JMIR Publications

ABSTRACT

Background:

Multimorbidity is characterized by the co-occurrence of two or more chronic diseases, garnering the attention of the health care sector and health policymakers due to severe adverse effects.

Objective:

This paper uses the latest fifth of a century of national health data in Brazil to analyze the impacts of demographic factors and predict the impact of various risk factors on multimorbidity.

Methods:

Data analysis methods include descriptive analysis, logistic regression, and nomogram prediction. The research illustrates a set of national cross-sectional data with a sample size of 877,032. The study period was from 1998, 2003, and 2008 in the Brazilian National Household Sample Survey (PNAD) and 2013 and 2019 in the Brazilian National Health Survey (PNS).

Results:

Overall, females were 1.7 times more likely to suffer from multimorbidity than males ([OR] 1.72, 95% CI 1.69-1.74). The prevalence of multimorbidity was 1.5 times higher among non-working individuals ([OR] 1.51, 95% CI 1.49-1.53) and significantly increased with age. People over 60 years old were about 20 times more likely to have multiple chronic diseases than those between 18-29 years of age ([OR] 19.6, 95% CI 19.15-20.07). The risk of multimorbidity in illiterate individuals was 1.2 times higher than in literate ones ([OR] 1.26, 95% CI 1.24-1.28). The subjective well-being of seniors with multiple chronic diseases was 15 times higher among people without multimorbidity ([OR] 15.29, 95% CI 14.97-15.63). Adults with multimorbidity were more than 1.5 times likely to be hospitalized([OR] 1.53, 95% CI 1.50-1.56) and 1.9 times likely to need medical care ([OR] 1.94, 95% CI 1.91-1.97). These patterns were similar in five cohorts and remained stable for over 21 years. A nomogram model was used to predict multimorbidity prevalence under the influence of various risk factors. The prediction results were consistent with the impacts of logistic regression, among which age and subject well-being had the highest impact on multimorbidity prevalence.

Conclusions:

Our study shows that multimorbidity prevalence has little variation in the latest fifth of a century but varies widely across social groups. We hope that predicting populations with higher risk factors of multimorbidity prevalence will champion policy-making on multimorbidity prevention and management.