Inequalities in multimorbidity among elderly: a population-based study in a city in Southern Brazil

Abstract

Lower socioeconomic level is positively related to multimorbidity and it is possible that the clustering of health conditions carries the same association. The aim of this study was to identify prevalence of multimorbidity and clusters of health conditions among elderly, as well the underlying socioeconomic inequalities. This was a cross-sectional population-based study carried out with 60-year-old individuals. Multimorbidity was defined as the presence of 2+, 3+, 4+ or 5+ health conditions in the same individual. Schooling levels and the National Economic Index were used to investigate inequalities in the prevalence of multimorbidities among elderly. Slope and concentration indexes of inequality were used to evaluate absolute and relative differences. A factorial analysis was performed to identify disease clusters. In every ten older adults, about nine, eight, seven and six presented, respectvely, 2+, 3+, 4+ and 5+ health conditions. Three clusters of health conditions were found, involving musculoskeletal/mental/functional disorders, cardiometabolic, and respiratory factors. Higher inequalities were found the higher amount of health conditions (5+), when considering economic level, and for 3+, 4+ and 5+, when considering educational level. These findings show high multimorbidity prevalence among elderly, highlighting the persistence of health inequalities in Southern Brazil. Strategies by the health services need to focus on elderly at lower socioeconomic levels.

Prevalence and Patterns of Multimorbidity Among Rural Elderly: Findings of the AHSETS Study

“…While Arthritis alone is placed in a cluster, hypertension, and acid peptic disease are in another cluster and in the last cluster all the rest conditions were placed. Different sets of chronic disease clusters have been reported from studies done in the United Kingdom (3 clusters), Brazil (3 clusters), Switzerland (4 clusters), USA (4 clusters), Portugal (6 clusters), and Australia (6 clusters) using separate methods of cluster analysis such as hierarchical agglomeration, correlation matrix with Yule's Q and exploratory factor analysis (27)(28)(29)(30)(31)(32). However, the cluster membership is variable and depends on both the nature of the study population and statistical methods used in analysis.…”

Multimorbidity of chronic non-communicable diseases in low- and middle-income countries: A scoping review

“…While most studies (n ¼ 56, 73.7%) defined multimorbidity as the presence of two or more chronic conditions, some studies 30,34,[38][39][40]50,57,101 used the presence of three or more chronic conditions as their definition. All studies used simple counting of the number of chronic conditions from a list of individual diseases with the list varying substantially from one study to another.…”

Consumo alimentar e multimorbidade entre idosos não institucionalizados de Pelotas, 2014: estudo transversal *

“…The same cut-off point was proposed in a study published recently. 15 The health problems included in this study were reported by elderly people, which may result in information bias. This may however be minimized by the fact of the questions asking about medical diagnosis 30 and approximately 90% of those elderly people having visited a doctor in the last year, thus minimizing possible selfreporting bias.…”

“…Multimorbidity was defined as the presence of five or more diseases, based on a study already conducted with the same target population as this study. 15 It should be noted that multimorbidity assessment worldwide is not uniform and that currently use of a list of at least 12 diseases is recommended for this assessment. 1 Nutritional status was also assessed based on body mass index (BMI), whereby overweight and obesity were considered to be components of multimorbidity and were defined as ≥25kg/m² and ≥30kg/m², respectively.…”