Burden, duration and costs of hospital bed closures due to acute gastroenteritis in England per winter, 2010/11-2015/16.

BACKGROUND: Bed closures due to acute gastroenteritis put hospitals under pressure each winter. In England, the National Health Service (NHS) has monitored the winter situation for all acute trusts since 2010/11. AIM: To estimate the burden, duration and costs of hospital bed closures due to acute gastroenteritis in winter. METHODS: A retrospective analysis of routinely collected time-series data of bed closures due to diarrhoea and vomiting was conducted for the winters 2010/11 to 2015/16. Two key issues were addressed by imputing non-randomly missing values at provider level, and filtering observations to a range of dates recorded in all six winters. The lowest and highest values imputed were taken to represent the best- and worst-case scenarios. Bed-days were costed using NHS reference costs, and potential staff absence costs were based on previous studies. FINDINGS: In the best-to-worst case, a median of 88,000-113,000 beds were closed due to gastroenteritis each winter. Of these, 19.6-20.4% were unoccupied. On average, 80% of providers were affected, and had closed beds for a median of 15-21 days each winter. Hospital costs of closed beds were £5.7-£7.5 million, which increased to £6.9-£10.0 million when including staff absence costs due to illness. CONCLUSIONS: The median number of hospital beds closed due to acute gastroenteritis per winter was equivalent to all general and acute hospital beds in England being unavailable for a median of 0.88-1.12 days. Costs for hospitals are high but vary with closures each winter

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

Quantifying the contribution of pathways of nosocomial acquisition of COVID-19 in English hospitals

BACKGROUND: Despite evidence of the nosocomial transmission of novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in hospitals worldwide, the contributions of the pathways of transmission are poorly quantified. METHODS: We analysed national records of hospital admissions and discharges, linked to data on SARS-CoV-2 testing, using an individual-based model that considers patient-to-patient, patient-to-healthcare worker (HCW), HCW-to-patient and HCW-to-HCW transmission. RESULTS: Between 1 March 2020 and 31 December 2020, SARS-CoV-2 infections that were classified as nosocomial were identified in 0.5% (0.34-0.74) of patients admitted to an acute National Health Service trust. We found that the most likely route of nosocomial transmission to patients was indirect transmission from other infected patients, e.g. through HCWs acting as vectors or contaminated fomites, followed by direct transmission between patients in the same bay. The risk of transmission to patients from HCWs over this time period is low, but can contribute significantly when the number of infected inpatients is low. Further, the risk of a HCW acquiring SARS-CoV-2 in hospital is approximately equal to that in the community, thereby doubling their overall risk of infection. The most likely route of transmission to HCWs is transmission from other infected HCWs. CONCLUSIONS: Current control strategies have successfully reduced the transmission of SARS-CoV-2 between patients and HCWs. In order to reduce the burden of nosocomial COVID-19 infections on health services, stricter measures should be enforced that would inhibit the spread of the virus between bays or wards in the hospital. There should also be a focus on inhibiting the spread of SARS-CoV-2 between HCWs. The findings have important implications for infection-control procedures in hospitals

Excess length of stay and mortality due to Clostridium difficile infection: a multi-state modelling approach.

BACKGROUND: The burden of healthcare-associated infections, such as healthcare-acquired Clostridium difficile (HA-CDI), can be expressed in terms of additional length of stay (LOS) and mortality. However, previous estimates have varied widely. Although some have considered time of infection onset (time-dependent bias), none considered the impact of severity of HA-CDI; this was the primary aim of this study. METHODS: The daily risk of in-hospital death or discharge was modelled using a Cox proportional hazards model, fitted to data on patients discharged in 2012 from a large English teaching hospital. We treated HA-CDI status as a time-dependent variable and adjusted for confounders. In addition, a multi-state model was developed to provide a clinically intuitive metric of delayed discharge associated with non-severe and severe HA-CDI respectively. FINDINGS: Data comprised 157 (including 48 severe) HA-CDI cases among 42,618 patients. HA-CDI reduced the daily discharge rate by nearly one-quarter [hazard ratio (HR): 0.72; 95% confidence interval (CI): 0.61-0.84] and increased the in-hospital death rate by 75% compared with non-HA-CDI patients (HR: 1.75; 95% CI: 1.16-2.62). Whereas overall HA-CDI resulted in a mean excess LOS of about seven days (95% CI: 3.5-10.9), severe cases had an average excess LOS which was twice (∼11.6 days; 95% CI: 3.6-19.6) that of the non-severe cases (about five days; 95% CI: 1.1-9.5). CONCLUSION: HA-CDI contributes to patients' expected LOS and risk of mortality. However, when quantifying the health and economic burden of hospital-onset of HA-CDI, the heterogeneity in the impact of HA-CDI should be accounted for

Does appropriate empiric antibiotic therapy modify intensive care unit-acquired Enterobacteriaceae bacteraemia mortality and discharge?

BACKGROUND: Conflicting results have been found regarding outcomes of intensive care unit (ICU)-acquired Enterobacteriaceae bacteraemia and the potentially modifying effect of appropriate empiric antibiotic therapy. AIM: To evaluate these associations while adjusting for potential time-varying confounding using methods from the causal inference literature. METHODS: Patients who stayed more than two days in two general ICUs in England between 2002 and 2006 were included in this cohort study. Marginal structural models with inverse probability weighting were used to estimate the mortality and discharge associated with Enterobacteriaceae bacteraemia and the impact of appropriate empiric antibiotic therapy on these outcomes. FINDINGS: Among 3411 ICU admissions, 195 (5.7%) ICU-acquired Enterobacteriaceae bacteraemia cases occurred. Enterobacteriaceae bacteraemia was associated with an increased daily risk of ICU death [cause-specific hazard ratio (HR): 1.48; 95% confidence interval (CI): 1.10-1.99] and a reduced daily risk of ICU discharge (HR: 0.66; 95% CI: 0.54-0.80). Appropriate empiric antibiotic therapy did not significantly modify ICU mortality (HR: 1.08; 95% CI: 0.59-1.97) or discharge (HR: 0.91; 95% CI: 0.63-1.32). CONCLUSION: ICU-acquired Enterobacteriaceae bacteraemia was associated with an increased daily risk of ICU mortality. Furthermore, the daily discharge rate was also lower after acquiring infection, even when adjusting for time-varying confounding using appropriate methodology. No evidence was found for a beneficial modifying effect of appropriate empiric antibiotic therapy on ICU mortality and discharge

Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey

Background: Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. Methods: Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated cross-sectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. Findings: Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280 327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29–0·54) to 0·06% (0·04–0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient-facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17–24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17–24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45–68%, dependent on calendar time. Interpretation: Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards. Funding: Department of Health and Social Care

The challenge of antimicrobial resistance: What economics can contribute

BACKGROUND: Antimicrobial resistance (AMR) is increasing, driven by widespread antibiotic use. The wide availability of effective antibiotics is under threat, jeopardizing modern health care. Forecasts of the economic costs are similar to those of a 2°C rise in global average surface temperature, above preindustrial levels. AMR is becoming an urgent priority for policy-makers, and pressure is mounting to secure international commitments to tackle the problem. // ADVANCES: Estimating the value of interventions to reduce antibiotic use requires predictions of future levels of antibiotic resistance. However, modeling the trajectory of antibiotic resistance, and how marginal changes in antibiotic consumption contribute to resistance, is complex. The challenge of estimating the resulting impact on health and the economy is similarly daunting. As with the cost of climate change, estimates of total AMR costs are fraught with uncertainty and may be far too low. Much of the uncertainty arises from the complexity of estimating the cost of changes in overall resistance levels. This cost depends on various factors: which drug and pathogen are involved, the mechanism of antibiotic resistance, the prevalence of that pathogen, the types of infections it causes and their level of transmissibility, the health burden of those infections, and whether alternative treatments are available. Effective new antibiotics are urgently needed. However, without government intervention, R&D for antibiotics is rarely profitable, and most major pharmaceutical companies have left the field. New ways are needed to make antibiotic development profitable, decoupling profits from volumes sold. // OUTLOOK: Analogies can be drawn between climate change and AMR, both of which have been described as a global “tragedy of the commons.” There is some consensus that we should treat carbon emissions reduction as an insurance policy against the possibility of a catastrophic climate outcome—and avoid waiting for a definitive optimum-abatement policy. A similar paradigm shift is needed to incentivize both the introduction and valuation of interventions to reduce antibiotic use and R&D of new antibiotics. Rather than taxing the price and letting the market dictate the quantity of antibiotics supplied, an alternative may be to establish a regulatory body that issues prescribers tradable permits and to allow the market to determine the price. Such an approach could create a predictable revenue stream through more-foreseeable licensing fees for important antibiotics by decoupling the return on investment from the volume used. Approaches such as this could incentivize industry to develop new antibiotics for which there would otherwise be too small a market to provide a sufficient return on investment. Reducing inappropriate antibiotic use while expanding essential access is a difficult challenge, especially in low- and middle-income countries. However, policy-makers and philanthropists are alert to the importance of AMR and increasingly are making substantial research funds available, with much of these funds devoted to the social sciences. We need economists, across many different fields, to engage with this pressing global problem

Seasonality of urinary tract infections in the United Kingdom in different age groups: longitudinal analysis of The Health Improvement Network (THIN)

Evidence regarding the seasonality of urinary tract infection (UTI) consultations in primary care is conflicting and methodologically poor. To our knowledge, this is the first study to determine whether this seasonality exists in the UK, identify the peak months and describe seasonality by age. The monthly number of UTI consultations (N = 992 803) and nitrofurantoin and trimethoprim prescriptions (N = 1 719 416) during 2008-2015 was extracted from The Health Improvement Network (THIN), a large nationally representative UK dataset of electronic patient records. Negative binomial regression models were fitted to these data to investigate seasonal fluctuations by age group (14-17, 18-24, 25-45, 46-69, 70-84, 85+) and by sex, accounting for a change in the rate of UTI over the study period. A September to November peak in UTI consultation incidence was observed for ages 14-69. This seasonality progressively faded in older age groups and no seasonality was found in individuals aged 85+, in whom UTIs were most common. UTIs were rare in males but followed a similar seasonal pattern than in females. We show strong evidence of an autumnal seasonality for UTIs in individuals under 70 years of age and a lack of seasonality in the very old. These findings should provide helpful information when interpreting surveillance reports and the results of interventions against UTI

A Risk Assessment of Antibiotic Pan-Drug-Resistance in the UK: Bayesian Analysis of an Expert Elicitation Study.

To inform the UK antimicrobial resistance strategy, a risk assessment was undertaken of the likelihood, over a five-year time-frame, of the emergence and widespread dissemination of pan-drug-resistant (PDR) Gram-negative bacteria that would pose a major public health threat by compromising effective healthcare delivery. Subsequent impact over five- and 20-year time-frames was assessed in terms of morbidity and mortality attributable to PDR Gram-negative bacteraemia. A Bayesian approach, combining available data with expert prior opinion, was used to determine the probability of the emergence, persistence and spread of PDR bacteria. Overall probability was modelled using Monte Carlo simulation. Estimates of impact were also obtained using Bayesian methods. The estimated probability of widespread occurrence of PDR pathogens within five years was 0.2 (95% credibility interval (CrI): 0.07-0.37). Estimated annual numbers of PDR Gram-negative bacteraemias at five and 20 years were 6800 (95% CrI: 400-58,600) and 22,800 (95% CrI: 1500-160,000), respectively; corresponding estimates of excess deaths were 1900 (95% CrI: 0-23,000) and 6400 (95% CrI: 0-64,000). Over 20 years, cumulative estimates indicate 284,000 (95% CrI: 17,000-1,990,000) cases of PDR Gram-negative bacteraemia, leading to an estimated 79,000 (95% CrI: 0-821,000) deaths. This risk assessment reinforces the need for urgent national and international action to tackle antibiotic resistance