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2.
CMAJ ; 193(24): E921-E930, 2021 06 14.
Article in French | MEDLINE | ID: covidwho-1551317

ABSTRACT

CONTEXTE: Les interventions non pharmacologiques demeurent le principal moyen de maîtriser le coronavirus du syndrome respiratoire aigu sévère 2 (SRAS-CoV-2) d'ici à ce que la couverture vaccinale soit suffisante pour donner lieu à une immunité collective. Nous avons utilisé des données de mobilité anonymisées de téléphones intelligents afin de quantifier le niveau de mobilité requis pour maîtriser le SRAS-CoV-2 (c.-à-d., seuil de mobilité), et la différence par rapport au niveau de mobilité observé (c.-à-d., écart de mobilité). MÉTHODES: Nous avons procédé à une analyse de séries chronologiques sur l'incidence hebdomadaire du SRAS-CoV-2 au Canada entre le 15 mars 2020 et le 6 mars 2021. Le paramètre mesuré était le taux de croissance hebdomadaire, défini comme le rapport entre les cas d'une semaine donnée et ceux de la semaine précédente. Nous avons mesuré les effets du temps moyen passé hors domicile au cours des 3 semaines précédentes à l'aide d'un modèle de régression log-normal, en tenant compte de la province, de la semaine et de la température moyenne. Nous avons calculé le seuil de mobilité et l'écart de mobilité pour le SRAS-CoV-2. RÉSULTATS: Au cours des 51 semaines de l'étude, en tout, 888 751 personnes ont contracté le SRAS-CoV-2. Chaque augmentation de 10 % de l'écart de mobilité a été associée à une augmentation de 25 % du taux de croissance des cas hebdomadaires de SRAS-CoV-2 (rapport 1,25, intervalle de confiance à 95 % 1,20­1,29). Comparativement à la mobilité prépandémique de référence de 100 %, le seuil de mobilité a été plus élevé au cours de l'été (69 %, écart interquartile [EI] 67 %­70 %), et a chuté à 54 % pendant l'hiver 2021 (EI 52 %­55 %); un écart de mobilité a été observé au Canada entre juillet 2020 et la dernière semaine de décembre 2020. INTERPRÉTATION: La mobilité permet de prédire avec fiabilité et constance la croissance des cas hebdomadaires et il faut maintenir des niveaux faibles de mobilité pour maîtriser le SRAS-CoV-2 jusqu'à la fin du printemps 2021. Les données de mobilité anonymisées des téléphones intelligents peuvent servir à guider le relâchement ou le resserrement des mesures de distanciation physique provinciales et régionales.


Subject(s)
COVID-19/prevention & control , Geographic Mapping , Mobile Applications/standards , Patient Identification Systems/methods , COVID-19/epidemiology , COVID-19/transmission , Canada/epidemiology , Humans , Mobile Applications/statistics & numerical data , Patient Identification Systems/statistics & numerical data , Quarantine/methods , Quarantine/standards , Quarantine/statistics & numerical data , Regression Analysis , Time Factors
3.
Clin Microbiol Infect ; 28(4): 491-501, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1540547

ABSTRACT

BACKGROUND: The prevalence of bacterial infection in patients with COVID-19 is low, however, empiric antibiotic use is high. Risk stratification may be needed to minimize unnecessary empiric antibiotic use. OBJECTIVE: To identify risk factors and microbiology associated with respiratory and bloodstream bacterial infection in patients with COVID-19. DATA SOURCES: We searched MEDLINE, OVID Epub and EMBASE for published literature up to 5 February 2021. STUDY ELIGIBILITY CRITERIA: Studies including at least 50 patients with COVID-19 in any healthcare setting. METHODS: We used a validated ten-item risk of bias tool for disease prevalence. The main outcome of interest was the proportion of COVID-19 patients with bloodstream and/or respiratory bacterial co-infection and secondary infection. We performed meta-regression to identify study population factors associated with bacterial infection including healthcare setting, age, comorbidities and COVID-19 medication. RESULTS: Out of 33 345 studies screened, 171 were included in the final analysis. Bacterial infection data were available from 171 262 patients. The prevalence of co-infection was 5.1% (95% CI 3.6-7.1%) and secondary infection was 13.1% (95% CI 9.8-17.2%). There was a higher odds of bacterial infection in studies with a higher proportion of patients in the intensive care unit (ICU) (adjusted OR 18.8, 95% CI 6.5-54.8). Female sex was associated with a lower odds of secondary infection (adjusted OR 0.73, 95% CI 0.55-0.97) but not co-infection (adjusted OR 1.05, 95% CI 0.80-1.37). The most common organisms isolated included Staphylococcus aureus, coagulase-negative staphylococci and Klebsiella species. CONCLUSIONS: While the odds of respiratory and bloodstream bacterial infection are low in patients with COVID-19, meta-regression revealed potential risk factors for infection, including ICU setting and mechanical ventilation. The risk for secondary infection is substantially greater than the risk for co-infection in patients with COVID-19. Understanding predictors of co-infection and secondary infection may help to support improved antibiotic stewardship in patients with COVID-19.


Subject(s)
Antimicrobial Stewardship , Bacterial Infections , COVID-19 , Respiratory Tract Infections , Bacteria , Bacterial Infections/drug therapy , Bacterial Infections/epidemiology , COVID-19/epidemiology , Female , Humans , Respiratory Tract Infections/drug therapy
6.
Sci Data ; 8(1): 173, 2021 07 15.
Article in English | MEDLINE | ID: covidwho-1315604

ABSTRACT

The COVID-19 pandemic has demonstrated the need for real-time, open-access epidemiological information to inform public health decision-making and outbreak control efforts. In Canada, authority for healthcare delivery primarily lies at the provincial and territorial level; however, at the outset of the pandemic no definitive pan-Canadian COVID-19 datasets were available. The COVID-19 Canada Open Data Working Group was created to fill this crucial data gap. As a team of volunteer contributors, we collect daily COVID-19 data from a variety of governmental and non-governmental sources and curate a line-list of cases and mortality for all provinces and territories of Canada, including information on location, age, sex, travel history, and exposure, where available. We also curate time series of COVID-19 recoveries, testing, and vaccine doses administered and distributed. Data are recorded systematically at a fine sub-national scale, which can be used to support robust understanding of COVID-19 hotspots. We continue to maintain this dataset, and an accompanying online dashboard, to provide a reliable pan-Canadian COVID-19 resource to researchers, journalists, and the general public.


Subject(s)
COVID-19 , Databases, Factual , Vaccination/statistics & numerical data , COVID-19/epidemiology , COVID-19/prevention & control , Canada/epidemiology , Data Collection , Humans , Pandemics
7.
CMAJ ; 193(17): E592-E600, 2021 04 26.
Article in English | MEDLINE | ID: covidwho-1207650

ABSTRACT

BACKGROUND: Nonpharmaceutical interventions remain the primary means of controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until vaccination coverage is sufficient to achieve herd immunity. We used anonymized smartphone mobility measures to quantify the mobility level needed to control SARS-CoV-2 (i.e., mobility threshold), and the difference relative to the observed mobility level (i.e., mobility gap). METHODS: We conducted a time-series study of the weekly incidence of SARS-CoV-2 in Canada from Mar. 15, 2020, to Mar. 6, 2021. The outcome was weekly growth rate, defined as the ratio of cases in a given week versus the previous week. We evaluated the effects of average time spent outside the home in the previous 3 weeks using a log-normal regression model, accounting for province, week and mean temperature. We calculated the SARS-CoV-2 mobility threshold and gap. RESULTS: Across the 51-week study period, a total of 888 751 people were infected with SARS-CoV-2. Each 10% increase in the mobility gap was associated with a 25% increase in the SARS-CoV-2 weekly case growth rate (ratio 1.25, 95% confidence interval 1.20-1.29). Compared to the prepandemic baseline mobility of 100%, the mobility threshold was highest in the summer (69%; interquartile range [IQR] 67%-70%), and dropped to 54% in winter 2021 (IQR 52%-55%); a mobility gap was present in Canada from July 2020 until the last week of December 2020. INTERPRETATION: Mobility strongly and consistently predicts weekly case growth, and low levels of mobility are needed to control SARS-CoV-2 through spring 2021. Mobility measures from anonymized smartphone data can be used to guide provincial and regional loosening and tightening of physical distancing measures.


Subject(s)
COVID-19 Testing/trends , COVID-19/prevention & control , Disease Transmission, Infectious/prevention & control , COVID-19/epidemiology , Canada/epidemiology , Female , Forecasting , Humans , Incidence , Interrupted Time Series Analysis , Male , Physical Distancing , Public Health , Quarantine/trends
8.
Clin Infect Dis ; 74(2): 368-370, 2022 01 29.
Article in English | MEDLINE | ID: covidwho-1227648
9.
Clin Microbiol Infect ; 27(4): 520-531, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1009396

ABSTRACT

BACKGROUND: The proportion of patients infected with SARS-CoV-2 that are prescribed antibiotics is uncertain, and may contribute to patient harm and global antibiotic resistance. OBJECTIVE: The aim was to estimate the prevalence and associated factors of antibiotic prescribing in patients with COVID-19. DATA SOURCES: We searched MEDLINE, OVID Epub and EMBASE for published literature on human subjects in English up to June 9 2020. STUDY ELIGIBILITY CRITERIA: We included randomized controlled trials; cohort studies; case series with ≥10 patients; and experimental or observational design that evaluated antibiotic prescribing. PARTICIPANTS: The study participants were patients with laboratory-confirmed SARS-CoV-2 infection, across all healthcare settings (hospital and community) and age groups (paediatric and adult). METHODS: The main outcome of interest was proportion of COVID-19 patients prescribed an antibiotic, stratified by geographical region, severity of illness and age. We pooled proportion data using random effects meta-analysis. RESULTS: We screened 7469 studies, from which 154 were included in the final analysis. Antibiotic data were available from 30 623 patients. The prevalence of antibiotic prescribing was 74.6% (95% CI 68.3-80.0%). On univariable meta-regression, antibiotic prescribing was lower in children (prescribing prevalence odds ratio (OR) 0.10, 95% CI 0.03-0.33) compared with adults. Antibiotic prescribing was higher with increasing patient age (OR 1.45 per 10 year increase, 95% CI 1.18-1.77) and higher with increasing proportion of patients requiring mechanical ventilation (OR 1.33 per 10% increase, 95% CI 1.15-1.54). Estimated bacterial co-infection was 8.6% (95% CI 4.7-15.2%) from 31 studies. CONCLUSIONS: Three-quarters of patients with COVID-19 receive antibiotics, prescribing is significantly higher than the estimated prevalence of bacterial co-infection. Unnecessary antibiotic use is likely to be high in patients with COVID-19.


Subject(s)
Anti-Bacterial Agents/therapeutic use , COVID-19 , Drug Prescriptions , Drug Utilization , Age Factors , Antimicrobial Stewardship , Bacterial Infections/complications , Bacterial Infections/drug therapy , Bacterial Infections/epidemiology , COVID-19/complications , Coinfection/drug therapy , Coinfection/epidemiology , Female , Humans , Male
10.
Clin Microbiol Infect ; 26(12): 1622-1629, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-664356

ABSTRACT

BACKGROUND: Bacterial co-pathogens are commonly identified in viral respiratory infections and are important causes of morbidity and mortality. The prevalence of bacterial infection in patients infected with SARS-CoV-2 is not well understood. AIMS: To determine the prevalence of bacterial co-infection (at presentation) and secondary infection (after presentation) in patients with COVID-19. SOURCES: We performed a systematic search of MEDLINE, OVID Epub and EMBASE databases for English language literature from 2019 to April 16, 2020. Studies were included if they (a) evaluated patients with confirmed COVID-19 and (b) reported the prevalence of acute bacterial infection. CONTENT: Data were extracted by a single reviewer and cross-checked by a second reviewer. The main outcome was the proportion of COVID-19 patients with an acute bacterial infection. Any bacteria detected from non-respiratory-tract or non-bloodstream sources were excluded. Of 1308 studies screened, 24 were eligible and included in the rapid review representing 3338 patients with COVID-19 evaluated for acute bacterial infection. In the meta-analysis, bacterial co-infection (estimated on presentation) was identified in 3.5% of patients (95%CI 0.4-6.7%) and secondary bacterial infection in 14.3% of patients (95%CI 9.6-18.9%). The overall proportion of COVID-19 patients with bacterial infection was 6.9% (95%CI 4.3-9.5%). Bacterial infection was more common in critically ill patients (8.1%, 95%CI 2.3-13.8%). The majority of patients with COVID-19 received antibiotics (71.9%, 95%CI 56.1 to 87.7%). IMPLICATIONS: Bacterial co-infection is relatively infrequent in hospitalized patients with COVID-19. The majority of these patients may not require empirical antibacterial treatment.


Subject(s)
Bacterial Infections/epidemiology , COVID-19/complications , COVID-19/microbiology , Coinfection/epidemiology , Asia/epidemiology , Bacteria/classification , Bacteria/isolation & purification , Bacteria/pathogenicity , Bacterial Infections/microbiology , Coinfection/microbiology , Coinfection/virology , Critical Illness/epidemiology , Data Management , Female , Humans , Male , Pandemics , Prevalence , Respiratory Tract Infections , United States/epidemiology
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