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Socio-economic Correlates and Spatial Heterogeneity in the Prevalence of Asthma among Young Women in India
Authors: Shri Kant Singh, Jitendra Gupta, Himani Sharma, Sarang P. Pedgaonkar, and Nidhi Gupta
Source: BMC Pulmonary Medicine, 20, Article number: 190
Topic(s): Asthma
Rural-urban differentials
Spatial analysis
Women's health
Country: Asia
Published: JUL 2020
Abstract: Background: Asthma is one of the leading causes of disease burden when measured in terms of disability adjusted life years, despite low prevalence of self-reported cases among young women. This paper deals with the meso-scale correlates and spatial heterogeneity in the prevalence of self-reported Asthma across 640 districts in India, using a nationally representative sample of 699,686 women aged 15–49?years from all 36 States/UTs under NFHS-4 (2015–16). Methods: Analytical methods used in this paper include multivariate logistic regression to examine the adjusted effects of various independent variables on self-reported Asthma and poor-rich ratios (PRR) and concentration index (CI) to understand the economic inequalities in the prevalence of Asthma. For the spatial analysis in the prevalence of Asthma, univariate and bivariate local Moran’s I statistic have been computed in addition to measure of spatial autocorrelation and auto regression using spatial error and spatial lag models. Results: Results highlight that women’s education was an important marker to the prevalence of Asthma. Smoking tobacco in any form among women were significantly more likely to suffer from Asthma. The prevalence of Asthma was further aggravated among women from the households without a separate room for kitchen, as well as those using unclean fuel for cooking. The poor-rich ratio in the prevalence of Asthma across various States/UTs in India depict inherent inequality. An analysis of spatial clustering in the prevalence of Asthma based on spatial autocorrelation portrays that Moran’s I values were significant for improved source of drinking water, clean fuel used for cooking, and household environment. When spatial weights are taken into consideration, the autoregression model noticeably becomes stronger in predicting the prevalence of Asthma. Conclusions: Any programmatic effort to curb the prevalence of Asthma through vertical interventions may hinge around the use of clean fuel, poverty, and lifestyle of subjects, irrespective of urban-rural place of their residence, environmental and ecological factors.