A Markov model to quantify the transitions in the psychological health of young adults in India during the COVID-19 pandemic
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Resumo
A longitudinal data set where the system is characterized by its states in place of the values of the underlying random variables taken over time, can be modeled using a Markov model. In case of psychological data, the Markov models are beneficial as problems may progress or regress over time thus exhibiting the shift in the states of the system. These models, when applied to cohort studies may indicate at a general shift in the psychological health of the cohort under study. In a study regarding the psychological health of young adults in higher education in India during COVID-19 pandemic period, three independent surveys were conducted using the Strength and Difficulty Questionnaire (SDQ). 162 respondents were found to have been participated in all three surveys. A Markov chain model was used to study the transition of the respondents’ psychological health over different phases of the pandemic duration in respect of the observed scores of the two components of SDQ viz, the ‘Difficulty’ score and the ‘Impact’ score; and the estimated ‘Impact’ scores obtained from the observed ‘Difficulty’ scores on application of the Quantile Regression and Quantile Regression Neural Network. For all the three data sets, the Markov model indicated at the prominent shift from a ‘Normal’ state to the ‘Borderline’ and the ‘Abnormal’ states of SDQ. Moreover, the stationary distributions showed significantly higher probabilities of being in the ‘Borderline’ and the ‘Abnormal’ states during the pandemic period than what is suggested by the psychological manuals in standard times.
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Referências
1. American College Health Association-National College Health Assessment Spring 2007 Reference Group Data Report (Abridged). Journal of American College Health 56(5), 469–480, 2008. https://doi.org/10.3200/jach.56.5.469-480
2. Cannon, A. J. Quantile regression neural networks: implementation in R and application to precipitation downscaling. Computers & Geosciences, 37, 1277-1284 (2011). https://doi.org/10.1016/j.cageo.2010.07.005
3. Cipresso, P., Borghesi, F., & Chirico, A. Affects affect affects: A Markov Chain. Frontiers in Psychology, 14 (2023). https://doi.org/10.3389/fpsyg.2023.1162655
4. Claudio, D., Moyce, S., Albano, T., Ibe, E., Miller, N., & O’Leary, M. A Markov Chain model for mental health interventions. International Journal of Environmental Research and Public Health, 20(4), 3525 (2023). https://doi.org/10.3390/ijerph20043525
5. Collins, L. M., & Lanza, S. T. Latent class and Latent transition analysis. In Wiley series in probability and statistics. (2009). https://doi.org/10.1002/9780470567333
6. Duffy, S. W., Day, N. E., Tabár, L., Chen, H. H., Smith, T. C. Markov models of breast tumor progression: some age-specific results. J Natl Cancer Inst Monogr. 93 (7) (1997). https://doi.org/10.1093/jncimono/1997.22.93
7. Farooqi, A. A Comparative Study of Kendall-TheilSen, Siegel Vs Quantile Regression with Outliers. Wayne State University Dissertations. 2352. (2019). https://digitalcommons.wayne.edu/oa_dissertations/2352
8. Goel, K., Grover, G., Sharma, A. & Bae, S. Multistate Markov model for predicting the natural disease progression of type 2 diabetes based on hemoglobin A1c. Journal of Nephropharmacology, 8(1), 4 (2018). https://doi.org/10.15171/npj.2019.04
9. Goldberg, D. P., & Williams, P. D A user’s guide to the General Health Questionnaire. In NFER-Nelson eBooks. (1988). https://ci.nii.ac.jp/ncid/BA23507592
10. Goodman, R. The Extended Version of the Strengths and Difficulty Questionnaire as a Guide to Child Psychiatric Caseness and Consequent Burden. The Journal of Child Psychology and Psychiatry and Consequent Burden, 40(5), 791-799 (1999). https://doi.org/10.1111/1469-7610.00494
11. Goyal, B., Sabharwal, A., Chauhan, V, & Joshi, L. M. Psychological health of Indian youth during COVID-19: a study through three chronological surveys. International Journal of Public Health, 12(2), 752-763 (2023). http://doi.org/10.11591/ijphs.v12i2.22420
12. Grover, G., Sabharwal, A., Kumar, S., & Thakur, A. K. A Multi-State Markov model for the progression of chronic kidney disease. Turkiye Klinikleri Journal of Biostatistics, 11(1), 1–14 (2019). https://doi.org/10.5336/biostatic.2018-62156
13. Grover, G., Seth, D., Vajala, R., & Swain, P. K. A multistate Markov model for the progression of liver cirrhosis in the presence of various prognostic factors. Chilean Journal of Statistics, 15–27 (2014). https://www.researchgate.net/publication/272576126
14. Huang Q, Zhang H, Chen J, & He MJJBB. Quantile regression models and their applications: a review. Journal of Biometrics & Biostatistics, 8(3), 1-6 (2017). https://doi.org/10.4172/2155-6180.1000354
15. Jackson, C. Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software, 38(8), 1–28 (2011). https://doi.org/10.18637/jss.v038.i08
16. Kemeny, J. G., & Snell, J. L. Finite Markov chains. (1976). http://ci.nii.ac.jp/ncid/BA03539406
17. Koenker R. Quantile Regression. In: Econometric Society Monographs, 38. Cambridge University Press, Cambridge, New York 368pp. (2005) https://doi.org/10.1017/CBO9780511754098
18. Koenker R. & Machdo JAF. Goodness of fit and related inferences processes for Quantile Regression: Journal of the American Statistical Association. 94 1296-1310 (1999). http://doi.org/10.1080/01621459.1999.10473882
19. Koenker RW & Bassett G. Quantile Regression, Cambridge: Cambridge University Press. (2005). https://assets.cambridge.org/97805218/45731/frontmatter/9780521845731_frontmatter.pdf
20. Kumar, G., Jain, A., & Hegde, S. Prevalence of depression and its associated factors using Beck Depression Inventory among students of a medical college in Karnataka. Indian Journal of Psychiatry, 54(3), 223 (2012). https://doi.org/10.4103/0019-5545.102412
21. Kumar, U. D. Business Analytics, 2ed: The Science of Data-Driven Decision Making. Wiley (2022). https://www.wileyindia.com/business-analytics-2ed-the-science-of-data-driven-decision-making.html
22. Kwok, A. Research on the application of Markov chain. Theoretical and Natural Science, 11(1), 147–150 (2023). https://doi.org/10.54254/2753-8818/11/20230395
23. Lovibond, S. H., & Lovibond, P. F. Manual for the Depression Anxiety Stress Scales (2nd ed.). Sydney: Psychology Foundation of Australia. (1995). https://search.worldcat.org/title/Manual-for-the-depression-anxiety-stress-scales/oclc/222009504
24. McCarthy, K. J., Liu, S. H., Kennedy, J., Chan, H. T., Mayer, V. L., Vieira, L., Glazer, K. B., Van Wye, G., & Janevic, T. Prospective transitions in hemoglobin A1c following gestational diabetes using Multistate Markov Models. American Journal of Epidemiology. (2024). https://doi.org/10.1093/aje/kwae219
25. Medhi, J. Stochastic processes (2nd ed.). New Age International (P) Ltd. (2017). https://books.google.co.in/books/about/Stochastic_Processes.html?id=DnCWgeoalKQC&redir_esc=y
26. Merlo, L., Petrella, L., & Tzavidis, N. Quantile Mixed Hidden Markov Models for Multivariate Longitudinal Data: An Application to Children’s Strengths and Difficulties Questionnaire scores. Journal of the Royal Statistical Society Series C (Applied Statistics), 71(2), 417–448 (2022). https://doi.org/10.1111/rssc.12539
27. Norris, J. Markov Chains. Cambridge University Press, Cambridge. (1998). https://doi.org/10.1017/CBO9780511810633
28. Putter, H., Van Der Hage, J., De Bock, G. H., Elgalta, R., & Van De Velde, C. J. H. Estimation and prediction in a Multi-State model for breast cancer. Biometrical Journal, 48(3), 366–380 (2006). https://doi.org/10.1002/bimj.200510218
29. Rabiner, L. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286 (1989). https://doi.org/10.1109/5.18626
30. Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. Factorizing personalized Markov chains for next-basket recommendation. Proceedings of the 19th International Conference on World Wide Web, 811–820 (2010). https://doi.org/10.1145/1772690.1772773
31. Sabharwal, A., Goyal, B., & Joshi, L. M. Psychological Health of Young Adults as Measured through Internalising and Externalising Scores of Strength and Difficulty Questionnaire. Migration Letters, 21(S4), 1511-1520 (2024a). Retrieved from https://migrationletters.com/index.php/ml/article/view/7565
32. Sabharwal, A., Goyal, B., & Joshi, L. M. A Study to Assess the Psychological Health of Young Adults through Quantile Regression and Quantile Regression Neural Network Models. Afr.J.Bio.Sc. 6(9) (2024b). https://doi.org/10.33472/AFJBS.6.9.2024.4693-4708
33. Sabharwal, A., Goyal, B., Chauhan, V. S., Joshi, L. M., & Goyal, V. Evaluating the effect of COVID-19 pandemic on the psychological health of young adults in India. International Journal of Public Health Science (IJPHS), 12(1), 311 (2023). https://doi.org/10.11591/ijphs.v12i1.21527
34. Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., & Kuntz, K. M. State-Transition Modeling: A report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3. Value in Health, 15(6), 812–820 (2012). https://doi.org/10.1016/j.jval.2012.06.014
35. Sonnenberg, F. A., & Beck, J. R. Markov models in medical decision making. Medical Decision Making, 13(4), 322–338 (1993). https://doi.org/10.1177/0272989x9301300409
36. Srikanth, P. Using Markov Chains to predict the natural progression of diabetic retinopathy. DOAJ (DOAJ: Directory of Open Access Journals), 8(1), 132–137 (2015). https://doi.org/10.3980/j.issn.2222-3959.2015.01.25
37.Taylor, J. W. (2000). A quantile regression neural network approach to estimating the conditional density of multiperiod returns. Journal of Forecasting, 19(4), 299–311 (2000). https://doi.org/10.1002/1099-131x(200007)19:4
38. Zhang, G. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175 (2003). https://doi.org/10.1016/s0925-2312(01)00702-0