TY - JOUR
T1 - Understanding Variation in Postacute Care
T2 - Developing Rehabilitation Service Areas Through Geographic Mapping
AU - Reistetter, Timothy A.
AU - Eschbach, Karl
AU - Prochaska, John
AU - Jupiter, Daniel C.
AU - Hong, Ickpyo
AU - Haas, Allen M.
AU - Ottenbacher, Kenneth J.
N1 - Publisher Copyright:
© Wolters Kluwer Health, Inc. All rights reserved.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - 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.
AB - 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.
KW - Cluster Analysis
KW - Geographic Mapping
KW - Health Services
KW - Small Area Variation
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U2 - 10.1097/PHM.0000000000001577
DO - 10.1097/PHM.0000000000001577
M3 - Article
C2 - 32858537
AN - SCOPUS:85103994225
SN - 0894-9115
VL - 100
SP - 465
EP - 472
JO - American Journal of Physical Medicine and Rehabilitation
JF - American Journal of Physical Medicine and Rehabilitation
IS - 5
ER -