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  Table of Contents 
Year : 2021  |  Volume : 25  |  Issue : 1  |  Page : 11-16

A diagnostic assistant tool for work-related low back pain in hospital workers

1 Department of Occupational Health, Saraburi Regional Hospital, Thailand
2 Department of Community Medicine, Faculty of Medicine, Thammasart University, Thailand
3 Department of Clinical Epidemiology, Faculty of Medicine, Thammasart University, Thailand

Date of Submission23-Jun-2019
Date of Decision02-May-2020
Date of Acceptance25-May-2020
Date of Web Publication26-Apr-2021

Correspondence Address:
Dr. Oopara Saengdao
Saraburi Regional Hospital 18 Thesaban 4 Rd., Tambon Pak Prieo, Amphoe Mueang Saraburi, Saraburi - 18000
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijoem.IJOEM_153_19

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Aims: The study objective was to develop a clinical risk score to assist occupational medicine physicians in diagnosing hospital workers' occupational lower back pain (LBP). Settings and Design: A cross-sectional data collection design was conducted at Saraburi Hospital, Thailand. Methods and Materials: The sample consisted of 220 hospital workers who cared for patients and had LBP. They were assessed for the frequency of targeted activities (CPR, lifting, transferring patients) and other activities from work as well as ergonomic assessments, and diagnosed with LBP by three occupational medicine physicians. Statistical Analysis Used: Predicted factors of multivariable logistic regression were analysed to find clinical risk scores to help the diagnosis. Results: The physicians agreed on the diagnosis, based on ergonomic risk factors and their experiences that 86 persons have occupational LBP.   A diagnostic assistant tool consists of six predictors: the duration of LBP, having LBP within the last 7 days, bending, twisting, lateral bending, and reaching. The scores predicted occupational LBP correctly with an AuROC of 90.0% (95% CI; 84.8–93.5%). The positive likelihood ratio for occupational LBP was 0 in the low risk category (<6 points) and 16.8 (95% CI; 8.0–35.6) in the high risk (>8 points). Conclusions:   A diagnostic assistant tool is used to assist occupational medicine physicians in diagnosing hospital workers' occupational LBP.

Keywords: Assistant Tool, clinical prediction rule, clinical risk scores, diagnosis, hospital workers, occupational low back pain

How to cite this article:
Saengdao O, Surasak B, Jayanton P. A diagnostic assistant tool for work-related low back pain in hospital workers. Indian J Occup Environ Med 2021;25:11-6

How to cite this URL:
Saengdao O, Surasak B, Jayanton P. A diagnostic assistant tool for work-related low back pain in hospital workers. Indian J Occup Environ Med [serial online] 2021 [cited 2022 Aug 8];25:11-6. Available from:

  Introduction Top

Work-related musculoskeletal disorders (WMSDs) are occupational health and safety concerns. The Thai Compensation Fund statistics show the most problems with WMSDs. In hospital or healthcare settings, these problems are commonly found as well due to manual lifting of patients with variations in body size, physical disability, impaired cognitive function, vary levels of cooperation and fluctuations. Unlike factories, there are specific medical ethics and emergency conditions that do not align with ergonomics laws or regulations. The highest prevalence of hospital workers WMSDs in the world is the lower back.[1],[2] The prevalence varies roughly from 47% to 70%.[3],[4],[5],[6],[7],[8],[9],[10],[11] Risk factors include reasonable evidence such as ergonomics risk factors: awkward posture (repetitive motion, bending, twisting[12]), heavy physical work, lifting; psychosocial factors: age, body mass index (BMI), as well as insufficient evidence such as gender, race, smoking, and comorbidity.[13] Other risk factors are working conditions such as work experience or job seniority, work days per week, work hours per day, break time, cumulative trauma, and shift work.[14],[15]   The impacts of lower back pain (LBP) in hospital workers manifest in both direct costs such as workers' compensation, healthcare expenses, and legal services, and indirect costs such as pain and suffering, training for replacement employees, accident investigation, and lost productivity.

In 2004, the Thai Labor Ministerial Regulations limit employees' capacity to lift, push, and pull. Male employees are required to lift a weight not exceeding 55 kg, and female employees not exceeding 25 kg. If the weight exceeds the limit, employers must provide the appropriate lift assist devices.[16] These Thai Ministerial Regulations cover industrial works, but exclude works in both public and private hospitals since healthcare workers focus on patient care. Healthcare workers cannot refuse to lift overweight patients due to medical ethics and some emergency conditions.   There are lift assistive devices (such as patient transfer slide boards and electric medical beds) but they are insufficient or difficult to retrieve or broken as a result of a lack of budget. The barriers to the use of assistive devices in patient handling study confirmed that time constraints contribute to fewer instances of assistive device use and difficult patient handling situations.[17]

The hospital workers, having risk factors and lacking lift assistive devices use, have occupational LBP. They need to be diagnosed for compensation. Occupational LBP in Thailand is diagnosed by occupational medicine physicians by using diagnostic tools which are complicated, full of details for both general and work posture information, and difficult to use as they are specifically for occupational diseases. The limitation in the diagnosis of occupational LBP is inappropriate diagnostic tools; there are only basic suggestions from nine steps in occupational diseases. This is a simplistic and controversial approach in the opinion of experts who use the knowledge (baseline characteristics, ergonomic risk factors) and experience for diagnosis, less quantitative evidences are used. A diagnostic assistant tool that is suitable for the hospital setting should be easy to use and accurate to make a claim for compensation as well as present the true cause of occupational LBP which leads to correct solutions. That tool comes from hospital data collection which is a specific job, different from the industrial setting, and is derived from the research methodology that is suitable for scoring to help diagnose. This study aims to develop a diagnostic assistant tool for occupational medicine physicians to diagnose hospital workers' occupational LBP.

  Subjects and Methods Top

A cross-sectional data collection design was conducted at Saraburi Regional Hospital in Saraburi, located in the central part of Thailand, from February to May 2017. The hospital has a capacity of 740 active beds with a total of 2,521 personnel. 1,002 hospital workers who have close contact with and care for the patients were screened by Nordic Musculoskeletal Questionnaire (NMQ). The sample consisted of 220 hospital workers who cared for the patients and who had lower back pain by NMQ screening. This research was a cross-sectional study to determine the  diagnostic assistant tool for clinical diagnosis in hospital workers. Data was collected using the ergonomic tools, with both self-report and observation methods. Advanced video-based observational techniques were used to evaluate the variety of gestures in different target settings in each ward.   The researcher observed and recorded VDOs by using mobile phones and video cameras of each hospital worker, various views, to collect detailed work gestures especially those with high risk activities. They were assessed for frequency of targeted activities (External cardiac compression, lifting, transferring and repositioning patients) and other work activities. A diagnosis of occupational LBP was conducted by three occupational medicine physicians. The physicians agreed on the diagnosis based on baseline characteristics, ergonomic risk factors, and their experiences.

Occupational LBP and non-occupational LBP were compared for evidence of differences (P value) in clinical characteristics with t-tests, rank sum tests, or exact probability tests as appropriate. Predictions for each characteristic were calculated by univariable logistic regression and presented as an area under the receiver operating characteristic (AuROC) curve and its 95% confidence interval (95% CI). Strong (high AuROC curve) and significant (P < 0.05) clinical predictors were categorized into two and three levels to facilitate odds ratio calculation, under the multivariable logistic regression. Discriminative performance of the model was calculated by an AuROC curve. Regression coefficients of each level for each clinical predictor were divided by the smallest coefficient of the model and rounded to the nearest half (0.5) to transform into an item risk score. Scores for each clinical predictor were added up to obtain a total risk score. Score prediction of occupational LBP was done by using a total score as the only summary predictor in the logistic model. Discrimination of the score was presented with an AuROC curve. Calibration of the prediction was analyzed with Hosmer–Lemeshow statistics. Scores predicting risk and observed risk were compared and presented in a graph.

Internal validation of the score was done with the bootstrap method (220 replications). Risk scores were categorized into three risk levels: high, moderate, and low. Predictive ability of each risk score level was calculated and presented as a likelihood ratio of positive, 95% CI and its significance level.

The research proposal, data collection, and analysis plan were approved by Saraburi Hospital Research Ethics Committee and Human Ethics Committee of Thammasat University No. 1 (Faculty of Medicine). Informed consents were performed.

  Results Top

In a total of 220 LBP hospital workers, 86 persons have occupational LBP, female more than male. In comparison to non-occupational LBP, occupational LBP cases were older, shorter and heavier, with greater body mass index, larger waist circumference, more work experience, higher hospital admission, and change of duties due to LBP, but not significant.   Occupational LBP cases have significantly more working period, proportion of shift work, duration of LBP, more than 1 month of last 12 months LBP, were unable to fully perform working activity within the last 12 months due to LBP, had seen the doctor due to last 12 months LBP and had LBP within the last 7 days [Table 1].   Occupational LBP cases had more frequency of lifting and transferring patients, maximum weight of patient who received lifting and transferring, had more degree of back bending, twisting, lateral bending, reaching [Table 2]. Among all clinical predictors, the prediction ability as measured by the AuROC curve was highest for the duration of LBP.
Table 1: Clinical characteristics of occupational LBP cases and non-occupational LBP cases, evidence of difference (p value), area under receiver operating curve (AuROC) and 95% confidence interval (CI)

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Table 2: Ergonomic risk factors of Occupational LBP in hospital workers

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The best multivariable clinical predictors for occupational LBP were the duration of LBP, had LBP within the last 7 days, back bending, twisting, lateral bending, and reaching. These clinical predictors were each categorized into three levels; the optimal cutoff points for each characteristic were determined by the values at which the level yielded the smallest P values, and also the largest likelihood ratio obtained in logistic regression.   An item score was assigned to each level of the six clinical characteristics by simple transformation of its logistic regression coefficient [Table 3]. A summary risk score was obtained by adding up the item scores. The discriminative ability of the derived risk score, which ranged from 0 to 13, could directly be observed by the different percentage distribution between occupational LBP cases and non-occupational LBP cases [Figure 1]. The risk score predicted occupational LBP with an AuROC curve of 90.0% (95% CI; 84.8, 93.5) [Figure 2] and with the P value for the Hosmer–Lemeshow goodness-of-fit test of 0.674. Internal validation by the bootstrapping method reduced the AuROC curve to 86.6–92.2%. When translating into absolute risks, the score predicted risk of occupational LBP increased when the risk score moved upward, with close calibration to the actual or observed risks [Figure 3]. The risk scores were categorized into three risk groups, low (below 6) when the slope of the risk curve was lowest, moderate (6 to 8), and high (above 8) when the slope was highest, to facilitate clinical interpretation.   The likelihood ratio of positive for occupational LBP was 0 in the low risk category, 1.4 (95% CI; 0.7, 2.6) in the moderate and 16.8 (95% CI; 8.0, 35.6) in the high category [Table 4].
Figure 1: Percentage distribution of clinical risk score of occupational LBP cases (n = 86) vs non-occupational LBP cases (n = 134)

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Figure 2: Area under receiver operating characteristics curve of clinical risk score and 95% confidence interval (CI) on prediction of occupational LBP

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Figure 3: Observed risk (circle) vs score predicted risk (solid line) of occupational LBP, size of circle represent frequency of hospital workers in each score

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Table 3: Best multivariable clinical predictors, odds ratio (OR), 95% confidence interval (CI), logistic regression beta coefficient (β) and assigned item scores

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Table 4: Distribution of occupational LBP vs non-occupational LBP into low, moderate and high probability categories, likelihood ratio of positive (LHR+) and 95% confidence interval (CI)

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Results and reasons for predicting a better diagnostic assistant tool than other tools, because it is a tool derived from all possible risk factors causing occupational lower back pain in healthcare workers with the most appropriate research methodology (Clinical prediction rule or Clinical prediction scoring) to find predictors affecting lower back pain. Predictors are classified according to the theory of ergonomics and practical use. The tools are easy to use. The ability to predict the diagnosis of occupational lower back pain, this is illustrated from [Table 4] Distribution of occupational LBP vs non occupational LBP into low, moderate and high probability categories, likelihood ratio of positive (LHR+) and 95% confidence interval (CI) and the predictive ability of the overall diagnostic assistant tool from the AuROC values of 90.0% (95% CI; 84.8–93.5%).

  Discussion Top

Saraburi Regional Hospital is a large tertiary hospital in the central region of Thailand. There are several levels of hospital workers and several jobs that can represent work activities in all types of hospitals. This hospital has sufficient numbers of hospital workers diagnosed with low back pain to be able to collect data for diagnosis from work.   The reasons for the better prediction of this diagnostic assistant tool are the collection of all possible risk factors of low back pain from theory (such as ergonomics, disc pressure, barriers to use lift assisting devices), the researcher's experience in diagnosing occupational diseases and observing the practice of other physicians in occupational LBP diagnosis, through appropriate research methodology (Clinical prediction rules) to generate scores for the diagnosis. This study identifies the most accurate, appropriate, and useful diagnostic assistant tool for the occupational medicine physicians to diagnose occupational LBP.

The six clinical predictors for diagnosing occupational LBP were assessed by both quantitative and qualitative data. Qualitative data was obtained from a LBP screening. There may be bias in the memory of the past (recall bias), concerning the date of shooting video to evaluate work posture, there are interviews on the frequency of activities and the weights of the patients. Participants were limited in their ability to respond to the questions due to their limited workload and time. However, in general, the data can predict occupational LBP.

The rating of severity of the occupational LBP scores such as back bending, too much bend and the disc pressure is higher. Twisting and lateral bending are considered equal awkward position.

The diagnostic assistant tool predictive value is good (AuROC = 90.0% (95% CI; 84.8–93.5%). Quantitative data may allow patients to perform various tasks, see or observe hospital workers behaviour in real work situations.

The diagnostic assistant tool from this study is a new, appropriate, accurate, and user-friendly tool for occupational physicians to diagnose occupational LBP in several objectives. It solves the problem of compensation management and constructs preventive management. Also, it helps increase quality of life and work productivity of hospital workers, decrease sickness absence, and reduce cost of health care expenses.

Clinical characteristics used as clinical predictors in our setting may not be directly applicable to other settings; Model adjustment, either selection of different clinical predictors and/or different scoring weight, should always be considered for application to a new setting.

  Conclusions Top

A diagnostic assistant tool is used to assist occupational medicine physicians in diagnosing hospital workers' occupational LBP. The six occupational LBP predictors were used as a tool to assist in the diagnosis of occupational LBP in hospital workers. Hospital workers in a high-risk category may be informed about their risk of occupational LBP, allowing them to apply for compensation, prevention, and correction.


The authors wish to thank the hospital workers in Saraburi Hospital for their assistance in data collection from routine practices.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form the patient (s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

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  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2], [Table 3], [Table 4]


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