Association between Health Literacy and Subgroups of Health Risk Behaviors among Chinese Adolescents in Six Cities: A Study Using Regression Mixture Modeling
Abstract
:1. Introduction
2. Methods
2.1. Study Participants and Procedures
2.2. Measures
2.3. Statistical Analysis
3. Results
3.1. Prevalence of HRBs
3.2. Class Enumeration
3.3. Characteristics of the Final Four Class Model
3.4. Effect of HL on the Best-Fitting Latent Classes of HRBs
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
HRBs | Health risk behaviors |
AU | Alcohol use |
ST | Screen time |
NSSI | Non-suicidal self-injury |
SB | Suicidal behavior |
UI | Unintentional injury |
HL | Health literacy |
RMM | Regression mixture modeling |
LCA | Latent class analysis |
df | Degrees of freedom |
AIC | Akaike Information Criteria |
BIC | Bayesian Information Criteria |
aBIC | Adjusted Bayesian Information Criteria |
LMR-LRT | Lo-Mendell-Rubin Likelihood Ratio |
BLRT | Bootstrapped Likelihood Ratio Tests |
OR | Odds ratio |
CI | Confidence interval |
Appendix A
Variable | Measures |
---|---|
Health literacy | Chinese Adolescent Interactive Health Literacy Questionnaire |
Current smoking | During the past 30 days, how many days did you smoke cigarettes? |
Current AU | During the past 30 days, on how many days did you have at least one drink of alcohol? |
ST | The average hours on weekdays spent on playing games or doing things unrelated to study on the computer every day |
Non-suicidal self-injury | Adolescent Non-suicidal Self-injury Assessment Questionnaire |
SB | Have you ever thought about killing yourself in the past 12 months? Have you ever made a plan to kill yourself in the past 12 months? Have you ever tried to kill yourself in the past 12 months? |
UI | Children and teenager injury monitoring method. UI are divided into road traffic incident, crush, falling and tripping, scratches, puncture or cut, bites and pricks, explosive impact, enclosed anoxic space, drowning, electric shock, chemical or other substances poisoning and other twelve injuries. |
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Variable | Total Sample (%) |
---|---|
Gender | |
Male | 10,990 (48.6) |
Female | 11,638 (51.4) |
Grade | |
Middle school | 11,993 (53.0) |
High school | 10,635 (47.0) |
Registered residence | |
Rural | 10,882 (48.1) |
Urban | 11,746 (51.9) |
Any siblings | |
Yes | 12,908 (57.0) |
No | 9720 (43.0) |
Accommodation type | |
Boarding student | 11,320 (50.0) |
Commuting student | 11,308 (50.0) |
Father’s educational level a | |
<High school degree | 13,006 (57.5) |
≥High school degree | 9424 (41.6) |
Mother’s educational level b | |
<High school degree | 14,335 (63.4) |
≥High school degree | 8105 (35.8) |
Self-reported family economy | |
Bad | 3240 (14.3) |
General | 16,345 (72.2) |
Good | 3043 (13.4) |
Statistic | 2 Classes | 3 Classes | 4 Classes | 5 Classes |
---|---|---|---|---|
df | 50 | 43 | 36 | 29 |
AIC | 120,896.912 | 119,991.261 | 119,844.588 | 119,822.264 |
BIC | 121,001.263 | 120,151.800 | 120,061.315 | 120,095.180 |
aBIC | 120,959.949 | 120,088.241 | 119,975.510 | 119,987.129 |
LMR-LRT | <0.001 | <0.001 | <0.001 | 0.0592 |
BLRT | <0.001 | <0.001 | <0.001 | <0.001 |
Entropy | 0.549 | 0.725 | 0.692 | 0.579 |
Classification probability | 0.28730 0.71270 | 0.24032 0.04698 0.71270 | 0.28774 0.04472 0.64089 0.02665 | 0.19299 0.28774 0.01003 0.47795 0.03129 |
Variable | Low-Risk Class | Moderate-Risk Class 1 (Smoking/AU/ST) | Moderate-Risk Class 2 (NSSI/SB/UI) | High-Risk Class (Smoking/AU/ST/NSSI/SB/UI) |
---|---|---|---|---|
Adjusted OR (95% CI) | Adjusted OR (95% CI) | Adjusted OR (95% CI) | Adjusted OR (95% CI) | |
HL | ref. | 0.990 (0.982–0.998) ** | 0.981 (0.979–0.983) *** | 0.965 (0.959–0.970) *** |
Age | ref. | 1.327 (1.242–1.419) *** | 0.838 (0.814–0.863) *** | 0.978 (0.908–1.054) |
Gender | ||||
Male | ref. | ref. | ref. | ref. |
Female | ref. | 0.183 (0.137–0.245) *** | 0.725 (0.662–0.795) *** | 0.359 (0.265–0.487) *** |
Registered residence | ||||
Rural | ref. | ref. | ref. | ref. |
Urban | ref. | 1.176 (0.920–1.502) | 0.900 (0.816–0.993) ** | 0.843 (0.629–1.129) |
Household structure | ||||
Only child | ref. | ref. | ref. | ref. |
More than one child | ref. | 0.940 (0.753–1.173) | 0.957 (0.871–1.051) | 1.157 (0.885–1.514) |
Accommodation type | ||||
Boarding student | ref. | ref. | ref. | ref. |
Commuting student | ref. | 1.687 (1.323–2.151)*** | 0.736 (0.665–0.815) *** | 1.636 (1.207–2.216) ** |
Father’s educational level | ||||
<High school degree | ref. | ref. | ref. | ref. |
≥High school degree | ref. | 1.313 (1.035–1.664)** | 0.977 (0.874–1.093) | 1.461 (1.084–1.968) ** |
Mother’s educational level | ||||
< High school degree | ref. | ref. | ref. | ref. |
≥ High school degree | ref. | 0.945 (0.735–1.214) | 0.994 (0.885–1.116) | 0.951 (0.691–1.309) |
Self-reported family economy (per level change) | ref. | 1.015 (0.795–1.297) | 0.791 (0.715–0.874) *** | 1.752 (1.234–2.489) ** |
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Yang, R.; Li, D.; Hu, J.; Tian, R.; Wan, Y.; Tao, F.; Fang, J.; Zhang, S. Association between Health Literacy and Subgroups of Health Risk Behaviors among Chinese Adolescents in Six Cities: A Study Using Regression Mixture Modeling. Int. J. Environ. Res. Public Health 2019, 16, 3680. https://doi.org/10.3390/ijerph16193680
Yang R, Li D, Hu J, Tian R, Wan Y, Tao F, Fang J, Zhang S. Association between Health Literacy and Subgroups of Health Risk Behaviors among Chinese Adolescents in Six Cities: A Study Using Regression Mixture Modeling. International Journal of Environmental Research and Public Health. 2019; 16(19):3680. https://doi.org/10.3390/ijerph16193680
Chicago/Turabian StyleYang, Rong, Danlin Li, Jie Hu, Run Tian, Yuhui Wan, Fangbiao Tao, Jun Fang, and Shichen Zhang. 2019. "Association between Health Literacy and Subgroups of Health Risk Behaviors among Chinese Adolescents in Six Cities: A Study Using Regression Mixture Modeling" International Journal of Environmental Research and Public Health 16, no. 19: 3680. https://doi.org/10.3390/ijerph16193680