Health index scores and health insurance coverage across India: A state level spatiotemporal analysis

Introduction: Since the Government of India has decided to continue with the publicly funded health insurance (PFHI) strategy, it is now pertinent to attempt to determine the factors that drive health insurance coverage in India. The NITI (National Institute for Transforming India) Aayog (i.e. Commission) is the apex public policy think tank of the Government of India. The NITI Aayog assesses the health status of the states through its acclaimed health index consisting of 24 indicators for health outcomes, governance and infrastructure. All states and Union Territories (UTs) are ranked on the index. This study aims to assess associations between NITI Aayog health index scores and health insurance coverage across India through a state-level lens. Methods: Health insurance coverage data has been extracted from the National Family Health Survey (NFHS) 4 and NFHS-5 data. NFHS-4 was conducted during 2015-16. NFHS-5 was interrupted by the COVID-19 pandemic and conducted in two phases from 2019 to 2021. This change in health insurance coverage is mapped to the NITI Aayog health index scores for the states and UTs. The NITI Aayog has classified states into two categories: Larger states and smaller states. Based on performance in health indices, NITI Aayog also classifies the states and UTs as Aspirants, Achievers and Front runners. Results and discussion: There is a positive linear relationship between the health index scores of front-runners (Pearson's r = 0.6037, p = 0.029) and the total insurance coverage. We observe poor linear relationship between the health index scores of achievers (Pearson's r = 0.2822, p = 0.498) and the total insurance coverage. There is no linear relationship between the NFHS-5 Total Insurance Coverage and the NITI Health Index Scores (Pearson's r = 0.2766, p = 0.125). Also, we observe a moderate positive linear relationship between the health index scores and the total insurance coverage among the Union Territories which is not statistically significant (Pearson's r = 0.4343, p = 0.465). A similar conclusion is made in the context of smaller states (Pearson's r = 0.3692, p = 0.368) and larger states (Pearson's r = 0.2103, p = 0.387). At the same time, we observe a decrease in insurance coverage across NFHS-4 and NFHS-5 in some states and UTs. Further research is needed to identify the determinants of these spatial changes across a span of five years, from a temporal lens.