Evidence map›Paper›PMID 38836174›Full record

ReviewIntractable & rare diseases research2024

Epidemiology and distribution of 207 rare diseases in China: A systematic literature review.

Yukun Wang, Yicheng Liu, Guoyao Du, Yonghui Liu, Ying Zeng

Abstract readReview
In one paragraph

Review in Intractable & rare diseases research, 2024. 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
–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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  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

5 authors.

Yukun WangSchool of Medicine, Tongji University, Shanghai, China.
Yicheng LiuSchool of Medicine, Tongji University, Shanghai, China.
Guoyao DuSchool of Medicine, Tongji University, Shanghai, China.
Yonghui LiuSchool of Medicine, Tongji University, Shanghai, China.
Ying ZengSchool of Medicine, Tongji University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epidemiological data on rare diseases in China are currently limited. The objective of this study was to provide a comprehensive understanding of the prevalence and incidence of rare diseases by systematically analyzing the available epidemiological data. We conducted a comprehensive search of English and Chinese databases, the Incidence and Prevalence Database, the Chinese Rare Disease Guideline, and the Taiwan Health Promotion Administration from 2010 to 2023. We identified the top diseases and regions based on epidemiological data and present the maximum, minimum, and median prevalence and incidence values in tables and forest plots. 1,264 prevalence and incidence data were retrieved from 277 studies, guidelines and official websites, covering 110 rare diseases (53.1%) and 32 regions (94.1%). In terms of geographical regions, incidence or prevalence data were available for 32 regions (94.1%), excluding Tibet Hui Autonomous Region and Macao Special Administrative Region. In terms of rate, 60 and 77 out of 207 diseases (29.0% and 37.2%) had available incidence and prevalence data, respectively. Eight diseases had an incidence rate equal to or greater than that of 1,000 patients per million. The present study provides a comprehensive epidemiological analysis and valuable insights into the prevalence and incidence of rare diseases in China. Our findings underscore the pressing need for sustained drug research and medical support for individuals and families impacted by rare diseases.

Indexed as

Chinaepidemiologyrare disease

Identifiers

PMID38836174
PMCPMC11145401

What OpenQuestion holds

Textmetadata
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.