Modeling the impact of urban and hospital eco-exposomes on antibiotic-resistance dynamics in wastewaters.

The emergence and selection of antibiotic resistance is a major public health problem worldwide. The presence of antibiotic-resistant bacteria (ARBs) in natural and anthropogenic environments threatens the sustainability of efforts to reduce resistance in human and animal populations. Here, we use mathematical modeling of the selective effect of antibiotics and contaminants on the dynamics of bacterial resistance in water to analyze longitudinal spatio-temporal data collected in hospital and urban wastewater between 2012 and 2015. Samples were collected monthly during the study period at four different sites in Haute-Savoie, France: hospital and urban wastewater, before and after water treatment plants. Three different categories of exposure variables were collected simultaneously: 1) heavy metals, 2) antibiotics and 3) surfactants for a total of 13 drugs/molecules; in parallel to the normalized abundance of 88 individual genes and mobile genetic elements, mostly conferring resistance to antibiotics. A simple hypothesis-driven model describing weekly antibiotic resistance gene (ARG) dynamics was proposed to fit the available data, assuming that normalized gene abundance is proportional to antibiotic resistant bacteria (ARB) populations in water. The detected compounds were found to influence the dynamics of 17 genes found at multiple sites. While mercury and vancomycin were associated with increased ARG and affected the dynamics of 10 and 12 identified genes respectively, surfactants antagonistically affected the dynamics of three genes. The models proposed here make it possible to analyze the relationship between the persistence of resistance genes in the aquatic environment and specific compounds associated with human activities from longitudinal data. Our analysis of French data over 2012-2015 identified mercury and vancomycin as co-selectors for some ARGs.

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.

Preventing iatrogenic HCV infection: A quantitative risk assessment based on observational data in an Egyptian hospital.

When compliance with infection control recommendations is non-optimal, hospitals may play an important role in hepatitis C (HCV) transmission. However, few studies have analyzed the nosocomial HCV acquisition risk based on detailed empirical data. Here, we used data from a prospective cohort study conducted on 500 patients in the Ain Shams hospital (Cairo, Egypt) in 2017 with the objective of identifying (i) high-risk patient profiles and (ii) transmission hotspots within the hospital. Data included information on patient HCV status upon admission, their trajectories between wards and the invasive procedures they underwent. We first performed a sequence analysis to identify different hospitalization profiles. Second, we estimated each patient's individual risk of HCV acquisition based on ward-specific prevalence and procedures undergone, and risk hotspots by computing ward-level risks. Then, using a beta regression model, we evaluated upon-admission factors linked to HCV acquisition risk and built a score estimating the risk of HCV infection during hospitalization based on these factors. Finally, we assessed and compared ward-focused and patient-focused HCV control strategies. The sequence analysis based on patient trajectories allowed us to identify four distinct patient trajectory profiles. The risk of HCV infection was greater in the internal medicine department, compared to the surgery department (0·188% [0·142%-0·235%] vs. 0·043%, CI 95%: [0·036%-0·050%]), with risk hotspots in the geriatric, tropical medicine and intensive-care wards. Upon-admission risk predictors included source of admission, age, reason for hospitalization, and medical history. Interventions focused on the most at-risk patients were most effective to reduce HCV infection risk. Our results might help reduce the risk of HCV acquisition during hospitalization in Egypt by targeting enhanced control measures to ward-level transmission hotspots and to at-risk patients identified upon admission.

Sick leave due to COVID-19 during the first pandemic wave in France, 2020.

OBJECTIVES: To quantify the burden of COVID-19-related sick leave during the first pandemic wave in France, accounting for sick leaves due to symptomatic COVID-19 ('symptomatic sick leaves') and those due to close contact with COVID-19 cases ('contact sick leaves'). METHODS: We combined data from a national demographic database, an occupational health survey, a social behaviour survey and a dynamic SARS-CoV-2 transmission model. Sick leave incidence from 1 March 2020 to 31 May 2020 was estimated by summing daily probabilities of symptomatic and contact sick leaves, stratified by age and administrative region. RESULTS: There were an estimated 1.70M COVID-19-related sick leaves among France's 40M working-age adults during the first pandemic wave, including 0.42M due to COVID-19 symptoms and 1.28M due to COVID-19 contacts. There was great geographical variation, with peak daily sick leave incidence ranging from 230 in Corse (Corsica) to 33 000 in Île-de-France (the greater Paris region), and greatest overall burden in regions of north-eastern France. Regional sick leave burden was generally proportional to local COVID-19 prevalence, but age-adjusted employment rates and contact behaviours also contributed. For instance, 37% of symptomatic infections occurred in Île-de-France, but 45% of sick leaves. Middle-aged workers bore disproportionately high sick leave burden, owing predominantly to greater incidence of contact sick leaves. CONCLUSIONS: France was heavily impacted by sick leave during the first pandemic wave, with COVID-19 contacts accounting for approximately three-quarters of COVID-19-related sick leaves. In the absence of representative sick leave registry data, local demography, employment patterns, epidemiological trends and contact behaviours can be synthesised to quantify sick leave burden and, in turn, predict economic consequences of infectious disease epidemics.

Meta-analysis: risk of hepatitis C virus infection associated with hospital-based invasive procedures.

BACKGROUND: Healthcare settings, where invasive procedures are frequently performed, may play an important role in the transmission dynamics of blood-borne pathogens when compliance with infection control precautions is suboptimal. AIMS: To understand and quantify the role of hospital-based invasive procedures on hepatitis C virus (HCV) transmission. METHODS: We conducted a systematic review and meta-analysis to identify recent studies reporting association measures of HCV infection risk that are linked to iatrogenic procedures. Based on expert opinion, invasive procedures were categorised into 10 groups for which pooled measures were calculated. Finally, the relationship between pooled measures and the country-level HCV prevalence or the Healthcare Access and Quality (HAQ) index was assessed by meta-regression. RESULTS: We included 71 studies in the analysis. The most frequently evaluated procedures were blood transfusion (66 measures) and surgery (43 measures). The pooled odds ratio (OR) of HCV infection varied widely, ranging from 1.46 (95% confidence interval: 1.14-1.88) for dental procedures to 3.22 (1.7-6.11) for transplantation. The OR for blood transfusion was higher for transfusions performed before 1998 (3.77, 2.42-5.88) than for those without a specified/recent date (2.20, 1.77-2.75). In procedure-specific analyses, the HCV infection risk was significantly negatively associated with the HAQ for endoscopy and positively associated with HCV prevalence for endoscopy and surgery. CONCLUSIONS: Various invasive procedures were significantly associated with HCV infection. Our results provide a ranking of procedures in terms of HCV risk that may be used for prioritisation of infection control interventions, especially in high HCV prevalence settings.