Evidence map›Paper›PMID 36597533›Full record

ArticleResearch square2022

COVID-19 non-vaccination among older adults in China: a nationwide survey based on the China Health and Retirement Longitudinal Study (CHARLS).

Gewei Wang, Yao Yao, Yafeng Wang, Jinquan Gong, Qinqin Meng, Hui Wang, Wenjin Wang, Xinxin Chen, Yaohui Zhao

Open access · greenAbstract readPreprint
In one paragraph

Article in Research square, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Gewei WangInstitute of Social Science Survey, Peking University, Beijing, China.
Yao YaoChina Center for Health Development Studies, Peking University, Beijing, China.
Yafeng WangInstitute of Social Science Survey, Peking University, Beijing, China.
Jinquan GongInstitute of Social Science Survey, Peking University, Beijing, China.
Qinqin MengInstitute of Social Science Survey, Peking University, Beijing, China.
Hui WangChina Center for Economic Research, National School of Development, Peking University Beijing, China.
Wenjin WangChina Center for Economic Research, National School of Development, Peking University Beijing, China.
Xinxin ChenInstitute of Social Science Survey, Peking University, Beijing, China.
Yaohui ZhaoChina Center for Economic Research, National School of Development, Peking University Beijing, China.
Peking University · CN

Funding

China Health and Retirement Longitudinal StudyR01AG037031 · NIA · PEKING UNIVERSITY · PI STRAUSS, JOHN A, WANG, YAFENG · 2010 to 2024
$15.1M
NIA NIH HHS R01 AG037031
6 · The paper itself

Abstract

China has a lower rate of vaccination among older adults and those who have chronic conditions and functional disabilities. As China has recently ended the zero-COVID policy, understanding the factors behind low vaccination rates among these vulnerable populations can inform immediate policy priorities to save lives for China and offer lessons for the world at large. We used the fifth wave (2021-22) of the China Health and Retirement Longitudinal Study (CHARLS), which represented mainland Chinese 45 and older. Vaccination status was updated in the summer of 2022, reflecting the current situation because very few additional vaccinations were administered afterward. For those who were unvaccinated, self-reported reasons were recorded. Using regression analysis, we investigated the determinants of non-vaccination, including demographics, functional status, and chronic conditions. In addition, two-thirds of the respondents had their vaccination status recorded twice in 2021 and 2022, allowing us to examine changes in vaccination rates in the recent year, zeroing in on the effects of the government's most recent vaccination campaign. Finally, we corroborated the regression results using self-reported reasons for non-vaccination in both years. A total of 12900 participants were included in the analysis. By the summer of 2022, the weighted COVID-19 vaccination rate among older Chinese people (≥60 years old) was 92.3%, with 88.8% having completed the primary series and 72.7% having received boosters. Only 72.0% of the oldest-old (≥80 years old) had completed the primary series, and 47.1% had had boosters. Regression analysis showed that participants who were older, female, unmarried, registered with urban Hukou residence, functionally dependent, and comorbid with chronic conditions were less likely to receive COVID-19 vaccines. A significant increase in vaccination rates among ethnic minorities, older adults, rural residents, and those with chronic conditions and functional dependency was observed in the year after the winter of 2021 when the government started to push for universal vaccination. The self-reported reasons for non-vaccination in 2022 were contraindications (48%), advanced ages/frailty/health conditions (21%), problems in accessing vaccines (18%), concerns about side effects or efficacy (9%), and having never heard of COVID-19 vaccine (6%). Nevertheless, as China has ended the zero-COVID policy, many older people, especially the oldest and those with chronic conditions and disabilities, have not yet been fully vaccinated with the primary series or booster doses, exposing them to the danger of infection. Therefore, health authorities should immediately abandon the previous practice of refusing to vaccinate those with chronic conditions, change people's mistaken perceptions of contraindications and side effects, and improve access to vaccines. Most importantly, China should strengthen public trust in vaccines by making information transparent regarding the vaccine's protection rates and side effects.

Identifiers

PMID36597533
PMCPMC9810236
OpenAlexW4311951425

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.