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

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

Nosocomial transmission of C. difficile in English hospitals from patients with symptomatic infection.

BACKGROUND: Recent evidence suggests that less than one-quarter of patients with symptomatic nosocomial Clostridium difficile infections (CDI) are linked to other in-patients. However, this evidence was limited to one geographic area. We aimed to investigate the level of symptomatic CDI transmission in hospitals located across England from 2008 to 2012. METHODS: A generalized additive mixed-effects Poisson model was fitted to English hospital-surveillance data. After adjusting for seasonal fluctuations and between-hospital variation in reported CDI over time, possible clustering (transmission between symptomatic in-patients) of CDI cases was identified. We hypothesised that a temporal proximity would be reflected in the degree of correlation between in-hospital CDI cases per week. This correlation was modelled through a latent autoregressive structure of order 1 (AR(1)). FINDINGS: Forty-six hospitals (33 general, seven specialist, and six teaching hospitals) located in all English regions met our criteria. In total, 12,717 CDI cases were identified; seventy-five per cent of these occurred >48 hours after admission. There were slight increases in reports during winter months. We found a low, but statistically significant, correlation between successive weekly CDI case incidences (phi = 0.029, 95%CI: 0.009-0.049). This correlation was five times stronger in a subgroup analysis restricted to teaching hospitals (phi = 0.104, 95%CI: 0.048-0.159). CONCLUSIONS: The results suggest that symptomatic patient-to-patient transmission has been a source of CDI-acquisition in English hospitals in recent years, and that this might be a more important transmission route in teaching hospitals. Nonetheless, the weak correlation indicates that, in line with recent evidence, symptomatic cases might not be the primary source of nosocomial CDI in England

Reconstructing transmission trees for communicable diseases using densely sampled genetic data.

Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding important insights into disease dynamics and the impact of control measures. We considered disease outbreaks in a setting with dense genomic sampling, and formulated stochastic epidemic models to investigate person-to-person transmission, based on observed genomic and epidemiological data. We constructed models in which the genetic distance between sampled genotypes depends on the epidemiological relationship between the hosts. A data augmented Markov chain Monte Carlo algorithm was used to sample over the transmission trees, providing a posterior probability for any given transmission route. We investigated the predictive performance of our methodology using simulated data, demonstrating high sensitivity and specificity, particularly for rapidly mutating pathogens with low transmissibility. We then analyzed data collected during an outbreak of methicillin-resistant Staphylococcus aureus in a hospital, identifying probable transmission routes and estimating epidemiological parameters. Our approach overcomes limitations of previous methods, providing a framework with the flexibility to allow for unobserved infection times, multiple independent introductions of the pathogen, and within-host genetic diversity, as well as allowing forward simulation.Funding received from the following: The European Community [Mastering Hospital Antimicrobial Resistance (MOSAR) network contract LSHP-CT-2007-037941]. The National Institute of General Medical Sciences of the National Institutes of Health under award number U54GM088558. The UK Medical Research Council (Unit Programme number U105260566). The UKCRC Translational Infection Research Initiative (MRC Grant number G1000803) and Public Health England. The Medical Research Council and Department for International Development (Grant number MR/K006924/1). The Mahidol Oxford Tropical Medicine Research Unit is part of the Wellcome Trust Major Overseas Programme in SE Asia (Grant number 106698/Z/14/Z).This is the final version of the article. It first appeared from the Institute of Mathematical Statistics via http://dx.doi.org/10.1214/15-AOAS89

Screening, isolation, and decolonisation strategies in the control of meticillin resistant Staphylococcus aureus in intensive care units: cost effectiveness evaluation

Objective To assess the cost effectiveness of screening, isolation, and decolonisation strategies in the control of meticillin resistant Staphylococcus aureus (MRSA) in intensive care units