Weller Daniel, Shiwakoti Suvash, Bergholz Peter, Grohn Yrjo, Wiedmann Martin, Strawn Laura K
Department of Food Science, Cornell University, Ithaca, New York, USA.
Department of Veterinary and Microbiological Sciences, North Dakota State University, Fargo, North Dakota, USA.
Appl Environ Microbiol. 2015 Nov 20;82(3):797-807. doi: 10.1128/AEM.03088-15. Print 2016 Feb 1.
Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce.
技术进步，尤其是在地理信息系统（GIS）领域，使得利用地理空间模型预测农产品生产环境中食源性病原体污染的可能性成为可能。然而，很少有研究考察这些模型的有效性和稳健性。本研究旨在测试和完善与先前开发的地理空间模型相关的规则，该模型预测纽约州（NYS）农产品农场中单核细胞增生李斯特菌的流行情况。根据田间可用蓄水量（AWS）及其与水源、不透水覆盖物和牧场的距离，将四个参与研究的农产品农场的每个农产品种植田划分为预测单核细胞增生李斯特菌流行率高或低的区域。从分配到每个风险类别的地块采集拖拭样本（n = 1056）。逻辑回归测试了每个规则准确预测单核细胞增生李斯特菌流行率的能力，验证了基于水和牧场的规则。与远离水源和牧场采集的样本相比，在水源附近（优势比[OR]，3.0）和牧场附近（OR，2.9）采集的样本显示单核细胞增生李斯特菌分离的可能性显著增加。广义线性混合模型确定了与单核细胞增生李斯特菌分离可能性增加相关的其他土地覆盖因素，如靠近湿地。这些发现验证了先前开发的预测农产品生产环境中单核细胞增生李斯特菌流行率的部分规则。这表明GIS和地理空间模型可用于准确预测农场中单核细胞增生李斯特菌的流行率，并可前瞻性地用于将农产品收获前污染的风险降至最低。
