ABSTRACT
Background: COVID-19 infectivity has been linked to various background factors, but there is no data on preCOVID determinants of COVID-19 diagnosis by the clinician, RT-PCR, and diagnosis by both methods. In an adult representative sample with pre-COVID data, we aimed to identify determinants of subsequent COVID-19 diagnosis by the clinician and RT-PCR. Method(s): In the cohort of 42,621 adults, 5,705 were diagnosed with COVID-19 by a clinician based on ICD-10 codes and 3,936 using RT-PCR. Pre-COVID data were available for several demographic factors, socio-economic (SES) factors, and several indicators of respiratory symptoms. Result(s): In total, 6,560 (15.4%) were diagnosed with COVID-19 by either clinician or RT-PCR;47% had both clinician diagnosis and RT-PCR;40% had clinician diagnosis but not RT-PCR confirmation;and 13% had only RTPCR confirmation. Proportion of those diagnosed by a clinician increased by age, but those of age >= 60 years were less likely to be confirmed with RT-PCR than those younger. Clinician diagnosis of COVID-19 did not differ by smoking, BMI, childhood on a farm, education, SES, or respiratory symptoms, but those with >= 2 co-morbidities were more likely to be diagnosed than those with <2 co-morbidities. For RT-PCR, ex-smokers, those who grew up on a farm, those with less high school education, those with respiratory symptoms, asthma, COPD, and >=2 co-morbidities were less likely to be diagnosed with COVID-19. Conclusion(s): Pre-COVID factors may influence COVID diagnosis and these act differentially for clinician-based diagnosis and based on RT-PCR. Such information can be useful for planning future screening efforts for COVID or other similar outbreaks.
ABSTRACT
Background: Impairment of lung function is one of the main complications observed with the coronavirus disease 2019 (COVID-19), but risk factors and outcomes associated with lung function are yet to be fully elucidated, partly due to limited pre-COVID-19 clinical data on lung health. Method(s): Spirometry data were retrieved from a population-representative adult cohort in Sweden. These were linked to national registers of COVID-19 diagnosis by real-time polymerase chain reaction (RT-PCR) or recommended ICD10 codes set by clinicians. We compared pre-COVID-19 lung function in COVID-19 cases and non-cases using independent t-test, presenting measurements as a percentage of predicted normal values. Result(s): From the cohort of initially 24,534 adults aged 16-75 years, spirometry, measured 2017-2019, was performed to 951 subjects (n=633 with asthma, n=316 no asthma). Of these, 201 (21.1%) had COVID-19. Overall, there was no difference between those who had and those who had no COVID-19 in FEV1 (93.99 +/- 1.05 vs 91.77 +/- 0.62, p 0.09), FVC (99.66 +/- 0.94 vs 98.78 +/- 0.57, p 0.47), or FEV1/FVC (94.25 +/- 0.16 vs 95.31 +/- 2.72, p 0.84). Stratifying by gender, FEV1 was higher in women who had COVID-19 than in women who had no COVID-19 (96.35 +/- 1.27 vs 92.54 +/- 0.78, p 0.02), and FEV1/FVC was higher in subjects with BMI >=30 who had COVID-19 than those with BMI >= 30 and no COVID-19 (98.42 +/- 1.07 vs 94.21 +/- 0.93, p 0.03). Conclusion(s): Pre-COVID-19 lung function in those who had COVID-19 was similar to those who have never been diagnosed. There is need to compare these baseline data with post-COVID-19 lung function data in order to ascertain possible changes in respiratory health due to the disease.
ABSTRACT
Background: Due to the high transmissibility of SARS-Cov-2, the virus causing COVID-19, accurate diagnostic methods are essential for effective infection control, but the gold standard method of real-time polymerase chain reaction (RT-PCR) is costly, slow, and test capacity has at times been insufficient. Method(s): Diagnosis data were retrieved from registers, based on positive RT-PCR or ICD-10 codes set by clinicians. Through linkage to a population-representative adult cohort in Sweden, we assessed the accuracy of clinician diagnosis against RT-PCR, stratified by number and severity of comorbidities. Result(s): A total of 42,621 subjects were included. Of these, 6,560 had COVID-19. Clinician diagnosis was found in 5,705 subjects, while 3,936 had a positive RT-PCR and 3,081 got diagnosed with both methods. Of those with at least one comorbidity, sensitivity for clinician diagnosis ranged from 69% (95% CI 44-86) for those with two "severe" comorbidities (diabetes and chronic obstructive pulmonary disease) to 84% (95% CI 73-91) for those with two "moderately severe" comorbidities (asthma and hypertension). Specificity was > 90% for all comorbidity groups. Youden's index increased slightly with the number of comorbidities in both "severe" and "moderately severe" categories, but for those with "light" comorbidities (eczema, rhinitis, sleep disorders), it was the lowest with >=2 comorbidities (69% (95% CI 66-72)). Youden's index was 71% (95% CI 70-72) for those with no comorbidities and 71% (95% 69-73) for the whole cohort. Conclusion(s): Clinicians identify non-cases to a high degree, but RT-PCR is needed for adequate sensitivity, regardless of comorbidity.
ABSTRACT
Background: Whilst the gold standard real-time polymerase chain reaction (RT-PCR) is costly and can take time to obtain results, there is a dearth of data comparing clinician diagnosis based on recommended ICD codes and RTPCR. Aim(s): In this study, we compared clinician diagnosis of COVID-19 with RT-PCR in a general adult population and evaluated any differences in accuracy by age, gender, pre-COVID-19 BMI, and obstructive airway diseases. Material(s) and Method(s): Data from a cohort of 42,621 adult-representative samples in Sweden, included 5705 clinician-diagnosed and 3936 RT-PCR-diagnosed COVID-19 patients. Using RT-PCR as the reference standard, estimates of the accuracy of clinician's diagnosis were determined. Result(s): The sensitivity and specificity of clinician diagnosis in identifying COVID-19 was 78% (95%CI 77-80%) and 93% (95%CI 93-93%), respectively. The positive predictive value was 54% (95%CI 53-55%), negative predictive value was 98% (95%CI 98-98%) and the Youden's Index was 71% (95%CI 70-72%). These accuracy measures were similar between men and women, across age groups, BMI categories, and between patients with and without asthma. However, while the specificity, negative predictive value, and Youden's index were similar between patients with and without COPD, the sensitivity was slightly higher in patients with COPD (84%, 95%CI 74-90%) than those without (78%, 95%CI 77-79%) COPD. Conclusion(s): The accuracy of clinician's diagnosis for COVID-19 is adequate, regardless of gender, age, pre-COVID19 BMI, asthma, and COPD, thus can be used for screening purposes to supplement RT-PCR.
ABSTRACT
Background: Incidence and clinical outcomes of COVID-19 appear to differ between allergic and non- allergic asthma, but evidence for other asthma phenotypes, such as obesity-related asthma, is scarce. We sought to determine whether pre-COVID-19 obesityrelated asthma phenotypes are associated with risk of COVID-19 incidence in a Swedish populationrepresentative adult cohort. Method: Clinical examination data from 2,006 subjects aged 16-75 years collected during 2009-2012 were linked to register data of COVID-19 diagnosis, based on real-time polymerase chain reaction or ICD-10 codes set by clinicians. Obese asthma was defined as current asthma and body mass index ≥30 kg/m2. Allergic obese asthma was further based on sensitization to any aeroallergen measured by specific immunoglobulin E. Results: In total, 344 (17.1%) of the subjects had COVID-19. After adjustment for gender, age, allergy history, farm childhood, urbanization, dust exposure, smoking, education, and occupation, there was no association between having allergic obese asthma (adjusted risk ratio (aRR) 0.92, 95% CI 0.63-1.34), non-allergic obese asthma (aRR 0.94, 95% CI 0.68-1.29), or any obese asthma (aRR 0.93, 95% CI 0.72-1.19), and getting COVID-19. Stratifying by gender and age produced similar results. Conclusion: We found no association between pre- COVID-19 obesity-related asthma phenotypes and being diagnosed with COVID-19. Further analyses are needed regarding long-term outcomes and disease severity of COVID-19 in relation to obesity-related asthma phenotypes.