Since the start of COVID-19 there have been widely spread notions that men can contract the disease at a higher rate than women and are more likely to die from it. Furthermore, age has been reported as a risk factor in COVID-19 mortality, and a vast majority of COVID-19 deaths have been among elderly people. Therefore, it is interesting to conduct a statistical analysis on COVID-19 deaths to draw conclusions on whether men are more likely to die of COVID-19 than women, and the influence of age groups on the number of reported deaths of COVID-19 and the interaction between these two factors. This paper uses a two-way analysis of variance (two-way ANOVA) as a statistical tool to analyze COVID-19 deaths in the US. Two-way ANOVA can effectively determine whether the age and gender are significant factors in COVID-19 death cases in the US. The dependent variable in the analysis is the number of COVID deaths in the entire US, and the two independent variables are the age groups and gender (sex). The age groups consist of 11 subgroups or levels ranging from babies to elderly people. The sex is either male or female. Results showed that age group is a significant factor in COVID deaths, while gender was found to be insignificant factor in the mortality of COVID.
Cite this paper
Abdulhafedh, A. (2023). Analyzing the Impact of Age and Gender on COVID-19 Deaths Using Two-Way ANOVA. Open Access Library Journal, 10, e9658. doi: http://dx.doi.org/10.4236/oalib.1109658.
Griffith, D.M., Sharma, G., Holliday, C.S., Enyia, O.K., Valliere, M., Semlow, A.R., et al. (2020) Men and COVID-19: A Biopsychosocial Approach to Understanding Sex Differences in Mortality and Recommendations for Practice and Policy Interventions. Preventing Chronic Disease, 17, Article ID: 200247.
https://doi.org/10.5888/pcd17.200247
Danielsen, A.C., Boulicault, M., Gompers, A., Rushovich, T., Lee, K.M.N. and Richardson, S.S. (2022) How Cumulative Statistics Can Mislead: The Temporal Dynamism of Sex Disparities in COVID-19 Mortality in New York State. International Journal of Environmental Research and Public Health, 19, Article No. 14066.
https://doi.org/10.3390/ijerph192114066
Wenham, C., Smith, J. and Morgan, R. (2020) COVID-19: The Gendered Impacts of the Outbreak. Lancet, 395, 846-848. https://doi.org/10.1016/S0140-6736(20)30526-2
Barber, S.J. and Kim, H. (2020) COVID-19 Worries and Behavior Changes in Older and Younger Men and Women. Innovation in Aging, 4, 939-940.
https://doi.org/10.1093/geroni/igaa057.3441
Sharma, G., Volgman, A.S. and Michos, E.D. (2020) Sex Differences in Mortality from COVID-19 Pandemic: Are Men Vulnerable and Women Protected? JACC: Case Reports, 2, 1407-1410. https://doi.org/10.1016/j.jaccas.2020.04.027
Alkhouli, M., Nanjundappa, A., Annie, F., Bates, M.C. and Bhatt, D.L. (2020) Sex Differences in COVID-19 Case Fatality Rate: Insights from a Multinational Registry. Mayo Clinic Proceedings, 95, 1613-1620.
https://doi.org/10.1016/j.mayocp.2020.05.014
Willard, C.A. (2020) Statistical Methods: An Introduction to Basic Statistical Concepts and Analysis. 2nd Edition, Routledge, New York.
https://doi.org/10.4324/9780429261039
Abdulhafedh, A. (2022) Comparison between Common Statistical Modeling Techniques Used in Research, Including: Discriminant Analysis vs Logistic Regression, Ridge Regression vs LASSO, and Decision Tree vs Random Forest. Open Access Library Journal, 9, e8414. https://doi.org/10.4236/oalib.1108414
Armstrong, R.A., Eperjesi, F. and Gilmartin, B. (2002) The Application of Analysis of Variance (ANOVA) to Different Experimental Designs in Optometry. Ophthalmic and Physiological Optics, 22, 248-256.
https://doi.org/10.1046/j.1475-1313.2002.00020.x
Shin, J.H. (2009) Application of Repeated-Measures Analysis of Variance and Hierarchical Linear Model in Nursing Research. Nursing Research, 58, 211-217.
https://doi.org/10.1097/NNR.0b013e318199b5ae
Mendes, M. and Yigit, S. (2013) Comparison of ANOVA-F and ANOM Tests with Regard to Type I Error Rate and Test Power. Journal of Statistical Computation and Simulation, 83, 2093-2104. https://doi.org/10.1080/00949655.2012.679942
Cohen, J. (1973) Eta-Squared and Partial Eta-Squared in Fixed Factor Anova Designs. Educational and Psychological Measurement, 33, 107-112.
https://doi.org/10.1177/001316447303300111
Olejnik, S. and Algina, J. (2000) Measures of Effect Size for Comparative Studies: Applications, Interpretations, and Limitations. Contemporary Educational Psychology, 25, 241-286. https://doi.org/10.1006/ceps.2000.1040
Okada, K. (2013) Is Omega Squared Less Biased? A Comparison of Three Major Effect Size Indices in One-Way Anova. Behaviormetrika, 40, 129-147.
https://doi.org/10.2333/bhmk.40.129