IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i13p8168-d855055.html
   My bibliography  Save this article

A Longitudinal Study on Trajectories of Night Work and Sickness Absence among Hospital Employees

Author

Listed:
  • Oxana Krutova

    (Finnish Institute of Occupational Health, 00032 Helsinki, Finland)

  • Aki Koskinen

    (Finnish Institute of Occupational Health, 00032 Helsinki, Finland)

  • Laura Peutere

    (Finnish Institute of Occupational Health, 00032 Helsinki, Finland
    School of Educational Sciences and Psychology, University of Eastern Finland, 80101 Joensuu, Finland)

  • Jenni Ervasti

    (Finnish Institute of Occupational Health, 00032 Helsinki, Finland)

  • Marianna Virtanen

    (School of Educational Sciences and Psychology, University of Eastern Finland, 80101 Joensuu, Finland
    Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden)

  • Mikko Härmä

    (Finnish Institute of Occupational Health, 00032 Helsinki, Finland)

  • Annina Ropponen

    (Finnish Institute of Occupational Health, 00032 Helsinki, Finland
    Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden)

Abstract

This study aimed to investigate trajectories of night shift work in irregular shift work across a 12-year follow-up among hospital employees with and without sickness absence (SA). The payroll-based register data of one hospital district in Finland included objective working hours and SA from 2008 to 2019. The number of night shifts per year was used in group-based trajectory modeling (GBTM). The results indicate that, among those who had any sickness absence episodes, the amount of night work decreased prior to the first SA. In general, trajectories of night shift work varied from stably high to low-but-increasing trajectories in terms of the number of shifts. However, a group with decreasing pattern of night work was identified only among those with sickness absence episodes but not among those without such episodes. To conclude, the identified trajectories of night work with or without sickness absences may indicate that, among those with sickness absence episodes, night work was reduced due to increasing health problems. Hence, the hospital employees working night shifts are likely a selected population because the employees who work at night are supposed to be healthier than those not opting for night work.

Suggested Citation

  • Oxana Krutova & Aki Koskinen & Laura Peutere & Jenni Ervasti & Marianna Virtanen & Mikko Härmä & Annina Ropponen, 2022. "A Longitudinal Study on Trajectories of Night Work and Sickness Absence among Hospital Employees," IJERPH, MDPI, vol. 19(13), pages 1-9, July.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:8168-:d:855055
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/13/8168/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/13/8168/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    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. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    2. 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).
    3. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    4. Peter Willemé, 2017. "Working Paper 14-17 - Modelling unobserved heterogeneity in distribution - Finite mixtures of the Johnson family of distributions," Working Papers 1714, Federal Planning Bureau, Belgium.
    5. Marc A. Scott & Kaushik Mohan & Jacques‐Antoine Gauthier, 2020. "Model‐based clustering and analysis of life history data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1231-1251, June.
    6. Fabian Dvorak, 2020. "stratEst: Strategy Estimation in R," TWI Research Paper Series 119, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    7. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
    8. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    9. Frick, Hannah & Strobl, Carolin & Leisch, Friedrich & Zeileis, Achim, 2012. "Flexible Rasch Mixture Models with Package psychomix," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i07).
    10. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    11. Salvatore Ingrassia & Simona Minotti & Giorgio Vittadini, 2012. "Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions," Journal of Classification, Springer;The Classification Society, vol. 29(3), pages 363-401, October.
    12. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2023. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3313-3335, June.
    13. Ahonen, Ilmari & Nevalainen, Jaakko & Larocque, Denis, 2019. "Prediction with a flexible finite mixture-of-regressions," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 212-224.
    14. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    15. Ciarleglio, Adam & Todd Ogden, R., 2016. "Wavelet-based scalar-on-function finite mixture regression models," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 86-96.
    16. Maik Dehnert & Josephine Schumann, 2022. "Uncovering the digitalization impact on consumer decision-making for checking accounts in banking," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1503-1528, September.
    17. Chun-Yu Chang & Yueh-Tseng Hou & Yung-Jiun Chien & Yu-Long Chen & Po-Chen Lin & Chien-Sheng Chen & Meng-Yu Wu, 2020. "Two-Thumb or Two-Finger Technique in Infant Cardiopulmonary Resuscitation by a Single Rescuer? A Meta-Analysis with GOSH Analysis," IJERPH, MDPI, vol. 17(14), pages 1-19, July.
    18. Prates, Marcos Oliveira & Lachos, Victor Hugo & Barbosa Cabral, Celso Rômulo, 2013. "mixsmsn: Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i12).
    19. Papastamoulis, Panagiotis & Martin-Magniette, Marie-Laure & Maugis-Rabusseau, Cathy, 2016. "On the estimation of mixtures of Poisson regression models with large number of components," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 97-106.
    20. Salvatore Ingrassia & Antonio Punzo, 2020. "Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 526-547, July.

    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:gam:jijerp:v:19:y:2022:i:13:p:8168-:d:855055. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.