Trends in clinical workload in UK primary care 2005-2019.

BACKGROUND: Substantial increases in UK consulting rates, mean consultation duration and clinical workload were observed between 2007 and 2014. No analysis of more recent trends in clinical workload has been published to date. This study updates and builds on previous research, identifying underlying changes in population morbidity levels affecting demand for primary health care. AIM: To describe the changes in clinical workload in UK primary care since 2005. DESIGN AND SETTING: Retrospective cohort study. METHOD: Over 500 million anonymised electronic health records were obtained from IQVIA Medical Research Data to examine consulting rates with GPs and practice nurses together with the duration of these consultations to determine total patient-level workload per person-year. RESULTS: Age-standardised mean GP direct (face-to-face and telephone) consulting rates fell steadily by 2.0% a year from 2014 to 2019. Between 2005 and 2019 mean GP direct consulting rates fell by 5.8% overall whereas mean workload per person-year increased by 25.8%, due in part to a 36.9% increase in mean consultation duration. Indirect GP workload almost tripled over the fifteen years, contributing to a 48.3% increase in overall clinical workload per person-year. The proportion of the study population with two or more serious chronic conditions increased from 22.5% to 31.6%, accounting for almost 55.0% of total clinical workload in 2019. CONCLUSION: Findings show sustained increases in consulting rates, consultation duration and clinical workload until 2014. From 2015, however, rising demand for healthcare and a larger administrative workload have led to capacity constraints as the system nears saturation.

Healthcare resource utilisation and economic burden attributable to back pain in primary care: A matched case-control study in the United Kingdom.

Objective: Incremental healthcare costs attributed to back pain, and characterisation by patient and clinical factors have rarely been documented. This study aimed to assess annual healthcare resource utilisation and costs associated with back pain in primary care. Methods: Using the IQVIA Medical Research Data (IMRD), patients with back pain were identified (study period: 01 January 2006 to 31 December 2015) using diagnostic records and analgesics prescriptions (n = 133,341), and propensity score matched 1:1 to patients without back pain. The annual incremental costs of back pain associated with consultations and prescriptions were estimated and extrapolated to a national level. Sensitivity analysis was conducted by restricting the study population to the most recent diagnosis of back pain. Variations in cost were assessed stratified by gender, age-groups, deprivation, and comorbidity categories. Results: The mean age was 57 years, and 62% were females in both the case and control groups. The total incremental healthcare costs associated with back pain was £32.5 million in 2015 (£35.9 million in 2020), with per-patient cost of £244 (£265 in 2020) per year. On a national level, this translated to an estimated £3.2 billion (£3.5 billion in 2020). Eighty percent of the costs were attributed to consultations; and female gender, older age, higher deprivation, and higher comorbidity were all associated with increased mean healthcare costs of patients with back pain. Conclusion: Our findings confirm the substantial healthcare costs attributed to back pain, even with primacy care costs only. The data also revealed significant cost variations across socio-demographic and clinical factors.

Perceptions on artificial intelligence-based decision-making for coexisting multiple long-term health conditions: protocol for a qualitative study with patients and healthcare professionals.

INTRODUCTION: Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates prescribing decisions for these patients. Artificial intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of the multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions. METHODS AND ANALYSIS: A qualitative study will be conducted using semistructured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British HCP bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide is designed to explore participants' attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes towards risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants' opinions on how their experiences compare. Data will be analysed thematically using the Framework Method. ETHICS AND DISSEMINATION: This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer-reviewed publications, conferences and lay summaries.

The representation of priors and decisions in the human parietal cortex.

Animals actively sample their environment through orienting actions such as saccadic eye movements. Saccadic targets are selected based both on sensory evidence immediately preceding the saccade, and a "salience map" or prior built-up over multiple saccades. In the primate cortex, the selection of each individual saccade depends on competition between target-selective cells that ramp up their firing rate to saccade release. However, it is less clear how a cross-saccade prior might be implemented, either in neural firing or through an activity-silent mechanism such as modification of synaptic weights on sensory inputs. Here, we present evidence from magnetoencephalography for 2 distinct processes underlying the selection of the current saccade, and the representation of the prior, in human parietal cortex. While the classic ramping decision process for each saccade was reflected in neural firing rates (measured in the event-related field), a prior built-up over multiple saccades was implemented via modulation of the gain on sensory inputs from the preferred target, as evidenced by rapid frequency tagging. A cascade of computations over time (initial representation of the prior, followed by evidence accumulation and then an integration of prior and evidence) provides a mechanism by which a salience map may be built up across saccades in parietal cortex. It also provides insight into the apparent contradiction that inactivation of parietal cortex has been shown not to affect performance on single-trials, despite the presence of clear evidence accumulation signals in this region.

EEGManyPipelines: A Large-scale, Grassroots Multi-analyst Study of Electroencephalography Analysis Practices in the Wild.

The ongoing reproducibility crisis in psychology and cognitive neuroscience has sparked increasing calls to re-evaluate and reshape scientific culture and practices. Heeding those calls, we have recently launched the EEGManyPipelines project as a means to assess the robustness of EEG research in naturalistic conditions and experiment with an alternative model of conducting scientific research. One hundred sixty-eight analyst teams, encompassing 396 individual researchers from 37 countries, independently analyzed the same unpublished, representative EEG data set to test the same set of predefined hypotheses and then provided their analysis pipelines and reported outcomes. Here, we lay out how large-scale scientific projects can be set up in a grassroots, community-driven manner without a central organizing laboratory. We explain our recruitment strategy, our guidance for analysts, the eventual outputs of this project, and how it might have a lasting impact on the field.

The cost of primary care consultations associated with long COVID in non-hospitalised adults: a retrospective cohort study using UK primary care data.

BACKGROUND: The economic impact of managing long COVID in primary care is unknown. We estimated the costs of primary care consultations associated with long COVID and explored the relationship between risk factors and costs. METHODS: Data were obtained on non-hospitalised adults from the Clinical Practice Research Datalink Aurum primary care database. We used propensity score matching with an incremental cost method to estimate additional primary care consultation costs associated with long COVID (12 weeks after COVID-19) at an individual and UK national level. We applied multivariable regression models to estimate the association between risk factors and consultations costs beyond 12 weeks from acute COVID-19. RESULTS: Based on an analysis of 472,173 patients with COVID-19 and 472,173 unexposed individuals, the annual incremental cost of primary care consultations associated with long COVID was £2.44 per patient and £23,382,452 at the national level. Among patients with COVID-19, a long COVID diagnosis and reporting of longer-term symptoms were associated with a 43% and 44% increase in primary care consultation costs respectively, compared to patients without long COVID symptoms. Older age, female sex, obesity, being from a white ethnic group, comorbidities and prior consultation frequency were all associated with increased primary care consultation costs. CONCLUSIONS: The costs of primary care consultations associated with long COVID in non-hospitalised adults are substantial. Costs are significantly higher among those diagnosed with long COVID, those with long COVID symptoms, older adults, females, and those with obesity and comorbidities.

Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England.

BACKGROUND: Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions. METHODS AND FINDINGS: We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results. CONCLUSIONS: Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.

Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records.

BACKGROUND: Primary care electronic health records (EHR) are widely used to study long-term conditions in epidemiological and health services research. Therefore, it is important to understand how well the recorded prevalence of these conditions in EHRs, compares to other reliable sources overall, and varies by socio-demographic characteristics. We aimed to describe the prevalence and socio-demographic variation of cardiovascular, renal, and metabolic (CRM) and mental health (MH) conditions in a large, nationally representative, English primary care database and compare with prevalence estimates from other population-based studies. METHODS: This was a cross-sectional study using the Clinical Practice Research Datalink (CPRD) Aurum primary care database. We calculated prevalence of 18 conditions and used logistic regression to assess how this varied by age, sex, ethnicity, and socio-economic status. We searched the literature for population prevalence estimates from other sources for comparison with the prevalences in CPRD Aurum. RESULTS: Depression (16.0%, 95%CI 16.0-16.0%) and hypertension (15.3%, 95%CI 15.2-15.3%) were the most prevalent conditions among 12.4 million patients. Prevalence of most conditions increased with socio-economic deprivation and age. CRM conditions, schizophrenia and substance misuse were higher in men, whilst anxiety, depression, bipolar and eating disorders were more common in women. Cardiovascular risk factors (hypertension and diabetes) were more prevalent in black and Asian patients compared with white, but the trends in prevalence of cardiovascular diseases by ethnicity were more variable. The recorded prevalences of mental health conditions were typically twice as high in white patients compared with other ethnic groups. However, PTSD and schizophrenia were more prevalent in black patients. The prevalence of most conditions was similar or higher in the primary care database than diagnosed disease prevalence reported in national health surveys. However, screening studies typically reported higher prevalence estimates than primary care data, especially for PTSD, bipolar disorder and eating disorders. CONCLUSIONS: The prevalence of many clinically diagnosed conditions in primary care records closely matched that of other sources. However, we found important variations by sex and ethnicity, which may reflect true variation in prevalence or systematic differences in clinical presentation and practice. Primary care data may underrepresent the prevalence of undiagnosed conditions, particularly in mental health.

Quantifying decision-making in dynamic, continuously evolving environments.

During perceptual decision-making tasks, centroparietal electroencephalographic (EEG) potentials report an evidence accumulation-to-bound process that is time locked to trial onset. However, decisions in real-world environments are rarely confined to discrete trials; they instead unfold continuously, with accumulation of time-varying evidence being recency-weighted towards its immediate past. The neural mechanisms supporting recency-weighted continuous decision-making remain unclear. Here, we use a novel continuous task design to study how the centroparietal positivity (CPP) adapts to different environments that place different constraints on evidence accumulation. We show that adaptations in evidence weighting to these different environments are reflected in changes in the CPP. The CPP becomes more sensitive to fluctuations in sensory evidence when large shifts in evidence are less frequent, and the potential is primarily sensitive to fluctuations in decision-relevant (not decision-irrelevant) sensory input. A complementary triphasic component over occipito-parietal cortex encodes the sum of recently accumulated sensory evidence, and its magnitude covaries with parameters describing how different individuals integrate sensory evidence over time. A computational model based on leaky evidence accumulation suggests that these findings can be accounted for by a shift in decision threshold between different environments, which is also reflected in the magnitude of pre-decision EEG activity. Our findings reveal how adaptations in EEG responses reflect flexibility in evidence accumulation to the statistics of dynamic sensory environments.

Within-subject variation of HbA1c: A systematic review and meta-analysis.

BACKGROUND: Glycosylated haemoglobin (HbA1c) measurement is used to diagnose and to guide treatment of diabetes mellitus. Within-subject variability in measured HbA1c affects its clinical utility and interpretation, but no comprehensive systematic review has described within-subject variability. METHODS: A systematic review and meta-analysis was performed of within-subject variability of HbA1c. Multiple databases were searched from inception to November 2022 for follow-up studies of any design in adults or children, with repeated measures of HbA1c or glycosylated haemoglobin. Title and abstract screening was performed in duplicate, full text screening and data extraction by one reviewer and verified by a second. Risk of bias of included papers was assessed using a modified consensus-based standards for the selection of health measurement Instruments (COSMIN) tool. Intraclass correlation coefficient (ICC) results were pooled with a meta-analysis and coefficient of variation (CV) results were described by median and range. RESULTS: Of 2675 studies identified, 111 met the inclusion criteria. Twenty-five studies reported variability data in healthy patients, 19 in patients with type 1 diabetes and 59 in patients with type 2 diabetes. Median within-subject coefficient of variation (CV) was 0.070 (IQR 0.034 to .09). For healthy subjects the median CV for HbA1c % was 0.017 (IQR 0.013 to 0.022), for patients with type 1 diabetes 0.084 (IQR 0.067 to 0.89) and for type 2 diabetes 0.083 (IQR 0.06 to 0.10). CV increased with mean population HbA1c. LIMITATIONS: Assessment of variability was not the main aim of many of the included studies and some relevant papers may have been missed. Many included papers had few participants or few repeated measurements. CONCLUSIONS: Within-subject variability of HbA1c is higher for patients with than without diabetes and increases with mean population HbA1c. This may confound observed relationships between HbA1c variability and health outcomes. Because of its importance in clinical decision-making there is a need for better estimates and understanding of factors associated with of HbA1c variability.

Medicalising diagnoses and treatment preferences: a retrospective cohort study of throat-related consultations in electronic primary care records.

BACKGROUND: Rather than first diagnosing and then deciding on treatment, GPs may intuitively decide on treatment and justify this through choice of diagnosis. AIM: To investigate the relationship between choice of a medicalising diagnosis and antibiotic treatment for throat-related consultations. DESIGN & SETTING: A retrospective cohort study in a large database of UK electronic primary care records between 1 January 2010 and 1 January 2020. METHOD: All first throat-related consultations were included, categorised as either pharyngitis/tonsillitis or sore throat. The outcome was any antibiotic prescription on the consultation date. GP-level random effects on prescribing and on diagnosis were estimated in a series of mixed-effects regression models, including age, sex, weekday, month, and clinician characteristics as fixed effects. GPs were grouped into quintiles by antibiotic prescribing propensity, and described the proportion of patients they diagnosed with pharyngitis/tonsillitis or sore throat in each quintile. RESULTS: The analysis dataset included 393 590 throat-related consultations with 6881 staff. Diagnosis of pharyngitis/tonsillitis was strongly associated with antibiotic prescribing (adjusted odds ratio = 13.41, 95% confidence interval = 12.8 to 14.04). GP random effect accounted for 18% of variation in prescribing and for 26% of variation in diagnosis. GPs in the lowest quintile of antibiotic prescribing propensity diagnosed pharyngitis/tonsillitis on 31% of occasions, compared with 55% in the highest quintile. CONCLUSION: There is substantial variation among GPs in diagnosis and treatment of throat-related problems. Preference for a medicalising diagnosis is associated with a preference for antibiotics, suggesting a common propensity to both diagnose and treat.

Non-Pharmacological Therapies for Post-Viral Syndromes, Including Long COVID: A Systematic Review.

BACKGROUND: Post-viral syndromes (PVS), including Long COVID, are symptoms sustained from weeks to years following an acute viral infection. Non-pharmacological treatments for these symptoms are poorly understood. This review summarises the evidence for the effectiveness of non-pharmacological treatments for PVS. METHODS: We conducted a systematic review to evaluate the effectiveness of non-pharmacological interventions for PVS, as compared to either standard care, alternative non-pharmacological therapy, or placebo. The outcomes of interest were changes in symptoms, exercise capacity, quality of life (including mental health and wellbeing), and work capability. We searched five databases (Embase, MEDLINE, PsycINFO, CINAHL, MedRxiv) for randomised controlled trials (RCTs) published between 1 January 2001 to 29 October 2021. The relevant outcome data were extracted, the study quality was appraised using the Cochrane risk-of-bias tool, and the findings were synthesised narratively. FINDINGS: Overall, five studies of five different interventions (Pilates, music therapy, telerehabilitation, resistance exercise, neuromodulation) met the inclusion criteria. Aside from music-based intervention, all other selected interventions demonstrated some support in the management of PVS in some patients. INTERPRETATION: In this study, we observed a lack of robust evidence evaluating the non-pharmacological treatments for PVS, including Long COVID. Considering the prevalence of prolonged symptoms following acute viral infections, there is an urgent need for clinical trials evaluating the effectiveness and cost-effectiveness of non-pharmacological treatments for patients with PVS. REGISTRATION: The study protocol was registered with PROSPERO [CRD42021282074] in October 2021 and published in BMJ Open in 2022.

Rapid invisible frequency tagging (RIFT): a promising technique to study neural and cognitive processing using naturalistic paradigms.

Frequency tagging has been successfully used to investigate selective stimulus processing in electroencephalography (EEG) or magnetoencephalography (MEG) studies. Recently, new projectors have been developed that allow for frequency tagging at higher frequencies (>60 Hz). This technique, rapid invisible frequency tagging (RIFT), provides two crucial advantages over low-frequency tagging as (i) it leaves low-frequency oscillations unperturbed, and thus open for investigation, and ii) it can render the tagging invisible, resulting in more naturalistic paradigms and a lack of participant awareness. The development of this technique has far-reaching implications as oscillations involved in cognitive processes can be investigated, and potentially manipulated, in a more naturalistic manner.

In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm.

BACKGROUND AND OBJECTIVES: To investigate the reproducibility and validity of latent class analysis (LCA) and hierarchical cluster analysis (HCA), multiple correspondence analysis followed by k-means (MCA-kmeans) and k-means (kmeans) for multimorbidity clustering. METHODS: We first investigated clustering algorithms in simulated datasets with 26 diseases of varying prevalence in predetermined clusters, comparing the derived clusters to known clusters using the adjusted Rand Index (aRI). We then them investigated the medical records of male patients, aged 65 to 84 years from 50 UK general practices, with 49 long-term health conditions. We compared within cluster morbidity profiles using the Pearson correlation coefficient and assessed cluster stability using in 400 bootstrap samples. RESULTS: In the simulated datasets, the closest agreement (largest aRI) to known clusters was with LCA and then MCA-kmeans algorithms. In the medical records dataset, all four algorithms identified one cluster of 20-25% of the dataset with about 82% of the same patients across all four algorithms. LCA and MCA-kmeans both found a second cluster of 7% of the dataset. Other clusters were found by only one algorithm. LCA and MCA-kmeans clustering gave the most similar partitioning (aRI 0.54). CONCLUSION: LCA achieved higher aRI than other clustering algorithms.

An interrupted time-series analysis of the effects of withdrawal of financial incentives on diagnosis of atrial fibrillation as resolved. Does withdrawal of an incentive reverse its unintended effects?

BACKGROUND: The UK introduced financial incentives for management of atrial fibrillation (AF) in 2006, after which there was an increase in the proportion of patients with AF diagnosed as resolved. Removal of incentives in Scotland provides a natural experiment to investigate the effects of withdrawal of an incentive on diagnosis of resolved AF. AIM: To investigate the effects of introduction and withdrawal of financial incentives on the diagnosis of resolved AF. DESIGN & SETTING: Cohort study in a large database of UK primary care records, before and after introduction of incentives in April 2006 in Scotland, England, and Northern Ireland, and their withdrawal in April 2016 in Scotland. METHOD: Interrupted time-series analysis of monthly rates of resolved AF from January 2000-September 2019. RESULTS: A total of 251 526 adult patients with AF were included, of whom 14 674 were diagnosed as resolved AF. In April 2006 there were similar shift-changes in rates of resolved AF per 1000 in England 1.55 (95% confidence interval [CI] = 1.11 to 2.00) and Northern Ireland 1.54 (95% CI = 0.91 to 2.18), and a smaller increase in Scotland 0.79 (95% CI = 0.04 to 1.53). There were modest downward post-introduction trends in all countries. After Scotland's withdrawal of the incentive in April 2016 there was a small, statistically non-significant, downward shift in rate of resolved AF per 1000 (0.39 [95% CI = -3.21 to 2.42]) and no change in post-removal trend. CONCLUSION: Introduction of a financial incentive coincided with an increase in resolved AF but no evidence was found that its withdrawal led to a reduction.

Enhancing the health of NHS staff: eTHOS - protocol for a randomised controlled pilot trial of an employee health screening clinic for NHS staff to reduce absenteeism and presenteeism, compared with usual care.

BACKGROUND: Staff absenteeism and presenteeism incur high costs to the NHS and are associated with adverse health outcomes. The main causes are musculoskeletal complaints and mental ill-health, which are potentially modifiable, and cardiovascular risk factors are also common. We will test the feasibility of an RCT to evaluate the clinical and cost-effectiveness of an employee health screening clinic on reducing sickness absenteeism and presenteeism. METHODS: This is an individually randomised controlled pilot trial aiming to recruit 480 participants. All previously unscreened employees from four hospitals within three UK NHS hospital Trusts will be eligible. Those randomised to the intervention arm will be invited to attend an employee health screening clinic consisting of a screening assessment for musculoskeletal (STarT MSK and STarT Back), mental (PHQ-9 and GAD-7) and cardiovascular (NHS Health Check if aged ≥ 40, lifestyle check if < 40 years) health. Screen positives will be given advice and/or referral to recommended services. Those randomised to the control arm will receive usual care. Participants will complete a questionnaire at baseline and 26 weeks; anonymised absenteeism and staff demographics will also be collected from personnel records. The co-primary outcomes are as follows: recruitment, referrals and uptake of recommended services in the intervention arm. Secondary outcomes include the following: results of screening assessments, uptake of individual referrals, reported changes in health behaviours, acceptability and feasibility of intervention, indication of contamination and costs. Outcomes related to the definitive trial include self-reported and employee records of absenteeism with reasons. Process evaluation to inform a future trial includes interviews with participants, intervention delivery staff and service providers receiving referrals. Analyses will include presentation of descriptive statistics, framework analysis for qualitative data and costs and consequences presented for health economics. DISCUSSION: The study will provide data to inform the design of a definitive RCT which aims to find an effective and cost-effective method of reducing absenteeism and presenteeism amongst NHS staff. The feasibility study will test trial procedures, and process outcomes, including the success of strategies for including underserved groups, and provide information and data to help inform the design and sample size for a definitive trial. TRIAL REGISTRATION: ISRCTN reference number 10237475 .

Symptoms and risk factors for long COVID in non-hospitalized adults.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is associated with a range of persistent symptoms impacting everyday functioning, known as post-COVID-19 condition or long COVID. We undertook a retrospective matched cohort study using a UK-based primary care database, Clinical Practice Research Datalink Aurum, to determine symptoms that are associated with confirmed SARS-CoV-2 infection beyond 12 weeks in non-hospitalized adults and the risk factors associated with developing persistent symptoms. We selected 486,149 adults with confirmed SARS-CoV-2 infection and 1,944,580 propensity score-matched adults with no recorded evidence of SARS-CoV-2 infection. Outcomes included 115 individual symptoms, as well as long COVID, defined as a composite outcome of 33 symptoms by the World Health Organization clinical case definition. Cox proportional hazards models were used to estimate adjusted hazard ratios (aHRs) for the outcomes. A total of 62 symptoms were significantly associated with SARS-CoV-2 infection after 12 weeks. The largest aHRs were for anosmia (aHR 6.49, 95% CI 5.02-8.39), hair loss (3.99, 3.63-4.39), sneezing (2.77, 1.40-5.50), ejaculation difficulty (2.63, 1.61-4.28) and reduced libido (2.36, 1.61-3.47). Among the cohort of patients infected with SARS-CoV-2, risk factors for long COVID included female sex, belonging to an ethnic minority, socioeconomic deprivation, smoking, obesity and a wide range of comorbidities. The risk of developing long COVID was also found to be increased along a gradient of decreasing age. SARS-CoV-2 infection is associated with a plethora of symptoms that are associated with a range of sociodemographic and clinical risk factors.

Multimorbidity and associations with clinical outcomes in a middle-aged population in Iran: a longitudinal cohort study.

BACKGROUND: As the populations of lower-income and middle-income countries age, multimorbidity is increasing, but there is little information on its long-term consequences. We aimed to show associations between multimorbidity and outcomes of mortality and hospitalisation in Iran, a middle-income country undergoing rapid economic transition. METHODS: We conducted a secondary analysis of longitudinal data collected in the Golestan Cohort Study. Data on demographics, morbidities and lifestyle factors were collected at baseline, and information on hospitalisations or deaths was captured annually. Logistic regression was used to analyse the association between baseline multimorbidity and 10-year mortality, Cox-proportional hazard models to measure lifetime risk of mortality and zero-inflation models to investigate the association between hospitalisation and multimorbidity. Multimorbidity was classified as ≥2 conditions or number of conditions. Demographic, lifestyle and socioeconomic variables were included as covariables. RESULTS: The study recruited 50 045 participants aged 40-75 years between 2004 and 2008, 47 883 were available for analysis, 416 (57.3%) were female and 12 736 (27.94%) were multimorbid. The odds of dying at 10 years for multimorbidity defined as ≥2 conditions was 1.99 (95% CI 1.86 to 2.12, p<0.001), and it increased with increasing number of conditions (OR of 3.57; 95% CI 3.12 to 4.08, p<0.001 for ≥4 conditions). The survival analysis showed the hazard of death for those with ≥4 conditions was 3.06 (95% CI 2.74 to 3.43, p<0.001). The number of hospital admissions increased with number of conditions (OR of not being hospitalised of 0.36; 95% CI 0.31 to 0.52, p<0.001, for ≥4 conditions). CONCLUSION: The long-terms effects of multimorbidity on mortality and hospitalisation are similar in this population to those seen in high-income countries.

Temporal trends in associations between severe mental illness and risk of cardiovascular disease: A systematic review and meta-analysis.

BACKGROUND: Severe mental illness (SMI; schizophrenia, bipolar disorders (BDs), and other nonorganic psychoses) is associated with increased risk of cardiovascular disease (CVD) and CVD-related mortality. To date, no systematic review has investigated changes in population level CVD-related mortality over calendar time. It is unclear if this relationship has changed over time in higher-income countries with changing treatments. METHODS AND FINDINGS: To address this gap, a systematic review was conducted, to assess the association between SMI and CVD including temporal change. Seven databases were searched (last: November 30, 2021) for cohort or case-control studies lasting ≥1 year, comparing frequency of CVD mortality or incidence in high-income countries between people with versus without SMI. No language restrictions were applied. Random effects meta-analyses were conducted to compute pooled hazard ratios (HRs) and rate ratios, pooled standardised mortality ratios (SMRs), pooled odds ratios (ORs), and pooled risk ratios (RRs) of CVD in those with versus without SMI. Temporal trends were explored by decade. Subgroup analyses by age, sex, setting, world region, and study quality (Newcastle-Ottawa scale (NOS) score) were conducted. The narrative synthesis included 108 studies, and the quantitative synthesis 59 mortality studies (with (≥1,841,356 cases and 29,321,409 controls) and 28 incidence studies (≥401,909 cases and 14,372,146 controls). The risk of CVD-related mortality for people with SMI was higher than controls across most comparisons, except for total CVD-related mortality for BD and cerebrovascular accident (CVA) for mixed SMI. Estimated risks were larger for schizophrenia than BD. Pooled results ranged from SMR = 1.55 (95% confidence interval (CI): 1.33 to 1.81, p < 0.001), for CVA in people with BD to HR/rate ratio = 2.40 (95% CI: 2.25 to 2.55, p < 0.001) for CVA in schizophrenia. For schizophrenia and BD, SMRs and pooled HRs/rate ratios for CHD and CVD mortality were larger in studies with outcomes occurring during the 1990s and 2000s than earlier decades (1980s: SMR = 1.14, 95% CI: 0.57 to 2.30, p = 0.71; 2000s: SMR = 2.59, 95% CI: 1.93 to 3.47, p < 0.001 for schizophrenia and CHD) and in studies including people with younger age. The incidence of CVA, CVD events, and heart failure in SMI was higher than controls. Estimated risks for schizophrenia ranged from HR/rate ratio 1.25 (95% CI: 1.04 to 1.51, p = 0.016) for total CVD events to rate ratio 3.82 (95% CI: 3.1 to 4.71, p < 0.001) for heart failure. Incidence of CHD was higher in BD versus controls. However, for schizophrenia, CHD was elevated in higher-quality studies only. The HR/rate ratios for CVA and CHD were larger in studies with outcomes occurring after the 1990s. Study limitations include the high risk of bias of some studies as they drew a comparison cohort from general population rates and the fact that it was difficult to exclude studies that had overlapping populations, although attempts were made to minimise this. CONCLUSIONS: In this study, we found that SMI was associated with an approximate doubling in the rate ratio of CVD-related mortality, particularly since the 1990s, and in younger groups. SMI was also associated with increased incidence of CVA and CHD relative to control participants since the 1990s. More research is needed to clarify the association between SMI and CHD and ways to mitigate this risk.

Therapies for Long COVID in non-hospitalised individuals: from symptoms, patient-reported outcomes and immunology to targeted therapies (The TLC Study).

INTRODUCTION: Individuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4-12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies. METHODS AND ANALYSIS: A cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink, and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability and patient-reported outcome measures. Data will be collected monthly for 1 year.Statistical clustering methods will be used to identify distinct Long COVID-19 symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear substudy which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy.We will review existing evidence on interventions for postviral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulative evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation.Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers. TRIAL REGISTRATION NUMBER: 1567490.