Showing papers by "Mark Ashworth published in 2022"

Inequalities in developing multimorbidity over time: A population-based cohort study from an urban, multi-ethnic borough in the United Kingdom

Abstract: Background Social and material deprivation accelerate the development of multimorbidity, yet the mechanisms which drive multimorbidity pathways and trajectories remain unclear. We aimed to examine the association between health inequality, risk factors and accumulation or resolution of LTCs, taking disease sequences into consideration. Methods We conducted a retrospective cohort of adults aged 18 years and over, registered between April 2005 and May 2020 in general practices in one inner London borough (n = 826,936). Thirty-two long term conditions (LTCs) were selected using a consensus process, based on a definition adapted to the demographic characteristics of the local population. sThe development and resolution of these LTCs were examined according to sociodemographic and clinical risk factors (hypertension; moderate obesity (BMI 30·0–39·9 kg/m2), high cholesterol (total cholesterol > 5 mmol/L), smoking, high alcohol consumption (>14 units per week), and psychoactive substance use), through the application of multistate Markov chain models. Findings Participants were followed up for a median of 4.2 years (IQR = 1·8 - 8·4); 631,760 (76%) entered the study with no LTCs, 121,424 (15%) with 1 LTC, 41,720 (5%) with 2 LTCs, and 31,966 (4%) with three or more LTCs. At the end of follow-up, 194,777 (24%) gained one or more LTCs, while 45,017 (5%) had resolved LTCs and 27,021 (3%) died. In multistate models, deprivation (hazard ratio [HR] between 1·30 to 1·64), female sex (HR 1·13 to 1·20), and Black ethnicity (HR 1·20 to 1·30; vs White) were independently associated with increased risk of transition from one to two LTCs, and shorter time spent in a healthy state. Substance use was the strongest risk factor for multimorbidity with an 85% probability of gaining LTCs over the next year. First order Markov chains identified consistent disease sequences including: chronic pain or osteoarthritis followed by anxiety and depression; alcohol and substance dependency followed by HIV, viral hepatitis, and liver disease; and morbid obesity followed by diabetes, hypertension, and chronic pain. Interpretation We examined the relations among 32 LTCs, taking the order of disease occurrence into consideration. Distinctive patterns for the development and accumulation of multimorbidity have emerged, with increased risk of transitioning from no conditions to multimorbidity and mortality related to ethnicity, deprivation and gender. Musculoskeletal disorders, morbid obesity and substance abuse represent common entry points to multimorbidity trajectories.

Idiographic patient reported outcome measures (I-PROMs) for routine outcome monitoring in psychological therapies: Position paper.

Abstract: Idiographic patient-reported outcome measures (I-PROMs) are a growing set of individualized tools for use in routine outcome monitoring (ROM) in psychological therapies. This paper presents a position statement on their conceptualization, use, and analysis, based on contemporary evidence and clinical practice. Four problem-based, and seven goal-based, I-PROMs, with some evidence of psychometric evaluation and use in psychotherapy, were identified. I-PROMs may be particularly valuable to the evaluation of psychological therapies because of their clinical utility and their alignment with a patient-centered approach. However, there are several challenges for I-PROMs: how to generate items in a robust manner, their measurement model, methods for establishing their reliability and validity, and the meaning of an aggregated I-PROM score. Based on the current state of the literature, we recommend that I-PROMs are used to complement nomothetic measures. Research recommendations are also made regarding the most appropriate methods for analyzing I-PROM data.

Prospective associations between depression and risk of hospitalisation for infection: Findings from the UK Biobank

Abstract: Associations between depression and non-communicable disease have been well-described. However, the evidence for its role in the development of infectious disease is less understood. We aimed to examine prospective associations between depression and risk of hospitalisation for infection in middle-aged adults from the UK Biobank (linked with Hospital Episode Statistics) and assessed the role of several depression-related factors.We assessed prospective associations between depression status at the baseline assessment (2006-2010) and hospitalisations for infection up to the end of March 2016 in 460,418 middle-aged adults enrolled in the UK Biobank (mean age = 56.23 ± 8.11 years, 53.5% female). Cox regression was used to assess associations between depression and subsequent hospitalisations for any infections, as well as infection subtypes, viral infections, and bacterial infections. Amongst those with depression, we also examined the role of depression duration, the age of onset, and the use of antidepressants in hospitalisation risk.Depression at baseline was prospectively associated with an increased risk of hospitalisation for infection (adjusted hazard ration (aHR) = 1.20, 95% confidence interval (CI) = 1.16 to 1.25). This association was found for all infection subtypes apart from infections of the central nervous system (p = 0.911) and the skin (p = 0.313). Receipt of a depression diagnosis in late adulthood and use of antidepressants (but only in those with none/mild depressive symptoms at baseline) increased the risk of hospitalisation for infection amongst those with depression.These findings suggest that depression might be a risk factor which could be used to identify those at risk of hospitalisation for infection. Future research is required to understand the underlying factors that might result in this increased risk, so that targeted interventions can be developed.AD and AR are funded by Guy's Charity grant number EIC180702 (MLTC Challenge Fund); AD and JAT are co-funded by MRC and NIHR through grant number MR/S028188/1. IB is supported by the NIHR Maudsley BRC and by the NIHR Collaboration for Leadership in Applied Health Research and Care South Londnoo at King's College Hospital NHS Foundation Trust, King's College London. The views expressed are those of the author[s] and not necessarily those of the ESRC, NIHR, the Department of Health and Social Care or King's College London.

Associations between air pollution and multimorbidity in the UK Biobank: A cross-sectional study

Abstract: Background Long-term exposure to air pollution concentrations is known to be adversely associated with a broad range of single non-communicable diseases, but its role in multimorbidity has not been investigated in the UK. We aimed to assess associations between long-term air pollution exposure and multimorbidity status, severity, and patterns using the UK Biobank cohort. Methods Multimorbidity status was calculated based on 41 physical and mental conditions. We assessed cross-sectional associations between annual modeled particulate matter (PM)2.5, PMcoarse, PM10, and nitrogen dioxide (NO2) concentrations (μg/m3–modeled to residential address) and multimorbidity status at the baseline assessment (2006–2010) in 364,144 people (mean age: 52.2 ± 8.1 years, 52.6% female). Air pollutants were categorized into quartiles to assess dose-response associations. Among those with multimorbidity (≥2 conditions; n = 156,395) we assessed associations between air pollutant exposure levels and multimorbidity severity and multimorbidity patterns, which were identified using exploratory factor analysis. Associations were explored using generalized linear models adjusted for sociodemographic, behavioral, and environmental indicators. Results Higher exposures to PM2.5, and NO2 were associated with multimorbidity status in a dose-dependent manner. These associations were strongest when we compared the highest air pollution quartile (quartile 4: Q4) with the lowest quartile (Q1) [PM2.5: adjusted odds ratio (adjOR) = 1.21 (95% CI = 1.18, 1.24); NO2: adjOR = 1.19 (95 % CI = 1.16, 1.23)]. We also observed dose-response associations between air pollutant exposures and multimorbidity severity scores. We identified 11 multimorbidity patterns. Air pollution was associated with several multimorbidity patterns with strongest associations (Q4 vs. Q1) observed for neurological (stroke, epilepsy, alcohol/substance dependency) [PM2.5: adjOR = 1.31 (95% CI = 1.14, 1.51); NO2: adjOR = 1.33 (95% CI = 1.11, 1.60)] and respiratory patterns (COPD, asthma) [PM2.5: adjOR = 1.24 (95% CI = 1.16, 1.33); NO2: adjOR = 1.26 (95% CI = 1.15, 1.38)]. Conclusions This cross-sectional study provides evidence that exposure to air pollution might be associated with having multimorbid, multi-organ conditions. Longitudinal studies are needed to further explore these associations.

Measuring continuity of care in general practice: a comparison of two methods using routinely collected data

Abstract: Background Despite well-documented clinical benefits of longitudinal doctor–patient continuity in primary care, continuity rates have declined. Assessment by practices or health commissioners is rarely undertaken. Aim Using the Usual Provider of Care (UPC) score this study set out to measure continuity across 126 practices in the mobile, multi-ethnic population of East London, comparing these scores with the General Practice Patient Survey (GPPS) responses to questions on GP continuity. Design and setting A retrospective, cross-sectional study in all 126 practices in three East London boroughs. Method The study population included patients who consulted three or more times between January 2017 and December 2018. Anonymised demographic and consultation data from the electronic health record were linked to results from Question 10 (‘seeing the doctor you prefer’) of the 2019 GPPS. Results The mean UPC score for all 126 practices was 0.52 (range 0.32 to 0.93). There was a strong correlation between practice UPC scores measured in the 2 years to December 2018 and responses to the 2019 GPPS Question 10, Pearson’s r correlation coefficient, 0.62. Smaller practices had higher scores. Multilevel analysis showed higher continuity for patients ≥65 years compared with children and younger adults (β coefficient 0.082, 95% confidence interval = 0.080 to 0.084) and for females compared with males. Conclusion It is possible to measure continuity across all practices in a local health economy. Regular review of practice continuity rates can be used to support efforts to increase continuity within practice teams. In turn this is likely to have a positive effect on clinical outcomes and on satisfaction for both patients and doctors.

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

Abstract: 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.

The relationship of socio-demographic factors and patient attitudes to connected health technologies: A survey of stroke survivors

Abstract: More evidence is needed on technology implementation for remote monitoring and self-management across the various settings relevant to chronic conditions. This paper describes the findings of a survey designed to explore the relevance of socio-demographic factors to attitudes towards connected health technologies in a community of patients. Stroke survivors living in the UK were invited to answer questions about themselves and about their attitudes to a prototype remote monitoring and self-management app developed around their preferences. Eighty (80) responses were received and analysed, with limitations and results presented in full. Socio-demographic factors were not found to be associated with variations in participants’ willingness to use the system and attitudes to data sharing. Individuals’ levels of interest in relevant technology was suggested as a more important determinant of attitudes. These observations run against the grain of most relevant literature to date, and tend to underline the importance of prioritising patient-centred participatory research in efforts to advance connected health technologies.

Ethnic inequalities in mental and physical multimorbidity in women of reproductive age: a data linkage cohort study

Abstract: Objectives Explore inequalities in risk factors, mental and physical health morbidity in non-pregnant women of reproductive age in contact with mental health services and how these vary per ethnicity. Design Retrospective cohort study. Setting Data from Lambeth DataNet, anonymised primary care records of this ethnically diverse London borough, linked to anonymised electronic mental health records (‘CRIS secondary care database’). Participants Women aged 15–40 years with an episode of secondary mental health care between January 2008 and December 2018 and no antenatal or postnatal Read codes (n=3817) and a 4:1 age-matched comparison cohort (n=14 532). Main outcome measures Preconception risk factors including low/high body mass index, smoking, alcohol, substance misuse, micronutrient deficiencies and physical diagnoses. Results Women in contact with mental health services (whether with or without severe mental illness (SMI)) had a higher prevalence of all risk factors and physical health diagnoses studied. Women from minority ethnic groups were less likely to be diagnosed with depression in primary care compared with white British women (adjusted OR 0.66 (0.55–0.79) p<0.001), and black women were more likely to have a SMI (adjusted OR 2.79 (2.13–3.64) p<0.001). Black and Asian women were less likely to smoke or misuse substances and more likely to be vitamin D deficient. Black women were significantly more likely to be overweight (adjusted OR 3.47 (3.00–4.01) p<0.001), be diagnosed with hypertension (adjusted OR 3.95 (2.67–5.85) p<0.00) and have two or more physical health conditions (adj OR 1.94 (1.41–2.68) p<0.001) than white British women. Conclusions Our results challenge the perspective that regular monitoring of physical health in primary care should be exclusively encouraged in people with a l diagnosis. The striking differences in multimorbidity for women in contact with mental health services and those of ethnic minority groups emphasise a need of integrative models of care.

Determinants of long‐term opioid prescribing in an urban population: A cross‐sectional study

Abstract: Opioid prescribing has more than doubled in the UK between 1998 and 2016. Potential adverse health implications include dependency, falls and increased health expenditure.

Managing stroke risk in patients with atrial fibrillation: a cross-sectional analysis of socio-demographic inequalities in a London borough.

Abstract: BACKGROUND Stroke prevention is essential for patients with atrial fibrillation (AF), but some receive sub-optimal management. We reviewed those with a recorded AF diagnosis assessed with CHA2DS2-VASc stroke risk score (SRS) and socio-demographic determinants of anticoagulation prescribing. The objective was to compare with national guidance recommendations, which recommend anticoagulant therapy for SRS ≥ 2, to determine if there were inequalities in management. METHODS A cross-sectional design was used to analyze records from all (n = 41) general practices in one London borough. Patients were excluded if they were <18 years, had AF resolved or diagnosed < 3 months. Logistic regression identified socio-demographic factors associated with high SRS and anticoagulant prescribing. RESULTS Of 2913 patients, 2885 (99.0%) had an SRS, and 2411 (83.6%) a score ≥ 2 and 82.9% (1999 of 2411) were prescribed anticoagulation. Women (compared with men), Black and Mixed/Multiple ethnic groups (compared with White), and those living in most deprived areas (compared with least) were more likely to have a score ≥ 2. Patients with a high SRS from Black and Mixed/Multiple ethnic groups and aged 18-74 years were less likely to be prescribed anticoagulation. CONCLUSION We found evidence of age and ethnic inequity in anticoagulation prescribing for stroke prevention in patients with AF.

Effects of the COVID-19 pandemic on secondary care for cardiovascular disease in the UK: an electronic health record analysis across three countries

Abstract: Abstract Background Although morbidity and mortality from COVID-19 have been widely reported, the indirect effects of the pandemic beyond 2020 on other major diseases and health service activity have not been well described. Methods and results Analyses used national administrative electronic hospital records in England, Scotland, and Wales for 2016–21. Admissions and procedures during the pandemic (2020–21) related to six major cardiovascular conditions [acute coronary syndrome (ACS), heart failure (HF), stroke/transient ischaemic attack (TIA), peripheral arterial disease (PAD), aortic aneurysm (AA), and venous thromboembolism(VTE)] were compared with the annual average in the pre-pandemic period (2016–19). Differences were assessed by time period and urgency of care. In 2020, there were 31 064 (−6%) fewer hospital admissions [14 506 (−4%) fewer emergencies, 16 560 (−23%) fewer elective admissions] compared with 2016–19 for the six major cardiovascular diseases (CVDs) combined. The proportional reduction in admissions was similar in all three countries. Overall, hospital admissions returned to pre-pandemic levels in 2021. Elective admissions remained substantially below expected levels for almost all conditions in all three countries [−10 996 (−15%) fewer admissions]. However, these reductions were offset by higher than expected total emergency admissions [+25 878 (+6%) higher admissions], notably for HF and stroke in England, and for VTE in all three countries. Analyses for procedures showed similar temporal variations to admissions. Conclusion The present study highlights increasing emergency cardiovascular admissions during the pandemic, in the context of a substantial and sustained reduction in elective admissions and procedures. This is likely to increase further the demands on cardiovascular services over the coming years.

Associations between air pollution and multimorbidity: results from the population-based UK Biobank study

Abstract: Background and aims Long and short-term air pollution exposures are well established to be associated with single conditions, but its role in multimorbidity (MM) has not been investigated. We aimed to assess associations between air pollution exposure and MM status and severity, as well MM patterns using the UK Biobank cohort. Methods MM status was calculated based on 41 physical and mental conditions We assessed cross-sectional associations between particulate matter (PM)2.5, PM10, nitrogen dioxide (NO2), and total nitrogen oxides (NOx) in &#x3bc;g/m3 and MM status at the baseline assessment (2006-2010) in 364,144 people (mean age: 52.2&#xb1;8.1 years, 52.6% female) who had complete data for all study variables. Air pollutants were categorised into quartiles to assess dose response associations. Among those with MM (&#x2265;2 conditions; n=156,395) we assessed associations between air pollutants and MM severity. We used factor analysis to identify MM patterns. Potential associations were explored using generalised linear models adjusted for socioeconomic and neighbourhood-level indicators. Results Higher exposures to PM2.5, NO2, and NOx was associated with MM status in a clear dose response manner (PM2.5 quartile 1 vs quartile 4: adjusted odds ratio (aOR)=1.21; 95% CI=1.18, 1.24; NO2 quartile 1 vs quartile 4: aOR=1.19; 95 % CI=1.16, 1.23). Among people with MM, higher exposure to air pollutants was linked with increased MM severity. We identified 11 MM patterns. Air pollution was associated with multiple MM patterns with strongest associations observed for neurological (stroke, epilepsy) (PM2.5: aOR=1.31; 95% CI=1.14, 1.51; NO2: aOR=1.33; 95% CI=1.11, 1.60) and respiratory patterns (COPD, asthma) (PM2.5: aOR=1.24; 95% CI=1.16, 1.33; NO2: aOR=1.26; 95% CI=1.15, 1.38). Conclusions This cross-sectional study suggests that exposure to air pollution might be associated with the accumulation of long-term, multi-organ conditions. Prospective studies are needed to investigate associations between air pollution and multimorbidity trajectories. Keywords: Air pollution; multimorbidity; Factor analysis

Ethnic inequities in the patterns of Personalised Care Adjustments for Informed Dissent and Patient Unsuitable: A retrospective study using Clinical Practice Research Datalink

Abstract: Objectives To examine patterns of PCA reporting for Informed Dissent and Patient Unsuitable, how they vary by ethnic group, and whether ethnic inequities can be explained by socio-demographic factors or comorbidities. Design A retrospective study using routinely collected electronic health records. Setting Individual patient data from Clinical Practice Research Datalink collected from UK general practice. Participants Patients with at least one of the 12 Quality and Outcomes Framework (QOF) conditions which had PCA coding options from a random sample of 690,00 patients aged 18+ years on the 1st of Jan 2016. Main outcomes measures The associations between ethnicity and two PCA reasons (Informed Dissent and Patient Unsuitable) were examined using logistic regressions after adjustment for age, sex, multiple QOF conditions and area-level deprivation. Results The association between ethnicity and the two PCA reasons were in opposite directions. After accounting for age, gender, multiple QOF conditions and area-level deprivation, people of Bangladeshi [OR: 0.69, 95% CI: 0.55 to 0.87], Black African [OR: 0.70, 95% CI: 0.61 to 0.81] , Black Caribbean, OR: 0.67, 95% CI: 0.58 to 0.76], Indian [OR: 0.74, 95% CI: 0.66 to 0.83], mixed [OR: 0.86, 95% CI: 0.74 to 0.99], other Asian [OR: 0.74 95% CI: 0.64 to 0.86] and other ethnicity [OR: 0.66, 95% CI: 0.55 to 0.80] were less likely to have a PCA record for Informed Dissent than people of white ethnicity. Only people of Indian ethnicity were significantly less likely than people of white ethnicity to have a PCA record for Patient Unsuitable in fully adjusted models [OR: 0.80, 95% CI: 0.67 to 0.94]. We found ethnic inequities in PCA reporting for Patient Unsuitable among people of Black Caribbean, Black other, Pakistani, and other ethnicity, but these attenuated after adjusting for multiple QOF conditions and/or area level deprivation. Conclusion Study findings counter the narratives that suggest that people from minoritised ethnic groups often refuse medical intervention. They illuminate the complex relationship between Informed Dissent and (dis)empowerment which requires further scrutiny. They also show ethnic inequalities in PCA reporting for Patient Unsuitable that are linked to clinical and social complexity and should be tackled to improve health outcomes for all.

Prior antibiotics and risk of subsequent Herpes zoster: A population-based case control study

Abstract: Background The effect of antibiotics on the human microbiome is now well established, but their indirect effect on the related immune response is less clear. The possible association of Herpes zoster, which involves a reactivation of a previous varicella zoster virus infection, with prior antibiotic exposure might indicate a potential link with the immune response. Methods A case-control study was carried out using a clinical database, the UK’s Clinical Practice Research Datalink. A total of 163,754 patients with varicella zoster virus infection and 331,559 age/sex matched controls were identified and their antibiotic exposure over the previous 10 years, and longer when data permitted, was identified. Conditional logistic regression was used to identify the association between antibiotic exposure and subsequent infection in terms of volume and timing. Results The study found an association of antibiotic prescription and subsequent risk of varicella zoster virus infection (adjusted odds ratio of 1.50; 95%CIs: 1.42–1.58). The strongest association was with a first antibiotic over 10 years ago (aOR: 1.92; 95%CIs: 1.88–1.96) which was particularly pronounced in the younger age group of 18 to 50 (aOR 2.77; 95%CIs: 1.95–3.92). Conclusions By finding an association between prior antibiotics and Herpes zoster this study has shown that antibiotics may be involved in the reactivation of the varicella zoster virus. That effect, moreover, may be relatively long term. This indirect effect of antibiotics on viruses, possibly mediated through their effect on the microbiome and immune system, merits further study.

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

Abstract: 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.

Multimorbidity clusters among people with serious mental illness: a representative primary and secondary data linkage cohort study.

Abstract: BACKGROUND People with serious mental illness (SMI) experience higher mortality partially attributable to higher long-term condition (LTC) prevalence. However, little is known about multiple LTCs (MLTCs) clustering in this population. METHODS People from South London with SMI and two or more existing LTCs aged 18+ at diagnosis were included using linked primary and mental healthcare records, 2012-2020. Latent class analysis (LCA) determined MLTC classes and multinominal logistic regression examined associations between demographic/clinical characteristics and latent class membership. RESULTS The sample included 1924 patients (mean (s.d.) age 48.2 (17.3) years). Five latent classes were identified: 'substance related' (24.9%), 'atopic' (24.2%), 'pure affective' (30.4%), 'cardiovascular' (14.1%), and 'complex multimorbidity' (6.4%). Patients had on average 7-9 LTCs in each cluster. Males were at increased odds of MLTCs in all four clusters, compared to the 'pure affective'. Compared to the largest cluster ('pure affective'), the 'substance related' and the 'atopic' clusters were younger [odds ratios (OR) per year increase 0.99 (95% CI 0.98-1.00) and 0.96 (0.95-0.97) respectively], and the 'cardiovascular' and 'complex multimorbidity' clusters were older (ORs 1.09 (1.07-1.10) and 1.16 (1.14-1.18) respectively). The 'substance related' cluster was more likely to be White, the 'cardiovascular' cluster more likely to be Black (compared to White; OR 1.75, 95% CI 1.10-2.79), and both more likely to have schizophrenia, compared to other clusters. CONCLUSION The current study identified five latent class MLTC clusters among patients with SMI. An integrated care model for treating MLTCs in this population is recommended to improve multimorbidity care.

The Interface Between Colon Cancer and Mental Health Morbidities.

Abstract: In this landmark study by Benitez Majano et al,1 the authors use linked primary care data to study pathways to diagnosis and diagnostic delay and the association with mental health conditions. Unsurprisingly, patients with a coexistent mental health diagnosis experienced diagnostic delay. However, the delay was not merely a statistical delay of little clinical significance. The authors concluded that diagnostic delays were “prognostically consequential,” in other words, delays that were likely to be associated with reduced 5-year and 10-year cancer survival rates. What is more surprising is that a study of this type has not been previously conducted, even though in the everyday experience of primary care physicians, mental health symptoms so commonly coexist with symptoms that may be an early warning signal associated with cancer or explicit red-flag symptoms. Disentangling this web of symptoms and determining when to consider diagnostic testing and referral is a continually challenging task of primary care. This research and this study are long overdue. These findings will confirm and quantify long-held concerns of primary care physicians that mental health conditions may detract and distract from a diagnosis of bowel cancer and likely other cancers, as well. Although not a primary outcome of this study, the findings concerning diagnostic delay after presentation with red-flag symptoms should be of great concern to primary care physicians. In this study, it took more than 4 months (133 days) between a primary care record of a new–onset red-flag symptom and colon cancer diagnosis in patients without a mental health diagnosis and a disturbing delay of almost 1 year (326 days) delay in those with a mental health diagnosis. That these delays need to be drastically reduced is self-evident. How can delay be reduced, however? For all the talk about integrated care and educational programs, these delays continue and are clearly the reality for a large number of patients. Urgent referrals for suspected cancer in England have more than doubled in the past 10 years, to more than 2 million referrals per year,2 including more 400 000 referrals for suspected bowel cancer per year. However, while increasing symptomatic referral has been associated with improved outcomes, including stage at diagnosis for many cancers, this seems less the case for bowel cancer.3 Ultimately, to make a large difference to colon cancer survival rates, diagnosis may need to be made before symptom onset and certainly before recurrent red–flag symptom presentations. The rollout and uptake of fecal immunochemical test (FIT) for the triage of symptomatic presentations and in screening has the potential to improve early detection rates and prioritize use of stretched and expensive endoscopy capacity. With FIT requiring 1 small sample, ease of use and acceptability may be improved. The UK recently introduced a national bowel cancer–screening program involving use of FIT. Bowel cancer mortality reductions of 41% were reported in an observational study that screened participants using FIT, which compares with a 16% reduction in patients using previous fecal occult blood screening.4 In the UK, 2-year bowel cancer screening using FIT currently begins at age 60 years, with the aim to reduce the eligibility age to include all adults aged 50 years and older.5 A further reason for putting more faith in bowel cancer–screening programs is that 50% of colorectal cancers have no red-flag symptoms.6 The very real difficulty faced by primary care physicians is that all bowel symptoms have poor sensitivity and specificity for bowel cancer.7 It is now clear that sensitivity and specificity for bowel cancer may be even worse in individuals with concurrent mental health symptoms. Although FIT screening promises gains in early diagnosis and reduced mortality, primary care physicians will always + Related article

Cost outcomes of potentially inappropriate prescribing in middle‐aged adults: A Delphi consensus and cross‐sectional study

Abstract: Potentially inappropriate prescribing (PIP) is common in older adults and is associated with increased medication costs and costs of associated adverse drug events. PIP also affects almost 1/5 of middle‐aged adults (45–64 y), as defined by the PRescribing Optimally in Middle‐aged People's Treatments (PROMPT) criteria. However, there has been little research on PIP medication costs within this age group.

Ethnic disparities in multi-morbidity in women of reproductive age in the UK: a data linkage study

Abstract: Introduction Few studies have explored ethnic inequalities in physical and mental health in women at preconception. Objectives Explore inequalities in multimorbidity in women of reproductive age. Methods Data from Lambeth DataNet, anonymized primary care records of this ethnically diverse London borough, linked to anonymized electronic mental health records (“CRIS secondary care database”) were extracted on preconception risk factors including BMI, smoking, alcohol, substance misuse, micronutrient deficiencies and physical health diagnoses for women aged 15-40 with an episode of secondary mental health care (January 2008-December 2018) and no pregnancy codes (n=3,633) and a 4:1 age-matched comparison cohort (n=14,532) . Results Women in contact with mental health services (whether with or without SMI diagnoses) had a higher prevalence of all risk factors and physical health diagnoses studied after adjustment for deprivation and ethnicity. Women from minority ethnic groups [79.5% of total sample] were less likely to be diagnosed with depression in primary care compared to White British women [adj OR 0.66 (0.55- 0.79) p<0.001] and Black women were more likely to have a severe mental illness [adj OR 3.41(2.63-4.43), p<0.001]. Black and Asian women were less likely to smoke or misuse substances and more likely to be vitaminD deficient. Black women were also significantly more likely to be overweight [adj OR 4.56(3.96-5.24 p <0.001] and have two or more physical health conditions [adj OR 2.98(2.19-4.07) p<0.001] than White British women after adjustment for deprivation and SMI diagnoses. Conclusions Our results highlight a need for culturally centered integrative models of care across primary and secondary mental health services. Disclosure Closing the Gap is funded by UK Research and Innovation and their support is gratefully acknowledged (Grant reference: ES/S004459/1). Any views expressed here are those of the project investigators and do not necessarily represent the views of the Closing