ArticleFrontiers in public health2026
Factors influencing the decision to opt out of basic medical insurance among China's migrant population: a logistic regression analysis.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: For countries with large internal migrant populations, reducing the opt-out rate from basic medical insurance among this group remains a major obstacle to achieving universal health coverage (UHC). Within China's basic medical insurance system, migrants whose registered household locations differ from their places of residence have comparatively high opt-out rates. If they forgo their entitlement to basic medical insurance, they may face substantial out-of-pocket medical costs and the risk of being unable to access necessary healthcare services when suffering from serious illnesses. This study conceptualizes opting out as a multistage decision-making process influenced by rational and irrational factors. By identifying key associated factors of opting out among migrants, it aims to inform targeted policies to improve their insurance coverage. Methods: The analytical sample originates from the 2018 China Migrant Population Dynamic Monitoring Survey. After data cleaning and excluding respondents with unclear insurance status, 144,048 valid cases were identified, including 8,355 respondents who reported no enrollment in any scheme. This study first conducted a descriptive analysis of opt-out rates across different migrant groups, across demographic, migration, economic, employment, and health characteristics. Subsequently, a binary logistic regression model was employed to explore the key determinants of opting out. The final model included 11 explanatory variables (age group, educational attainment, marital status, migration duration, mobility range, reason for migration, expected local stay duration, household income, household expenditure, employment status, and recent health status), with the region of residence as a control variable. Results: Descriptive analysis revealed an overall opt-out rate of 5.80% among the migrant population. Opt-out rates varied across the demographic groups. Binary logistic regression analysis further indicated that the probability of opting out was significantly associated with multiple factors, including age, educational attainment, marital status, migration distance, income level, employment stability, and health status. For example, compared with migrants aged 15-19, those aged 60 or older had lower odds of opting out (OR = 0.403, Conclusion: Under China's current basic medical insurance system, opt-out behavior among the migrant population is the result of the combined effects of the multidimensional individual variables. To effectively reduce the opt-out rate, targeted adjustments to the current medical insurance policies should be proactively made, thereby advancing the full achievement of UHC.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.