Kathrin Gödde1, Bob Siegerink2, Hella Fügemann1,3, Dietmar Keune4, Steffen Sander4, Alice Schneider5, Jacqueline Müller-Nordhorn1,6, Christine Holmberg1,3,7, Nina Rieckmann1, Nikolaj Frost8, Ulrich Keilholz4, Ute Goerling4. 1. Institute of Public Health, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany. 2. Center for Stroke Research (CSB), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany. 3. Institute of Social Medicine and Epidemiology, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany. 4. Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany. 5. Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany. 6. Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Bayerisches Krebsregister, Erlangen, Germany. 7. Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Potsdam, Germany. 8. Department of Infectious Diseases and Pulmonary Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Abstract
OBJECTIVES: Several patient factors have been described to influence access to optimal cancer care like socioeconomic factors or place of residence. In this study, we investigate whether data routinely collected in a clinical cancer registry can be used to identify populations of lung cancer patients with increased risk of not receiving optimal cancer care. METHODS: We analysed data of 837 lung cancer patients extracted from the clinical cancer registry of a German university hospital. We compared patient populations by two indicators of optimal care, namely implementation of tumour board meeting recommendations as well as the timeliness of care. RESULTS: There was a high rate of implementation of tumour board meeting recommendations of 94.4%. Reasons for non-implementation were mainly a patient's own wish or a worsening of the health situation. Of all patient parameters, only tumour stage was associated with the two optimal care indicators. CONCLUSION: Using routine data from a clinical cancer registry, we were not able to identify patient populations at risk of not getting optimal care and the implementation of guideline-conform care appeared to be very high in this setting. However, limitations were the ambiguity of optimal care indicators and availability of parameters predictive for patients' vulnerability.
OBJECTIVES: Several patient factors have been described to influence access to optimal cancer care like socioeconomic factors or place of residence. In this study, we investigate whether data routinely collected in a clinical cancer registry can be used to identify populations of lung cancerpatients with increased risk of not receiving optimal cancer care. METHODS: We analysed data of 837 lung cancerpatients extracted from the clinical cancer registry of a German university hospital. We compared patient populations by two indicators of optimal care, namely implementation of tumour board meeting recommendations as well as the timeliness of care. RESULTS: There was a high rate of implementation of tumour board meeting recommendations of 94.4%. Reasons for non-implementation were mainly a patient's own wish or a worsening of the health situation. Of all patient parameters, only tumour stage was associated with the two optimal care indicators. CONCLUSION: Using routine data from a clinical cancer registry, we were not able to identify patient populations at risk of not getting optimal care and the implementation of guideline-conform care appeared to be very high in this setting. However, limitations were the ambiguity of optimal care indicators and availability of parameters predictive for patients' vulnerability.