Investigating the Male and Older People Susceptibility to Death from (COVID-19) Using Statistical Models

Rabia Almamlook^*
*Correspondence: Rabia Almamlook, Department of Industrial and Entrepreneurial Engineering & Engineering Management, Western Michigan University, 1903 W. Michigan Ave., Kalamazoo, MI 49008-5336, United States, Tel: +1 2698304004, Email:

Keywords

COVID-19 • Death • Age • Odds Rate • logistic Regression • Older People • Male

Introduction

COVID-19 is an infectious disease caused by a specific virus called SARS3 CoV-2. The COVID-19 outbreak was first reported in Wuhan, China in late December 2019 [1-3] and it has spread quickly in many other countries, including Europe and the United States causing it to become a global health emergency [3]. As of March 24, 2020, according to the World Health Organization (WHO) and figures from state government leaders and health officials, over 523,163 cases 8 have been confirmed and more than 23,639 have died of the virus since the start of the spread [3]. Figure 1 illustrates how the number of deaths is increased weekly for all countries. A study based on 1099 Covid-19 patients from 552 hospitals in 30 provinces in mainland China through January 29, 2020, showed patients often presented without fever, and many did not have abnormal radiologic findings [4].

This has been a huge challenge to distinguish Covid-19 patients from healthy ones. Many cases of COVID-19 are mild and can recover quickly, but some cases can be severe and deadly, with the highest mortality rate of around 3.4% [3,5]. Scientists work around the clock to test medications for curing patients of this disease. Unfortunately, there is no evidence to support a drug that 18 is guaranteed to be effective. A recent study shows no benefit was observed with 19 lopinavir-ritonavir treatment beyond standard care [6]. Another study found that age was mortality risk factors. This study aimed to identify risk factors for mortality in elderly patients with COVID-19 [7]. This study used age as risk factor for predicting mortality in elderly patients with COVID-19.Moreover, elderly people has been one of the largest problems in several countries that there are many old people who will face the risk of infected with COVID-19, which impressive a heavy problem on the health care systems in the world [8,9].

This opportunistic virus can affect all people of any age or gender. Early reports of the outbreak in China suggested that males were especially at risk. Therefore, a study of 99 patients at a hospital in Wuhan, where the virus originated, found that males made up two-thirds of patients. It showed a strong gender breakdown of deaths, which were 64% male [10]. In a recent study published in the Lancet, found that 80% of the deaths were in males and just 20% were in female [11]. Previous studies of COVID-19 were based on information from the general population, limited data are available for patients with COVID-19. Another study found that age was mortality risk factors. This study aimed to identify risk factors for mortality in elderly patients with COVID-19. There is limited research on COVID-19 by age and gender. The primary purpose of the study is to examine death rates by gender, age, and both age and gender using various statistical analyses (Figure 1).

Therefore, the main purpose of this study is to conduct a statistical analysis on factors such as age group and gender that may result in death from COVID-19 and associate those among them that have a more pronounced impact on comprehensive analysis. In this study, we analyzed using binary logistic regressions by assuming the death is independent among group age and gender. The remainder of the paper is organized as follows. Section 2 discusses the data and Methodology. Section 3 describes the results and discussion. Finally, Section 4 concludes the paper and presents directions for 48 future researches.

Data and Methodology

Description of the Date

The analyses in this study are based on the COVID-19 dataset. The data was obtained from the Kaggle [12] that allows data analysts to compete with each other’s to solve real and complex data knowledge problems. All patients admitted to the hospital and diagnosed with COVID-19 from January 2020 to March 2020 were included in this study. The data contains 1085 record. Variables that were considered in the models included: gender, age, and death. Additionally, patients 57 were classified into 9 groups according to age including 0-9; 10-19; 20-29; 30- 58 39; 40-49; 50-59; 60-69; 70-79 and 80+ years old. Age groups less than 39 are 59 excluded from this study due to no death cases.

Statistical methods

Descriptive statistics were used to identify the potential factors that had a statistically significant influence on the likelihood of death. Chi-square test (x²) of homogeneity was used to test the relationship between the potential predictors (age and gender) on the outcome (death). his test is a non-parametric test with no assumed distribution. It has been used broadly as it does not execute conditions in the data, such as equality of variance or residual homoscedasticity. Therefore, to further understand this relationship, the logistic regression model was developed to identify the degree of significance of each independent predictor. The null hypothesis the authors wish to reject in this test is that there is no significant relationship between two variables. For this study, we rejected the null hypothesis if the p-value was less than 0.05. Chi-square statistics can be computed as shown in the equation below:

(1)

where by O_ij is the observed frequency and E_ij is the expected frequency across row i and column j of the contingency. The calculated is x² associated with the critical value found from Chi-square distribution. The definite degrees of freedom (df) for the critical value can be subtracted as (c-1) *(r-1) where c signifies the number of columns and r represents the number of rows.

Logistic regression Model

Binary logistic regression was used because it is a preferred method when the response variable is dummy variable [13]. The logistic regression estimated the probability of a patient to choose either death or recover to, given a set of explanatory or predictor variables. Mathematical formulation of logistic regression can be presented as shown in the equation below:

(2)

whereby, p is the probability of a person death or recover and x_i is the explanatory variable of interest with its corresponding coefficient β_i

Here, β is the coefficient of the predictor or input variable used in a regression equation [14]. The logit is the natural logarithm of the odds that the dependent variable is 1 (death) as opposed to 0 (recovery). When an independent variable xi increases by one unit, with all other factors remaining constant, the odds increase by a factor exp which is called the odds ratio (OR), ranging from 0 to positive infinity. It indicates the relative amount by which the odds of the outcome (death) increase (OR>1) or decrease (0<OR<1) when the value of the corresponding independent variables increases by one unit.

Odds ratio

Odds ratio (OR) have become commonly used in medical studied [15]. OR is used to compare the relative odds of the occurrence of the outcome of interest(death) given exposure to the variable of interest (age and gender). 95% confidence intervals (CIs) are calculated to estimate the precision of the odds ratio. Mathematical formulation of odds ratio can be presented as shown in the equation below:

(3)

Whereby, p is the probability of a person death or recover and x_i is the explanatory variable of interest with its corresponding coefficient β_i. The term called odds ratio of event.

• OR = 1 Indicate does not affect odds of outcome

• OR > 1 Indicate a positive relationship with higher odds of outcome (event likely to occur)

• OR < 1 Indicate a negative relationship with less odds of outcome (event less likely to occur).

Conclusion

This study investigates if older people and males are more at susceptible to the novel Coronavirus. In this regard, the results revealed that death probability was significantly related to age and gender. This indicated that males are more likely to die of COVID-19 than females by 2.5 times. Our findings also indicate an increase in the probability of death in patients aged 60 and older. The COVID-19 infection is generally susceptible with death in older people and male, we should pay more attention to the elderly people and male with COVID-19 infection. Further studies are needed to understand why mortality is more strongly associated with males than in females. This implies that further studies need to analyze the causes of having higher death risk in males to protect this population and reduce the overall risk factor. Further studies are needed to understand why mortality is more strongly associated with men than in women according to some factors such as smoking and complicating conditions.

References

1. Chih-Cheng Lai, Tzu-Ping Shih, Wen-Chien Ko, and Hung-Jen Tang, et al. “Severe acute repiratory syndrome coronavirus 2 (sars-cov-2) and corona virus disease-2019 (covid-19): the epidemic and the challenges” International journal of antimi crobial agents (2020) 105924.
2. Chih-Cheng Lai, Yen Hung Liu, Cheng-Yi Wang and Yih-Sharng Chen, et al. "Asymptomatic carrier state, acute respiratory disease, and pneumonia due to severe acute respiratory syndrome coronavirus 2 (sarscov-2): Facts and myths "Journal of Microbiology, Immunology and Infection (2020).
3. Shajeea Arshad Ali, Mariam Baloch, N Naseem Ahmed and Asadullah Arshad Ali. "The outbreak of coronavirus disease 2019 (covid-19)-an emerging global health threat" Journal of Infection and Public Health (2020).
4. Wei-jie Guan, Zheng-yi Ni, Yu Hu and Wen-hua Liang, et al. "Clinical characteristics of coronavirus disease 2019 in china" New England Journal of Medicine (2020).
5. W. H. Organization. Coronavirus disease 2019 (covid-19): situationreport, 59 (2020).
6. Bin Cao, Yeming Wang, Danning Wen, and Wen Liu, et al. "A trial of lopinavir–ritonavir in adults hospitalized with severe covid-19" New England Journal of Medicine (2020).
7. Char Leung. "Risk factors for predicting mortality in elderly patients with covid19: a review of clinical data in china" Mechanisms of Ageing and Development (2020) 111255.
8. Lian Leng Low, Yu Heng Kwan, Michelle Shi Min Ko and Cheng Teng Yeam V. et al. "Epidemiologic characteristics of multimorbidity and sociode mographic factors associated with multimorbidity in a rapidly aging asian country" JAMA network open 2 (2019) e1915245–e1915245.
9. Huijun Chen, Juanjuan Guo, Chen Wang and Fan Luo, et al. "Clinical characteristics and intrauterine vertical transmis sion potential of covid-19 infection in nine pregnant women: a retrospective review of medical records" The Lancet 395 (2020) 809-815.
10. Nanshan Chen, Min Zhou, Xuan Dong. "Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in wuhan, china: a descriptive study" The Lancet 395 (2020) 507–513.
11. Remuzzi, G. Remuzzi. "Covid-19 and italy: what next?" The Lancet 220 (2020).
12. J. H. University, Novel corona virus 2019 dataset - day level information on covid-19 affected cases, 2020. Data retrieved from Kaggle covid-19
13. Hailpern SM, Visintainer PF. "Odds ratios and logistic regression: further examples of their use and interpretation" The Stata Journal 3 (2003) 213- 228.
14. Xing Liu. "Applied ordinal logistic regression using Stata: From single-level to multilevel modeling" Sage Publications 2015. 231
15. J Martin Bland, Douglas G Altman, The odds ratio, Bmj 320 (2000) 1468.
16. Ahmed Mandil, Abdulaziz BinSaeed, Shaffi Ahmad and Rufaidah Al-Dabbagh, et al. "Smoking among university students: a gender analysis" Journal of infection and public health 3 (2010) 179-187.
17. Efraim Jaul, Jeremy Barron "Age-related diseases and clinical and public health impli cations for the 85 years old and over population" Frontiers in public health 5 237 (2017) 335.
18. Fiona Macrae. "Man flu is not a myth: Female hormones give women strongerimmune systems" Daily Mail (Published 13 May 2009) (2009).