Factors determining the rural-urban disparity in unmet healthcare needs among households in Eastern India

Abstract Background Unmet healthcare needs is an important concern in India’s social development, which limit progress towards the goal of health equity and achieving SDG-3, i.e., good health and well-being. Objectives This paper examines the factors behind rural-urban disparity in unmet healthcare needs in Eastern India. Methods 75th round of the National Sample Survey (NSS) on Social Consumption: Health (July 2017-June 2018) has been used. The study involves four East Indian states, i.e., Bihar, Jharkhand, Odisha, and West Bengal. Binary percentage distribution, binary logistic regressions, and Fairlie’s decomposition technique (1999) have been applied. Anderson’s healthcare use model has been applied as a framework of analysis. Results Logistic regression result shows, besides the place of residence, social groups, age, state, religion, nature of ailment, and spells of ailments are major determinants of unmet needs in Eastern India. Decomposition technique shows that economic status, social strata, and the nature of ailments significantly contribute to the rural-urban gap (6.9%). Discussion and conclusion Both structural determinants (predisposing and enabling factors), as well as the nature and severity of diseases (need factors), play a significant role in shaping the inequality. It also suggests the expansion of health schemes as a policy-relevant modifiable factor to reduce healthcare inequality.

Authors

Publication Details

Journal
Discover Public Health
Published
2026-03-21
DOI
https://doi.org/10.1186/s12982-026-01713-8
Primary Topic
Healthcare Systems and Reforms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors determining the rural-urban disparity in unmet healthcare needs among households in Eastern India

Sujoy Pal
Discover Public Health
Healthcare Systems and Reforms
article

Factors determining the rural-urban disparity in unmet healthcare needs among households in Eastern India

Sujoy Pal
article en

Abstract

Abstract Background Unmet healthcare needs is an important concern in India’s social development, which limit progress towards the goal of health equity and achieving SDG-3, i.e., good health and well-being. Objectives This paper examines the factors behind rural-urban disparity in unmet healthcare needs in Eastern India. Methods 75th round of the National Sample Survey (NSS) on Social Consumption: Health (July 2017-June 2018) has been used. The study involves four East Indian states, i.e., Bihar, Jharkhand, Odisha, and West Bengal. Binary percentage distribution, binary logistic regressions, and Fairlie’s decomposition technique (1999) have been applied. Anderson’s healthcare use model has been applied as a framework of analysis. Results Logistic regression result shows, besides the place of residence, social groups, age, state, religion, nature of ailment, and spells of ailments are major determinants of unmet needs in Eastern India. Decomposition technique shows that economic status, social strata, and the nature of ailments significantly contribute to the rural-urban gap (6.9%). Discussion and conclusion Both structural determinants (predisposing and enabling factors), as well as the nature and severity of diseases (need factors), play a significant role in shaping the inequality. It also suggests the expansion of health schemes as a policy-relevant modifiable factor to reduce healthcare inequality.

Discover Public HealthVol. 23(1)
No poverty
Openalex Percentile: Top 15%
Healthcare Systems and Reforms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.