Evidence map›Paper›PMID 35968418›Full record

ArticleFrontiers in public health2022

Health shocks and changes in preventive behaviors: Results from the China Health and Retirement Longitudinal Study.

Peng Zhang, Hongli Jiang, Wen Chen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
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

12 citing papers in PubMed, 18 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Peng ZhangSchool of Public Health, Fudan University, Shanghai, China.
Hongli JiangSchool of Public Health, Fudan University, Shanghai, China.
Wen ChenSchool of Public Health, Fudan University, Shanghai, China.
Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: China is facing the challenge of rising prevalence and ballooning burden of chronic non-communicable diseases (NCDs); however, the Chinese middle- and older-aged population considerably lack preventive behaviors. Health shocks (HS), widely defined as sudden health deterioration brought on by diseases or accidents, bring a "teachable moment" to motivate changes in preventive behaviors. Objective: This study aims to examine the effect of HS on changes in preventive behaviors, including personal health practices and preventive care utilization. Methods: HS was defined as any five chronic disease diagnoses (cancer, heart disease, stroke, diabetes, and hypertension). The impacts of HS on smoking, drinking, and exercise, physical examination were estimated. The panel data of 13,705 respondents were obtained from the latest two waves of the China Health and Retirement Longitudinal Study (CHARLS) in 2015 and 2018. A multilevel propensity score match difference-in-difference (multilevel PSM-DID) model was constructed. Results: HS significantly decreased smoking (OR = 0.59, Conclusion: After HS, there is a teachable moment to promote positive changes in preventive behaviors. Guided by the 5A's brief intervention model (Ask, Advise, Assess, Assist, Arrange), tailored interventions should be targeted at these populations to reduce the risk of the progression and complications of existing diseases, prevent the related comorbidity, and prolong the expected life-span.

Indexed as

RetirementSmokingChinaChronic DiseaseHumansLongitudinal StudiesChinahealth shockslongitudinal studiespreventive behaviorsthe China Health and Retirement Longitudinal Study

Identifiers

PMID35968418
PMCPMC9363769
OpenAlexW4288033669

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.