A composite indicator to measure universal health care coverage in India

Way forward for post-2015 health system performance monitoring framework

Bibliographic Data

ID11492379
AuthorsShankar Prinja (0000-0001-7719-6986, Post Graduate Institute of Medical Education and Research, corresponding author), Rakesh Gupta (0000-0002-6051-8923, Government of Haryana), Pankaj Bahuguna (0000-0001-9952-6077, Post Graduate Institute of Medical Education and Research), Atul Sharma (0000-0002-8960-5449, Post Graduate Institute of Medical Education and Research), Arun Kumar Aggarwal (0000-0002-9795-784X, Post Graduate Institute of Medical Education and Research), Amit Phogat, Rajesh Kumar (0000-0001-9750-3437, Post Graduate Institute of Medical Education and Research)
Year2017
Volume32
Issue1
Pages43-56
Publication date2017-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueHealth Policy and Planning (JOURNAL)
Journal identifiersISSN: 0268-1080 • E-ISSN: 1460-2237
PublisherOxford University Press (PUBLISHER • GB)
DOI10.1093/heapol/czw097
PMID27497138
OpenAlexW2484550329
LanguageEN
Citations received11
References cited20

BACKGROUND: There is limited work done on developing methods for measurement of universal health coverage. We undertook a study to develop a methodology and demonstrate the practical application of empirically measuring the extent of universal health coverage at district level. Additionally, we also develop a composite indicator to measure UHC. METHODS: A cross-sectional survey was undertaken among 51 656 households across 21 districts of Haryana state in India. Using the WHO framework for UHC, we identified indicators of service coverage, financial risk protection, equity and quality based on the Government of India and the Haryana Government's proposed UHC benefit package. Geometric mean approach was used to compute a composite UHC index (CUHCI). Various statistical approaches to aggregate input indicators with or without weighting, along with various incremental combinations of input indicators were tested in a comprehensive sensitivity analysis. FINDINGS: The population coverage for preventive and curative services is presented. Adjusting for inequality, the coverage for all the indicators were less than the unadjusted coverage by 0.1-6.7% in absolute term and 0.1-27% in relative term. There was low unmet need for curative care. However, about 11% outpatient consultations were from unqualified providers. About 30% households incurred catastrophic health expenditures, which rose to 38% among the poorest 20% population. Summary index (CUHCI) for UHC varied from 12% in Mewat district to 71% in Kurukshetra district. The inequality unadjusted coverage for UHC correlates highly with adjusted coverage. CONCLUSION: Our paper is an attempt to develop a methodology to measure UHC. However, careful inclusion of others indicators of service coverage is recommended for a comprehensive measurement which captures the spirit of universality. Further, more work needs to be done to incorporate quality in the measurement framework.

Business · Composite indicator · Data mining · Economic growth · Economics · Environmental economics · Environmental health · Health care · Measure (data warehouse) · Universal coverage · Computer Science · Global Maternal and Child Health · Healthcare Systems and Reforms · Medicine · Primary Care and Health Outcomes · Health Policy

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