COVID-19 mortality rate and its associated factors during the first and second waves in Nigeria

COVID-19 mortality rate has not been formally assessed in Nigeria. Thus, we aimed to address this gap and identify associated mortality risk factors during the first and second waves in Nigeria. This was a retrospective analysis of national surveillance data from all 37 States in Nigeria between February 27, 2020, and April 3, 2021. The outcome variable was mortality amongst persons who tested positive for SARS-CoV-2 by Reverse-Transcriptase Polymerase Chain Reaction. Incidence rates of COVID-19 mortality was calculated by dividing the number of deaths by total person-time (in days) contributed by the entire study population and presented per 100,000 person-days with 95% Confidence Intervals (95% CI). Adjusted negative binomial regression was used to identify factors associated with COVID-19 mortality. Findings are presented as adjusted Incidence Rate Ratios (aIRR) with 95% CI. The first wave included 65,790 COVID-19 patients, of whom 994 (1∙51%) died;the second wave included 91,089 patients, of whom 513 (0∙56%) died. The incidence rate of COVID-19 mortality was higher in the first wave [54∙25 (95% CI: 50∙98–57∙73)] than in the second wave [19∙19 (17∙60–20∙93)]. Factors independently associated with increased risk of COVID-19 mortality in both waves were: age ≥45 years, male gender [first wave aIRR 1∙65 (1∙35–2∙02) and second wave 1∙52 (1∙11–2∙06)], being symptomatic [aIRR 3∙17 (2∙59–3∙89) and 3∙04 (2∙20–4∙21)], and being hospitalised [aIRR 4∙19 (3∙26–5∙39) and 7∙84 (4∙90–12∙54)]. Relative to South-West, residency in the South-South and North-West was associated with an increased risk of COVID-19 mortality in both waves. In conclusion, the rate of COVID-19 mortality in Nigeria was higher in the first wave than in the second wave, suggesting an improvement in public health response and clinical care in the second wave. However, this needs to be interpreted with caution given the inherent limitations of the country's surveillance system during the study.

Comparison of the AmpFire and MA-6000 polymerase chain reaction platforms for high-risk human papillomavirus testing in cervical precancer screening.

High-risk human papillomavirus (hr-HPV) testing for primary cervical precancer screening offers an opportunity to improve screening in low-middle income countries (LMICs). This study aimed to compare the analytic performances of the AmpFire and MA-6000 platforms for hr-HPV DNA testing in three groups of women screened for hr-HPV types in Ghana: group 1 with 33 GeneXpert-archived ThinPrep/liquid-based samples subjected to both tests, group 2 with 50 AmpFire-archived dry brush samples subjected to MA-6000 testing, and group 3 involving 143 cotton swab samples simultaneously subjected to both tests without archiving. The overall agreement rates were 73 %, 92 %, and 84 %, for groups 1-3, respectively, and 84 % (95 % CI, 78.6-88.6) for the entire group. Neither AmpFire nor MA-6000 was more likely to test hr-HPV positive in all three groups and the combined group. Group 1 showed fair agreement without statistical significance (κ = 0.224, 95 % CI, -0.118 to 0.565), while group 3 showed significant moderate agreement (κ = 0.591, 95% CI, 0.442-0.741). Group 2 showed an almost perfect significant level of agreement (κ = 0.802; 95 % CI, 0.616-0.987). Thus, both platforms showed statistically significant moderate to near-perfect agreement for detecting hr-HPV in cervicovaginal samples, with variation according to archiving conditions and duration between sample collection and retesting. For LMICs using these platforms for COVID-19 testing, as the COVID-19 pandemic subsides, the platforms can become available for running other tests such as hr-HPV DNA testing for cervical precancer screening.

COVID-19 mortality rate and its associated factors during the first and second waves in Nigeria.

COVID-19 mortality rate has not been formally assessed in Nigeria. Thus, we aimed to address this gap and identify associated mortality risk factors during the first and second waves in Nigeria. This was a retrospective analysis of national surveillance data from all 37 States in Nigeria between February 27, 2020, and April 3, 2021. The outcome variable was mortality amongst persons who tested positive for SARS-CoV-2 by Reverse-Transcriptase Polymerase Chain Reaction. Incidence rates of COVID-19 mortality was calculated by dividing the number of deaths by total person-time (in days) contributed by the entire study population and presented per 100,000 person-days with 95% Confidence Intervals (95% CI). Adjusted negative binomial regression was used to identify factors associated with COVID-19 mortality. Findings are presented as adjusted Incidence Rate Ratios (aIRR) with 95% CI. The first wave included 65,790 COVID-19 patients, of whom 994 (1∙51%) died; the second wave included 91,089 patients, of whom 513 (0∙56%) died. The incidence rate of COVID-19 mortality was higher in the first wave [54∙25 (95% CI: 50∙98-57∙73)] than in the second wave [19∙19 (17∙60-20∙93)]. Factors independently associated with increased risk of COVID-19 mortality in both waves were: age ≥45 years, male gender [first wave aIRR 1∙65 (1∙35-2∙02) and second wave 1∙52 (1∙11-2∙06)], being symptomatic [aIRR 3∙17 (2∙59-3∙89) and 3∙04 (2∙20-4∙21)], and being hospitalised [aIRR 4∙19 (3∙26-5∙39) and 7∙84 (4∙90-12∙54)]. Relative to South-West, residency in the South-South and North-West was associated with an increased risk of COVID-19 mortality in both waves. In conclusion, the rate of COVID-19 mortality in Nigeria was higher in the first wave than in the second wave, suggesting an improvement in public health response and clinical care in the second wave. However, this needs to be interpreted with caution given the inherent limitations of the country's surveillance system during the study.

Stock market response to COVID-19 pandemic: A comparative evidence from two emerging markets.

This paper presents the first comparative study of emerging stock markets' response to the COVID-19 pandemic with evidence from Ghana and Botswana. Using daily time-series data from March 1, 2020, to September 30, 2021, the study estimates parametric, semi-parametric and non-parametric models, and provides evidence to support the negative effects of the COVID-19 pandemic (i.e., the total number of reported COVID-19 cases and deaths) on the stock market performances of Ghana and Botswana. Interestingly, the study shows that the impact of the pandemic on Ghana's stock market is quantitatively greater than the stock market of Botswana. The study calls for fiscal and monetary policies to help firms on the stock market to survive the shock. Going forward, measures aimed at building a robust stock market to withstand such external shocks are critical.

A Correlational Study on the Gross Domestic Product and Gross Domestic Product Per Capita of a Country and Its COVID-19 Incidence and Mortality Rates (preprint)

Some studies had shown that there is a relationship between the state of the economy of a country and COVID-19 incidence and mortality rates. However, these studies are mostly done on countries that are already developed. This study aims to find the relationship between GDP and GDP per capita and COVID-19 incidence and mortality rates in all countries. In addition, they will also be analyzed based on their different income levels. The data collected are from databases from World Bank and WHO and are analyzed through MS Excel and JASP. Spearman’s rho is used to analyze the overall data and stratified data. It has been found that the GDP per capita and incidence (r = .656, p < .001) and mortality rates (r = .521, p < .001) have a strong and moderate correlation, respectively. GDP’s relationship with incidence (r = .295, p < .001) and mortality rates (r = .346, p < .001) resulted in both weak correlations. Stratified analysis resulted in no significant relationships, except for GDP per capita’s relationship with incidence (r = .362, p = .011) and mortality rates (r = .348, p = .014) in low-middle countries, which yielded both weak correlations. These results show that there is indeed a relationship between the incidence and mortality rates and the economic status of a country before a pandemic, however, more factors need to be accounted for in order to help countries improve their pandemic response in the future.

Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study.

OBJECTIVES: This study aimed to develop and validate a symptom prediction tool for COVID-19 test positivity in Nigeria. DESIGN: Predictive modelling study. SETTING: All Nigeria States and the Federal Capital Territory. PARTICIPANTS: A cohort of 43 221 individuals within the national COVID-19 surveillance dataset from 27 February to 27 August 2020. Complete dataset was randomly split into two equal halves: derivation and validation datasets. Using the derivation dataset (n=21 477), backward multivariable logistic regression approach was used to identify symptoms positively associated with COVID-19 positivity (by real-time PCR) in children (≤17 years), adults (18-64 years) and elderly (≥65 years) patients separately. OUTCOME MEASURES: Weighted statistical and clinical scores based on beta regression coefficients and clinicians' judgements, respectively. Using the validation dataset (n=21 744), area under the receiver operating characteristic curve (AUROC) values were used to assess the predictive capacity of individual symptoms, unweighted score and the two weighted scores. RESULTS: Overall, 27.6% of children (4415/15 988), 34.6% of adults (9154/26 441) and 40.0% of elderly (317/792) that had been tested were positive for COVID-19. Best individual symptom predictor of COVID-19 positivity was loss of smell in children (AUROC 0.56, 95% CI 0.55 to 0.56), either fever or cough in adults (AUROC 0.57, 95% CI 0.56 to 0.58) and difficulty in breathing in the elderly (AUROC 0.53, 95% CI 0.48 to 0.58) patients. In children, adults and the elderly patients, all scoring approaches showed similar predictive performance. CONCLUSIONS: The predictive capacity of various symptom scores for COVID-19 positivity was poor overall. However, the findings could serve as an advocacy tool for more investments in resources for capacity strengthening of molecular testing for COVID-19 in Nigeria.