Abstract
This study investigates disparities in healthcare services in the districts of Uttar Pradesh, the most populous and one of the poorest states of India in the health sector. To measure the disparities in healthcare services, a composite index has been computed using Principal Component Analysis in three domains namely, health availability, health amenities and health affordability and 12 representative indicators. On the basis of the results, districts have been clubbed into five categories, such as high, high-medium, medium, medium–low and low according to their composite scores. The study also uses k-means cluster analysis to find out the set of districts which are similar within the group, but they are different between the groups. Key results indicate that the districts of the western region are more developed in comparison to the districts of the eastern region in terms of three indices such as availability, amenities, affordability of healthcare services and overall composite health index. Also, k-means cluster analysis shows that there are many districts which are identical in many respects; however, they are located in different regions of the state. This study may be helpful to understand the poor availability and affordability of healthcare services in the districts of Uttar Pradesh, which should be improved to meet the objectives of the basic-needs approach and the millennium development goals.
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Availability of data and material
Government open access sources, NFHS 2015–16 (Government of India), data from the Uttar Pradesh official site, Government of Uttar Pradesh, India and National Sample Survey 75th round data for the year 2018–19.
Code availability
Arc GIS and SPSS version 22 are used for analysis and we have permission for the use of these software.
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RK-data analysis, data collection, tabulation, mapping, and analysis work. RR-Proof-reading, analysis work, result interpretation.
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Kumari, R., Raman, R. Regional disparities in healthcare services in Uttar Pradesh, India: a principal component analysis. GeoJournal 87, 5027–5050 (2022). https://doi.org/10.1007/s10708-021-10542-y
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DOI: https://doi.org/10.1007/s10708-021-10542-y