Predictors of Return to Work After Stroke in Hungary: A Mixed-Methods Economic and Clinical Data Analysis.

BACKGROUND

Return to work (RTW) is a fundamental aspect of recovery after stroke, importantly, for workers of working age. Evidence indicates there is little known about the clinical and systematic predictors of RTW in Hungary. We aimed to determine the independent predictors of RTW for stroke survivors using aggregate hospital data and expert opinion.

METHODS

A mixed-method study using aggregated national level administrative data from the Pulvita platform (the National Health Insurance Fund of Hungary) and expert interpretation from the physicians who treat stroke survivors. The data analyzed 13,572 inpatient records for stroke hospitalizations from 2015-2024 across Hungarian counties.

RESULTS

Stroke severity, cognitive and psychological recovery, and presence of comorbidities were important clinical predictors of RTW. Rehabilitation intensity was greater for people aged 51-65 years, and work-age men appeared to have slightly better access to rehabilitation compared to work-aged women. Patients accessed more medical rehabilitation services than they did occupational or psychosocial services. Access to rehabilitation services may have varied geographically, with patients in counties such as Budapest and Pest having better access due to higher provider availability and cross-county patient movement. In addition, economic extrapolations from the literature on post-stroke care costs may have introduced bias in estimating annual social productivity losses, reported as EUR 19,953 per patient.

CONCLUSIONS

Clinical and economic factors both impact RTW potential among stroke survivors in Hungary. Although rehabilitation intensity can indicate likelihood of RTW, the lack of a national RTW program acts as a significant barrier to RTW for stroke survivors. This study suggests a need for integrated rehabilitation and RTW systems, with associated future research linking clinical, economic, and labor market status data to develop effective and efficient policy for stroke survivors.