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.
【저자키워드】 COVID-19, public health, Epidemiology, 【초록키워드】 children, Nigeria, Symptom, elderly patients, cough, Cohort, Surveillance, Real-time PCR, Fever, Patient, dataset, molecular, characteristic, resource, patients, COVID-19 test, Predictive, Loss of Smell, Capital, 95% CI, individual, multivariable logistic regression, difficulty in breathing, measure, participant, coefficient, AUROC, National, datasets, positive, territory, approach, state, objective, setting, statistical, Randomly, individual symptoms, Result, tested, identify, was used, develop, were used, with COVID-19, 【제목키워드】 Symptom, predict, validation cohort, National, Assessing,