Modeling COVID-19 Binary Data in the Aspect of Neoplasms as a Potential Indicator of Cancer by Logit and Probit Regression Models
Abstract views: 240
/ 
PDF downloads: 119
DOI:
https://doi.org/10.59287/ijanser.754Keywords:
COVID-19 Pandemic, Non-Communicable Diseases, Generalized Linear Model, Probit Model, Logit ModelAbstract
In this study, the effects of disability-adjusted life years (DALYs) from neoplasms and concomitant non-communicable diseases (NCDs) on total deaths from the COVID-19 pandemic until 21 July 2021 are examined globally for 179 countries. For this purpose, the explanatory variables are taken as DALYs as a measure of total burden of diseases in life lost years and lived with a disability years from neoplasm and NCDs. In this study, the total number of deaths caused by the COVID-19 pandemic has been made categorical with the help of the indicator variable and then taken as the response variable. Thus, in this study, the effects of neoplasms and concomitant NCDs on the COVID-19 pandemic are investigated by using binary logit and binary probit regression models in the family of generalized linear models (GLMs) as statistical methods. Specific to this study, the superiority of the probit model which is based on the assumption that the errors have a normal distribution in the statistical sense over the logit model which is based on the assumption that the errors have a logistic distribution is emphasized. As principle results and major conclusion from this study, neoplasms, cirrhosis and other chronic liver diseases, cardiovascular diseases, skin and subcutaneous diseases and other non-communicable diseases have been found to have statistically significant effects on deaths due to the COVID-19 pandemic.
Downloads
References
Ciotti, M., Ciccozzi, M., Terrinoni, A., Jiang, W. C., Wang, C. B., & Bernardini, S. (2020). The COVID-19 pandemic. Critical reviews in clinical laboratory sciences, 57(6), 365-388.
Lone, S. A., & Ahmad, A. (2020). COVID-19 pandemic–an African perspective. Emerging microbes & infections, 9(1), 1300-1308.
Tuttle, K. R. (2020). Impact of the COVID-19 pandemic on clinical research. Nature Reviews Nephrology, 16(10), 562-564.
Suryasa, I. W., Rodríguez-Gámez, M., & Koldoris, T. (2021). The COVID-19 pandemic. International Journal of Health Sciences, 5(2).
Maeda, J. M., & Nkengasong, J. N. (2021). The puzzle of the COVID-19 pandemic in Africa. Science, 371(6524), 27-28.
Anand, S., & Hanson, K. (1997). Disability-adjusted life years: a critical review. Journal of health economics, 16(6), 685-702.
(2023) World Health Organization (WHO) website. [Online]. Available: https://www.who.int/data/gho/indicator-metadata-registry/imr-details/158
Akman, M., & Civek, S. (2022). Dünyada ve Türkiye’de kardiyovasküler hastalıkların sıklığı ve riskin değerlendirilmesi. The Journal of Turkish Family Physician, 13(1), 21-28.
Horton, R. (2013). Non-communicable diseases: 2015 to 2025. The Lancet, 381(9866), 509-510.
Wagner, K. H., & Brath, H. (2012). A global view on the development of non communicable diseases. Preventive medicine, 54, S38-S41.
Islam, S. M. S., Purnat, T. D., Phuong, N. T. A., Mwingira, U., Schacht, K., & Fröschl, G. (2014). Non‐Communicable Diseases (NCDs) in developing countries: a symposium report. Globalization and health, 10(1), 1-8.
Arsang-Jang, S., Belasi, M. T., Najafi, F., Darbandi, M., Raza, M. Z., Akhuanzada, H., ... & Azarpazhooh, M. R. (2021). The Association Between Healthcare Resources, Non-communicable Diseases, and Covid-19 Mortality: An Epidemiological Study of 139 Countries.
Azarpazhooh, M. R., Morovatdar, N., Avan, A., Phan, T. G., Divani, A. A., Yassi, N., ... & Di Napoli, M. (2020). COVID-19 pandemic and burden of non-communicable diseases: an ecological study on data of 185 countries. Journal of Stroke and Cerebrovascular Diseases, 29(9), 105089.
Azadnajafabad, S., Ghasemi, E., Moghaddam, S. S., Rezaei, N., & Farzadfar, F. (2021). Non-communicable diseases’ contribution to the COVID-19 mortality: a global warning on the emerging syndemics. Archives of Iranian Medicine, 24(5), 445.
Sousa, B. L. A., Brentani, A., Ribeiro, C. C. C., Dolhnikoff, M., Grisi, S. J. F. E., Ferrer, A. P. S., & Ferraro, A. A. (2021). Non-communicable diseases, sociodemographic vulnerability and the risk of mortality in hospitalised children and adolescents with COVID-19 in Brazil: a cross-sectional observational study. BMJ open, 11(9), e050724.
Youn, H. M., Quan, J., Mak, I. L., Yu, E. Y. T., Lau, C. S., Ip, M. S. M., ... & Wan, E. Y. F. (2022). Long-term spill-over impact of COVID-19 on health and healthcare of people with non-communicable diseases: a study protocol for a population-based cohort and health economic study. BMJ open, 12(8), e063150.
Nicoletti-Rojas, D., Retamal, R., Cerda-Rioseco, R., Rodríguez-Osiac, L., Fuentes-Alburquenque, M., & Araya-Bannout, M. (2022). Effects of sociodemographic and health factors on the self-management of non-communicable diseases among Chilean adults during the Covid-19 pandemic. PLOS Global Public Health, 2(7), e0000763.
Gaur, K., Khedar, R. S., Mangal, K., Sharma, A. K., Dhamija, R. K., & Gupta, R. (2021). Macrolevel association of COVID-19 with non-communicable disease risk factors in India. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 15(1), 343-350.
Pécout, C., Pain, E., Chekroun, M., Champeix, C., Kulak, C., Prieto, R., et al. (2021). Impact of the COVID-19 Pandemic on Patients Affected by Non-Communicable Diseases in Europe and in the USA. International Journal of Environmental Research and Public Health, 18(13), 6697.
Mistry, S. K., Ali, A. M., Yadav, U. N., Ghimire, S., Hossain, M. B., Das Shuvo, S., et al. (2021). Older adults with non-communicable chronic conditions and their health care access amid COVID-19 pandemic in Bangladesh: Findings from a cross-sectional study. PLoS One, 16(7), e0255534.
Gutierrez, J. P., & Bertozzi, S. M. (2020). Non-communicable diseases and inequalities increase risk of death among COVID-19 patients in Mexico. PLoS One, 15(10), e0240394.
Cobre, A. F., Surek, M., Vilhena, R. O., Böger, B., Fachi, M. M., Momade, D. R., et al. (2022). Influence of foods and nutrients on COVID-19 recovery: A multivariate analysis of data from 170 countries using a generalized linear model. Clinical Nutrition, 41(12), 3077-3084.
Formenti, B., Gregori, N., Crosato, V., Marchese, V., Tomasoni, L. R., & Castelli, F. (2022). The impact of COVID-19 on communicable and non-communicable diseases in Africa: a narrative review. Le Infezioni in Medicina, 30(1), 30.
Azzouzi, S., Stratton, C., Muñoz-Velasco, L. P., Wang, K., Fourtassi, M., Hong, B. Y., et al. (2022). The impact of the COVID-19 pandemic on healthy lifestyle behaviors and perceived mental and physical health of people living with non-communicable diseases: An international cross-sectional survey. International Journal of Environmental Research and Public Health, 19(13), 8023.
Yadav, U. N., Rayamajhee, B., Mistry, S. K., Parsekar, S. S., & Mishra, S. K. (2020). A syndemic perspective on the management of non-communicable diseases amid the COVID-19 pandemic in low-and middle-income countries. Frontiers in public health, 8, 508.
(2023) Our World in Data website. [Online]. Available: https://ourworldindata.org/coronavirus.
(2023) Our World in Data website. [Online]. Available: https://ourworldindata.org/grapher/disease-burden-from-ncds.
Özkaya, U., & Öztürk, Ş. (2022). Gaussian Regression Models for Day-Level Forecasting of COVID-19 in European Countries. Understanding COVID-19: The Role of Computational Intelligence, 339-356.
İyit, N., Sevim, F., & Kahraman, Ü. M. (2023). Investigating the impact of CO2 emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions. Open Chemistry, 21(1), 20220301.
Yonar, H., & Neslihan, İ. Y. İ. T. (2018). Modeling the causality relationships between Gdp/Gni and electricity consumption according to income levels of countries by Generalized Estimating Equations. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (39), 191-200.
Yonar, H., & İyit, N. (2021). Some Generalized Estimating Equations Models Based on Causality Tests for Investigation of The Economic Growth of The Country Groups. Foundations of Computing and Decision Sciences, 46(3), 297-315.
Iyit, N., & Genc, A. (2011). Constitution of random intercept and slope model (RISM) as a special case of linear mixed models (LMMs) for repeated measurements data. Applied Mathematics and Computation, 218(3), 827-831.
Tekin, K. U., Mestav, B., & Neslihan, İ. Y. İ. T. (2021). Robust Logistic Modelling for Datasets with Unusual Points. Journal of New Theory, (36), 49-63.
Hilbe, J. M. (2011). Negative binomial regression. Cambridge University Press.
İyıt, N., Yonar, H., & Genç, A. (2016). Generalized linear models for European Union countries energy data. Acta Physica Polonica A, 130(1), 397-400.
İyit, N. (2021). An applicatıon of generalized lınear model approach on econometric studies. Research & Reviews in Science and Mathematics-II.
Iyit N, Al Mashhadani AA. An application of generalized linear model (GLM) to child mortality data in Iraq based on socio-economic indicators. In: Ugur A, Tozak K, Yatbaz A, editors. Turkish World Socio Economic Strategies. Beau Bassin: LAP Lambert Academic Publishing; 2017. p. 195-203.
Iyit, N. (2018). Modelling world energy security data from multinomial distribution by generalized linear model under different cumulative link functions. Open Chemistry, 16(1), 377-385.
Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications.
Oznur, O., & Iyit, N. (2018). Modelling the US diabetes mortality rates via generalized linear model with the Tweedie distribution. Int. J. Sci. Res, 7(2), 1326-1334.
Hardin, J. W., Hardin, J. W., Hilbe, J. M., & Hilbe, J. (2007). Generalized linear models and extensions. Stata press.
Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723.
Cavanaugh, J. E. (1997). Unifying the derivations for the Akaike and corrected Akaike information criteria. Statistics & Probability Letters, 33(2), 201-208.
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461-464.
Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345-370.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2021. Available from: https://www.R-project.org/.
