Evidence map›Paper›PMID 39839412›Full record

ArticleFrontiers in public health2024

Level measurement, regional variations, and the dynamic evolution of active aging in China-analysis based on CHARLS tracking data.

Jiru Guo, Xiaoli Zhang, Longyin Chen, Hong Yang

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jiru GuoSchool of Economics and Management, Shanghai University of Sport, Shanghai, China.
Xiaoli ZhangSchool of Physical Education, Xi'an Physical Education University, Xi'an, China.
Longyin ChenSchool of Art, Xi'an Physical Education University, Xi'an, China.
Hong YangSchool of Sports Economics and Management, Xi'an Physical Education University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Given the world's largest and increasingly serious aging population, China has elevated "positively responding to aging of population" to a national strategy. Exploring the current state and evolutionary trends of active aging over the past decade is a fundamental prerequisite and the primary task for implementing this strategy. Methods: Based on data from the China Health and Retirement Longitudinal Study (2011-2018), this study primarily employs methods such as the entropy method, Gini coefficient, Moran index, and Kernel density estimation to analyze the development level, regional differences, and dynamic evolution of active aging in China. Results: (1) From a general point of view, the overall level of active aging in China has not been high in the past decade, but has shown a rising trend year by year. Older Chinese people exhibit high levels of physical and mental health, but social participation and economic status remain areas of weakness in active aging. (2) Inter-regional differences are the main source of the overall differences in the level of active aging in China. (3) There is a spatial clustering of the active aging level in China, along with a neighborhood effect. (4) The bifurcation phenomenon of active aging in China has intensified over time. While the eastern region exhibits uneven development, the central and western regions have generally had more balanced growth. Discussion: To improve the level of active aging among older adult individuals in China, policymakers should continuously optimize policies and pay more attention to the economic status and social participation of the older adult. Local governments should not only fully leverage their regional advantages but also interact with other regions to achieve cross-regional joint development.

Indexed as

AgingAgedAged, 80 and overChinaFemaleHumansLongitudinal StudiesMaleMiddle Agedactive agingChina Health and Retirement Longitudinal Studydynamic evolutionlevel measurementregional variations

Identifiers

PMID39839412
PMCPMC11748547

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.