An international study to investigate and optimise the safety of discontinuing valproate in young men and women with epilepsy: Protocol.

Valproate is the most effective treatment for idiopathic generalised epilepsy. Currently, its use is restricted in women of childbearing potential owing to high teratogenicity. Recent evidence extended this risk to men's offspring, prompting recommendations to restrict use in everybody aged <55 years. This study will evaluate mortality and morbidity risks associated with valproate withdrawal by emulating a hypothetical randomised-controlled trial (called a "target trial") using retrospective observational data. The data will be drawn from ~250m mainly US patients in the TriNetX repository and ~60m UK patients in Clinical Practice Research Datalink (CPRD). These will be scanned for individuals aged 16-54 years with epilepsy and on valproate who either continued, switched to lamotrigine or levetiracetam, or discontinued valproate between 2014-2024, creating four groups. Randomisation to these groups will be emulated by baseline confounder adjustment using g-methods. Mortality and morbidity outcomes will be assessed and compared between groups over 1-10 years, employing time-to-first-event and recurrent events analyses. A causal prediction model will be developed from these data to aid in predicting the safest alternative antiseizure medications. Together, these findings will optimise informed decision-making about valproate withdrawal and alternative treatment selection, providing immediate and vital information for patients, clinicians and regulators.

A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.

INTRODUCTION: Structured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its potential, there is limited empirical evidence on the implementation of SMRs, and the challenges faced in the process. This study is part of a larger DynAIRx (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) project which aims to introduce Artificial Intelligence (AI) to SMRs and develop machine learning models and visualisation tools for patients with multimorbidity. Here, we explore how SMRs are currently undertaken and what barriers are experienced by those involved in them. METHODS: Qualitative focus groups and semi-structured interviews took place between 2022-2023. Six focus groups were conducted with doctors, pharmacists and clinical pharmacologists (n = 21), and three patient focus groups with patients with multimorbidity (n = 13). Five semi-structured interviews were held with 2 pharmacists, 1 trainee doctor, 1 policy-maker and 1 psychiatrist. Transcripts were analysed using thematic analysis. RESULTS: Two key themes limiting the effectiveness of SMRs in clinical practice were identified: 'Medication Reviews in Practice' and 'Medication-related Challenges'. Participants noted limitations to the efficient and effectiveness of SMRs in practice including the scarcity of digital tools for identifying and prioritising patients for SMRs; organisational and patient-related challenges in inviting patients for SMRs and ensuring they attend; the time-intensive nature of SMRs, the need for multiple appointments and shared decision-making; the impact of the healthcare context on SMR delivery; poor communication and data sharing issues between primary and secondary care; difficulties in managing mental health medications and specific challenges associated with anticholinergic medication. CONCLUSION: SMRs are complex, time consuming and medication optimisation may require multiple follow-up appointments to enable a comprehensive review. There is a need for a prescribing support system to identify, prioritise and reduce the time needed to understand the patient journey when dealing with large volumes of disparate clinical information in electronic health records. However, monitoring the effects of medication optimisation changes with a feedback loop can be challenging to establish and maintain using current electronic health record systems.

Bayesian estimation of the prevalence of antimicrobial resistance: a mathematical modelling study.

BACKGROUND: Estimates of the prevalence of antimicrobial resistance (AMR) underpin effective antimicrobial stewardship, infection prevention and control, and optimal deployment of antimicrobial agents. Typically, the prevalence of AMR is determined from real-world antimicrobial susceptibility data that are time delimited, sparse, and often biased, potentially resulting in harmful and wasteful decision-making. Frequentist methods are resource intensive because they rely on large datasets. OBJECTIVES: To determine whether a Bayesian approach could present a more reliable and more resource-efficient way to estimate population prevalence of AMR than traditional frequentist methods. METHODS: Retrospectively collected, open-source, real-world pseudonymized healthcare data were used to develop a Bayesian approach for estimating the prevalence of AMR by combination with prior AMR information from a contextualized review of literature. Iterative random sampling and cross-validation were used to assess the predictive accuracy and potential resource efficiency of the Bayesian approach compared with a standard frequentist approach. RESULTS: Bayesian estimation of AMR prevalence made fewer extreme estimation errors than a frequentist estimation approach [n = 74 (6.4%) versus n = 136 (11.8%)] and required fewer observed antimicrobial susceptibility results per pathogen on average [mean = 28.8 (SD = 22.1) versus mean = 34.4 (SD = 30.1)] to avoid any extreme estimation errors in 50 iterations of the cross-validation. The Bayesian approach was maximally effective and efficient for drug-pathogen combinations where the actual prevalence of resistance was not close to 0% or 100%. CONCLUSIONS: Bayesian estimation of the prevalence of AMR could provide a simple, resource-efficient approach to better inform population infection management where uncertainty about AMR prevalence is high.

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.

Early Detection and Monitoring of Gastrointestinal Infections Using Syndromic Surveillance: A Systematic Review.

The underreporting of laboratory-reported cases of community-based gastrointestinal (GI) infections poses a challenge for epidemiologists understanding the burden and seasonal patterns of GI pathogens. Syndromic surveillance has the potential to overcome the limitations of laboratory reporting through real-time data and more representative population coverage. This systematic review summarizes the utility of syndromic surveillance for early detection and surveillance of GI infections. Relevant articles were identified using the following keyword combinations: 'early warning', 'detection', 'gastrointestinal activity', 'gastrointestinal infections', 'syndrome monitoring', 'real-time monitoring', 'syndromic surveillance'. In total, 1820 studies were identified, 126 duplicates were removed, and 1694 studies were reviewed. Data extraction focused on studies reporting the routine use and effectiveness of syndromic surveillance for GI infections using relevant GI symptoms. Eligible studies (n = 29) were included in the narrative synthesis. Syndromic surveillance for GI infections has been implemented and validated for routine use in ten countries, with emergency department attendances being the most common source. Evidence suggests that syndromic surveillance can be effective in the early detection and routine monitoring of GI infections; however, 24% of the included studies did not provide conclusive findings. Further investigation is necessary to comprehensively understand the strengths and limitations associated with each type of syndromic surveillance system.

An analysis of published study designs in PubMed prisoner health abstracts from 1963 to 2023: a text mining study.

BACKGROUND: The challenging nature of studies with incarcerated populations and other offender groups can impede the conduct of research, particularly that involving complex study designs such as randomised control trials and clinical interventions. Providing an overview of study designs employed in this area can offer insights into this issue and how research quality may impact on health and justice outcomes. METHODS: We used a rule-based approach to extract study designs from a sample of 34,481 PubMed abstracts related to epidemiological criminology published between 1963 and 2023. The results were compared against an accepted hierarchy of scientific evidence. RESULTS: We evaluated our method in a random sample of 100 PubMed abstracts. An F1-Score of 92.2% was returned. Of 34,481 study abstracts, almost 40.0% (13,671) had an extracted study design. The most common study design was observational (37.3%; 5101) while experimental research in the form of trials (randomised, non-randomised) was present in 16.9% (2319). Mapped against the current hierarchy of scientific evidence, 13.7% (1874) of extracted study designs could not be categorised. Among the remaining studies, most were observational (17.2%; 2343) followed by systematic reviews (10.5%; 1432) with randomised controlled trials accounting for 8.7% (1196) of studies and meta-analysis for 1.4% (190) of studies. CONCLUSIONS: It is possible to extract epidemiological study designs from a large-scale PubMed sample computationally. However, the number of trials, systematic reviews, and meta-analysis is relatively small - just 1 in 5 articles. Despite an increase over time in the total number of articles, study design details in the abstracts were missing. Epidemiological criminology still lacks the experimental evidence needed to address the health needs of the marginalized and isolated population that is prisoners and offenders.

Management of antipsychotics in primary care: Insights from healthcare professionals and policy makers in the United Kingdom.

INTRODUCTION: Antipsychotic medication is increasingly prescribed to patients with serious mental illness. Patients with serious mental illness often have cardiovascular and metabolic comorbidities, and antipsychotics independently increase the risk of cardiometabolic disease. Despite this, many patients prescribed antipsychotics are discharged to primary care without planned psychiatric review. We explore perceptions of healthcare professionals and managers/directors of policy regarding reasons for increasing prevalence and management of antipsychotics in primary care. METHODS: Qualitative study using semi-structured interviews with 11 general practitioners (GPs), 8 psychiatrists, and 11 managers/directors of policy in the United Kingdom. Data was analysed using thematic analysis. RESULTS: Respondents reported competency gaps that impaired ability to manage patients prescribed antipsychotic medications, arising from inadequate postgraduate training and professional development. GPs lacked confidence to manage antipsychotic medications alone; psychiatrists lacked skills to address cardiometabolic risks and did not perceive this as their role. Communication barriers, lack of integrated care records, limited psychology provision, lowered expectation towards patients with serious mental illness by professionals, and pressure to discharge from hospital resulted in patients in primary care becoming 'trapped' on antipsychotics, inhibiting opportunities to deprescribe. Organisational and contractual barriers between services exacerbate this risk, with socioeconomic deprivation and lack of access to non-pharmacological interventions driving overprescribing. Professionals voiced fears of censure if a catastrophic event occurred after stopping an antipsychotic. Facilitators to overcome these barriers were suggested. CONCLUSIONS: People prescribed antipsychotics experience a fragmented health system and suboptimal care. Several interventions could be taken to improve care for this population, but inadequate availability of non-pharmacological interventions and socioeconomic factors increasing mental distress need policy change to improve outcomes. The role of professionals' fear of medicolegal or regulatory censure inhibiting antipsychotic deprescribing was a new finding in this study.

Compatibility in Missing Data Handling Across the Prediction Model Pipeline: A Simulation Study.

Careful handling of missing data is crucial to ensure that clinical prediction models are developed, validated, and implemented in a robust manner. We determined the bias in estimating predictive performance of different combinations of approaches for handling missing data across validation and implementation. We found four strategies that are compatible across the model pipeline and have provided recommendations for handling missing data between model validation and implementation under different missingness mechanisms.

Antimicrobial treatment imprecision: an outcome-based model to close the data-to-action loop.

Health-care systems, food supply chains, and society in general are threatened by the inexorable rise of antimicrobial resistance. This threat is driven by many factors, one of which is inappropriate antimicrobial treatment. The ability of policy makers and leaders in health care, public health, regulatory agencies, and research and development to deliver frameworks for appropriate, sustainable antimicrobial treatment is hampered by a scarcity of tangible outcome-based measures of the damage it causes. In this Personal View, a mathematically grounded, outcome-based measure of antimicrobial treatment appropriateness, called imprecision, is proposed. We outline a framework for policy makers and health-care leaders to use this metric to deliver more effective antimicrobial stewardship interventions to future patient pathways. This will be achieved using learning antimicrobial systems built on public and practitioner engagement; solid implementation science; advances in artificial intelligence; and changes to regulation, research, and development. The outcomes of this framework would be more ecologically and organisationally sustainable patterns of antimicrobial development, regulation, and prescribing. We discuss practical, ethical, and regulatory considerations involved in the delivery of novel antimicrobial drug development, and policy and patient pathways built on artificial intelligence-augmented measures of antimicrobial treatment imprecision.

COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region.

OBJECTIVES: To understand severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission risks, perceived risks and the feasibility of risk mitigations from experimental mass cultural events before coronavirus disease 2019 (COVID-19) restrictions were lifted. DESIGN: Prospective, population-wide observational study. SETTING: Four events (two nightclubs, an outdoor music festival and a business conference) open to Liverpool City Region UK residents, requiring a negative lateral flow test (LFT) within the 36 h before the event, but not requiring social distancing or face-coverings. PARTICIPANTS: A total of 12,256 individuals attending one or more events between 28 April and 2 May 2021. MAIN OUTCOME MEASURES: SARS-CoV-2 infections detected using audience self-swabbed (5-7 days post-event) polymerase chain reaction (PCR) tests, with viral genomic analysis of cases, plus linked National Health Service COVID-19 testing data. Audience experiences were gathered via questionnaires, focus groups and social media. Indoor CO2 concentrations were monitored. RESULTS: A total of 12 PCR-positive cases (likely 4 index, 8 primary or secondary), 10 from the nightclubs. Two further cases had positive LFTs but no PCR. A total of 11,896 (97.1%) participants with scanned tickets were matched to a negative pre-event LFT: 4972 (40.6%) returned a PCR within a week. CO2 concentrations showed areas for improving ventilation at the nightclubs. Population infection rates were low, yet with a concurrent outbreak of >50 linked cases around a local swimming pool without equivalent risk mitigations. Audience anxiety was low and enjoyment high. CONCLUSIONS: We observed minor SARS-CoV-2 transmission and low perceived risks around events when prevalence was low and risk mitigations prominent. Partnership between audiences, event organisers and public health services, supported by information systems with real-time linked data, can improve health security for mass cultural events.

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.

Antimicrobial learning systems: an implementation blueprint for artificial intelligence to tackle antimicrobial resistance.

The proliferation of various forms of artificial intelligence (AI) brings many opportunities to improve health care. AI models can harness complex evolving data, inform and augment human actions, and learn from health outcomes such as morbidity and mortality. The global public health challenge of antimicrobial resistance (AMR) needs large-scale optimisation of antimicrobial use and wider infection care, which could be enabled by carefully constructed AI models. As AI models become increasingly useful and robust, health-care systems remain challenging places for their deployment. An implementation gap exists between the promise of AI models and their use in patient and population care. Here, we outline an adaptive implementation and maintenance framework for AI models to improve antimicrobial use and infection care as a learning system. The roles of AMR problem identification, law and regulation, organisational support, data processing, and AI development, assessment, maintenance, and scalability in the implementation of AMR-targeted AI models are considered.

Health, economic, and equity consequences of future tobacco control policies in England: a microsimulation study.

BACKGROUND: Smoking still generates a huge, costly, and inequitable burden of disease. The UK tobacco-free generation target to reduce smoking prevalence to below 5% by 2030 will be missed if current trends continue. We aimed to determine whether additional policies could speed progress towards meeting the tobacco-free generation target. METHODS: We developed, calibrated, and validated a microsimulation model, IMPACTHINT simulating English adults aged 30-89 years from 2023 to 2072. The model included a detailed smoking history and quantified policy health outcomes including smoking prevalence and smoking-related diseases, economics, and equity. We simulated five scenarios: (1) baseline trends; (2) increasing the minimum age of access to tobacco to 21 years (MinAge21); (3) a 30% increase in tobacco duty (TaxUP); (4) improved smoking cessation services (ServicesUP); and (5) a combination of TaxUP and ServicesUP. We estimated the smoking prevalence, smoking-related diseases and cumulative cases prevented or postponed, and deaths. We evaluated the scenario cost-effectiveness from the societal perspective. Lastly, we analysed the results by deprivation quintile. We present in our findings cumulative cases prevented or postponed over 50 years. FINDINGS: None of the scenarios would reduce overall smoking prevalence to below 5% by 2030. However, that goal could be reached by 2035 under the TaxUP and the combination of TaxUP and ServicesUP scenarios, by 2037 under the ServicesUP scenario, or by 2038 under the MinAge21 and the baseline scenarios. By 2072, the combined scenario might reduce smoking-related diseases by 160 000 cases (95% CI 140 000-200 000), greatly exceeding the reductions by 140 000 cases (120 000-180 000) with TaxUP, 69 000 cases (53 000-86 000) with MinAge21, or 22 000 cases (14 000-31 000) with ServicesUP. Some 50% of all disease-years reduced by TaxUP would occur in the most deprived quintile. The most affluent quintile could reach the 5% goal sooner than the most deprived quintile (by 2032 for the least deprived vs 2038 for the most deprived), and it could reach the 5% target by 2030 under the combined TaxUP and ServicesUP scenario. Finally, all policies would save costs compared with the baseline trend. INTERPRETATION: Affluent groups will achieve the 5% tobacco-free goal a decade sooner than the most deprived. However, that goal could be achieved in all groups by 2035 through a 30% increase in tax and enhanced smoking cessation services. Our limitations included the uncertainties of any 50-year forecast. However, that long time-horizon can capture the potential policy benefits for younger age groups. FUNDING: Economic and Social Research Council.

Evaluating the impact of using mobile vaccination units to increase COVID-19 vaccination uptake in Cheshire and Merseyside, UK: a synthetic control analysis.

OBJECTIVE: To evaluate the impact of mobile vaccination units on COVID-19 vaccine uptake of the first dose, the percentage of vaccinated people among the total eligible population. We further investigate whether such an effect differed by deprivation, ethnicity and age. DESIGN: Synthetic control analysis. SETTING: The population registered with general practices (GPs) in nine local authority areas in Cheshire and Merseyside in Northwest England, UK. INTERVENTION: Mobile vaccination units that visited 37 sites on 54 occasions between 12 April 2021 and 28 June 2021. We defined intervention neighbourhoods as having their population weighted centroid located within 1 km of mobile vaccination sites (338 006 individuals). A weighted combination of neighbourhoods that had not received the intervention (1 495 582 individuals) was used to construct a synthetic control group. OUTCOME: The weekly number of first-dose vaccines received among people aged 18 years and over as a proportion of the population. RESULTS: The introduction of a mobile vaccination unit into a neighbourhood increased the number of first vaccinations conducted in the neighbourhood by 25% (95% CI 21% to 28%) within 3 weeks after the first visit to a neighbourhood, compared with the synthetic control group. Interaction analyses showed smaller or no effect among older age groups, Asian and black ethnic groups, and the most socioeconomically deprived populations. CONCLUSIONS: Mobile vaccination units are effective interventions for increasing vaccination uptake, at least in the short term. While mobile units can be geographically targeted to reduce inequalities, we found evidence that they may increase inequalities in vaccine uptake within targeted areas, as the intervention was less effective among groups that tended to have lower vaccination uptake. Mobile vaccination units should be used in combination with activities to maximise outreach with black and Asian communities and socioeconomically disadvantaged groups.

Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People With Depression.

Depression is a common mental health condition that often occurs in association with other chronic illnesses, and varies considerably in severity. Electronic Health Records (EHRs) contain rich information about a patient's medical history and can be used to train, test and maintain predictive models to support and improve patient care. This work evaluated the feasibility of implementing an environment for predicting mental health crisis among people living with depression based on both structured and unstructured EHRs. A large EHR from a mental health provider, Mersey Care, was pseudonymised and ingested into the Natural Language Processing (NLP) platform CogStack, allowing text content in binary clinical notes to be extracted. All unstructured clinical notes and summaries were semantically annotated by MedCAT and BioYODIE NLP services. Cases of crisis in patients with depression were then identified. Random forest models, gradient boosting trees, and Long Short-Term Memory (LSTM) networks, with varying feature arrangement, were trained to predict the occurrence of crisis. The results showed that all the prediction models can use a combination of structured and unstructured EHR information to predict crisis in patients with depression with good and useful accuracy. The LSTM network that was trained on a modified dataset with only 1000 most-important features from the random forest model with temporality showed the best performance with a mean AUC of 0.901 and a standard deviation of 0.006 using a training dataset and a mean AUC of 0.810 and 0.01 using a hold-out test dataset. Comparing the results from the technical evaluation with the views of psychiatrists shows that there are now opportunities to refine and integrate such prediction models into pragmatic point-of-care clinical decision support tools for supporting mental healthcare delivery.

Automatic Extraction of Research Themes in Epidemiological Criminology From PubMed Abstracts From 1946 to 2020: Text Mining Study.

BACKGROUND: The emerging field of epidemiological criminology studies the intersection between public health and justice systems. To increase the value of and reduce waste in research activities in this area, it is important to perform transparent research priority setting considering the needs of research beneficiaries and end users along with a systematic assessment of the existing research activities to address gaps and harness opportunities. OBJECTIVE: In this study, we aimed to examine published research outputs in epidemiological criminology to assess gaps between published outputs and current research priorities identified by prison stakeholders. METHODS: A rule-based method was applied to 23,904 PubMed epidemiological criminology abstracts to extract the study determinants and outcomes (ie, "themes"). These were mapped against the research priorities identified by Australian prison stakeholders to assess the differences from research outputs. The income level of the affiliation country of the first authors was also identified to compare the ranking of research priorities in countries categorized by income levels. RESULTS: On an evaluation set of 100 abstracts, the identification of themes returned an F1-score of 90%, indicating reliable performance. More than 53.3% (11,927/22,361) of the articles had at least 1 extracted theme; the most common was substance use (1533/11,814, 12.97%), followed by HIV (1493/11,814, 12.64%). The infectious disease category (2949/11,814, 24.96%) was the most common research priority category, followed by mental health (2840/11,814, 24.04%) and alcohol and other drug use (2433/11,814, 20.59%). A comparison between the extracted themes and the stakeholder priorities showed an alignment for mental health, infectious diseases, and alcohol and other drug use. Although behavior- and juvenile-related themes were common, they did not feature as prison priorities. Most studies were conducted in high-income countries (10,083/11,814, 85.35%), while countries with the lowest income status focused half of their research on infectious diseases (47/91, 52%). CONCLUSIONS: The identification of research themes from PubMed epidemiological criminology research abstracts is possible through the application of a rule-based text mining method. The frequency of the investigated themes may reflect historical developments concerning disease prevalence, treatment advances, and the social understanding of illness and incarcerated populations. The differences between income status groups are likely to be explained by local health priorities and immediate health risks. Notable gaps between stakeholder research priorities and research outputs concerned themes that were more focused on social factors and systems and may reflect publication bias or self-publication selection, highlighting the need for further research on prison health services and the social determinants of health. Different jurisdictions, countries, and regions should undertake similar systematic and transparent research priority-setting processes.

Cardiovascular risk estimation in rural psychiatric inpatient populations: Cross-sectional preliminary study.

Cardiovascular risk was evaluated in patients admitted to rural inpatient psychiatric services over a one-year period in a sparsely populated region of the United Kingdom. Care records were analysed for risk factor recording, and cardiovascular risk estimated using the QRISK3 calculator, which estimates 10-year risk of myocardial infarction or stroke. Of eligible patients, risk factor recording as part of routine care was completed in 86% of possible QRISK3 inputs, enabling QIRSK3 estimation in all eligible patients. QRISK3 for this group was significantly raised relative to an age, sex and ethnicity-matched population, and high risk of cardiovascular disease (QRISK3 score >10%) was detected in 28% of patients. The results demonstrate that there is significant unmet need in rural patients for cardiovascular risk reduction that could be identified as part of routine care. An opportunity exists to integrate mental and physical healthcare by routinely assessing cardiovascular risk in rural psychiatric inpatients. Resources and training are needed to produce this risk information and act on it.

A Systematic Review of the Prevalence of Persistent Gastrointestinal Symptoms and Incidence of New Gastrointestinal Illness after Acute SARS-CoV-2 Infection.

It is known that SARS-CoV-2 infection can result in gastrointestinal symptoms. For some, these symptoms may persist beyond acute infection, in what is known as 'post-COVID syndrome'. We conducted a systematic review to examine the prevalence of persistent gastrointestinal symptoms and the incidence of new gastrointestinal illnesses following acute SARS-CoV-2 infection. We searched the scientific literature using MedLine, SCOPUS, Europe PubMed Central and medRxiv from December 2019 to July 2023. Two reviewers independently identified 45 eligible articles, which followed participants for various gastrointestinal outcomes after acute SARS-CoV-2 infection. The study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools. The weighted pooled prevalence for persistent gastrointestinal symptoms of any nature and duration was 10.8% compared with 4.9% in healthy controls. For seven studies at low risk of methodological bias, the symptom prevalence ranged from 0.2% to 24.1%, with a median follow-up time of 18 weeks. We also identified a higher risk for future illnesses such as irritable bowel syndrome, dyspepsia, hepatic and biliary disease, liver disease and autoimmune-mediated illnesses such as inflammatory bowel disease and coeliac disease in historically SARS-CoV-2-exposed individuals. Our review has shown that, from a limited pool of mostly low-quality studies, previous SARS-CoV-2 exposure may be associated with ongoing gastrointestinal symptoms and the development of functional gastrointestinal illness. Furthermore, we show the need for high-quality research to better understand the SARS-CoV-2 association with gastrointestinal illness, particularly as population exposure to enteric infections returns to pre-COVID-19-restriction levels.

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.

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.