Dataset Information

Predicting prolonged sick leave among trauma survivors.

ABSTRACT: Many survivors after trauma suffer from long-term morbidity. The aim of this observational cohort study was to develop a prognostic prediction tool for early assessment of full-time sick leave one year after trauma. Potential predictors were assessed combining individuals from a trauma register with national health registers. Two models were developed using logistic regression and stepwise backward elimination. 4458 individuals were included out of which 488 were on sick leave full-time 12 months after the trauma. One comprehensive and one simplified model were developed including nine and seven predictors respectively. Both models showed excellent discrimination (AUC 0.81). The comprehensive model had very good calibration, and the simplified model good calibration. Prediction models can be used to assess post-trauma sick leave using injury-related variables as well as factors not related to the trauma per se. Among included variables, pre-injury sick leave was the single most important predictor for full-time sick leave one year after trauma. These models could facilitate a more efficient use of resources, targeting groups for follow-up interventions to improve outcome. External validation is necessary in order to evaluate generalizability.

SUBMITTER: von Oelreich E

PROVIDER: S-EPMC6329751 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Predicting prolonged sick leave among trauma survivors.

von Oelreich Erik E Eriksson Mikael M Brattström Olof O Discacciati Andrea A Strömmer Lovisa L Oldner Anders A Larsson Emma E

Scientific reports 20190111 1

Many survivors after trauma suffer from long-term morbidity. The aim of this observational cohort study was to develop a prognostic prediction tool for early assessment of full-time sick leave one year after trauma. Potential predictors were assessed combining individuals from a trauma register with national health registers. Two models were developed using logistic regression and stepwise backward elimination. 4458 individuals were included out of which 488 were on sick leave full-time 12 month ...[more]

PMID: 30635611

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Project description:BackgroundBy the end of 2022, nearly 20 million workers in the United States have gained paid-sick-leave coverage from mandates that require employers to provide benefits to qualified workers, including paid time off for the use of preventive services. Although the lack of paid-sick-leave coverage may hinder access to preventive care, current evidence is insufficient to draw meaningful conclusions about its relationship to cancer screening.MethodsWe examined the association between paid-sick-leave mandates and screening for breast and colorectal cancers by comparing changes in 12- and 24-month rates of colorectal-cancer screening and mammography between workers residing in metropolitan statistical areas (MSAs) that have been affected by paid-sick-leave mandates (exposed MSAs) and workers residing in unexposed MSAs. The comparisons were conducted with the use of administrative medical-claims data for approximately 2 million private-sector employees from 2012 through 2019.ResultsPaid-sick-leave mandates were present in 61 MSAs in our sample. Screening rates were similar in the exposed and unexposed MSAs before mandate adoption. In the adjusted analysis, cancer-screening rates were higher among workers residing in exposed MSAs than among those in unexposed MSAs by 1.31 percentage points (95% confidence interval [CI], 0.28 to 2.34) for 12-month colorectal cancer screening, 1.56 percentage points (95% CI, 0.33 to 2.79) for 24-month colorectal cancer screening, 1.22 percentage points (95% CI, -0.20 to 2.64) for 12-month mammography, and 2.07 percentage points (95% CI, 0.15 to 3.99) for 24-month mammography.ConclusionsIn a sample of private-sector workers in the United States, cancer-screening rates were higher among those residing in MSAs exposed to paid-sick-leave mandates than among those residing in unexposed MSAs. Our results suggest that a lack of paid-sick-leave coverage presents a barrier to cancer screening. (Funded by the National Cancer Institute.).

Dataset Information

Predicting prolonged sick leave among trauma survivors.

Publications

Predicting prolonged sick leave among trauma survivors.

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