Review GLOBAL PREVALENCE OF NON-COMMUNICABLE DISEASES MORBIDITY AND COMORBIDITY AMONG ELDERLY INDIVIDUALS: A SYSTEMATIC REVIEW OF OBSERVATIONAL STUDIES
Bibliographic record
Abstract
A systematic review was carried out to assess prevalence of noncommunicable disease morbidity and comorbidity in relationship to socioeconomic, behavioural health and environmental risk factors for among elderly people globally. Observational studies were culled from public databases, such as PubMed, SCOPUS, Science Direct, and Google Scholar from January 2015 to December 2020, and a Newcastle-Ottawa Quality Assessment Scale tool and PRISMA 2009 checklist were used to assess risk of bias in selected material. Analysis was performed using a weighted mean of morbidity prevalence and disease subgroup together with an R program for data analysis. Among 16 eligible studies and 14 cross-sectional studies weighted mean of morbidity and comorbidity prevalence was 75.1 and 60.6% respectively. NCDs constituted mainly coronary heart disease, diabetes mellitus, hypercholesterolemia, hypertension, and stroke, with hypertension having the highest prevalence among all subjects (62.2%), both females (65.9%) and males (65.4%). In the elderly population individual and behavioural risk factors were the main categories related to morbidity, with behavioral risk factors contributing to comorbidities. In conclusion, morbidity and comorbidity prevalence are high among the elderly population worldwide. Diabetes mellitus and hypertension are the most common illnesses. Age, socioeconomic status, environmental settings and behavioral risk factors influenced comorbidity occurrence.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".