Evidence map›Paper›PMID 31510992›Full record

ArticleBMC public health2019

Development and validation of a pre-scoring system for nonspecific low back pain among general population in Guangzhou: a cross-sectional study.

Kai Wang, Jing-Wen Zhang, Shao-Xiong Min, Xin-Yi Xu, Sheng-Li An

Abstract read
In one paragraph

Article in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Kai WangDepartment of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, No. 1838 North Guangzhou Avenue, Guangzhou, 510515, People's Republic of China.
Jing-Wen ZhangDepartment of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, No. 1838 North Guangzhou Avenue, Guangzhou, 510515, People's Republic of China.
Shao-Xiong MinOrthopaedic Center, Zhujiang Hospital, Southern Medical University, No. 253 Gongye Avenue, Guangzhou, 510282, People's Republic of China.
Xin-Yi XuOrthopaedic Center, Zhujiang Hospital, Southern Medical University, No. 253 Gongye Avenue, Guangzhou, 510282, People's Republic of China.
Sheng-Li AnDepartment of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, No. 1838 North Guangzhou Avenue, Guangzhou, 510515, People's Republic of China. asl0418@126.com.ORCID http://orcid.org/0000-0002-2811-7759

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNonspecific Low Back Pain (NLBP) is a common disease with a low cure rate and significant impact on the population. This study aimed to develop and validate a pre-scoring system for identifying the risk of suffering from NLBP among the general population in Guangzhou.

methodsA total of 1439 eligible subjects were surveyed in Guangzhou by stratified random sampling and was divided randomly into the development dataset (69.6%) and validation dataset (30.4%) subsequently. Based on the development dataset, potential associated factors (average exercise times weekly, the intensity of daily work, etc.) with NLBP were tested by the sequential logistic regression, and a pre-scoring system was formulated with Sullivan's method and graded afterward. The internal validity of the system was assessed by AUC and calibration plot, and the external validation was performed in the validation dataset.

resultsThe prevalence rates of NLBP in the development dataset and the validation dataset were 12.97 and 13.27%, respectively. Age, BMI, average exercise times weekly, gender, educational level, the intensity of daily work, place of residence, monthly income, overall evaluation of health condition and physiology health were identified as significant factors. The total risk score ranged from 0 to 38, which was split into three risk grades: low risk (0 to 18), intermediate risk (19 to 22) and high risk (23 to 38). The pre-scoring system had an adequate calibration and a good discriminating ability with bootstrap-corrected AUC equaling 0.861 in the development dataset and 0.821 in the validation dataset.

conclusionsA pre-scoring system that could help clinicians to assess the risk of NLBP in the general population was validated. Further validation of the system in a new population or prospective cohort study is suggested.

Indexed as

AdultChinaCross-Sectional StudiesFemaleHumansLow Back PainMaleMiddle AgedPrevalenceReproducibility of ResultsRisk AssessmentYoung AdultAssociated factorsGeneral populationNon-specific low back painPre-scoring system

Identifiers

PMID31510992
PMCPMC6739945

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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.