BMC Digital Health (Jul 2024)

An analysis of predictors and wealth-based inequality in internet use among women in India: aiming for better digital health outcomes

  • Rakesh Chandra,
  • Jeetendra Kumar Patel,
  • Sonal Srivastava,
  • Aditya Singh,
  • Saradiya Mukherjee

DOI
https://doi.org/10.1186/s44247-024-00090-z
Journal volume & issue
Vol. 2, no. 1
pp. 1 – 11

Abstract

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Abstract Background Digital health, aiming to boost healthcare accessibility, is an emerging concept in the domain of healthcare administration and delivery in developing countries. In India, according to the National Family Health Survey (NFHS-5), more than half (55%) of the men have reported ever using the internet, while only one-third (33%) of the women have done so. This gendered digital divide is further complicated and worsened by the fact that individuals in the lowest wealth quintile exhibit significantly lower internet usage rates, with only 9% of women and 26% of men. Such intersectionality of the gendered digital divide might prove a barrier to realizing the full potential of digital health in India. Eliminating digital inequalities in all forms and ensuring universal digitalization is essential for desired digital health outcomes. Methods This study aims to explore India’s readiness for digital health in terms of access to basic digital infrastructure, i.e., the internet. We analyze access to the Internet among Indian women of reproductive age using pan-India survey data from the fifth round of the National Family Health Survey (2019–21). We investigate predictors of Internet use in a regression model and apply the Erreygers Concentration Index (ECI) to examine inequalities in Internet access. Using decomposition analyses, we analyze factors contributing to digital inequality in terms of internet use. Results Our inequality analysis based on the ECI [ECI- 0.4444 (p < 0.001)] suggests that a significant inequality exists in internet access. Furthermore, the decomposition analyses in the study find women’s educational level to be the most prominent (28.19%) contributing factor to internet inequality, followed by wealth (25.67%), place of residence (23.16%), and caste (1.10%). Recommendation We suggest a comprehensive readiness and need assessment, revamping of digital infrastructure, and moving with caution in implementing digital health innovation in the country as it may further exacerbate the existing healthcare access inequities.

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