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NEWCOURTLAND CENTER FOR TRANSITIONS AND HEALTH University of Pennsylvania School of Nursing

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:

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NEWCOURTLAND CENTER FOR TRANSITIONS AND HEALTH University of Pennsylvania School of Nursing

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  1. NEWCOURTLAND CENTER FOR TRANSITIONS AND HEALTH University of Pennsylvania School of Nursing

  2. 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

  3. 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

  4. Our Common Focus • Improve the nursing care of older adults • Accomplished by • Faculty Development • Leadership Development • Collaboration • Dissemination

  5. 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

  6. 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

  7. Controlling for Observed Confounding Covariates in Non-Experimental Study Designs: An Application of Propensity Score Analysis and the Full-Matching MethodMelissa O’Connor, PhD, MBA, RN, COS-C Alexandra Hanlon, PhD; Mary D. Naylor, PhD, RN, FAAN; Kathryn H. Bowles, PhD, RN, FAANSeptember 15, 2012 NEWCOURTLAND CENTER FOR TRANSITIONS AND HEALTH University of Pennsylvania School of Nursing

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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)

  13. 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

  14. Propensity Score Analysis

  15. Propensity Score Analysis

  16. 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

  17. Advantages of Full-Matching • Employs the entire sample • Forms a series of matched sets with either: • One treated subject and multiple control subjects or • One control subject and multiple treated subjects

  18. Matching Methods

  19. Bias Reduction Using Full-Matching

  20. Chain of Events

  21. 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

  22. 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

  23. Questions & Comments Thank you NewCourtland Center for Transitions and Health University of Pennsylvania School of Nursing

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