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144Identification of non-vaccinated children using decision trees


 
 
 Non-vaccinated children are a particularly vulnerable and understudied group. Machine learning algorithms, such as decision trees, might be useful for identifying subgroups with high prevalence of zero dose (neither BCG, polio, DPT nor measles vaccines received).
 
 
 
 We developed Classification and Regression Tree models using data from DHS surveys of India 2015 and Chad 2014 in order to identify risk groups of zero dose.
 
 
 
 The first split variable for India was the child’s place of delivery, followed by the mother’s tetanus vaccination status for the higher-risk subgroup of children born in noninstitutional facilities. For Chad, administrative region was selected, and two high zero dose regions were defined. For those regions, children whose mother did not receive any dose of tetanus vaccine were also considered a higher-risk subgroup.
 
 
 
 Two trees were created with only two splits each. Subgroups with zero dose prevalence higher than 40% were identified.
 
 
 
 Decision trees might be valuable tools for exploratory data analysis and risk groups identification in epidemiological research.
