The association between healthcare needs, socioeconomic status, and life satisfaction from a Chinese rural population cohort, 2012–2018

Caiyun Chen, Richard Huan Xu (Corresponding Author), Eliza Wong, Dong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

This study aimed to examine the prevalence of unmet healthcare needs and clarify its impact on socioeconomic status (SES) and life satisfaction in a longitudinal cohort of the Chinese rural population. Data used in this study were obtained from a nationally representative sample of 1387 eligible rural residents from the Chinese Family Panel Studies. Generalized estimating equation (GEE) logistic regression models were used to examine the factors associated with unmet healthcare needs and the impact of unmet healthcare needs on respondents’ perceived SES and life satisfaction. Approximately 34.6% of respondents were male, 18.2% were ≤ 40 years, and 66.7% had completed primary education or below. Around 19% and 32.6% of individuals who healthcare needs were met reported an above average socioeconomic status and life satisfaction, respectively in the baseline survey. GEE models demonstrated that unmet healthcare needs were significantly associated with low perceived SES (Odds ratio = 1.57, p < 0.001) and life satisfaction (Odds ratio = 1.23, p = 0.03) adjusted by covariates. Respondents who were older, reported moderate or severe illness, and with chronic conditions were more likely to report the unmet healthcare needs.Unmet healthcare needs are longitudinally associated with low SES and life satisfaction among the Chinese rural population, the disparity in access to healthcare exists among this population.

Original languageEnglish
Article number14129
JournalScientific Reports
Volume12
Issue number1
DOIs
Publication statusPublished - Dec 2022

Fingerprint

Dive into the research topics of 'The association between healthcare needs, socioeconomic status, and life satisfaction from a Chinese rural population cohort, 2012–2018'. Together they form a unique fingerprint.

Cite this