Hypertension Prevalence Among People Lifted Out of Poverty in China in 2018-2023: Retrospective Spatiotemporal Analysis
Bibliographic record
Abstract
Background: Hypertension is a significant global public health concern, with particular concern in China due to its widespread prevalence. The spatial distribution of hypertension varies significantly, revealing important regional disparities that may impact public health strategies and interventions. Objective: This study aimed to investigate the temporal trends and spatial distribution characteristics of hypertension prevalence among individuals lifted out of poverty in China, covering the period from 2018 to 2023. Methods: We used data from the National Health Poverty Alleviation Dynamic Management System to analyze hypertension prevalence among people lifted out of poverty from 2018 to 2023. Long-term trends were assessed using the Joinpoint regression model. Spatial distribution characteristics were examined through global and local spatial autocorrelation, cluster and outlier analyses, and trend surface analyses. These methods provided insights into the spatial aggregation and variability of hypertension prevalence across different regions. Results: From 2018 to 2023, the prevalence of hypertension among people lifted out of poverty in China increased from 2.71% to 7.29%. The highest rates were observed in the northeast, with Jilin Province ranking first for 3 consecutive years (2020-2023), reaching 27.19% in 2023. Linxi County in Inner Mongolia had prevalence rates exceeding 40% for 5 years (2019-2023), peaking at 47.99% in 2022. Among the 22 provinces containing poverty-stricken counties, 9 showed significant annual increases, with Guangxi having the highest annual percentage change at 27.9018% (95% CI 7.4095%-52.3038%). Spatial analysis identified high-high clusters in northern provinces such as Hebei, Jilin, and Inner Mongolia, and low-low clusters in southwestern provinces such as Yunnan and Guizhou. Trend surface analysis revealed a distinct spatial gradient, with the northeast highest and the southwest lowest. Conclusions: The study revealed a generally increasing trend in hypertension prevalence among people lifted out of poverty in China from 2018 to 2023. The highest prevalence rates were concentrated in northeastern poverty-alleviated counties, while southwestern counties exhibited the lowest prevalence rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".