Measuring the burden of comorbidity for ischaemic heart disease and four common non-communicable diseases in Iran, 1990–2017: a modelling study based on global burden of diseases data
Bibliographic record
Abstract
OBJECTIVE: This modelling study aimed to estimate the comorbidity burden for four common non-communicable diseases with ischaemic heart diseases (IHD) in Iran during a period of 28 years. DESIGN: Analysis of the burden of comorbidity with IHD based on data included prevalence rates and the disability weight (DW) average for calculating years lived with disability (YLDs) from the Iran population based on the Global Burden of Disease (GBD) study. SETTING: Population-based available data in GBD 2017 study of Iran population. PARTICIPANT: The source of data was the GBD 2017 Study. We evaluated IHD, major depressive disorder (MDD), diabetes mellitus (DM), ischaemic stroke (IS), and osteoarthritis (OA) age-standardised prevalence rates and their DW. MAIN OUTCOME MEASURES: A new formula that modified the GBD calculator was used to measure the comorbidity YLDs. In the new formula, some multipliers were considered, measuring the departure from independence. RESULT: The contribution of total comorbidity for each combination of IHD with DM, MDD, IS and OA was 2.5%, 2.0%, 1.6% and 2.9%, respectively. The highest YLD rates were observed for IHD_MDD, 16.5 in 1990 and 17.0 in 2017. This was followed by IHD_DM, from 11.5 to 16.9 per 100 000. The YLD rates for IHD_OA changed slightly (6.5-6.7) per 100 000, whereas there was a gradual reduction in the trends of IHD-IS, from 4.0-4.5 per 100 000. CONCLUSION: Of the four comorbidities studied, the highest burden was due to the coexistence of MDD with IHD. Our results highlight the importance of addressing the burden of comorbidities when studying the burden of IHD or any other non-communicable disease.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".