Performance of predictive algorithms in estimating the risk of being a zero-dose child in India, Mali and Nigeria.

ABSTRACT:

Introduction

Many children in low-income and middle-income countries fail to receive any routine vaccinations. There is little evidence on how to effectively and efficiently identify and target such 'zero-dose' (ZD) children.

Methods

We examined how well predictive algorithms can characterise a child's risk of being ZD based on predictor variables that are available in routine administrative data. We applied supervised learning algorithms with three increasingly rich sets of predictors and multiple years of data from India, Mali and Nigeria. We assessed performance based on specificity, sensitivity and the F1 Score and investigated feature importance. We also examined how performance decays when the model is trained on older data. For data from India in 2015, we further comp

SUBMITTER: Biswas A 

PROVIDER: S-EPMC10583101 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature