Prediction of Multimorbidity in Brazil: Latest Fifth of a Century Population Study
Bibliographic record
Abstract
BACKGROUND: Multimorbidity is characterized by the co-occurrence of 2 or more chronic diseases and has been a focus of the health care sector and health policy makers due to its severe adverse effects. OBJECTIVE: This paper aims to use the latest 2 decades of national health data in Brazil to analyze the effects 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 study makes use of a set of national cross-sectional data with a sample size of 877,032. The study used data from 1998, 2003, and 2008 from the Brazilian National Household Sample Survey, and from 2013 and 2019 from the Brazilian National Health Survey. We developed a logistic regression model to assess the influence of risk factors on multimorbidity and predict the influence of the key risk factors in the future, based on the prevalence of multimorbidity in Brazil. RESULTS: Overall, females were 1.7 times more likely to experience multimorbidity than males (odds ratio [OR] 1.72, 95% CI 1.69-1.74). The prevalence of multimorbidity among unemployed individuals was 1.5 times that of employed individuals (OR 1.51, 95% CI 1.49-1.53). Multimorbidity prevalence increased significantly with age. People over 60 years of age were about 20 times more likely to have multiple chronic diseases than those between 18 and 29 years of age (OR 19.6, 95% CI 19.15-20.07). The prevalence of multimorbidity in illiterate individuals was 1.2 times that in literate ones (OR 1.26, 95% CI 1.24-1.28). The subjective well-being of seniors without multimorbidity was 15 times that among people with multimorbidity (OR 15.29, 95% CI 14.97-15.63). Adults with multimorbidity were more than 1.5 times more likely to be hospitalized than those without (OR 1.53, 95% CI 1.50-1.56) and 1.9 times more likely need medical care (OR 1.94, 95% CI 1.91-1.97). These patterns were similar in all 5 cohort studies 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 effects of logistic regression; older age and poorer participant well-being had the strongest correlation with multimorbidity. CONCLUSIONS: Our study shows that multimorbidity prevalence varied little in the past 2 decades but varies widely across social groups. Identifying populations with higher rates of multimorbidity prevalence may improve policy making around multimorbidity prevention and management. The Brazilian government can create public health policies targeting these groups, and provide more medical treatment and health services to support and protect the multimorbidity population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".