Multimorbidity patterns in the elderly: a prospective cohort study with cluster analysis

Abstract

BackgroundMultimorbidity is the coexistence of more than two chronic diseases in the same individual; however, there is no consensus about the best definition. In addition, few studies have described the variability of multimorbidity patterns over time. The aim of this study was to identify multimorbidity patterns and their variability over a 6-year period in patients older than 65 years attended in primary health care.MethodsA cohort study with yearly cross-sectional analysis of electronic health records from 50 primary health care centres in Barcelona. Selected patients had multimorbidity and were 65 years of age or older in 2009. Diagnoses (International Classification of Primary Care, second edition) were extracted using O’Halloran criteria for chronic diseases. Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex and age group (65–79 and ≥80 years) at the beginning of the study period.ResultsAnalysis of 2009 electronic health records from 190,108 patients with multimorbidity (59.8% women) found a mean age of 71.8 for the 65–79 age group and 84.16 years for those over 80 (Standard Deviation [SD] 4.35 and 3.46, respectively); the median number of chronic diseases was seven (Interquartil range [IQR] 5–10). We obtained 6 clusters of multimorbidity patterns (1 nonspecific and 5 specifics) in each group, being the specific ones: Musculoskeletal, Endocrine-metabolic, Digestive/Digestive-respiratory, Neurological, and Cardiovascular patterns. A minimum of 42.5% of the sample remained in the same pattern at the end of the study, reflecting the stability of these patterns.ConclusionsThis study identified six multimorbidity patterns per each group, one nonnspecific pattern and five of them with a specific pattern related to an organic system. The multimorbidity patterns obtained had similar characteristics throughout the study period. These data are useful to improve clinical management of each specific subgroup of patients showing a particular multimorbidity pattern.Electronic supplementary materialThe online version of this article (10.1186/s12877-018-0705-7) contains supplementary material, which is available to authorized users.

Association of Symptoms of Obstructive Lung Disease and All‐Cause Mortality in Older Adult Smokers

“…Further, our study shows that 9% in More Dyspnea‐No OLD, 44% in Less Dyspnea‐With OLD, and 78% in More Dyspnea‐With OLD have an FEV 1 % predicted lower than 80% and therefore should be considered high risk related to multimorbidity and potential risk of death. The typical presentation of OLD including asthma in older adults may lead to misdiagnosis or underdiagnosis and therefore can lead to undertreatment due to multimorbidity patterns . Older adults with asthma may have a variable presentation and do not all present with airflow limitation .…”

“…The typical presentation of OLD including asthma in older adults may lead to misdiagnosis or underdiagnosis and therefore can lead to undertreatment due to multimorbidity patterns. 30,31 Older adults with asthma may have a variable presentation and do not all present with airflow limitation. 32 Further, there is no agreed upon spirometry-defined cutoff for the diagnosis of COPD in older adults.…”

Survival in relation to multimorbidity patterns in older adults in primary care in Barcelona, Spain (2010–2014): a longitudinal study based on electronic health records

“…In a previous study of the same sample, seven chronic multimorbidity patterns (CMPs) were generated by cluster analysis (K-means clustering), with consideration only of diseases with a prevalence ≥1% to avoid spurious associations and obtain coherent epidemiological patterns (online supplementary file S2). 13 …”

Trends of multimorbidity in 15 European countries: a population-based study in community-dwelling adults aged 50 and over

“…Another aspect that should be considered is the method used to collect the information. Some studies have used clinical records in hospitals or primary care units, while others collected information through self-report 12,[19][20][21] . Selfreport it is the most feasible method for population-based epidemiological studies; however, it has the potential to underestimate the prevalence 22 .…”

“…Several studies show that the prevalence of multimorbidity increases with age 20,32,33 . Stratified analysis for 10-year-old groups indicates in our results a possible cohort effect in some countries, such as the reduction among men and women in Italy for the younger age groups (50-59 and 60-69) and increase in men in group 80 or more.…”