Open AccessPatterns of multimorbidity and demographic profile of latent classes in a Danish population—A register-based study
Author(s) -
Sanne Pagh Møller,
Bjarne Laursen,
Caroline Klint Johannesen,
Janne S Tolstrup,
Stine Schramm
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0237375
Subject(s) - latent class model , demography , marital status , social class , danish , multinomial logistic regression , odds , population , medicine , socioeconomic status , logistic regression , odds ratio , gerontology , environmental health , linguistics , statistics , philosophy , mathematics , machine learning , sociology , political science , computer science , law
Background Multimorbidity is an increasing public health concern and is associated with a range of further adverse outcomes. Identification of disease patterns as well as characteristics of populations affected by multimorbidity is important for prevention strategies to identify those at risk. Aim The aim of the study was to identify and describe demographic characteristics of multimorbidity classes in three age groups (16–44 years, 45–64 years, and 65+ years). Methods Based on register information on 47 chronic diseases and conditions, we used latent class analysis to identify multimorbidity classes in a random sample of the Danish population (n = 470,794). Information on sociodemographic characteristics (age, sex, region of origin, educational level, employment status, and marital status) was obtained from registers and linked to the study population. Age- and sex-adjusted multinomial logistic regression models were used to examine associations between multimorbidity classes and sociodemographic characteristics. Results We identified seven classes among individuals in the age groups 45–64 years and 65+ years and five classes in the age group 16–44 years. Overall, the classes were similar in the three age groups, but varied in size, i.e. the class ‘No or few diseases’ was larger in the younger age group. The class ‘Many diseases’ (a class with both somatic diseases and mental illnesses) was only seen in individuals aged 45–64 years and 65+ years. There were social inequalities in odds of belonging to the multimorbidity classes compared to the healthier class. These social inequalities varied but were especially strong in the classes named ‘Many diseases’ and ‘Mental illness, epilepsy’. Conclusion The results of the study suggest that there are social inequalities in multimorbidity but that these inequalities are not universal to all types of multimorbidity. This supports that multimorbidity is diverse and should be prevented and treated accordingly.