ABSTRACT
OBJECTIVES: As the world transitions to COVID-19 endemicity, studies focusing on aerosol shedding of highly transmissible SARS-CoV-2 variants of concern (VOCs) are vital for the calibration of infection control measures against VOCs that are likely to circulate seasonally. This follow-up Gesundheit-II aerosol sampling study aims to compare the aerosol shedding patterns of Omicron VOC samples with pre-Omicron variants analyzed in our previous study. DESIGN: Coarse and fine aerosol samples from 47 patients infected with SARS-CoV-2 were collected during various respiratory activities (passive breathing, talking, and singing) and analyzed using reverse transcription-quantitative polymerase chain reaction and virus culture. RESULTS: Compared with patients infected with pre-Omicron variants, comparable SARS-CoV-2 RNA copy numbers were detectable in aerosol samples of patients infected with Omicron despite being fully vaccinated. Patients infected with Omicron also showed a slight increase in viral aerosol shedding during breathing activities and were more likely to have persistent aerosol shedding beyond 7 days after disease onset. CONCLUSION: This follow-up study reaffirms the aerosol shedding properties of Omicron and should guide continued layering of public health interventions even in highly vaccinated populations.
Subject(s)
COVID-19 , Humans , Follow-Up Studies , RNA, Viral , SARS-CoV-2ABSTRACT
BACKGROUND: Free-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms. OBJECTIVE: The aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records. METHODS: CHESS is a keyword-based natural language processing algorithm to extract 48 signs and symptoms suggesting respiratory infections, gastrointestinal infections, constitutional, as well as other signs and symptoms potentially associated with infectious diseases. The algorithm also captured the assertion status (affirmed, negated, or suspected) and symptom duration. Electronic medical records from the National Healthcare Group Polyclinics, a major public sector primary care provider in Singapore, were randomly extracted and manually reviewed by 2 human reviewers, with a third reviewer as the adjudicator. The algorithm was evaluated based on 1680 notes against the human-coded result as the reference standard, with half of the data used for training and the other half for validation. RESULTS: The symptoms most commonly present within the 1680 clinical records at the episode level were those typically present in respiratory infections such as cough (744/7703, 9.66%), sore throat (591/7703, 7.67%), rhinorrhea (552/7703, 7.17%), and fever (928/7703, 12.04%). At the episode level, CHESS had an overall performance of 96.7% precision and 97.6% recall on the training dataset and 96.0% precision and 93.1% recall on the validation dataset. Symptoms suggesting respiratory and gastrointestinal infections were all detected with more than 90% precision and recall. CHESS correctly assigned the assertion status in 97.3%, 97.9%, and 89.8% of affirmed, negated, and suspected signs and symptoms, respectively (97.6% overall accuracy). Symptom episode duration was correctly identified in 81.2% of records with known duration status. CONCLUSIONS: We have developed an natural language processing algorithm dubbed CHESS that achieves good performance in extracting signs and symptoms from primary care free-text clinical records. In addition to the presence of symptoms, our algorithm can also accurately distinguish affirmed, negated, and suspected assertion statuses and extract symptom durations.
ABSTRACT
There is a lack of representative samples to provide reliable and accurate seroprevalence of sexually transmitted infections (STIs) and human immunodeficiency virus (HIV) as well as behavioural information among men who have sex with men (MSM) in Singapore. We used respondent driven sampling (RDS) to recruit MSM. Participants completed a survey used by Asian Internet MSM Sex Survey (AIMSS) and were tested for HIV and syphilis. We compared the characteristics of the RDS participants with STI diagnosis against those who did not have any STI diagnosis in the past 6 months. We compared RDS participants with AIMSS participants. Of 72 MSM recruited, 1 was positive for HIV (1.3%) and 4 (5.5%) tested positive for syphilis. Median age was 30 years and majority was Chinese (69.4%). RDS participants who had any STI diagnosis reported to have more use of recreational drugs (P = 0.006), and lower condom use (P = 0.054). Comparing RDS participants (n = 72) with the AIMSS participants (n = 2075), RDS respondents had ≥1 male partner in the past 6 months (P = 0.003), more casual sex partners (P = 0.012) and more STI symptoms (P = 0.019). There was no difference in terms of HIV testing and recreational drug use. The HIV and syphilis seroprevalence rates from our study are similar to previous reports conducted in high-risk MSM. In contrast to other settings, RDS did not work well among MSM in Singapore. The public health implications of our study highlight the challenges in obtaining data for HIV surveillance in assessing prevalence and risk behaviours among MSM.
