ArticleBMC public health2024
Health lifestyles of six Zhiguo ethnic groups in China: a latent class analysis.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Ethnic differences in maternal health care preferences in rural Yunnan, China: a discrete choice experiment.BMC health services research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundZhiguo ethnic groups, commonly known as "the directly-entering-socialism ethnic groups", represent Chinese ethnic minorities who have undergone a unique social development trajectory by transforming directly from primitive societies to the socialist stage. In recent decades, significant lifestyle transformations have occurred among Zhiguo ethnic groups. Understanding their health lifestyles can play a strategic role in China's pursuit of universal health coverage. This study aims to examine patterns of health-related lifestyle among Zhiguo ethnic groups and explore whether sociodemographic features and specific indicators related to health status are associated with particular classes.
methodsA cross-sectional study was conducted in Yunnan Province, China, from July to December 2022. Stratified random sampling method was employed to recruit residents belonging to six Zhiguo ethnic groups aged between 15 and 64. Latent class analysis was performed to identify clusters of health-related behaviors within each ethnic group. Logistic regression was utilized to determine the predictors of health lifestyles.
resultsA total of 1,588 individuals from the Zhiguo ethnic groups participated in this study. Three latent classes representing prevalent health lifestyles among the Zhiguo ethnic groups were identified: "unhealthy lifestyle" (31.80%), "mixed lifestyle" (57.37%), and "healthy lifestyle" (10.83%). In the overall population, individuals belonging to the "healthy lifestyle" group exhibited a higher likelihood of being non-farmers (OR: 2.300, 95% CI: 1.347-3.927), women (OR: 21.459, 95% CI: 13.678-33.667), married individuals (OR: 1.897, 95% CI: 1.146-3.138), and those residing within a walking distance of less than 15 min from the nearest health facility (OR: 2.133, 95% CI: 1.415-3.215). Conversely, individuals in the age cohorts of 30-39 years (OR: 0.277, 95% CI: 0.137-0.558) and 40-49 years (OR: 0.471, 95% CI: 0.232-0.958) showed a decreased likelihood of adopting a healthy lifestyle.
conclusionsA considerable proportion of the Zhiguo ethnic groups have not adopted healthy lifestyles. Targeted interventions aimed at improving health outcomes within these communities should prioritize addressing the clustering of unfavorable health behaviors, with particular emphasis on single male farmers aged 30-49, and expanding healthcare coverage for individuals residing more than 15 min away from accessible facilities.
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.