Evidence map›Paper›PMID 34823606›Full record

ArticleSystematic reviews2021

Prevalence and risk factors of depression in Chinese patients with type 2 diabetes mellitus: a protocol of systematic review and meta-analysis.

Xiaobo Liu, Chao Dong, Hong Jiang, Dongling Zhong, Yuxi Li, Huiling Zhang, Jun Zhang, Jin Fan, Juan Li, Li Guan and 1 more

Open access · goldAbstract read
In one paragraph

Article in Systematic reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 27% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
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  3. Review
  4. Article
  5. Article
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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

11 authors at 3 institutions in 1 country.

Xiaobo Liu *School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Chao Dong *The General Hospital of Western Theater Command, Chengdu, China.
Hong Jiang *Department of Rehabilitation, Xichong County People's Hospital, Nanchong, China.
Dongling ZhongSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Yuxi LiSchool of Acupuncture-Moxibustion and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Huiling ZhangSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Jun ZhangSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Jin FanSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Juan LiSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China. 785939016@qq.com.ORCID 0000-0002-8530-9001
Li GuanDepartment of Rehabilitation, Fushun County People's Hospital, Zigong, China. 172739721@qq.com.
Rongjiang JinSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China. cdzyydxjrj@126.com.
Chengdu University of Traditional Chinese Medicine · CNNanchong Central Hospital · CNZigong First People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevalence of type 2 diabetes mellitus (T2DM) is growing in China. Depression is a significant complication of T2DM, leading to poor management of T2DM. Thus, early detection and treatment of depression in patients with T2DM are essential and effective. Therefore, we plan to conduct a systematic review and meta-analysis to evaluate the prevalence of depression in Chinese patients with T2DM and explore potential risk factors of depression in T2DM.

methodsWe will search literatures recorded in MEDLINE, EMBASE, the Cochrane Library, Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), Chinese Science and Technology Periodical Database (VIP), and WanFang Database from their inception onwards. We will manually search gray literatures, reference lists of identified studies, relevant websites, and consult experts in this field. We will include population-based, cross-sectional surveys that investigated the prevalence of depression in Chinese patients with T2DM or/and the possible risk factors of depression in T2DM. Two reviewers will screen studies, extract data, and evaluate risk of bias independently. Agency for Healthcare Research and Quality methodology checklist will be used to assess the risk of bias. If feasible, we will conduct random effects meta-analysis of observational data to summarize the pooled prevalence, and use odds ratio for categorical data to explore potential risk factors. Prevalence estimates will be stratified according to age, gender, and other factors. Statistical heterogeneity will be estimated using Cochran's Q and I DISCUSSION: This systematic review and meta-analysis will provide comprehensive evidence of the prevalence and potential risk factors of depression in Chinese patients with T2DM. We expect to provide evidence for healthcare practitioners and policy makers to pay attention to the mental health of patients with T2DM. Our data will highlight the need and importance of early detection and intervention for depression in patients with T2DM. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020182979.

Indexed as

Diabetes Mellitus, Type 2Cross-Sectional StudiesDepressionHumansMeta-Analysis as TopicPrevalenceRisk FactorsSystematic Reviews as TopicChinaDepressionPrevalenceRisk factorsType 2 diabetes mellitus

Identifiers

PMID34823606
PMCPMC8620640
OpenAlexW3119861520

What OpenQuestion holds

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LicenceCC BY
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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.