Evidence map›Paper›PMID 39232685›Full record

SynthesisBMC public health2024

The impact of social relationships on the risk of stroke and post-stroke mortality: a systematic review and meta-analysis.

Mingxian Meng, Zheng Ma, Hangning Zhou, Yanming Xie, Rui Lan, Shirui Zhu, Deyu Miao, Xiaoming Shen

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BMC public health, 2024. 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. Review
  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

8 authors.

Mingxian MengEncephalopathy Hospital, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan, China.
Zheng MaSchool of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
Hangning ZhouState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Yanming XieInstitute of Clinical Basic Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Rui LanEncephalopathy Hospital, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan, China.
Shirui ZhuEncephalopathy Hospital, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan, China.
Deyu MiaoEncephalopathy Hospital, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan, China.
Xiaoming ShenEncephalopathy Hospital, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan, China. sxmdoc@hactcm.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe association between poor social relationships and post-stroke mortality remains uncertain, and the evidence regarding the relationship between poor social relationships and the risk of stroke is inconsistent. In this meta-analysis, we aim to elucidate the evidence concerning the risk of stroke and post-stroke mortality among individuals experiencing a poor social relationships, including social isolation, limited social networks, lack of social support, and loneliness.

methodsA thorough search of PubMed, Embase, and the Cochrane Library databases to systematically identify pertinent studies. Data extraction was independently performed by two researchers. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using either a random-effects or fixed-effects model. Sensitivity analyses were conducted to evaluate the reliability of the results. Random-effects meta-regression was performed to explore the sources of heterogeneity in stroke risk estimates between studies. Assessment for potential publication bias was carried out using Egger's and Begg's tests.

resultsNineteen studies were included, originating from 4 continents and 12 countries worldwide. A total of 1,675,707 participants contributed to this meta-analysis. Pooled analyses under the random effect model revealed a significant association between poor social relationships and the risk of stroke (OR = 1.30; 95%CI: 1.17-1.44), as well as increased risks for post-stroke mortality (OR = 1.36; 95%CI: 1.07-1.73). Subgroup analyses demonstrated associations between limited social network (OR = 1.52; 95%CI = 1.04-2.21), loneliness (OR = 1.31; 95%CI = 1.13-1.51), and lack of social support (OR = 1.66; 95%CI = 1.04-2.63) with stroke risk. The meta-regression explained 75.21% of the differences in reported stroke risk between studies. Random-effect meta-regression results indicate that the heterogeneity in the estimated risk of stroke may originate from the continent and publication year of the included studies.

conclusionSocial isolation, limited social networks, lack of social support, and feelings of loneliness have emerged as distinct risk factors contributing to both the onset and subsequent mortality following a stroke. It is imperative for public health policies to prioritize the multifaceted influence of social relationships and loneliness in stroke prevention and post-stroke care.

trial registrationThe protocol was registered on May 1, 2024, on the Prospero International Prospective System with registration number CRD42024531036.

Indexed as

LonelinessSocial IsolationSocial SupportStrokeHumansInterpersonal RelationsRisk FactorsMeta-analysisMortalitySocial isolationSocial networkSocial supportStroke

Identifiers

PMID39232685
PMCPMC11373457

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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.