Evidence map›Paper›PMID 31574957›Full record

ArticleInternational journal of environmental research and public health2019

Association between Health Literacy and Subgroups of Health Risk Behaviors among Chinese Adolescents in Six Cities: A Study Using Regression Mixture Modeling.

Rong Yang, Danlin Li, Jie Hu, Run Tian, Yuhui Wan, Fangbiao Tao, Jun Fang, Shichen Zhang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  17. Physical Disorders are Associated with Health Risk Behaviors in Chinese Adolescents: A Latent Class Analysis.International journal of environmental research and public health · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Rong YangDepartment of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, China. yangrong@stu.ahmu.edu.cn.
Danlin LiDepartment of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, China. lidanlin@stu.ahmu.edu.cn.
Jie HuDepartment of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, China. hujie@stu.ahmu.edu.cn.
Run TianDepartment of Occupational and Environmental Health, School of Public Health, Guilin Medical University, No 1 Zhiyuan Road, Guilin 541199, China. tianrun@stu.glmc.edu.cn.
Yuhui WanDepartment of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, China. wyhayd@163.com.
Fangbiao TaoDepartment of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, China. fbtao@ahmu.edu.cn.
Jun FangMOE Key Laboratory of Population Health across Life Cycle/Anhui Provincial Key Laboratory of Population Health and Aristogenics, No 81 Meishan Road, Hefei 230032, Anhui, China. fangjun@ph.sojo-u.ac.jp.
Shichen ZhangDepartment of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, China. zhangshichen@ahmu.edu.cn.ORCID 0000-0002-2649-7321

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adolescents engage in health risk behaviors (HRBs) that influence their current and future health status. Health literacy (HL) is defined as how well a person can get and understand the health information and services, and use them to make good health decisions. HL can be used to participate in everyday activities actively and apply new information to the changing circumstances. HRBs commonly co-occur in adolescence, and few researchers have examined how HL predicts multiple HRBs in adolescence. In this study we examined the subgroups of HRBs, and investigated heterogeneity in the effects of HL on the subgroups. In total, 22,628 middle school students (10,990 males and 11,638 females) in six cities were enrolled by multistage stratified cluster sampling from November 2015 to January 2016. The measurement of HL was based on the Chinese Adolescent Interactive Health Literacy Questionnaire (CAIHLQ). Analyses were conducted with regression mixture modeling approach (RMM) by Mplus. By this study we found four latent classes among Chinese adolescents: Low-risk class, moderate-risk class 1 (smoking/alcohol use (AU)/screen time (ST)), moderate-risk class 2 (non-suicidal self-injury (NSSI)/suicidal behaviors (SB)/unintentional injury (UI)), and high-risk class (smoking/AU/ST/NSSI/SB/UI) which were 64.0%, 4.5%, 28.8% and 2.7% of involved students, respectively. Negative correlations were found between HL and HRBs: higher HL accompanied decreased HBRs. Compared to the low-risk class, moderate-risk class 1 (smoking/AU/ST), moderate-risk class 2 (NSSI/SB/UI), and high-risk class (smoking/AU/ST/NSSI/SB/UI) showed OR (95%CI) values of 0.990 (0.982-0.998), 0.981 (0.979-0.983) and 0.965 (0.959-0.970), respectively. Moreover, there was heterogeneity in the profiles of HRBs and HL in different classes. It is important for practitioners to examine HRBs in multiple domains concurrently rather than individually in isolation. Interventions and research should not only target adolescents engaging in high levels of risky behavior but also adolescents who are engaging in lower levels of risky behavior.

Indexed as

Asian PeopleHealth LiteracyHealth Risk BehaviorsAdolescentChinaCitiesData CollectionFemaleHumansMaleSmokingSurveys and QuestionnairesYoung AdultChinese adolescentshealth literacyhealth risk behaviorslatent class analysisregression mixture modeling

Identifiers

PMID31574957
PMCPMC6801655

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Registered trials

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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.