Flood is one of the most common disasters and contributes to many-disaster related deaths worldwide. According to the EM-DAT database, floods account for 38.7% of all incidences, 6.2% of the deaths and every year 43% of world population is affected by flood. There are various factors which magnify the impact of flood to the community in the catchment area. The nature and extent of the flood is determined by the physical location and topography, and by the built environment. The built environment comprises of the existing political, social and economic structures inherent in a community. In the post-flood period, the risk of diarrhoea is significantly higher for those with lower educational level, living in a household with a non-concrete roof, drinking tube-well water (vs. tap water), using a distant water source and unsanitary toilets. The 1998 Bangladesh flood study confirms that low socio-economic groups and poor hygiene and sanitation groups are most vulnerable to flood-related diarrhoea (Annya et al., 2010). The Bihar flood of 2008 affected 100 villages out of which Supaul, Araria, Saharsa, Madhepura and Purnea were the most severly affected districts. Nearly 2.5 million people were affected by the floodwater. Various disease outbreaks are a common phenomenon in a post flood scenario. In Bihar, the population witnessed outbreaks of malaria, diarrhoea, measles and cholera. This paper examines the various factors causing diarrhoeal spread in the five flood affected districts of Bihar. Also, the paper analyses the above factors in terms of most contributing and least contributing factor towards diarrhoeal outbreak. The methodology used for the study is the statistical tool of Standard Multiple Regression Analyses where several independent variables will predict the dependent variable. The various independent variables have been taken all the five affected districts in Bihar floods. They are malnutrition, population density, awareness levels, socio-economic conditions, literacy, extent of flood and availability of health facilities. These independent variables will determine how much each predicts the dependent variable and as a result help in analysing the linear relationship between the dependent and independent variables.