Distributions of recorded pain in mental health records: a natural language processing based study.

OBJECTIVE: The objective of this study is to determine demographic and diagnostic distributions of physical pain recorded in clinical notes of a mental health electronic health records database by using natural language processing and examine the overlap in recorded physical pain between primary and secondary care. DESIGN, SETTING AND PARTICIPANTS: The data were extracted from an anonymised version of the electronic health records of a large secondary mental healthcare provider serving a catchment of 1.3 million residents in south London. These included patients under active referral, aged 18+ at the index date of 1 July 2018 and having at least one clinical document (≥30 characters) between 1 July 2017 and 1 July 2019. This cohort was compared with linked primary care records from one of the four local government areas. OUTCOME: The primary outcome of interest was the presence of recorded physical pain within the clinical notes of the patients, not including psychological or metaphorical pain. RESULTS: A total of 27 211 patients were retrieved. Of these, 52% (14,202) had narrative text containing relevant mentions of physical pain. Older patients (OR 1.17, 95% CI 1.15 to 1.19), females (OR 1.42, 95% CI 1.35 to 1.49), Asians (OR 1.30, 95% CI 1.16 to 1.45) or black (OR 1.49, 95% CI 1.40 to 1.59) ethnicities, living in deprived neighbourhoods (OR 1.64, 95% CI 1.55 to 1.73) showed higher odds of recorded pain. Patients with severe mental illnesses were found to be less likely to report pain (OR 0.43, 95% CI 0.41 to 0.46, p<0.001). 17% of the cohort from secondary care also had records from primary care. CONCLUSION: The findings of this study show sociodemographic and diagnostic differences in recorded pain. Specifically, lower documentation across certain groups indicates the need for better screening protocols and training on recognising varied pain presentations. Additionally, targeting improved detection of pain for minority and disadvantaged groups by care providers can promote health equity.

Transforming post pandemic cancer services.

This paper outlines the impact of the COVID-19 pandemic on cancer services in the UK including screening, symptomatic diagnosis, treatment pathways and projections on clinical outcomes as a result of these care disruptions. A restoration of cancer services to pre-pandemic levels is not likely to mitigate this adverse impact, particularly with an ageing population and increased cancer burden. New cancer cases are projected to rise to over 500,000 per year by 2035, with over 4 million people living with and beyond cancer. This paper calls for a strategic transformation to prioritise effort on the basis of available datasets and evidence-in particular, to prioritise cancers where an earlier diagnosis is feasible and clinically useful with a focus on mortality benefit by preventing emergency presentations by harnessing data and analytics. This could be delivered by a focus on underperforming groups/areas to try and reduce inequity, linking near real-time datasets with clinical decision support systems at the primary and secondary care levels, promoting the use of novel technologies to improve patient uptake of services, screening and diagnosis, and finally, upskilling and cross-skilling healthcare workers to expand supply of diagnostic and screening services.

Disease patterns in high-cost individuals with multimorbidity: a retrospective cross-sectional study in primary care.

BACKGROUND: 'High-cost' individuals with multimorbidity account for a disproportionately large share of healthcare costs and are at most risk of poor quality of care and health outcomes. AIM: To compare high-cost with lower-cost individuals with multimorbidity and assess whether these populations can be clustered based on similar disease patterns. DESIGN AND SETTING: A cross-sectional study based on 2019/2020 electronic medical records from adults registered to primary care practices (n = 41) in a London borough. METHOD: Multimorbidity is defined as having ≥2 long-term conditions (LTCs). Primary care costs reflected consultations, which were costed based on provider and consultation types. High cost was defined as the top 20% of individuals in the cost distribution. Descriptive analyses identified combinations of 32 LTCs and their contribution to costs. Latent class analysis explored clustering patterns. RESULTS: Of 386 238 individuals, 101 498 (26%) had multimorbidity. The high-cost group (n = 20 304) incurred 53% of total costs and had 6833 unique disease combinations, about three times the diversity of the lower-cost group (n = 81 194). The trio of anxiety, chronic pain, and depression represented the highest share of costs (5%). High-cost individuals were best grouped into five clusters, but no cluster was dominated by a single LTC combination. In three of five clusters, mental health conditions were the most prevalent. CONCLUSION: High-cost individuals with multimorbidity have extensive heterogeneity in LTCs, with no single LTC combination dominating their primary care costs. The frequent presence of mental health conditions in this population supports the need to enhance coordination of mental and physical health care to improve outcomes and reduce costs.

Factors Associated With Oral Anticoagulant Use in Patients With Atrial Fibrillation and Mental Disorders.

Objective: This study aims to identify how mental illness severity interacts with oral anticoagulant (OAC) patterns among people with atrial fibrillation (AF).Methods: AF patients with comorbid mental illness (classified using ICD-10) were identified from the South London and Maudsley Biomedical Research Centre Case Register. CHA2DS2-VASc and ORBIT scales were used to calculate stroke and bleeding risks, respectively, whereas Health of the Nation Outcome Scales (HoNOS) assessment was used for functional impairment.Results: Overall, 2,105 AF patients were identified between 2011 and 2019. Serious mental illness (SMI) was associated with lower prescription of any OAC (adjusted risk ratio [aRR]: 0.94; 95% CI, 0.90-0.99). A total of 62% of SMI patients at risk of stroke were not prescribed an OAC. In the AF cohort, alcohol or substance dependence and activities of daily living (ADL) impairment were associated with lower prescription of warfarin (aRR: 0.92; 95% CI, 0.86-0.98 and aRR: 0.96; 95% CI, 0.93-0.99, respectively). Among people with AF and SMI, warfarin was less likely to be prescribed to people with self-injury (aRR: 0.84; 95% CI, 0.77-0.91), hallucinations or delusions (aRR: 0.92; 95% CI, 0.85-0.99), ADL impairment (aRR: 0.91; 95% CI, 0.84-0.99), or alcohol or substance dependence (aRR: 0.92; 95% CI, 0.87-0.98). Among people with AF and comorbid substance use disorder, self-injury (aRR: 0.78; 95% CI, 0.64-0.96), cognitive problems (aRR: 0.84; 95% CI, 0.70-0.99), and other mental illnesses (aRR: 0.83; 95% CI, 0.70-0.99) were associated with lower prescription of warfarin.Conclusions: An OAC treatment gap for AF patients with comorbid SMI relative to other mental illnesses was identified. The gap was wider in those with dependence comorbidities, positive symptoms, self-injury, or functional impairment.J Clin Psychiatry 2024;85(1):23m14824. Author affiliations are listed at the end of this article.

Risk of fibromyalgia following antibiotic prescriptions: A population-based case-control study.

BACKGROUND: The health of the gut microbiome is now recognized to be an important component of the gut-brain axis which itself appears to be implicated in pain perception. Antibiotics are known to create dysbiosis in the microbiome, so whether fibromyalgia is more commonly diagnosed after antibiotic prescriptions provides a means of exploring the role of the microbiome in the experience of chronic pain. METHODS: A case-control study was carried out using electronic health records collected in the UK's Clinical Practice Research Datalink (CPRD), a comprehensive database of primary care consultations. For each case of diagnosed fibromyalgia, three controls were identified and matched by age, gender and GP practice. The exposure variable was the number and timing of antibiotic prescriptions over previous years. The analysis involved adjusting for a wide range of co-variates that might be possible confounders. RESULTS: A total of 44,674 cases of fibromyalgia were identified together with 133,513 controls. After adjusting for co-variates, it was found that both the total number of prescriptions and their timing was associated with an FM diagnosis. For example, the quartile with the highest number of prescriptions and that with the longest exposure had a greater than three-fold increase in FM diagnoses (number of prescriptions: odds ratio 3.92; 95% CIs: 3.71-4.13; exposure odds ratio 3.28; CIs: 3.13-3.43). Some antibiotics (such as tetracyclines and metronidazole) seemed to confer greater risk than others. CONCLUSIONS: The results lend support for prior antibiotics being an important risk factor for a diagnosis of FM. SIGNIFICANCE: This study shows an association between the volume as well as timing of prior antibiotic prescriptions and of a subsequent diagnosis of fibromyalgia in primary care.

Hypertension prevalence, coding and control in an urban primary care setting in the UK between 2014 and 2021.

OBJECTIVE: Hypertension is a leading preventable cause of mortality, yet high rates of undiagnosed and uncontrolled hypertension continue. The burden falls most heavily on some ethnic minorities and the socially deprived, with the COVID-19 pandemic having further widened inequalities. We sought to determine the prevalence and predictors of unmeasured blood pressure (BP), uncoded elevated BP and uncontrolled hypertension in primary care across 2014-2021. METHODS: A population-based cohort study using data from all 41 general practices in a socioeconomically diverse inner-city borough. BP measurements, sociodemographic, lifestyle and clinical factors were extracted from anonymized primary care data. Hypertension and BP control were defined using NICE guidelines. Associations between patient characteristics and hypertension outcomes were identified using logistical regression modelling. RESULTS: Of 549 082 patients, 39.5% had unmeasured BP; predictors included male sex [AOR 2.40, 95% confidence interval (95% CI) 2.26-2.43] and registration in the pandemic years. Of 71 970 adults with elevated BP, 36.0% were uncoded; predictors included obesity (AOR 2.51, 95% CI 2.42-2.60) and increasing age. Of 44 648 adults on the hypertension register, 46.8% had uncontrolled hypertension; predictors included black ethnicity compared to white (AOR 1.54, 95% CI 1.41-1.68) and cardiovascular co-morbidities (AOR 1.23, 95% CI 1.21-1.25). Social deprivation was only weakly or not significantly associated with hypertension outcomes. CONCLUSION: The burden of uncoded elevated BP and uncontrolled hypertension is high. Obesity and male sex were associated with uncoded elevated BP and uncontrolled hypertension. Black ethnicity was associated with uncontrolled hypertension. Initiatives are needed to optimize hypertension coding and control, with an emphasis on specific population subgroups.

The Effectiveness of Interventions for Improving Chronic Pain Symptoms Among People With Mental Illness: A Systematic Review.

Chronic pain (CP) and mental illness (MI) are leading causes of years lived with disability and commonly co-occur. However, it remains unclear if available interventions are effective in improving pain outcomes in patients with co-existing CP and MI. This systematic review synthesised evidence for the effectiveness of interventions to improve pain outcomes for people with comorbid CP and clinically diagnosed MI. Ten electronic databases were searched from inception until May 2023. Randomised controlled trials (RCTs) were included if they evaluated interventions for CP-related outcomes among people with comorbid CP and clinically diagnosed MI. Pain-related and mental health outcomes were reported as primary and secondary outcomes, respectively. 26 RCTs (2,311 participants) were included. Four trials evaluated the effectiveness of cognitive-behavioural therapy, 6 mindfulness-based interventions, 1 interpersonal psychotherapy, 5 body-based interventions, 5 multi-component interventions, and 5 examined pharmacological-based interventions. Overall, there was considerable heterogeneity in sample characteristics and interventions, and included studies were generally of poor quality with insufficient trial details being reported. Despite the inconsistency in results, preliminary evidence suggests interventions demonstrating a positive effect on CP may include cognitive-behavioural therapy for patients with depression (with a small to medium effect size) and multi-component intervention for people with substance use disorders (with a small effect size). Despite the high occurrence/burden of CP and MI, there is a relative paucity of RCTs investigating interventions and none in people with severe MI. More rigorously designed RCTs are needed to further support our findings. PERSPECTIVE: This systematic review presents current evidence evaluating interventions for CP-related and MH outcomes for people with comorbid CP and clinically diagnosed MI. Our findings could potentially help clinicians identify the most effective treatments to manage these symptoms for this vulnerable patient group.

Risk factors associated with overweight and obesity in people with severe mental illness in South Asia: cross-sectional study in Bangladesh, India, and Pakistan.

Obesity is one of the major contributors to the excess mortality seen in people with severe mental illness (SMI) and in low- and middle-income countries people with SMI may be at an even greater risk. In this study, we aimed to determine the prevalence of obesity and overweight in people with SMI and investigate the association of obesity and overweight with sociodemographic variables, other physical comorbidities, and health-risk behaviours. This was a multi-country cross-sectional survey study where data were collected from 3989 adults with SMI from three specialist mental health institutions in Bangladesh, India, and Pakistan. The prevalence of overweight and obesity was estimated using Asian BMI thresholds. Multinomial regression models were then used to explore associations between overweight and obesity with various potential determinants. There was a high prevalence of overweight (17·3 %) and obesity (46·2 %). The relative risk of having obesity (compared to normal weight) was double in women (RRR = 2·04) compared with men. Participants who met the WHO recommendations for fruit and vegetable intake had 2·53 (95 % CI: 1·65-3·88) times greater risk of having obesity compared to those not meeting them. Also, the relative risk of having obesity in people with hypertension is 69 % higher than in people without hypertension (RRR = 1·69). In conclusion, obesity is highly prevalent in SMI and associated with chronic disease. The complex relationship between diet and risk of obesity was also highlighted. People with SMI and obesity could benefit from screening for non-communicable diseases, better nutritional education, and context-appropriate lifestyle interventions.

Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) - protocol for a research collaboration.

Background: Most people living with multiple long-term condition multimorbidity (MLTC-M) are under 65 (defined as 'early onset'). Earlier and greater accrual of long-term conditions (LTCs) may be influenced by the timing and nature of exposure to key risk factors, wider determinants or other LTCs at different life stages. We have established a research collaboration titled 'MELD-B' to understand how wider determinants, sentinel conditions (the first LTC in the lifecourse) and LTC accrual sequence affect risk of early-onset, burdensome MLTC-M, and to inform prevention interventions. Aim: Our aim is to identify critical periods in the lifecourse for prevention of early-onset, burdensome MLTC-M, identified through the analysis of birth cohorts and electronic health records, including artificial intelligence (AI)-enhanced analyses. Design: We will develop deeper understanding of 'burdensomeness' and 'complexity' through a qualitative evidence synthesis and a consensus study. Using safe data environments for analyses across large, representative routine healthcare datasets and birth cohorts, we will apply AI methods to identify early-onset, burdensome MLTC-M clusters and sentinel conditions, develop semi-supervised learning to match individuals across datasets, identify determinants of burdensome clusters, and model trajectories of LTC and burden accrual. We will characterise early-life (under 18 years) risk factors for early-onset, burdensome MLTC-M and sentinel conditions. Finally, using AI and causal inference modelling, we will model potential 'preventable moments', defined as time periods in the life course where there is an opportunity for intervention on risk factors and early determinants to prevent the development of MLTC-M. Patient and public involvement is integrated throughout.

A conceptual framework for characterising lifecourse determinants of multiple long-term condition multimorbidity.

Objective: Social, biological and environmental factors in early-life, defined as the period from preconception until age 18, play a role in shaping the risk of multiple long-term condition multimorbidity. However, there is a need to conceptualise these early-life factors, how they relate to each other, and provide conceptual framing for future research on aetiology and modelling prevention scenarios of multimorbidity. We develop a conceptual framework to characterise the population-level domains of early-life determinants of future multimorbidity. Method: This work was conducted as part of the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) study. The conceptualisation of multimorbidity lifecourse determinant domains was shaped by a review of existing research evidence and policy, and co-produced with public involvement via two workshops. Results: Early-life risk factors incorporate personal, social, economic, behavioural and environmental factors, and the key domains discussed in research evidence, policy, and with public contributors included adverse childhood experiences, socioeconomics, the social and physical environment, and education. Policy recommendations more often focused on individual-level factors as opposed to the wider determinants of health discussed within the research evidence. Some domains highlighted through our co-production process with public contributors, such as religion and spirituality, health screening and check-ups, and diet, were not adequately considered within the research evidence or policy. Conclusions: This co-produced conceptualisation can inform research directions using primary and secondary data to investigate the early-life characteristics of population groups at risk of future multimorbidity, as well as policy directions to target public health prevention scenarios of early-onset multimorbidity.

Identifying populations with chronic pain in primary care: developing an algorithm and logic rules applied to coded primary care diagnostic and medication data.

BACKGROUND: Estimates of chronic pain prevalence using coded primary care data are likely to be substantially lower than estimates derived from community surveys. Most primary care studies have estimated chronic pain prevalence using data searches confined to analgesic medication prescriptions. Increasingly, following recent NICE guideline recommendations, patients and doctors opt for non-drug treatment of chronic pain thus excluding these patients from prevalence estimates based on medication codes. We aimed to develop and test an algorithm combining medication codes with selected diagnostic codes to estimate chronic pain prevalence using coded primary care data. METHODS: Following a scoping review 4 criteria were developed to identify cohorts of people with chronic pain. These were (1) people with one of 12 ('tier 1') conditions that almost always results in the individual having chronic pain (2) people with one of 20 ('tier 2') conditions included when there are also 3 or more prescription-only analgesics issued in the last 12 months (3) chronic neuropathic pain, or (4) 4 or more prescription-only analgesics issued in the last 12 months. These were translated into 8 logic rules which included 1,932 SNOMED CT codes. RESULTS: The algorithm was run on primary care data from 41 GP Practices in Lambeth. The total population consisted of 386,238 GP registered adults ≥ 18 years as of the 31st March 2021. 64,135 (16.6%) were identified as people with chronic pain. This definition demonstrated notably high rates in Black ethnicity females, and higher rates in the most deprived, and older population. CONCLUSIONS: Estimates of chronic pain prevalence using structured healthcare data have previously shown lower prevalence estimates for chronic pain than reported in community surveys. This has limited the ability of researchers and clinicians to fully understand and address the complex multifactorial nature of chronic pain. Our study demonstrates that it may be possible to establish more representative prevalence estimates using structured data than previously possible. Use of logic rules offers the potential to move systematic identification and population-based management of chronic pain into mainstream clinical practice at scale and support improved management of symptom burden for people experiencing chronic pain.

Ethnic inequities in the patterns of personalized care adjustments for 'informed dissent' and 'patient unsuitable': a retrospective study using Clinical Practice Research Datalink.

BACKGROUND: In England, general practitioners voluntarily take part in the Quality and Outcomes Framework, which is a program that seeks to improve care by rewarding good practice. They can make personalized care adjustments (PCAs), e.g. if patients choose not to have the treatment/intervention offered ('informed dissent') or because they are considered to be clinically 'unsuitable'. METHODS: Using data from the Clinical Practice Research Datalink (Aurum), this study examined patterns of PCA reporting for 'informed dissent' and 'patient unsuitable', how they vary across ethnic groups and whether ethnic inequities were explained by sociodemographic factors or co-morbidities. RESULTS: The odds of having a PCA record for 'informed dissent' were lower for 7 of the 10 minoritized ethnic groups studied. Indian patients were less likely than white patients to have a PCA record for 'patient unsuitable'. The higher likelihood of reporting for 'patient unsuitable' among people from Black Caribbean, Black Other, Pakistani and other ethnic groups was explained by co-morbidities and/or area-level deprivation. CONCLUSIONS: The findings counter narratives that suggest that people from minoritized ethnic groups often refuse medical intervention/treatment. The findings also illustrate ethnic inequities in PCA reporting for 'patient unsuitable', which are linked to clinical and social complexity and should be tackled to improve health outcomes for all.

Antidepressant Advisor (ADeSS): a decision support system for antidepressant treatment for depression in UK primary care - a feasibility study.

OBJECTIVES: To develop and probe the first computerised decision-support tool to provide antidepressant treatment guidance to general practitioners (GPs) in UK primary care. DESIGN: A parallel group, cluster-randomised controlled feasibility trial, where individual participants were blind to treatment allocation. SETTING: South London NHS GP practices. PARTICIPANTS: Ten practices and eighteen patients with treatment-resistant current major depressive disorder. INTERVENTIONS: Practices were randomised to two treatment arms: (a) treatment-as-usual, (b) computerised decision support tool. RESULTS: Ten GP practices participated in the trial, which was within our target range (8-20). However, practice and patient recruitment were slower than anticipated and only 18 of 86 intended patients were recruited. This was due to fewer than expected patients being eligible for the study, as well as disruption resulting from the COVID-19 pandemic. Only one patient was lost to follow-up. There were no serious or medically important adverse events during the trial. GPs in the decision tool arm indicated moderate support for the tool. A minority of patients fully engaged with the mobile app-based tracking of symptoms, medication adherence and side effects. CONCLUSIONS: Overall, feasibility was not shown in the current study and the following modifications would be needed to attempt to overcome the limitations found: (a) inclusion of patients who have only tried one Selective Serotonin Reuptake Inhibitor, rather than two, to improve recruitment and pragmatic relevance of the study; (b) approaching community pharmacists to implement tool recommendations rather than GPs; (c) further funding to directly interface between the decision support tool and self-reported symptom app; (d) increasing the geographic reach by not requiring detailed diagnostic assessments and replacing this with supported remote self-report. TRIAL REGISTRATION NUMBER: NCT03628027.

Completeness, agreement, and representativeness of ethnicity recording in the United Kingdom's Clinical Practice Research Datalink (CPRD) and linked Hospital Episode Statistics (HES).

BACKGROUND: This descriptive study assessed the completeness, agreement, and representativeness of ethnicity recording in the United Kingdom (UK) Clinical Practice Research Datalink (CPRD) primary care databases alone and, for those patients registered with a GP in England, when linked to secondary care data from Hospital Episode Statistics (HES). METHODS: Ethnicity records were assessed for all patients in the May 2021 builds of the CPRD GOLD and CPRD Aurum databases for all UK patients. In analyses of the UK, English data was from combined CPRD-HES, whereas data from Northern Ireland, Scotland, and Wales drew from CPRD only. The agreement of ethnicity records per patient was assessed within each dataset (CPRD GOLD, CPRD Aurum, and HES datasets) and between datasets at the highest level ethnicity categorisation ('Asian', 'black', 'mixed', 'white', 'other'). Representativeness was assessed by comparing the ethnic distributions at the highest-level categorisation of CPRD-HES to those from the Census 2011 across the UK's devolved administrations. Additionally, CPRD-HES was compared to the experimental ethnic distributions for England and Wales from the Office for National Statistics in 2019 (ONS2019) and the English ethnic distribution from May 2021 from NHS Digital's General Practice Extraction Service Data for Pandemic Planning and Research with HES data linkage (GDPPR-HES). RESULTS: In CPRD-HES, 81.7% of currently registered patients in the UK had ethnicity recorded in primary care. For patients with multiple ethnicity records, mismatched ethnicity within individual primary and secondary care datasets was < 10%. Of English patients with ethnicity recorded in both CPRD and HES, 93.3% of records matched at the highest-level categorisation; however, the level of agreement was markedly lower in the 'mixed' and 'other' ethnic groups. CPRD-HES was less proportionately 'white' compared to the UK Census 2011 (80.3% vs. 87.2%) and experimental ONS2019 data (80.4% vs. 84.3%). CPRD-HES was aligned with the ethnic distribution from GDPPR-HES ('white' 80.4% vs. 80.7%); however, with a smaller proportion classified as 'other' (1.1% vs. 2.8%). CONCLUSIONS: CPRD-HES has suitable representation of all ethnic categories with some overrepresentation of minority ethnic groups and a smaller proportion classified as 'other' compared to the UK general population from other data sources. CPRD-HES data is useful for studying health risks and outcomes in typically underrepresented groups.

Predicting type 2 diabetes prevalence for people with severe mental illness in a multi-ethnic East London population.

BACKGROUND AND AIMS: Prevalence of type two diabetes mellitus (T2DM) in people with severe mental illness (SMI) is 2-3 times higher than in general population. Predictive modelling has advanced greatly in the past decade, and it is important to apply cutting-edge methods to vulnerable groups. However, few T2DM prediction models account for the presence of mental illness, and none seemed to have been developed specifically for people with SMI. Therefore, we aimed to develop and internally validate a T2DM prevalence model for people with SMI. METHODS: We utilised a large cross-sectional sample representative of a multi-ethnic population from London (674,000 adults); 10,159 people with SMI formed our analytical sample (1,513 T2DM cases). We fitted a linear logistic regression and XGBoost as stand-alone models and as a stacked ensemble. Age, sex, body mass index, ethnicity, area-based deprivation, past hypertension, cardiovascular diseases, prescribed antipsychotics, and SMI illness were the predictors. RESULTS: Logistic regression performed well while detecting T2DM presence for people with SMI: area under the receiver operator curve (ROC-AUC) was 0.83 (95 % CI 0.79-0.87). XGBoost and LR-XGBoost ensemble performed equally well, ROC-AUC 0.83 (95 % CI 0.79-0.87), indicating a negligible contribution of non-linear terms to predictive power. Ethnicity was the most important predictor after age. We demonstrated how the derived models can be utilised and estimated a 2.14 % (95 %CI 2.03 %-2.24 %) increase in T2DM prevalence in East London SMI population in 20 years' time, driven by the projected demographic changes. CONCLUSIONS: Primary care data, the setting where prediction models could be most fruitfully used, provide enough information for well-performing T2DM prevalence models for people with SMI. We demonstrated how thorough internal cross-validation of an ensemble of a linear and machine-learning model can quantify the predictive value of non-linearity in the data.

The impact of the COVID-19 pandemic on cardiovascular disease prevention and management.

How the Coronavirus Disease 2019 (COVID-19) pandemic has affected prevention and management of cardiovascular disease (CVD) is not fully understood. In this study, we used medication data as a proxy for CVD management using routinely collected, de-identified, individual-level data comprising 1.32 billion records of community-dispensed CVD medications from England, Scotland and Wales between April 2018 and July 2021. Here we describe monthly counts of prevalent and incident medications dispensed, as well as percentage changes compared to the previous year, for several CVD-related indications, focusing on hypertension, hypercholesterolemia and diabetes. We observed a decline in the dispensing of antihypertensive medications between March 2020 and July 2021, with 491,306 fewer individuals initiating treatment than expected. This decline was predicted to result in 13,662 additional CVD events, including 2,281 cases of myocardial infarction and 3,474 cases of stroke, should individuals remain untreated over their lifecourse. Incident use of lipid-lowering medications decreased by 16,744 patients per month during the first half of 2021 as compared to 2019. By contrast, incident use of medications to treat type 2 diabetes mellitus, other than insulin, increased by approximately 623 patients per month for the same time period. In light of these results, methods to identify and treat individuals who have missed treatment for CVD risk factors and remain undiagnosed are urgently required to avoid large numbers of excess future CVD events, an indirect impact of the COVID-19 pandemic.

Ethnic inequalities in the impact of COVID-19 on primary care consultations: a time series analysis of 460,084 individuals with multimorbidity in South London.

BACKGROUND: The COVID-19 pandemic caused rapid changes in primary care delivery in the UK, with concerns that certain groups of the population may have faced increased barriers to access. This study assesses the impact of the response to the COVID-19 pandemic on primary care consultations for individuals with multimorbidity and identifies ethnic inequalities. METHODS: A longitudinal study based on monthly data from primary care health records of 460,084 patients aged ≥18 years from 41 GP practices in South London, from February 2018 to March 2021. Descriptive analysis and interrupted time series (ITS) models were used to analyse the effect of the pandemic on primary care consultations for people with multimorbidity and to identify if the effect varied by ethnic groups and consultation type. RESULTS: Individuals with multimorbidity experienced a smaller initial fall in trend at the start of the pandemic. Their primary care consultation rates remained stable (879 (95% CI 869-890) per 1000 patients in February to 882 (870-894) March 2020), compared with a 7% decline among people without multimorbidity (223 consultations (95% CI 221-226) to 208 (205-210)). The gap in consultations between the two groups reduced after July 2020. The effect among individuals with multimorbidity varied by ethnic group. Ethnic minority groups experienced a slightly larger fall at the start of the pandemic. Individuals of Black, Asian, and Other ethnic backgrounds also switched from face-to-face to telephone at a higher rate than other ethnic groups. The largest fall in face-to-face consultations was observed among people from Asian backgrounds (their consultation rates declined from 676 (659-693) in February to 348 (338-359) in April 2020), which may have disproportionately affected their quality of care. CONCLUSIONS: The COVID-19 pandemic significantly affected primary care utilisation in patients with multimorbidity. While there is evidence of a successful needs-based prioritisation of multimorbidity patients within primary care at the start of the pandemic, inequalities among ethnic minority groups were found. Strengthening disease management for these groups may be necessary to control widening inequalities in future health outcomes.

Ethnic differences in physical and mental multimorbidity in working age adults with a history of depression and/or anxiety.

BACKGROUND: The current study used data from an ethnically diverse population from South London to examine ethnic differences in physical and mental multimorbidity among working age (18-64 years) adults in the context of depression and anxiety. METHOD: The study included 44 506 patients who had previously attended Improving Access to Psychological Therapies services in the London Borough of Lambeth. Multinomial logistic regression examined cross-sectional associations between ethnicity with physical and mental multimorbidity. Patterns of multimorbidity were identified using hierarchical cluster analysis. RESULTS: Within 44 056 working age adults with a history of depression or anxiety from South London there were notable ethnic differences in physical multimorbidity. Adults of Black Caribbean ethnicity were more likely to have physical multimorbidity [adjusted relative risk ratio (aRRR) = 1.25, 95% confidence interval (CI) 1.15-1.36] compared to adults of White ethnicity. Relative to adults of White ethnicity, adults of Asian ethnicity were more likely to have physical multimorbidity at higher thresholds only (e.g. 4 + conditions; aRRR = 1.53, 95% CI 1.17-2.00). Three physical (atopic, cardiometabolic, mixed) and three mental (alcohol/substance use, common/severe mental illnesses, personality disorder) multimorbidity clusters emerged. Ethnic minority groups with multimorbidity had a higher probability of belonging to the cardiometabolic cluster. CONCLUSION: In an ethnically diverse population with a history of common mental health disorders, we found substantial between- and within-ethnicity variation in rates of physical, but not mental, multimorbidity. The findings emphasised the value of more granular definitions of ethnicity when examining the burden of physical and mental multimorbidity.

Continuity of care in diverse ethnic groups: a general practice record study in England.

BACKGROUND: GPs and patients value continuity of care. Ethnic differences in continuity could contribute to inequalities in experience and outcomes. AIM: To describe relational continuity of care in general practice by ethnicity and long-term conditions. DESIGN AND SETTING: In total, 381 474 patients in England were included from a random sample from the Clinical Practice Research Datalink (January 2016 to December 2019). METHOD: Face-to-face, telephone, and online consultations with a GP were included. Continuity, measured by the Usual Provider of Care and Bice-Boxerman indices, was calculated for patients with ≥3 consultations. Ethnicity was taken from the GP record or linked Hospital Episode Statistics data, and long-term conditions were counted at baseline. Multilevel regression models were used to describe continuity by ethnicity sequentially adjusted for: a) the number of consultations, follow-up time, age, sex, and practice-level random intercept; b) socioeconomic deprivation in the patient's residential area; and c) long-term conditions. RESULTS: On full adjustment, 5 of 10 ethnic minority groups (Bangladeshi, Pakistani, Black African, Black Caribbean, and any other Black background) had lower continuity of care compared with White patients. Continuity was lower for patients in more deprived areas and younger patients but this did not account for ethnic differences in continuity. Differences by ethnicity were also seen in patients with ≥2 long-term conditions. CONCLUSION: Ethnic minority identity and socioeconomic deprivation have additive associations with lower continuity of care. Structural factors affecting demand for, and supply of, GPs should be assessed for their contribution to ethnic inequalities in relational continuity and other care quality domains.