Dataset Information

Status and prediction of disapproval of the Korean workers' compensation insurance for diseases and injuries.

ABSTRACT:

Objective

Workers' diseases and injuries are often highly related to work. However, due to limited resources and unclear work relatedness, workers' compensation insurance cannot cover all diseases or injuries among workers. This study aimed to estimate the status and probability of disapproval from national workers' compensation insurance using basic information from Korean workers' compensation system.

Methods

The compensation insurance data for Korean workers consists of personal, occupational, and claims data. We describe the status of disapproval by workers' compensation insurance according to the type of disease or injury. A prediction model for disapproval by workers' compensation insurance was established by applying two machine-learning methods with a logistic regression model.

Results

Among 42 219 cases, there were significantly higher risks of disapproval by workers' compensation insurance for women, younger workers, technicians, and associate professionals. We established a disapproval model for workers' compensation insurance after the feature selection. The prediction model for workers' disease disapproval by the workers' compensation insurance showed a good performance, and the prediction model for workers' injury disapproval showed a moderate performance.

Conclusion

This study is the first attempt to demonstrate the status and prediction of disapproval by workers' compensation insurance using basic information from the Korean workers' compensation data. These findings suggest that diseases or injuries have a low level of evidence of work relatedness or there is a lack of research on occupational health. It is also expected to contribute to the efficiency of the management of workers' diseases or injuries.

SUBMITTER: Choi SB

PROVIDER: S-EPMC9939877 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Status and prediction of disapproval of the Korean workers' compensation insurance for diseases and injuries.

Choi Soo Beom SB Lee Seunghyun S Lee Wanhyung W

Journal of occupational health 20230101 1

<h4>Objective</h4>Workers' diseases and injuries are often highly related to work. However, due to limited resources and unclear work relatedness, workers' compensation insurance cannot cover all diseases or injuries among workers. This study aimed to estimate the status and probability of disapproval from national workers' compensation insurance using basic information from Korean workers' compensation system.<h4>Methods</h4>The compensation insurance data for Korean workers consists of persona ...[more]

PMID: 36808797

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Project description:ObjectiveOccupational injuries (OIs) cause an immense burden on the US population. Prediction models help focus resources on those at greatest risk of a delayed return to work (RTW). RTW depends on factors that develop over time; however, existing methods only utilize information collected at the time of injury. We investigate the performance benefits of dynamically estimating RTW, using longitudinal observations of diagnoses and treatments collected beyond the time of initial injury.Materials and methodsWe characterize the difference in predictive performance between an approach that uses information collected at the time of initial injury (baseline model) and a proposed approach that uses longitudinal information collected over the course of the patient's recovery period (proposed model). To control the comparison, both models use the same deep learning architecture and differ only in the information used. We utilize a large longitudinal observation dataset of OI claims and compare the performance of the two approaches in terms of daily prediction of future work state (working vs not working). The performance of these two approaches was assessed in terms of the area under the receiver operator characteristic curve (AUROC) and expected calibration error (ECE).ResultsAfter subsampling and applying inclusion criteria, our final dataset covered 294 103 OIs, which were split evenly between train, development, and test datasets (1/3, 1/3, 1/3). In terms of discriminative performance on the test dataset, the proposed model had an AUROC of 0.728 (90% confidence interval: 0.723, 0.734) versus the baseline's 0.591 (0.585, 0.598). The proposed model had an ECE of 0.004 (0.003, 0.005) versus the baseline's 0.016 (0.009, 0.018).ConclusionThe longitudinal approach outperforms current practice and shows potential for leveraging observational data to dynamically update predictions of RTW in the setting of OI. This approach may enable physicians and workers' compensation programs to manage large populations of injured workers more effectively.

Project description:IntroductionPrevious reviews have demonstrated that patient outcomes following orthopaedic surgery are strongly influenced by the presence of Workers' Compensation. However, the variability in the reviews' methodology may have inflated the estimated strength of this association. The main objective of this meta-analysis is to evaluate the influence of Workers' Compensation on the outcomes of orthopaedic surgical procedures.MethodsWe conducted a systematic search of the literature published in this area from 1992-2012, with no language restrictions. The following databases were used MEDLINE (Ovid), Embase (Ovid), CINAHL, Google Scholar, LILACS and Pubmed. We also hand-searched the reference sections of all selected papers. We included all prospective studies evaluating the effect of compensation status on outcomes in adult patients who had undergone surgery due to orthopaedic conditions or diseases. Outcomes of interest included disease specific, region specific and/or overall quality of life scales/questionnaires and surgeons' personal judgment of the results. We used an assessment tool to appraise the quality of all included studies. We used Review Manager to create forest plots to summarize study data and funnel plots for the assessment of publication bias.ResultsTwenty studies met our eligibility criteria. The overall risk ratio for experiencing an unsatisfactory result after orthopaedic surgery for patients with compensation compared to non-compensated patients is 2.08 (95% CI 1.54-2.82). A similar association was shown for continuous data extracted from the studies using assessment scales or questionnaires (Standard Mean Difference = -0.70 95% CI -0.97- -0.43).ConclusionsAmong patients who undergo orthopaedic surgical procedures, those receiving Workers' Compensation experience a two-fold greater risk of a negative outcome. Our findings show a considerably lower estimate of risk compared to previous reviews that include retrospective data. Further research is warranted to determine the etiological explanation for the influence of compensation status on patient outcomes. SYSTEMATIC REVIEW REGISTRATION NUMBER: CRD42012002121.

Dataset Information

Status and prediction of disapproval of the Korean workers' compensation insurance for diseases and injuries.

Objective

Methods

Results

Conclusion

Publications

Status and prediction of disapproval of the Korean workers' compensation insurance for diseases and injuries.

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