Methodological Approaches to Comparative Trend Analyses: The Case of Adolescent Toothbrushing

. 2024 ; 69 () : 1607669. [epub] 20250110

Jazyk angličtina Země Švýcarsko Médium electronic-ecollection

Typ dokumentu časopisecké články, srovnávací studie

Perzistentní odkaz   https://www.medvik.cz/link/pmid39867841

OBJECTIVES: Research questions about how and why health trends differ between populations require decisions about data analytic procedure. The objective was to document and compare the information returned from stratified, fixed effect and random effect approaches to data modelling for two prototypical descriptive research questions about comparative trends in toothbrushing. METHODS: Data included five cycles of the Health Behaviour in School-aged Children 2006 to 2022, which provided a sample of 980192 11- to 15- year olds from 35 countries. Using logistic regression models and generalized linear mixed models, toothbrushing daily was regressed on time, following the three approaches to analysis of trends. RESULTS: The stratified approach suggested a positive but non-linear trend in toothbrushing from 2006 to 2022 in most countries but provided no statistical inference on the variation. The fixed effect and the random effect approach converged on a positive but flattening overall trend, with a statistically significant country variation in trends. CONCLUSION: Only the fixed effect approach and the random effects approach provided clear answers to the research question. Additional methodological considerations for making an informed choice of analytical approach are discussed.

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Brisson R, Furstova J, Sokolová L, Eriksson C, Boniel-Nissim M, Badura P. Trends in the Link Between Perceived Social Support and Life Satisfaction in Adolescents (2013/14–2021/22): A Cross-National Study. Int J Public Health (2024) 69:1607283. 10.3389/ijph.2024.1607283 PubMed DOI PMC

Cosma A, Stevens G, Martin G, Duinhof EL, Walsh SD, Garcia-Moya I, et al. Cross-National Time Trends in Adolescent Mental Well-Being From 2002 to 2018 and the Explanatory Role of Schoolwork Pressure. J Adolesc Health (2020) 66(6):S50–8. 10.1016/j.jadohealth.2020.02.010 PubMed DOI PMC

Chatelan A, Rouche M, Dzielska A, Lebacq T, Fismen A, Kelly C, et al. Time Trends in Consumption of Sugar-Sweetened Beverages and Related Socioeconomic Differences Among Adolescents in Eastern Europe: Signs of a Nutrition Transition? Am J Clin Nutr (2021) 114(4):1476–85. 10.1093/ajcn/nqab175 PubMed DOI

Ottová-Jordan V, Smith O, Gobina I, Mazur J, Augustine L, Cavallo F, et al. Trends in Multiple Recurrent Health Complaints in 15-Year-Olds in 35 Countries in Europe, North America and Israel From 1994 to 2010. Eur J Public Health (2015) 25:24–7. 10.1093/eurpub/ckv015 PubMed DOI

Honkala S, Vereecken C, Niclasen B, Honkala E. Trends in Toothbrushing in 20 Countries/Regions From 1994 to 2010. Eur J Public Health (2015) 25:20–3. 10.1093/eurpub/ckv013 PubMed DOI

Boniel-Nissim M, Lenzi M, Zsiros E, de Matos M, Gommans R, Harel-Fisch Y, et al. International Trends in Electronic Media Communication Among 11-to 15-Year-Olds in 30 Countries From 2002 to 2010: Association With Ease of Communication With Friends of the Opposite Sex. Eur J Public Health (2015) 25:41–5. 10.1093/eurpub/ckv025 PubMed DOI

Schnohr CW, Molcho M, Rasmussen M, Samdal O, de Looze M, Levin K, et al. Trend Analyses in the Health Behaviour in School-Aged Children Study: Methodological Considerations and Recommendations. Eur J Public Health (2015) 25(Suppl. l_2):7–12. 10.1093/eurpub/ckv010 PubMed DOI

Roberts C, Currie C, Samdal O, Currie D, Smith R, Maes L. Measuring the Health and Health Behaviours of Adolescents Through Cross-National Survey Research: Recent Developments in the Health Behaviour in School-Aged Children (HBSC) Study. J Public Health (2007) 15(3):179–86. 10.1007/s10389-007-0100-x DOI

Roberts C, Freeman J, Samdal O, Schnohr CW, de Looze ME, Nic GS, et al. The Health Behaviour in School-Aged Children (HBSC) Study: Methodological Developments and Current Tensions. Int J Public Health (2009) 54(2):140–50. 10.1007/s00038-009-5405-9 PubMed DOI PMC

Schmidt-Catran AW, Fairbrother M. The Random Effects in Multilevel Models: Getting Them Wrong and Getting Them Right. Eur Sociol Rev (2016) 32(1):23–38. 10.1093/esr/jcv090 DOI

Zaborskis A, Kavaliauskiene A, Levi S, Tesler R, Dimitrova E. Adolescent Toothbrushing and Its Association With Sociodemographic Factors-Time Trends From 1994 to 2018 in Twenty Countries. Healthcare (2023) 11(24):3148. 10.3390/healthcare11243148 PubMed DOI PMC

R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; (2024). Available from: https://www.R-project.org/ (Accessed October 19, 2024).

Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw (2015) 67(1):1–48. 10.18637/jss.v067.i01 DOI

Giner G, Smyth GK. Statmod: Probability Calculations for the Inverse Gaussian Distribution. R J (2016) 8(1):339–51. 10.32614/rj-2016-024 DOI

Dunn PK, Smyth GK. Generalized Linear Models With Examples in R. Springer Texts in Statistics. New York, NY: Springer; (2018). Available from: http://link.springer.com/10.1007/978-1-4419-0118-7 (Accessed October 19, 2024). DOI

Lüdecke LD. ggeffects: Tidy Data Frames of Marginal Effects From Regression Models. J Open Source Softw (2018) 3(26):772. 10.21105/joss.00772 DOI

Lüdecke D. sjPlot: Data Visualization for Statistics in Social Science (2024). Available from: https://CRAN.R-project.org/package=sjPlot (Accessed October 19, 2024).

Bryan ML, Jenkins SP. Multilevel Modelling of Country Effects: A Cautionary Tale. Eur Sociol Rev (2016) 32(1):3–22. 10.1093/esr/jcv059 DOI

Stegmueller D. How Many Countries for Multilevel Modeling? A Comparison of Frequentist and Bayesian Approaches. Am J Polit Sci (2013) 57(3):748–61. 10.1111/ajps.12001 DOI

Wood SN. Generalized Additive Models: An Introduction with R. 2nd ed. Boca Raton London New York: Chapman and Hall/CRC; (2017). p. 476.

de Looze ME, Henking C, Torsheim T, Currie DB, Weber MW, Aleman-Diaz AY. The Association Between MPOWER Tobacco Control Policies and Adolescent Smoking Across 36 Countries: An Ecological Study Over Time (2006–2014). Int J Drug Policy (2022) 109:103871. 10.1016/j.drugpo.2022.103871 PubMed DOI

Fairbrother M. Two Multilevel Modeling Techniques for Analyzing Comparative Longitudinal Survey Datasets*. Polit Sci Res Methods (2014) 2(1):119–40. 10.1017/psrm.2013.24 DOI

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