NEWS Score’System to Assess the Severity of Patients During the Outbreak of Food Poisoning in Phrao District, Chiang Mai Province, 2017.

Abstract

          Food poisoning is a common disease in Thailand. According to the outbreak of food poisoning in many areas of Chiang Mai Province, there are some patients have severe symptoms and death risk. Assessment of severity of patients and providing appropriate treatment are essential. This study is a retrospective study. The objective is to assess the severity of food poisoning patients by using Nakornping Early Warning Signs score: NEWS score for the outbreak of food poisoning in the temple’s participants in Chiang Mai Province duing 27-30 March 2017. A total number of 80 patients were collected from the investigation forms and patient medical records, which are general information, clinical symptoms and laboratory results of patients who are compatible with the definition of food poisoning. Data were analyzed using descriptive statistics such as frequency, arithmetic mean, percentage, and standard deviation. Comparison of variables between patients with severe symptoms and patients with mild symptoms using Student’s t test, Mann-Whitney U test and Chi–square test, including sensitivity, specificity, positive predictive value, and negative predictive value, and the area under the curve of ROC ( AUC ) to determine the ability of the NEWS score to classify severe and mild cases of food poisoning.

          The study revealed that of 80 cases were defined as food poisoning definition who had completed vital signs and laboratory records. The results showed the fever, average blood pressure, and creatinine value were significantly difference between severe and mild cases (p-value < 0.05). The cut-off point of the NEWS score to identify severe food poisoning patients was ≥ 3 scores with 100% sensitivity, 94.4% specificity, 98.4% Positive predictive value (PPV), 100% Negative predictive value (NPV), 95.2 % predictive accuracy, and the AUC was 0.952. Therefore the NEWS score was useful to classify severe cases of food poisoning and waterborne diseases outbreaks.