302P
Aging-Friendly Neighborhood Influences on Late-Life Disability: An Examination of Socioeconomic Differences

Schedule:
Saturday, January 17, 2015
Bissonet, Third Floor (New Orleans Marriott)
* noted as presenting author
Kyeongmo Kim, MSW, PhD Student, University of Maryland at Baltimore, Baltimore, MD
Amanda Lehning, PhD, Assistant Professor, University of Maryland at Baltimore, Baltimore, MD
Richard Smith, PhD, Assistant Professor, Wayne State University, Detroit, MI
Background and Purpose: Previous studies typically examine late-life disability in terms of limitations of activities of daily living (e.g., feeding, bathing) and instrumental activities of daily living (e.g., cooking, shopping). In recent years, however, researchers have noted the importance of measuring disability in terms of an elder’s ability to participate in valued activities. Aging-friendly communities provide physical and social environments that are thought to promote elder health and well-being. Conceptual work proposes that aging-friendly communities can lead to a number of beneficial outcomes, including the ability to maintain life-long activities even when experiencing the challenges of aging. To date, however, research has yet to examine whether aging-friendly neighborhoods influence this outcome. In addition, prior work rarely considers the potential differential effects of socioeconomic status.

Methods: We examined the association between aging-friendly neighborhoods and late-life disability using data from the 2011 wave of the National Health and Aging Trends Survey (NHATS), a publicly-available data set with a sample representative of the Medicare population age 65+. We restricted our analyses to 6,517 community-dwelling older adults without a proxy respondent. We measured the dependent dichotomous variable of late-life disability based on whether the respondent 1) reported not participating in any of four activities because of their health and functioning, and 2) the activity was important to them. Activities included family visits, religious services, club activities, and going out for enjoyment. Aging-friendly neighborhood characteristics included self-report and interviewer-rated aspects of the social and physical environment (e.g., absence of housing and neighborhood problems, social cohesion, mobility options). We adjusted for individual demographic and health characteristics. We conducted separate logistic regression models for Medicaid recipients (n=901) and Medicare-only recipients (n=5616) to examine potential socioeconomic differences. 

Results: 30% of Medicaid recipients and 17% of those with higher incomes reported restrictions in at least one valued activity. For both groups, individual health characteristics (i.e., chronic conditions, hospital stay in the previous year, fall in the previous year, and depression/anxiety) were positively associated with late-life disability as measured by restriction in at least one valued activity. None of the aging-friendly characteristics were significant for Medicaid recipients. In contrast, for those with higher incomes we found that aging-friendly measures of mobility, specifically driving a car (B =.46, SE = .05, p<.001) and walking in the neighborhood (B =.68, SE = .06, p<.001) were associated with a decreased odds of late-life disability.

Conclusions and Implications:  This poster adds to the limited literature documenting differential effects of aging-friendly neighborhoods on a variety of elder outcomes between those who are low-income and higher income. We found that the ability to drive and walk around the neighborhood may allow higher-income elders to continue to engage in valued activities, while the same relationship was not observed for low-income elders. We will discuss strategies for social work scholars and practitioners to modify aging-friendly initiatives to potentially protect low-income elders from late-life disability. We will also provide specific recommendations for future research on socioeconomic differences by including aging-friendly measures from other sources, such as U.S. Census data.