Christopher J Louis1, Jonathan R Clark2, Marianne M Hillemeier3, Fabian Camacho4, Nengliang Yao4, Roger T Anderson4. 1. Department of Health Law, Policy and Management, Boston University, Boston, Massachusetts. 2. College of Business, The University of Texas at San Antonio, San Antonio, Texas. 3. Department of Health Policy and Administration, The Pennsylvania State University, University Park, Pennsylvania. 4. Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia.
Abstract
PURPOSE: Despite being generally accepted that delays in diagnosing breast cancer are of prognostic and psychological concern, the influence of hospital characteristics on such delays remains poorly understood, especially in rural and underserved areas. However, hospital characteristics have been tied to greater efficiency and warrant further investigation as they may have implications for breast cancer care in these areas. METHODS: Study data were derived from the Kentucky, North Carolina, Ohio, and Pennsylvania state central cancer registries (2006-2008). We then linked Medicare enrollment files and claims data (2005-2009), the Area Resource File (2006-2008), and the American Hospital Association Annual Survey of Hospitals (2007) to create an integrated data set. Hierarchical linear modeling was used to regress the natural log of breast cancer diagnosis delay on a number of hospital-level, demographic, and clinical characteristics. FINDINGS: The baseline study sample consisted of 4,547 breast cancer patients enrolled in Medicare that lived in Appalachian counties at the time of diagnosis. We found that hospitals with for-profit ownership (P < .01) had shorter diagnosis delays than their counterparts. Estimates for comprehensive oncology services, system membership and size were not statistically significant at conventional levels. CONCLUSIONS: Some structural characteristics of hospitals (eg, for-profit ownership) in the Appalachian region are associated with having shorter delays in diagnosing breast cancer. Researchers and practitioners must go beyond examining patient-level demographic and tumor characteristics to better understand the drivers of timely cancer diagnosis, especially in rural and underserved areas.
PURPOSE: Despite being generally accepted that delays in diagnosing breast cancer are of prognostic and psychological concern, the influence of hospital characteristics on such delays remains poorly understood, especially in rural and underserved areas. However, hospital characteristics have been tied to greater efficiency and warrant further investigation as they may have implications for breast cancer care in these areas. METHODS: Study data were derived from the Kentucky, North Carolina, Ohio, and Pennsylvania state central cancer registries (2006-2008). We then linked Medicare enrollment files and claims data (2005-2009), the Area Resource File (2006-2008), and the American Hospital Association Annual Survey of Hospitals (2007) to create an integrated data set. Hierarchical linear modeling was used to regress the natural log of breast cancer diagnosis delay on a number of hospital-level, demographic, and clinical characteristics. FINDINGS: The baseline study sample consisted of 4,547 breast cancerpatients enrolled in Medicare that lived in Appalachian counties at the time of diagnosis. We found that hospitals with for-profit ownership (P < .01) had shorter diagnosis delays than their counterparts. Estimates for comprehensive oncology services, system membership and size were not statistically significant at conventional levels. CONCLUSIONS: Some structural characteristics of hospitals (eg, for-profit ownership) in the Appalachian region are associated with having shorter delays in diagnosing breast cancer. Researchers and practitioners must go beyond examining patient-level demographic and tumor characteristics to better understand the drivers of timely cancer diagnosis, especially in rural and underserved areas.
Authors: Samilia Obeng-Gyasi; Sheetal M Kircher; Kelsey P Lipking; Benjamin J Keele; Al B Benson; Lynne I Wagner; Ruth C Carlos Journal: Cancer Date: 2019-06-28 Impact factor: 6.860