Evidence map›Paper›PMID 39103903›Full record

ArticleHealth research policy and systems2024

Achilles' heel: elderly COVID-19 vaccination policy in China.

Ziru Deng, Karen A Grépin

Abstract read
In one paragraph

Article in Health research policy and systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. 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

2 authors.

Ziru DengSchool of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Patrick Manson Building, 7 Sassoon Road, Pokfulam, Hong Kong.
Karen A GrépinSchool of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Patrick Manson Building, 7 Sassoon Road, Pokfulam, Hong Kong. kgrepin@hku.hk.ORCID http://orcid.org/0000-0003-4368-0045

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite high overall COVID-19 vaccine coverage, the continuously low elderly vaccination rate in mainland China remains a dangerous threat as the country shifts away from its zero-Covid policy. This retrospective study uses the Multiple Streams Framework to examine how macro-level factors may explain poor elderly vaccination outcomes.

methodsWe performed a thematic analysis of qualitative data obtained from 95 official press conferences from October 20, 2020, to February 27, 2023, vaccination-related policy documents, and media coverage, using both inductive and deductive coding approaches.

resultsOur findings suggest that in the problem stream, elderly vaccination was not a "focusing event" during the initial vaccine rollout, resulting in delayed outreach to this population. Additionally, ideologically driven complacency and discrepancies in top-down implementation undermined elderly vaccination in the political stream. In the policy stream, precautious and ambiguous statements, inconsistent policy content, radical shifting media messages, and less age-friendly digital technologies also affected elderly vaccination.

conclusionsThe poor convergence of the three streams led the elderly to be the Achilles' heel of China's COVID-19 containment strategy. Future studies should focus on priority identification, adoption of enforcement measures, and timely and effective policy dissemination. The empirical lessons from China can inform and optimize elderly vaccination policy design and implementation in the post-pandemic era.

Indexed as

COVID-19COVID-19 VaccinesHealth PolicySARS-CoV-2VaccinationVaccination CoverageAgedAged, 80 and overChinaHumansRetrospective StudiesCOVID-19 VaccinesCOVID-19Elderly vaccinationMultiple streams frameworkPolicy analysis

Identifiers

PMID39103903
PMCPMC11299290

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.