[Application of Bayes discriminatory analysis to malaria stratification in Hainan province]

The present study was conducted to explore a new method for stratification of malaria endemicity. Several socioeconomic and geographic factors deemed appropriate to be employed in association with malaria epidemic potential were deal with Bayes discriminatory analysis.55 local villages in 12 counties of Hainan province were selected for socioeconomic survey, among which 33 villages were taken as the modelling sample and 22 villages were the non-modelling sample. The questionnaire contained a total of 12 relevant items. After socioeconomic inquiry was performed, the data were analysed through Bayes discriminatory theorem. Six important factors ware selected, i.e. topography (X1), proportion of labour forces (X4), income per person (X5), house structure (X8), knowledge of malaria control (X10), frequency of over-night stay in mountain forest (X12). Three discriminatory function formulas referring respectively to hyper-, meso-, and hypo-endemicity were set up. These 3 discriminatory function formulas were further applied to fit the endemicity of 33 model-sampling villages and 22 non-model-sampling villages. The agreement rate were 91.0% and 77.3% respectively.