Evidence map›Paper›PMID 34682392›Full record

ArticleInternational journal of environmental research and public health2021

Vulnerable Older Adults' Identification, Geographic Distribution, and Policy Implications in China.

Xiaoyi Jin, Yanjun Liu, Zhaoyuan Hu, Wei Du

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
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.

Xiaoyi JinSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China.
Yanjun LiuSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China.
Zhaoyuan HuSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China.
Wei DuSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the population aging and urbanization in China, vulnerable older adults tend to show more complex characteristics, bringing great challenges to public health policies. Using China Longitudinal Aging Social Survey data 2014, this paper builds a comprehensive index system for the identification of vulnerable older adults from three dimensions, including health, economy, and social support, then divides older adults into four support levels and six small classes by using the typological method. The results show that older adults in urgent need of assistance or priority are those poor in health and economic conditions, 1.46% of them are highly vulnerable because of the lack of social support; 12.76% of them obtain a certain social support are moderately vulnerable; and 34.72% of them are slightly vulnerable with disadvantage in only one dimension. The geographic distribution of different types of vulnerable older adults varies significantly. The paper provides evidence to design more feasible and specific policies with comprehensive considerations for different types of vulnerable older adults residing in different regions.

Indexed as

PolicySocial SupportChinaLongitudinal Studiesgeographic distributionlatent class analysispolicy implicationsvulnerable older adults

Identifiers

PMID34682392
PMCPMC8535285

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.