Innovative approach to improve information accuracy in a two-district cross-sectional study in Bihar, India

ObjectiveCombine Health Management Information Systems (HMIS) and probability survey data using the statistical annealing technique (AT) to produce more accurate health coverage estimates than either source of data and a measure of HMIS data error.SettingThis study is set in Bihar, the fifth poorest...

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Bibliographic Details
Published in:BMJ open 2022-01, Vol.12 (1), p.e051427-e051427
Main Authors: Jeffery, Caroline, Pagano, Marcello, Devkota, Baburam, Valadez, Joseph J
Format: Article
Language:English
Subjects:
Accuracy
Childrens health
Cross-Sectional Studies
Decision making
Estimates
Female
Global Health
Health facilities
health informatics
Health Personnel
health policy
Health services
Households
Humans
Infant, Newborn
Information systems
international health services
Maternal & child health
Population
Pregnancy
Prenatal Care
Prevalence
Public health
Surveys and Questionnaires
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Summary:ObjectiveCombine Health Management Information Systems (HMIS) and probability survey data using the statistical annealing technique (AT) to produce more accurate health coverage estimates than either source of data and a measure of HMIS data error.SettingThis study is set in Bihar, the fifth poorest state in India, where half the population lives below the poverty line. An important source of data, used by health professionals for programme decision making, is routine health facility or HMIS data. Its quality is sometimes poor or unknown, and has no measure of its uncertainty. Using AT, we combine district-level HMIS and probability survey data (n=475) for the first time for 10 indicators assessing antenatal care, institutional delivery and neonatal care from 11 blocks of Aurangabad and 14 blocks of Gopalganj districts (N=6 253 965) in Bihar state, India.ParticipantsBoth districts are rural. Bihar is 82.7% Hindu and 16.9% Islamic.Primary outcome measuresSurvey prevalence measures for 10 indicators, corresponding prevalences using HMIS data, combined prevalences calculated with AT and SEs for each type of data.ResultsThe combined and survey estimates differ by
ISSN:2044-6055
2044-6055
DOI:10.1136/bmjopen-2021-051427