Additive interaction of family medical history of cardiovascular diseases with hypertension and diabetes on the diagnosis of cardiovascular diseases among older adults in India
Bibliographic record
Abstract
Introduction: The present study aimed to examine the additive interaction of family medical history of cardiovascular disease (CVD) and self-reported hypertension and diabetes on the diagnosis of CVD among older adults aged 45 years and above in India. A family medical history of CVD in individuals with hypertension and diabetes could identify a subpopulation with a higher risk of CVD. Methods: The study used the data from the Longitudinal Ageing Study in India (LASI) Wave 1 (2017-2018). The total sample size for the study was 58,734 older adults aged 45 years and above. An additive model was applied to determine the additive interaction effect of the family medical history of CVD with hypertension and diabetes on the diagnosis of CVD by calculating three different measures of additive interaction: the relative excess risk due to interaction (RERI), attribution proportion due to interaction (AP), and synergy index (S). Results: The prevalence of CVD was higher among hypertensive individuals with a family medical history of CVD (18.6%) than individuals without the coexistence of family medical history of CVD and hypertension (4.7%), and hypertensive individuals without family medical history of CVD (11.3%). On the other hand, the prevalence of CVD was higher among individuals with diabetes and family history of CVD (20.5%) than individuals without the coexistence of family history of CVD and diabetes (5.0%). Individuals with parental and sibling medical history had two times higher odds of having chronic heart diseases and strokes, respectively than those without parental and sibling history. In the adjusted model, RERI, AP, and S for CVD were 2.30 (95% CI: 0.87-3.74), 35% (0.35; 95% CI: 0.20-0.51), and 1.71 (95% CI: 1.27-2.28) respectively, demonstrating significant positive interaction between family medical history and hypertension on the diagnosis of cardiovascular diseases. Conclusions: The present study revealed that in the additive model, the interaction effects of family medical history and hypertension were significantly positive on cardiovascular diseases even after adjustment with potential confounding factors. Therefore, it is crucial to consider the presence of family medical history of CVD among individuals with hypertension and diabetes measured in research and clinical practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".