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NEWCOURTLAND CENTER FOR TRANSITIONS AND HEALTH University of Pennsylvania School of Nursing. Background. Older adults ~ aged 65 and older: Comprised almost 13% of the US population in 2009 Estimated to comprise 20% of the US population by 2030 In 2007: - PowerPoint PPT Presentation
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NEWCOURTLAND CENTER FOR TRANSITIONS AND HEALTHUniversity of Pennsylvania School of Nursing
Background
• Older adults ~ aged 65 and older:– Comprised almost 13% of the US population in
2009– Estimated to comprise 20% of the US population
by 2030• In 2007:
– 12.9 million older adults were discharged from hospitals
(3 times the rate of persons of all ages)– Older adults encompassed 1.4 million DAILY home
health patients– Older adults occupied over 88% of the nursing
home beds• However, 25% of US nursing programs lack a
faculty member specializing in gerontology
Who We Are
• Building Academic Geriatric Nursing Capacity Alumni
• Funded by the John A. Hartford Foundation
• To cultivate better prepared and more highly skilled geriatric health care practitioners and faculty
Our Common Focus
• Improve the nursing care of older adults
• Accomplished by– Faculty Development– Leadership Development– Collaboration– Dissemination
Today’s Symposium
• Janet Van Cleave– Factors affecting older adults’ symptom
distress following cancer surgery
• Sarah L. Szanton– An intervention to improve function and health-
related quality of life in disabled, older adults
• Dana Carthron– Multicaregiving among African-American
caregiving grandmothers
Today’s Symposium
• LuAnn Etcher– Sleep characteristics in early and late-
onset Alzheimer’s dementia • Melissa O’Connor
– Innovative study design of propensity score analysis and full-matching
Controlling for Observed Confounding Covariates in Non-Experimental Study Designs: An Application of Propensity Score Analysis and the Full-Matching Method
Melissa O’Connor, PhD, MBA, RN, COS-C
Alexandra Hanlon, PhD; Mary D. Naylor, PhD, RN, FAAN; Kathryn H. Bowles, PhD, RN, FAAN
September 15, 2012
NEWCOURTLAND CENTER FOR TRANSITIONS AND HEALTHUniversity of Pennsylvania School of Nursing
Funding
John A. Hartford Foundation’s Building Academic Geriatric Nursing Capacity Scholar [2010-2012]
Ruth L. Kirschstein NRSA Predoctoral Fellowship, National Institute for Nursing Research [1F31NR012090]
Frank Morgan Jones Fund
Introduction
Non-Experimental Study
Not randomized
Significantly differ on observed and unobserved characteristics/covariates
Difference in outcome between the groups could be due to the treatment or the background covariates
Presentation Aims
Compare groups in a non-experimental study and separate the effect of treatment from the background covariates
Application example ~ Determine the impact of length of stay on the
rate of hospitalization after discharge from home health services
Preparation of the Data Sets
Exclusion criteria applied Variables created and recoded Merging of the data sets Sample
52,000 eligible Medicare beneficiaries Randomized sample of 4,500
Method
Propensity Score Analysis (Rosenbaum and Rubin, 1983)
Full-matching (Stuart, 2010)
Five CMS-owned national data sets from 2009 Outcomes Assessment Information Set
(OASIS) Home Health Standard Analytic File
(HHSAF) Medicare Provider and Analysis Review
File (MedPAR) Beneficiary Summary Provider of Services File (POS)
Propensity Score Analysis
Conditional probability of receiving treatment, given the distribution of observed covariates
Reduces the potentially confounding covariates into a single variable - the propensity score
Predictor of interest must be dichotomous
Conducted in R statistical software
Propensity Score Analysis
Matched Covariates
Female White Hispanic Dyspnea
Living Alone Stasis Ulcer Pressure Ulcer Age
Number of Diagnoses
Urinary Incontinence/ Urinary Catheter
Requiring Assistance with Ambulation
Alzheimer’s, Cardiomyopathy,CAD, Diabetes, COPD, Renal Failure, Anxiety, Depression, Dysrythmia, HF, HIV/AIDS, Ischemic Heart Disease, MI, Osteoporosis
Requiring Assistance with Transfers
Requiring Assistance with Bathing
Requiring Assistance with Oral Medications
Requiring Assistance with Eating
Requiring Assistance with ADLs
Requiring Assistance with IADLs
Propensity Score Analysis
Matched Covariates
Cognitive Function
Confusion Anxiety Memory Deficits
Inpatient Stay prior to Home Health
Depressed Mood
Severity of Illness
For-Profit Home Health Agency
Guarded Rehabilitation Prognosis
Lacking an Informal Caregiver
LOS Group Skilled Nursing Visit Group
Matching Techniques
Conducted in R using the MatchIt package
Several matching methods One to One One to One with Replacement One to One with Calipers Subclassification Full-Matching
Advantages of Full-Matching
Employs the entire sample Forms a series of matched sets with
either: One treated subject and multiple control
subjectsor
One control subject and multiple treated subjects
Matching Methods
Bias Reduction Using Full-Matching
Chain of Events
Prepare Data Set
Take Random Sample
Export via CSV file;
Import into R
Conduct PSA
Conduct Matching
Techniques
Choose the Technique that
Reduces the most Bias
Export the Matched Data Set via CSV file
Import into Analytic Software
Ready for Analysis
Limitations
Predictor of interest must be dichotomous LOS (Group 1: < 21 days; Group 3: > 42
days)
Potentially confounding factors not measured Having poor access to primary care Number of medications Non-adherence Socioeconomic factors
Conclusions
Despite limitations, Propensity Score Analysis and Matching techniques are: rigorous allow us learn how to better care for older
adults
Using existing large, administrative data sets
Questions & Comments
Thank you
NEWCOURTLAND CENTER FOR TRANSITIONS AND HEALTHUniversity of Pennsylvania School of Nursing
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