Predictors of COVID-19 Positivity in Ndokwa-West District in Nigeria. A retrospective ‎Cohort Study‎‎

• The study showed the prevalence of Covid-19 in Ndokwa District of Delta State
• The study highlighted independent factors predicting Covid-19 positivity in Ndokwa District of Delta State
• The study was unique because it presented local data and addressed the paucity of studies on independent factors predicting Covid-19 positivity in Delta State.

Aim of the work: to investigate the likely predictors of Covid-19 positivity in Ndokwa-West ‎Local Government Area of Delta State Nigeria.‎

PATIENTS AND METHODS

Study Design: This retrospective cohort study involves the retrospective analysis of patients’ data from April 2020 to August 2021.

Study Settings: The study was conducted in the Ndokwa-West Local Government Area of Delta State. Ndokwa-West is one of the local government areas of Delta North senatorial district and has its local government headquarters in Kwale.

Study Population: The study population comprised all individuals who were investigated for Covid-19 using real-time PCR (RT-PCR) testing and captured on the Surveillance, Outbreak, Response, Management and Analysis System (SORMAS) from April 2020 to August 2021 in Ndokwa-West Local Government Area of Delta State. 

Sample Size and Sampling Procedure: All 1133 patients who presented for the Covid-19 test in Ndokwa-West Local Government Area from April 2020 to August 2021 were extracted, reviewed and purposively selected for the study.

Data Sources: The data was extracted from SORMAS. This is an electronic health surveillance database maintained by NCDC [8]. The SORMAS is domiciled in the Disease Surveillance and Notification office of the Ndokwa-West Local Government Area of Delta State.

Data Collection: Samples were collected from individuals who met the eligibility criteria of RT-PCR test of NCDC COVID-19 suspect case definition [9]. These samples were transported to Asaba the state capital where we have the nearest NCDC-certified laboratory for COVID-19 while maintaining a temperature range of 2°C–4°C in line with the WHO guidelines [10], and analysis were performed according to the NCDC guidelines for identifying and reporting suspected COVID-19 cases [11]. RT-PCR was used for the laboratory diagnosis of COVID-19. Laboratory results, pertinent patients’ characteristics and clinical data were entered into the SORMAS by a designated public health officer at the Disease Surveillance and Notification office of the Ndokwa-West Local Government Area of Delta State.

Instrument for data collection: A designed proforma containing information on a number of individuals who were tested for Covid-19, socio-demographic characteristics, signs and symptoms, co-morbidity and other severe life-threatening illnesses were used to extract the data. Data Analysis: The data was analysed using SPSS version 23.0 and qualitative data presented in frequency tables and percentages. Chi-square test (χ2) were used in performing bivariate analysis for socio-demographic and clinical variables vs Number of patients that took the Covid-19 test which was either positive or negative. Those that were significant in the Chi-Square analysis were further subjected to multivariate analysis using logistic regression as predictors of Covid-19 positivity. All statistical comparisons were two-tailed with significance Level of P-value < 0.05 indicates significant while P> 0.05 indicates a Non-significant difference.

RESULTS:

Prevalence of Covid-19

According to figure 1, 67(5.9%) of the patients tested positive for Covid-19, while 1066(94.1%) tested negative for Covid-19.

Bivariate analysis in table 1 shows a significant association between ages of the patients (ρ=0.000), sex (ρ=0.034), occupation (ρ=0.035) and travelling in the last 14 days before the covid-19 test (ρ=0.046) and Covid-19 positivity.

In table 2 multivariate analysis shows ages 56-65 years (OR=188.0, 95% CI=17.011-2077.717), 25-40 years (OR=54.923, 95% CI=5.338-565.064), and 41-55 years (OR=36.240, 95% CI=3.635-361.333) predicted Covid-19 positivity; while male sex (OR=1.623, 95% CI=0.950-2.775) and travelling in the last 14 days prior to the taking the covid-19 test (OR=1.761, 95% CI=1.003-3.092) predicted Covid-19 positivity.

According to table 3, patients having co-morbidity (ρ=0.021), Cough (ρ=0.000), shortness of breath (ρ=0.000), nausea (ρ=0.000) and other severe life-threatening illness (ρ=0.008) all showed significant association with Covid-19 Positivity.

In table 4, multivariate analysis shows that co-morbidity (OR=188.0, 95% CI=17.011-2077.717), shortness of breath (OR=23.202, 95% CI=8.821-61.030), vomiting (OR=2.211, 95% CI=1.077-4.540), and having other life threatening illness (OR=1.994, 95% CI=1.187-3.348) significantly predicted Covid-19 positivity.

The study was a retrospective study carried out in Ndokwa-West Local Government Area of Delta State Southern Nigeria. According to the finding of the study, age was a significant predictor of Covid-19 positivity. Patients within ages 25-95 years were more likely to show positivity of Covid-19 than patients with a lower age range. Although Covid-19 affects all age groups, patients age has been reported as one of the critical factors predicting clinical outcomes of affected patients [12-14].In addition to predicting infected patients clinical outcomes, age is also an independent demographic factor predicting Covid-19 positivity. This was corroborated by a national study in Nigeria where older age was independently associated with Covid-19 positivity [7].

Furthermore, being male has been reported to be a significant risk factor for contracting Covid-19 [15-16]. This was substantiated by the finding of the current study where male patients were more likely to be diagnosed with Covid-19 positivity than females. This was also substantiated by the results of previous studies in the United Kingdom and Nigeria, where male sex was independently associated with Covid-19 positivity [7,17]. This observed sex difference could be attributed to a combination of genetic, physiological and socio-cultural factors. Two possible explanations are the broader distribution of SARS- CoV-2 cellular receptor, ACE-2, in men compared with those of women [18].Also, the cultural perspective in Nigeria of males being ‘bread winners’ in their respective families and are therefore more likely to go out, travel and interact with their close community than females, consequently increasing their chances of exposure to the virus. Besides, patients who travelled 14 days before the Covid-19 test were more likely to show Covid-19 positivity than patients who had not travelled. This finding differed from that of the study in Nigeria, where lower odds for infection were observed for both local and international travelers [7].Travelling both local and internationally was more likely to expose travellers to Covid-19. Although, various measures such as the enforcement of physical distancing for local travels, provision of Covid-19 information, National Centre for Disease Control contact details, use of face masks and routine hand hygiene for international travellers were put in place by the Nigerian Government. The likelihood of exposure to Covid-19 for travellers was higher than non-travellers.

Common clinical manifestations associated with Covid-19 infection were fever, cough, breathlessness, vomiting and diarrhoea [5,19]. In addition, co-morbidities such as hypertension, diabetes, cardiovascular disease, chronic lung disease and cancer have been associated with poor patient clinical outcomes and Covid-19 related death [20,21].Multivariate analysis shows that having co-morbidity, other severe life-threatening illnesses, presentation with shortness of breath, vomiting, nausea, and diarrhoea were significant predictors of Covid-19 positivity. This finding was similar to the national study finding in Nigeria, where presenting symptoms such as cough, fever, loss of smell and taste were independently associated with Covid-19 positivity [7]. A limitation for the study is limiting the sample size to patients with complete socio-demographic data, consequently reducing the sample size.

CONCLUSION

The study highlighted possible factors independently associated with Covid-19 positivity in the low resource setting like the study area, which was similar to that of the national level.  Therefore, this finding should guide health workers and policymakers at the local and national levels in implementing best practices for Covid-19 testing and care.

Recommendations:

‎1.‎  Continuous research both locally and nationally on the various independent factors ‎influencing Covid-19 positivity.‎

‎2.‎  The various highlighted local factors should be incorporated into Covid-19 care and ‎prevention in the study area and beyond.‎

‎3.‎  Specifically, age is a very pertinent independent factor which should be focused on in ‎protecting the aged and elderly especially those with co-morbid and other life threatening ‎conditions from Covid-19 related morbidity and mortality.‎.

Funding: None.

Conflict of interest: None.

Ethical Considerations: Approval for the study was obtained from the Department of Public and ‎Community Health, Novena University Ogume Delta State and the head of the Disease ‎Surveillance and Notification office of the Ndokwa-West Local Government Area of Delta State.‎