Calibration plots for multistate risk predictions models.

INTRODUCTION: There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation. METHODS: We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. RESULTS: The pseudo-value, BLR-IPCW, and MLR-IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low-density regions of predicted transition probability. CONCLUSIONS: We recommend implementing either the pseudo-value or BLR-IPCW approaches to produce a calibration curve, combined with the MLR-IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the "calibmsm" R package available on CRAN.

Sepsis and case fatality rates and associations with deprivation, ethnicity, and clinical characteristics: population-based case-control study with linked primary care and hospital data in England.

PURPOSE: Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. The purpose of the study was to measure the associations of specific exposures (deprivation, ethnicity, and clinical characteristics) with incident sepsis and case fatality. METHODS: Two research databases in England were used including anonymized patient-level records from primary care linked to hospital admission, death certificate, and small-area deprivation. Sepsis cases aged 65-100 years were matched to up to six controls. Predictors for sepsis (including 60 clinical conditions) were evaluated using logistic and random forest models; case fatality rates were analyzed using logistic models. RESULTS: 108,317 community-acquired sepsis cases were analyzed. Severe frailty was strongly associated with the risk of developing sepsis (crude odds ratio [OR] 14.93; 95% confidence interval [CI] 14.37-15.52). The quintile with most deprived patients showed an increased sepsis risk (crude OR 1.48; 95% CI 1.45-1.51) compared to least deprived quintile. Strong predictors for sepsis included antibiotic exposure in prior 2 months, being house bound, having cancer, learning disability, and diabetes mellitus. Severely frail patients had a case fatality rate of 42.0% compared to 24.0% in non-frail patients (adjusted OR 1.53; 95% CI 1.41-1.65). Sepsis cases with recent prior antibiotic exposure died less frequently compared to non-users (adjusted OR 0.7; 95% CI 0.72-0.76). Case fatality strongly decreased over calendar time. CONCLUSION: Given the variety of predictors and their level of associations for developing sepsis, there is a need for prediction models for risk of developing sepsis that can help to target preventative antibiotic therapy.

Multiple adverse outcomes associated with antipsychotic use in people with dementia: population based matched cohort study.

OBJECTIVE: To investigate risks of multiple adverse outcomes associated with use of antipsychotics in people with dementia. DESIGN: Population based matched cohort study. SETTING: Linked primary care, hospital and mortality data from Clinical Practice Research Datalink (CPRD), England. POPULATION: Adults (≥50 years) with a diagnosis of dementia between 1 January 1998 and 31 May 2018 (n=173 910, 63.0% women). Each new antipsychotic user (n=35 339, 62.5% women) was matched with up to 15 non-users using incidence density sampling. MAIN OUTCOME MEASURES: The main outcomes were stroke, venous thromboembolism, myocardial infarction, heart failure, ventricular arrhythmia, fracture, pneumonia, and acute kidney injury, stratified by periods of antipsychotic use, with absolute risks calculated using cumulative incidence in antipsychotic users versus matched comparators. An unrelated (negative control) outcome of appendicitis and cholecystitis combined was also investigated to detect potential unmeasured confounding. RESULTS: Compared with non-use, any antipsychotic use was associated with increased risks of all outcomes, except ventricular arrhythmia. Current use (90 days after a prescription) was associated with elevated risks of pneumonia (hazard ratio 2.19, 95% confidence interval (CI) 2.10 to 2.28), acute kidney injury (1.72, 1.61 to 1.84), venous thromboembolism (1.62, 1.46 to 1.80), stroke (1.61, 1.52 to 1.71), fracture (1.43, 1.35 to 1.52), myocardial infarction (1.28, 1.15 to 1.42), and heart failure (1.27, 1.18 to 1.37). No increased risks were observed for the negative control outcome (appendicitis and cholecystitis). In the 90 days after drug initiation, the cumulative incidence of pneumonia among antipsychotic users was 4.48% (4.26% to 4.71%) versus 1.49% (1.45% to 1.53%) in the matched cohort of non-users (difference 2.99%, 95% CI 2.77% to 3.22%). CONCLUSIONS: Antipsychotic use compared with non-use in adults with dementia was associated with increased risks of stroke, venous thromboembolism, myocardial infarction, heart failure, fracture, pneumonia, and acute kidney injury, but not ventricular arrhythmia. The range of adverse outcomes was wider than previously highlighted in regulatory alerts, with the highest risks soon after initiation of treatment.

Rapid systematic review on risks and outcomes of sepsis: the influence of risk factors associated with health inequalities.

BACKGROUND AND AIMS: Sepsis is a serious and life-threatening condition caused by a dysregulated immune response to an infection. Recent guidance issued in the UK gave recommendations around recognition and antibiotic treatment of sepsis, but did not consider factors relating to health inequalities. The aim of this study was to summarise the literature investigating associations between health inequalities and sepsis. METHODS: Searches were conducted in Embase for peer-reviewed articles published since 2010 that included sepsis in combination with one of the following five areas: socioeconomic status, race/ethnicity, community factors, medical needs and pregnancy/maternity. RESULTS: Five searches identified 1,402 studies, with 50 unique studies included in the review after screening (13 sociodemographic, 14 race/ethnicity, 3 community, 3 care/medical needs and 20 pregnancy/maternity; 3 papers examined multiple health inequalities). Most of the studies were conducted in the USA (31/50), with only four studies using UK data (all pregnancy related). Socioeconomic factors associated with increased sepsis incidence included lower socioeconomic status, unemployment and lower education level, although findings were not consistent across studies. For ethnicity, mixed results were reported. Living in a medically underserved area or being resident in a nursing home increased risk of sepsis. Mortality rates after sepsis were found to be higher in people living in rural areas or in those discharged to skilled nursing facilities while associations with ethnicity were mixed. Complications during delivery, caesarean-section delivery, increased deprivation and black and other ethnic minority race were associated with post-partum sepsis. CONCLUSION: There are clear correlations between sepsis morbidity and mortality and the presence of factors associated with health inequalities. To inform local guidance and drive public health measures, there is a need for studies conducted across more diverse setting and countries.

Adverse drug reactions and hospital admissions: Large case-control study of patients aged 65-100 years using linked English primary care and hospital data.

BACKGROUND: Adverse drug reactions (ADRs) are common and a leading cause of injury. However, information on ADR risks of individual medicines is often limited. The aim of this hypothesis-generating study was to assess the relative importance of ADR-related and emergency hospital admission for large group of medication classes. METHODS: This study was a propensity-matched case-control study in English primary care. Data sources were Clinical Practice Research Databank and Aurum with longitudinal, anonymized, patient level electronic health records (EHRs) from English general practices linked to hospital records. Cases aged 65-100 with ADR-related or emergency hospital admission were matched to up to six controls by age, sex, morbidity and propensity scores for hospital admission risk. Medication groups with systemic administration as listed in the British National Formulary (used by prescribers for medication advice). Prescribing in the 84 days before the index date was assessed. Only medication groups with 50+ cases exposed were analysed. The outcomes of interest were ADR-related and emergency hospital admissions. Conditional logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CI). RESULTS: The overall population included 121 546 cases with an ADR-related and 849 769 cases with emergency hospital admission. The percentage of hospitalizations with an ADR-related code for admission diagnosis was 1.83% and 6.58% with an ADR-related code at any time during hospitalization. A total of 137 medication groups was included in the main ADR analyses. Of these, 13 (9.5%) had statistically non-significant adjusted ORs, 58 (42.3%) statistically significant ORs between 1.0 and 1.5, 37 (27.0%) between 1.5-2.0, 18 (13.1%) between 2.0-3.0 and 11 (8.0%) 3.0 or higher. Several classes of antibiotics (including penicillins) were among medicines with largest ORs. Evaluating the 14 medications most often associated with ADRs, a strong association was found between the number of these medicines and the risk of ADR-related hospital admission (adjusted OR of 7.53 (95% CI 7.15-7.93) for those exposed to 6+ of these medicines). CONCLUSIONS AND RELEVANCE: There is a need for a regular systematic assessment of the harm-benefit ratio of medicines, harvesting the information in large healthcare databases and combining it with causality assessment of individual case histories.

Risk of Falls and Fractures in Individuals With Cataract, Age-Related Macular Degeneration, or Glaucoma.

Importance: Three leading disease causes of age-related visual loss are cataract, age-related macular degeneration (AMD), and glaucoma. Although all 3 eye diseases have been implicated with falls and fracture risk, evidence is mixed, with the contribution of different eye diseases being uncertain. Objective: To examine whether people with cataract, AMD, or glaucoma have higher risks of falls or fractures than those without. Design, Setting, and Participants: This cohort study was a population-based study in England using routinely collected electronic health records from the Clinical Practice Research Datalink (CPRD) GOLD and Aurum primary care databases with linked hospitalization and mortality records from 2007 to 2020. Participants were people with cataract, AMD, or glaucoma matched to comparators (1:5) by age, sex, and general practice. Data were analyzed from May 2021 to June 2023. Exposures: For each eye disease, we estimated the risk of falls or fractures using separate multivariable Cox proportional hazards regression models. Main Outcomes: Two primary outcomes were incident falls and incident fractures derived from general practice, hospital, and mortality records. Secondary outcomes were incident fractures of specific body sites. Results: A total of 410 476 people with cataract, 75 622 with AMD, and 90 177 with glaucoma were matched (1:5) to 2 034 194 (no cataract), 375 548 (no AMD), and 448 179 (no glaucoma) comparators. The mean (SD) age was 73.8 (11.0) years, 79.4 (9.4) years, and 69.8 (13.1) years for participants with cataract, AMD, or glaucoma, respectively. Compared with comparators, there was an increased risk of falls in those with cataract (adjusted hazard ratio [HR], 1.36; 95% CI, 1.35-1.38), AMD (HR, 1.25; 95% CI, 1.23-1.27), and glaucoma (HR, 1.38; 95% CI, 1.35-1.41). Likewise for fractures, there were increased risks in all eye diseases, with an HR of 1.28 (95% CI, 1.27-1.30) in the cataract cohort, an HR of 1.18 (95% CI, 1.15-1.21) for AMD, and an HR of 1.31 (95% CI, 1.27-1.35) for glaucoma. Site-specific fracture analyses revealed increases in almost all body sites (including hip, spine, forearm, skull or facial bones, pelvis, ribs or sternum, and lower leg fractures) compared with matched comparators. Conclusions and Relevance: The results of this study support recognition that people with 1 or more of these eye diseases are at increased risk of both falls and fractures. They may benefit from improved advice, access, and referrals to falls prevention services.

Expert guidance on target product profile development for AMR diagnostic tests.

Diagnostics are widely considered crucial in the fight against antimicrobial resistance (AMR), which is expected to kill 10 million people annually by 2030. Nevertheless, there remains a substantial gap between the need for AMR diagnostics versus their development and implementation. To help address this problem, target product profiles (TPP) have been developed to focus developers' attention on the key aspects of AMR diagnostic tests. However, during discussion between a multisectoral working group of 51 international experts from industry, academia and healthcare, it was noted that specific AMR-related TPPs could be extended by incorporating the interdependencies between the key characteristics associated with the development of such TPPs. Subsequently, the working group identified 46 characteristics associated with six main categories (ie, Intended Use, Diagnostic Question, Test Description, Assay Protocol, Performance and Commercial). The interdependencies of these characteristics were then identified and mapped against each other to generate new insights for use by stakeholders. Specifically, it may not be possible for diagnostics developers to achieve all of the recommendations in every category of a TPP and this publication indicates how prioritising specific TPP characteristics during diagnostics development may influence (or not) a range of other TPP characteristics associated with the diagnostic. The use of such guidance, in conjunction with specific TPPs, could lead to more efficient AMR diagnostics development.

The impact of the COVID-19 pandemic on rates and predictors of missed hospital appointments in multiple outpatient clinics of The Royal Hospital, Sultanate of Oman: a retrospective study.

BACKGROUND: The global outbreak of the COVID-19 pandemic resulted in significant changes in the delivery of health care services such as attendance of scheduled outpatient hospital appointments. This study aimed to evaluate the impact of COVID-19 on the rate and predictors of missed hospital appointment in the Sultanate of Oman. METHODS: A retrospective single-centre analysis was conducted to determine the effect of COVID-19 on missed hospital appointments at various clinics at The Royal Hospital (tertiary referral hospital) in Muscat, Sultanate of Oman. The study population included scheduled face-to-face and virtual appointments between January 2019 and March 2021. Logistic regression models were used with interaction terms (post COVID-19) to assess changes in the predictors of missed appointments. RESULTS: A total of 34, 3149 scheduled appointments was analysed (320,049 face-to-face and 23,100 virtual). The rate of missed face-to-face hospital appointments increased from 16.9% pre to 23.8% post start of COVID-19, particularly in early pandemic (40.5%). Missed hospital appointments were more frequent (32.2%) in virtual clinics (post COVID-19). Increases in missed face-to-face appointments varied by clinic (Paediatrics from 19.3% pre to 28.2% post; Surgery from 12.5% to 25.5%; Obstetrics & Gynaecology from 8.4% to 8.5%). A surge in the frequency of missed appointments was seen during national lockdowns for face-to-face and virtual appointments. Most predictors of missed appointments did not demonstrate any appreciable changes in effect (i.e., interaction term not statistically significant). Distance of patient residence to the hospital revealed no discernible changes in the relative effect pre and post COVID-19 for both face-to-face and virtual clinic appointments. CONCLUSION: The rate of missed visits in most clinics was directly impacted by COVID-19. The case mix of patients who missed their appointments did not change. Virtual appointments, introduced after start of the pandemic, also had substantial rates of missed appointments and cannot be viewed as the single approach that can overcome the problem of missing hospital appointments.

The Impact of Oral Antibiotics Prior to Cancer Diagnosis on Overall Patient Survival: Findings from an English Population-Based Cohort Study.

BACKGROUND: There is limited evidence in humans as to whether antibiotics impact the effectiveness of cancer treatments. Rodent studies have shown that disruption in gut microbiota due to antibiotics decreases cancer therapy effectiveness. We evaluated the associations between the antibiotic treatment of different time periods before cancer diagnoses and long-term mortality. METHODS: Using the Clinical Practice Research Datalink GOLD, linked to the Cancer Registry's and the Office for National Statistics' mortality records, we delineated a study cohort that involved cancer patients who were prescribed antibiotics 0-3 months; 3-24 months; or more than 24 months before cancer diagnosis. Patients' exposure to antibiotics was compared according to the recency of prescriptions and time-to-event (all-cause mortality) by applying Cox models. RESULTS: 111,260 cancer patients from England were included in the analysis. Compared with antibiotic prescriptions that were issued in the past, patients who had been prescribed antibiotics shortly before cancer diagnosis presented an increased hazard ratio (HR) for mortality. For leukaemia, the HR in the Cancer Registry was 1.32 (95% CI 1.16-1.51), for lymphoma it was 1.22 (1.08-1.36), for melanoma it was 1.28 (1.10-1.49), and for myeloma it was 1.19 (1.04-1.36). Increased HRs were observed for cancer of the uterus, bladder, and breast and ovarian and colorectal cancer. CONCLUSIONS: Antibiotics that had been issued within the three months prior to cancer diagnosis may reduce the effectiveness of chemotherapy and immunotherapy. Judicious antibiotic prescribing is needed among cancer patients.

The impact of the COVID-19 pandemic on the treatment of common infections in primary care and the change to antibiotic prescribing in England.

BACKGROUND: There is concern that the COVID-19 pandemic altered the management of common infections in primary care. This study aimed to evaluate infection-coded consultation rates and antibiotic use during the pandemic and how any change may have affected clinical outcomes. METHODS: With the approval of NHS England, a retrospective cohort study using the OpenSAFELY platform analysed routinely collected electronic health data from GP practices in England between January 2019 and December 2021. Infection coded consultations and antibiotic prescriptions were used estimate multiple measures over calendar months, including age-sex adjusted prescribing rates, prescribing by infection and antibiotic type, infection consultation rates, coding quality and rate of same-day antibiotic prescribing for COVID-19 infections. Interrupted time series (ITS) estimated the effect of COVID-19 pandemic on infection-coded consultation rates. The impact of the pandemic on non- COVID-19 infection-related hospitalisations was also estimated. RESULTS: Records from 24 million patients were included. The rate of infection-related consultations fell for all infections (mean reduction of 39% in 2020 compared to 2019 mean rate), except for UTI which remained stable. Modelling infection-related consultation rates highlighted this with an incidence rate ratio of 0.44 (95% CI 0.36-0.53) for incident consultations and 0.43 (95% CI 0.33-0.54) for prevalent consultations. Lower respiratory tract infections (LRTI) saw the largest reduction of 0.11 (95% CI 0.07-0.17). Antibiotic prescribing rates fell with a mean reduction of 118.4 items per 1000 patients in 2020, returning to pre-pandemic rates by summer 2021. Prescribing for LRTI decreased 20% and URTI increased 15.9%. Over 60% of antibiotics were issued without an associated same-day infection code, which increased during the pandemic. Infection-related hospitalisations reduced (by 62%), with the largest reduction observed for pneumonia infections (72.9%). Same-day antibiotic prescribing for COVID-19 infection increased from 1 to 10.5% between the second and third national lockdowns and rose again during 2022. CONCLUSIONS: Changes to consultations and hospital admissions may be driven by reduced transmission of non-COVID-19 infections due to reduced social mixing and lockdowns. Inconsistencies in coding practice emphasises the need for improvement to inform new antibiotic stewardship policies and prevent resistance to novel infections.

Repeated antibiotic exposure and risk of hospitalisation and death following COVID-19 infection (OpenSAFELY): a matched case-control study.

Background: Identifying potential risk factors related to severe COVID-19 outcomes is important. Repeated intermittent antibiotic use is known be associated with adverse outcomes. This study aims to examine whether prior frequent antibiotic exposure is associated with severe COVID-19 outcomes. Methods: With the approval of NHS England, we used the OpenSAFELY platform, which integrated primary and secondary care, COVID-19 test, and death registration data. This matched case-control study included 0.67 million patients (aged 18-110 years) from an eligible 2.47 million patients with incident COVID-19 by matching with replacement. Inclusion criteria included registration within one general practice for at least 3 years and infection with incident COVID-19. Cases were identified according to different severity of COVID-19 outcomes. Cases and eligible controls were 1:6 matched on age, sex, region of GP practice, and index year and month of COVID-19 infection. Five quintile groups, based on the number of previous 3-year antibiotic prescriptions, were created to indicate the frequency of prior antibiotic exposure. Conditional logistic regression used to compare the differences between case and control groups, adjusting for ethnicity, body mass index, comorbidities, vaccination history, deprivation, and care home status. Sensitivity analyses were done to explore potential confounding and the effects of missing data. Findings: Based on our inclusion criteria, between February 1, 2020 and December 31, 2021, 98,420 patients were admitted to hospitals and 22,660 died. 55 unique antibiotics were prescribed. A dose-response relationship between number of antibiotic prescriptions and risk of severe COVID-19 outcome was observed. Patients in the highest quintile with history of prior antibiotic exposure had 1.80 times greater odds of hospitalisation compared to patients without antibiotic exposure (adjusted odds ratio [OR] 1.80, 95% Confidence Interval [CI] 1.75-1.84). Similarly, the adjusted OR for hospitalised patients with death outcomes was 1.34 (95% CI 1.28-1.41). Larger number of prior antibiotic type was also associated with more severe COVID-19 related hospital admission. The adjusted OR of quintile 5 exposure (the most frequent) with more than 3 antibiotic types was around 2 times larger than quintile 1 (only 1 type; OR 1.80, 95% CI 1.75-1.84 vs. OR 1.03, 95% CI 1.01-1.05). Interpretation: Our observational study has provided evidence that antibiotic exposure frequency and diversity may be associated with COVID-19 severity, potentially suggesting adverse effects of repeated intermittent antibiotic use. Future work could work to elucidate causal links and potential mechanisms. Antibiotic stewardship should put more emphasis on long-term antibiotic exposure and its adverse outcome to increase the awareness of appropriate antibiotics use. Funding: Health Data Research UK and National Institute for Health Research.

Knowledge support for optimising antibiotic prescribing for common infections in general practices: evaluation of the effectiveness of periodic feedback, decision support during consultations and peer comparisons in a cluster randomised trial (BRIT2) - study protocol.

INTRODUCTION: This project applies a Learning Healthcare System (LHS) approach to antibiotic prescribing for common infections in primary care. The approach involves iterations of data analysis, feedback to clinicians and implementation of quality improvement activities by the clinicians. The main research question is, can a knowledge support system (KSS) intervention within an LHS implementation improve antibiotic prescribing without increasing the risk of complications? METHODS AND ANALYSIS: A pragmatic cluster randomised controlled trial will be conducted, with randomisation of at least 112 general practices in North-West England. General practices participating in the trial will be randomised to the following interventions: periodic practice-level and individual prescriber feedback using dashboards; or the same dashboards plus a KSS. Data from large databases of healthcare records are used to characterise heterogeneity in antibiotic uses, and to calculate risk scores for clinical outcomes and for the effectiveness of different treatment strategies. The results provide the baseline content for the dashboards and KSS. The KSS comprises a display within the electronic health record used during the consultation; the prescriber (general practitioner or allied health professional) will answer standard questions about the patient's presentation and will then be presented with information (eg, patient's risk of complications from the infection) to guide decision making. The KSS can generate information sheets for patients, conveyed by the clinicians during consultations. The primary outcome is the practice-level rate of antibiotic prescribing (per 1000 patients) with secondary safety outcomes. The data from practices participating in the trial and the dashboard infrastructure will be held within regional shared care record systems of the National Health Service in the UK. ETHICS AND DISSEMINATION: Approved by National Health Service Ethics Committee IRAS 290050. The research results will be published in peer-reviewed journals and also disseminated to participating clinical staff and policy and guideline developers. TRIAL REGISTRATION NUMBER: ISRCTN16230629.

UK research data resources based on primary care electronic health records: review and summary for potential users.

BACKGROUND: The range and scope of electronic health record (EHR) data assets in the UK has recently increased, which has been mainly in response to the COVID-19 pandemic. Summarising and comparing the large primary care resources will help researchers to choose the data resources most suited to their needs. AIM: To describe the current landscape of UK EHR databases and considerations of access and use of these resources relevant to researchers. DESIGN & SETTING: Narrative review of EHR databases in the UK. METHOD: Information was collected from the Health Data Research Innovation Gateway, publicly available websites and other published data, and from key informants. The eligibility criteria were population-based open-access databases sampling EHRs across the whole population of one or more countries in the UK. Published database characteristics were extracted and summarised, and these were corroborated with resource providers. Results were synthesised narratively. RESULTS: Nine large national primary care EHR data resources were identified and summarised. These resources are enhanced by linkage to other administrative data to a varying extent. Resources are mainly intended to support observational research, although some can support experimental studies. There is considerable overlap of populations covered. While all resources are accessible to bona fide researchers, access mechanisms, costs, timescales, and other considerations vary across databases. CONCLUSION: Researchers are currently able to access primary care EHR data from several sources. Choice of data resource is likely to be driven by project needs and access considerations. The landscape of data resources based on primary care EHRs in the UK continues to evolve.

Impact of COVID-19 on broad-spectrum antibiotic prescribing for common infections in primary care in England: a time-series analyses using OpenSAFELY and effects of predictors including deprivation.

Background: The COVID-19 pandemic impacted the healthcare systems, adding extra pressure to reduce antimicrobial resistance. Therefore, we aimed to evaluate changes in antibiotic prescription patterns after COVID-19 started. Methods: With the approval of NHS England, we used the OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system in primary care and selected patients prescribed antibiotics from 2019 to 2021. To evaluate the impact of COVID-19 on broad-spectrum antibiotic prescribing, we evaluated prescribing rates and its predictors and used interrupted time series analysis by fitting binomial logistic regression models. Findings: Over 32 million antibiotic prescriptions were extracted over the study period; 8.7% were broad-spectrum. The study showed increases in broad-spectrum antibiotic prescribing (odds ratio [OR] 1.37; 95% confidence interval [CI] 1.36-1.38) as an immediate impact of the pandemic, followed by a gradual recovery with a 1.1-1.2% decrease in odds of broad-spectrum prescription per month. The same pattern was found within subgroups defined by age, sex, region, ethnicity, and socioeconomic deprivation quintiles. More deprived patients were more likely to receive broad-spectrum antibiotics, which differences remained stable over time. The most significant increase in broad-spectrum prescribing was observed for lower respiratory tract infection (OR 2.33; 95% CI 2.1-2.50) and otitis media (OR 1.96; 95% CI 1.80-2.13). Interpretation: An immediate reduction in antibiotic prescribing and an increase in the proportion of broad-spectrum antibiotic prescribing in primary care was observed. The trends recovered to pre-pandemic levels, but the consequence of the COVID-19 pandemic on AMR needs further investigation. Funding: This work was supported by Health Data Research UK and by National Institute for Health Research.

Real-world evidence: new opportunities for osteoporosis research. Recommendations from a Working Group from the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO).

This narrative review summarises the recommendations of a Working Group of the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) for the conduct and reporting of real-world evidence studies with a focus on osteoporosis research. PURPOSE: Vast amounts of data are routinely generated at every healthcare contact and activity, and there is increasing recognition that these real-world data can be analysed to generate scientific evidence. Real-world evidence (RWE) is increasingly used to delineate the natural history of disease, assess real-life drug effectiveness, understand adverse events and in health economic analysis. The aim of this work was to understand the benefits and limitations of this type of data and outline approaches to ensure that transparent and high-quality evidence is generated. METHODS: A ESCEO Working Group was convened in December 2022 to discuss the applicability of RWE to osteoporosis research and approaches to best practice. RESULTS: This narrative review summarises the agreed recommendations for the conduct and reporting of RWE studies with a focus on osteoporosis research. CONCLUSIONS: It is imperative that research using real-world data is conducted to the highest standards with close attention to limitations and biases of these data, and with transparency at all stages of study design, data acquisition and curation, analysis and reporting to increase the trustworthiness of RWE study findings.

The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing.

BACKGROUND: This study aimed to predict risks of potentially inappropriate antibiotic type and repeat prescribing and assess changes during COVID-19. METHODS: With the approval of NHS England, we used OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system and selected patients prescribed antibiotics from 2019 to 2021. Multinomial logistic regression models predicted patient's probability of receiving inappropriate antibiotic type or repeat antibiotic course for each common infection. RESULTS: The population included 9.1 million patients with 29.2 million antibiotic prescriptions. 29.1% of prescriptions were identified as repeat prescribing. Those with same day incident infection coded in the EHR had considerably lower rates of repeat prescribing (18.0%) and 8.6% had potentially inappropriate type. No major changes in the rates of repeat antibiotic prescribing during COVID-19 were found. In the 10 risk prediction models, good levels of calibration and moderate levels of discrimination were found. CONCLUSIONS: Our study found no evidence of changes in level of inappropriate or repeat antibiotic prescribing after the start of COVID-19. Repeat antibiotic prescribing was frequent and varied according to regional and patient characteristics. There is a need for treatment guidelines to be developed around antibiotic failure and clinicians provided with individualised patient information.

Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques.

INTRODUCTION: This study considers the prediction of the time until two survival outcomes have both occurred. We compared a variety of analytical methods motivated by a typical clinical problem of multimorbidity prognosis. METHODS: We considered five methods: product (multiply marginal risks), dual-outcome (directly model the time until both events occur), multistate models (msm), and a range of copula and frailty models. We assessed calibration and discrimination under a variety of simulated data scenarios, varying outcome prevalence, and the amount of residual correlation. The simulation focused on model misspecification and statistical power. Using data from the Clinical Practice Research Datalink, we compared model performance when predicting the risk of cardiovascular disease and type 2 diabetes both occurring. RESULTS: Discrimination was similar for all methods. The product method was poorly calibrated in the presence of residual correlation. The msm and dual-outcome models were the most robust to model misspecification but suffered a drop in performance at small sample sizes due to overfitting, which the copula and frailty model were less susceptible to. The copula and frailty model's performance were highly dependent on the underlying data structure. In the clinical example, the product method was poorly calibrated when adjusting for 8 major cardiovascular risk factors. DISCUSSION: We recommend the dual-outcome method for predicting the risk of two survival outcomes both occurring. It was the most robust to model misspecification, although was also the most prone to overfitting. The clinical example motivates the use of the methods considered in this study.

Primary care staff's views and experience of patients' online access to their electronic health record: a qualitative exploration.

BACKGROUND: NHS England have announced plans to enable all adult patients to have full prospective access to their primary care record by default. Despite this, little is known about the views and experiences of primary care staff regarding patients' online records access (ORA). AIM: To examine the views and experiences of primary care staff regarding patients having online access to their primary care health record, and how this service could be supported and improved. DESIGN AND SETTING: A qualitative study of a purposive sample of 30 primary care staff in England. METHOD: Online semi-structured interviews with primary care staff were conducted between December 2021 and March 2022. Verbatim transcripts were analysed inductively using thematic analysis. RESULTS: Most staff agreed with the principle of patient access to online health records but had mixed feelings regarding the potential benefits and drawbacks of applying this in practice. Staff identified opportunities for improving patient engagement, health literacy, and efficiencies in some administrative workloads, as well as concerns about maintaining the clinical integrity of patient records and ensuring that staff and patient safety and wellbeing are protected. CONCLUSION: Participants acknowledged that ORA may transform the purpose and function of the record and that ORA has potential to instigate a significant cultural shift in primary care, changing how staff work and relate to patients. This underlines the need for additional staff training and support to expand capability and capacity to adapt practice and enhance patient engagement with, and understanding of, their health records.

Clinician acceptability of an antibiotic prescribing knowledge support system for primary care: a mixed-method evaluation of features and context.

BACKGROUND: Overprescribing of antibiotics is a major concern as it contributes to antimicrobial resistance. Research has found highly variable antibiotic prescribing in (UK) primary care, and to support more effective stewardship, the BRIT Project (Building Rapid Interventions to optimise prescribing) is implementing an eHealth Knowledge Support System. This will provide unique individualised analytics information to clinicians and patients at the point of care. The objective of the current study was to gauge the acceptability of the system to prescribing healthcare professionals and highlight factors to maximise intervention uptake. METHODS: Two mixed-method co-design workshops were held online with primary care prescribing healthcare professionals (n = 16). Usefulness ratings of example features were collected using online polls and online whiteboards. Verbal discussion and textual comments were analysed thematically using inductive (participant-centred) and deductive perspectives (using the Theoretical Framework of Acceptability). RESULTS: Hierarchical thematic coding generated three overarching themes relevant to intervention use and development. Clinician concerns (focal issues) were safe prescribing, accessible information, autonomy, avoiding duplication, technical issues and time. Requirements were ease and efficiency of use, integration of systems, patient-centeredness, personalisation, and training. Important features of the system included extraction of pertinent information from patient records (such as antibiotic prescribing history), recommended actions, personalised treatment, risk indicators and electronic patient communication leaflets. Anticipated acceptability and intention to use the knowledge support system was moderate to high. Time was identified as a focal cost/ burden, but this would be outweighed if the system improved patient outcomes and increased prescribing confidence. CONCLUSION: Clinicians anticipate that an eHealth knowledge support system will be a useful and acceptable way to optimise antibiotic prescribing at the point of care. The mixed method workshop highlighted issues to assist person-centred eHealth intervention development, such as the value of communicating patient outcomes. Important features were identified including the ability to efficiently extract and summarise pertinent information from the patient records, provide explainable and transparent risk information, and personalised information to support patient communication. The Theoretical Framework of Acceptability enabled structured, theoretically sound feedback and creation of a profile to benchmark future evaluations. This may encourage a consistent user-focused approach to guide future eHealth intervention development.

Combinations of medicines in patients with polypharmacy aged 65-100 in primary care: Large variability in risks of adverse drug related and emergency hospital admissions.

BACKGROUND: Polypharmacy can be a consequence of overprescribing that is prevalent in older adults with multimorbidity. Polypharmacy can cause adverse reactions and result in hospital admission. This study predicted risks of adverse drug reaction (ADR)-related and emergency hospital admissions by medicine classes. METHODS: We used electronic health record data from general practices of Clinical Practice Research Datalink (CPRD GOLD) and Aurum. Older patients who received at least five medicines were included. Medicines were classified using the British National Formulary sections. Hospital admission cases were propensity-matched to controls by age, sex, and propensity for specific diseases. The matched data were used to develop and validate random forest (RF) models to predict the risk of ADR-related and emergency hospital admissions. Shapley Additive eXplanation (SHAP) values were calculated to explain the predictions. RESULTS: In total, 89,235 cases with polypharmacy and hospitalised with an ADR-related admission were matched to 443,497 controls. There were over 112,000 different combinations of the 50 medicine classes most implicated in ADR-related hospital admission in the RF models, with the most important medicine classes being loop diuretics, domperidone and/or metoclopramide, medicines for iron-deficiency anaemias and for hypoplastic/haemolytic/renal anaemias, and sulfonamides and/or trimethoprim. The RF models strongly predicted risks of ADR-related and emergency hospital admission. The observed Odds Ratio in the highest RF decile was 7.16 (95% CI 6.65-7.72) in the validation dataset. The C-statistics for ADR-related hospital admissions were 0.58 for age and sex and 0.66 for RF probabilities. CONCLUSIONS: Polypharmacy involves a very large number of different combinations of medicines, with substantial differences in risks of ADR-related and emergency hospital admissions. Although the medicines may not be causally related to increased risks, RF model predictions may be useful in prioritising medication reviews. Simple tools based on few medicine classes may not be effective in identifying high risk patients.