Couple concordance in diabetes, hypertension and dyslipidaemia in urban India and Pakistan and associated socioeconomic and household characteristics and modifiable risk factors
Bibliographic record
Abstract
Background: Concordance in chronic disease status has been observed within couples. In urban India and Pakistan, little is known about couple concordance in diabetes, hypertension, and dyslipidaemia and associated socioeconomic characteristics and modifiable risk factors.Methods: We analysed cross-sectional data from 2548 couples from the Centre for cArdio-metabolic Risk Reduction in South Asia cohort in Chennai, Delhi and Karachi. We estimated couple concordance in presence of ≥1 of diabetes, hypertension and dyslipidaemia (positive concordance: both spouses (W+H+); negative concordance: neither spouse (W-H-); discordant wife: only wife (W+H-); or discordant husband: only husband (W-H+)). We assessed associations of five socioeconomic and household characteristics, and six modifiable risk factors with couple concordance using multinomial logistic regression models with couples as the unit of analysis (reference: W-H-).Results: Of the couples, 59.4% (95% CI 57.4% to 61.3%) were concordant in chronic conditions (W+H+: 29.2% (95% CI 27.4% to 31.0%); W-H-: 30.2% (95% CI 28.4%- to 32.0%)); and 40.6% (95% CI 38.7% to 42.6%) discordant (W+H-: 13.1% (95% CI 11.8% to 14.4%); W-H+: 27.6% (95% CI 25.9% to 29.4%)). Compared with couples with no conditions (W-H-), couples had higher relative odds of both having at least one condition if they had higher versus lower levels of: income (OR 2.03 (95% CI 1.47 to 2.80)), wealth (OR 2.66 (95% CI 1.98 to 3.58)) and education (wives' education: OR 1.92 (95% CI 1.29 to 2.86); husbands' education: OR 2.98 (95% CI 1.92 to 4.66)) or weight status (overweight or obesity in both spouses ORs 7.17 (95% CI 4.99 to 10.30)).Conclusions: Positive couple concordance in major chronic conditions is high in urban India and Pakistan, especially among couples with relatively higher socioeconomic position. This suggests that prevention and management focusing on couples at high risk for concordant chronic conditions may be effective and more so in higher socioeconomic groups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".