Estimates of influenza vaccine effectiveness in primary care in Scotland vary with clinical or laboratory endpoint and method : experience across the 2010/11 season

Aim: This study examines estimation of seasonal influenza vaccine effectiveness (VE) for a cohort of patients attending general practice in Scotland in 2010/11. The study focuses on the variation in estimation of VE for both virological and clinical consultation outcomes and understanding the dependency on date of analysis during the season, methodological approach and the effect of use of a propensity score model. Methods: For the clinical outcomes, three methodological approaches were considered; adjusted Poissonmulti-level modelling splitting consultations in vaccinated individuals into those before and after vaccination, adjusted Cox proportional hazards modelling and finally the screening method. For the virological outcome, the test-negative case–control study design was employed. Results: VE was highest for the most specific outcomes of ILI (Poisson end-of-season VE = 47% (95% CI:−69%, 83%); Cox VE = 34% (95% CI: −64%, 73.2%); Screening VE = 52.8% (95% CI: 3.8%, 76.8%)) and a viro-logical diagnosis (VE = 54% (95% CI: −37%, 85%)). Using the Cox approach, adjusted for propensity scoreonly gave VE = 46.5% (95% CI: −30.4%, 78.0%). Conclusion: Our approach illustrated the ability to achieve relatively consistent estimates of seasonalinfluenza VE using both specific and less specific outcomes. Construction of a propensity score and usefor bias adjustment increased the estimate of ILI VE estimated from the Cox model and made estimatesmore similar to the Poisson approach, which models differences in consultation behaviour of vacci-nated individuals more inherently in its structure. VE estimation for the same data was found to vary bymethodology which should be noted when comparing results from different studies and countries

Building a national Infection Intelligence Platform to improve antimicrobial stewardship and drive better patient outcomes:the Scottish experience

Background: The better use of new and emerging data streams to understand the epidemiology of infectious disease and to inform and evaluate antimicrobial stewardship improvement programmes is paramount in the global fight against antimicrobial resistance. Objectives: To create a national informatics platform that synergises the wealth of disjointed, infection-related health data, building intelligence capability that allows rapid enquiry, generation of new knowledge and feedback to clinicians and policy makers. Methods: A multi-stakeholder community, led by the Scottish Antimicrobial Prescribing Group, secured government funding to deliver a national program of work centred on three key aspects: technical platform development with record linkage capability across multiple datasets; a proportionate governance approach to enhance responsiveness; generation of new evidence to guide clinical practice. Results: The National Health Service Scotland Infection Intelligence Platform (IIP) is now hosted within the national health data repository to assure resilience and sustainability. New technical solutions include simplified “data views” of complex, linked datasets and embedded statistical programmes to enhance capability. These developments have enabled responsiveness, flexibility and robustness in conducting population-based studies including a focus on intended and unintended effects of antimicrobial stewardship interventions and quantification of infection risk factors and clinical outcomes. Conclusion: We have completed the build and test phase of IIP, overcoming the technical and governance challenges and produced new capability in infection informatics, generating new evidence for improved clinical practice. This provides a foundation for expansion and opportunity for global collaborations

Risk factors for resistance and MDR in community urine isolates:population-level analysis using the NHS Scotland Infection Intelligence Platform

Background: Urinary tract infections (UTI) are common. Antibiotic treatment is usually empirical, with the risk of under-treatment of resistant infections. Objectives: To characterise risk factors for antibiotic resistant community urine isolates using routine record linked health data. Methods: Within the National Health Service Scotland Infection Intelligence Platform, national surveillance patient-level data on community urine isolates (January 2012-June 2015) were linked to hospital activity and community prescribing data. Associations between age, gender, comorbidity, care home residence, previous hospitalisations, antibiotic exposure, and resistant (any antibiotic) or MDR (≥1 antibiotic from ≥3 categories) urinary isolates were quantified using multivariable logistic regression. Results: Of 40,984 isolates, 28% were susceptible, 45% resistant, and 27% MDR. Exposure to ≥ 4 different antibiotics in the prior six months increased MDR risk, OR 6.81 (95%CI 5.73-8.11). MDR was associated with ≥29 DDD cumulative exposure, in the prior six months, for any antibiotic (OR 6.54, 95%CI 5.88-7.27), nitrofurantoin (OR 8.56, 95%CI 6.56-11.18) and trimethoprim (OR 14.61, 95%CI10.53-20.27). Associations persisted for 10-12 months for nitrofurantoin (OR 2.31, 95%CI 1.93-2.76) and trimethoprim (OR 1.81, 95%CI 1.57-2.09). Increasing age, comorbidity, previous hospitalisation and care home residence were independently associated with MDR. For resistant isolates the factors were the same but with weaker associations. Conclusion: We have demonstrated, using national capability at scale, the risk of MDR in community urine isolates for the first time and quantified the cumulative and sustained impact of antibiotic exposure. These data will inform the development of decision support tools for UTI treatment

Development and validation of a digital biomarker predicting acute kidney injury following cardiac surgery on an hourly basis

Objectives To develop and validate a digital biomarker for predicting the onset of acute kidney injury (AKI) on an hourly basis up to 24 hours in advance in the intensive care unit after cardiac surgery. Methods The study analyzed data from 6056 adult patients undergoing coronary artery bypass graft (CABG) and/or valve surgery between 1st April 2012 and 31st December 2018 (development phase, training, and testing) and 3572 patients between 1st January 2019 and 30th June 2022 (validation phase). The study utilized two dynamic predictive modeling approaches, namely logistic regression and bootstrap aggregated regression trees machine (BARTm), to predict AKI. The mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV and NPV) across all lead times before the occurrence of AKI were reported. The clinical practicality was assessed using calibration. Results Of all included patients, 8.45% and 16.66% had AKI in the development and validation phases, respectively. When applied to testing data, AKI was predicted with the mean AUC of 0.850 and 0.802 by BARTm and logistic regression, respectively. When applied to validation data, BARTm and LR resulted in a mean AUC of 0.844 and 0.786, respectively. Conclusions This study demonstrated the successful prediction of AKI on an hourly basis up to 24 hours in advance. The digital biomarkers developed and validated in this study have the potential to assist clinicians in optimizing treatment and implementing preventive strategies for patients at risk of developing AKI after cardiac surgery in the ICU

Predicting the onset of delirium on hourly basis in an intensive care unit following cardiac surgery

Delirium, affecting up to 52% of cardiac surgery patients, can have serious long-term effects on patients by damaging cognitive ability and causing subsequent functional decline. This study reports on the development and evaluation of predictive models aimed at identifying the likely onset of delirium on an hourly basis in intensive care unit following cardiac surgery. Most models achieved a mean AUC > 0.900 across all lead times. A support vector machine achieved the highest performance across all lead times of AUC = 0.941 and Sensitivity = 0.907, and BARTm, where missing values were replaced with missForest imputation, achieved the highest Specificity of 0.892. Being able to predict delirium hours in advance gives clinicians the ability to intervene and optimize treatments for patients who are at risk and avert potentially serious and life-threatening consequences

Cumulative and temporal associations between antimicrobial prescribing and community-associated <i>Clostridium difficile</i> infection:population-based case-control study using administrative data

Background. Community-associated (CA) Clostridium difficile infection (CDI) is a major public health problem. This study estimates the magnitude of the association between temporal and cumulative prescription of antimicrobials in primary care and CA-CDI. CA-CDI is defined as cases without prior hospitalisation in the previous 12 weeks who were either tested outside of hospital or tested within 2 days of admission to hospital. Methods. Three National patient level datasets –covering CDI cases, community prescriptions and hospitalisations were linked by the NHS Scotland unique patient identifier, the community health index, CHI. All validated cases of CDI from August 2010 to July 2013 were extracted and up to six population-based controls were matched to each case from the CHI register for Scotland. Statistical analysis used conditional logistic regression. Results. 1446 unique cases of CA-CDI were linked with 7964 age, sex and location matched controls. Cumulative exposure to any antimicrobial in the previous 6 months has a monotonic dose-response association with CA-CDI. Individuals with excess of 28 defined daily doses (DDD) to any antimicrobial (19.9% of cases) had an odds ratio (OR)=4.4 (95% CI:3.4-5.6) compared to those unexposed. Individuals exposed to 29+ DDD of high risk antimicrobials (cephalosporins, clindamycin co-amoxiclav, or fluoroquinolones) had an OR=17.9 (95% CI:7.6-42.2). Elevated CA-CDI risk following high risk antimicrobial exposure was greatest in the first month (OR=12.5 (8.9-17.4)) but was still present 4-6 months later (OR=2.6 (1.7-3.9)). Cases exposed to 29+DDD had prescription patterns more consistent with repeated therapeutic courses, using different antimicrobials, than long term prophylactic use. Conclusions. This analysis demonstrated temporal and dose-response associations between CA-CDI risk and antimicrobials with an impact of exposure to high risk antimicrobials remaining 4-6 months later

Seasonal Influenza Vaccine Effectiveness in the community (SIVE): protocol for a cohort study exploiting a unique national linked data set

Introduction Seasonal influenza vaccination is recommended for all individuals aged 65 years and over and in individuals younger than 65 years with comorbidities. There is good evidence of vaccine effectiveness (VE) in young healthy individuals but less robust evidence for effectiveness in the populations targeted for influenza vaccination. Undertaking a randomised controlled trial to assess VE is now impractical due to the presence of national vaccination programmes. Quasi-experimental designs offer the potential to advance the evidence base in such scenarios, and the authors have therefore been commissioned to undertake a naturalistic national evaluation of seasonal influenza VE by using data derived from linkage of a number of Scottish health databases. The aim of this study is to examine the effectiveness of the seasonal influenza vaccination in the Scottish population. Methods and analysis A cohort study design will be used pooling data over nine seasons. A primary care database covering 4% of the Scottish population for the period 2000–2009 has been linked to the national database of hospital admissions and the death register and is being linked to the Health Protection Scotland virology database. The primary outcome is VE measured in terms of rate of hospital admissions due to respiratory illness. Multivariable regression will be used to produce estimates of VE adjusted for confounders. The major challenge of this approach is addressing the strong effect of confounding due to vaccinated individuals being systematically different from unvaccinated individuals. Analyses using propensity scores and instrumental variables will be undertaken, and the effect of an unknown confounder will be modelled in a sensitivity analysis to assess the robustness of the estimates

Data feedback and behavioural change intervention to improve primary care prescribing safety (EFIPPS):multicentre, three arm, cluster randomised controlled trial

Objective: To evaluate the effectiveness of feedback on safety of prescribing compared with moderately enhanced usual care. Design: Three arm, highly pragmatic cluster randomised trial. Setting and participants: 262/278 (94%) primary care practices in three Scottish health boards. Interventions: Practices were randomised to: "usual care," consisting of emailed educational material with support for searching to identify patients (88 practices at baseline, 86 analysed); usual care plus feedback on practice's high risk prescribing sent quarterly on five occasions (87 practices, 86 analysed); or usual care plus the same feedback incorporating a behavioural change component (87 practices, 86 analysed). Main outcome measures: The primary outcome was a patient level composite of six prescribing measures relating to high risk use of antipsychotics, non-steroidal anti-inflammatories, and antiplatelets. Secondary outcomes were the six individual measures. The primary analysis compared high risk prescribing in the two feedback arms against usual care at 15 months. Secondary analyses examined immediate change and change in trend of high risk prescribing associated with implementation of the intervention within each arm. Results: In the primary analysis, high risk prescribing as measured by the primary outcome fell from 6.0% (3332/55 896) to 5.1% (2845/55 872) in the usual care arm, compared with 5.9% (3341/56 194) to 4.6% (2587/56 478) in the feedback only arm (odds ratio 0.88 (95% confidence interval 0.80 to 0.96) compared with usual care; P=0.007) and 6.2% (3634/58 569) to 4.6% (2686/58 582) in the feedback plus behavioural change component arm (0.86 (0.78 to 0.95); P=0.002). In the pre-specified secondary analysis of change in trend within each arm, the usual care educational intervention had no effect on the existing declining trend in high risk prescribing. Both types of feedback were associated with significantly more rapid decline in high risk prescribing after the intervention compared with before. Conclusions: Feedback of prescribing safety data was effective at reducing high risk prescribing. The intervention would be feasible to implement at scale in contexts where electronic health records are in general use

Residual effect of community antimicrobial exposure on risk of hospital onset healthcare associated Clostridioides difficile infection:a case-control study using national linked data

Background: Associations between antimicrobial exposure in the community and community-associated Clostridioides difficile infection (CA-CDI) are well documented but associations with healthcare-associated CDI (HA-CDI) are less clear. This study estimates the association between antimicrobial prescribing in the community and HA-CDI. Methods: A matched case–control study was conducted by linking three national patient level datasets covering CDI cases, community prescriptions and hospitalizations. All validated cases of HA-CDI (August 2010 to July 2013) were extracted and up to three hospital-based controls were matched to each case on the basis of gender, age, hospital and date of admission. Conditional logistic regression was applied to estimate the association between antimicrobial prescribing in the community and HA-CDI. A sensitivity analysis was conducted to consider the impact of unmeasured hospital antimicrobial prescribing. Results: Nine-hundred and thirty unique cases of HA-CDI with onset in hospital and no hospital discharge in the 12 weeks prior to index admission were linked with 1810 matched controls. Individuals with prior prescription of any antimicrobial in the community had an odds ratio (OR) = 1.41 (95% confidence interval (CI) 1.13–1.75) for HA-CDI compared with those without. Individuals exposed to high-risk antimicrobials (cephalosporins, clindamycin, co-amoxiclav or fluoroquinolones) had an OR = 1.86 (95% CI: 1.33–2.59). After accounting for the likely impact of unmeasured hospital prescribing, the community exposure, particulary to high-risk antimicrobials, was still associated with elevated HA-CDI risk. Conclusions: Community antimicrobial exposure is an independent risk factor for HA-CDI and should be considered as part of the risk assessment of patients developing diarrhoea in hospital