Exploring factors shaping antibiotic resistance patterns in Streptococcus pneumoniae during the 2020 COVID-19 pandemic.

Non-pharmaceutical interventions implemented to block SARS-CoV-2 transmission in early 2020 led to global reductions in the incidence of invasive pneumococcal disease (IPD). By contrast, most European countries reported an increase in antibiotic resistance among invasive Streptococcus pneumoniae isolates from 2019 to 2020, while an increasing number of studies reported stable pneumococcal carriage prevalence over the same period. To disentangle the impacts of the COVID-19 pandemic on pneumococcal epidemiology in the community setting, we propose a mathematical model formalizing simultaneous transmission of SARS-CoV-2 and antibiotic-sensitive and -resistant strains of S. pneumoniae. To test hypotheses underlying these trends five mechanisms were built into the model and examined: (1) a population-wide reduction of antibiotic prescriptions in the community, (2) lockdown effect on pneumococcal transmission, (3) a reduced risk of developing an IPD due to the absence of common respiratory viruses, (4) community azithromycin use in COVID-19 infected individuals, (5) and a longer carriage duration of antibiotic-resistant pneumococcal strains. Among 31 possible pandemic scenarios involving mechanisms individually or in combination, model simulations surprisingly identified only two scenarios that reproduced the reported trends in the general population. They included factors (1), (3), and (4). These scenarios replicated a nearly 50% reduction in annual IPD, and an increase in antibiotic resistance from 20% to 22%, all while maintaining a relatively stable pneumococcal carriage. Exploring further, higher SARS-CoV-2 R0 values and synergistic within-host virus-bacteria interaction mechanisms could have additionally contributed to the observed antibiotic resistance increase. Our work demonstrates the utility of the mathematical modeling approach in unraveling the complex effects of the COVID-19 pandemic responses on AMR dynamics.

Determinants of worldwide antibiotic resistance dynamics across drug-bacterium pairs: a multivariable spatial-temporal analysis using ATLAS.

BACKGROUND: Antibiotic resistance (ABR) is a major concern for global health. However, factors driving its emergence and dissemination are not fully understood. Identification of such factors is crucial to explain heterogeneity in ABR rates observed across space, time, and species and antibiotics. METHODS: We analysed count data of clinical isolates from 51 countries over 2006-19 for thirteen drug-bacterium pairs taken from the ATLAS database. We characterised ABR spatial and temporal patterns and used a mixed-effect negative binomial model, accounting for country-year dependences with random effects, to investigate associations with potential drivers, including antibiotic sales, economic and health indicators, meteorological data, population density, and tourism. FINDINGS: ABR patterns were strongly country and drug-bacterium pair dependent. In 2019, median ABR rates ranged from 6·3% (IQR 19·7% [0·5-20·2]) for carbapenem-resistant Klebsiella pneumoniae to 80·7% (41·8% [50·4-92·2]) for fluoroquinolone-resistant Acinetobacter baumannii, with heterogeneity across countries. From 2006 to 2019, carbapenem resistance increased in more than 60% of investigated countries; no global trend was observed for other resistances. Multivariable analyses identified significant associations of ABR with country-level selecting antibiotic sales, but only in fluoroquinolone-resistant-Escherichia coli, fluoroquinolone-resistant-Pseudomonas aeruginosa, and carbapenem-resistant-A baumannii. We also found a correlation between temperature and resistance in Enterobacteriaceae and with the health system quality for all drug-bacterium pairs except Enterococci and Streptococcus pneumoniae pairs. Despite wide consideration of possible explanatory variables, drug-bacterium pair ABR rates still showed unexplained spatial random effects variance. INTERPRETATION: Our findings reflect the diversity of mechanisms driving global antibiotic resistance across pathogens and stress the need for tailored interventions to tackle bacterial resistance. FUNDING: Independent research Pfizer Global Medical Grant and ANR Labex IBEID.

Implementation of a Self-Triage Web Application for Suspected COVID-19 and Its Impact on Emergency Call Centers: Observational Study.

BACKGROUND: We developed a self-triage web application for COVID-19 symptoms, which was launched in France in March 2020, when French health authorities recommended all patients with suspected COVID-19 call an emergency phone number. OBJECTIVE: Our objective was to determine if a self-triage tool could reduce the burden on emergency call centers and help predict increasing burden on hospitals. METHODS: Users were asked questions about their underlying conditions, sociodemographic status, postal code, and main COVID-19 symptoms. Participants were advised to call an emergency call center if they reported dyspnea or complete loss of appetite for over 24 hours. Data on COVID-19-related calls were collected from 6 emergency call centers and data on COVID-19 hospitalizations were collected from Santé Publique France and the French Ministry of Health. We examined the change in the number of emergency calls before and after the launch of the web application. RESULTS: From March 17 to April 2, 2020, 735,419 questionnaires were registered in the study area. Of these, 121,370 (16.5%) led to a recommendation to call an emergency center. The peak number of overall questionnaires and of questionnaires leading to a recommendation to call an emergency center were observed on March 22, 2020. In the 17 days preceding the launch of the web application, emergency call centers in the study area registered 66,925 COVID-19-related calls and local hospitals admitted 639 patients for COVID-19; the ratio of emergency calls to hospitalizations for COVID-19 was 104.7 to 1. In the 17 days following the launch of the web application, there were 82,347 emergency calls and 6009 new hospitalizations for COVID-19, a ratio of 13.7 calls to 1 hospitalization (chi-square test: P<.001). CONCLUSIONS: The self-triage web application launch was followed by a nearly 10-fold increase in COVID-19-related hospitalizations with only a 23% increase in emergency calls. The peak of questionnaire completions preceded the peak of COVID-19-related hospitalizations by 5 days. Although the design of this study does not allow us to conclude that the self-triage tool alone contributed to the alleviation of calls to the emergency call centers, it does suggest that it played a role, and may be used for predicting increasing burden on hospitals. TRIAL REGISTRATION: ClinicalTrials.gov NCT04331171; https://clinicaltrials.gov/ct2/show/NCT04331171.