Using Administrative Data to Assess the Risk of Permanent Work Disability: A Cohort Study
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Using Administrative Data to Assess the Risk of Permanent Work Disability: A Cohort Study Matthias Bethge1 · Katja Spanier1 · Marco Streibelt2
© The Author(s) 2020
Abstract Purpose Unmet rehabilitation needs are common. We therefore developed a risk score using administrative data to assess the risk of permanent work disability. Such a score may support the identification of individuals with a high likelihood of receiving a disability pension. Methods Our sample was a random and stratified 1% sample of individuals aged 18–65 years paying pension contributions. From administrative records, we extracted sociodemographic data and data about employment and welfare benefits covering 2010–2012. Our outcome was a pension due to work disability that was requested between January 2013 and December 2017. We developed a comprehensive logistic regression model and used the model estimates to determine the risk score. Results We included 352,140 individuals and counted 6,360 (1.8%) disability pensions during the 5-year follow-up. The area under the receiver operating curve was 0.839 (95% CI 0.834 to 0.844) for the continuous risk score. Using a threshold of ≥ 50 points (20.2% of all individuals), we correctly classified 80.6% of all individuals (sensitivity: 71.5%; specificity: 80.8%). Using ≥ 60 points (9.9% of all individuals), we correctly classified 90.3% (sensitivity: 54.9%; specificity: 91.0%). Individuals with 50 to 0.5 implies that the predictive power is better than random; values of ≥ 0.8 are considered good [10]. As the apparent performance of a prognostic model on a development sample is usually better than the performance on other samples, even if the latter sample consist of individuals from the same population we internally validated our model [11]. We used the bootstrapping technique described by Harrell et al. [12] with 200 repetitions. To assess the prognostic accuracy of a categorized risk score, we calculated sensitivity and specificity, the correct classification rate, and the positive and negative likelihood ratios for each potential threshold. The index J (J = sensitivity + specificity − 1) proposed by Youden was used to determine the optimal threshold for categorization of our risk score [13]. The optimized threshold was determined as the score for which the Youden index reached its maximum. Finally, Kaplan–Meier curves were generated to describe the prognostic relevance of our categorized risk score. Time-to-event was computed from 1 January 2013 until the date of application for an approved disability pension. The serial time of people who received an old-age pension was censored at the date of start of the old-age pension. Individuals’ serial time without an event ended on 31 December 2017. In addition, a proportional hazard model using the categorized risk score as the independent variable was calculated, and hazard ratios and their 95% confidence intervals were determined. Statistical tests were considered significant if the twotailed level of significance was less than 5%. All analyses were
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