Timothy A Reistetter1, Karl Eschbach, John Prochaska, Daniel C Jupiter, Ickpyo Hong, Allen M Haas, Kenneth J Ottenbacher. 1. From the Department of Occupational Therapy, University of Texas Health Science Center at San Antonio, School of Health Professions, San Antonio, Texas (TAR); Department of Preventive Medicine and Population Health, University of Texas Medical Branch, School of Medicine, Galveston, Texas (KE, JP, DCJ, AMH); Department of Occupational Therapy, Yonsei University, College of Health Sciences, Gangwon-do, Republic of Korea (IH); and Division of Rehabilitation Sciences, University of Texas Medical Branch, School of Health Professions, Galveston, Texas (KJO).
Abstract
OBJECTIVE: The aims of the study were to demonstrate a method for developing rehabilitation service areas and to compare service areas based on postacute care rehabilitation admissions to service areas based on acute care hospital admissions. DESIGN: We conducted a secondary analysis of 2013-2014 Medicare records for older patients in Texas (N = 469,172). Our analysis included admission records for inpatient rehabilitation facilities, skilled nursing facilities, long-term care hospitals, and home health agencies. We used Ward's algorithm to cluster patient ZIP Code Tabulation Areas based on which facilities patients were admitted to for rehabilitation. For comparison, we set the number of rehabilitation clusters to 22 to allow for comparison to the 22 hospital referral regions in Texas. Two methods were used to evaluate rehabilitation service areas: intraclass correlation coefficient and variance in the number of rehabilitation beds across areas. RESULTS: Rehabilitation service areas had a higher intraclass correlation coefficient (0.081 vs. 0.076) and variance in beds (27.8 vs. 21.4). Our findings suggest that service areas based on rehabilitation admissions capture has more variation than those based on acute hospital admissions. CONCLUSIONS: This study suggests that the use of rehabilitation service areas would lead to more accurate assessments of rehabilitation geographic variations and their use in understanding rehabilitation outcomes.
OBJECTIVE: The aims of the study were to demonstrate a method for developing rehabilitation service areas and to compare service areas based on postacute care rehabilitation admissions to service areas based on acute care hospital admissions. DESIGN: We conducted a secondary analysis of 2013-2014 Medicare records for older patients in Texas (N = 469,172). Our analysis included admission records for inpatient rehabilitation facilities, skilled nursing facilities, long-term care hospitals, and home health agencies. We used Ward's algorithm to cluster patient ZIP Code Tabulation Areas based on which facilities patients were admitted to for rehabilitation. For comparison, we set the number of rehabilitation clusters to 22 to allow for comparison to the 22 hospital referral regions in Texas. Two methods were used to evaluate rehabilitation service areas: intraclass correlation coefficient and variance in the number of rehabilitation beds across areas. RESULTS: Rehabilitation service areas had a higher intraclass correlation coefficient (0.081 vs. 0.076) and variance in beds (27.8 vs. 21.4). Our findings suggest that service areas based on rehabilitation admissions capture has more variation than those based on acute hospital admissions. CONCLUSIONS: This study suggests that the use of rehabilitation service areas would lead to more accurate assessments of rehabilitation geographic variations and their use in understanding rehabilitation outcomes.
Authors: S Samuel Bederman; Charles D Rosen; Nitin N Bhatia; P Douglas Kiester; Ranjan Gupta Journal: Clin Orthop Relat Res Date: 2011-08-05 Impact factor: 4.176
Authors: Jane L Givens; Susan L Mitchell; Sylvia Kuo; Pedro Gozalo; Vince Mor; Joan Teno Journal: J Am Geriatr Soc Date: 2013-10-01 Impact factor: 5.562
Authors: John D Birkmeyer; Bradley N Reames; Peter McCulloch; Andrew J Carr; W Bruce Campbell; John E Wennberg Journal: Lancet Date: 2013-09-28 Impact factor: 79.321
Authors: David C Goodman; Stephen S Mick; David Bott; Therese Stukel; Chiang-hua Chang; Nancy Marth; Jim Poage; Henry J Carretta Journal: Health Serv Res Date: 2003-02 Impact factor: 3.402
Authors: Robert E Burke; Christine D Jones; Eric A Coleman; Jason R Falvey; Jennifer E Stevens-Lapsley; Adit A Ginde Journal: Am J Accountable Care Date: 2017-03-10