Lung cancer patients' journey from first symptom to treatment: Results from a Greek registry |
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| Institution: | 1. ECONCARE LP, Athens, Greece;2. Oncology Unit, 3rd Department of Medicine, Athens Medical School, National & Kapodistrian University of Athens, Greece;1. Moffitt Cancer Center, Department of Health Outcomes and Behavior, 4115 E. Fowler Ave., Tampa, FL 33617, United States;2. Moffitt Cancer Center, Center for Immunization and Infection Research in Cancer, 12902 USF Magnolia Drive, Tampa, FL 33612, United States;3. Moffitt Cancer Center, Department of Cancer Epidemiology, 12902 USF Magnolia Drive, Tampa, FL 33612, United States;4. Moffitt Cancer Center, Department of Biostatistics and Bioinformatics, 12902 USF Magnolia Drive, Tampa, FL 33612, United States;5. University of South Florida, Department of Family Medicine, 13330 USF Laurel Drive, Tampa, FL 33612, United States;6. University of South Florida, Department of Epidemiology & Biostatistics, 13201 Bruce B Downs Blvd, Tampa, FL 33612, United States;8. University of Florida, Department of Medicine, 1600 SW Archer Rd., Gainesville, FL 32608, United States;9. University of Florida Health, Department of Health Outcomes and Biomedical Informatics, 2004 Mowry Road, Ste 2245, Gainesville, FL 32610, United States;10. University of Florida Health, Cancer Population Sciences, 2004 Mowry Road, Ste 2245, Gainesville, FL 32610, United States;1. Department of Oncology, Lund University, and Skane University Hospital, Lund, Sweden;2. Research Unit of Clinical Epidemiology, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark;3. Center for Clinical Epidemiology, Odense University Hospital, Odense, Denmark;4. Institute of Pathology, Aalborg Hospital, Aalborg, Denmark;5. Medical Oncology Department, Instituto Português de Oncologia (IPO-Porto), Porto, Portugal;6. Department of Epidemiological Methods and Etiologic Research, Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany;7. Department of Health Information and Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal;8. Centre de recherche en Épidémiologie et Santé des Populations (CESP), Inserm U1018, France;9. Cancer and Environment Team, Université Paris-Sud, Villejuif, France;10. Institute for Medical Informatics, Biometry and Epidemiology, University Clinic Essen, Germany;11. Unit of Public Health and Environmental Care, Department of Preventive Medicine, University of Valencia, Valencia, Spain;12. CIBER Epidemiologia y Salud Pública (CIBERESP), Madrid, Spain;13. Unit of Cancer Epidemiology, Centre for Oncologic Prevention, University of Turin, Italy;14. Registre des Tumeurs de l´Hérault, Montpellier, France;15. Division of Occupational and Environmental Medicine, Department of Clinical and Experimental Medicine, Linköping University, Sweden;p. Department of Oncology, University of Padua, Italy;q. Department of Public Health, University of Aarhus, Denmark;1. Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China;2. Department of Medical Oncology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Affiliated Cancer Hospital of Nanjing Medical University, Nanjing China;3. State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210023, China;4. State Key Laboratory of Bioelectronics, Southeast University, Nanjing, China;1. Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;2. Department of Health and Human Performance, University of Houston, TX, USA;3. Health Disparities Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;1. Centre for Big Data Research in Health, University of New South Wales Sydney, NSW, Australia;2. Centre for Primary Health Care and Equity, University of New South Wales Sydney, NSW, Australia;3. School of Medicine, University of Wollongong, NSW, Australia;4. National Drug and Alcohol Research Centre, University of New South Wales Sydney, NSW, Australia;5. Faculty of Medicine and Health, University of Sydney, NSW, Australia;6. Cancer Voices NSW, NSW, Australia;1. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States;2. Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences Durham, NC, United States;3. Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States;4. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States;5. Biospecimen Processing Center, University of North Carolina, Chapel Hill, NC, United States;6. Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA, United States;7. Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States;8. Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States;9. Showers Center for Childhood Cancer and Blood Disorder, Akron Children’s Hospital, Akron, OH, United States;10. Department of Biostatistics, Colleges of Medicine and Public Health & Health Professions, University of Florida, Children’s Oncology Group Statistics & Data Center, Gainesville, FL, United States;11. Department of Otolaryngology, Washington University School of Medicine, St Louis, MO, 63110 |
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| Abstract: | BackgroundTo map the patients’ journey from symptoms onset to treatment initiation for the most frequent histological types of lung cancer in Greece and describe the initial treatment that patients receive.MethodsThe primary data source was a Greek hospital-based registry. Demographic, anthropometric, lifestyle, and diagnostic-related characteristics as well as treatment-related data were extracted from the registry for patients diagnosed with Adenocarcinoma, Squamous and Small Cell Lung Cancer (SCLC). The time intervals from symptoms onset to diagnosis (StD), diagnosis to treatment initiation (DtT), symptoms onset to treatment initiation (StT) and surgery to post–surgery treatment (SRGtT) were estimated.Results231, 120 and 122 patients were diagnosed with Adenocarcinoma, SCLC and Squamous, respectively. The percentage of patients diagnosed at stage III/IV ranged from 75% in Adenocarcinoma to 97.5% in SCLC (p < 0.001). The median (IQR) StD was 52 (28–104) days and no difference was detected across the three histological types (p = 0.301). Cough as first symptom was the only determinant of StD (p = 0.001). The median (IQR) DtT was 23 (13–36) days, with this time interval being shorter among patients with SCLC compared to patients with Adenocarcinoma and Squamous (p < 0.001). The median (IQR) StT was 81 (51–139) days. Almost one third of patients with Adenocarcinoma and Squamous were subjected first to surgery and the median (IQR) SRGtT was 42 (34–55) days.ConclusionsOur results indicate that time interval from symptoms onset to treatment initiation in Greece is substantially prolonged, highlighting the need for strategies to expedite lung cancer diagnosis and access to evidence-based treatment. |
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| Keywords: | Lung neoplasm Delay Time interval Diagnosis Non-small cell lung cancer |
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