Evidence map›Paper›PMID 31035622›Full record

ArticleInternational journal of environmental research and public health2019

Social Integration, Social Support, and All-Cause, Cardiovascular Disease and Cause-Specific Mortality: A Prospective Cohort Study.

Jinke Tan, Yafeng Wang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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

19 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

2 authors.

Jinke TanSchool of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China. tanjinke@ecupl.edu.cn.
Yafeng WangDepartment of Epidemiology and Biostatistics, School of Health Sciences, Wuhan University, Wuhan 430071, China. wonyhfon@whu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social relationships are associated with all-cause mortality. Substantial uncertainties remain, however, for the associations of social relationships with mortality from subtypes of cardiovascular disease (CVD) and major non-vascular diseases. This prospective cohort study estimated mortality risks according to social support and social integration utilizing a nationally representative sample of 29,179 adults ages 18 years and older. Cox proportional hazards regression models were employed. Social integration, but not social support was associated with all-cause mortality risk. For CVD mortality, social integration predicted a 33% lower risk (HR = 0.67, 95% CI = 0.53-0.86). The results were similar in magnitude for heart disease mortality. Participants with the highest social integration level had a 53%, 30%, and 47% decreased mortality risk of diabetes, Alzheimer's disease, and chronic lower respiratory diseases (CLRD) than those with the lowest level. These social integration associations were linear and consistent across baseline age, sex and socioeconomic status. We did not observe an association of social integration with the risk of cancer mortality. Our findings support the linear association of social integration but not social support with mortality from a range of major chronic diseases in the US adult population, independent of socioeconomic status (SES), behavioral risk factors, and health status.

Indexed as

Cause of DeathSocial BehaviorSocial SupportAdolescentAdultAgedAlzheimer DiseaseCardiovascular DiseasesDiabetes MellitusFemaleHealth StatusHumansMaleMiddle AgedProportional Hazards ModelsProspective Studiescardiovascular diseasecohortmortalitysocial integrationsocial support

Identifiers

PMID31035622
PMCPMC6539343

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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.