Evidence map›Paper›PMID 42406396›Full record

ArticleHuman vaccines & immunotherapeutics2026

Vaccination practice and safety in children with special health care needs: A retrospective study of 1278 children.

Yu Song, Jingjing Sun, Yu Ling, Sixiang Liu, Zhen Xu, Min Ma, Ou Zhang, Xueyan Zhao

Abstract read
In one paragraph

Article in Human vaccines & immunotherapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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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

8 authors.

Yu SongDepartment of Child Healthcare, Kunming Children's Hospital, Kunming, Yunnan, China.
Jingjing SunDepartment of Child Healthcare, Kunming Children's Hospital, Kunming, Yunnan, China.
Yu LingDepartment of Child Healthcare, Kunming Children's Hospital, Kunming, Yunnan, China.
Sixiang LiuDepartment of Child Healthcare, Kunming Children's Hospital, Kunming, Yunnan, China.
Zhen XuDepartment of Child Healthcare, Kunming Children's Hospital, Kunming, Yunnan, China.
Min MaDepartment of Child Healthcare, Kunming Children's Hospital, Kunming, Yunnan, China.
Ou ZhangDepartment of Child Healthcare, Kunming Children's Hospital, Kunming, Yunnan, China.
Xueyan ZhaoPuer City Center for Disease Control and Prevention, Pu'er, Yunnan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Children with special health care needs (CSHCN) are at increased risk of vaccine-preventable diseases, yet concerns about safety often lead to delayed or incomplete immunization. We conducted a retrospective analysis of 1278 CSHCN attending the Special Vaccination Consultation Clinic of Kunming Children's Hospital in Yunnan Province, China, between January 2023 to December 2024. We examined their disease spectrum, vaccination recommendations, and incidence of adverse events following immunization (AEFI). The cohort had a male-to-female ratio of 1.3:1, with infants aged 0-1 y constituting the majority (56.4%). Neurological (38.3%), neonatal (33.6%), and cardiovascular diseases (21.4%) predominated the consultation spectrum, with a marked age-dependent shift from neonatal conditions in early infancy to neurological disorders thereafter. Following specialist evaluation, 92.5% of children received vaccination advice: 76.7% were recommended for standard or single-dose sequential vaccination, and 15.8% for inactivated vaccines only; only 2.7% were advised to defer all vaccinations. Among 1182 children who were subsequently vaccinated, 174 reported AEFI, yielding an incidence of 14.7%. Fever was the predominant manifestation (70.7% of AEFI), and the vast majority (99.4%) of events occurred in children under 4 y of age. This study demonstrates that under the guidance of a specialized vaccination clinic, over 90% of CSHCN can be safely vaccinated. Individualized assessment based on age and disease characteristics is pivotal to ensuring immunization in this vulnerable population, thereby safeguarding their right to protection against vaccine-preventable diseases.

Indexed as

VaccinationVaccinesChildChild, PreschoolChinaFemaleHumansImmunization ScheduleInfantInfant, NewbornMaleRetrospective StudiesVaccinesadverse events following immunization (AEFI)congenital heart diseaseindividualized vaccine recommendationneurological diseasespediatric risk populationxpreterm infantretrospective studyVaccination consultation

Identifiers

PMID42406396
PMCPMC13349029

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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.