scholarly journals O1E.5 Short-term disability leave and employment termination: using marginal structural models to estimate counterfactual risks

2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Sally Picciotto ◽  
Andreas Neophytou ◽  
Mark Cullen ◽  
Ellen Eisen

IntroductionShort-term disability leave can be considered as a measure of not being well enough to work. The American Manufacturing Cohort, followed 1996–2013, consists of employees of a light-metal company that provided short-term disability insurance to all employees: coverage to replace wages for up to 6 months of work absence due to medical issues. We hypothesized that since brief short-term disability leave allows workers time to recover from illness or injury without losing their jobs, it should be protective against employment termination.MethodsWe analyzed 18 386 (83% male, 80% white) hourly employees. We censored workers once their accumulated disability leave exceeded 6 weeks because longer time spent on short-term disability leave suggests more serious illness or injury that may prevent return to work. To analyze the effect of short-term disability leave on employment termination, we applied a marginal structural pooled logistic model that allowed for a time-varying hazard function. We adjusted for time-varying confounding by occupational exposures and health-related variables using inverse probability weighting. Using the estimated coefficients, we compared the predicted probabilities (by person-month) of terminating employment with the corresponding counterfactual probabilities if the worker had never taken disability leave. These probabilities yielded estimated survival curves under the two scenarios.ResultsThe average worker was followed for 5.5 years. Approximately 42% of the workers took at least one day of disability leave, and 48% terminated employment during follow-up. We estimated that 1058 (29%) more workers would have terminated employment within 5 years from cohort entry if the company had had no disability leave benefit than were predicted under the natural course.ConclusionShort-term disability leave is a potentially relevant health variable for occupational epidemiologists. This analysis suggests that short-term disability leave can help employees retain their jobs when a temporary health issue prevents them from working.

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Sahamoddin Khailaie ◽  
Tanmay Mitra ◽  
Arnab Bandyopadhyay ◽  
Marta Schips ◽  
Pietro Mascheroni ◽  
...  

Abstract Background SARS-CoV-2 has induced a worldwide pandemic and subsequent non-pharmaceutical interventions (NPIs) to control the spread of the virus. As in many countries, the SARS-CoV-2 pandemic in Germany has led to a consecutive roll-out of different NPIs. As these NPIs have (largely unknown) adverse effects, targeting them precisely and monitoring their effectiveness are essential. We developed a compartmental infection dynamics model with specific features of SARS-CoV-2 that allows daily estimation of a time-varying reproduction number and published this information openly since the beginning of April 2020. Here, we present the transmission dynamics in Germany over time to understand the effect of NPIs and allow adaptive forecasts of the epidemic progression. Methods We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states using our model. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread in different regions of Italy, the model was optimized to fit data from the Robert Koch Institute. Results The time-varying reproduction number (Rt) in Germany decreased to <1 in early April 2020, 2–3 weeks after the implementation of NPIs. Partial release of NPIs both nationally and on federal state level correlated with moderate increases in Rt until August 2020. Implications of state-specific Rt on other states and on national level are characterized. Retrospective evaluation of the model shows excellent agreement with the data and usage of inpatient facilities well within the healthcare limit. While short-term predictions may work for a few weeks, long-term projections are complicated by unpredictable structural changes. Conclusions The estimated fraction of immunized population by August 2020 warns of a renewed outbreak upon release of measures. A low detection rate prolongs the delay reaching a low case incidence number upon release, showing the importance of an effective testing-quarantine strategy. We show that real-time monitoring of transmission dynamics is important to evaluate the extent of the outbreak, short-term projections for the burden on the healthcare system, and their response to policy changes.


2009 ◽  
Vol 24 (2) ◽  
pp. 133-139 ◽  
Author(s):  
Jan Krul ◽  
Armand R. J. Girbes

AbstractObjective:The objective of this study was to report on a nine years of experience of providing medical support during house parties (raves) in the Netherlands, where they can be organized legally.Design:This was a prospective, observational study of self-referred patients from 1997 to 2005. During raves, first aid stations are staffed with specifically trained medical and paramedical personnel. Self-referred patients were diagnosed, treated, and recorded using standardized methods.Results:During a nine-year period with 219 raves occurred, involving approximately three million participants, 23,581 patients visited the first aid stations. The medical usage rate (MUR) varied from 59–170 patients per 10,000 rave participants. The mean age increased from 1997 to 2005 from 18.7 ±2.7 to 23.3 ±5.7 years. The mean stay at the first aid station was 18 ±46 minutes. Most health problems were mild. Fifteen cases of severe incidents were observed with one death.Conclusions:Unique data from the Netherlands demonstrate a low number of serious, health-related, short-term problems during raves.


2013 ◽  
Vol 113 (2) ◽  
pp. 260-265 ◽  
Author(s):  
Michael A. Poch ◽  
Andrew P. Stegemann ◽  
Shabnam Rehman ◽  
Mohamed A. Sharif ◽  
Abid Hussain ◽  
...  

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