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Antimicrobial resistance is a major global health concern, driven by overuse of antibiotics. We aimed to assess the effectiveness of a national antimicrobial stewardship intervention, the National Health Service (NHS) England Quality Premium implemented in 2015–16, on broad-spectrum antibiotic prescribing and Escherichia coli bacteraemia resistance to broad-spectrum antibiotics in England. In this quasi-experimental, ecological, data linkage study, we used longitudinal data on bacteraemia for patients registered with a general practitioner in the English National Health Service and patients with E coli bacteraemia notified to the national mandatory surveillance programme between Jan 1, 2013, and Dec 31, 2018. We linked these data to data on antimicrobial susceptibility testing of E coli from Public Health England's Second-Generation Surveillance System. We did an ecological analysis using interrupted time-series analyses and generalised estimating equations to estimate the change in broad-spectrum antibiotics prescribing over time and the change in the proportion of E coli bacteraemia cases for which the causative bacteria were resistant to each antibiotic individually or to at least one of five broad-spectrum antibiotics (co-amoxiclav, ciprofloxacin, levofloxacin, moxifloxacin, ofloxacin), after implementation of the NHS England Quality Premium intervention in April, 2015. Before implementation of the Quality Premium, the rate of antibiotic prescribing for all five broad-spectrum antibiotics was increasing at rate of 0·2% per month (incidence rate ratio [IRR] 1·002 [95% CI 1·000–1·004], p=0·046). After implementation of the Quality Premium, an immediate reduction in total broad-spectrum antibiotic prescribing rate was observed (IRR 0·867 [95% CI 0·837–0·898], p<0·0001). This effect was sustained until the end of the study period; a 57% reduction in rate of antibiotic prescribing was observed compared with the counterfactual situation (ie, had the Quality Premium not been implemented). In the same period, the rate of resistance to at least one broad-spectrum antibiotic increased at rate of 0·1% per month (IRR 1·001 [95% CI 0·999–1·003], p=0·346). On implementation of the Quality Premium, an immediate reduction in resistance rate to at least one broad-spectrum antibiotic was observed (IRR 0·947 [95% CI 0·918–0·977], p=0·0007). Although this effect was also sustained until the end of the study period, with a 12·03% reduction in resistance rate compared with the counterfactual situation, the overall trend remained on an upward trajectory. On examination of the long-term effect following implementation of the Quality Premium, there was an increase in the number of isolates resistant to at least one of the five broad-spectrum antibiotics tested (IRR 1·002 [1·000–1·003]; p=0·047). Although interventions targeting antibiotic use can result in changes in resistance over a short period, they might be insufficient alone to curtail antimicrobial resistance. National Institute for Health Research, Economic and Social Research Council, Rosetrees Trust, and The Stoneygate Trust.

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. 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. 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). 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.

Background Infective endocarditis is an uncommon but serious infection, where evidence for giving antibiotic prophylaxis before invasive dental procedures is inconclusive. In England, antibiotic prophylaxis was offered routinely to patients at risk of infective endocarditis until March 2008, when new guidelines aimed at reducing unnecessary antibiotic use were issued. We investigated whether changes in infective endocarditis incidence could be detected using electronic health records, assessing the impact of inclusion criteria/statistical model choice on inferences about the timing/type of any change. Methods Using national data from Hospital Episode Statistics covering 1998–2017, we modelled trends in infective endocarditis incidence using three different sets of inclusion criteria plus a range of regression models, identifying the most likely date for a change in trends if evidence for one existed. We also modelled trends in the proportions of different organism groups identified during infection episodes, using secondary diagnosis codes and data from national laboratory records. Lastly, we applied non-parametric local smoothing to visually inspect any changes in trend around the guideline change date. Results Infective endocarditis incidence increased markedly over the study (22.2–41.3 per million population in 1998 to 42.0–67.7 in 2017 depending on inclusion criteria). The most likely dates for a change in incidence trends ranged from September 2001 (uncertainty interval August 2000–May 2003) to May 2015 (March 1999–January 2016), depending on inclusion criteria and statistical model used. For the proportion of infective endocarditis cases associated with streptococci, the most likely change points ranged from October 2008 (March 2006–April 2010) to August 2015 (September 2013–November 2015), with those associated with oral streptococci decreasing in proportion after the change point. Smoothed trends showed no notable changes in trend around the guideline date. Conclusions Infective endocarditis incidence has increased rapidly in England, though we did not detect any change in trends directly following the updated guidelines for antibiotic prophylaxis, either overall or in cases associated with oral streptococci. Estimates of when changes occurred were sensitive to inclusion criteria and statistical model choice, demonstrating the need for caution in interpreting single models when using large datasets. More research is needed to explore the factors behind this increase.

ObjectivesInappropriate prescribing of antibiotics is a key driver of antimicrobial resistance. This study aimed to describe urine sampling rates and antibiotic prescribing for patients with lower urinary tract infections (UTIs) in English general practice.DesignA retrospective population-based study using administrative data.SettingIQVIA Medical Research Database (IMRD) data from general practices in England, 2015–2022.ParticipantsPatients who have consulted with an uncomplicated UTI in England general practices captured in the IMRD.Outcome measuresTrends in UTI episodes (episodes were defined as UTI diagnosis codes occurring within 14 days of each other), testing and antibiotic prescribing on the same day as initial UTI consultation were assessed from January 2015 to December 2022. Associations, using univariate and multivariate logistic regressions, were examined between consultation and demographic factors on the odds of a urine test.ResultsThere were 743 350 UTI episodes; 50.8% had a urine test. Testing rates fluctuated with an upward trend and large decline in 2020. Same-day UTI antibiotic prescribing occurred in 78.2% of episodes. In multivariate modelling, factors found to decrease odds of a urine test included age ≥85 years (0.83, 95% CI 0.82 to 0.84), consultation type (remote vs face to face, 0.45, 95% CI 0.45 to 0.46), episodes in London compared with the South (0.74, 95% CI 0.72 to 0.75) and increasing practice size (0.77, 95% CI 0.76 to 0.78). Odds of urine tests increased in males (OR 1.11, 95% CI 1.10 to 1.13), for those episodes without a same-day UTI antibiotic (1.10, 95% CI 1.04 to 1.16) for episodes for those with higher deprivation status (Indices of Multiple Deprivation 8 vs 1, 1.51, 95% CI 1.48 to 1.54). Compared with 2015, 2016–2019 saw increased odds of testing while 2020 and 2021 saw decreases, with 2022 showing increased odds.ConclusionUrine testing for UTI in general practice in England showed an upward trend, with same-day antibiotic prescribing remaining consistent, suggesting greater alignment to national guidelines. The COVID-19 pandemic impacted testing rates, though as of 2022, they began to recover.

Changes in antibacterial prescribing during the COVID-19 pandemic were anticipated given that the clinical features of severe respiratory infection syndrome caused by SARS-CoV-2 mirror bacterial respiratory tract infections. Antibacterial consumption was measured in items/1000 population for primary care and in Defined Daily Doses (DDDs)/1000 admissions for secondary care in England from 2015 to October 2020. Interrupted time-series analyses were conducted to evaluate the effects of the pandemic on antibacterial consumption. In the community, the rate of antibacterial items prescribed decreased further in 2020 (by an extra 1.4% per month, 95% CI: −2.3 to −0.5) compared to before COVID-19. In hospitals, the volume of antibacterial use decreased during COVID-19 overall (−12.1% compared to pre-COVID, 95% CI: −19.1 to −4.4), although the rate of usage in hospitals increased steeply in April 2020. Use of antibacterials prescribed for respiratory infections and broad-spectrum antibacterials (predominately ‘Watch’ antibacterials in hospitals) increased in both settings. Overall volumes of antibacterial use at the beginning of the COVID-19 pandemic decreased in both primary and secondary settings, although there were increases in the rate of usage in hospitals in April 2020 and in specific antibacterials. This highlights the importance of antimicrobial stewardship during pandemics to ensure appropriate prescribing and avoid negative consequences on patient outcomes and antimicrobial resistance.

Linkage of risk-factor data for blood-stream infection (BSI) in paediatric intensive care (PICU) with bacteraemia surveillance data to monitor risk-adjusted infection rates in PICU is complicated by a lack of unique identifiers and under-ascertainment in the national surveillance system. We linked, evaluated and performed preliminary analyses on these data to provide a practical guide on the steps required to handle linkage of such complex data sources. Data on PICU admissions in England and Wales for 2003-2010 were extracted from the Paediatric Intensive Care Audit Network. Records of all positive isolates from blood cultures taken for children <16 years and captured by the national voluntary laboratory surveillance system for 2003-2010 were extracted from the Public Health England database, LabBase2. \"Gold-standard\" datasets with unique identifiers were obtained directly from three laboratories, containing microbiology reports that were eligible for submission to LabBase2 (defined as \"clinically significant\" by laboratory microbiologists). Reports in the gold-standard datasets were compared to those in LabBase2 to estimate ascertainment in LabBase2. Linkage evaluated by comparing results from two classification methods (highest-weight classification of match weights and prior-informed imputation using match probabilities) with linked records in the gold-standard data. BSI rate was estimated as the proportion of admissions associated with at least one BSI. Reporting gaps were identified in 548/2596 lab-months of LabBase2. Ascertainment of clinically significant BSI in the remaining months was approximately 80-95%. Prior-informed imputation provided the least biased estimate of BSI rate (5.8% of admissions). Adjusting for ascertainment, the estimated BSI rate was 6.1-7.3%. Linkage of PICU admission data with national BSI surveillance provides the opportunity for enhanced surveillance but analyses based on these data need to take account of biases due to ascertainment and linkage error. This study provides a generalisable guide for linkage, evaluation and analysis of complex electronic healthcare data.

Background Diagnostic codes from electronic health records are widely used to assess patterns of disease. Infective endocarditis is an uncommon but serious infection, with objective diagnostic criteria. Electronic health records have been used to explore the impact of changing guidance on antibiotic prophylaxis for dental procedures on incidence, but limited data on the accuracy of the diagnostic codes exists. Endocarditis was used as a clinically relevant case study to investigate the relationship between clinical cases and diagnostic codes, to understand discrepancies and to improve design of future studies. Methods Electronic health record data from two UK tertiary care centres were linked with data from a prospectively collected clinical endocarditis service database (Leeds Teaching Hospital) or retrospective clinical audit and microbiology laboratory blood culture results (Oxford University Hospitals Trust). The relationship between diagnostic codes for endocarditis and confirmed clinical cases according to the objective Duke criteria was assessed, and impact on estimations of disease incidence and trends. Results In Leeds 2006–2016, 738/1681(44%) admissions containing any endocarditis code represented a definite/possible case, whilst 263/1001(24%) definite/possible endocarditis cases had no endocarditis code assigned. In Oxford 2010–2016, 307/552(56%) reviewed endocarditis-coded admissions represented a clinical case. Diagnostic codes used by most endocarditis studies had good positive predictive value (PPV) but low sensitivity (e.g. I33-primary 82% and 43% respectively); one (I38-secondary) had PPV under 6%. Estimating endocarditis incidence using raw admission data overestimated incidence trends twofold. Removing records with non-specific codes, very short stays and readmissions improved predictive ability. Estimating incidence of streptococcal endocarditis using secondary codes also overestimated increases in incidence over time. Reasons for discrepancies included changes in coding behaviour over time, and coding guidance allowing assignment of a code mentioning ‘endocarditis’ where endocarditis was never mentioned in the clinical notes. Conclusions Commonly used diagnostic codes in studies of endocarditis had good predictive ability. Other apparently plausible codes were poorly predictive. Use of diagnostic codes without examining sensitivity and predictive ability can give inaccurate estimations of incidence and trends. Similar considerations may apply to other diseases. Health record studies require validation of diagnostic codes and careful data curation to minimise risk of serious errors.

To evaluate variation in trends in bloodstream infection (BSI) rates in neonatal units (NNUs) in England according to the data sources and linkage methods used. We used deterministic and probabilistic methods to link clinical records from 112 NNUs in the National Neonatal Research Database (NNRD) to national laboratory infection surveillance data from Public Health England. We calculated the proportion of babies in NNRD (aged <1 year and admitted between 2010-2017) with a BSI caused by clearly pathogenic organisms between two days after admission and two days after discharge. We used Poisson regression to determine trends in the proportion of babies with BSI based on i) deterministic and probabilistic linkage of NNRD and surveillance data (primary measure), ii) deterministic linkage of NNRD-surveillance data, iii) NNRD records alone, and iv) linked NNRD-surveillance data augmented with clinical records of laboratory-confirmed BSI in NNRD. Using deterministic and probabilistic linkage, 5,629 of 349,740 babies admitted to a NNU in NNRD linked with 6,660 BSI episodes accounting for 38% of 17,388 BSI records aged <1 year in surveillance data. The proportion of babies with BSI due to clearly pathogenic organisms during their NNU admission was 1.0% using deterministic plus probabilistic linkage (primary measure), compared to 1.0% using deterministic linkage alone, 0.6% using NNRD records alone, and 1.2% using linkage augmented with clinical records of BSI in NNRD. Equivalent proportions for babies born before 32 weeks of gestation were 5.0%, 4.8%, 2.9% and 5.9%. The proportion of babies who linked to a BSI decreased by 7.5% each year (95% confidence interval [CI]: -14.3%, -0.1%) using deterministic and probabilistic linkage but was stable using clinical records of BSI or deterministic linkage alone. Linkage that combines BSI records from national laboratory surveillance and clinical NNU data sources, and use of probabilistic methods, substantially improved ascertainment of BSI and estimates of BSI trends over time, compared with single data sources.

We determined the generalisability and cost-impact of adopting antibiotic-impregnated CVCs in all paediatric intensive care units (PICUs) in England, based on results from a large randomised controlled trial (the CATCH trial; ISRCTN34884569). BSI rates using standard CVCs were estimated through linkage of national PICU audit data (PICANet) with laboratory surveillance data. We estimated the number of BSI averted if PICUs switched from standard to antibiotic-impregnated CVCs by applying the CATCH trial rate-ratio (0.40; 95% CI 0.17,0.97) to the BSI rate using standard CVCs. The value of healthcare resources made available by averting one BSI as estimated from the trial economic analysis was £10,975; 95% CI -£2,801,£24,751. The BSI rate using standard CVCs was 4.58 (95% CI 4.42,4.74) per 1000 CVC-days in 2012. Applying the rate-ratio gave 232 BSI averted using antibiotic CVCs. The additional cost of purchasing antibiotic-impregnated compared with standard CVCs was £36 for each child, corresponding to additional costs of £317,916 for an estimated 8831 CVCs required in PICUs in 2012. Based on 2012 BSI rates, management of BSI in PICUs cost £2.5 million annually (95% uncertainty interval: -£160,986, £5,603,005). The additional cost of antibiotic CVCs would be less than the value of resources associated with managing BSI in PICUs with standard BSI rates >1.2 per 1000 CVC-days. The cost of introducing antibiotic-impregnated CVCs is less than the cost associated with managing BSIs occurring with standard CVCs. The long-term benefits of preventing BSI could mean that antibiotic CVCs are cost-effective even in PICUs with extremely low BSI rates.

Background Predicting antimicrobial resistance (AMR), a top global health threat, nationwide at an aggregate hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR. Methods Antimicrobial use and AMR prevalence in bloodstream infections in hospitals in England were obtained per hospital group (Trust) and financial year (FY, April–March) for 22 pathogen–antibiotic combinations (FY2016-2017 to FY2021-2022). Extreme Gradient Boosting (XGBoost) model predictions were compared to the previous value taken forwards, the difference between the previous two years taken forwards and linear trend forecasting (LTF). XGBoost feature importances were calculated to aid interpretability. Results Here we show that XGBoost models achieve the best predictive performance. Relatively limited year-to-year variability in AMR prevalence within Trust–pathogen–antibiotic combinations means previous value taken forwards also achieves a low mean absolute error (MAE), similar to or slightly higher than XGBoost. Using the difference between the previous two years taken forward or LTF performs consistently worse. XGBoost considerably outperforms all other methods in Trusts with a larger change in AMR prevalence from FY2020-2021 (last training year) to FY2021-2022 (held-out test set). Feature importance values indicate that besides historical resistance to the same pathogen–antibiotic combination as the outcome, complex relationships between resistance in different pathogens to the same antibiotic/antibiotic class and usage are exploited for predictions. These are generally among the top ten features ranked according to their mean absolute SHAP values. Conclusions Year-to-year resistance has generally changed little within Trust–pathogen–antibiotic combinations. In those with larger changes, XGBoost models can improve predictions, enabling informed decisions, efficient resource allocation, and targeted interventions. Plain language summary Antibiotics play an important role in treating serious bacterial infections. However, with the increased usage of antibiotics, they are becoming less effective. In our study, we use machine learning to learn from past antibiotic resistance and usage in order to predict what resistance will look like in the future. Different hospitals across England have very different resistance levels, however, within each hospital, these levels remain stable over time. When larger changes in resistance occurred over time in individual hospitals, our methods were able to predict these. Understanding how much resistance there is in hospital populations, and what may occur in the future can help determine where resources and interventions should be directed. Vihta et al. use past hospital data including bloodstream infection cases, susceptibilities, and antimicrobial use to predict future resistance prevalence. Machine learning can improve the accuracy of predictions potentially impacting interventions.