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Year : 2020  |  Volume : 1  |  Issue : 1  |  Page : 15-20

Spatial clustering and impact of household characteristics on under-five mortality in India: A secondary data analysis

1 Department of Community and Family Medicine, AIIMS, Rishikesh, Uttarakhand, India
2 Department of Architecture and Planning, IIT, Roorkee, Uttarakhand, India
3 Department of Nephrology, AIIMS, Rishikesh, Uttarakhand, India

Correspondence Address:
Dr. Jatin Chaudary
Department of Community and Family Medicine, AIIMS, Rishikesh, Uttarakhand
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/JME.JME_40_20

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Background: Under-five mortality rate (U5MR) is one among the best social indicators that identifies social development and well-being. Various risk factors are seen contributing to its increase. Aim: The present correlational study aims to identify the spatial clustering of U5MR in different states and union territories (UTs) of India and determines its association with household characteristics. Patients and Methods: The present study incorporates primary data for 29 states and 2 UTs (Delhi and Chandigarh) of India from the National Family and Health Survey-4 (2015–2016) for secondary data analysis. The data include the outcome variable which was the U5MR and predictor variables such as households with electricity; improved source of drinking water; toilet facility; solid fuels being used for cooking; anyone smoking and living in a pucca house. Primary data were analysed using GeoDa software, employing Univariate Local Indicators of Spatial Association, Spearman's correlation coefficient, ordinary least square (OLS) and spatial error model (SEM). Results: The study showed significant spatial clustering of high U5MR in six states and one UT, namely Rajasthan, Madhya Pradesh, Chhattisgarh, Uttar Pradesh, Bihar, Jharkhand and Delhi and clustering of lower U5MR in southern states of Tamil Nadu and Karnataka. The Spearman's correlation showed a significant positive association of U5MR with households using solid fuel for cooking and negative association with households using electricity, with toilet facility and Living in a pucca house. The OLS and SEM spatial regression models model showed an association between households with toilet facility and in which anyone smokes at home with under-five mortality. Conclusions: U5MR shows a significant clustering geographically. This mortality indicator is influenced by the external environment such as household characteristics.

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