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.

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.

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.

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.

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.

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.

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.

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.

Pregnancy-Related Complications and Incidence of Atrial Fibrillation: A Systematic Review.

Pregnancy-related complications are associated with a higher risk of various incident cardiovascular diseases, but their specific potential relationship with incident atrial fibrillation (AF) is less clear. This systematic review summarises the available evidence from observational studies which have examined associations between pregnancy-related complications and the risk of AF. MEDLINE and EMBASE (Ovid) were searched for studies between 1990 to 10 February 2022. Pregnancy-related complications examined included hypertensive disorders of pregnancy (HDP), gestational diabetes, placental abruption, preterm birth, small-for-gestational-age and stillbirth. Study selection, data extraction and quality assessment were completed independently by two reviewers. Narrative synthesis was used to evaluate the results of the included studies. Nine observational studies were included, with eight eligible for narrative synthesis. Sample sizes ranged from 1839 to 2,359,386. Median follow-up ranged from 2 to 36 years. Six studies reported that pregnancy-related complications were associated with a significantly increased risk of incident AF. Hazard ratios (HRs) (95% confidence intervals) for the four studies that evaluated HDP ranged from 1.1 (0.8-1.6) to 1.9 (1.4-2.7). For the four studies that evaluated pre-eclampsia, HRs ranged from 1.2 (0.9-1.6) to 1.9 (1.7-2.2). Current evidence from observational studies suggests pregnancy-related complications are associated with a significantly higher risk of incident AF. However, only a small number of studies examining each pregnancy-related complication were identified, and considerable statistical heterogeneity was observed. Further large-scale prospective studies are required to confirm the association between pregnancy-related complications and incident AF.

The clustering of physical health conditions and associations with co-occurring mental health problems and problematic alcohol use: a cross-sectional study.

BACKGROUND: There is strong evidence for the co-occurrence of mental health conditions and alcohol problems, yet physical health outcomes among this group are not well characterised. This study aimed to identify clusters of physical health conditions and their associations with mental health and problematic alcohol use in England's general population. METHODS: Cross-sectional analysis of the 2014 Adult Psychiatric Morbidity Survey (N = 7546) was conducted. The survey used standardised measures of problematic alcohol use and mental health conditions, including the Alcohol Use Disorders Identification Test (AUDIT) and the Clinical Interview Schedule-Revised. Participants self-reported any lifetime physical health conditions. Latent class analysis considered 12 common physical illnesses to identify clusters of multimorbidity. Multinomial logistic regression (adjusting for age, gender, ethnicity, education, and occupational grade) was used to explore associations between mental health, hazardous drinking (AUDIT 8 +), and co-occurring physical illnesses. RESULTS: Five clusters were identified with statistically distinct and clinically meaningful disease patterns: 'Physically Healthy' (76.62%), 'Emerging Multimorbidity' (3.12%), 'Hypertension & Arthritis' (14.28%), 'Digestive & Bowel Problems'' (3.17%), and 'Complex Multimorbidity' (2.8%). Having a mental health problem was associated with increased odds of 'Digestive & Bowel Problems' (adjusted multinomial odds ratio (AMOR) = 1.58; 95% CI [1.15-2.17]) and 'Complex Multimorbidity' (AMOR = 2.02; 95% CI [1.49-2.74]). Individuals with co-occurring mental health conditions and problematic alcohol use also had higher odds of 'Digestive & Bowel Problems' (AMOR = 2.64; 95% CI [1.68-4.15]) and 'Complex Multimorbidity' (AMOR = 2.62; 95% CI [1.61-4.23]). CONCLUSIONS: Individuals with a mental health condition concurrent with problematic alcohol use experience a greater burden of physical illnesses, highlighting the need for timely treatment which is likely to include better integration of alcohol and mental health services.

Association of SARS-CoV-2 viral load distributions with individual demographics and suspected variant type: results from the Liverpool community testing pilot, England, 6 November 2020 to 8 September 2021.

BackgroundThe PCR quantification cycle (Cq) is a proxy measure of the viral load of a SARS-CoV-2-infected individual.AimTo investigate if Cq values vary according to different population characteristics, in particular demographic ones, and within the COVID-19 pandemic context, notably the SARS-CoV-2 type/variant individuals get infected with.MethodsWe considered all positive PCR results from Cheshire and Merseyside, England, between 6 November 2020 and 8 September 2021. Cq distributions were inspected with Kernel density estimates. Multivariable quantile regression models assessed associations between people's features and Cq.ResultsWe report Cq values for 188,821 SARS-CoV-2 positive individuals. Median Cqs increased with decreasing age for suspected wild-type virus and Alpha variant infections, but less so, if not, for Delta. For example, compared to 30-39-year-olds (median age group), 5-11-year-olds exhibited 1.8 (95% CI: 1.5 to 2.1), 2.2 (95% CI: 1.8 to 2.6) and 0.8 (95% CI: 0.6 to 0.9) higher median Cqs for suspected wild-type, Alpha and Delta positives, respectively, in multivariable analysis. 12-18-year-olds also had higher Cqs for wild-type and Alpha positives, however, not for Delta. Overall, in univariable analysis, suspected Delta positives reported 2.8 lower median Cqs than wild-type positives (95% CI: 2.7 to 2.8; p < 0.001). Suspected Alpha positives had 1.5 (95% CI: 1.4 to 1.5; p < 0.001) lower median Cqs than wild type.ConclusionsWild-type- or Alpha-infected school-aged children (5-11-year-olds) might transmit less than adults (> 18 years old), but have greater mixing exposures. Smaller differences in viral loads with age occurred in suspected Delta infections. Suspected-Alpha- or Delta-infections involved higher viral loads than wild type, suggesting increased transmission risk. COVID-19 control strategies should consider age and dominant variant.

Identifying best modelling practices for tobacco control policy simulations: a systematic review and a novel quality assessment framework.

BACKGROUND: Policy simulation models (PSMs) have been used extensively to shape health policies before real-world implementation and evaluate post-implementation impact. This systematic review aimed to examine best practices, identify common pitfalls in tobacco control PSMs and propose a modelling quality assessment framework. METHODS: We searched five databases to identify eligible publications from July 2013 to August 2019. We additionally included papers from Feirman et al for studies before July 2013. Tobacco control PSMs that project tobacco use and tobacco-related outcomes from smoking policies were included. We extracted model inputs, structure and outputs data for models used in two or more included papers. Using our proposed quality assessment framework, we scored these models on population representativeness, policy effectiveness evidence, simulated smoking histories, included smoking-related diseases, exposure-outcome lag time, transparency, sensitivity analysis, validation and equity. FINDINGS: We found 146 eligible papers and 25 distinct models. Most models used population data from public or administrative registries, and all performed sensitivity analysis. However, smoking behaviour was commonly modelled into crude categories of smoking status. Eight models only presented overall changes in mortality rather than explicitly considering smoking-related diseases. Only four models reported impacts on health inequalities, and none offered the source code. Overall, the higher scored models achieved higher citation rates. CONCLUSIONS: While fragments of good practices were widespread across the reviewed PSMs, only a few included a 'critical mass' of the good practices specified in our quality assessment framework. This framework might, therefore, potentially serve as a benchmark and support sharing of good modelling practices.