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Row and Column Effects Modelling of Elderly Age Groups and Chronic Health Problem on COVID-19

Year 2024, , 175 - 181, 28.03.2024

Gokcen Altun , Serpil Aktaş

https://doi.org/10.17776/csj.1325410

Abstract

Statistical analysis of COVID-19 data from China and NYC, using log-linear models, helps identifying high-risk groups like those aged over 65 and individuals with chronic health issues. According to the results of row effects model applied to the COVID-19 data set of China, we conclude that when the age group increases by one unit, the risk of getting COVID-19 disease is approximately 8 times higher for the patients having Chronic Obstructive Pulmonary Disease (COPD) than patients having hypertension, 9.37 times higher than patients with coronary heart disease, 13.37 times higher than patients having diabetes and cerebrovascular diseases and 10.16 times higher than patients having other diseases. According to the results of column effects model applied to the COVID-19 data set of NYC, we conclude that when the age group increases by one unit, the risk of death from the COVID-19 disease is approximately 2 times higher for the patients having choric health problem than the patients not having a chronic health problem. We believe that the empirical findings of the presented study will guide the policymakers to make provision for these disadvantageous groups for COVID-19 disease

Keywords

Categorical data analysis Row effects model Column effects model COVID-19 data

References

  • [1] Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., ... & Tan, W., Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding, The Lancet, 395(10224) (2020) 565-574.
  • [2] Lauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H. R., ... & Lessler, J., The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application, Annals of Internal Medicine., 172(9) (2020) 577-582.
  • [3] Chen N, Zhou M, Dong X, et al., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study, Lancet, 395 (2020) 507–13.
  • [4] Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., ... & Zhong, N. S., Clinical characteristics of 2019 novel coronavirus infection in China, Med. Rxiv ,(2020).
  • [5] Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., ... & Zhong, N. S., Clinical characteristics of coronavirus disease 2019 in China, New England Journal Of Medicine, 382(18) (2020) 1708-1720.
  • [6] Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., ... & Cao, B, Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China., The Lancet, 395(10223) (2020) 497-506.
  • [7] Gomes, C., Report of the WHO-China joint mission on coronavirus disease 2019 (COVID-19), Brazilian Journal Of Implantology And Health Sciences , 2(3) (2020).
  • [8] Liu, K., Chen, Y., Lin, R., & Han, K., Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients, Journal of Infection, 80(6) (2020) e14-e18.
  • [9] Likassa, H. T., The impacts of covariates on spatial distribution of corona virus 2019 (COVID-19): what do the data show through ANCOVA and MANCOVA, EJMO, 4(2) (2020) 141-148.
  • [10] Centers for Disease Control and Prevention. Coronavirus Disease 2019 (COVID-19) Available at: https://www.cdc.gov/coronavirus/2019- ncov/about/symptoms.htm
  • [11] Sasson, I., Age and COVID-19 mortality, Demographic Research, 44 (2021) 379-396.
  • [12] Ahrenfeldt, L. J., Otavova, M., Christensen, K., & Lindahl-Jacobsen, R. Sex and age differences in COVID-19 mortality in Europe, Wiener klinische Wochenschrift, 133 (2021) 393-398.
  • [13] Agresti A., Analysis of Ordinal Categorical Data, 2nd Edition, Wiley and Sons, New York, (2010).
  • [14] Davis, C., Estimation of row and column scores in the linear-by-linear association model for two-way ordinal contingency tables, In Proceedings of the 13th Annual SAS Users Group International Conference., (1988) 946-951.
  • [15] Goodman, L. A., Simple models for the analysis of association in cross-classifications having ordered categories, Journal of the American Statistical Association, 74(367) (1979) 537-552
  • [16] Goodman, L.A., The analysis of cross-classified data having ordered and/or unordered categories: association models, correlation models, and asymmetry models for contingency tables with or without missing entries, The Annals of Statistics, 13 (1985) 10-69.
  • [17] Niu, S., Tian, S., Lou, J., Kang, X., Zhang, L., Lian, H., & Zhang, J., Clinical characteristics of older patients infected with COVID-19: A descriptive study, Archives of Gerontology and Geriatrics, 89 (2020) 104058.
  • [18] Saraçbaşı T., Aktaş Altunay, S., Kategorik Veri Çözümlemesi, Hacettepe Ünv. Yayınları, (2016)
  • [19] Dowd, J. B., Andriano, L., Brazel, D. M., Rotondi, V., Block, P., Ding, X., ... & Mills, M. C., Demographic science aids in understanding the spread and fatality rates of COVID-19., Proceedings of the National Academy of Sciences, 117(18) (2020) 9696-9698.
  • [20] İlgili, Ö., & Kutsal, Y. G., Impact of COVID-19 among the elderly population, Turkish Journal of Geriatrics, 23(4) (2020) 419-423.

Year 2024, , 175 - 181, 28.03.2024

Gokcen Altun , Serpil Aktaş

https://doi.org/10.17776/csj.1325410

Abstract

References

  • [1] Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., ... & Tan, W., Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding, The Lancet, 395(10224) (2020) 565-574.
  • [2] Lauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H. R., ... & Lessler, J., The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application, Annals of Internal Medicine., 172(9) (2020) 577-582.
  • [3] Chen N, Zhou M, Dong X, et al., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study, Lancet, 395 (2020) 507–13.
  • [4] Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., ... & Zhong, N. S., Clinical characteristics of 2019 novel coronavirus infection in China, Med. Rxiv ,(2020).
  • [5] Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., ... & Zhong, N. S., Clinical characteristics of coronavirus disease 2019 in China, New England Journal Of Medicine, 382(18) (2020) 1708-1720.
  • [6] Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., ... & Cao, B, Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China., The Lancet, 395(10223) (2020) 497-506.
  • [7] Gomes, C., Report of the WHO-China joint mission on coronavirus disease 2019 (COVID-19), Brazilian Journal Of Implantology And Health Sciences , 2(3) (2020).
  • [8] Liu, K., Chen, Y., Lin, R., & Han, K., Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients, Journal of Infection, 80(6) (2020) e14-e18.
  • [9] Likassa, H. T., The impacts of covariates on spatial distribution of corona virus 2019 (COVID-19): what do the data show through ANCOVA and MANCOVA, EJMO, 4(2) (2020) 141-148.
  • [10] Centers for Disease Control and Prevention. Coronavirus Disease 2019 (COVID-19) Available at: https://www.cdc.gov/coronavirus/2019- ncov/about/symptoms.htm
  • [11] Sasson, I., Age and COVID-19 mortality, Demographic Research, 44 (2021) 379-396.
  • [12] Ahrenfeldt, L. J., Otavova, M., Christensen, K., & Lindahl-Jacobsen, R. Sex and age differences in COVID-19 mortality in Europe, Wiener klinische Wochenschrift, 133 (2021) 393-398.
  • [13] Agresti A., Analysis of Ordinal Categorical Data, 2nd Edition, Wiley and Sons, New York, (2010).
  • [14] Davis, C., Estimation of row and column scores in the linear-by-linear association model for two-way ordinal contingency tables, In Proceedings of the 13th Annual SAS Users Group International Conference., (1988) 946-951.
  • [15] Goodman, L. A., Simple models for the analysis of association in cross-classifications having ordered categories, Journal of the American Statistical Association, 74(367) (1979) 537-552
  • [16] Goodman, L.A., The analysis of cross-classified data having ordered and/or unordered categories: association models, correlation models, and asymmetry models for contingency tables with or without missing entries, The Annals of Statistics, 13 (1985) 10-69.
  • [17] Niu, S., Tian, S., Lou, J., Kang, X., Zhang, L., Lian, H., & Zhang, J., Clinical characteristics of older patients infected with COVID-19: A descriptive study, Archives of Gerontology and Geriatrics, 89 (2020) 104058.
  • [18] Saraçbaşı T., Aktaş Altunay, S., Kategorik Veri Çözümlemesi, Hacettepe Ünv. Yayınları, (2016)
  • [19] Dowd, J. B., Andriano, L., Brazel, D. M., Rotondi, V., Block, P., Ding, X., ... & Mills, M. C., Demographic science aids in understanding the spread and fatality rates of COVID-19., Proceedings of the National Academy of Sciences, 117(18) (2020) 9696-9698.
  • [20] İlgili, Ö., & Kutsal, Y. G., Impact of COVID-19 among the elderly population, Turkish Journal of Geriatrics, 23(4) (2020) 419-423.
There are 20 citations in total.

Details

Primary Language English
Subjects Applied Statistics
Journal Section Natural Sciences
Authors

Gokcen Altun 0000-0003-4311-6508

Serpil Aktaş 0000-0003-3364-6388

Publication Date March 28, 2024
Submission Date July 10, 2023
Acceptance Date February 27, 2024
Published in Issue Year 2024

Cite

APA Altun, G., & Aktaş, S. (2024). Row and Column Effects Modelling of Elderly Age Groups and Chronic Health Problem on COVID-19. Cumhuriyet Science Journal, 45(1), 175-181. https://doi.org/10.17776/csj.1325410