Addressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic

Addressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic

Abstract Background Poor data quality is limiting the use of data sourced from routine health information systems (RHIS), especially in low- and middle-income countries. An important component of this data quality issue comes from missing values, where health facilities, for a variety of reasons, fa...

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Enregistré dans:
Détails bibliographiques
Auteurs principaux: Shuo Feng, Celestin Hategeka, Karen Ann Grépin
Format: article
Langue:EN
Publié: BMC 2021
Sujets:
Missing data
Routine health information systems (RHIS)
Health management information system (HMIS)
Health services research
Low- and middle-income countries (LMICs)
Multiple imputation
Computer applications to medicine. Medical informatics
R858-859.7
Public aspects of medicine
RA1-1270
Accès en ligne:https://doaj.org/article/d3a35b234ffb4c9b9c2e82128fa9708c
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https://doaj.org/article/d3a35b234ffb4c9b9c2e82128fa9708c

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