Radiotherapy continuity for cancer treatment: lessons learned from natural disasters
Müller-Polyzou, R.; Reuter-Oppermann, M.
Show abstract
BackgroundThe contemporary world is challenged by natural disasters accelerated by climate change, affecting a growing world population. Simultaneously, cancer remains a persistent threat as a leading cause of death, killing 10 million people annually. The efficacy of radiotherapy, a cornerstone in cancer treatment worldwide, depends on an uninterrupted course of therapy. However, natural disasters cause significant disruptions to the continuity of radiotherapy services, posing a critical challenge to cancer treatment. This paper explores how natural disasters impact radiotherapy practice, compares them to man-made disasters, and outlines strategies to mitigate adverse effects of natural disasters. Through this analysis, the study seeks to contribute to developing resilient healthcare frameworks capable of sustaining essential cancer treatment amidst the challenges posed by natural disasters. MethodWe conducted a Structured Literature Review to investigate this matter comprehensively, gathering and evaluating relevant academic publications. We explored how natural disasters affected radiotherapy practice and examined the experience of radiotherapy centres worldwide in resuming operations after such events. Subsequently, we validated and extended our research findings through a global online survey involving radiotherapy professionals. ResultsThe Structured Literature Review identified twelve academic publications describing hurricanes, floods, and earthquakes as the primary disruptors of radiotherapy practice. The analysis confirms and complements risk mitigation themes identified in our previous research, which focused on the continuity of radiotherapy practice during the COVID-19 pandemic. Our work describes nine overarching themes, forming the basis for a taxonomy of 36 distinct groups. The subsequent confirmative online survey supported and solidified our findings and served as a basis for developing a conceptual framework for natural disaster-resilient radiotherapy. DiscussionThe growing threat posed by natural disasters underscores the need to develop business continuity programs and define risk mitigation measures to ensure the uninterrupted provision of radiotherapy services. By drawing lessons from past disasters, we can better prepare for future hazards, supporting disaster management and planning efforts, particularly enhancing the resilience of radiotherapy practice. Additionally, our study can serve as a resource for shaping policy initiatives aimed at mitigating the impact of natural hazards.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Early user experience and lessons learned using ultra-portable digital X-ray with computer-aided detection (DXR-CAD) products: A qualitative study from the perspective of healthcare providers 94%
- Optimizing costs and sustainability for Gamma Knife Radiosurgery: A cost and breakeven analysis at India’s largest Neurosurgery Centre 94%
- Predictive factors for the development of peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma 93%
Similar papers in this journal
- The Use of Machine Learning in Occupational Risk Communication for Healthcare Workers – Protocol for scoping review 93%
- Surgical Resection, Radiotherapy, And Percutaneous Thermal Ablation for Treatment of Stage 1 Non-Small Cell Lung Cancer: A Systematic Review and Network Meta-Analysis 92%
- Purchasing high-cost medical equipment in hospitals: A systematic review 92%
Similar papers in this journal
- Development of a customised data management system for a COVID-19-adapted colorectal cancer pathway 91%
- Network Graph Representation of COVID-19 Scientific Publications to Aid Knowledge Discovery 90%
- Connecting Artificial Intelligence and Primary Care Challenges: Findings from a Multi-Stakeholder Collaborative Consultation 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.