Innovative approach to improve information accuracy in a two-district cross-sectional study in Bihar, India. Issue 1 (6th January 2022)

• Record Type: Journal Article
• Authors: Jeffery, Caroline; Pagano, Marcello; Devkota, Baburam; Valadez, Joseph J
• Is Part Of: BMJ open. Volume 12:Issue 1(2022)
• Publication Date: 2022-01-06
• Subjects: health policy -- public health -- health informatics -- international health services; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2021-051427
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objective: Combine 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. Setting: This 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. Participants: Both districts are rural. Bihar is 82.7% Hindu and 16.9% Islamic. Primary outcome measures: Survey prevalence measures for 10 indicators, corresponding prevalences using HMIS data, combined prevalences calculated with AT and SEs for each type of data. Results: The combined and survey estimates differ by <0.10. The combined and HMIS estimates differ by up to 84.2%, with the HMIS having 1.4–32.3 times larger error. Of 20 HMIS versus survey coverage estimate comparisons across the two districts only five differed by <0.10. Of 250 subdistrict-level comparisons of HMIS versus combined estimates, only 36.4% of the HMIS estimates are within the 95% CI of the combined estimate. Conclusions: Our statistical innovation increases the accuracy of information available for local health system decision making, allows evaluation of indicator accuracy and increases the accuracy of HMIS estimates. The combined estimates with a measure of error better informs health system professionals about their risks when using HMIS estimates, so they can reduce waste by making better decisions. Our results show that AT is an effective method ready for additional international assessment while also being used to provide affordable information to improve health services.