Evidence map›Paper›PMID 37497032›Full record

ArticleFrontiers in public health2023

Construction and validation of a musculoskeletal disease risk prediction model for underground coal miners.

Haili Zhao, Hong Dou, Xianting Yong, Wei Liu, Saiyidan Yalimaimaiti, Ying Yang, Xiaoqiao Liang, Lili Sun, Jiwen Liu, Li Ning

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.7field-weighted citation impact, top 17% of its field
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

4 citing papers in PubMed, 7 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Haili ZhaoCollege of Public Health, Xinjiang Medical University, Urumqi, China.
Hong DouXinjiang Uygur Autonomous Region Third People's Hospital, Urumqi, China.
Xianting YongCollege of Public Health, Xinjiang Medical University, Urumqi, China.
Wei LiuXinjiang Uygur Autonomous Region Third People's Hospital, Urumqi, China.
Saiyidan YalimaimaitiCollege of Public Health, Xinjiang Medical University, Urumqi, China.
Ying YangCollege of Public Health, Xinjiang Medical University, Urumqi, China.
Xiaoqiao LiangCollege of Public Health, Xinjiang Medical University, Urumqi, China.
Lili SunThe Fifth Affiliated Hospital, Xinjiang Medical University, Urumqi, China.
Jiwen LiuCollege of Public Health, Xinjiang Medical University, Urumqi, China.
Li NingCollege of Public Health, Xinjiang Medical University, Urumqi, China.
Xinjiang Medical University · CNPeople's Hospital of Xinjiang Uygur Autonomous Region · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To understand the prevalence among underground coal miners of musculoskeletal disorders (MSDs), analyze the risk factors affecting MSDs, and develop and validate a risk prediction model for the development of MSDs. Materials and methods: MSD questionnaires were used to investigate the prevalence of work-related musculoskeletal disorders among 860 underground coal miners in Xinjiang. The Chinese versions of the Effort-Reward Imbalance Questionnaire (ERI), the Burnout Scale (MBI), and the Self-Rating Depression Inventory (SDS) were used to investigate the occupational mental health status of underground coal miners. The R4.1.3 software cart installation package was applied to randomly divide the study subjects into a 1:1 training set and validation set, screen independent predictors using single- and multi-factor regression analysis, and draw personalized nomogram graph prediction models based on regression coefficients. Subject work characteristic (ROC) curves, calibration (Calibrate) curves, and decision curves (DCA) were used to analyze the predictive value of each variable on MSDs and the net benefit. Results: (1) The prevalence of MSDs was 55.3%, 51.2%, and 41.9% since joining the workforce, in the past year, and in the past week, respectively; the highest prevalence was in the lower back (45.8% vs. 38.8% vs. 33.7%) and the lowest prevalence was in the hips and buttocks (13.3% vs. 11.4% vs. 9.1%) under different periods. (2) Underground coal miners: the mean total scores of occupational stress, burnout, and depression were 1.55 ± 0.64, 51.52 ± 11.53, and 13.83 ± 14.27, respectively. (3) Univariate regression revealed a higher prevalence of MSDs in those older than 45 years (49.5%), length of service > 15 years (56.4%), annual income <$60,000 (79.1%), and moderate burnout (43.2%). (4) Binary logistic regression showed that the prevalence of MSDs was higher for those with 5-20 years of service (OR = 0.295, 95% CI: 0.169-0.513), >20 years of service (OR = 0.845, 95% CI: 0.529-1.350), annual income ≥$60,000 (OR = 1.742, 95% CI: 1.100-2.759), and severe burnout (OR = 0.284, 95% CI: 0.109-0.739), and that these were independent predictors of the occurrence of MSDs among workers in underground coal mine operations ( Conclusion: The prevalence of MSDs among workers working underground in coal mines was high, and the constructed nomogram showed good discriminatory ability and optimal accuracy.

Indexed as

Coal MiningMusculoskeletal DiseasesCoalHumansRisk FactorsSurveys and QuestionnairesCoaldepressionmusculoskeletal disordersnomogramoccupational job burnoutoccupational stressunderground coal miners

Identifiers

PMID37497032
PMCPMC10368395
OpenAlexW4383874510

What OpenQuestion holds

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LicenceCC BY
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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.