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Smieszek, T.

Publications and source records attributed to Smieszek, T..

3 recordsLinked to original sources
bioRxiv2019

Selection and co-selection of antibiotic resistances among Escherichia coli by antibiotic use in primary care: an ecological analysis

The majority of studies that link antibiotic usage and resistance focus on simple associations between the resistance against a specific antibiotic and the use of that specific antibiotic. However, the relationship between antibiotic use and resistance is more complex. Here we evaluate which antibiotics, including those mainly prescribed for respiratory tract infections, are associated with increased resistance among Escherichia coli isolated from urinary samples.\n\nMonthly primary care prescribing data were obtained from National Health Service (NHS) Digital. Positive E. coli records from urine samples in English primary care (n=888,207) between April 2014 and January 2016 were obtained from the Second Generation Surveillance System. Elastic net regularization was used to evaluate associations between prescribing of different antibiotic groups and resistance against amoxicillin, cephalexin, ciprofloxacin, co-amoxiclav and nitrofurantoin at the clinical commissioning group (CCG) level. England is divided into 209 CCGs, with each NHS practice prolonging to one CCG.\n\nAmoxicillin prescribing (measured in DDD/ 1000 inhabitants / day) was positively associated with amoxicillin (RR 1.03, 95% CI 1.01 - 1.04) and ciprofloxacin (RR 1.09, 95% CI 1.04 - 1.17) resistance. In contrast, nitrofurantoin prescribing was associated with lower levels of resistance to amoxicillin (RR 0.92, 95% CI 0.84 - 0.97). CCGs with higher levels of trimethoprim prescribing also had higher levels of ciprofloxacin resistance (RR 1.34, 95% CI 1.10 - 1.59).\n\nAmoxicillin, which is mainly (and often unnecessarily) prescribed for respiratory tract infections is associated with increased resistance against various antibiotics among E. coli causing urinary tract infections. Our findings suggest that when predicting the potential impact of interventions on antibiotic resistances it is important to account for use of other antibiotics, including those typically used for other indications.\n\nAuthor summaryAntibiotic resistance is increasingly recognised as a threat to modern healthcare. Effective antibiotics are crucial for treatment of serious bacterial infections and are necessary to avoid that complicated surgical procedures and chemotherapy becoming life-threatening. Antibiotic use is one of the main drivers of antibiotic resistance. The majority of antibiotic prescriptions are prescribed in primary care, however, a large proportion of these antibiotic prescriptions are unnecessary. Understanding which antibiotics are causing antibiotic resistance to what extent is needed to prevent under- or over-investment in interventions lowering use of specific antibiotics, such as rapid diagnostic tests for respiratory tract infection.\n\nWe have statistically evaluated which antibiotics are associated with higher and lower levels of antibiotic resistance against common antibiotics among Escherichia coli bacteria sampled from the urinary tract by comparing antibiotic prescribing and resistance in different geographical areas in England. Our model shows that amoxicillin, the most commonly used antibiotic in England and mainly used for respiratory tract infections, is associated with increased resistance against several other antibiotics among bacteria causing urinary tract infections. The methods used in this study, that overcome several of the limitations of previous studies, can be used to explore the complex relationships between antibiotic use and antibiotic resistance in other settings.

epidemiology↗
bioRxiv2019

Using hospital network-based surveillance for antimicrobial resistance as a more robust alternative to self-reporting

Hospital performance is often measured using self-reported statistics, such as the incidence of hospital-transmitted micro-organisms or those exhibiting antimicrobial resistance (AMR), encouraging hospitals with high levels to improve their performance. However, hospitals that increase screening efforts will appear to have a higher incidence and perform poorly, undermining comparison between hospitals and disincentivising testing, thus hampering infection control. We propose a surveillance system in which hospitals test patients previously discharged from other hospitals and report observed cases. Using NHS Hospital Episode Statistics data, we analysed patient movements across England and assessed the number of hospitals required to participate in such a reporting scheme to deliver robust estimates of incidence. With over 1.2 million admissions to English hospitals previously discharged from other hospitals annually, even when only a fraction of hospitals (41/155) participate (each screening at least 1000 of these admissions), the proposed surveillance system can estimate incidence across all hospitals. By reporting on other hospitals, the reporting of incidence is separated from the task of improving own performance. Therefore the incentives for increasing performance can be aligned to increase (rather than decrease) screening efforts, thus delivering both more comparable figures on the AMR problems across hospitals and improving infection control efforts.

epidemiology↗
bioRxiv2017

Assessing The Dynamics And Control Of Droplet- and Aerosol-Transmitted Influenza Using An Indoor Positioning System

There is increasing evidence that aerosol transmission is a major contributor to the spread of influenza. Despite this, virtually all studies assessing the dynamics and control of influenza assume that it is transmitted solely through direct contact and large droplets, requiring close physical proximity. Here, we use wireless sensors to measure simultaneously both the location and close proximity contacts in the population of a US high school. This dataset, highly resolved in space and time, allows us to model both droplet and aerosol transmission either in isolation or in combination. In particular, it allows us to computationally assess the effectiveness of overlooked mitigation strategies such as improved ventilation that are available in the case of aerosol transmission. While the effects of the type of transmission on disease outbreak dynamics appear to be weak, we find that good ventilation could be as effective in mitigating outbreaks as vaccinating the majority of the population. In simulations using empirical transmission levels observed in households, we find that bringing ventilation to recommended levels has the same mitigating effect as a vaccination coverage of 50% to 60%. Our results therefore suggest that improvements of ventilation in public spaces could be an important and easy-to-implement strategy supplementing vaccination efforts for effective control of influenza spread.

epidemiology↗