Evaluating concordance between government administrative data and externally collected data among high-volume government health facilities in Uttar Pradesh, India

Abstract

Background : Globally, opportunities to validate government reports through external audits are rare, notably in India. A cross-sectional maternal health study in Uttar Pradesh, India’s most populous state, compares government administrative data and externally collected data on maternal health service indicators. Objectives : Our study aims to determine the level of concordance between government-reported health facility data compared to externally collected health facility data on the same maternal healthcare quality indicators. Second, our study aims to explore whether the level of agreement between government administrative data versus the externally collected data differs by level of facility or by type of maternal health service. Methods : Facility assessment surveys were administered to key health staff by government-hired enumerators from January 2017 to March 2017 at nearly 750 government health facilities across UP. The same survey was re-conducted by external data collectors from August 2017 to October 2017 at 40 of the same facilities. We conduct comparative analyses of the two datasets for agreement among the same measures of maternal healthcare quality. Results : The findings indicate concordance between most indicators across government administrative data and externally collected health facility data. However, when stratified by facility-level or service type, results suggest significant over-reporting in the government administrative data on indicators that are incentivized. This finding is consistent across all levels of facilities; however, the most significant disparities appear at higher-level facilities, namely District Hospitals. Conclusion : This study has a number of important programmatic and policy implications. Government administrative health data have the potential to be highly critical in informing large-scale quality improvements in maternal healthcare quality, but its credibility must be readily verifiable and accessible to politicians, researchers, funders, and most importantly, the public, to improve the overall health, patient experience, and well-being of women and newborns.

Biometric Tracking, Healthcare Provision, and Data Quality: Experimental Evidence from Tuberculosis Control

“…Little improvement has been made in the past decades, so that the quality even of statistics as fundamental as birth and death rates remains poor overall in the world (Mahapatra et al, 2007). In the context of health systems in India, Morton et al (2016) show that data from a hospital insurance program are practically unusable, eliminating critical information for state efforts to improve healthcare for the poor, and Phillips et al (2019) find significant overreporting in government health facility data relative to externally collected data, in particular on incentivized indicators. Despite the im-portance of data quality for healthcare delivery, there is a paucity of rigorous evidence on the causes of health data incompleteness and misreporting and on interventions that can successfully address them.…”

Quality of routine health facility data used for newborn indicators in low- and middle-income countries: A systematic review

“…The data sources evaluated in included studies were predominantly registers, with very few assessing individual patient case notes directly. Several studies also assessed aggregate reports such as those included in DHIS [ 30 , 33 , 50 , 51 , 53 , 61 , 63 , 64 , 59 ]. Only a few studies explored whether assessment of quality of specific data for newborn indicators was feasible given the design of registers or case notes [ 39 , 41 , 42 , 44 - 46 ].…”

National guidelines for data quality in surveys

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