Target Setting to Reduce Infant Mortality Across Indian States: A Statistical Approach

• Published in: International Journal of Social Science Vol. 9; no. 2; pp. 85 - 96
• Main Author: Mishra, Ananya
• Format: Journal Article
• Language: English
• Published: New Delhi New Delhi Publishers 19.06.2020
• Subjects: Gender; Health care policy; Health surveys; Infant mortality; Infrastructure; Literacy; Prenatal care; Regression analysis; Socioeconomic factors; Sustainable development; Womens health; India
• ISSN: 2249-6637; 2321-5771
• DOI: 10.30954/2249-6637.02.2020.5

Background: An important area in healthcare policy is Child survival. The metric for Child survival is Infant Mortality Rate. As a country, India has registered significant decline in Infant Mortality Rate, however, the progress of individual States have varied, with many well performing and various laggard States. States are important units for healthcare policy since the subject of health comes under the State List in the Constitution; therefore implementation of interventions is the responsibility of the State. This paper therefore seeks to develop a Statistical model to estimate Infant Mortality Rate and set targets based on the factors that determine the predictive model. Methodology: Infant Mortality Rate for different States were regressed against relevant healthcare service delivery metrics, quality of infrastructure metrics, socio-economic factors as recorded in National Family Health Survey 4 (2015-16). A multivariate model was developed estimating IMR at given values of operational factors. For policy target setting, States were clustered on the basis of Infant Mortality Rates and target for each cluster was defined in terms of factors and in turn, a target IMR value for different clusters were derived. Results: The result demonstrate that the Infant Mortality Rate levels for reference year 2015-16 had a significant relationship with "Percentage of women with mobile phone" and "Percentage of women who received at least four Antenatal Care visits during their pregnancy". The second metric as a proxy for gender development had a higher beta coefficient than the first metric, a proxy for health infrastructure. Conclusion: Improving the metric for institutional birth percentage, registration of pregnancies and using technology to follow up and manage data on pregnancies are important. The second factor highlights the convergence of development outcomes that is, lower IMR with higher agency (higher literacy and access to technology) and development of infrastructure (increased mobile penetration). Therefore, for gender development indicator, access to technology, internet penetration, digital literacy and incentivizing higher agency to women of the household are recommended.