Epidemiology and predictors of multimorbidity in Kharameh cohort study: A population‐based cross‐sectional study in southern Iran
Bibliographic record
Abstract
Abstract Background and Aim Multimorbidity is one of the problems and concerns of public health. The aim of this study was to estimate the prevalence and identify the risk factors associated with multimorbidity based on the data of the Kherameh cohort study. Methods This cross‐sectional study was performed on 10,663 individuals aged 40–70 years in the south of Iran in 2015 to 2017. Demographic and behavioral characteristics were investigated. Multimorbidity was defined as the coexistence of two or more of two chronic diseases in a person. In this study, the prevalence of multimorbidity was calculated. Logistic regression was used to identify the predictors of multimorbidity. Results The prevalence of multimorbidity was 24.4%. The age‐standardized prevalence rate was 18.01% in males and 29.6% in females. The most common underlying diseases were gastroesophageal reflux disease with hypertension (33.5%). Multiple logistic regression results showed that the age of 45–55 years (adjusted odds ratio [OR adj] ] = 1.22, 95% confidence interval [CI], 1.07–1.38), age of over 55 years (OR adj = 1.21, 95% CI, 1.06–1.37), obesity (OR adj = 3.65, 95% CI, 2.55–5.24), and overweight (OR adj = 2.92, 95% CI, 2.05–4.14) were the risk factors of multimorbidity. Also, subjects with high socioeconomic status (OR adj = 1.27, 95% CI, 1.1–1.45) and very high level of socioeconomic status (OR adj = 1.53, 95% CI, 1.31–1.79) had a higher chance of having multimorbidity. The high level of education, alcohol consumption, having job, and high physical activity had a protective role against it. Conclusion The prevalence of multimorbidity was relatively high in the study area. According to the results of our study, age, obesity, and overweight had an important effect on multimorbidity. Therefore, determining interventional strategies for weight loss and control and treatment of chronic diseases, especially in the elderly, is very useful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".