Evidence map›Paper›PMID 35386277›Full record

ArticleRisk management and healthcare policy2022

Development and Validation of a Risk Score Screening Tool to Identify People at Risk for Hypertension in Shanghai, China.

Qiyun Jiang, Dan Gong, Huiqi Li, Donglan Zhang, Suzhen Hu, Qinghua Xia, Hong Yuan, Peng Zhou, Yiying Zhang, Xing Liu and 3 more

Open access · goldAbstract read
In one paragraph

Article in Risk management and healthcare policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 25% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

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

13 authors at 5 institutions in 4 countries.

Qiyun JiangDepartment of Health Policy and Management, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Dan GongDepartment of Health Policy and Management, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Huiqi LiSchool of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
Donglan ZhangDepartment of Health Policy and Management, College of Public Health, University of Georgia, Athens, GA, USA.
Suzhen HuDepartment of Medical Affairs, Liaocheng People's Hospital, Liaocheng, People's Republic of China.
Qinghua XiaDepartment of Chronic Disease Prevention and Control, Changning District Center for Disease Control and Prevention, Shanghai, People's Republic of China.
Hong YuanDepartment of Chronic Disease Prevention and Control, Jiading District Center for Disease Control and Prevention, Shanghai, People's Republic of China.
Peng ZhouDepartment of Chronic Disease Prevention and Control, Changning District Center for Disease Control and Prevention, Shanghai, People's Republic of China.
Yiying ZhangDepartment of Chronic Disease Prevention and Control, Jiading District Center for Disease Control and Prevention, Shanghai, People's Republic of China.
Xing LiuDepartment of Epidemiology, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Mei SunDepartment of Health Policy and Management, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Jun LvDepartment of Health Policy and Management, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Chengyue LiDepartment of Health Policy and Management, School of Public Health, Fudan University, Shanghai, People's Republic of China.ORCID 0000-0001-5829-5129
Fudan University · CNTaiwan Centers for Disease Control · TWLiaocheng People's Hospital · CNUniversity of Georgia · USUniversity of Gothenburg · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to develop a screening tool based on a risk scoring approach that could identify individuals at high risk for hypertension in Shanghai, China. Methods: A total of 3147 respondents from the 2013 Shanghai Chronic Disease and Risk Factor Surveillance were randomly divided into the derivation group and validation group. The coefficients obtained from multivariable logistic regression were used to assign a score to each variable category. The receiver operating characteristic (ROC) curve was used to find the optimal cut-off point and to evaluate the screening performance. Results: Age, family history of hypertension, having diabetes, having dyslipidemia, body mass index, and having abdominal obesity contributed to the risk score. The area under the ROC curve was 0.817 (95% CI: 0.797-0.836). The optimal cut-off value of 20 had a sensitivity of 83.4%, and a specificity of 64.3%, demonstrating good performance. Conclusion: We developed a simple and valid screening tool to identify individuals at risk for hypertension. Early detection could be beneficial for high-risk groups to better manage their conditions and delay the progression of hypertension and related complications.

Indexed as

Eastern Chinahigh-risk populationhypertensionrisk scoring methodscreening tool

Identifiers

PMID35386277
PMCPMC8977866
OpenAlexW4220978853

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.