Clinical characteristics, predictors of symptomatic coronavirus disease 2019 and duration of hospitalisation in a cohort of 632 Patients in Lagos State, Nigeria

Olufemi A Erinoso; Kikelomo Ololade Wright; Samuel Anya; Abimbola Bowale; Olusola Adejumo; Sunday Adesola; Bodunrin Osikomaiya; Bamidele Mutiu; Babatunde Saka; Ayodeji Falana; Disu Ola-Ayinde; Ephraim I Akase; Henry Owuna; Hussein Abdur-Razzaq; Dayo Lajide; Oliver Ezechi; Olusegun Ogboye; Akin Osibogun; Akin Abayomi

doi:10.4103/npmj.npmj_272_20

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ORIGINAL ARTICLE

Year : 2020 | Volume : 27 | Issue : 4 | Page : 285-292

Clinical characteristics, predictors of symptomatic coronavirus disease 2019 and duration of hospitalisation in a cohort of 632 Patients in Lagos State, Nigeria

Olufemi A Erinoso¹, Kikelomo Ololade Wright², Samuel Anya³, Abimbola Bowale⁴, Olusola Adejumo³, Sunday Adesola⁴, Bodunrin Osikomaiya⁵, Bamidele Mutiu⁶, Babatunde Saka⁶, Ayodeji Falana⁴, Disu Ola-Ayinde⁴, Ephraim I Akase⁷, Henry Owuna⁴, Hussein Abdur-Razzaq³, Dayo Lajide³, Oliver Ezechi⁸, Olusegun Ogboye⁹, Akin Osibogun¹⁰, Akin Abayomi⁹
¹ Department of Oral and Maxillofacial Surgery, Lagos State University Teaching Hospital, Lagos State, Lagos, Nigeria
² Department of Community Health and Primary Health Care, Lagos State University Teaching Hospital, Lagos State, Lagos, Nigeria
³ Research Unit, Directorate of Health Planning Research and Statistics Lagos State Ministry of Health, Lagos State, Lagos, Nigeria
⁴ Infectious Disease Unit, Mainland Hospital, Lagos State, Lagos, Nigeria
⁵ Lagos State Blood Transfusion Services, Lagos State, Lagos, Nigeria
⁶ Lagos State Bio-Security Laboratory, Mainland Hospital Yaba, Lagos State, Lagos, Nigeria
⁷ Department of Medicine, Infectious Disease Unit, Lagos University Teaching Hospital, Lagos State, Lagos, Nigeria
⁸ Directorate of Research, Institute of Medical Research, Lagos State, Lagos, Nigeria
⁹ Ministry of Health, Lagos State, Lagos, Nigeria
¹⁰ Department of Community and Primary Health, College of Medicine, University of Lagos, Lagos, Nigeria

Correspondence Address:
Dr. Kikelomo Ololade Wright
Department of Community Health and Primary Health Care, Lagos State University Teaching Hospital/ Lagos State University College of Medicine, Lagos
Nigeria

Source of Support: None, Conflict of Interest: None

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DOI: 10.4103/npmj.npmj_272_20

Objective: The clinical spectrum of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is still evolving. This study describes the clinical characteristics and investigates factors that predict symptomatic presentation and duration of hospitalisation in a cohort of coronavirus disease 2019 (COVID-19) patients managed in Lagos, Nigeria. Methodology: This was a retrospective assessment of patients hospitalised with COVID-19 disease in six dedicated facilities in Lagos, Nigeria, between April 1^st and May 31^st 2020. Participants were individuals with laboratory-confirmed SARS-CoV-2 infection. The outcome measures were presence of symptoms and duration of hospitalisation. Demographic and comorbidity data were also obtained. Statistical analysis was done using STATA 15.0 software, with P < 0.05 being considered statistically significant. Results: A total of 632 cases were analysed. The median age was 40 years (IQR: 30.5–49); male patients accounted for 60.1%. About 63% of patients were asymptomatic at presentation. Among the symptomatic, the most common symptoms were cough (47.4%) and fever (39.7%). The most common comorbidities were hypertension (16.8%) and diabetes (5.2%). The median duration of hospitalisation was 10 days (IQR: 8–14). Comorbidities increased the odds of presenting with symptoms 1.6-fold (P = 0.025) for one comorbidity and 3.2-fold (P = 0.005) for ≥2 comorbidities. Individuals aged ≥50 years were twice as likely to be hospitalised for more than 14 days compared to individuals aged <50 years (P = 0.016). Conclusion: Most individuals had no symptoms with comorbidities increasing the likelihood of symptoms. Older age was associated with longer duration of hospitalisation. Age and comorbidities should be used for COVID-19 triaging for efficient resource allocation.

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