Knee osteoarthritis (OA) is a costly and worsening problem in the United States with nearly 14 million
adults suffering with symptoms of pain and stiffness. Although many treatments exist, they often lack evidence
for efficacy, bare significant risk, or are exercise programs that are difficult to maintain. Walking in one’s
neighborhood provides a low-cost solution that has been shown to control OA symptoms. However, most
Americans, and further, most OA patients do not engage in enough physical activity. Barriers to walking are
built and social environment in their neighborhood, such as walkability and fear of crime. Although walkability
and social environment have been associated with walking and physical activity, no studies have yet bridged
the gap to prevalent knee OA. Investigating and identifying walkability and social environmental components
that most influence prevalence of knee OA and trajectory of knee OA to end stage total knee arthroplasty
(TKA) can offer targets for intervention. This 5-year K23 award and strong mentorship in “Big Data”, Geospatial
Information Science (GISc) and qualitative methods will help Dr. Gebauer become a leader in integrating
Electronic Medical Record Data (EMR) and GISc and gain skills in qualitative methodology to facilitate mixed
methods studies to explore the influence of neighborhood on walking and painful conditions. Training in spatial
statistics and “Big Data” management will facilitate Aims 1 & 2, exploring the associations between
neighborhood factors, such as walkability, social capital, and violent crime rates and prevalent cases of knee
OA, as well as TKA across the United States utilizing the rich Veterans Affairs (VA) EMR Data set. Innovative
GISc modeling with time-varying measures of neighborhood walkability and social characteristics will be used
to follow veterans across the country in a dynamic retrospective cohort for 11 years, providing much needed
longitudinal data to offer evidence to inform public health action. Training in qualitative methods and
triangulation will facilitate Aim 3, using qualitative interviews with primary care providers (PCPs) and OA
patients to help understand how patient or PCP knowledge of neighborhood barriers and resources can be
leveraged to enhance shared decision making between PCPs and patients and establish concordance around
walking for knee OA treatment.
Through the completion of these aims and additional training in longitudinal/multilevel modeling
strategies, grantsmanship, and networking with experts in neighborhoods and physical activity, Dr. Gebauer
will provide novel risk factors for knee OA and time to TKA, introduce innovative spatial survival models to
chronic disease epidemiology, as well as a new understanding of shared decision making for patients and their
PCPs, providing data for follow up R01 studies examining integration of neighborhood characteristics to
increase walking in knee OA patients, replicating this study in civilian populations and other metropolitan areas,
and begin to explore other musculoskeletal conditions that benefit from walking in the neighborhood.
