Evidence map›Paper›PMID 35784203›Full record

ArticleFrontiers in public health2022

Trends in Musculoskeletal Rehabilitation Needs in China From 1990 to 2030: A Bayesian Age-Period-Cohort Modeling Study.

Ningjing Chen, Daniel Yee Tak Fong, Janet Yuen Ha Wong

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
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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

20 citing papers in PubMed.

  1. Review
  2. [Current status and Joinpoint regression analysis of the burden of musculoskeletal diseases in China from 1990 to 2023].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2026
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  8. [Advances of low-intensity pulsed ultrasound for treatment of musculoskeletal disorders in the past decade].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025
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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

3 authors.

Ningjing ChenSchool of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Daniel Yee Tak FongSchool of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Janet Yuen Ha WongSchool of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Disability and medical expenses caused by musculoskeletal disorders in China had a great impact on the global health and economy. Rehabilitation is essential for dealing with musculoskeletal disorders. However, China's musculoskeletal rehabilitation needs remain unknown. This study aimed to examine the secular trends for musculoskeletal rehabilitation needs in China from 1990 to 2030. Methods: Data on musculoskeletal rehabilitation needs were extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) repository. Estimated annual percentage changes (EAPCs) were calculated to reflect fluctuations in the age-standardized rates. The Bayesian age-period-cohort models were used to project rehabilitation needs. Results: The number of prevalent cases and years lived with disability (YLD) counts in need of musculoskeletal rehabilitation increased greatly in China from 1990 to 2019. There will be 465.9 million Chinese people in need of rehabilitation, with the age-standardized prevalence rate increasing to 21,151.0 [2.5-97.5% predictive interval (95% Conclusions: Increasing trends in musculoskeletal rehabilitation needs were found from 1990 to 2019, which will be anticipated through 2030. Rehabilitation is suggested to be integrated into primary care settings.

Indexed as

Musculoskeletal DiseasesPersons with DisabilitiesBayes TheoremChinaCohort StudiesHumansmusculoskeletal disorderspredictionprevalencerehabilitationyears lived with disability

Identifiers

PMID35784203
PMCPMC9240767

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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.