Cognitive impairment and reporting of hypertension among adults in india: Evidence from a population-based study
Bibliographic record
Abstract
The study investigates the disparities between the prevalence of self-reported and measured hypertension among adults and the role of cognitive impairment in such disparities. The study used data from the first wave of the Longitudinal Aging Study in India, a nationally representative survey of 72,250 individuals. Percentage distributions were calculated for cognitive impairment, self-reported hypertension, and objective measures of hypertension along with the explanatory variables. Multivariable logistic regression was performed to assess the association of cognitive impairment and other factors with self-reported and measured hypertension. Furthermore, the Propensity Score Matching method was used to estimate the effect of cognitive impairment on self-reported hypertension, measured hypertension, and the misreporting of hypertension. Cognitive impairment was found in 9.8% of Indian adults in this study. Cognitive impairment was most prevalent among females and those over 75 years of age. Hypertension too was higher among females as well as among rural residents and those with no education compared to their respective counterparts. The likelihood of cognitively impaired adults having hypertension was 24% more than that of their cognitively unimpaired counterparts [OR = 1.24, CI = 1.18-1.32]. Other risk factors of hypertension were age, alcohol consumption, and place of residence. The PSM analysis revealed that individuals with cognitive impairment were 2.7% more likely to underreport their hypertensive status compared to those without cognitive impairment. The study underscores the significance of acknowledging reporting bias among individuals with cognitive impairment. Addressing this bias in healthcare systems is crucial. The policy recommendations encompass creating tailored healthcare interventions, improving access to healthcare, enhancing communication strategies, and providing robust support to those with cognitive impairment to ensure accurate diagnosis and proper disease management. Healthcare providers require training to identify and mitigate reporting biases in this vulnerable group. Doing so will ultimately enhance healthcare outcomes.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".