicrobial resistance using Salmonella surveillance data in poultry in Spain

EntericSalmonellain humans and food in the Middle East and North Africa: protocol of a systematic review

Critical Orientation in the Jungle of Currently Available Methods and Types of Data for Source Attribution of Foodborne Diseases

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