Objectives To compare receipt of guideline-informed myocardial infarction (MI) care by mental disorder and assess how the COVID-19 pandemic affected associations. Design A population-based cohort study using linked electronic health records. Setting England, November 2019 to February 2023. Participants 131 075 adults with non-ST-elevation MI (NSTEMI) and 79 045 adults with ST-elevation MI (STEMI) were identified from the Myocardial Ischaemia National Audit Project, and their prior diagnoses of mental disorder were ascertained from linked hospitalisation and primary care records. Outcome measures We compared guideline-informed care standards for each of NSTEMI and STEMI between people with schizophrenia, bipolar disorder or depression versus those without any of these disorders. We used logistic regression to adjust for confounders and investigate differences over time. Results Mental disorder disparities were more evident for NSTEMI than STEMI. Following NSTEMI, people with a mental disorder had lower odds of angiography eligibility and receipt, cardiac ward admission and cardiac rehabilitation referral. ORs (95% CIs) ranged from 0.25 (0.20 to 0.31) for angiography receipt for schizophrenia to 0.92 (0.89 to 0.96) for cardiac ward admission for depression. Following STEMI, people with bipolar disorder were less likely to meet the 150 min call-to-balloon target (OR 0.72; 95% CI 0.55 to 0.93), and people with schizophrenia were less likely to receive rehabilitation referral (OR 0.38; 95% CI 0.23 to 0.61) or indicated secondary prevention medication (OR 0.46, 95% CI 0.27 to 0.77). There was no clear evidence that the COVID-19 pandemic affected disparities. Conclusions People with a mental disorder are less likely to receive guideline-informed MI care, with disparities greatest following NSTEMI and for people with schizophrenia.

Aim To investigate the association between pre-existing mental illness and out-of-hospital cardiac arrest (OHCA) survival. Methods We performed a nationwide retrospective cohort study using Scottish Ambulance Service OHCA data linked to unscheduled care and death data in Scotland. We identified adults 18 years or older with non-traumatic OHCA between 2011 and 2022 and defined pre-existing mental illness as a record of mental illness within an unscheduled acute hospital admission or a record of unscheduled psychiatric hospital admission prior to OHCA. We used logistic regression models to obtain crude and adjusted odds ratios (ORs) for the association between mental illness and OHCA 30-day survival and conducted subgroup analyses based on patient and event characteristics, including age, sex, initial heart rhythm and bystander cardiopulmonary resuscitation. Results We included 30,523 patients with OHCA, of whom 12.8% had a pre-existing mental illness. Those with pre-existing mental illness had a higher prevalence of physical comorbidities, lower rates of initial shockable heart rhythm and significantly lower odds of 30-day survival compared to those without mental illness, after adjusting for age (OR 0.22, 95% confidence interval 0.18–0.26). This association persisted after adjusting the model for sex, year of arrest, comorbidities and deprivation, and was consistent across the different subgroups analysed. Conclusion Compared to people without pre-existing mental illness, those with pre-existing mental illness have lower odds of OHCA survival even after accounting for patient and event characteristics. Further research should investigate factors that may be responsible for this association and inform interventions to address disparities.

AIMS:People with schizophrenia experience poorer cardiovascular disease (CVD) outcomes, but other mental disorders have been little studied. We compared post-myocardial infarction (MI) mortality by mental disorder, explored whether differences in care contributed to mortality disparities, and investigated whether disparities worsened during the COVID-19 pandemic. METHODS AND RESULTS:We identified people with MI in England (November 2019-February 2023) from the Myocardial Ischaemia National Audit Programme, ascertaining mental disorder diagnoses from linked electronic health records and mortality from national death records. We used logistic regression to compare 30-day and one-year mortality between people with each of schizophrenia, bipolar disorder, or depression (of any severity) vs. those without these disorders for ST-elevation MI (STEMI) and non-STEMI (NSTEMI), adjusting for confounders and investigating differences by calendar time period. For one-year mortality, we additionally adjusted for receipt of guideline-informed care. We included 131 075 patients with NSTEMI and 79 045 with STEMI. For NSTEMI, schizophrenia [odds ratio (OR) 1.73, 95% CI 1.20-2.49] and depression (1.17, 1.08-1.26) were associated with higher 30-day mortality. For STEMI, 30-day mortality was higher in people with schizophrenia (1.61, 1.09-2.37), bipolar disorder (1.68, 1.08-2.59), and depression (1.10, 1.01-1.20). All disorders were associated with higher one-year mortality following NSTEMI and STEMI, with adjustment for care attenuating estimates. Relative mortality disparities were generally unaffected by the COVID-19 pandemic. CONCLUSION:Our findings highlight the increased risk of post-MI mortality in people with mental disorders. Improved implementation of acute cardiac care standards may help to close the gap.

Background Physical health checks in primary care for people with severe mental illness ((SMI) defined as schizophrenia, bipolar disorders and non-organic psychosis) aim to reduce health inequalities. Patients who decline or are deemed unsuitable for screening are removed from the denominator used to calculate incentivisation, termed exception reporting. Aims To describe the prevalence of, and patient characteristics associated with, exception reporting in patients with SMI. Method We identified adult patients with SMI from the UK Clinical Practice Research Datalink (CPRD), registered with a general practice between 2004 and 2018. We calculated the annual prevalence of exception reporting and investigated patient characteristics associated with exception reporting, using logistic regression. Results Of 193 850 patients with SMI, 27.7% were exception reported from physical health checks at least once. Exception reporting owing to non-response or declining screening increased over the study period. Patients of Asian or Black ethnicity (Asian: odds ratio 0.72, 95% CI 0.65–0.80; Black: odds ratio 0.86, 95% CI 0.76–0.97; compared with White) and women (odds ratio 0.90, 95% CI 0.88–0.92) had a reduced odds of being exception reported, whereas patients diagnosed with ‘other psychoses’ (odds ratio 1.19, 95% CI 1.15–1.23; compared with bipolar disorder) had increased odds. Younger patients and those diagnosed with schizophrenia were more likely to be exception reported owing to informed dissent. Conclusions Exception reporting was common in people with SMI. Interventions are required to improve accessibility and uptake of physical health checks to improve physical health in people with SMI.

BACKGROUND:People with severe mental illness (SMI) have a higher risk of premature mortality than the general population. AIMS:To investigate whether the life expectancy gap for people with SMI is widening, by determining time trends in excess life-years lost. METHOD:This population-based study included people with SMI (schizophrenia, bipolar disorder and major depression) alive on 1 January 2000. We ascertained SMI from psychiatric hospital admission records (1981-2019), and deaths via linkage to the national death register (2000-2019). We used the Life Years Lost (LYL) method to estimate LYL by SMI and sex, compared LYL to the Scottish population and assessed trends over 18 3-year rolling periods. RESULTS:We included 28 797 people with schizophrenia, 16 657 with bipolar disorder and 72 504 with major depression. Between 2000 and 2019, life expectancy increased in the Scottish population but the gap widened for people with schizophrenia. For 2000-2002, men and women with schizophrenia lost an excess 9.4 (95% CI 8.5-10.3) and 8.2 (95% CI 7.4-9.0) life-years, respectively, compared with the general population. In 2017-2019, this increased to 11.8 (95% CI 10.9-12.7) and 11.1 (95% CI 10.0-12.1). The life expectancy gap was lower for bipolar disorder and depression and unchanged over time. CONCLUSIONS:The life expectancy gap in people with SMI persisted or widened from 2000 to 2019. Addressing this entrenched disparity requires equitable social, economic and health policies, healthcare re-structure and improved resourcing, and investment in interventions for primary and secondary prevention of SMI and associated comorbidities.

ABSTRACTObjectiveCurrent evidence on the association between depression and cancer risk is conflicting, with little understanding of how associations vary by time period or sociodemographic factors. We aimed to compare cancer incidence in people with versus without a previous hospital admission record for depression, by sociodemographic factors and over time.MethodsWe conducted a cohort study using national linked data in Scotland from 1991 to 2019. We calculated sex‐stratified age standardised incidence rates for all cancers, lung, female breast, colorectal and prostate cancer, and used quasi‐Poisson regression models to obtain sex‐specific estimates of cancer incidence and relative risks of cancer in those with versus without a prior hospital admission record of depression.ResultsThere were 128,654 people with a hospital record of depression with 12,802 incident cancers and 847,656 cancers among those without depression. Age‐standardised cancer incidence rates were higher in both males and females with versus without depression. Depression was associated with a 20%–30% increased risk of all cancers combined, a difference that did not vary by sex, age or deprivation and persisted over three decades. Depression was associated with higher risks of lung (RR 1.79, 95% CI 1.70–1.88) and colorectal cancer (RR 1.12, 95% CI 1.05–1.19), but not breast or prostate cancer.ConclusionsWe identified an entrenched disparity in cancer incidence by depression status. Further research should identify underlying mechanisms and inform cancer prevention strategies in this vulnerable group. Meanwhile, health care professionals have a key role to play in optimising physical health care for people with depression.

Background We aimed to estimate mental disorder disparities in cardiovascular disease (CVD) incidence and determine whether these disparities were worsened by the COVID-19 pandemic.Methods For each outcome (myocardial infarction (MI), heart failure and stroke), we created a population-based cohort of people without a prior diagnosis of the outcome using linked electronic health records, with follow-up from November 2019 until December 2023. We ascertained pre-existing schizophrenia, bipolar disorder and depression, and each CVD outcome from primary care and hospital admission records and (for CVD outcomes) mortality records. We calculated sex-stratified age-standardised incidence rates by mental disorder diagnosis and used quasi-Poisson modelling to obtain rate ratios (RRs) of CVD among people with each of schizophrenia, bipolar disorder or depression versus those without any of these disorders, adjusting for sociodemographic factors and time period. We investigated whether mental disorder disparities changed as a consequence of the COVID-19 pandemic by including an interaction term between mental disorder and time.Results During follow-up, 383 365 people had incident MI, 868 590 had incident heart failure and 455 300 had incident stroke. Age-standardised incidence of each CVD outcome decreased markedly between February and April 2020, with incidence levels returning to, but not exceeding, prepandemic levels in subsequent years. Mental disorder was associated with a higher incidence of each CVD outcome, with RRs ranging from 1.31 (95% CI 1.25 to 1.38) to 2.15 (95% CI 2.05 to 2.24). There was generally no evidence of interaction between mental disorder and time, with mental disorder disparities in CVD incidence stable over time.Conclusion We found no clear evidence that the mental disorder disparities in CVD incidence widened during the acute period of the pandemic or during the subsequent years. Continued monitoring of the CVD burden in the general population and among marginalised groups is critical to identifying longer-term impacts on CVD and worsening disparities.

Background and Aims People with a mental disorder have poorer myocardial infarction (MI) outcomes, with differences in cardiac care thought to be partly responsible. We compared receipt of guideline-informed acute MI care by mental disorder and assessed how the COVID-19 pandemic affected associations. Methods We identified people with MI in England (November 2019 - February 2023) from the Myocardial Ischaemia National Audit Project (MINAP), ascertaining prior mental disorder from linked hospitalisation and primary care records and extracting care standards from MINAP. We used logistic regression to compare care standards for ST-elevation MI (STEMI) and non-STEMI (NSTEMI) between people with each of schizophrenia, bipolar disorder or depression versus those without any of these disorders, adjusting for confounders and investigating differences over time. Results We included 131,075 NSTEMI and 79,045 STEMI cases. For NSTEMI, people with prior mental disorder had lower odds of angiography eligibility and receipt, cardiac ward admission and cardiac rehabilitation referral. Odds ratios (95% CIs) ranged from 0.25 (0.20, 0.31) for angiography receipt for schizophrenia to 0.92 (0.89, 0.96) for cardiac ward admission for depression. For STEMI, there was no evidence of care differences for depression; however, people with bipolar disorder were less likely to meet call-to-balloon targets and people with schizophrenia were less likely to be referred for cardiac rehabilitation and receive indicated secondary prevention medication. Disparities were generally unaffected by the COVID-19 pandemic. Conclusions People with a mental disorder are less likely to receive guideline-informed MI care, with variation by MI type, care standard and mental disorder. ### Competing Interest Statement K.F., J.N., D.C., S.W.M., S.P., D.J.S., R.S., A.V. and C.A.J. declare no disclosures of interest for this work. C.B. is employed by the University of Glasgow which holds consultancy and research agreements for his work with Abbott Vascular, AstraZeneca, Boehringer Ingelheim, CorFlow, MSD, Novartis, Servier, Siemens Healthcare, Xylocor and Zoll Medical. ### Clinical Protocols ### Funding Statement This study was funded by a Chief Scientist Office Scotland grant (Ref HIPS/21/48) awarded to C.J., K.F., D.C., D.J.S., S.W.M. and S.P. The authors K.F., D.J.S. and C.J. are also supported by the UKRI-funded University of Edinburgh Hub for Metabolic Psychiatry (Ref MR/Z503563/1), within the UK Mental Health Platform (Ref MR/Z000548/1). The British Heart Foundation Data Science Centre (grant No SP/19/3/34678, awarded to Health Data Research (HDR) UK) funded co-development (with NHS England) of the Secure Data Environment service for England, provision of linked datasets, data access, user software licences, computational usage, and data management and wrangling support, with additional contributions from the HDR UK Data and Connectivity component of the UK Government Chief Scientific Adviser's National Core Studies programme to coordinate national COVID-19 priority research. Consortium partner organisations funded the time of contributing data analysts, biostatisticians, epidemiologists, and clinicians. RS is part-funded by: i) the NIHR Maudsley Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and King's College London; ii) the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust; iii) UKRI - Medical Research Council through the DATAMIND HDR UK Mental Health Data Hub (MRC reference: MR/W014386); iv) the UK Prevention Research Partnership (Violence, Health and Society; MR-VO49879/1), an initiative funded by UK Research and Innovation Councils, the Department of Health and Social Care (England) and the UK devolved administrations, and leading health research charities. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The North East - Newcastle and North Tyneside 2 research ethics committee provided ethical approval for the CVD-COVID-UK/COVID-IMPACT research programme (REC No 20/NE/0161) to access, within secure trusted research environments, unconsented, whole-population, de-identified data from electronic health records collected as part of the routine healthcare of patients. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The protocol, code lists and data curation and analysis code for this study are available on GitHub (https://github.com/BHFDSC/CCU046_01). The data used in this study are available in the NHS England Secure Data Environment (SDE) service for England, but as restrictions apply they are not publicly available (https://digital.nhs.uk/services/secure-data-environment-service). The CVD-COVID-UK/COVID-IMPACT programme, led by the BHF Data Science Centre (https://bhfdatasciencecentre.org/), received approval to access data in the NHS England SDE service for England from the Independent Group Advising on the Release of Data (IGARD) (https://digital.nhs.uk/about-nhs-digital/corporate-information-and-documents/independent-group-advising-on-the-release-of-data) via an application made in the Data Access Request Service (DARS) Online system (ref. DARS-NIC-381078-Y9C5K) (https://digital.nhs.uk/services/data-access-request-service-dars/dars-products-and-services). The CVD-COVID-UK/COVID-IMPACT Approvals & Oversight Board (https://bhfdatasciencecentre.org/areas/cvd-covid-uk-covid-impact/) subsequently granted approval to this project to access the data within the NHS England SDE service for England. The de-identified data used in this study were made available to accredited researchers only. Those wishing to gain access to the data should contact bhfdsc{at}hdruk.ac.uk in the first instance.

AIM:We aimed to determine whether glucose-lowering medication (GLM) prescribing differs by severe mental illness (SMI) status. MATERIALS AND METHODS:We conducted a population-based cohort study using routinely collected linked data, including people in Scotland diagnosed with type 2 diabetes between January 2004 and March 2022. We used Cox proportional hazards models to compare the time from diabetes diagnosis to the first prescription of each of metformin and insulin in people with and without a hospital admission record of SMI (schizophrenia, bipolar disorder or depression prior to diabetes diagnosis). We adjusted for age, sex, area-based socio-economic deprivation, smoking status and baseline HbA1c and estimated glomerular filtration rate. RESULTS:Among 317 761 people with type 2 diabetes, 14 600 (4.6%) had pre-existing SMI. During a median follow-up of 1.6 years, 10 859 (74%) of those with SMI and 219 823 (73%) without SMI were prescribed metformin. After accounting for confounders, people with SMI received their first metformin (HR 1.09, 95% CI: 1.07-1.11) and insulin (HR 1.24, 95% CI: 1.18-1.31) prescriptions at a faster rate than people without SMI. Body mass index (BMI) was missing in 21% of participants, but similar estimates were obtained when we additionally adjusted for BMI in analyses that only included people with BMI data. CONCLUSIONS:Among people with type 2 diabetes each of metformin and insulin is prescribed sooner in people with compared to those without SMI. Further research is needed to understand the reasons for this, and to investigate the implications of these differences in prescribing patterns.

Background People with severe mental illness (SMI) are at increased risk of cardiovascular disease (CVD), and initiatives for CVD risk factor screening in the UK have not reduced disparities.Objectives To describe the annual screening prevalence for CVD risk factors in people with SMI from April 2000 to March 2018, and to identify factors associated with receiving no screening and regular screening.Methods We identified adults with a diagnosis of SMI (schizophrenia, bipolar disorder or ‘other psychosis’) from UK primary care records in Clinical Practice Research Datalink. We calculated the annual prevalence of screening for blood pressure, cholesterol, glucose, body mass index, alcohol consumption and smoking status using multinomial logistic regression to identify factors associated with receiving no screening and complete screening.Results Of 216 136 patients with SMI, 55% received screening for all six CVD risk factors at least once during follow-up and 35% received all six within a 1-month period. Our findings suggest that patient characteristics and financial incentivisation influence screening prevalence of individual CVD risk factors, the likelihood of receiving screening for all six CVD risk factors annually and risk of receiving no screening.Conclusions The low proportion of people with SMI receiving regular comprehensive CVD risk factor screening is concerning. Screening needs to be embedded as part of broad physical health checks to ensure the health needs of people with SMI are being met. If we are to improve cardiovascular health, interventions are needed where risk of receiving no screening or not receiving regular screening is highest.

BACKGROUND:Measurement of multimorbidity, the co-occurrence of two or more conditions in the same individual, is highly variable which limits the consistency and reproducibility of research. METHODS:Using data from 172,563 UK Biobank (UKB) participants and a cross-sectional approach, we examined how choice of data source affected estimated prevalence of 80 individual long-term conditions (LTCs) and multimorbidity. We developed code-list-based algorithms to determine the prevalence of 80 LTCs in (1) primary care records, (2) UKB baseline assessment, (3) hospital/cancer registry records, and (4) all three data sources together. RESULTS:Using records from all three data sources, 146,811 (85.1%) participants have at least one and 109,609 (63.5%) have at least two LTCs at baseline. A median of 4.7% (IQR 1.0-16.6) of participants with a condition are identified by all three data sources. Agreement is highest for endocrine, nutritional and metabolic disorders, with a median of 32.9% (IQR 20.5-34.1) of individuals with a condition identified by all three data sources. Agreement is lowest for diseases of the genitourinary system and mental and behavioural disorders where perfect agreement varies from zero to 4.9% and zero to 12.3% across conditions, respectively. The low agreement between data sources is accompanied by high proportions of individuals with a condition identified only in primary care data (i.e. not in either of the other two sources), with a median of 59.3% (IQR 47.4-75.9) for diseases of the genitourinary system and 66.9% (IQR 42.8-79.2) for mental and behavioural disorders. CONCLUSIONS:Our study highlights the impact of the choice of which data source is used in research on individual LTCs and multimorbidity, and the importance of clearly justifying choices made.

Depression is associated with multiple physical health conditions, and inflammation is a mechanism commonly proposed to explain this association. We aimed to investigate the association between depression and the incidence of physical health conditions thought to have an inflammatory etiological component, including coronary heart disease, peripheral arterial disease, type 2 diabetes, inflammatory bowel disease, inflammatory arthritis and Parkinson’s Disease. We conducted a cohort study using UK Biobank (UKB) data linked to primary care, hospital admission and death data. We ascertained depression at baseline using primary care and hospital records, and self-report at the UKB baseline assessment. We identified incident physical health conditions during follow-up using primary care, hospital admission and death data. We used Cox proportional hazards models to determine hazard ratios of each incident inflammation-related condition in those with versus without depression at baseline, serially adjusting for sociodemographic factors, lifestyle factors and baseline count of morbidities. We included 172,556 UKB participants who had continuous primary care records. Of these, 30,770 (17.8

BACKGROUND:Depression is associated with a range of adverse physical health outcomes. We aimed to quantify the association between depression and the subsequent rate of accrual of long-term physical health conditions in middle and older age. METHODS AND FINDINGS:We included 172,556 participants from the UK Biobank (UKB) cohort study, aged 40-71 years old at baseline assessment (2006-2010), who had linked primary care data available. Using self-report, primary care, hospital admission, cancer registry, and death records, we ascertained 69 long-term physical health conditions at both UKB baseline assessment and during a mean follow-up of 6.9 years. We used quasi-Poisson models to estimate associations between history of depression at baseline and subsequent rate of physical condition accrual. Within our cohort, 30,770 (17.8%) had a history of depression. Compared to those without depression, participants with depression had more physical conditions at baseline (mean 2.9 [SD 2.3] versus 2.1 [SD 1.9]) and accrued additional physical conditions at a faster rate (mean 0.20 versus 0.16 additional conditions/year during follow-up). After adjustment for age and sex, participants with depression accrued physical morbidities at a faster rate than those without depression (RR 1.32, 95% confidence interval [CI] [1.31, 1.34]). After adjustment for all sociodemographic characteristics, the rate of condition accrual remained higher in those with versus without depression (RR 1.30, 95% CI [1.28, 1.32]). This association attenuated but remained statistically significant after additional adjustment for baseline condition count and social/lifestyle factors (RR 1.10, 95% CI [1.09, 1.12]). The main limitation of this study is healthy volunteer selection bias, which may limit generalisability of findings to the wider population. CONCLUSIONS:Middle-aged and older adults with a history of depression have more long-term physical health conditions at baseline and accrue additional physical conditions at a faster rate than those without a history of depression. Our findings highlight the importance of integrated approaches to managing both mental and physical health outcomes.

[This corrects the article DOI: 10.1371/journal.pmed.1004532.].

Background: Patients with severe mental illness (SMI) die 10-20 years earlier than the general population. They have a higher risk of cardiovascular disease (CVD) yet may experience lower cardioprotective medication prescribing. Aim: To understand the challenges experienced by GPs in prescribing cardioprotective medication to patients with SMI. Design & setting: A qualitative study with 15 GPs from 11 practices in two Scottish health boards, including practices servicing highly deprived areas (Deep End). Method: Semi- structured one- to- one interviews with fully qualified GPs with clinical experience of patients with SMI. Interviews were transcribed verbatim and analysed thematically. Results: Participants aimed to routinely prescribe cardioprotective medication to relevant patients with SMI but were hampered by various challenges. These structural and contextual barriers included the following: lack of funding for chronic disease management; insufficient consultation time; workforce shortages; IT infrastructure; and navigating boundaries with mental health services. Patient- related barriers included patients' complex health and social needs, their understandable prioritisation of mental health needs or existing physical conditions, and presentation during crises. Professional barriers comprised GPs' desire to practise holistic medicine rather than treating via cardioprotective prescribing in isolation, and concerns about patients' medication concordance if patients were not prioritising this aspect of their health care at that particular time. In terms of enablers for cardioprotective prescribing, participants emphasised continuity of care as fundamental in engaging this patient group in effective cardiovascular health management. A cross- cutting theme was the current GP workforce crisis leading to 'firefighting' and diminishing capacity for primary prevention. This was particularly acute in Deep End practices, which have a high proportion of patients with complex needs and greater resource challenges. Conclusion: Although participants aspire to prescribe cardioprotective medication to patients with SMI, professional-, system- and patient- level barriers often make this challenging, particularly in deprived areas owing to patient complexity and the inverse care law.

Aims To compare receipt of acute cardiac care in people with versus without severe mental illness (SMI) and investigate the impact of the COVID-19 pandemic on any differences in care. We hypothesised that, compared with those without SMI, patients with an SMI are less likely to receive guideline recommended acute cardiac care and that disparities worsened as a result of the pandemic. Methods We conducted a cohort study using data from the CVD-COVID-UK resource, which links electronic health data from multiple sources. Our cohort included 95,125 adults with a non-ST-elevation MI (NSTEMI) recorded in the Myocardial Infarction National Audit Programme (MINAP) dataset between 1 November 2019 and 31 March 2022. We defined SMI as schizophrenia, schizoaffective disorders or bipolar disorder (BD), ascertained through recorded diagnosis in primary care or hospital admission records. We examined receipt of cardiac care standards for NSTEMI, including: admission to a cardiac ward; angiogram eligibility; receipt of angiogram (in those eligible); angiogram within 72 hours; secondary prevention medication prescribing at discharge, and arrangement of post-discharge cardiac rehabilitation. We used logistic regression to obtain odds ratios (ORs) for the association between SMI and receipt of each care indicator, adjusting for age, sex and time period. We tested for an interaction between SMI and time period in order to determine if any disparities had changed since the start of the COVID-19 pandemic. Results Within our cohort, 620 patients (0.6%) had schizophrenia and 575 (0.6%) had BD. Compared with people without SMI and after adjusting for age, sex and period, patients with an SMI were less likely to receive each of the cardiac care standards. For example, compared with those without SMI, those with SMI were less likely to: be admitted to a cardiac ward (schizophrenia: OR 0.72, 95% CI 0.61–0.85; BD: 0.74, 95% CI 0.63–0.88); be eligible for an angiogram (schizophrenia: 0.37, 95% CI 0.29–0.47; BD: 0.52, 95% CI 0.40–0.68); receive an angiogram (schizophrenia: 0.22, 95% CI 0.18–0.28; BD: 0.51, 95% CI 0.39–0.66); and receive an angiogram within 72 hours (schizophrenia: 0.71, 95% CI 0.56–0.90); BD: 0.80, 95% CI 0.64–1.00). We generally found no evidence that disparities had changed since the start of the COVID-19 pandemic. Conclusion We identified marked SMI disparities in receipt of acute cardiac care among people treated in hospital for a NSTEMI. Further research should seek to identify reasons for, and inform interventions to, address these disparities.

Aims/hypothesis The aim of this study was to compare cardiovascular risk management among people with type 2 diabetes according to severe mental illness (SMI) status. Methods We used linked electronic data to perform a retrospective cohort study of adults diagnosed with type 2 diabetes in Scotland between 2004 and 2020, ascertaining their history of SMI from hospital admission records. We compared total cholesterol, systolic BP and HbA 1c target level achievement 1 year after diabetes diagnosis, and receipt of a statin prescription at diagnosis and 1 year thereafter, by SMI status using logistic regression, adjusting for sociodemographic factors and clinical history. Results We included 291,644 individuals with type 2 diabetes, of whom 1.0% had schizophrenia, 0.5% had bipolar disorder and 3.3% had major depression. People with SMI were less likely to achieve cholesterol targets, although this difference did not reach statistical significance for all disorders. However, people with SMI were more likely to achieve systolic BP targets compared to those without SMI, with effect estimates being largest for schizophrenia (men: adjusted OR 1.72; 95% CI 1.49, 1.98; women: OR 1.64; 95% CI 1.38, 1.96). HbA 1c target achievement differed by SMI disorder and sex. Among people without previous CVD, statin prescribing was similar or better in those with vs those without SMI at diabetes diagnosis and 1 year later. In people with prior CVD, SMI was associated with lower odds of statin prescribing at diabetes diagnosis (schizophrenia: OR 0.54; 95% CI 0.43, 0.68, bipolar disorder: OR 0.75; 95% CI 0.56, 1.01, major depression: OR 0.92; 95% CI 0.83, 1.01), with this difference generally persisting 1 year later. Conclusions/interpretation We found disparities in cholesterol target achievement and statin prescribing by SMI status. This reinforces the importance of clinical review of statin prescribing for secondary prevention of CVD, particularly among people with SMI. Graphical Abstract

Aim Prescribing of antidepressant and antipsychotic drugs in general populations has increased in the UK, but prescribing trends in people with type 2 diabetes (T2D) have not previously been investigated. The aim of this study was to describe time trends in annual prevalence of antidepressant and antipsychotic drug prescribing in adult patients with T2D. Methods Repeated annual cross-sectional analyses of a population-based diabetes registry, derived from primary and secondary care data in Scotland, from 2004 to 2021. For each cross-sectional calendar year time period, we calculated the prevalence of antidepressant and antipsychotic drug prescribing, overall and by sociodemographic characteristics and drug subtype. Results The number of patients with a T2D diagnosis in Scotland increased from 161,915 in 2004 to 309,288 in 2021. Prevalence of antidepressant and antipsychotic prescribing in patients with T2D increased markedly between 2004 and 2021 (from 20.0 per 100 person-years to 33.3 per 100 person-years and from 2.8 per 100 person-years to 4.7 per 100 person-years, respectively). We observed this pattern for all drug subtypes except for first-generation antipsychotics, prescribing of which remained largely stable. The degree of increase, as well as overall prevalence of prescribing, differed by age, sex, socioeconomic status, and subtype of drug class. Conclusion There has been a marked increase in the prevalence of antidepressant and antipsychotic prescribing in patients with T2D in Scotland. Further research should identify the reasons for this increase, including indication for use and the extent to which this reflects increases in incident prescribing rather than increased duration.