Charles P Schade1, John G Brehm. 1. West Virginia Medical Institute, 3001 Chesterfield Ave., Charleston, WV 25304, USA. cschade@wvmi.org
Abstract
OBJECTIVES: (1) To demonstrate average length of service (ALOS) bias in the currently used acute-care hospitalization (ACH) home health quality measure, limiting comparability across agencies, and (2) to propose alternative ACH measures. DATA SOURCES/STUDY SETTING: Secondary analysis of Medicare home health service data 2004-2007; convenience sample of Medicare fee-for-service hospital discharges. STUDY DESIGN: Cross-sectional analysis and patient-level simulation. DATA COLLECTION/EXTRACTION METHODS: We aggregated outcome and ALOS data from 2,347 larger Medicare-certified home health agencies (HHAs) in the United States between 2004 and 2007, and calculated risk-adjusted monthly ACH rates. We used multiple regression to identify agency characteristics associated with ACH. We simulated ACH during and immediately after home health care using patient and agency characteristics similar to those in the actual data, comparing the existing measure with alternative fixed-interval measures. PRINCIPAL FINDINGS: Of agency characteristics studied, ALOS had by far the highest partial correlation with the current ACH measure (r(2)=0.218, p<.0001). We replicated the correlation between ACH and ALOS in the patient-level simulation. We found no correlation between ALOS and the alternative measures. CONCLUSIONS: Alternative measures do not exhibit ALOS bias and would be appropriate for comparing HHA ACH rates with one another or over time.
OBJECTIVES: (1) To demonstrate average length of service (ALOS) bias in the currently used acute-care hospitalization (ACH) home health quality measure, limiting comparability across agencies, and (2) to propose alternative ACH measures. DATA SOURCES/STUDY SETTING: Secondary analysis of Medicare home health service data 2004-2007; convenience sample of Medicare fee-for-service hospital discharges. STUDY DESIGN: Cross-sectional analysis and patient-level simulation. DATA COLLECTION/EXTRACTION METHODS: We aggregated outcome and ALOS data from 2,347 larger Medicare-certified home health agencies (HHAs) in the United States between 2004 and 2007, and calculated risk-adjusted monthly ACH rates. We used multiple regression to identify agency characteristics associated with ACH. We simulated ACH during and immediately after home health care using patient and agency characteristics similar to those in the actual data, comparing the existing measure with alternative fixed-interval measures. PRINCIPAL FINDINGS: Of agency characteristics studied, ALOS had by far the highest partial correlation with the current ACH measure (r(2)=0.218, p<.0001). We replicated the correlation between ACH and ALOS in the patient-level simulation. We found no correlation between ALOS and the alternative measures. CONCLUSIONS: Alternative measures do not exhibit ALOS bias and would be appropriate for comparing HHA ACH rates with one another or over time.
Authors: Mary D Naylor; Dorothy A Brooten; Roberta L Campbell; Greg Maislin; Kathleen M McCauley; J Sanford Schwartz Journal: J Am Geriatr Soc Date: 2004-05 Impact factor: 5.562