Comparing the income-related inequity of tested prevalence and self-reported prevalence of hypertension in China
Bibliographic record
Abstract
BACKGROUND: Hypertension has become a global health challenge given its high prevalence and but low awareness and detection. Whether the actual prevalence of hypertension has been estimated is important, especially for the poor. This study aimed to measure tested prevalence and self-reported prevalence of hypertension and compare the inequity between them in China. METHODS: Data were derived from China Health and Nutrition Survey (CHNS) conducted in 2011. By using the multistage, stratified, random sampling method, 12,168 respondents aged 18 or older were identified for analysis. Both tested prevalence (systolic blood pressure ≥ 140 mmHg or/and diastolic blood pressure ≥ 90 mmHg or /and current use any of antihypertensive medication) and self-reported prevalence (ever diagnosed with hypertension by a doctor) were used to measure the prevalence of hypertension. The concentration index was employed to measure the extent of inequality in tested prevalence and self-reported prevalence. A decomposition method, based on a Probit model, was used to analyze income-related horizontal inequity of tested prevalence and self-reported prevalence. RESULTS: The tested prevalence and self-reported prevalence of total respondents were 28.8% [95% CI (28.0%, 29.6%)] and 15.7% [95% CI (15.0%, 16.3%)], and 26.4% [95% CI (25.1%, 27.6%)] and 19.0% [95% CI (17.9%, 20.1%)] in urban areas, and 30.3% [95% CI (29.3%, 31.4%)] and 13.5% [95% CI (12.7%, 14.3%)] in rural areas. The horizontal inequity indexes of mean tested prevalence and self-reported prevalence were - 0.0494 and 0.1203 of total respondents, - 0.0736 and 0.0748 in urban area, and - 0.0177 and 0.0466 in rural area respectively, indicating pro-poor inequity in tested prevalence and pro-rich inequity in self-reported prevalence of hypertension. Economic status, education attainment and age were key factors of the pro-poor inequity in tested prevalence. Economic status, area and age were key factors to explain the poor-rich inequity in self-reported prevalence. CONCLUSIONS: This study revealed self-reported prevalence of hypertension was much lower than tested prevalence in China, while a larger gap between self-reported and tested prevalence was found in rural areas. Our study suggested social strategies aiming at narrowing economic gap and regional disparities, reducing educational inequity, and facilitating health conditions of the elderly should be implemented. Finally, awareness raising campaigns to test hypertension in rural area need be strengthened by health education programs and improving the access to public health service, especially for those who do not engage with regular health checkups.
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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 | 0.002 | 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".