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

Caroline Jeffery,Marcello Pagano,Baburam Devkota,Joseph J Valadez

doi:10.1136/bmjopen-2021-051427

Abstract

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...

Highlights

For six indicators, all block-level weighting factors w, are below 0.10 in both districts, indicating the contribution of Health Management Information Systems (HMIS) estimator to the combined estimator is no higher than 10%
This result indicates that there are more discrepancies between the HMIS estimates and the LQAS estimates in Aurangabad when compared with Gopalganj for the first three indicators, while the discrepancies are greater in Gopalganj for the latter seven indicators
The LQAS survey in Bihar was undertaken for another purpose and so its use in annealing technique (AT) was at no additional expense

Summary

Introduction

The 17 Sustainable Development Goals (SDGs) were adopted by all United Nations member states in 2015 as an urgent call for action to end poverty and deprivation by Strengths and limitations of this study► Household survey data were captured as a stratified random sample leading to an efficient use of information.► Administrative data comprise 100% of the available recurrent information available in the two selected districts.► The study population is very large covering a large geographical area, reducing the likelihood that the results are pertinent only to a small group of mothers with children; results may be generalisable.► The process for combining probability and administrative data has been assessed using a statistically principled approach prior to use in this study.► The study is confined to two districts of Bihar, India which indicates the need for replicating the study in additional States of India and in other country settings.following strategies that improve health and education, reduce inequality and spur economic growth. Good-q uality data to measure the prevalence of disease conditions, or the population’s coverage with health services coverage, is an indispensable resource for programme managers and health policy makers to understand their context. This point is true in higher-income countries as it is in low-income and middle- income countries (LMICs). Data generated routinely through the Health Management Information Systems (HMIS) is more frequently used for decision- making and for annual reviews than household surveys.

Methods

Results

Discussion

Conclusion