Abstract
Covid-19 is an emergent disease transmitted to humans from animals. Since its influx, several studies have been conducted on zoonotic spread and social-economic impact of the disease. However, only a few studies have been conducted to assess risk factors that positively correlate with COVID-19 death rates. The current study will investigate the correlation between social-economic variables, age, means of commuting, and underlying clinical conditions with COVID-19 fatality rates in nursing homes in the county. This paper highlights the background of the problem under study, the research problem and the research questions. Also, the paper points out some of the literature reviewed in relation to the study and the gap to be filled by the current study.
Background
Emergent diseases that transmit from animals to human beings have proven to be among the deadliest viruses known. Corona Virus Disease of 2019 (COVID -19) is believed to be spread from animal, although it has not been ascertained precisely from which animal (Law, 2020). However, recent studies have established that SARS-CoV-2, the agent that cause COVID-19 is significantly similar (96%) to corona virus in bats (Karmakar et al., 2021; Shereen et al., 2020 & Law, 2020). Estimates indicate that the global death rates as a result of COVID-19 is 0.66 percent with a sharp increase to 7.8 percent among elderly people in the age category above eighty years. The average death rate from the virus is considerably low (0.002 percent) among children below nine years (Wolff et al., 2020). Other than age, variables like initial medical condition and use of public means of transport have been associate with increasing risked of contracting the virus, which in some instances result in deaths (Li et al., 2020). Based on this background, I will conduct a research to ascertain the factors that positively correlate with death rates from COVID-19 and give recommendation based on the findings. The study will be conducted using a sample size representative of nursing home in the county.
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Problem Statement and Research Questions
Since the emergent of the virus, several studies have been conducted focusing on zoonotic spread of the virus to human beings and the effects of other underlying medical conditions on death rate. While the majority of researches on COVID-19 have focus on influence of age and underlying medical condition on death rates, this study will investigate the correlation of four variables with rates of COVID-19 deaths. The variables will include social-economic variables, mode of commuting (private or public means), age and existence of previous medical conditions which will be defined in the methodology. The research questions will be informed by the variables under investigation as follows:
What specific social-economic variables correlate with death rates occasioned by COVID-19?
How do age and means of commuting influence COVID-19 death rates?
What is the effect of underlying medical conditions in COVID-19 patients on death rates?
Review of the literature
Knittel and Ozaltun (20202) conducted a study to ascertain what factors do and do not have a relation with death rates due to COVID-19. The research established that both across and within states, higher proportion of black Americans, higher share of elderly people, commuting by public means had a significant and positive correlations with death rates occasioned by COVID-19. Climate was also found to have a correlation with COVID-19 as regions with high summer and low winter temperatures had comparably higher fatality rates.
Guo et al., (2020) performed a research during the advent of COVID-19 in 2020 to account the implications of underlying conditions on outcomes of patients. 27.8 percent of the 187 COVID-19 patients who participated in the study had myocardial trauma resulting in cardiac dysfunction (Tehrani et al., 2021) . Patients who exhibited myocadiac trauma suffered fatal results of COVID -19 while the outcome was relatively better for patients with cardiovascular disease devoid of myocadiac injury.
References
Guo, T., Fan, Y., Chen, M., Wu, X., Zhang, L., He, T., ... & Lu, Z. (2020). Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID-19). JAMA cardiology , 5 (7), 811-818.
Karmakar, M., Lantz, P. M., & Tipirneni, R. (2021). Association of social and demographic factors with COVID-19 incidence and death rates in the US. JAMA network open , 4 (1), e2036462-e2036462.
Knittel, C. R., & Ozaltun, B. (2020). What does and does not correlate with COVID-19 death rates (No. w27391). National Bureau of Economic Research.
Law, P. K. (2020). COVID-19 Pandemic: Its Origin, Implications and Treatments. Open Journal of Regenerative Medicine , 9 (02), 43.
Li, Y., Horowitz, M. A., Liu, J., Chew, A., Lan, H., Liu, Q., ... & Yang, C. (2020). Individual-level fatality prediction of COVID-19 patients using AI methods. Frontiers in Public Health , 8 , 566.
Shereen, M. A., Khan, S., Kazmi, A., Bashir, N., & Siddique, R. (2020). COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. Journal of advanced research , 24 , 91-98.
Tehrani, S., Killander, A., Åstrand, P., Jakobsson, J., & Gille-Johnson, P. (2021). Risk factors for death in adult COVID-19 patients: Frailty predicts fatal outcome in older patients. International Journal of Infectious Diseases , 102 , 415-421.
Wolff, D., Nee, S., Hickey, N. S., & Marschollek, M. (2020). Risk factors for Covid-19 severity and fatality: a structured literature review. Infection , 1-14.
