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Background: Maternal mortality remains a major public health problem in low and middle-income countries. Adequate utilization of maternal health care services could be an effective means for reducing maternal mortality. Objective: This study aims to examine the socio-demographic factors of maternal health care utilization among Indian women. Methods: A cross-sectional study was conducted using the data from the 2015-2016 National Family Health Survey (NFHS-4) in India.A total of 190,898 ever-married women who had at least one live birth in the past five years preceding the survey were utilized for this study. Bivariate and multivariate analyses were performed for the analysis of the data. Results: Our study has indicated that educational attainment of women and household wealth status are the most significant predictors of maternal health care utilization. Other important socio-demographic factors include rural-urban residence, caste, religion, women's age, age at marriage, exposure to mass media and region. Conclusion: Our study has found that socio-demographic factors play a significant role in determining utilization of maternal health care services in India. Therefore, policymaker and programme administrators should address socioeconomic and demographic vulnerabilities of women to improve the use of maternal health care services, which eventually could reduce the risk of maternal morbidity and mortality.

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Objectives COVID-19 Pandemic has brought a threatening challenge to the world and as well as for Indian society and economy. In India, it has become a public health disaster and its' intensity increasing continuously. For the disaster risk reduction, and capacity building against the COVID-19 pandemic understanding of the relationship between socio-environmental conditions with the pandemic is very necessary. The objective of the present work is to construct a socio-environmental vulnerability index of the potential risk of community spread of COVID-19 using socio-economic and environmental variables. Methodology In this, cross-sectional study principal component analyses have been used to drive SoEVI. 4 uncorrelated sub-index has been extracted from 16 sub-indicators which reflects 59% of the variance. Aggregation of 4 Sub-Index has been done to obtain the final vulnerability Index. Results Results show that there is spatial variability in vulnerability based on environmental and socio-economic conditions. Districts of north and central India found more vulnerable then south India. Statistical significance has been tested using regression analysis, positive relation has been found between vulnerability index and confirmed and active cases. Conclusion The vulnerability index has highlighted environmentaly and socioeconomicallybackward districts. These areas will suffer more critical problems against COVID-19 pandemic for their socio-environmental problem.