Dataset Information

Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs.

ABSTRACT: Animal movements between farms are a major route of pathogen spread in the pig production sector. This study aimed to pair network analysis and epidemiological data in order to evaluate the impact of animal movements on pathogen prevalence in farms and assess the risk of local areas being exposed to diseases due to incoming movements. Our methodology was applied to hepatitis E virus (HEV), an emerging foodborne zoonotic agent of concern that is highly prevalent in pig farms. Firstly, the pig movement network in France (data recorded in 2013) and the results of a nation-wide seroprevalence study (data collected in 178 farms in 2009) were modelled and analysed. The link between network centrality measures of farms and HEV seroprevalence levels was explored using a generalised linear model. The in-degree and ingoing closeness of farms were found to be statistically associated with high HEV within-farm seroprevalence (p<0.05). Secondly, the risk of a French département (i.e. French local administrative areas) being exposed to HEV was calculated by combining the distribution of farm-level HEV prevalence in source départements with the number of movements coming from those same départements. By doing so, the risk of exposure for départements was mapped, highlighting differences between geographical patterns of HEV prevalence and the risk of exposure to HEV. These results suggest that not only highly prevalent areas but also those having at-risk movements from infected areas should be monitored. Pathogen management and surveillance options in the pig production sector should therefore take animal movements into consideration, paving the way for the development of targeted and risk-based disease surveillance strategies.

SUBMITTER: Salines M

PROVIDER: S-EPMC7126927 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundClinical trial enrollment and completion is challenging, with nearly half of all trials not being completed or not completed on time. In 2014, the National Institutes of Health StrokeNet in collaboration with stroke epidemiologists from GCNKSS (Greater Cincinnati/Northern Kentucky Stroke Study) began providing proposed clinical trials with formal trial feasibility assessments. Herein, we describe the process of prospective feasibility analyses using epidemiological data that can be used to improve enrollment and increase the likelihood a trial is completed.MethodsIn 2014, DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trialists, National Institutes of Health StrokeNet, and stroke epidemiologists from GCNKSS collaborated to evaluate the initial inclusion/exclusion criteria for the DEFUSE 3 study. Trial criteria were discussed and an assessment was completed to evaluate the percent of the stroke population that might be eligible for the study. The DEFUSE 3 trial was stopped early with the publication of DAWN (Thrombectomy 6 to 24 Hours After Stroke With a Mismatch Between Deficit and Infarct), and the Wilcoxon rank-sum statistic was used to analyze whether the trial would have been stopped had the proposed changes not been made, following the DEFUSE 3 statistical analysis plan.ResultsAfter initial epidemiological analysis, 2.4% of patients with acute stroke in the GCNKSS population would have been predicted to be eligible for the study. After discussion with primary investigators and modifying 4 key exclusion criteria (upper limit of age increased to 90 years, baseline modified Rankin Scale broadened to 0-2, time since last well expanded to 16 hours, and decreased lower limit of National Institutes of Health Stroke Scale score to <6), the number predicted to be eligible for the trial increased to 4%. At the time of trial conclusion, 57% of the enrolled patients qualified only by the modified criteria, and the trial was stopped at an interim analysis that demonstrated efficacy. We estimated that the Wilcoxon rank-sum value for the unadjusted predicted enrollment would not have crossed the threshold for efficacy and the trial not stopped.ConclusionsObjectively assessing trial inclusion/exclusion criteria using a population-based resource in a collaborative and iterative process including epidemiologists can lead to improved recruitment and can increase the likelihood of successful trial completion.

Dataset Information

Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs.

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