RESUMEN
Prolonged exposure to fine particulate matter (PM2.5) is a known risk to respiratory health, causing chronic lung impairment. Yet, the immediate, acute effects of PM2.5 exposure on respiratory symptoms, such as cough, are less understood. This pilot study aims to investigate this relationship using objective PM2.5 and cough monitors. Fifteen participants from rural Madagascar were followed for three days, equipped with an RTI Enhanced Children's MicroPEM PM2.5 sensor and a smartphone with the ResApp Cough Counting Software application. Univariable Generalized Estimating Equation (GEE) models were applied to measure the association between hourly PM2.5 exposure and cough counts. Peaks in both PM2.5 concentration and cough frequency were observed during the day. A 10-fold increase in hourly PM2.5 concentration corresponded to a 39% increase in same-hour cough frequency (incidence rate ratio (IRR) = 1.40; 95% CI: 1.12, 1.74). The strength of this association decreased with a one-hour lag between PM2.5 exposure and cough frequency (IRR = 1.21; 95% CI: 1.01, 1.44) and was not significant with a two-hour lag (IRR = 0.93; 95% CI: 0.71, 1.23). This study demonstrates the feasibility of objective PM2.5 and cough monitoring in remote settings. An association between hourly PM2.5 exposure and cough frequency was detected, suggesting that PM2.5 exposure may have immediate effects on respiratory health. Further investigation is necessary in larger studies to substantiate these findings and understand the broader implications.
RESUMEN
Objectives: We evaluated the added value of infection control-guided, on demand, and locally performed severe acute respiratory coronavirus virus 2 (SARS-CoV-2) genomic sequencing to support outbreak investigation and control in acute-care settings. Design and setting: This 18-month prospective molecular epidemiology study was conducted at a tertiary-care hospital in Montreal, Canada. When nosocomial transmission was suspected by local infection control, viral genomic sequencing was performed locally for all putative outbreak cases. Molecular and conventional epidemiology data were correlated on a just-in-time basis to improve understanding of coronavirus disease 2019 (COVID-19) transmission and reinforce or adapt control measures. Results: Between April 2020 and October 2021, 6 outbreaks including 59 nosocomial infections (per the epidemiological definition) were investigated. Genomic data supported 7 distinct transmission clusters involving 6 patients and 26 healthcare workers. We identified multiple distinct modes of transmission, which led to reinforcement and adaptation of infection control measures. Molecular epidemiology data also refuted (n = 14) suspected transmission events in favor of community acquired but institutionally clustered cases. Conclusion: SARS-CoV-2 genomic sequencing can refute or strengthen transmission hypotheses from conventional nosocomial epidemiological investigations, and guide implementation of setting-specific control strategies. Our study represents a template for prospective, on site, outbreak-focused SARS-CoV-2 sequencing. This approach may become increasingly relevant in a COVID-19 endemic state where systematic sequencing within centralized surveillance programs is not available. Trial registration: clinicaltrials.gov identifier: NCT05411562.
RESUMEN
PURPOSE: Recent developments in the field of artificial intelligence and acoustics have made it possible to objectively monitor cough in clinical and ambulatory settings. We hypothesized that time patterns of objectively measured cough in COVID-19 patients could predict clinical prognosis and help rapidly identify patients at high risk of intubation or death. METHODS: One hundred and twenty-three patients hospitalized with COVID-19 were enrolled at University of Florida Health Shands and the Centre Hospitalier de l'Université de Montréal. Patients' cough was continuously monitored digitally along with clinical severity of disease until hospital discharge, intubation, or death. The natural history of cough in hospitalized COVID-19 disease was described and logistic models fitted on cough time patterns were used to predict clinical outcomes. RESULTS: In both cohorts, higher early coughing rates were associated with more favorable clinical outcomes. The transitional cough rate, or maximum cough per hour rate predicting unfavorable outcomes, was 3·40 and the AUC for cough frequency as a predictor of unfavorable outcomes was 0·761. The initial 6 h (0·792) and 24 h (0·719) post-enrolment observation periods confirmed this association and showed similar predictive value. INTERPRETATION: Digital cough monitoring could be used as a prognosis biomarker to predict unfavorable clinical outcomes in COVID-19 disease. With early sampling periods showing good predictive value, this digital biomarker could be combined with clinical and paraclinical evaluation and is well adapted for triaging patients in overwhelmed or resources-limited health programs.