Screening strategies in surveillance and control of methicillin-resistant Staphylococcus aureus (MRSA)

With reports of hospital-acquired methicillin-resistant Staphylococcus aureus (MRSA) continuing to increase and therapeutic options decrease, infection control methods are of increasing importance. Here we investigate the relationship between surveillance and infection control. Surveillance plays two roles with respect to control: it allows detection of infected/colonized individuals necessary for their removal from the general population, and it allows quantification of control success. We develop a stochastic model of MRSA transmission dynamics exploring the effects of two screening strategies in an epidemic setting: random and on admission. We consider both hospital and community populations and include control and surveillance in a single framework. Random screening was more efficient at hospital surveillance and allowed nosocomial control, which also prevented epidemic behaviour in the community. Therefore, random screening was the more effective control strategy for both the hospital and community populations in this setting. Surveillance strategies have significant impact on both ascertainment of infection prevalence and its control

The epidemiology, transmission dynamics and control of healthcare-associated infections

This thesis presents research on the epidemiology and transmission dynamics of healthcare-associated infections (HCAI) and focuses on the antibiotic resistant hospital pathogen methicillin-resistant Staphylococcits aureus (MRSA). First, a stochastic mathematical model of MRSA transmission dynamics is developed in which patient movement within and between both hospital and community populations is considered. The effects on transmission of both surveillance and control within this setting are explored. Significant interplay is found to exist between surveillance and control; surveillance is shown to be essential to control success and in addition allows quantification of the level of control achieved. Furthermore, patient movement between hospital and community populations is shown to have a considerable impact on transmission dynamics and on the success of infection control strategies. Analyses of the demographics of a hospital population using a real hospital dataset are presented and the heterogeneous nature of the patient population described. Differences in admission patterns and length of hospital stay between age groups, gender and speciality are explored. Combining these analyses highlights the patient groups constituting the majority of patient days. Further to this, the heterogeneous nature of patient readmissions is described and the existence of a 'core group' of most frequently readmitted patients is illustrated. Overall, readmissions are found to be far more likely than previously thought, with the majority of patient admissions to hospital being readmissions. Given this finding of increased readmission, the hospital admission data is used to inform the development of a model in which real patient movements between the hospital and community are simulated and transmission within this setting explored. Endemic behaviour results and the change in movement patterns is found to influence control strategy success. Further to this, the model is extended to simulate transmission within a multi-centre setting where patient movements within a three-hospital and community network are simulated. This increase in heterogeneity within the patient population appears to allow endemic behaviour throughout all hospitals 11 within the network

Estimating the effect of healthcare-associated infections on excess length of hospital stay using inverse probability-weighted survival curves

Background: Studies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact. We present a methodology based on inverse probability–weighted survival curves to address this limitation. Methods: A case study focusing on intensive care unit–acquired bacteremia using data from 2 general intensive care units (ICUs) from 2 London teaching hospitals were used to illustrate the methodology. The area under the curve of a conventional Kaplan-Meier curve applied to the observed data was compared with that of an inverse probability–weighted Kaplan-Meier curve applied after treating bacteremia as censoring events. Weights were based on the daily probability of acquiring bacteremia. The difference between the observed average LOS and the average LOS that would be observed if all bacteremia cases could be prevented was multiplied by the number of admitted patients to obtain the total excess LOS. Results: The estimated total number of extra ICU days caused by 666 bacteremia cases was estimated at 2453 (95% confidence interval [CI], 1803–3103) days. The excess number of days was overestimated when ignoring time-varying confounding (2845 [95% CI, 2276–3415]) or when completely ignoring confounding (2838 [95% CI, 2101–3575]). Conclusions: ICU-acquired bacteremia was associated with a substantial excess LOS. Wider adoption of inverse probability–weighted survival curves or alternative techniques that address time-varying confounding could lead to better informed decision making around nosocomial infections and other time-dependent exposures

Estimating the opportunity costs of bed-days.

Opportunity costs of bed-days are fundamental to understanding the value of healthcare systems. They greatly influence burden of disease estimations and economic evaluations involving stays in healthcare facilities. However, different estimation techniques employ assumptions that differ crucially in whether to consider the value of the second-best alternative use forgone, of any available alternative use, or the value of the actually chosen alternative. Informed by economic theory, this paper provides a taxonomic framework of methodologies for estimating the opportunity costs of resources. This taxonomy is then applied to bed-days by classifying existing approaches accordingly. We highlight differences in valuation between approaches and the perspective adopted, and we use our framework to appraise the assumptions and biases underlying the standard approaches that have been widely adopted mostly unquestioned in the past, such as the conventional use of reference costs and administrative accounting data. Drawing on these findings, we present a novel approach for estimating the opportunity costs of bed-days in terms of health forgone for the second-best patient, but expressed monetarily. This alternative approach effectively re-connects to the concept of choice and explicitly considers net benefits. It is broadly applicable across settings and for other resources besides bed-days

Identifying English practices that are high antibiotic prescribers accounting for comorbidities and other legitimate medical reasons for variation

Background: Seeing one’s practice as a high antibiotic prescriber compared to general practices with similar patient populations can be one of the best motivators for change. Current comparisons are based on age-sex weighting of the practice population for expected prescribing rates (STAR-PU). Here, we investigate whether there is a need to additionally account for further potentially legitimate medical reasons for higher antibiotic prescribing. Methods: Publicly available data from 7,376 general practices in England between April 2014 and March 2015 were used. We built two different negative binomial regression models to compare observed versus expected antibiotic dispensing levels per practice: one including comorbidities as covariates and another with the addition of smoking prevalence and deprivation. We compared the ranking of practices in terms of items prescribed per STAR-PU according to i) conventional STAR-PU methodology, ii) observed vs expected prescribing levels using the comorbidity model, and iii) observed vs expected prescribing levels using the full model. Findings: The median number of antibiotic items prescribed per practice per STAR-PU was 1.09 (25th -75th percentile, 0.92-1.25). 1,133 practices (76.8% of 1,476) were consistently identified as being in the top 20% of high antibiotic prescribers. However, some practices that would be classified as high prescribers using the current STAR-PU methodology would not be classified as high prescribers if comorbidity was accounted for (n=269, 18.2%) and if additionally smoking prevalence and deprivation were accounted for (n=312, 21.1%). Interpretation: Current age-sex weighted comparisons of antibiotic prescribing rates in England are fair for many, but not all practices. This new metric that accounts for legitimate medical reasons for higher antibiotic prescribing may have more credibility among general practitioners and, thus, more likely to be acted upon

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

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. 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. 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. 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

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

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

Duration of antibiotic treatment for common infections in English primary care: cross sectional analysis and comparison with guidelines

Objectives: To evaluate antibiotic therapy durations for common infections in English primary care and to compare this with guidelines. Design: Cross-sectional study. Setting: General practices contributing to The Health Improvement Network database, 2013-2015. Participants: 931,015 consultations that resulted in an antibiotic prescription for one of the following indications: acute sinusitis, acute sore throat, acute cough and bronchitis, pneumonia, acute exacerbation of chronic obstructive pulmonary disease (COPD), acute otitis media, acute cystitis, prostatitis, pyelonephritis, cellulitis, impetigo, scarlet fever and gastroenteritis. Main outcome measures: The main outcomes were the percentage of antibiotic prescriptions with a duration exceeding the guideline recommendation and the total number of days beyond the recommended duration for each indication. Results: The most common reasons for the prescriptions were patients consulting with acute bronchitis and cough (386,972), acute sore throat (239,231), acute otitis media (83,054), and acute sinusitis (76,683). Antibiotic treatments for upper respiratory indications and acute bronchitis accounted for more than two thirds of the total prescriptions considered, and ≥80% of these treatment courses exceeded guideline recommendations. Notable exceptions were acute sinusitis, where only 9.6% (95% CI 9.4 to 9.9%) of prescriptions exceeded 7 days and acute sore throat where only 2.1% (95% CI 2.0 to 2.1) exceed 10 days (recent guidance recommends 5 days). More than half of antibiotic prescriptions were longer than guidelines recommend for acute cystitis among females (54.6%, 95% CI 54.1 to 55.0%). The percentage of antibiotic prescriptions exceeding the recommended duration was lower for most non-respiratory infections. For the 931,015 included consultations resulting in antibiotic prescriptions, approximately 1.3 million days were beyond the durations recommended by the guidelines. Conclusion: For most common infections treated in primary care, a substantial proportion of antibiotic prescriptions have durations exceeding those recommended in guidelines. Substantial reductions in antibiotic exposure can be accomplished by aligning antibiotic prescription durations with guidelines

Quantifying the economic cost of antibiotic resistance and the impact of related interventions rapid methodological review, conceptual framework and recommendations for future studies

BACKGROUND: Antibiotic resistance (ABR) poses a major threat to health and economic wellbeing worldwide. Reducing ABR will require government interventions to incentivise antibiotic development, prudent antibiotic use, infection control and deployment of partial substitutes such as rapid diagnostics and vaccines. The scale of such interventions needs to be calibrated to accurate and comprehensive estimates of the economic cost of ABR. METHODS: A conceptual framework for estimating costs attributable to ABR was developed based on previous literature highlighting methodological shortcomings in the field and additional deductive epidemiological and economic reasoning. The framework was supplemented by a rapid methodological review. RESULTS: The review identified 110 articles quantifying ABR costs. Most were based in high-income countries only (91/110), set in hospitals (95/110), used a healthcare provider or payer perspective (97/110), and used matched cohort approaches to compare costs of patients with antibiotic-resistant infections and antibiotic-susceptible infections (or no infection) (87/110). Better use of methods to correct biases and confounding when making this comparison is needed. Findings also need to be extended beyond their limitations in (1) time (projecting present costs into the future), (2) perspective (from the healthcare sector to entire societies and economies), (3) scope (from individuals to communities and ecosystems), and (4) space (from single sites to countries and the world). Analyses of the impact of interventions need to be extended to examine the impact of the intervention on ABR, rather than considering ABR as an exogeneous factor. CONCLUSIONS: Quantifying the economic cost of resistance will require greater rigour and innovation in the use of existing methods to design studies that accurately collect relevant outcomes and further research into new techniques for capturing broader economic outcomes