Providing laypeople with results from dynamic infectious disease modelling studies affects their allocation preference for scarce medical resources-a factorial experiment.

BACKGROUND: Allocation of scarce medical resources can be based on different principles. It has not yet been investigated which allocation schemes are preferred by medical laypeople in a particular situation of medical scarcity like an emerging infectious disease and how the choices are affected by providing information about expected population-level effects of the allocation scheme based on modelling studies. We investigated the potential benefit of strategic communication of infectious disease modelling results. METHODS: In a two-way factorial experiment (n = 878 participants), we investigated if prognosis of the disease or information about expected effects on mortality at population-level (based on dynamic infectious disease modelling studies) influenced the choice of preferred allocation schemes for prevention and treatment of an unspecified sexually transmitted infection. A qualitative analysis of the reasons for choosing specific allocation schemes supplements our results. RESULTS: Presence of the factor "information about the population-level effects of the allocation scheme" substantially increased the probability of choosing a resource allocation system that minimized overall harm among the population, while prognosis did not affect allocation choices. The main reasons for choosing an allocation scheme differed among schemes, but did not differ among those who received additional model-based information on expected population-level effects and those who did not. CONCLUSIONS: Providing information on the expected population-level effects from dynamic infectious disease modelling studies resulted in a substantially different choice of allocation schemes. This finding supports the importance of incorporating model-based information in decision-making processes and communication strategies.

Explaining variation in antibiotic prescribing between general practices in the UK.

Objectives: Primary care practices in England differ in antibiotic prescribing rates, and, anecdotally, prescribers justify high prescribing rates based on their individual case mix. The aim of this paper was to explore to what extent factors such as patient comorbidities explain this variation in antibiotic prescribing. Methods: Primary care consultation and prescribing data recorded in The Health Improvement Network (THIN) database in 2013 were used. Boosted regression trees (BRTs) and negative binomial regression (NBR) models were used to evaluate associations between predictors and antibiotic prescribing rates. The following variables were considered as potential predictors: various infection-related consultation rates, proportions of patients with comorbidities, proportion of patients with inhaled/systemic corticosteroids or immunosuppressive drugs, and demographic traits. Results: The median antibiotic prescribing rate was 65.6 (IQR 57.4-74.0) per 100 registered patients among 348 English practices. In the BRT model, consultation rates had the largest total relative influence on antibiotic prescribing rate (53.5%), followed by steroid and immunosuppressive drugs (31.6%) and comorbidities (12.2%). Only 21% of the deviance could be explained by an NBR model considering only comorbidities and age and gender, whereas 57% of the deviance could be explained by the model considering all variables. Conclusions: The majority of practice-level variation in antibiotic prescribing cannot be explained by variation in prevalence of comorbidities. Factors such as high consultation rates for respiratory tract infections and high prescribing rates for corticosteroids could explain much of the variation, and as such may be considered in determining a practice's potential to reduce prescribing.

Actual versus 'ideal' antibiotic prescribing for common conditions in English primary care.

Objectives: Previous work based on guidelines and expert opinion identified 'ideal' prescribing proportions-the overall proportion of consultations that should result in an antibiotic prescription-for common infectious conditions. Here, actual condition-specific prescribing proportions in primary care in England were compared with ideal prescribing proportions identified by experts. Methods: All recorded consultations for common infectious conditions (cough, bronchitis, exacerbations of asthma or chronic obstructive pulmonary disease, sore throat, rhinosinusitis, otitis media, lower respiratory tract infection, upper respiratory tract infection, influenza-like illness, urinary tract infection, impetigo, acne, gastroenteritis) for 2013-15 were extracted from The Health Improvement Network (THIN) database. The proportions of consultations resulting in an antibiotic prescription were established, concentrating on acute presentations in patients without relevant comorbidities. These actual prescribing proportions were then compared with previously established 'ideal' proportions by condition. Results: For most conditions, substantially higher proportions of consultations resulted in an antibiotic prescription than was deemed appropriate according to expert opinion. An antibiotic was prescribed in 41% of all acute cough consultations when experts advocated 10%. For other conditions the proportions were: bronchitis (actual 82% versus ideal 13%); sore throat (actual 59% versus ideal 13%); rhinosinusitis (actual 88% versus ideal 11%); and acute otitis media in 2- to 18-year-olds (actual 92% versus ideal 17%). Substantial variation between practices was found. Conclusions: This work has identified substantial overprescribing of antibiotics in English primary care, and highlights conditions where this is most pronounced, particularly in respiratory tract conditions.

Defining the appropriateness and inappropriateness of antibiotic prescribing in primary care.

Objectives: To assess the appropriateness of prescribing systemic antibiotics for different clinical conditions in primary care, and to quantify 'ideal' antibiotic prescribing proportions in conditions for which antibiotic treatment is sometimes but not always indicated. Methods: Prescribing guidelines were consulted to define the appropriateness of antibiotic therapy for the conditions that resulted in antibiotic prescriptions between 2013 and 2015 in The Health Improvement Network (THIN) primary care database. The opinions of subject experts were then formally elicited to quantify ideal antibiotic prescribing proportions for 10 common conditions. Results: Of the antibiotic prescriptions in THIN, 52.5% were for conditions that could be assessed using prescribing guidelines. Among these, the vast majority of prescriptions (91.4%) were for conditions where antibiotic appropriateness is conditional on patient-specific indicators. Experts estimated low ideal prescribing proportions in acute, non-comorbid presentations of many of these conditions, such as cough (10% of patients), rhinosinusitis (11%), bronchitis (13%) and sore throat (13%). Conversely, antibiotics were believed to be appropriate in 75% of non-pregnant women with non-recurrent urinary tract infection. In impetigo and acute exacerbation of chronic obstructive pulmonary disease, experts clustered into distinct groups that believed in either high or low prescribing. Conclusions: In English primary care, most antibiotics are prescribed for conditions that only sometimes require antibiotic treatment, depending on patient-specific indicators. Experts estimated low ideal prescribing proportions in many of these conditions. Incomplete prescribing guidelines and disagreement about prescribing in some conditions highlight further research needs.

Antibiotics in primary care in England: which antibiotics are prescribed and for which conditions?

Objectives: To analyse antibiotic prescribing behaviour in English primary care with particular regard to which antibiotics are prescribed and for which conditions. Methods: Primary care data from 2013-15 recorded in The Health Improvement Network (THIN) database were analysed. Records with a prescription for systemic antibiotics were extracted and linked to co-occurring diagnostic codes, which were used to attribute prescriptions to clinical conditions. We further assessed which antibiotic classes were prescribed and which conditions resulted in the greatest share of prescribing. Results: The prescribing rate varied considerably among participating practices, with a median of 626 prescriptions/1000 patients (IQR 543-699). In total, 69% of antibiotic prescriptions (n = 3 156 507) could be linked to a body system and/or clinical condition. Of these prescriptions, 46% were linked to conditions of the respiratory tract, including ear, nose and throat (RT/ENT); leading conditions within this group were cough symptoms (22.7%), lower respiratory tract infection (RTI) (17.9%), sore throat (16.7%) and upper RTI (14.5%). After RT/ENT infections, infections of the urogenital tract (22.7% of prescriptions linked to a condition) and skin/wounds (16.4%) accounted for the greatest share of prescribing. Penicillins accounted for 50% of all prescriptions, followed by macrolides (13%), tetracyclines (12%) and trimethoprim (11%). Conclusions: The majority of antibiotic prescriptions in English primary care were for infections of the respiratory and urinary tracts. However, in almost one-third of all prescriptions no clinical justification was documented. Antibiotic prescribing rates varied substantially between practices, suggesting that there is potential to reduce prescribing in at least some practices.

Association between use of different antibiotics and trimethoprim resistance: going beyond the obvious crude association.

Objectives: To evaluate the association between use of different antibiotics and trimethoprim resistance at the population level. Methods: Monthly primary care prescribing data were obtained from NHS Digital. Positive Enterobacteriaceae records from urine samples from patients between April 2014 and January 2016 in England were extracted from PHE's Second Generation Surveillance System (SGSS). Elastic net regularization and generalized boosted regression models were used to evaluate associations between antibiotic prescribing and trimethoprim resistance, both measured at Clinical Commission Group level. Results: In total, 2 487 635 (99%) of 2 513 285 urine Enterobacteriaceae samples from 1 667 839 patients were tested for trimethoprim resistance. Using both elastic net regularization and generalized boosted regression models, geographical variation in trimethoprim resistance among Enterobacteriaceae urinary samples could be partly explained by geographical variation in use of trimethoprim (relative risk = 1.14, 95% CI = 1.02-1.75; relative influence = 4.1) and penicillins with extended spectrum (mainly amoxicillin/ampicillin in England) (relative risk = 1.19, 95% CI = 1.11-1.30; relative influence = 7.4). Nitrofurantoin use was associated with lower trimethoprim resistance levels (relative risk = 0.83, 95% CI = 0.57-0.96; relative influence = 9.2). Conclusions: Use of amoxicillin/ampicillin explained more of the variance in trimethoprim resistance than trimethoprim use, suggesting that co-selection by these antibiotics is an important driver of trimethoprim resistance levels at the population level. Nitrofurantoin use was consistently associated with lower trimethoprim resistance levels, indicating that trimethoprim resistance levels could be lowered if trimethoprim use is replaced by nitrofurantoin.

Potential for reducing inappropriate antibiotic prescribing in English primary care.

Objectives: To identify and quantify inappropriate systemic antibiotic prescribing in primary care in England, and ultimately to determine the potential for reduction in prescribing of antibiotics. Methods: Primary care data from 2013-15 recorded in The Health Improvement Network (THIN) database were used. Potentially inappropriate prescribing events in the database were identified by: (i) comparing prescribing events against treatment guidelines; (ii) comparing actual proportions of consultations resulting in prescription for a set of conditions with the ideal proportions derived from expert opinion; and (iii) identifying high prescribers and their number of prescriptions above an age- and body-system-specific benchmark. Results: Applying the most conservative assumptions, 8.8% of all systemic antibiotic prescriptions in English primary care were identified as inappropriate, and in the least conservative scenario 23.1% of prescriptions were inappropriate. All practices had non-zero reduction potentials, ranging from 6.4% to 43.5% in the middle scenario. The four conditions that contributed most to inappropriate prescribing were sore throat (23.0% of identified inappropriate prescriptions), cough (22.2%), sinusitis (7.6%) and acute otitis media (5.7%). One-third of all antibiotic prescriptions lacked an informative diagnostic code. Conclusions: This work demonstrates (i) the existence of substantial inappropriate antibiotic prescribing and (ii) poor diagnostic coding in English primary care. All practices (not just the high prescribers) should engage in efforts to improve antimicrobial stewardship. Better diagnostic coding, more precise prescribing guidelines and a deeper understanding of appropriate long-term uses of antibiotics would allow identification of further potential for reductions.

Measuring distance through dense weighted networks: The case of hospital-associated pathogens.

Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014-2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult.

Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants' attitudes.

BACKGROUND: Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement - paper diaries vs. wearable proximity sensors - that were applied concurrently to the same population, and we measured acceptability. METHODS: We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability. RESULTS: There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for <5 min contact duration (significantly lower than the following, p < 0.05), P = 86 % for 5-15 min, P = 89 % for 15-60 min, and P = 94 % for >60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants' aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors. CONCLUSION: Reporting and recording were not complete; reporting was particularly incomplete for contacts <5 min. The types of contact that both methods are capable of detecting are partly different. Participants appear to have overestimated the duration of their contacts. Conducting a study with diaries or wearable sensors was acceptable to and mostly easily done by participants. Both methods can be applied meaningfully if their specific limitations are considered and incompleteness is accounted for.

How should social mixing be measured: comparing web-based survey and sensor-based methods.

BACKGROUND: Contact surveys and diaries have conventionally been used to measure contact networks in different settings for elucidating infectious disease transmission dynamics of respiratory infections. More recently, technological advances have permitted the use of wireless sensor devices, which can be worn by individuals interacting in a particular social context to record high resolution mixing patterns. To date, a direct comparison of these two different methods for collecting contact data has not been performed. METHODS: We studied the contact network at a United States high school in the spring of 2012. All school members (i.e., students, teachers, and other staff) were invited to wear wireless sensor devices for a single school day, and asked to remember and report the name and duration of all of their close proximity conversational contacts for that day in an online contact survey. We compared the two methods in terms of the resulting network densities, nodal degrees, and degree distributions. We also assessed the correspondence between the methods at the dyadic and individual levels. RESULTS: We found limited congruence in recorded contact data between the online contact survey and wireless sensors. In particular, there was only negligible correlation between the two methods for nodal degree, and the degree distribution differed substantially between both methods. We found that survey underreporting was a significant source of the difference between the two methods, and that this difference could be improved by excluding individuals who reported only a few contact partners. Additionally, survey reporting was more accurate for contacts of longer duration, and very inaccurate for contacts of shorter duration. Finally, female participants tended to report more accurately than male participants. CONCLUSIONS: Online contact surveys and wireless sensor devices collected incongruent network data from an identical setting. This finding suggests that these two methods cannot be used interchangeably for informing models of infectious disease dynamics.

A low-cost method to assess the epidemiological importance of individuals in controlling infectious disease outbreaks.

BACKGROUND: Infectious disease outbreaks in communities can be controlled by early detection and effective prevention measures. Assessing the relative importance of each individual community member with respect to these two processes requires detailed knowledge about the underlying social contact network on which the disease can spread. However, mapping social contact networks is typically too resource-intensive to be a practical possibility for most communities and institutions. METHODS: Here, we describe a simple, low-cost method - called collocation ranking - to assess individual importance for early detection and targeted intervention strategies that are easily implementable in practice. The method is based on knowledge about individual collocation which is readily available in many community settings such as schools, offices, hospitals, and so on. We computationally validate our method in a school setting by comparing the outcome of the method against computational predictions based on outbreak simulations on an empirical high-resolution contact network. We compare collocation ranking to other methods for assessing the epidemiological importance of the members of a population. To this end, we select subpopulations of the school population by applying these assessment methods to the population and adding individuals to the subpopulation according to their individual rank. Then, we assess how suited these subpopulations are for early detection and targeted intervention strategies. RESULTS: Likelihood and timing of infection during an outbreak are important features for early detection and targeted intervention strategies. Subpopulations selected by the collocation ranking method show a substantially higher average infection probability and an earlier onset of symptoms than randomly selected subpopulations. Furthermore, these subpopulations selected by the collocation ranking method were close to the optimum. CONCLUSIONS: Our results indicate that collocation ranking is a highly effective method to assess individual importance, providing critical low-cost information for the development of sentinel surveillance systems and prevention strategies.

Non-statin lipid-lowering therapy over time in very-high-risk patients: effectiveness of fixed-dose statin/ezetimibe compared to separate pill combination on LDL-C.

BACKGROUND: Many patients at very-high atherosclerotic cardiovascular disease risk do not reach guideline-recommended targets for LDL-C. There is a lack of data on real-world use of non-statin lipid-lowering therapies (LLT) and little is known on the effectiveness of fixed-dose combinations (FDC). We therefore studied prescription trends in oral non-statin LLT and their effects on LDL-C. METHODS: A retrospective analysis was conducted of electronic medical records of outpatients at very-high cardiovascular risk treated by general practitioners (GPs) and cardiologists, and prescribed LLT in Germany between 2013 and 2018. RESULTS: Data from 311,242 patients were analysed. Prescriptions for high-potency statins (atorvastatin and rosuvastatin) increased from 10.4% and 25.8% of patients treated by GPs and cardiologists, respectively, in 2013, to 34.7% and 58.3% in 2018. Prescription for non-statin LLT remained stable throughout the period and low especially for GPs. Ezetimibe was the most prescribed non-statin LLT in 2018 (GPs, 76.1%; cardiologists, 92.8%). Addition of ezetimibe in patients already prescribed a statin reduced LDL-C by an additional 23.8% (32.3 ± 38.4 mg/dL), with a greater reduction with FDC [reduction 28.4% (40.0 ± 39.1 mg/dL)] as compared to separate pills [19.4% (27.5 ± 33.8 mg/dL)]; p < 0.0001. However, only a small proportion of patients reached the recommended LDL-C level of < 70 mg/dL (31.5% with FDC and 21.0% with separate pills). CONCLUSIONS: Prescription for high-potency statins increased over time. Non-statin LLT were infrequently prescribed by GPs. The reduction in LDL-C when statin and ezetimibe were prescribed in combination was considerably larger for FDC; however, a large proportion of patients still remained with uncontrolled LDL-C levels.

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model.

UNLABELLED: world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread. METHODS: We present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns. RESULTS: The simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east. CONCLUSIONS: We show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficial.

Author Correction: Assessing the Dynamics and Control of Droplet- and Aerosol-Transmitted Influenza Using an Indoor Positioning System.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A mechanistic model of infection: why duration and intensity of contacts should be included in models of disease spread.

BACKGROUND: Mathematical models and simulations of disease spread often assume a constant per-contact transmission probability. This assumption ignores the heterogeneity in transmission probabilities, e.g. due to the varying intensity and duration of potentially contagious contacts. Ignoring such heterogeneities might lead to erroneous conclusions from simulation results. In this paper, we show how a mechanistic model of disease transmission differs from this commonly used assumption of a constant per-contact transmission probability. METHODS: We present an exposure-based, mechanistic model of disease transmission that reflects heterogeneities in contact duration and intensity. Based on empirical contact data, we calculate the expected number of secondary cases induced by an infector (i) for the mechanistic model and (ii) under the classical assumption of a constant per-contact transmission probability. The results of both approaches are compared for different basic reproduction numbers R0. RESULTS: The outcomes of the mechanistic model differ significantly from those of the assumption of a constant per-contact transmission probability. In particular, cases with many different contacts have much lower expected numbers of secondary cases when using the mechanistic model instead of the common assumption. This is due to the fact that the proportion of long, intensive contacts decreases in the contact dataset with an increasing total number of contacts. CONCLUSION: The importance of highly connected individuals, so-called super-spreaders, for disease spread seems to be overestimated when a constant per-contact transmission probability is assumed. This holds particularly for diseases with low basic reproduction numbers. Simulations of disease spread should weight contacts by duration and intensity.

Models of epidemics: when contact repetition and clustering should be included.

BACKGROUND: The spread of infectious disease is determined by biological factors, e.g. the duration of the infectious period, and social factors, e.g. the arrangement of potentially contagious contacts. Repetitiveness and clustering of contacts are known to be relevant factors influencing the transmission of droplet or contact transmitted diseases. However, we do not yet completely know under what conditions repetitiveness and clustering should be included for realistically modelling disease spread. METHODS: We compare two different types of individual-based models: One assumes random mixing without repetition of contacts, whereas the other assumes that the same contacts repeat day-by-day. The latter exists in two variants, with and without clustering. We systematically test and compare how the total size of an outbreak differs between these model types depending on the key parameters transmission probability, number of contacts per day, duration of the infectious period, different levels of clustering and varying proportions of repetitive contacts. RESULTS: The simulation runs under different parameter constellations provide the following results: The difference between both model types is highest for low numbers of contacts per day and low transmission probabilities. The number of contacts and the transmission probability have a higher influence on this difference than the duration of the infectious period. Even when only minor parts of the daily contacts are repetitive and clustered can there be relevant differences compared to a purely random mixing model. CONCLUSION: We show that random mixing models provide acceptable estimates of the total outbreak size if the number of contacts per day is high or if the per-contact transmission probability is high, as seen in typical childhood diseases such as measles. In the case of very short infectious periods, for instance, as in Norovirus, models assuming repeating contacts will also behave similarly as random mixing models. If the number of daily contacts or the transmission probability is low, as assumed for MRSA or Ebola, particular consideration should be given to the actual structure of potentially contagious contacts when designing the model.

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 quantify the potential effectiveness of overlooked mitigation strategies such as improved ventilation that are available in the case of aerosol transmission. Our model suggests that recommendation-abiding ventilation could be as effective in mitigating outbreaks as vaccinating approximately half of the population. In simulations using empirical transmission levels observed in households, we find that bringing ventilation to recommended levels had the same mitigating effect as a vaccination coverage of 50% to 60%. Ventilation is an easy-to-implement strategy that has the potential to support vaccination efforts for effective control of influenza spread.

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

BACKGROUND: 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 selection and co-selection by assessing which antibiotics, including those mainly prescribed for respiratory tract infections, are associated with increased resistance to various antibiotics among Escherichia coli isolated from urinary samples. METHODS: Monthly 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. RESULTS: Amoxicillin 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). CONCLUSION: Amoxicillin, 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.

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 English National Health Service (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.

Understanding the gender gap in antibiotic prescribing: a cross-sectional analysis of English primary care.

OBJECTIVES: To explore the causes of the gender gap in antibiotic prescribing, and to determine whether women are more likely than men to receive an antibiotic prescription per consultation. DESIGN: Cross-sectional analysis of routinely collected electronic medical records from The Health Improvement Network (THIN). SETTING: English primary care. PARTICIPANTS: Patients who consulted general practices registered with THIN between 2013 and 2015. PRIMARY AND SECONDARY OUTCOME MEASURES: Total antibiotic prescribing was measured in children (<19 years), adults (19-64 years) and the elderly (65+ years). For 12 common conditions, the number of adult consultations was measured, and the relative risk (RR) of being prescribed antibiotics when consulting as female or with comorbidity was estimated. RESULTS: Among 4.57 million antibiotic prescriptions observed in the data, female patients received 67% more prescriptions than male patients, and 43% more when excluding antibiotics used to treat urinary tract infection (UTI). These gaps were more pronounced in adult women (99% more prescriptions than men; 69% more when excluding UTI) than in children (9%; 0%) or the elderly (67%; 38%). Among adults, women accounted for 64% of consultations (62% among patients with comorbidity), but were not substantially more likely than men to receive an antibiotic prescription when consulting with common conditions such as cough (RR 1.01; 95% CI 1.00 to 1.02), sore throat (RR 1.01, 95% CI 1.00 to 1.01) and lower respiratory tract infection (RR 1.00, 95% CI 1.00 to 1.01). Exceptions were skin conditions: women were less likely to be prescribed antibiotics when consulting with acne (RR 0.67, 95% CI 0.66 to 0.69) or impetigo (RR 0.85, 95% CI 0.81 to 0.88). CONCLUSIONS: The gender gap in antibiotic prescribing can largely be explained by consultation behaviour. Although in most cases adult men and women are equally likely to be prescribed an antibiotic when consulting primary care, it is unclear whether or not they are equally indicated for antibiotic therapy.