Christine M McDonough1, Donald Carmichael2, Molly E Marino3, Pengsheng Ni4, Anna N A Tosteson5, Julie P W Bynum6. 1. Department of Orthopaedics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire; Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, Massachusetts; and Department of Physical Therapy, School of Health and Rehabilitation Sciences and Department of Orthopaedic Surgery, University of Pittsburgh School of Medicine, 100 Technology Drive Pittsburgh, PA 15219 (USA). 2. The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth. 3. Department of Health Law, Policy and Management, Boston University School of Public Health. Now with: Department of Quality Measurement and Health Policy, Research Triangle (RTI) International, Waltham, Massachusetts. 4. Department of Health Law, Policy and Management, Boston University School of Public Health. 5. Department of Orthopaedics, The Dartmouth Institute for Health Policy and Clinical Practice, and the Department of Medicine, Geisel School of Medicine at Dartmouth. 6. The Dartmouth Institute for Health Policy and Clinical Practice and the Department of Medicine, Geisel School of Medicine at Dartmouth. Now with: Department of Internal Medicine, University of Michigan Medical School, and the Institute for Health Policy and Innovation, University of Michigan, Ann Arbor, Michigan.
Abstract
OBJECTIVE: Although Medicare assessment files will include Standardized Patient Assessment Data Elements from 2016 forward, lack of uniformity of functional data prior to 2016 impedes longitudinal research. The purpose of this study was to create crosswalks for postacute care assessment measures and the basic mobility and daily activities scales of the Activity Measure for Post-Acute Care (AM-PAC) and to test their accuracy and validity in development and validation datasets. METHODS: This cross-sectional study is a secondary analysis of AM-PAC, the Inpatient Rehabilitation Facility Patient Assessment Instrument, the Minimum Data Set, and the Outcome and Assessment Information Set data from 300 adults receiving rehabilitation recruited from 6 health care networks in 1 metropolitan area. Rasch analysis was used to co-calibrate items from the 3 measures onto the AM-PAC metric and to create look-up tables to create estimated AM-PAC (eAM-PAC) scores. Mean scores and correlation and agreement between actual and estimated scores were examined in the development dataset. Scores were estimated in a cohort of Medicare beneficiaries with hip, humerus and radius fractures. Correlations between eAM-PAC and Functional Independence Measure motor scores were examined. Differences in mean eAM-PAC scores were evaluated across groups of known differences (age, fracture type, dementia). RESULTS: Strong correlations were found between actual and eAM-PAC scores in the development dataset. Moderate to strong correlations were found between the eAM-PAC basic mobility and Functional Independence Measure motor scores in the validation dataset. Differences in basic mobility scores across known groups were statistically significant and appeared to be clinically important. Differences between mean daily activities scores were statistically significant but appeared not to be clinically important. CONCLUSION: Although further testing is warranted, the basic mobility crosswalk appears to provide valid scores for aggregate analysis of Medicare postacute care data. IMPACT: This study reports on a method to take data from different Medicare administrative data sources and estimate scores on 1 scale. This approach was applied separately for data related to basic mobility and to daily activities. This may allow researchers to overcome challenges with using Medicare administrative data from different sources.
OBJECTIVE: Although Medicare assessment files will include Standardized Patient Assessment Data Elements from 2016 forward, lack of uniformity of functional data prior to 2016 impedes longitudinal research. The purpose of this study was to create crosswalks for postacute care assessment measures and the basic mobility and daily activities scales of the Activity Measure for Post-Acute Care (AM-PAC) and to test their accuracy and validity in development and validation datasets. METHODS: This cross-sectional study is a secondary analysis of AM-PAC, the Inpatient Rehabilitation Facility Patient Assessment Instrument, the Minimum Data Set, and the Outcome and Assessment Information Set data from 300 adults receiving rehabilitation recruited from 6 health care networks in 1 metropolitan area. Rasch analysis was used to co-calibrate items from the 3 measures onto the AM-PAC metric and to create look-up tables to create estimated AM-PAC (eAM-PAC) scores. Mean scores and correlation and agreement between actual and estimated scores were examined in the development dataset. Scores were estimated in a cohort of Medicare beneficiaries with hip, humerus and radius fractures. Correlations between eAM-PAC and Functional Independence Measure motor scores were examined. Differences in mean eAM-PAC scores were evaluated across groups of known differences (age, fracture type, dementia). RESULTS: Strong correlations were found between actual and eAM-PAC scores in the development dataset. Moderate to strong correlations were found between the eAM-PAC basic mobility and Functional Independence Measure motor scores in the validation dataset. Differences in basic mobility scores across known groups were statistically significant and appeared to be clinically important. Differences between mean daily activities scores were statistically significant but appeared not to be clinically important. CONCLUSION: Although further testing is warranted, the basic mobility crosswalk appears to provide valid scores for aggregate analysis of Medicare postacute care data. IMPACT: This study reports on a method to take data from different Medicare administrative data sources and estimate scores on 1 scale. This approach was applied separately for data related to basic mobility and to daily activities. This may allow researchers to overcome challenges with using Medicare administrative data from different sources.
Authors: Stephen M Haley; Wendy J Coster; Patricia L Andres; Larry H Ludlow; Pengsheg Ni; Tamara L Y Bond; Samuel J Sinclair; Alan M Jette Journal: Med Care Date: 2004-01 Impact factor: 2.983
Authors: Anne Deutsch; Carl V Granger; Roger C Fiedler; Gerben DeJong; Robert L Kane; Kenneth J Ottenbacher; Allen W Heinemann; John P Naughton; Maurizio Trevisan Journal: Med Care Date: 2005-09 Impact factor: 2.983
Authors: Wendy J Coster; Stephen M Haley; Patricia L Andres; Larry H Ludlow; Tamara L Y Bond; Peng-Sheng Ni Journal: Med Care Date: 2004-01 Impact factor: 2.983