Journal of multimorbidity and comorbidity最新文献

{"title":"Frequency and types of clusters of major chronic diseases in 0.5 million adults in urban and rural China.","authors":"Parisa Hariri, Robert Clarke, Fiona Bragg, Yiping Chen, Yu Guo, Ling Yang, Jun Lv, Canqing Yu, Liming Li, Zhengming Chen, Derrick A Bennett","doi":"10.1177/26335565221098327","DOIUrl":"10.1177/26335565221098327","url":null,"abstract":"<p><strong>Background: </strong>Little is known about the frequency and types of disease clusters involving major chronic diseases that contribute to multimorbidity in China. We examined the frequency of disease clusters involving major chronic diseases and their relationship with age and socioeconomic status in 0.5 million Chinese adults.</p><p><strong>Methods: </strong>Multimorbidity was defined as the presence of at least two or more of five major chronic diseases: stroke, ischaemic heart disease (IHD), diabetes, chronic obstructive pulmonary disease (COPD) and cancer. Multimorbid disease clusters were estimated using both self-reported doctor-diagnosed diseases at enrolment and incident cases during 10-year follow-up. Frequency of multimorbidity was assessed overall and by age, sex, region, education and income. Association rule mining (ARM) and latent class analysis (LCA) were used to assess clusters of the five major diseases.</p><p><strong>Results: </strong>Overall, 11% of Chinese adults had two or more major chronic diseases, and the frequency increased with age (11%, 24% and 33% at age 50-59, 60-69 and 70-79 years, respectively). Multimorbidity was more common in men than women (12% vs 11%) and in those living in urban than in rural areas (12% vs 10%), and was inversely related to levels of education. Stroke and IHD were the most frequent combinations, followed by diabetes and stroke. The patterns of self-reported disease clusters at baseline were similar to those that were recorded during the first 10 years of follow-up.</p><p><strong>Conclusions: </strong>Cardiometabolic and cardiorespiratory diseases were most common disease clusters. Understanding the nature of such clusters could have implications for future prevention strategies.</p>","PeriodicalId":73843,"journal":{"name":"Journal of multimorbidity and comorbidity","volume":"12 ","pages":"26335565221098327"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9125108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9150462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chronic condition clusters and associated disability over time.","authors":"Tara C Klinedinst, Lauren Terhorst, Juleen Rodakowski","doi":"10.1177/26335565221093569","DOIUrl":"10.1177/26335565221093569","url":null,"abstract":"<p><strong>Objectives: </strong>Recent evidence shows that more complex clusters of chronic conditions are associated with poorer health outcomes. Less clear is the extent to which these clusters are associated with different types of disability (activities of daily living (ADL) and functional mobility (FM)) over time; the aim of this study was to investigate this relationship.</p><p><strong>Methods: </strong>This was a longitudinal analysis using the National Health and Aging Trends Study (NHATS) (<i>n</i> = 6179). Using latent class analysis (LCA), we determined the optimal clusters of chronic conditions, then assigned each person to a best-fit class. Next, we used mixed-effects models with repeated measures to examine the effects of group (best-fit class), time (years from baseline), and the group by time interaction on each of the outcomes in separate models over 4 years.</p><p><strong>Results: </strong>We identified six chronic condition clusters: Minimal Disease, Cognitive/Affective, Multiple Morbidity, Osteoporosis, Vascular, and Cancer. Chronic condition cluster was related to ADL and FM outcomes, indicating that groups experienced differential disability over time. At time point 4, all chronic condition groups had worse FM than Minimal Disease.</p><p><strong>Discussion: </strong>The clusters of conditions identified here are plausible when considered clinically and in the context of previous research. All groups with chronic conditions carry risk for disability in FM and ADL; increased screening for disability in primary care could identify early disability and prevent decline.</p>","PeriodicalId":73843,"journal":{"name":"Journal of multimorbidity and comorbidity","volume":" ","pages":"26335565221093569"},"PeriodicalIF":0.0,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42522283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient centred care in an evidence based world? A meta-ethnography of multimorbidity interventions","authors":"Marianne McCallum, F. Mair, Oscar Ponce, N. Corcoran, Tiffany Keep, Guy Rughani","doi":"10.1370/afm.20.s1.2803","DOIUrl":"https://doi.org/10.1370/afm.20.s1.2803","url":null,"abstract":"","PeriodicalId":73843,"journal":{"name":"Journal of multimorbidity and comorbidity","volume":"151 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78436240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Burden of multimorbidity in sub-saharan africa: Preliminary findings from three community studies","authors":"B. Jani, F. Mair, M. Jobe, J. Seeley, I. Sekitoleko, A. Price, A. Prentice","doi":"10.1370/afm.20.s1.2740","DOIUrl":"https://doi.org/10.1370/afm.20.s1.2740","url":null,"abstract":"","PeriodicalId":73843,"journal":{"name":"Journal of multimorbidity and comorbidity","volume":"61 5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77654154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experiences of people living with multi morbidity in urban and rural Malawi","authors":"Edith F Chikumbu, Christopher Bunn, F. Mair, J. Seeley, B. Jani, S. Wyke","doi":"10.1370/afm.20.s1.2702","DOIUrl":"https://doi.org/10.1370/afm.20.s1.2702","url":null,"abstract":"","PeriodicalId":73843,"journal":{"name":"Journal of multimorbidity and comorbidity","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82365140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patterns of comorbidity and multimorbidity among middle-aged and elderly women in peri-urban Tanzania.","authors":"Laura-Marie Stieglitz, Till Bärnighausen, Germana H Leyna, Patrick Kazonda, Japhet Killewo, Julia K Rohr, Stefan Kohler","doi":"10.1177/26335565221076254","DOIUrl":"10.1177/26335565221076254","url":null,"abstract":"<p><strong>Background: </strong>Multimorbidity poses an increasing challenge to health care systems in Sub-Saharan Africa. We studied the extent of multimorbidity and patterns of comorbidity among women aged 40 years or older in a peri-urban area of Dar es Salaam, Tanzania.</p><p><strong>Methods: </strong>We assessed 15 chronic conditions in 1528 women who participated in a cross-sectional survey that was conducted within the Dar es Salaam Urban Cohort Study (DUCS) from June 2017 to July 2018. Diagnoses of chronic conditions were based on body measurements, weight, blood testing, screening instruments, and self-report.</p><p><strong>Results: </strong>The five most prevalent chronic conditions and most common comorbidities were hypertension (49.8%, 95% CI 47.2 to 52.3), obesity (39.9%, 95% CI 37.3 to 42.4), anemia (36.9%, 95% CI 33.3 to 40.5), signs of depression (32.5%, 95% CI 30.2 to 34.9), and diabetes (30.9%, 95% CI 27.6 to 34.2). The estimated prevalence of multimorbidity (2+ chronic conditions) was 73.8% (95% CI 71.2 to 76.3). Women aged 70 years or older were 4.1 (95% CI 1.5 to 10.9) times mores likely to be affected by multimorbidity and had 0.7 (95% CI 0.3 to 1.2) more chronic conditions than women aged 40 to 44 years. Worse childhood health, being widowed, not working, and higher food insecurity in the household were also associated with a higher multimorbidity risk and level.</p><p><strong>Conclusion: </strong>A high prevalence of multimorbidity in the general population of middle-aged and elderly women suggests substantial need for multimorbidity care in Tanzania. Comorbidity patterns can guide multimorbidity screening and help identify health care and prevention needs.</p>","PeriodicalId":73843,"journal":{"name":"Journal of multimorbidity and comorbidity","volume":" ","pages":"26335565221076254"},"PeriodicalIF":0.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46313585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The geriatrics research instrument library: A resource for guiding instrument selection for researchers studying older adults with multiple chronic conditions.","authors":"Mayra Tisminetzky, Christopher Delude, Heather G Allore, Kathryn Anzuoni, Sarah Bloomstone, Peter Charpentier, John P Hepler, Dalane W Kitzman, Gail J McAvay, Michael Miller, Nicholas M Pajewski, Jerry Gurwitz","doi":"10.1177/26335565221081200","DOIUrl":"10.1177/26335565221081200","url":null,"abstract":"<p><strong>Background: </strong>After the passage of the 21st Century Cures Act in the U.S., the Inclusion Across the Lifespan policy eliminates upper-age limits for research participation unless risk justified. Broader inclusion will necessitate the use of reliable instruments in research that characterize the health status and function of older adults with multiple chronic conditions. As there is a plethora of such instruments, the Geriatrics Research Instrument Library (GRIL) was developed as freely available online resource of data collection instruments commonly used in gerontological research. GRIL has been revised and updated by the Advancing Geriatrics Infrastructure and Network Growth (AGING) Initiative, a joint endeavor of the Health Care Systems Research Network (HCSRN) and the Older Americans Independence Centers (OAICs).</p><p><strong>Methods: </strong>Extensive PubMed literature searches and domain expert feedback were utilized to inventory and update GRIL through the addition of instruments and compiling of instrument metadata. GRIL is hosted on the National Institute on Aging OAIC Coordinating Center website with a platform utilizing Microsoft Structured Query Language (SQL) and an Adobe ColdFusion application server. Tracking statistics are collected using Google Analytics.</p><p><strong>Results: </strong>Presently, GRIL includes 175 instruments across 18 domains, including instrument metadata such as instrument description, copyright information, completion time estimates, keywords, available translations, and a link and reference to the original manuscript describing the instrument. The GRIL website includes user-friendly features such as mobile platforming and resource links.</p><p><strong>Conclusions: </strong>GRIL provides a user-friendly public resource that facilitates clinical researchers in efficiently selecting appropriate instruments to measure clinical outcomes relevant to older adults across a full range of domains.</p>","PeriodicalId":73843,"journal":{"name":"Journal of multimorbidity and comorbidity","volume":" ","pages":"26335565221081200"},"PeriodicalIF":0.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46987658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between pre-pregnancy multimorbidity and adverse maternal outcomes: A systematic review","authors":"H. Brown, Anthony McKnight, Amira M. Aker","doi":"10.1177/26335565221096584","DOIUrl":"https://doi.org/10.1177/26335565221096584","url":null,"abstract":"Objective We reviewed the literature on the association between pre-pregnancy multimorbidity (co-occurrence of two or more chronic conditions) and adverse maternal outcomes in pregnancy and postpartum. Data sources Medline, EMBASE, and CINAHL were searched from inception to September, 2021. Study selection Observational studies were eligible if they reported on the association between ≥ 2 co-occurring chronic conditions diagnosed before conception and any adverse maternal outcome in pregnancy or within 365 days of childbirth, had a comparison group, were peer-reviewed, and were written in English. Data extraction and synthesis Two reviewers used standardized instruments to extract data and rate study quality and the certainty of evidence. A narrative synthesis was performed. Results Of 6,381 studies retrieved, seven met our criteria. There were two prospective cohort studies, two retrospective cohort studies, and 3 cross-sectional studies, conducted in the United States (n=6) and Canada (n=1), and ranging in size from n=3,110 to n=57,326,681. Studies showed a dose-response relation between the number of co-occurring chronic conditions and risk of adverse maternal outcomes, including severe maternal morbidity or mortality, hypertensive disorders of pregnancy, and acute health care use in the perinatal period. Study quality was rated as strong (n=1), moderate (n=4), or weak (n=2), and the certainty of evidence was very low to moderate. Conclusion Given the increasing prevalence of chronic disease risk factors such as advanced maternal age and obesity, more research is needed to understand the impact of pre-pregnancy multimorbidity on maternal health so that appropriate preconception and perinatal supports can be developed.","PeriodicalId":73843,"journal":{"name":"Journal of multimorbidity and comorbidity","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42210290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimorbidity and mortality: A data science perspective","authors":"K. W. Siah, Chi Heem Wong, Jerry Gupta, A. Lo","doi":"10.1177/26335565221105431","DOIUrl":"https://doi.org/10.1177/26335565221105431","url":null,"abstract":"Background With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide. Methods Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005–2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs based on single snapshots of closed cohorts, we adopt a longitudinal approach to examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality. Results The proportion of the population with multimorbidity has increased by approximately 2.5 percentage points over the last decade, with more than 17% having at least two chronic morbidities. We find that the prevalence and the severity of multimorbidity, as quantified by the number of co-occurring chronic conditions, increase progressively with age. Stratifying by socioeconomic status, we find that people living in more deprived areas are more likely to be multimorbid compared to those living in more affluent areas at all ages. The same trend holds consistently for all years in our data. In general, hypertension, diabetes, and respiratory-related diseases demonstrate high in-degree centrality and eigencentrality, while cardiac disorders show high out-degree centrality. Conclusions We use data-driven methods to characterize multimorbidity patterns in different demographic groups and their evolution over the past decade. In addition to a number of strongly associated comorbid pairs (e.g., cardiac-vascular and cardiac-metabolic disorders), we identify three principal clusters: a respiratory cluster, a cardiovascular cluster, and a mixed cardiovascular-renal-metabolic cluster. These are supported by established pathophysiological mechanisms and shared risk factors, and largely confirm and expand on the results of existing studies in the medical literature. Our findings contribute to a more quantitative understanding of the epidemiology of multimorbidity, an important pre-requisite for developing more effective medical care and policy for multimorbid patients.","PeriodicalId":73843,"journal":{"name":"Journal of multimorbidity and comorbidity","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42480861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Definition of patient complexity in adults: A narrative review","authors":"S. Nicolaus, Baptiste Crelier, J. Donzé, C. Aubert","doi":"10.1177/26335565221081288","DOIUrl":"https://doi.org/10.1177/26335565221081288","url":null,"abstract":"Background Better identification of complex patients could help to improve their care. However, the definition of patient complexity itself is far from obvious. We conducted a narrative review to identify, describe, and synthesize the definitions of patient complexity used in the last 25 years. Methods We searched PubMed for articles published in English between January 1995 and September 2020, defining patient complexity. We extended the search to the references of the included articles. We assessed the domains presented in the definitions, and classified the definitions as based on (1) medical aspects (e.g., number of conditions) or (2) medical and/or non-medical aspects (e.g., socio-economic status). We assessed whether the definition was based on a tool (e.g., index) or conceptual model. Results Among 83 articles, there was marked heterogeneity in the patient complexity definitions. Domains contributing to complexity included health, demographics, behavior, socio-economic factors, healthcare system, medical decision-making, and environment. Patient complexity was defined according to medical aspects in 30 (36.1%) articles, and to medical and/or non-medical aspects in 53 (63.9%) articles. A tool was used in 36 (43.4%) articles, and a conceptual model in seven (8.4%) articles. Conclusion A consensus concerning the definition of patient complexity was lacking. Most definitions incorporated non-medical factors in the definition, underlining the importance of accounting not only for medical but also for non-medical aspects, as well as for their interrelationship.","PeriodicalId":73843,"journal":{"name":"Journal of multimorbidity and comorbidity","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47386513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}