Evidence map›Paper›PMID 37334292›Full record

ArticleFrontiers in endocrinology2023

COVID-19 vaccine coverage, safety, and perceptions among patients with diabetes mellitus in China: a cross-sectional study.

Haolong Li, Fan Ping, Xiaomeng Li, Zhihong Wang, Jianzhong Xiao, Hongwei Jiang, Yaoming Xue, Jinxing Quan, Hebin Yao, Xianling Zheng and 8 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.9field-weighted citation impact, top 15% 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, 7 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Longitudinal Evaluation of Metabolic Benefits of Inactivated COVID-19 Vaccination in Diabetic Patients in Tianjin, China.Medical science monitor : international medical journal of experimental and clinical research · 2025
    Article
  5. Article
  6. Article
  7. 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

18 authors at 11 institutions in 1 country.

Haolong LiDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Fan PingDepartment of Endocrinology, Peking Union Medical College Hospital, Beijing, China.
Xiaomeng LiDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Zhihong WangDepartment of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Jianzhong XiaoDepartment of Endocrinology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
Hongwei JiangThe First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Yaoming XueDepartment of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Jinxing QuanDepartment of Endocrinology, Gansu Provincial People's Hospital, Lanzhou, Gansu, China.
Hebin YaoDepartment of Endocrinology, Sixth Medical Center of PLA General Hospital, Beijing, China.
Xianling ZhengDepartment of Endocrinology, Handan Central Hospital, Handan, China.
Yanming ChenDepartment of Endocrinology & Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Yufeng LiDepartment of Endocrinology, Capital Medical University, Beijing, China.
Xiaohua YuDepartment of Endocrinology, Liaoyang Central Hospital, Liaoyang, Liaoning, China.
Lingling XuDepartment of Endocrinology, ShenZhen Hospital, Southern Medical University, Shenzhen, Guangdong, China.
Xinxin FengDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Siyu WangDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Yongzhe LiDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Xinhua XiaoDepartment of Endocrinology, Peking Union Medical College Hospital, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNCapital Medical University · CNBeijing Tsinghua Chang Gung Hospital · CNChinese PLA General Hospital · CNFirst Affiliated Hospital of Henan University of Science and Technology · CNGansu Provincial Hospital · CNHandan College · CNNanfang Hospital · CNSouthern Medical University Shenzhen Hospital · CNSun Yat-sen University · CNThe Affiliated Yongchuan Hospital of Chongqing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Diabetes mellitus (DM), one of the most common chronic diseases in China, is a risk factor for SARS-COV-2 infection and poor prognosis of COVID-19. The COVID-19 vaccine is one of the key measures to control the pandemic. However, the actual coverage of COVID-19 vaccination and associated factors remain unclear among DM patients in China. We conducted this study to investigate the COVID-19 vaccine coverage, safety, and perceptions among patients with DM in China. Methods: A cross-sectional study of a sample of 2200 DM patients from 180 tertiary hospitals in China was performed using a questionnaire developed through the Wen Juan Xing survey platform to collect information regarding their coverage, safety, and perceptions of COVID-19 vaccination. A multinomial logistic regression analysis model was performed to determine any independent relationships with COVID-19 vaccination behavior among DM patients. Results: In total, 1929 (87.7%) DM patients have received at least one dose COVID-19 vaccine, and 271 (12.3%) DM patients were unvaccinated. In addition, 65.2% (n = 1434) were booster vaccinated against COVID-19, while 16.2% (n = 357) were only fully vaccinated and 6.3% (n = 138) were only partially vaccinated. The prevalence of adverse effects after the first dose of vaccine, the second dose of vaccine, and the third dose of vaccine were 6.0%, 6.0%, and 4.3% respectively. Multinomial logistic regression analysis showed that DM patients complicated with immune and inflammatory diseases (partially vaccinated: OR = 0.12; fully vaccinated: OR = 0.11; booster vaccinated: OR = 0.28), diabetic nephropathy (partially vaccinated: OR = 0.23; fully vaccinated: OR = 0.50; booster vaccinated: OR = 0.30), and perceptions on the safety of COVID-19 vaccine (partially vaccinated: OR = 0.44; fully vaccinated: OR = 0.48; booster vaccinated: OR = 0.45) were all associated with the three of vaccination status. Conclusion: This study showed that higher proportion of COVID-19 vaccine coverage among patients with DM in China. The concern about the safety of the COVID-19 vaccine affected the vaccine behavior in patients with DM. The COVID-19 vaccine was relatively safe for DM patients due to all side effects were self-limiting.

Indexed as

COVID-19COVID-19 VaccinesDiabetes MellitusDiabetic NephropathiesDrug-Related Side Effects and Adverse ReactionsChinaCross-Sectional StudiesHumansSARS-CoV-2COVID-19 VaccinesChinaCOVID-19diabetes mellitusvaccinevaccine uptake

Identifiers

PMID37334292
PMCPMC10270113
OpenAlexW4379056547

What OpenQuestion holds

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LicenceCC BY
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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.