Objective: A controversy exists concerning the relationship, if any, between obstructive sleep apnea (OSA) and the anatomical position of the anterior teeth. Specifi cally, there has been speculation that extraction orthodontics and retraction of the anterior teeth contributes to OSA by crowding the tongue and decreasing airway space. This retrospective study utilized electronic medical and dental health records to examine the association between missing premolars and OSA. Methods: The sample (n = 5,584) was obtained from the electronic medical and dental health records of HealthPartners in Minnesota. Half of the subjects (n = 2,792) had one missing premolar in each quadrant. The other half had no missing premolars. Cases and controls were paired in a 1:1 match on age range, gender, and body mass index (BMI) range. The outcome was the presence or absence of a diagnosis of OSA confi rmed by polysomnography.

BackgroundThe first wave of the London COVID-19 epidemic peaked in April 2020. Attention initially focused on severe presentations, intensive care capacity, and the timely supply of equipment. While general practice has seen a rapid uptake of technology to allow for virtual consultations, little is known about the pattern of suspected COVID-19 presentations in primary care.AimTo quantify the prevalence and time course of clinically suspected COVID-19 presenting to general practices, to report the risk of suspected COVID-19 by ethnic group, and to identify whether differences by ethnicity can be explained by clinical data in the GP record.Design and settingCross-sectional study using anonymised data from the primary care records of approximately 1.2 million adults registered with 157 practices in four adjacent east London clinical commissioning groups. The study population includes 55% of people from ethnic minorities and is in the top decile of social deprivation in England.MethodSuspected COVID-19 cases were identified clinically and recorded using SNOMED codes. Explanatory variables included age, sex, self-reported ethnicity, and measures of social deprivation. Clinical factors included data on 16 long-term conditions, body mass index, and smoking status.ResultsGPs recorded 8985 suspected COVID-19 cases between 10 February and 30 April 2020.Univariate analysis showed a two-fold increase in the odds of suspected COVID-19 for South Asian and black adults compared with white adults. In a fully adjusted analysis that included clinical factors, South Asian patients had nearly twice the odds of suspected infection (odds ratio [OR] = 1.93, 95% confidence interval [CI] = 1.83 to 2.04). The OR for black patients was 1.47 (95% CI = 1.38 to 1.57).ConclusionUsing data from GP records, black and South Asian ethnicity remain as predictors of suspected COVID-19, with levels of risk similar to hospital admission reports. Further understanding of these differences requires social and occupational data.

Yeast are among the most frequent pathogens in humans. The dominant yeast causing human infections belong to the genus Candida and Candida albicans is the most frequently isolated species. However, several non-C. albicans species are becoming increasingly common in patients worldwide. The relationships between yeast in humans and the natural environments remain poorly understood. Furthermore, it is often difficult to identify or exclude the origins of disease-causing yeast from specific environmental reservoirs. In this study, we compared the yeast isolates from tree hollows and from clinics in Hamilton, Ontario, Canada. Our surveys and analyses showed significant differences in yeast species composition, in their temporal dynamics, and in yeast genotypes between isolates from tree hollows and hospitals. Our results are inconsistent with the hypothesis that yeast from trees constitute a significant source of pathogenic yeast in humans in this region. Similarly, the yeast in humans and clinics do not appear to contribute to yeast in tree hollows.

ObjectiveThe Familial Hypercholesterolaemia Case Ascertainment Tool (FAMCAT) has been proposed to enhance case finding in primary care. In this study, we test application of the FAMCAT algorithm to describe risks of familial hypercholesterolaemia (FH) in a large unselected and ethnically diverse primary care cohort.MethodWe studied patients aged 18–65 years from three contiguous areas in inner London. We retrospectively applied the FAMCAT algorithm to routine primary care data and estimated the numbers of possible cases of FH and the potential service implications of subsequent investigation and management.ResultsOf the 777 128 patients studied, the FAMCAT score estimated between 11 736 and 23 798 (1.5%–3.1%) individuals were likely to have FH, depending on an assumed FH prevalence of 1 in 250 or 1 in 500, respectively. There was over-representation of individuals of South Asian ethnicity among those likely to have FH, with this cohort making up 41.9%–45.1% of the total estimated cases, a proportion which significantly exceeded their 26% representation in the study population.ConclusionsWe have demonstrated feasibility of application of the FAMCAT as an aid to case finding for FH using routinely recorded primary care data. Further research is needed on validity of the tool in different ethnic groups and more refined consideration of family history should be explored. While FAMCAT may aid case finding, implementation requires information on the cost-effectiveness of additional health services to investigate, diagnose and manage case ascertainment in those identified as likely to have FH.

Getting access to administrative health data for research purposes is a difficult and time-consuming process due to increasingly demanding privacy regulations. An alternative method for sharing administrative health data would be to share synthetic datasets where the records do not correspond to real individuals, but the patterns and relationships seen in the data are reproduced. This paper assesses the feasibility of generating synthetic administrative health data using a recurrent deep learning model. Our data comes from 120,000 individuals from Alberta Health’s administrative health database. We assess how similar our synthetic data is to the real data using utility assessments that assess the structure and general patterns in the data as well as by recreating a specific analysis in the real data commonly applied to this type of administrative health data. We also assess the privacy risks associated with the use of this synthetic dataset. Generic utility assessments that used Hellinger distance to quantify the difference in distributions between real and synthetic datasets for event types (0.027), attributes (mean 0.0417), Markov transition matrices (order 1 mean absolute difference: 0.0896, sd: 0.159; order 2: mean Hellinger distance 0.2195, sd: 0.2724), the Hellinger distance between the joint distributions was 0.352, and the similarity of random cohorts generated from real and synthetic data had a mean Hellinger distance of 0.3 and mean Euclidean distance of 0.064, indicating small differences between the distributions in the real data and the synthetic data. By applying a realistic analysis to both real and synthetic datasets, Cox regression hazard ratios achieved a mean confidence interval overlap of 68% for adjusted hazard ratios among 5 key outcomes of interest, indicating synthetic data produces similar analytic results to real data. The privacy assessment concluded that the attribution disclosure risk associated with this synthetic dataset was substantially less than the typical 0.09 acceptable risk threshold. Based on these metrics our results show that our synthetic data is suitably similar to the real data and could be shared for research purposes thereby alleviating concerns associated with the sharing of real data in some circumstances.