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Owen RK, Lyons J, Akbari A, Guthrie B, Agrawal U, Alexander DC, Azcoaga-Lorenzo A, Brookes AJ, Denaxas S, Dezateux C, Fagbamigbe AF, Harper G, Kirk PDW, Özyiğit EB, Richardson S, Staniszewska S, McCowan C, Lyons RA, Abrams KR. Effect on life expectancy of temporal sequence in a multimorbidity cluster of psychosis, diabetes, and congestive heart failure among 1·7 million individuals in Wales with 20-year follow-up: a retrospective cohort study using linked data. Lancet Public Health 2023; 8:e535-e545. [PMID: 37393092 DOI: 10.1016/s2468-2667(23)00098-1] [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: 08/25/2022] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 07/03/2023]
Abstract
BACKGROUND To inform targeted public health strategies, it is crucial to understand how coexisting diseases develop over time and their associated impacts on patient outcomes and health-care resources. This study aimed to examine how psychosis, diabetes, and congestive heart failure, in a cluster of physical-mental health multimorbidity, develop and coexist over time, and to assess the associated effects of different temporal sequences of these diseases on life expectancy in Wales. METHODS In this retrospective cohort study, we used population-scale, individual-level, anonymised, linked, demographic, administrative, and electronic health record data from the Wales Multimorbidity e-Cohort. We included data on all individuals aged 25 years and older who were living in Wales on Jan 1, 2000 (the start of follow-up), with follow-up continuing until Dec 31, 2019, first break in Welsh residency, or death. Multistate models were applied to these data to model trajectories of disease in multimorbidity and their associated effect on all-cause mortality, accounting for competing risks. Life expectancy was calculated as the restricted mean survival time (bound by the maximum follow-up of 20 years) for each of the transitions from the health states to death. Cox regression models were used to estimate baseline hazards for transitions between health states, adjusted for sex, age, and area-level deprivation (Welsh Index of Multiple Deprivation [WIMD] quintile). FINDINGS Our analyses included data for 1 675 585 individuals (811 393 [48·4%] men and 864 192 [51·6%] women) with a median age of 51·0 years (IQR 37·0-65·0) at cohort entry. The order of disease acquisition in cases of multimorbidity had an important and complex association with patient life expectancy. Individuals who developed diabetes, psychosis, and congestive heart failure, in that order (DPC), had reduced life expectancy compared with people who developed the same three conditions in a different order: for a 50-year-old man in the third quintile of the WIMD (on which we based our main analyses to allow comparability), DPC was associated with a loss in life expectancy of 13·23 years (SD 0·80) compared with the general otherwise healthy or otherwise diseased population. Congestive heart failure as a single condition was associated with mean a loss in life expectancy of 12·38 years (0·00), and with a loss of 12·95 years (0·06) when preceded by psychosis and 13·45 years (0·13) when followed by psychosis. Findings were robust in people of older ages, more deprived populations, and women, except that the trajectory of psychosis, congestive heart failure, and diabetes was associated with higher mortality in women than men. Within 5 years of an initial diagnosis of diabetes, the risk of developing psychosis or congestive heart failure, or both, was increased. INTERPRETATION The order in which individuals develop psychosis, diabetes, and congestive heart failure as combinations of conditions can substantially affect life expectancy. Multistate models offer a flexible framework to assess temporal sequences of diseases and allow identification of periods of increased risk of developing subsequent conditions and death. FUNDING Health Data Research UK.
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Affiliation(s)
- Rhiannon K Owen
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK.
| | - Jane Lyons
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Ashley Akbari
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Bruce Guthrie
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Utkarsh Agrawal
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, Faculty of Engineering Sciences, University College London, London, UK
| | - Amaya Azcoaga-Lorenzo
- School of Medicine, University of St Andrews, St Andrews, UK; Hospital Rey Juan Carlos, Instituto de Investigación Sanitaria Fundación Jimenez Diaz, Madrid, Spain
| | | | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | - Carol Dezateux
- Clinical Effectiveness Group, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | | | - Gill Harper
- Clinical Effectiveness Group, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Paul D W Kirk
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
| | - Eda Bilici Özyiğit
- Centre for Medical Image Computing, Department of Computer Science, Faculty of Engineering Sciences, University College London, London, UK
| | | | - Sophie Staniszewska
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Colin McCowan
- School of Medicine, University of St Andrews, St Andrews, UK
| | - Ronan A Lyons
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Keith R Abrams
- Department of Statistics, University of Warwick, Coventry, UK; Centre for Health Economics, University of York, York, UK
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Rossom RC, Hooker SA, O’Connor PJ, Crain AL, Sperl‐Hillen JM. Cardiovascular Risk for Patients With and Without Schizophrenia, Schizoaffective Disorder, or Bipolar Disorder. J Am Heart Assoc 2022; 11:e021444. [PMID: 35261265 PMCID: PMC9075298 DOI: 10.1161/jaha.121.021444] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background To compare estimated 10‐year and 30‐year cardiovascular risk in primary care patients with and without serious mental illness (SMI; bipolar disorder, schizophrenia, or schizoaffective disorder). Methods and Results All patients aged 18 to 75 years with a primary care visit in January 2016 to September 2018 were included and were grouped into those with and without SMI using diagnosis codes. Ten‐year cardiovascular risk was estimated using atherosclerotic cardiovascular disease scores for patients aged 40 to 75 years without cardiovascular disease; 30‐year cardiovascular risk was estimated using Framingham risk scores for patients aged 18 to 59 years without cardiovascular disease. Demographic, vital sign, medication, diagnosis, and health insurance data were collected from the electronic health record by a clinical decision support system. Descriptive statistics examined unadjusted differences, while general linear models examined differences for continuous variables and logistic regression models for categorical variables. Models were then adjusted for age, sex, race, ethnicity, and insurance type. A total of 11 333 patients with SMI and 579 924 patients without SMI were included. After covariate adjustment, 10‐year cardiovascular risk was significantly higher in patients with SMI (mean, 9.44%; 95% CI, 9.29%–9.60%) compared with patients without SMI (mean, 7.99%; 95% CI, 7.97–8.02). Similarly, 30‐year cardiovascular risk was significantly higher in those with SMI (25% of patients with SMI in the highest‐risk group compared with 11% of patients without SMI; P<0.001). The individual cardiovascular risk factors contributing most to increased risk for those with SMI were elevated body mass index and smoking. Among SMI subtypes, patients with bipolar disorder had the highest 10‐year cardiovascular risk, while patients with schizoaffective disorder had the highest 30‐year cardiovascular risk. Conclusions The significantly increased cardiovascular risk associated with SMI is evident even in young adults. This suggests the importance of addressing uncontrolled major cardiovascular risk factors in those with SMI at as early an age as possible. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02451670.
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Affiliation(s)
- Rebecca C. Rossom
- HealthPartners InstituteMinneapolisMN
- University of Minnesota Medical SchoolMinneapolisMN
| | | | - Patrick J. O’Connor
- HealthPartners InstituteMinneapolisMN
- University of Minnesota Medical SchoolMinneapolisMN
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Westman J, Eberhard J, Gaughran FP, Lundin L, Stenmark R, Edman G, Eriksson SV, Jedenius E, Rydell P, Overgaard K, Abrams D, Greenwood KE, Smith S, Ismail K, Murray R, Ösby U. Outcome of a psychosocial health promotion intervention aimed at improving physical health and reducing alcohol use in patients with schizophrenia and psychotic disorders (MINT). Schizophr Res 2019; 208:138-144. [PMID: 30979666 DOI: 10.1016/j.schres.2019.03.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 02/23/2019] [Accepted: 03/26/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND Life expectancy is reduced by 19 years in men and 17 in women with psychosis in Sweden, largely due to cardiovascular disease. AIM Assess whether a psychosocial health promotion intervention improves cardiometabolic risk factors, quality of life, and severity of illness in patients with psychotic disorders more than treatment as usual. METHODS A pragmatic intervention trial testing a manual-based multi-component health promotion intervention targeting patients with psychosis. The Swedish intervention was adapted from IMPaCT therapy, a health-promotion program based on motivational interviewing and cognitive behavioral therapy, designed to be incorporated into routine care. The intervention group consisted of 119 patients and the control group of 570 patients from specialized psychosis departments. Outcome variables were assessed 6 months before intervention during the run-in period, again at the start of intervention, and 12 months after the intervention began. The control group received treatment as usual. RESULTS The intervention had no significant effect on any of the outcome variables. However, BMI, waist circumference, systolic BP, heart rate, HbA1c, general health, and Clinical Global Impressions Scale score improved significantly during the run-in period before the start of the active intervention (observer effect). The multi-component design meant that treatment effects could only be calculated for the intervention as a whole. CONCLUSION The results of the intervention are similar to those of the U.K. IMPaCT study, in which the modular health-promotion intervention had little effect on cardiovascular risk indicators. However, in the current study, the run-in period had a positive effect on cardiometabolic risk factors.
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Affiliation(s)
- Jeanette Westman
- Dept of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Academic Primary Health Care Centre, Region Stockholm, Sweden.
| | - Jonas Eberhard
- Division of Psychiatry, Dept of Clinical Sciences, Lund University, Lund, Sweden; Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Fiona P Gaughran
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Lennart Lundin
- Sahlgrenska University Hospital, Gothenburg, Sweden; Swedish Schizophrenia Fellowship, Stockholm, Sweden
| | - Richard Stenmark
- Division of Psychiatry, Dept of Clinical Sciences, Lund University, Lund, Sweden
| | - Gunnar Edman
- Norrtälje Hospital, Tiohundra AB, Norrtälje, Sweden; Dept of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Sven V Eriksson
- Department of Internal Medicine, Enköping Hospital, Enköping, Sweden; Aleris Specialist Care, Gothenburg, Sweden
| | - Erik Jedenius
- Dept of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Division of Psychiatry, Dept of Clinical Sciences, Lund University, Lund, Sweden
| | - Pia Rydell
- Sahlgrenska University Hospital, Gothenburg, Sweden
| | | | | | - Kathryn E Greenwood
- School of Psychology, University of Sussex, Brighton, UK; Sussex Partnership NHS Foundation Trust, UK
| | - Shubulade Smith
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Khalida Ismail
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Robin Murray
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Urban Ösby
- Dept of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
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Abstract
UNLABELLED AimsPeople who have schizophrenia die earlier from somatic diseases than do people in the general population, but information about cardiovascular deaths in people who have schizophrenia is limited. We analysed mortality in all age groups of people with schizophrenia by specific cardiovascular diseases (CVDs), focusing on five CVD diagnoses: coronary heart disease, acute myocardial infarction, cerebrovascular disease, heart failure and cardiac arrhythmias. We also compared hospital admissions for CVDs in people who had schizophrenia with hospital admissions for CVDs in the general population. METHODS This national register study of 10 631 817 people in Sweden included 46 911 people who were admitted to the hospital for schizophrenia between 1 January 1987 and 31 December 2010. Information from national registers was used to identify people who had schizophrenia and obtain data about mortality, causes of death, medical diagnoses and hospitalisations. RESULTS CVDs were the leading cause of death in people who had schizophrenia (5245 deaths), and CVDs caused more excess deaths than suicide. The mean age of CVD death was 10 years lower for people who had schizophrenia (70.5 years) than the general population (80.7 years). The mortality rate ratio (MRR) for CVDs in all people who had schizophrenia was 2.80 (95% confidence interval (CI) 2.73-2.88). In people aged 15-59 years who had schizophrenia, the MRR for CVDs was 6.16 (95% CI 5.79-6.54). In all people who had schizophrenia, the MRR for coronary heart disease was 2.83 (95% CI 2.73-2.94); acute myocardial infarction, 2.62 (95% CI 2.49-2.75); cerebrovascular disease, 2.4 (95% CI 2.25-2.55); heart failure, 3.25 (95% CI 2.94-3.6); and cardiac arrhythmias, 2.06 (95% CI 1.75-2.43). Hospital admissions for coronary heart disease were less frequent in people who had schizophrenia than in the general population (admission rate ratio, 0.88 (95% CI 0.83-0.94). In all age groups, survival after hospital admission for CVD was lower in people who had schizophrenia than in the general population. CONCLUSIONS People who had schizophrenia died 10 years earlier from CVDs than did people in the general population. For all five CVD diagnoses, mortality risk was higher for those with schizophrenia than those in the general population. Survival after hospitalisation for CVDs in people who had schizophrenia was comparable with that of people in the general population who were several decades older.
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