IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v57y2023i2d10.1007_s11135-022-01421-w.html
   My bibliography  Save this article

Latent Class approach to analyze children’s nutritional trajectory and school dropout. A longitudinal population-based application

Author

Listed:
  • Alejandra Marroig

    (Universidad de la República)

  • Graciela Muniz-Terrera

    (OHIO University
    University of Edinburgh)

Abstract

The study of the nutritional status is relevant during the entire life course, but in children it is relevant as malnutrition may be a marker of underlying functional and mental health deficits. Evidence of the association between malnutrition and school dropout is not conclusive. Our aim was to analyze children’s nutritional trajectory measured using their Body Mass Index (BMI) of a Uruguayan cohort and its association with school dropout. With this purpose, Latent Class and Joint Latent Class Mixed Models were fitted to children’s cohort study (N = 1392 girls and 1492 boys) in sex-stratified analyses adjusting for sociodemographic characteristics. We identified latent classes of boys and girls with similar BMI trajectories during school years and differences in relevant socioeconomic and anthropometric characteristics. Results indicated that boys dropped out at younger ages than girls. No association between age of school dropout and nutritional trajectory classes was found. None of the classes exhibited a deficit or decrease in BMI trajectories during school ages, although the obesity and overweight classes could be of concern. Results suggested no significant association between obesity or overweight and age of school dropout for children up to 14 years old. Future research on other samples may inform about trajectories in higher educational levels.

Suggested Citation

  • Alejandra Marroig & Graciela Muniz-Terrera, 2023. "Latent Class approach to analyze children’s nutritional trajectory and school dropout. A longitudinal population-based application," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1519-1531, April.
  • Handle: RePEc:spr:qualqt:v:57:y:2023:i:2:d:10.1007_s11135-022-01421-w
    DOI: 10.1007/s11135-022-01421-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-022-01421-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11135-022-01421-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Failache, Elisa & Salas, Gonzalo & Vigorito, Andrea, 2018. "Desarrollo en la infancia y trayectorias educativas de los adolescentes. Un estudio con base en datos de panel para Uruguay," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(337), pages .81-113, enero-mar.
    2. Marina Bassi & Matias Busso & Juan Sebastian Muñoz, 2015. "Enrollment, Graduation, and Dropout Rates in Latin America: Is the Glass Half Empty or Half Full?," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Fall 2015), pages 113-156, October.
    3. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    4. Proust-Lima, Cécile & Philipps, Viviane & Liquet, Benoit, 2017. "Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i02).
    5. Proust-Lima, Cécile & Joly, Pierre & Dartigues, Jean-François & Jacqmin-Gadda, Hélène, 2009. "Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1142-1154, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Proust-Lima, Cécile & Philipps, Viviane & Liquet, Benoit, 2017. "Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i02).
    2. Adrian O’Hagan & Arthur White, 2019. "Improved model-based clustering performance using Bayesian initialization averaging," Computational Statistics, Springer, vol. 34(1), pages 201-231, March.
    3. Amanda F. Mejia, 2022. "Discussion on “distributional independent component analysis for diverse neuroimaging modalities” by Ben Wu, Subhadip Pal, Jian Kang, and Ying Guo," Biometrics, The International Biometric Society, vol. 78(3), pages 1109-1112, September.
    4. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    5. Mélissa Lemoine & Gerhard Gmel & Simon Foster & Simon Marmet & Joseph Studer, 2020. "Multiple trajectories of alcohol use and the development of alcohol use disorder: Do Swiss men mature-out of problematic alcohol use during emerging adulthood?," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.
    6. Lebret, Rémi & Iovleff, Serge & Langrognet, Florent & Biernacki, Christophe & Celeux, Gilles & Govaert, Gérard, 2015. "Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i06).
    7. Faicel Chamroukhi, 2016. "Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 374-411, October.
    8. Fabian Dvorak, 2020. "stratEst: Strategy Estimation in R," TWI Research Paper Series 119, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    9. Christelis, Dimitris & Messina, Julián, 2019. "Partial Identification of Population Average and Quantile Treatment Effects in Observational Data under Sample Selection," IDB Publications (Working Papers) 9520, Inter-American Development Bank.
    10. Brisa N. Sánchez & Shan Kang & Bhramar Mukherjee, 2012. "A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures," Biometrics, The International Biometric Society, vol. 68(2), pages 466-476, June.
    11. repec:jss:jstsof:46:i06 is not listed on IDEAS
    12. Berlinski, Samuel & Busso, Matías & Dinkelman, Taryn & Martínez, Claudia, 2021. "Reducing Parent-School Information Gaps and Improving Education Outcomes: Evidence from High-Frequency Text Messages," IDB Publications (Working Papers) 11234, Inter-American Development Bank.
    13. Semhar Michael & Volodymyr Melnykov, 2016. "An effective strategy for initializing the EM algorithm in finite mixture models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 563-583, December.
    14. Hung Tong & Cristina Tortora, 2022. "Model-based clustering and outlier detection with missing data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 5-30, March.
    15. Marisa Bucheli & Andrea Vigorito, 2021. "Short- and Medium-term Effects of Parental Separation on Children’s Well-being. Evidence from Uruguay," Documentos de Trabajo (working papers) 21-09, Instituto de Economía - IECON.
    16. Xia Fan & Xiaowan Yang & Liming Chen, 2015. "Diversified resources and academic influence: patterns of university–industry collaboration in Chinese research-oriented universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(2), pages 489-509, August.
    17. Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini & Simona Minotti, 2015. "Erratum to: The Generalized Linear Mixed Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 327-355, July.
    18. Paolo Guarda & Abdelaziz Rouabah & John Theal, 2011. "An MVAR Framework to Capture Extreme Events in Macroprudential Stress Tests," BCL working papers 63, Central Bank of Luxembourg.
    19. Grün, Bettina & Leisch, Friedrich, 2008. "FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i04).
    20. Paolo Berta & Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini, 2016. "Multilevel cluster-weighted models for the evaluation of hospitals," METRON, Springer;Sapienza Università di Roma, vol. 74(3), pages 275-292, December.
    21. Luca Scrucca & Adrian Raftery, 2015. "Improved initialisation of model-based clustering using Gaussian hierarchical partitions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 447-460, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:57:y:2023:i:2:d:10.1007_s11135-022-01421-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.