A surveillance of food borne disease outbreaks in India: 2009–2018. (March 2021)

• Record Type: Journal Article
• Authors: Bisht, Akshay; Kamble, Manoj P.; Choudhary, Pritesh; Chaturvedi, Kartikey; Kohli, Gautam; Juneja, Vijay K.; Sehgal, Shalini; Taneja, Neetu Kumra
• Is Part Of: Food control. Volume 121(2021)
• Publication Date: 2021-03
• Subjects: India -- Food-borne disease -- Outbreaks -- Surveillance -- IDSP; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2020.107630
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Knowledge about distribution of food-borne outbreaks and implicated food-vehicles helps in mitigating the risk of food-borne diseases and is critical for designing strategies to control them. In this study data from Integrated Disease Surveillance Program (IDSP) and Open Government Data Platform India (OGDPI) on food-borne outbreaks for the period 2008–2018 was consolidated and analysed. The modelling methods of Gaussian distribution model (GAM) and Autoregressive Integrated Moving Average (ARIMA) were used to probe influence of climatic factors (temperature and rainfall) on food-borne outbreaks. Data analysis showed that states of West Bengal (31.22), Karnataka (29.11) and Gujarat (22.67) reported maximum average outbreaks and contributed to 31.5% illnesses and 8.7% deaths. Amongst 19.6% of outbreaks, grains and beans were found to be food-vehicle causing maximum outbreaks (32.7%), while chemically contaminated food caused maximal deaths (70%). Weak correlations of climatic factors with outbreaks resulted in poor performance of ARIMA models. GAM model was validated and predicted 356 outbreaks for the year 2020, late-April to mid-July being most prevalent months. The analysis also revealed inclination of current surveillance program towards chemically contaminated food that resulted in maximal deaths (70%), while biological agents were observed to be under-reported. Despite the limitations, available data shows that food-borne disease outbreaks remain a public health concern in India. Therefore, it is imperative for India to strengthen its disease surveillance program by undertaking capacity-building initiatives at state/local health-care levels and connecting causative agents of outbreaks. This would help in efficient implementation of risk assessment and risk management strategies. Highlights: Food-borne disease outbreaks have risen in India over the past decade. Seasonal and geographical variations affect the occurrence of an outbreak. Gaussian distribution model is used to predict the food-borne disease outbreaks. ARIMA models showed weak correlation for analysis and prediction of outbreaks with climatic data.