Decomposition Analysis of Depressive Symptom Differences Among Older Adults With Hypertension Between Urban and Rural Areas: Cross-Sectional Study
Bibliographic record
Abstract
Background: Hypertension is the most prevalent chronic disease among China's older population, which comprises a growing proportion of the overall demographic. Older individuals with chronic diseases have a higher risk of developing depressive symptoms than their healthy counterparts, as evidenced in China's older population, where patients with hypertension exhibit varying rates of depression depending on residing in urban or rural areas. Objective: This study aimed to investigate factors influencing and contributing to the disparities in depressive symptoms among older urban and rural patients with hypertension in China. Methods: We used a cross-sectional study design and derived data from the 8th Chinese Longitudinal Health Longevity Survey of 2018. The Fairlie model was applied to analyze the factors contributing to disparities in depressive symptoms between urban and rural older populations with hypertension. Results: The sample size for this study was 5210, and 12.8% (n=669) of participants exhibited depressive symptoms. The proportions of depressive symptoms in rural and urban areas were 14.1% (n=468) and 10.7% (n=201), respectively. In rural areas, years of education (1-6 years: odds ratio [OR] 0.68, 95% CI 1.10-1.21; ≥7 years: OR 0.47, 95% CI 0.24-0.94), alcohol consumption (yes: OR 0.52, 95% CI 0.29-0.93), exercise (yes: OR 0.78, 95% CI 0.56-1.08), and sleep duration (6.0-7.9 hours: OR 0.29, 95% CI 0.17-0.52; 8.0-9.9 hours: OR 0.24, 95% CI 0.13-0.43; ≥10.0 hours: OR 0.22, 95% CI 0.11-0.41) were protective factors against depressive symptoms in older adults with hypertension, while gender (female: OR 1.94, 95% CI 1.33-2.81), self-reported income status (poor: OR 3.07, 95% CI 2.16-4.37), and activities of daily living (ADL) dysfunction (mild: OR 1.69, 95% CI 1.11-2.58; severe: OR 3.03, 95% CI 1.46-6.32) were risk factors. In urban areas, age (90-99 years: OR 0.37, 95% CI 0.16-0.81; ≥100 years: OR 0.19, 95% CI 0.06-0.66), exercise (yes: OR 0.33, 95% CI 0.22-0.51), and sleep duration (6.0-7.9 hours: OR 0.27, 95% CI 0.10-0.71; 8.0-9.9 hours: OR 0.16, 95% CI 0.06-0.44; ≥10.0 hours: OR 0.18, 95% CI 0.06-0.57) were protective factors, while years of education (1-6 years: OR 1.91, 95% CI 1.05-3.49), self-reported income status (poor: OR 2.94, 95% CI 1.43-6.08), and ADL dysfunction (mild: OR 2.38, 95% CI 1.39-4.06; severe: OR 3.26, 95% CI 1.21-8.76) were risk factors. The Fairlie model revealed that 91.61% of differences in depressive symptoms could be explained by covariates, including years of education (contribution 63.1%), self-reported income status (contribution 13.2%), exercise (contribution 45.7%), sleep duration (contribution 20.8%), ADL dysfunction (contribution -9.6%), and comorbidities (contribution -22.9%). Conclusions: Older patients with hypertension in rural areas had more depressive symptoms than their counterparts residing in urban areas, which could be explained by years of education, self-reported income status, exercise, sleep duration, ADL dysfunction, and comorbidities. Factors influencing depressive symptoms had similarities regarding exercise, sleep duration, self-reported income status, and ADL dysfunction as well as differences regarding age, gender, years of education, and alcohol consumption.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".