Introduction: Falls are common post-stroke events but their relationship with healthcare costs is unclear. The aim of this study was to examine the relationship between healthcare costs in the first year after stroke and falls among survivors discharged to the community.
Patients And Methods: Survivors of acute stroke with planned home discharges from five large hospitals in Ireland were recruited. Falls and healthcare utilisation data were recorded using inpatient records, monthly calendars and post-discharge interviews. Cost of stroke was estimated for each participant from hospital admission for one year. The association of fall-status with overall cost was tested with multivariable linear regression analysis adjusting for pre-stroke function, stroke severity, age and living situation.
Results: A total of 109 stroke survivors with complete follow-up data (mean age = 68.5 years (SD = 13.5 years)) were included. Fifty-three participants (49%) fell following stroke, of whom 28 (26%) had recurrent falls. Estimated mean total healthcare cost was €20,244 (SD=€23,456). The experience of one fall and recurrent falls was independently associated with higher costs of care (p = 0.02 and p < 0.01, respectively).
Discussion: The observed relationship between falls and cost is likely to be underestimated as aids and adaptions, productivity losses, and nursing home care were not included.
Conclusion: This study points at differences across fall-status in several healthcare costs categories, namely the index admission, secondary/tertiary care (including inpatient re-admissions) and allied healthcare. Future research could compare the cost-effectiveness of inpatient versus community-based fall-prevention after stroke. Further studies are also required to inform post-stroke bone-health management and fracture-risk reduction.
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http://dx.doi.org/10.1177/2396987318764954 | DOI Listing |
Br J Hosp Med (Lond)
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Department of Medicine, Rawalpindi Medical University, Rawalpindi, Pakistan.
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Faculty of Food Science and Nutrition, University of Iceland, 102 Reykjavík, Iceland.
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School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety.
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