Young Infants Clinical Signs Study 8-sign Algorithm for Identification of Sick Infants Adapted for Routine Home Visits: A Systematic Review and Critical Appraisal of its Measurement Properties

Abstract

Objective. The 8-sign algorithm adapted from the Young Infants Clinical Signs Study (YICSS) is widely used to identify sick infants during home visits (YICSS-home algorithm). We aimed to critically appraise the development and evidence of measurement properties, including sensibility, reliability, and validity, of the YICSS-home algorithm. Methods. Relevant studies were identified through a systematic literature search. Results. The YICSS-home algorithm has good sensibility. The algorithm demonstrated at least moderate inter-rater reliability and sensitivity ranging from 69% to 80%. However, the algorithm was developed among sick infants brought for care to a health facility and not initially developed for use by community health workers (CHWs) during home visits. Some important risk factors were omitted at item generation. Inter-CHW reliability and construct validity have not been estimated. Conclusion. Future research should build on the strengths of the YICSS-home algorithm and address its limitations to develop a new algorithm with improved predictive accuracy.