Dataset Information

A forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosis.

ABSTRACT:

Background

This work presents a forecast model for non-typhoidal salmonellosis outbreaks.

Method

This forecast model is based on fitted values of multivariate regression time series that consider diagnosis and estimation of different parameters, through a very flexible statistical treatment called generalized auto-regressive and moving average models (GSARIMA).

Results

The forecast model was validated by analyzing the cases of Salmonella enterica serovar Enteritidis in Sydney Australia (2014-2016), the environmental conditions and the consumption of high-risk food as predictive variables.

Conclusions

The prediction of cases of Salmonella enterica serovar Enteritidis infections are included in a forecast model based on fitted values of time series modeled by GSARIMA, for an early alert of future outbreaks caused by this pathogen, and associated to high-risk food. In this context, the decision makers in the epidemiology field can led to preventive actions using the proposed model.

SUBMITTER: Rojas F

PROVIDER: S-EPMC7664469 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

A forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosis.

Rojas Fernando F Ibacache-Quiroga Claudia C

PeerJ 20201110

<h4>Background</h4>This work presents a forecast model for non-typhoidal salmonellosis outbreaks.<h4>Method</h4>This forecast model is based on fitted values of multivariate regression time series that consider diagnosis and estimation of different parameters, through a very flexible statistical treatment called generalized auto-regressive and moving average models (GSARIMA).<h4>Results</h4>The forecast model was validated by analyzing the cases of <i>Salmonella enterica</i> serovar Enteritidis ...[more]

PMID: 33240587

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Project description:IntroductionInvasive salmonellosis is a common cause of bloodstream infection in Southeast Asia. Limited epidemiologic and antimicrobial resistance data are available from the region.MethodsBlood cultures performed in all 20 hospitals in the northeastern province of Nakhon Phanom (NP) and eastern province of Sa Kaeo (SK), Thailand were captured in a bloodstream infection surveillance system. Cultures were performed as clinically indicated in hospitalized patients; patients with multiple positive cultures had only the first included. Bottles were incubated using the BacT/Alert system (bioMérieux, Thailand) and isolates were identified using standard microbiological techniques; all Salmonella isolates were classified to at least the serogroup level. Antimicrobial resistance was assessed using disk diffusion.ResultsSalmonella was the fifth most common pathogen identified in 147,535 cultures with 525 cases (211 in Nakhon Phanom (NP) and 314 in Sa Kaeo (SK)). The overall adjusted iNTS incidence rate in NP was 4.0 cases/100,000 person-years (95% CI 3.5-4.5) and in SK 6.4 cases/100,000 person-years (95% CI 5.7-7.1; p = 0.001). The most common serogroups were C (39.4%), D (35.0%) and B (9.9%). Serogroup D predominated in NP (103/211) with 59.2% of this serogroup being Salmonella serovar Enteritidis. Serogroup C predominated in SK (166/314) with 84.3% of this serogroup being Salmonella serovar Choleraesuis. Antibiotic resistance was 68.2% (343/503) for ampicillin, 1.2% (6/482) for ciprofloxacin (or 58.1% (280/482) if both intermediate and resistant phenotypes are considered), 17.0% (87/512) for trimethoprim-sulfamethoxazole, and 12.2% (59/484) for third-generation cephalosporins (cefotaxime or ceftazidime). Multidrug resistance was seen in 99/516 isolates (19.2%).ConclusionsThe NTS isolates causing bloodstream infections in rural Thailand are commonly resistant to ampicillin, cefotaxime, and TMP-SMX. Observed differences between NP and SK indicate that serogroup distribution and antibiotic resistance may substantially differ throughout Thailand and the region.

Project description:BackgroundSalmonella Typhi is a human pathogen that causes typhoid fever. It is a major cause of morbidity and mortality in developing countries and is responsible for several outbreaks in developed countries. Studying certain parameters of the pathogen, such as the incubation period, provides a better understanding of its pathophysiology and its characteristics within a population. Outbreak investigations and human experimental studies provide an avenue to study these relevant parameters.MethodsIn this study, the authors have undertaken a systematic review of outbreak investigation reports and experimental studies, extracted reported data, tested for heterogeneity, identified subgroups of studies with limited evidence of heterogeneity between them and identified factors that may contribute to the distribution of incubation period. Following identification of relevant studies, we extracted both raw and summary incubation data. We tested for heterogeneity by deriving the value of I2 and conducting a KS-test to compare the distribution between studies. We performed a linear regression analysis to identify the factors associated with incubation period and using the resulting p-values from the KS-test, we conducted a hierarchical cluster analysis to classify studies with limited evidence of heterogeneity into subgroups.ResultsWe identified thirteen studies to be included in the review and extracted raw incubation period data from eleven. The value of I2 was 84% and the proportion of KS test p-values that were less than 0.05 was 63.6% indicating high heterogeneity not due to chance. We identified vaccine history and attack rates as factors that may be associated with incubation period, although these were not significant in the multivariable analysis (p-value: 0.1). From the hierarchical clustering analysis, we classified the studies into five subgroups. The mean incubation period of the subgroups ranged from 9.7 days to 21.2 days. Outbreaks reporting cases with previous vaccination history were clustered in a single subgroup and reported the longest incubation period.ConclusionsWe identified attack rate and previous vaccination as possible associating factors, however further work involving analyses of individual patient data and developing mathematical models is needed to confirm these as well as examine additional factors that have not been included in our study.

Project description:BackgroundGlobally, foodborne diseases result in a significant disease burden with low- and middle-income countries disproportionately affected. Estimates of healthcare costs related to foodborne disease can aid decision makers to take action to mitigate risks and prevent illness. However, only limited data on the African continent are available, especially related to more severe sequelae. We provide estimates of direct and indirect (non)-medical costs of patients with diarrhoea, Guillain-Barré syndrome (GBS), and invasive non-typhoidal salmonellosis (iNTS) in three healthcare facilities in Gondar, Ethiopia.MethodsWe used healthcare data from patient records, interviews with family caregivers and 2020 healthcare resource unit costs. Descriptive statistical analysis was performed. For diarrhoea, differences in mean and median transformed costs between healthcare facilities and etiologies (Campylobacter spp., enterotoxigenic Escherichia coli, non-typhoidal Salmonella enterica) were analysed with ANOVA and chi squared tests. Contribution of healthcare facility, dehydration severity, sex, age and living area to transformed costs was identified with linear regression. Results are in 2020 USD per patient. To extrapolate to national level, 2017 national incidence estimates were used.ResultsMean direct medical costs were 8.96 USD for diarrhoea (health centre 6.50 USD, specialised hospital 9.53 USD, private clinic 10.56 USD), 267.70 USD for GBS, and 47.79 USD for iNTS. Differences in costs between diarrhoea patients were mainly associated with healthcare facility. Most costs did not differ between etiologies. Total costs of a diarrhoea patient in the specialised hospital were 67 USD, or 8% of gross national income per capita. For direct medical plus transport costs of a GBS and iNTS patient in the specialised hospital, this was 33% and 8%, respectively. Of the 83.9 million USD estimated national non-typhoidal Salmonella enterica related cost, 12.2% was due to iNTS, and of 187.8 million USD related to Campylobacter spp., 0.2% was due to GBS.ConclusionDirect medical costs per patient due to GBS and iNTS were 30 respectively five times those due to diarrhoea. Costs of a patient with diarrhoea, GBS or iNTS can be a substantial part of a household's income. More severe sequalae can add substantially to cost-of-illness of foodborne hazards causing diarrheal disease.

Dataset Information

A forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosis.

Background

Method

Results

Conclusions

Publications

A forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosis.

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