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