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Joury E, Beveridge E, Littlejohns J, Burns A, Copsey G, Philips J, Begum S, Shiers D, Chew‐Graham C, Klass C, Chin J. Physical Health Checks and Follow-Up Care in Deprived and Ethnically Diverse People With Severe Mental Illness: Co-Designed Recommendations for Better Care. Health Expect 2024; 27:e70005. [PMID: 39193859 PMCID: PMC11350427 DOI: 10.1111/hex.70005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Revised: 07/25/2024] [Accepted: 08/10/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND There is wide variation in premature mortality rates in adults with severe mental illness (SMI) across London, with Tower Hamlets (a highly deprived and ethnically diverse area) scoring the highest. OBJECTIVE To identify examples of best practice and co-design recommendations for improving physical health checks and follow-up care amongst people with SMI in Tower Hamlets. METHODS Data were collected through online questionnaires (using SMI physical health best practice checklists), one-on-one interviews (n = 7) and focus groups (n = 3) with general practices, secondary mental health services, commissioners and leads of community services and public health programmes, experts by experience and community, voluntary and social enterprise organisations in Tower Hamlets. Data were analysed using deductive and inductive thematic analysis. RESULTS Twenty-two participants representing 15 general practices (out of 32), secondary mental health services, commissioners and public health leads completed the online questionnaires. Twenty-one participants took part in interviews and focus groups. Examples of best practice included cleaning and validating the SMI register regularly by general practices, knowing the number of patients who had been offered and/or received physical health checks, having clear pathways to community and specialist care services, using various communication methods and having a key performance indicator (KPI) for tailored smoking cessation services for people with SMI. Recommendations included adopting evidence-informed frameworks for risk stratification and utilising the wider primary care workforce with specific training to follow up on results, offer interventions and support navigating pathways and taking up follow-up care. Incentivising schemes were needed to deliver additional physical health check components such as oral health, cancer screening, Covid-19 vaccination and sexual health checks. Including KPIs in other community services' specifications with reference to SMI people was warranted. Further engagement with experts by experience and staff training were needed. CONCLUSION The present initiative identified best practice examples and co-designed recommendations for improving physical health checks and follow-up care in deprived and ethnically diverse people with SMI. PATIENT OR PUBLIC CONTRIBUTION This initiative was supported by three experts with experience, and two community organisations, who were involved in data curation and interpretation, development of recommendations and/or dissemination activities including writing this manuscript.
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Affiliation(s)
- Easter Joury
- Institute of DentistryQueen Mary University of LondonLondonUK
- Royal London Dental HospitalBarts Health NHS TrustLondonUnited Kingdom
| | | | | | - Angela Burns
- Healthy Young Adults, London Borough of Tower HamletsLondonUK
| | | | - Justin Philips
- NHS North East London, North East London Health and Care PartnershipLondonUK
| | | | - David Shiers
- Greater Manchester Mental Health NHS Foundation TrustUniversity of ManchesterManchesterUK
- Division of Psychology and Mental HealthUniversity of ManchesterManchesterUK
- Primary Care and Health Sciences, School of MedicineUniversity of KeeleNewcastleUK
| | - Carolyn Chew‐Graham
- School of Medicine, Faculty of Medicine and Health SciencesKeele UniversityNewcastleUK
| | | | - Jackie Chin
- Department of Health and Social CareOffice for Health Improvement and Disparities, London RegionLondonUK
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Ng VWS, Leung MTY, Lau WCY, Chan EW, Hayes JF, Osborn DPJ, Cheung CL, Wong ICK, Man KKC. Lithium and the risk of fractures in patients with bipolar disorder: A population-based cohort study. Psychiatry Res 2024; 339:116075. [PMID: 39002502 DOI: 10.1016/j.psychres.2024.116075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/24/2024] [Accepted: 06/29/2024] [Indexed: 07/15/2024]
Abstract
Lithium is considered to be the most effective mood stabilizer for bipolar disorder. Evolving evidence suggested lithium can also regulate bone metabolism which may reduce the risk of fractures. While there are concerns about fractures for antipsychotics and mood stabilizing antiepileptics, very little is known about the overall risk of fractures associated with specific treatments. This study aimed to compare the risk of fractures in patients with bipolar disorder prescribed lithium, antipsychotics or mood stabilizing antiepileptics (valproate, lamotrigine, carbamazepine). Among 40,697 patients with bipolar disorder from 1993 to 2019 identified from a primary care electronic health record database in the UK, 13,385 were new users of mood stabilizing agents (lithium:2339; non-lithium: 11,046). Lithium was associated with a lower risk of fractures compared with non-lithium treatments (HR 0.66, 95 % CI 0.44-0.98). The results were similar when comparing lithium with prolactin raising and sparing antipsychotics, and individual antiepileptics. Lithium use may lower fracture risk, a benefit that is particularly relevant for patients with serious mental illness who are more prone to falls due to their behaviors. Our findings could help inform better treatment decisions for bipolar disorder, and lithium's potential to prevent fractures should be considered for patients at high risk of fractures.
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Affiliation(s)
- Vanessa W S Ng
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China
| | - Miriam T Y Leung
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China; Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia
| | - Wallis C Y Lau
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China; Research Department of Practice and Policy, School of Pharmacy, University College London, London, United Kingdom; Laboratory of Data Discovery for Health (D(2)4H), Hong Kong Science Park, Hong Kong, China; Centre for Medicines Optimization Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Esther W Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China; Laboratory of Data Discovery for Health (D(2)4H), Hong Kong Science Park, Hong Kong, China; Department of Pharmacy, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China; The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, China
| | - Joseph F Hayes
- Division of Psychiatry, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - David P J Osborn
- Division of Psychiatry, Faculty of Brain Sciences, University College London, London, United Kingdom; Camden and Islington NHS Foundation Trust. London NW10PE, United Kingdom
| | - Ching-Lung Cheung
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China
| | - Ian C K Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China; Research Department of Practice and Policy, School of Pharmacy, University College London, London, United Kingdom; Laboratory of Data Discovery for Health (D(2)4H), Hong Kong Science Park, Hong Kong, China; Aston Pharmacy School, Aston University, Birmingham B4 7ET, United Kingdom; School of Pharmacy, Medical Sciences Division, Macau University of Science and Technology, Macau.
| | - Kenneth K C Man
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China; Research Department of Practice and Policy, School of Pharmacy, University College London, London, United Kingdom; Laboratory of Data Discovery for Health (D(2)4H), Hong Kong Science Park, Hong Kong, China; Centre for Medicines Optimization Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom.
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3
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Kar N, Barreto S. Influence of Lifestyle Factors on Metabolic Syndrome in Psychiatric Patients Attending a Community Mental Health Setting: A Cross-sectional Study. Indian J Psychol Med 2024; 46:313-322. [PMID: 39056040 PMCID: PMC11268271 DOI: 10.1177/02537176231219770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/28/2024] Open
Abstract
Background Metabolic syndrome (MetS) is a concern in psychiatric patients. We aimed to study the influence of the modifiable lifestyle factors on MetS in adult psychiatric patients along with associated clinical factors and quality of life. Methods Factors such as diet (Healthy Eating Index), exercise, substance use, cardiovascular risk (QRISK), illness severity (Clinical Global Impression), medications, adverse events (Systematic Monitoring of Adverse Events Related to Treatments), and quality of life (Recovering Quality of Life Scale) were assessed along with clinical components for MetS in 323 psychiatric patients receiving routine care and monitoring in a Community Mental Health Team. Results MetS was present in 50.5% (95% CI: 45.0-55.9). It was significantly associated with higher age, duration of mental illness, body mass index (BMI), QTc, QRISK, and antipsychotic drugs. In logistic regression, age, QTc, QRISK, and BMI remained significantly linked to MetS. Patients with or without MetS were comparable in their lifestyle factors such as diet, exercise, and substance use, along with the family history of metabolic disorders, age at onset of mental illness, duration of antipsychotic medication, side effects, psychiatric diagnoses, and quality of life. However, many patients with or without MetS had poorer diet and physical inactivity, indicating scope for interventions. Conclusions Around half of the psychiatric patients had MetS, and modifiable lifestyle factors did not differentiate individuals with or without MetS. The need for further research on the prevention and management of MetS in psychiatric patients is highlighted.
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Affiliation(s)
- Nilamadhab Kar
- University of Wolverhampton, United Kingdom
- Dept. of Psychiatry, Black Country Healthcare NHS Foundation Trust, Wolverhampton, United Kingdom
| | - Socorro Barreto
- Dept. of Psychiatry, Black Country Healthcare NHS Foundation Trust, Wolverhampton, United Kingdom
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Halstead S, Cao C, Høgnason Mohr G, Ebdrup BH, Pillinger T, McCutcheon RA, Firth J, Siskind D, Warren N. Prevalence of multimorbidity in people with and without severe mental illness: a systematic review and meta-analysis. Lancet Psychiatry 2024; 11:431-442. [PMID: 38642560 DOI: 10.1016/s2215-0366(24)00091-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/02/2024] [Accepted: 03/05/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND People with severe mental illness, such as schizophrenia-spectrum disorder and bipolar disorder, face poorer health outcomes from multiple chronic illnesses. Physical multimorbidity, the coexistence of two or more chronic physical conditions, and psychiatric multimorbidity, the coexistence of three or more psychiatric disorders, are both emerging concepts useful in conceptualising disease burden. However, the prevalence of physical and psychiatric multimorbidity in this cohort is unknown. This study aimed to estimate the absolute prevalence of both physical and psychiatric multimorbidity in people with severe mental illness, and also compare the odds of physical multimorbidity prevalence against people without severe mental illness. METHODS We searched CINAHL, EMBASE, PubMed, and PsycINFO from inception until Feb 15, 2024, for observational studies that measured multimorbidity prevalence. To be included, studies had to have an observational study design, be conducted in an adult population (mean age ≥18 years) diagnosed with either schizophrenia-spectrum disorder or bipolar disorder, and include a measurement of occurrence of either physical multimorbidity (≥2 physical health conditions) or psychiatric multimorbidity (≥3 psychiatric conditions total, including the severe mental illness). From control studies, a random-effects meta-analysis compared odds of physical multimorbidity between people with and without severe mental illness. Absolute prevalence of physical and psychiatric multimorbidity in people with severe mental illness was also calculated. Sensitivity and meta-regression analyses tested an array of demographic, diagnostic, and methodological variables. FINDINGS From 11 144 citations we included 82 observational studies featuring 1 623 773 individuals with severe mental illness (specifically schizophrenia-spectrum disorder or bipolar disorder), of which 21 studies featured 13 235 882 control individuals without severe mental illness (descriptive data for the entire pooled cohorts were not available for numbers of males and females, age, and ethnicity). This study did not feature involvement of people with lived experience. The odds ratio (OR) of physical multimorbidity between people with and without severe mental illness was 2·40 (95% CI 1·57-3·65, k=11, p=0·0009). This ratio was higher in younger severe mental illness populations (mean age ≤40 years, OR 3·99, 95% CI 1·43-11·10) compared with older populations (mean age >40 years, OR 1·55, 95% CI 0·96-2·51; subgroup differences p=0·0013). For absolute prevalence, 25% of those with severe mental illness have physical multimorbidity (95% CI 0·19-0·32, k=29) and 14% have psychiatric multimorbidity (95% CI 0·08-0·23, k=21). INTERPRETATION This is the first meta-analysis to estimate physical alongside psychiatric multimorbidity prevalence, showing that these are common in people with schizophrenia-spectrum disorder and bipolar disorder. The greater burden of physical multimorbidity in people with severe mental illness compared with those without is higher for younger cohorts, reflecting a need for earlier intervention. Our findings speak to the utility of multimorbidity for characterising the disease burden associated with severe mental illness, and the importance of facilitating integrated physical and mental health care. FUNDING None.
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Affiliation(s)
- Sean Halstead
- The University of Queensland, Medical School, Brisbane, QLD, Australia; Metro South Addiction and Mental Health, Brisbane, QLD, Australia.
| | - Chester Cao
- Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia; School of Medicine and Dentistry, Griffith University, Gold Coast, QLD, Australia
| | - Grímur Høgnason Mohr
- Center for Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
| | - Bjørn H Ebdrup
- Center for Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark; Faculty of Health and Medical Sciences, Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Toby Pillinger
- South London & Maudsley NHS Foundation Trust, London, UK; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Robert A McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Joseph Firth
- Division of Psychology and Mental Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; Greater Manchester Mental Health NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Dan Siskind
- The University of Queensland, Medical School, Brisbane, QLD, Australia; Metro South Addiction and Mental Health, Brisbane, QLD, Australia
| | - Nicola Warren
- The University of Queensland, Medical School, Brisbane, QLD, Australia; Metro South Addiction and Mental Health, Brisbane, QLD, Australia
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5
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Defina S, Woofenden T, Baltramonaityte V, Tiemeier H, Fairchild G, Felix JF, Cecil CAM, Walton E. The role of lifestyle factors in the association between early-life stress and adolescent psycho-physical health: Moderation analysis in two European birth cohorts. Prev Med 2024; 182:107926. [PMID: 38447658 PMCID: PMC7616134 DOI: 10.1016/j.ypmed.2024.107926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/01/2024] [Accepted: 03/02/2024] [Indexed: 03/08/2024]
Abstract
OBJECTIVE Early-life stress (ELS) is an established risk factor for a host of adult mental and physical health problems, including both depression and obesity. Recent studies additionally showed that ELS was associated with an increased risk of comorbidity between mental and physical health problems, already in adolescence. Healthy lifestyle factors, including physical activity, sleep and diet have also been robustly linked to both emotional and physical wellbeing. However, it is yet unclear whether these lifestyle factors may moderate the association between ELS and psycho-physical comorbidity. METHODS We investigated whether (a) participation in physical activity, (b) sleep duration, and (c) adherence to a Mediterranean diet, moderated the relationship between cumulative ELS exposure over the first 10 years of life and psycho-physical comorbidity at the age of 13.5 years. Analyses were conducted in 2022-2023, using data from two large adolescent samples based in the UK (ALSPAC; n = 8428) and The Netherlands (Generation R; n = 4268). RESULTS Exposure to ELS was significantly associated with a higher risk of developing comorbidity, however this association was not modified by any of the three lifestyle factors investigated. Only physical activity was significantly associated with a reduced risk of comorbidity in one cohort (ORALSPAC [95%CI] = 0.73 [0.59;0.89]). CONCLUSIONS In conclusion, while we found some evidence that more frequent physical activity may be associated with a reduction in psycho-physical comorbidity, we did not find evidence in support of the hypothesised moderation effects. However, more research is warranted to examine how these associations may evolve over time.
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Affiliation(s)
- Serena Defina
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Tom Woofenden
- Department of Psychology, University of Bath, Bath, United Kingdom
| | | | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Social and Behavioral Sciences, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Graeme Fairchild
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - Janine F Felix
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Paediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Charlotte A M Cecil
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Esther Walton
- Department of Psychology, University of Bath, Bath, United Kingdom.
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Žlahtič B, Kokol P, Blažun Vošner H, Završnik J. The role of correspondence analysis in medical research. Front Public Health 2024; 12:1362699. [PMID: 38584915 PMCID: PMC10995278 DOI: 10.3389/fpubh.2024.1362699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/07/2024] [Indexed: 04/09/2024] Open
Abstract
Correspondence analysis (CA) is a multivariate statistical and visualization technique. CA is extremely useful in analyzing either two- or multi-way contingency tables, representing some degree of correspondence between columns and rows. The CA results are visualized in easy-to-interpret "bi-plots," where the proximity of items (values of categorical variables) represents the degree of association between presented items. In other words, items positioned near each other are more associated than those located farther away. Each bi-plot has two dimensions, named during the analysis. The naming of dimensions adds a qualitative aspect to the analysis. Correspondence analysis may support medical professionals in finding answers to many important questions related to health, wellbeing, quality of life, and similar topics in a simpler but more informal way than by using more complex statistical or machine learning approaches. In that way, it can be used for dimension reduction and data simplification, clustering, classification, feature selection, knowledge extraction, visualization of adverse effects, or pattern detection.
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Affiliation(s)
- Bojan Žlahtič
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Peter Kokol
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
- Community Healthcare Center dr. Adolf Drolc, Maribor, Slovenia
| | - Helena Blažun Vošner
- Community Healthcare Center dr. Adolf Drolc, Maribor, Slovenia
- Faculty of Health and Social Sciences Slovenj Gradec, Slovenj Gradec, Slovenia
| | - Jernej Završnik
- Community Healthcare Center dr. Adolf Drolc, Maribor, Slovenia
- Alma Mater Europaea, Maribor, Slovenia
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Halstead S, Siskind D, Warren N. Making meaning of multimorbidity and severe mental illness: A viewpoint. Aust N Z J Psychiatry 2024; 58:12-20. [PMID: 37655619 PMCID: PMC10756013 DOI: 10.1177/00048674231195560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
People living with severe mental illness, such as schizophrenia and bipolar affective disorder, frequently experience poorer physical health compared to those without mental illness. This issue has hitherto been approached through the disease-centred construct of comorbidity, where subsequent conditions are viewed as secondary to an 'index condition'. In contrast, this Viewpoint sets out to explain why multimorbidity, a patient-centred concept that instead refers to the coexistence of multiple chronic illnesses, is a more versatile and robust framework for tackling the issue of poor physical health in people with severe mental illness. In establishing this argument, this Viewpoint has sought to address three key areas. First, this article will discuss the epidemiology of both physical and psychiatric multimorbidity, with respect to how they manifest at greater frequency and at younger ages in people with severe mental illness. Second, the profound consequences of this multimorbidity burden will be explored, with respect to the 'three D's' of death (premature mortality), disability (functional impacts) and deficit (health-economic impacts). Finally, the utility of multimorbidity as a framework will be illustrated through a proposal for a three-dimensional multimorbidity construct composed of (1) quantity, (2) severity and (3) duration of an individual's chronic illnesses. Consequently, this Viewpoint aims to capture why it is necessary for modern psychiatry to grasp the concept of multimorbidity to facilitate holistic healthcare for people living with severe mental illness.
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Affiliation(s)
- Sean Halstead
- The University of Queensland, Faculty of Medicine, Brisbane, QLD, Australia
- Logan Hospital, Metro South Health, Meadowbrook, QLD, Australia
| | - Dan Siskind
- The University of Queensland, Faculty of Medicine, Brisbane, QLD, Australia
- Metro South Addiction and Mental Health Service, Brisbane, QLD, Australia
| | - Nicola Warren
- The University of Queensland, Faculty of Medicine, Brisbane, QLD, Australia
- Metro South Addiction and Mental Health Service, Brisbane, QLD, Australia
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Fagbamigbe AF, Agrawal U, Azcoaga-Lorenzo A, MacKerron B, Özyiğit EB, Alexander DC, Akbari A, Owen RK, Lyons J, Lyons RA, Denaxas S, Kirk P, Miller AC, Harper G, Dezateux C, Brookes A, Richardson S, Nirantharakumar K, Guthrie B, Hughes L, Kadam UT, Khunti K, Abrams KR, McCowan C. Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland. PLoS One 2023; 18:e0294666. [PMID: 38019832 PMCID: PMC10686427 DOI: 10.1371/journal.pone.0294666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 11/07/2023] [Indexed: 12/01/2023] Open
Abstract
There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.
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Affiliation(s)
- Adeniyi Francis Fagbamigbe
- School of Medicine, University of St Andrews, St Andrews, United Kingdom
- Department of Epidemiology and Medical Statistics, University of Ibadan, Ibadan, Nigeria
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom
- Research Methods and Evaluation Unit, Institute for Health & Wellbeing, Coventry University, Coventry, United Kingdom
| | - Utkarsh Agrawal
- Nuffield Department of Primary Care Health Science, University of Oxford, Oxford, United Kingdom
| | - Amaya Azcoaga-Lorenzo
- School of Medicine, University of St Andrews, St Andrews, United Kingdom
- Hospital Rey Juan Carlos, Instituto de Investigación Sanitaria Fundación Jimenez Diaz, Madrid, Spain
| | - Briana MacKerron
- School of Medicine, University of St Andrews, St Andrews, United Kingdom
| | - Eda Bilici Özyiğit
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, United Kingdom
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Rhiannon K. Owen
- Population Data Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Jane Lyons
- Population Data Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Ronan A. Lyons
- Population Data Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Spiros Denaxas
- Institute of Health Informatics, UCL, London, United Kingdom
- British Heart Foundation Data Science Centre, London, United Kingdom
| | - Paul Kirk
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Ana Corina Miller
- Centre for Public Health, Institute of Clinical Science, Queen’s University Belfast, Belfast, United Kingdom
| | - Gill Harper
- Clinical Effectiveness Group, Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
| | - Carol Dezateux
- Clinical Effectiveness Group, Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
| | - Anthony Brookes
- Department of Genetics & Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Sylvia Richardson
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | | | - Bruce Guthrie
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Lloyd Hughes
- School of Medicine, University of St Andrews, St Andrews, United Kingdom
| | - Umesh T. Kadam
- Department of Population Health Sciences, University of Leicester, Leicester, United Kingdom
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Keith R. Abrams
- Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - Colin McCowan
- School of Medicine, University of St Andrews, St Andrews, United Kingdom
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9
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Carolan A, Hynes C, McWilliams S, Ryan C, Strawbridge J, Keating D. Cardiometabolic risk in people under 40 years with severe mental illness: reading between the guidelines. Int J Clin Pharm 2023; 45:1299-1301. [PMID: 37212968 PMCID: PMC10600028 DOI: 10.1007/s11096-023-01600-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 04/24/2023] [Indexed: 05/23/2023]
Abstract
People with severe mental illness (SMI) have a shorter life expectancy than the rest of the population. Multimorbidity and poorer physical health contribute to this health inequality. Cardiometabolic multimorbidity confers a significant mortality risk in this population. Multimorbidity is not restricted to older people and people with SMI present with multimorbidity earlier in life. Despite this, most screening, prevention and treatment strategies target older people. People under 40 years with SMI are underserved by current guidelines for cardiovascular risk assessment and reduction. Research is needed to develop and implement interventions to reduce cardiometabolic risk in this population.
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Affiliation(s)
- Aoife Carolan
- Saint John of God Hospital, Stillorgan, Co. Dublin, Ireland.
- School of Pharmacy and Biomolecular Science, Royal College of Surgeons Ireland, 123, St Stephen's Green, Dublin 2, Ireland.
| | - Caroline Hynes
- Saint John of God Hospital, Stillorgan, Co. Dublin, Ireland
- School of Pharmacy and Biomolecular Science, Royal College of Surgeons Ireland, 123, St Stephen's Green, Dublin 2, Ireland
| | - Stephen McWilliams
- Saint John of God Hospital, Stillorgan, Co. Dublin, Ireland
- School of Medicine and Medical Sciences, University College Dublin, Belfield, Dublin 4, Ireland
| | - Cristín Ryan
- School of Pharmacy and Pharmaceutical Sciences, Trinity College , Dublin 2, Ireland
| | - Judith Strawbridge
- School of Pharmacy and Biomolecular Science, Royal College of Surgeons Ireland, 123, St Stephen's Green, Dublin 2, Ireland
| | - Dolores Keating
- Saint John of God Hospital, Stillorgan, Co. Dublin, Ireland
- School of Pharmacy and Biomolecular Science, Royal College of Surgeons Ireland, 123, St Stephen's Green, Dublin 2, Ireland
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10
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Launders N, Hayes JF, Price G, Marston L, Osborn DPJ. The incidence rate of planned and emergency physical health hospital admissions in people diagnosed with severe mental illness: a cohort study. Psychol Med 2023; 53:5603-5614. [PMID: 36069188 PMCID: PMC10482715 DOI: 10.1017/s0033291722002811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 08/10/2022] [Accepted: 08/13/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND People with severe mental illness (SMI) have more physical health conditions than the general population, resulting in higher rates of hospitalisations and mortality. In this study, we aimed to determine the rate of emergency and planned physical health hospitalisations in those with SMI, compared to matched comparators, and to investigate how these rates differ by SMI diagnosis. METHODS We used Clinical Practice Research DataLink Gold and Aurum databases to identify 20,668 patients in England diagnosed with SMI between January 2000 and March 2016, with linked hospital records in Hospital Episode Statistics. Patients were matched with up to four patients without SMI. Primary outcomes were emergency and planned physical health admissions. Avoidable (ambulatory care sensitive) admissions and emergency admissions for accidents, injuries and substance misuse were secondary outcomes. We performed negative binomial regression, adjusted for clinical and demographic variables, stratified by SMI diagnosis. RESULTS Emergency physical health (aIRR:2.33; 95% CI 2.22-2.46) and avoidable (aIRR:2.88; 95% CI 2.60-3.19) admissions were higher in patients with SMI than comparators. Emergency admission rates did not differ by SMI diagnosis. Planned physical health admissions were lower in schizophrenia (aIRR:0.80; 95% CI 0.72-0.90) and higher in bipolar disorder (aIRR:1.33; 95% CI 1.24-1.43). Accident, injury and substance misuse emergency admissions were particularly high in the year after SMI diagnosis (aIRR: 6.18; 95% CI 5.46-6.98). CONCLUSION We found twice the incidence of emergency physical health admissions in patients with SMI compared to those without SMI. Avoidable admissions were particularly elevated, suggesting interventions in community settings could reduce hospitalisations. Importantly, we found underutilisation of planned inpatient care in patients with schizophrenia. Interventions are required to ensure appropriate healthcare use, and optimal diagnosis and treatment of physical health conditions in people with SMI, to reduce the mortality gap due to physical illness.
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Affiliation(s)
- Naomi Launders
- Division of Psychiatry, UCL. 6th Floor Maple House, 149 Tottenham Court Road, London W1T 7NF, UK
| | - Joseph F. Hayes
- Division of Psychiatry, UCL. 6th Floor Maple House, 149 Tottenham Court Road, London W1T 7NF, UK
- Camden and Islington NHS Foundation Trust, St Pancras Hospital, 4 St Pancras Way, London, NW1 0PE, UK
| | - Gabriele Price
- Department of Health and Social Care, Office for Health Improvement and Disparities, Wellington House, 133-155 Waterloo Road, London SE1 8UG, UK
| | - Louise Marston
- Department of Primary Care and Population Health, UCL, Rowland Hill Street, NW3 2PF, London, UK
| | - David P. J. Osborn
- Division of Psychiatry, UCL. 6th Floor Maple House, 149 Tottenham Court Road, London W1T 7NF, UK
- Camden and Islington NHS Foundation Trust, St Pancras Hospital, 4 St Pancras Way, London, NW1 0PE, UK
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11
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McCarter R, Rosato M, Thampi A, Barr R, Leavey G. Physical health disparities and severe mental illness: A longitudinal comparative cohort study using hospital data in Northern Ireland. Eur Psychiatry 2023; 66:e70. [PMID: 37578131 PMCID: PMC10594365 DOI: 10.1192/j.eurpsy.2023.2441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND People with severe mental illness (SMI) die prematurely, mostly due to preventable causes. OBJECTIVE To examine multimorbidity and mortality in people living with SMI using linked administrative datasets. METHOD Analysis of linked electronically captured routine hospital administrative data from Northern Ireland (2010-2021). We derived sex-specific age-standardised rates for seven chronic life-limiting physical conditions (chronic kidney disease, malignant neoplasms, diabetes mellitus, chronic obstructive pulmonary disease, chronic heart failure, myocardial infarction, and stroke) and used logistic regression to examine the relationship between SMI, socio-demographic indicators, and comorbid conditions; survival models quantified the relationship between all-cause mortality and SMI. RESULTS Analysis was based on 929,412 hospital patients aged 20 years and above, of whom 10,965 (1.3%) recorded a diagnosis of SMI. Higher likelihoods of an SMI diagnosis were associated with living in socially deprived circumstances, urbanicity. SMI patients were more likely to have more comorbid physical conditions than non-SMI patients, and younger at referral to hospital for each condition, than non-SMI patients. Finally, in fully adjusted models, SMI patients had a twofold excess all-cause mortality. CONCLUSION Multiple morbidities associated with SMI can drive excess mortality. While SMI patients are younger at referral to treatment for these life-limiting conditions, their relatively premature death suggests that these conditions are also quite advanced. There is a need for a more aggressive approach to improving the physical health of this population.
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Affiliation(s)
- Rachel McCarter
- Bamford Centre for Mental Health and Wellbeing, Ulster University, Coleraine, UK
- Administrative Data Research – Northern Ireland (ADR-NI), Ulster University, Coleraine, UK
| | - Michael Rosato
- Bamford Centre for Mental Health and Wellbeing, Ulster University, Coleraine, UK
- Administrative Data Research – Northern Ireland (ADR-NI), Ulster University, Coleraine, UK
| | | | | | - Gerard Leavey
- Bamford Centre for Mental Health and Wellbeing, Ulster University, Coleraine, UK
- Administrative Data Research – Northern Ireland (ADR-NI), Ulster University, Coleraine, UK
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12
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O'Connor RC, Worthman CM, Abanga M, Athanassopoulou N, Boyce N, Chan LF, Christensen H, Das-Munshi J, Downs J, Koenen KC, Moutier CY, Templeton P, Batterham P, Brakspear K, Frank RG, Gilbody S, Gureje O, Henderson D, John A, Kabagambe W, Khan M, Kessler D, Kirtley OJ, Kline S, Kohrt B, Lincoln AK, Lund C, Mendenhall E, Miranda R, Mondelli V, Niederkrotenthaler T, Osborn D, Pirkis J, Pisani AR, Prawira B, Rachidi H, Seedat S, Siskind D, Vijayakumar L, Yip PSF. Gone Too Soon: priorities for action to prevent premature mortality associated with mental illness and mental distress. Lancet Psychiatry 2023; 10:452-464. [PMID: 37182526 DOI: 10.1016/s2215-0366(23)00058-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/17/2023] [Accepted: 02/28/2023] [Indexed: 05/16/2023]
Abstract
Globally, too many people die prematurely from suicide and the physical comorbidities associated with mental illness and mental distress. The purpose of this Review is to mobilise the translation of evidence into prioritised actions that reduce this inequity. The mental health research charity, MQ Mental Health Research, convened an international panel that used roadmapping methods and review evidence to identify key factors, mechanisms, and solutions for premature mortality across the social-ecological system. We identified 12 key overarching risk factors and mechanisms, with more commonalities than differences across the suicide and physical comorbidities domains. We also identified 18 actionable solutions across three organising principles: the integration of mental and physical health care; the prioritisation of prevention while strengthening treatment; and the optimisation of intervention synergies across social-ecological levels and the intervention cycle. These solutions included accessible, integrated high-quality primary care; early life, workplace, and community-based interventions co-designed by the people they should serve; decriminalisation of suicide and restriction of access to lethal means; stigma reduction; reduction of income, gender, and racial inequality; and increased investment. The time to act is now, to rebuild health-care systems, leverage changes in funding landscapes, and address the effects of stigma, discrimination, marginalisation, gender violence, and victimisation.
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Affiliation(s)
- Rory C O'Connor
- Suicidal Behaviour Research Laboratory, School of Health & Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
| | | | - Marie Abanga
- Hope for the Abused and Battered, Douala, Cameroon
| | | | | | - Lai Fong Chan
- Department of Psychiatry, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia
| | - Helen Christensen
- Faculty of Medicine & Health, University of New South Wales, Sydney and the Black Dog Institute, Sydney, NSW, Australia
| | - Jayati Das-Munshi
- Department of Psychological Medicine, King's College London, London, UK; Institute of Psychiatry, Psychology, and Neuroscience, and Centre for Society and Mental Health, King's College London, London, UK; South London and Maudsley NHS Trust, London, UK
| | - James Downs
- Royal College of Psychiatrists, UK and Faculty of Wellbeing, Education, and Language Studies, Open University, Milton Keynes, UK
| | | | | | - Peter Templeton
- The William Templeton Foundation for Young People's Mental Health, Cambridge, UK
| | - Philip Batterham
- Centre for Mental Health Research, College of Health and Medicine, The Australian National University, Canberra, ACT, Australia
| | | | | | - Simon Gilbody
- York Mental Health and Addictions Research Group, University of York, York, UK
| | - Oye Gureje
- WHO Collaborating Centre for Research and Training in Mental Health, Neuroscience, Drug and Alcohol Abuse, University of Ibadan, Ibadan, Nigeria
| | - David Henderson
- Department of Psychiatry, Boston University School of Medicine, Boston Medical Center, Boston, MA, USA
| | - Ann John
- Swansea University Medical School, Swansea University, Swansea, UK
| | | | - Murad Khan
- Brain & Mind Institute, Aga Khan University, Karachi, Pakistan
| | - David Kessler
- Bristol Population Health Science Institute, Centre for Academic Mental Health, Centre for Academic Primary Care, Bristol Medical School, University of Bristol, Bristol, UK
| | - Olivia J Kirtley
- Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | | | - Brandon Kohrt
- Department of Psychiatry and Behavioral Sciences, George Washington University, Washington, DC, USA
| | - Alisa K Lincoln
- Institute for Health Equity and Social Justice Research, Northeastern University, Boston, MA, USA
| | - Crick Lund
- Health Services and Population Research Department, King's College London, London, UK; Centre for Global Mental Health, King's College London, London, UK
| | - Emily Mendenhall
- Edmund A Walsh School of Foreign Service, Georgetown University, Washington, DC, USA
| | - Regina Miranda
- Hunter College, Department of Psychology, The Graduate Center, City University of New York, New York, NY, USA
| | - Valeria Mondelli
- Department of Psychological Medicine, King's College London, London, UK
| | - Thomas Niederkrotenthaler
- Department of Social and Preventive Medicine, Suicide Research & Mental Health Promotion Unit, Center for Public Health, Medical University of Vienna, Vienna, Austria
| | - David Osborn
- Division of Psychiatry, University College London and Camden and Islington NHS Foundation Trust, London, UK
| | - Jane Pirkis
- Centre for Mental Health, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Anthony R Pisani
- University of Rochester Center for the Study and Prevention of Suicide, SafeSide Prevention, Rochester, NY, USA
| | | | | | - Soraya Seedat
- Department of Psychiatry, Faculty of Medicine and Health Sciences, SAMRC Genomics of Brain Disorders Unit, Stellenbosch University, Cape Town, South Africa
| | - Dan Siskind
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | | | - Paul S F Yip
- Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong Special Administrative Region, China
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13
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Ramos-Vera C, Barrientos AS, Vallejos-Saldarriaga J, Calizaya-Milla YE, Saintila J. Network Structure of Comorbidity Patterns in U.S. Adults with Depression: A National Study Based on Data from the Behavioral Risk Factor Surveillance System. DEPRESSION RESEARCH AND TREATMENT 2023; 2023:9969532. [PMID: 37096248 PMCID: PMC10122603 DOI: 10.1155/2023/9969532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/14/2023] [Accepted: 03/21/2023] [Indexed: 04/26/2023]
Abstract
Background People with depression are at increased risk for comorbidities; however, the clustering of comorbidity patterns in these patients is still unclear. Objective The aim of the study was to identify latent comorbidity patterns and explore the comorbidity network structure that included 12 chronic conditions in adults diagnosed with depressive disorder. Methods A cross-sectional study was conducted based on secondary data from the 2017 behavioral risk factor surveillance system (BRFSS) covering all 50 American states. A sample of 89,209 U.S. participants, 29,079 men and 60,063 women aged 18 years or older, was considered using exploratory graphical analysis (EGA), a statistical graphical model that includes algorithms for grouping and factoring variables in a multivariate system of network relationships. Results The EGA findings show that the network presents 3 latent comorbidity patterns, i.e., that comorbidities are grouped into 3 factors. The first group was composed of 7 comorbidities (obesity, cancer, high blood pressure, high blood cholesterol, arthritis, kidney disease, and diabetes). The second pattern of latent comorbidity included the diagnosis of asthma and respiratory diseases. The last factor grouped 3 conditions (heart attack, coronary heart disease, and stroke). Hypertension reported higher measures of network centrality. Conclusion Associations between chronic conditions were reported; furthermore, they were grouped into 3 latent dimensions of comorbidity and reported network factor loadings. The implementation of care and treatment guidelines and protocols for patients with depressive symptomatology and multimorbidity is suggested.
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Affiliation(s)
- Cristian Ramos-Vera
- Research Area, Faculty of Health Sciences, Universidad César Vallejo, Lima, Peru
| | | | | | - Yaquelin E. Calizaya-Milla
- Research Group for Nutrition and Lifestyle, School of Human Nutrition, Universidad Peruana Unión, Lima, Peru
| | - Jacksaint Saintila
- Research Group for Nutrition and Lifestyle, School of Human Nutrition, Universidad Peruana Unión, Lima, Peru
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14
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Ramos-Vera C, Saintila J, O'Diana AG, Calizaya-Milla YE. Identifying latent comorbidity patterns in adults with perceived cognitive impairment: Network findings from the behavioral risk factor surveillance system. Front Public Health 2022; 10:981944. [PMID: 36203679 PMCID: PMC9530468 DOI: 10.3389/fpubh.2022.981944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/30/2022] [Indexed: 01/25/2023] Open
Abstract
Background People with cognitive impairment may be exposed to an increased risk of comorbidities; however, the clustering of comorbidity patterns in these patients is unclear. Objective To explore the network structure of chronic comorbidity in a U.S. national sample spanning all 50 U.S. states with more than 170,000 participants reporting perceived cognitive impairment. Methods This is a cross-sectional study conducted using Behavioral Risk Factor Surveillance System (BRFSS) secondary data collected in 2019 and covering 49 U.S. states, the District of Columbia, Guam, and the Commonwealth of Puerto Rico. A total of 15,621 non-institutionalized U.S. adult participants who reported "yes" to the subjective cognitive impairment question were considered, of whom 7,045 were men and 8,576 were women. All participants were aged 45 years or older. A statistical graphical model was used that included clustering algorithms and factorization of variables in a multivariate network relationship system [exploratory graphical analysis (EGA)]. Results The results of the EGA show associations between the comorbid conditions evaluated. These associations favored the clustering of various comorbidity patterns. In fact, three patterns of comorbidities have been identified: (1) arthritis, asthma, respiratory diseases, and depression, (2) obesity, diabetes, blood pressure high, and blood cholesterol high, and (3) heart attack, coronary heart disease, stroke, and kidney disease. Conclusion These results suggest the development of interdisciplinary treatment strategies in patients with perceived cognitive impairment, which could help to design an integrated prevention and management of the disease and other related health problems, such as Alzheimer's disease and related dementias.
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Affiliation(s)
- Cristian Ramos-Vera
- Research Area, Faculty of Health Sciences, Universidad César Vallejo, Lima, Peru
| | - Jacksaint Saintila
- Escuela de Medicina Humana, Universidad Señor de Sipán, Chiclayo, Peru,*Correspondence: Jacksaint Saintila
| | - Angel García O'Diana
- Research Area, Faculty of Health Sciences, Universidad César Vallejo, Lima, Peru
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15
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Launders N, Kirsh L, Osborn DPJ, Hayes JF. The temporal relationship between severe mental illness diagnosis and chronic physical comorbidity: a UK primary care cohort study of disease burden over 10 years. Lancet Psychiatry 2022; 9:725-735. [PMID: 35871794 PMCID: PMC9630158 DOI: 10.1016/s2215-0366(22)00225-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/06/2022] [Accepted: 06/08/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND Despite increased rates of physical health problems in people with schizophrenia, bipolar disorder, and other psychotic illnesses, the temporal relationship between physical disease acquisition and diagnosis of a severe mental illness remains unclear. We aimed to determine the cumulative prevalence of 24 chronic physical conditions in people with severe mental illness from 5 years before to 5 years after their diagnosis. METHODS In this cohort study, we used the UK Clinical Practice Research Datalink (CPRD) to identify patients aged 18-100 years who were diagnosed with severe mental illness between Jan 1, 2000, and Dec 31, 2018. Each patient with severe mental illness was matched with up to four individuals in the CPRD without severe mental illness by sex, 5-year age band, primary care practice, and year of primary care practice registration. Individuals in the matched cohort were assigned an index date equal to the date of severe mental illness diagnosis in the patient with severe mental illness to whom they were matched. Our primary outcome was the cumulative prevalence of 24 physical health conditions, based on the Charlson and Elixhauser comorbidity indices, at 5 years, 3 years, and 1 year before and after severe mental illness diagnosis and at the time of diagnosis. We used logistic regression to compare people with severe mental illness with the matched cohort, adjusting for key variables such as age, sex, and ethnicity. FINDINGS We identified 68 789 patients diagnosed with a severe mental illness between Jan 1, 2000, and Dec 31, 2018, and we matched them to 274 827 patients without a severe mental illness diagnosis. In both cohorts taken together, the median age was 40·90 years (IQR 29·46-56·00), 175 138 (50·97%) people were male, and 168 478 (49·03%) were female. The majority of patients were of White ethnicity (59 867 [87·03%] patients with a severe mental illness and 244 566 [88·99%] people in the matched cohort). The most prevalent conditions at the time of diagnosis in people with severe mental illness were asthma (10 581 [15·38%] of 68 789 patients), hypertension (8696 [12·64%]), diabetes (4897 [7·12%]), neurological disease (3484 [5·06%]), and hypothyroidism (2871 [4·17%]). At diagnosis, people with schizophrenia had increased odds of five of 24 chronic physical conditions compared with matched controls, and nine of 24 conditions were diagnosed less frequently than in matched controls. Individuals with bipolar disorder and other psychoses had increased odds of 15 conditions at diagnosis. At 5 years after severe mental illness diagnosis, these numbers had increased to 13 conditions for schizophrenia, 19 for bipolar disorder, and 16 for other psychoses. INTERPRETATION Elevated odds of multiple conditions at the point of severe mental illness diagnosis suggest that early intervention on physical health parameters is necessary to reduce morbidity and premature mortality. Some physical conditions might be under-recorded in patients with schizophrenia relative to patients with other severe mental illness subtypes. FUNDING UK Office For Health Improvement and Disparities.
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Affiliation(s)
- Naomi Launders
- Division of Psychiatry, University College London, London, UK
| | - Leiah Kirsh
- Division of Psychiatry, University College London, London, UK
| | - David P J Osborn
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK
| | - Joseph F Hayes
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK.
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16
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Launders N, Dotsikas K, Marston L, Price G, Osborn DPJ, Hayes JF. The impact of comorbid severe mental illness and common chronic physical health conditions on hospitalisation: A systematic review and meta-analysis. PLoS One 2022; 17:e0272498. [PMID: 35980891 PMCID: PMC9387848 DOI: 10.1371/journal.pone.0272498] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 07/20/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND People with severe mental illness (SMI) are at higher risk of physical health conditions compared to the general population, however, the impact of specific underlying health conditions on the use of secondary care by people with SMI is unknown. We investigated hospital use in people managed in the community with SMI and five common physical long-term conditions: cardiovascular diseases, COPD, cancers, diabetes and liver disease. METHODS We performed a systematic review and meta-analysis (Prospero: CRD42020176251) using terms for SMI, physical health conditions and hospitalisation. We included observational studies in adults under the age of 75 with a diagnosis of SMI who were managed in the community and had one of the physical conditions of interest. The primary outcomes were hospital use for all causes, physical health causes and related to the physical condition under study. We performed random-effects meta-analyses, stratified by physical condition. RESULTS We identified 5,129 studies, of which 50 were included: focusing on diabetes (n = 21), cardiovascular disease (n = 19), COPD (n = 4), cancer (n = 3), liver disease (n = 1), and multiple physical health conditions (n = 2). The pooled odds ratio (pOR) of any hospital use in patients with diabetes and SMI was 1.28 (95%CI:1.15-1.44) compared to patients with diabetes alone and pooled hazard ratio was 1.19 (95%CI:1.08-1.31). The risk of 30-day readmissions was raised in patients with SMI and diabetes (pOR: 1.18, 95%CI:1.08-1.29), SMI and cardiovascular disease (pOR: 1.27, 95%CI:1.06-1.53) and SMI and COPD (pOR:1.18, 95%CI: 1.14-1.22) compared to patients with those conditions but no SMI. CONCLUSION People with SMI and five physical conditions are at higher risk of hospitalisation compared to people with that physical condition alone. Further research is warranted into the combined effects of SMI and physical conditions on longer-term hospital use to better target interventions aimed at reducing inappropriate hospital use and improving disease management and outcomes.
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Affiliation(s)
| | | | - Louise Marston
- Department of Primary Care and Population Health, UCL, London, United Kingdom
| | - Gabriele Price
- Health Improvement Directorate, Public Health England, London, United Kingdom
| | - David P. J. Osborn
- Division of Psychiatry, UCL, London, United Kingdom
- Camden and Islington NHS Foundation Trust, St Pancras Hospital, London, United Kingdom
| | - Joseph F. Hayes
- Division of Psychiatry, UCL, London, United Kingdom
- Camden and Islington NHS Foundation Trust, St Pancras Hospital, London, United Kingdom
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