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Development and evaluation of a mobile application for case management of small and sick newborns in Bangladesh.

Authors :
Schaeffer, Lauren E.
Ahmed, Salahuddin
Rahman, Mahmoodur
Whelan, Rachel
Rahman, Sayedur
Roy, Arunangshu Dutta
Nijhum, Tanzia Ahmed
Bably, Nazmun Nahar
D'Couto, Helen
Hudelson, Carly
Jaben, Iffat Ara
Rubayet, Sayed
Baqui, Abdullah
Lee, Anne CC
Source :
BMC Medical Informatics & Decision Making; 6/20/2019, Vol. 19 Issue 1, pN.PAG-N.PAG, 1p, 1 Color Photograph, 1 Black and White Photograph, 3 Charts
Publication Year :
2019

Abstract

<bold>Background: </bold>In low-income settings, community health workers (CHWs) are frequently the first point of contact for newborns. Mobile technology may aid health workers in classifying illness and providing referral and management guidance for newborn care. This study evaluates the potential for mobile health technology to improve diagnosis and case management of newborns in Bangladesh.<bold>Methods: </bold>A mobile application based on Bangladesh's Comprehensive Newborn Care Package national guidelines (mCNCP) was developed to aid CHWs in identifying and managing small and sick infants. After a 2-day training, CHWs assessed newborns at Sylhet Osmani Medical College Hospital and in the Projahnmo research site (Sylhet, Bangladesh) using either mCNCP or a comparable paper form (pCNCP), similar to standard IMCI-formatted paper forms. CHWs were randomized to conduct a block of ~ 6 newborn assessments starting with either mCNCP or pCNCP, then switched to the alternate method. Physicians using mCNCP served as gold standard assessors. CHW performance with mCNCP and pCNCP were compared using chi-squared tests of independence for equality of proportions, and logistic regressions clustered by CHW.<bold>Results: </bold>Two hundred seven total CHW assessments were completed on 101 enrolled infants. mCNCP assessments were more often fully completed and completed faster than pCNCP assessments (100% vs 23.8%, p < 0.001; 17.5 vs 23.6 min; p < 0.001). mCNCP facilitated calculations of respiratory rate, temperature, and gestational age. CHWs using mCNCP were more likely to identify small newborns (Odds Ratio (OR): 20.8, Confidence Interval (CI): (7.1, 60.8), p < 0.001), and to correctly classify 7 out of 16 newborn conditions evaluated, including severe weight loss (OR: 13.1, CI: (4.6, 37.5), p < 0.001), poor movement (OR: 6.6, CI: (2.3, 19.3), p = 0.001), hypothermia (OR: 14.9, CI: (2.7, 82.2), p = 0.002), and feeding intolerance (OR: 2.1, CI: (1.3, 3.3), p = 0.003). CHWs with mCNCP were more likely to provide counseling as needed on 4 out of 7 case management recommendations evaluated, including kangaroo mother care.<bold>Conclusions: </bold>CHWs in rural Bangladesh with limited experience using tablets successfully used a mobile application for neonatal assessment after a two-day training. mCNCP may aid frontline health workers in Bangladesh to improve completion of neonatal assessment, classification of illnesses, and adherence to neonatal management guidelines. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14726947
Volume :
19
Issue :
1
Database :
Complementary Index
Journal :
BMC Medical Informatics & Decision Making
Publication Type :
Academic Journal
Accession number :
137096974
Full Text :
https://doi.org/10.1186/s12911-019-0835-7