Estimation of the case fatality rate of COVID-19 epidemiological data in Nigeria using statistical regression analysis

Suleiman, Ahmad Abubakar; Suleiman, Aminu; Abdullahi, Usman Aliyu; Suleiman, Suleiman Abubakar

doi:10.1016/j.bsheal.2020.09.003

Cited by 21 publications

(19 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This estimate would have been lower if there was widespread testing. Despite speculations that Africa would be the worst hit by the pandemic due to its weak healthcare systems and relatively higher population density, the predicted catastrophe is yet to happen [ 4 ]. As of September 24th, 2021, about 3 million COVID-19 tests (1.5% of the population) have been conducted with about 6% confirmed to be positive.…”

Section: Introductionmentioning

confidence: 99%

Determinants for Acceptance of COVID-19 Vaccine in Nigeria

Eze¹,

Ndoh²,

Ibisola³

et al. 2021

Cureus

View full text Add to dashboard Cite

Background: The coronavirus disease 2019 (COVID-19) pandemic heralded an unprecedented race to the development of several vaccine candidates at record speeds never seen in global health. Within nine months, Pfizer-BioNTech’s COVID-19 vaccine was approved by the United States FDA. Unfortunately, while these advances were ongoing, there was a burgeoning epidemic of disinformation about the virus and the vaccines that affected the willingness of people, especially minority groups, to get vaccinated. In Nigeria, this wave of vaccine hesitancy was happening against the backdrop of landmark pharmaceutical litigations such as the 2007 Pfizer trovafloxacin lawsuit in the country. Aim: To assess the determinants of the COVID-19 vaccine's acceptability among Nigerians. Materials and methods: Following ethical approval, a population-based cross-sectional study was conducted from November 2020 to January 2021 using an adapted pretested, self-administered questionnaire originally designed by Amyn Malik and colleagues who conducted a similar study at Yale University School of Public Health. The participants were recruited through simple random sampling using a list of community and corporate sites obtained from Google Maps in the three regional zones of Nigeria (north, east, and west) in diverse occupational and residential settings. Information obtained includes socio-demographics, medical history related to COVID-19, level of knowledge, risk perception, and attitudes toward COVID-19 and the vaccines. Descriptive and inferential statistics were done, and results were summarized into percentages and associations. The level of statistical significance was set at a p-value of <0.05. Using the open EpiR package (Emory), we determined a minimum of 340 participants for a statistical power of 80%. Results: A total of 358 responses were obtained out of the 120 questionnaires distributed in each of the three regions, of which 189 (53%) were females. The mean age of respondents was 32 years (±11.2 SD). About 75% of the participants had at least a college education. The majority (66.2%) of the participants were willing to accept the approved vaccine. The mean risk perception score for COVID-19 was 5.1 (±2.2 SD) out of 10, while the mean COVID-19 symptom knowledge score was 8.6 (±4.1 SD) out of 19. Variables such as being male, identifying as Christian, Hausa ethnicity, and living in northern Nigeria had a statistically significant relationship with the willingness to get vaccinated. Conclusion: Over 60% of Nigerians are willing to take the COVID-19 vaccines if recommended by health workers. We found male gender, religion, ethnicity, and geographical location to positively influence the willingness of Nigerians to get vaccinated against COVID-19. Health workers should be supported to go beyond the confines of the hospital to educate the general public in schools, marketplaces, churches, and corporate organizations on the efficacy and safety of the approved vaccines.

show abstract

Section: Introductionmentioning

confidence: 99%

Determinants for Acceptance of COVID-19 Vaccine in Nigeria

Eze¹,

Ndoh²,

Ibisola³

et al. 2021

Cureus

View full text Add to dashboard Cite

show abstract

“…The lack of significance in the multivariate analysis could be due to an over‐adjustment bias of confounders. 20 In terms of hospital mortality, it is possible that we were unable to accurately reflect risk since the length of hospitalization may be a variable dependent on ACEi or ARB as well as comorbid conditions which could lead to ACEi or ARB prescriptions. The absence of long‐term mortality is also a limitation.…”

Section: Discussionmentioning

confidence: 99%

“…Ourconclusion may have been drawn upona fallacy which overadjusts for factors that are not a part of the relationship between exposure and outcome. 20 While it is possible that there is increased mortality with ACEi use alone, that conclusion requires further research. Larger sample size and more studies are needed to achieve statistical significance to comment on the effects of ACEi alone.…”

Section: Subgroup Analysismentioning

confidence: 99%

Safety of ACEi and ARB in COVID‐19 management: A retrospective analysis

Kumar¹,

Nikravesh²,

Umeh³

et al. 2022

Clinical Cardiology

View full text Add to dashboard Cite

Background & Aims Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV2)is a highly contagious virus that has infected 260 million individuals since December 2019. The severity of coronavirus disease 2019 (COVID‐19) depends upon the complex interplay between viral factors and the host's inflammatory response, which can trigger a cascadeeventually leading to multiorgan failure. There is contradictory evidence that angiotensin‐converting enzyme (ACEi) or angiotensin receptor blockers (ARBs) may affect mortality in patients with severe COVID‐19, theoretically due to interaction with the bradykinin pathway. Therefore, we aim to explore the association between ACEi and ARB use and mortality in severe SARS‐CoV2 infection.Severe acute respiratory yndrome with coronavirus (SARS‐CoV2) is a highly contagious virus that has infected 260 million individuals since December 2019. The severity of COVID‐19 depends upon the complex interplay between viral factors and the host's inflammatory response, which can trigger a cascadeeventually leading to multiorgan failure. There is contradictory evidence that angiotensin‐converting enzyme (ACEi) or angiotensin receptor blockers (ARBs) may affect mortality in patients with severe COVID‐19, theoretically due to interaction with the bradykinin pathway. Therefore, we aim to explore the association between ACEi and ARB use and mortality in severe SARS‐CoV2 infection. Materials & Methodology This multicenter retrospective observational study enrolled 2935 COVID‐19 patients admitted at six hospitals in Southern California, USA, between March 2020 and August 2021. Our primary outcome was the association of pre‐hospital use of ACEi and ARB on in‐hospital mortality in COVID‐19 patients. First, relevant deidentified patient data were extracted using an SQL program from the electronic medical record. Then, a bivariate analysis of the relationship between ACEi and ARB use and different study variables using χ2 and t test was done. Finally, we did a backward selection Cox multivariate regression analysis using mortality as a dependent variable. Results Of the 2935 patients in the study, hypertension was present in 40.6%, and congestive heart failure in 13.8%. ACEi and ARB were used by 17.5% and 11.3% of patients, respectively, with 28.8% of patients on either medication. After adjusting for confounding variables in the multivariate analysis, the use of ACEi (HR: 1.226, 95% CI: 0.989–1.520) or ARB (HR: 0.923, 95% CI: 0.701–1.216) was not independently associated with increased mortality. Conclusion Our results are consistent with the clinical guidelines and position statements per the International Society of Hypertension, that there is no indication to stop the use of ACEi/ARB in COVID‐19 patients.

show abstract

“…The data is then pre-processed and features reduced, followed by data visualization. The existing regression models including linear regression [20], [21], polynomial regression [22], [23], and SVM regression [24], [25] are implemented to predict the confirmed cases over the next 17 days. Finally, an ensemble regression model for prediction is proposed, which outperforms the existing regression models.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 epidemic: analysis and prediction

Bhutia

Patra

Ray

2022

IJ-AI

View full text Add to dashboard Cite

“Novel Coronavirus”, commonly known as COVID-19 has spread nearly to the entire world. The number of impacted cases and deaths has increased significantly in each country, posing a challenge for the world’s health organizations. The goal of this paper was to better comprehend and analyze the growth of the disease in India, including confirmed, recovered, fatalities, and active cases of COVID-19. Data analysis affects an organization’s decision-making process with interactive visual representation. The proposed model was an ensemble model that was built using linear regression, polynomial regression, and support vector machine (SVM) regression models. The model predicted the number of confirmed cases from 30 th May 2021 to 15 th June 2021 based on the data available from 22 January 2020 to 29 May 2021 and improved accuracy was obtained when compared with the actual data. Forecasting the confirmed cases might assist health organizations in planning medical facilities. Following that, an appropriate machine leraning (ML) model must be found that can predict the number of new cases in the future.

show abstract

Estimation of the case fatality rate of COVID-19 epidemiological data in Nigeria using statistical regression analysis

Cited by 21 publications

References 9 publications

Determinants for Acceptance of COVID-19 Vaccine in Nigeria

Determinants for Acceptance of COVID-19 Vaccine in Nigeria

Safety of ACEi and ARB in COVID‐19 management: A retrospective analysis

COVID-19 epidemic: analysis and prediction

Contact Info

Product

Resources

About