Background Acne vulgaris is a skin disorder that affects males and females with significant impact on quality of life. The onset of the COVID-19 pandemic led to a series of non-pharmaceutical interventions globally to reduce the spread of the virus particularly since there have been no known cures or definitive treatment for the disease. One key non-pharmaceutical intervention was recommendation on wearing of face masks. There are reports of discomfort associated with wearing face mask including complaints of various skin rashes, acne and headaches which could hinder appropriate use of face masks. While the dermatological problems associated with face mask use have been comprehensively explored in high income countries, the data is sparse in sub-Saharan Africa. We aimed to determine the association between face mask use and development of acne vulgaris in our developing country setting. We subsequently determined risk factors for development of acne vulgaris such as duration of wearing face masks, type of face mask, and prior dermatological skin condition history. We aimed to also determine the potential of acne development secondary to face mask use to reducing predisposition to wearing face masks. Methods This was an observational cross-sectional study conducted in within two local government areas of the Federal Capital Territory, Abuja. Trained interviewers administered pre-tested questionnaires to 1316 consecutive consenting adult participants randomly approached for informed consent at various public locations. Information was inputted into MS Excel and analyzed using Epi-info. Results A total number of 1316 persons participated in this study with mean age 34.4 ±12.3 years and median age 35.5years. Male: female ratio was 1:1.41. New onset acne or worsening of acne following consistent wearing of face masks was reported by 323 (24.5%) of the 1316 participants in this study. The surgical face mask was the least likely to predispose to acne p<0.05. Compared with the surgical mask, persons using N95 face mask and cloth mask were 1.89 and 1.41 times more likely to have acne respectively. Persons with prior history of acne were more likely to develop new acne or experience worsening of acne following wearing of face mask OR 3.89, 95% CI 2.85, 5.33; p <0.05). The length of time of daily mask wearing was not significantly associated with occurrence of new onset acne or worsening of acne. Persons reporting prior histories of allergy were more likely to develop acne in this study (OR 2.01, 95% CI 1.50, 2.88; p<0.05). In this study, 192 (59.4%) of those who reported having acne following face masks use responded they have a negative predisposition to wearing masks. Conclusion Our finding of greater predisposition to development or worsening of acne following consistent use of face masks could have implications for the control strategy of COVID-19. The finding that the N95 face mask was more significantly associated with acne is of concern as this is the preferred face mask in healthcare settings. It is important for the medical community to investigate feasible and safe recommendations to help alleviate this condition.

ObjectivesThis study aimed to develop and validate a symptom prediction tool for COVID-19 test positivity in Nigeria.DesignPredictive modelling study.SettingAll Nigeria States and the Federal Capital Territory.ParticipantsA 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 measuresWeighted 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.ResultsOverall, 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.ConclusionsThe 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.

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.