Evidence map›Paper›PMID 42387108›Full record

ArticleHealth economics review2026

Latent profiles of health insurance literacy, numeracy and supplementary voluntary health insurance enrollment.

Tong Liu, Lei Liu, Lizheng Xu, Chaofan Li

Abstract read
In one paragraph

Article in Health economics review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Tong LiuDepartment of Social Medicine and Health Management, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wenhua Xi Rd, Jinan, Shandong, 250012, China.
Lei LiuDepartment of Social Medicine and Health Management, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wenhua Xi Rd, Jinan, Shandong, 250012, China.
Lizheng XuSchool of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
Chaofan LiDepartment of Social Medicine and Health Management, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wenhua Xi Rd, Jinan, Shandong, 250012, China. lcfan@sdu.edu.cn.

Funding

National Natural Science Foundation of China 72204146Natural Science Foundation of Shandong Province ZR2022QG018
6 · The paper itself

Abstract

backgroundLow enrollment threatens the sustainability of China's voluntary health insurance system. Limited health insurance literacy and numeracy may create a cognitive barrier to informed decision-making regarding enrollment. This study aims to identify latent profiles of health insurance literacy and numeracy, and to evaluate their associations with Supplementary Voluntary Health Insurance (SVHI) enrollment.

methodsA cross-sectional survey utilizing face-to-face computer-assisted personal interviews was conducted between July and August 2024 across three Chinese provinces (Shandong, Henan, and Sichuan). Using multistage stratified sampling, 1,326 valid responses were collected from residents aged 18 years or older (out of 1,359 approached). Health insurance literacy was assessed using a validated scale comprising self-rated abilities and an objective knowledge quiz, while numeracy was measured via an adapted 3-item General Numeracy Scale. Latent profile analysis identified unobserved cognitive subgroups. Multivariable logistic regression with robust standard errors evaluated the associations between latent profiles and SVHI enrollment, adjusting for relevant covariates.

resultsA total of 1,326 participants were included in the analysis, of whom 369 (27.8%) were male and 610 (46.0%) were aged between 18 and 44 years. Of the participants, 195 (14.7%) had an education level of elementary school or lower. Three distinct cognitive profiles were identified: Profile 1 ("High health insurance literacy, High numeracy", 32.9%), Profile 2 ("High health insurance literacy, Low numeracy", 52.0%), and Profile 3 ("Low health insurance literacy, Low numeracy", 15.1%). In multivariable logistic regression, participants in profile 1 (OR = 2.14; 95% CI, 1.23-3.70) and profile 2 (OR = 2.40; 95% CI, 1.43-4.02) were significantly more likely to enroll in SVHI than those in Profile 3.

conclusionsThis study found that distinct cognitive profiles of health insurance literacy and numeracy were associated with SVHI enrollment. Individuals with low health insurance literacy and low numeracy were significantly less likely to enroll in SVHI than those with higher levels of both. These findings may inform targeted communication and support strategies to improve equitable access to SVHI among populations with lower health insurance literacy and numeracy.

Indexed as

Health insurance literacyLatent profile analysisNumeracySupplementary voluntary health insurance

Identifiers

PMID42387108
PMCPMC13595556

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.