A comparison of the sniff magnitude test and the University of Pennsylvania Smell Identification Test in children and nonnative English speakers

The sniff magnitude test (SMT) is a reliable and rapid clinical test of olfactory function that is minimally dependent on cognitive and linguistic abilities. In this study, we compared performance on the SMT and University of Pennsylvania Smell Identification Test (UPSIT) in samples of children and nonnative English speakers. Previous research has shown that these populations perform poorly on the UPSIT as compared with young, healthy U.S. adults. Such performance differences may reflect variations in memory/cognition and language/culture rather than olfactory abilities. The UPSIT scores of children and of Indian and Chinese graduate students were found to be lower than those of young U.S. adults. By contrast, these groups did not perform more poorly than U.S. adults did on the SMT. The results are consistent with findings from our studies, with the elderly showing that performance on the UPSIT, but not the SMT, is significantly correlated with measures of memory, language and other cognitive abilities. The findings highlight the utility of the SMT when evaluating the olfactory ability of the very young, older adults and people with diverse linguistic and cultural backgrounds.     

Estimation of COVID-19 Period Prevalence and the Undiagnosed Population in Canadian Provinces: Model-Based Analysis

BackgroundThe development of a successful COVID-19 control strategy requires a thorough understanding of the trends in geographic and demographic distributions of disease burden. In terms of the estimation of the population prevalence, this includes the crucial process of unravelling the number of patients who remain undiagnosed.ObjectiveThis study estimates the period prevalence of COVID-19 between March 1, 2020, and November 30, 2020, and the proportion of the infected population that remained undiagnosed in the Canadian provinces of Quebec, Ontario, Alberta, and British Columbia.MethodsA model-based mathematical framework based on a disease progression and transmission model was developed to estimate the historical prevalence of COVID-19 using provincial-level statistics reporting seroprevalence, diagnoses, and deaths resulting from COVID-19. The framework was applied to three different age cohorts (< 30; 30-69; and ≥70 years) in each of the provinces studied.ResultsThe estimates of COVID-19 period prevalence between March 1, 2020, and November 30, 2020, were 4.73% (95% CI 4.42%-4.99%) for Quebec, 2.88% (95% CI 2.75%-3.02%) for Ontario, 3.27% (95% CI 2.72%-3.70%) for Alberta, and 2.95% (95% CI 2.77%-3.15%) for British Columbia. Among the cohorts considered in this study, the estimated total number of infections ranged from 2-fold the number of diagnoses (among Quebecers, aged ≥70 years: 26,476/53,549, 49.44%) to 6-fold the number of diagnoses (among British Columbians aged ≥70 years: 3108/18,147, 17.12%).ConclusionsOur estimates indicate that a high proportion of the population infected between March 1 and November 30, 2020, remained undiagnosed. Knowledge of COVID-19 period prevalence and the undiagnosed population can provide vital evidence that policy makers can consider when planning COVID-19 control interventions and vaccination programs.