Associations of Place-Based Factors with Service Use and Consumer-Reported Unmet Service Needs Among Older Adults Using Publicly Funded Home- and Community-Based Services in the United States

Tetyana P. Shippee (), Romil R. Parikh, Nicholas Musinguzi, Benjamin W. Langworthy, Jack M. Wolf, Stephanie Giordano and Eric Jutkowitz
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Tetyana P. Shippee: Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
Romil R. Parikh: Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
Nicholas Musinguzi: Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, USA
Benjamin W. Langworthy: Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, USA
Jack M. Wolf: Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, USA
Stephanie Giordano: Human Services Research Institute, Cambridge, MA 02140, USA
Eric Jutkowitz: Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA

IJERPH, 2025, vol. 22, issue 9, 1-22

Abstract: Access to home- and community-based services (HCBS) is critical for aging in place; yet many older adults continue to experience unmet needs. While individual-level factors are better-studied, less is known about how neighborhood-level place-based factors (PBFs, e.g., poverty, housing conditions, transportation, and internet access) shape access to and adequacy of HCBS. This study addresses that gap by examining the added explanatory value of PBFs in predicting HCBS use and unmet needs. We analyzed data from 6558 community-dwelling adults aged ≥ 65 years using the 2022–2023 National Core Indicators–Aging & Disability Adult Consumer Survey. Outcomes included use of six HCBS types, consumer-reported unmet needs for each type, and overall unmet HCBS needs. PBFs were measured at the ZIP code level using the 2016–2020 American Community Survey. Nested logistic regression models estimated incremental variance (McFadden’s R 2 ) explained by PBFs, adjusting for individual demographics, health status, state, and proxy response. Adding PBFs increased explained variance by 7.98–22.70% for HCBS use, 35.92–48.00% for unmet needs by service type, and 51.85% for overall unmet HCBS needs. PBFs meaningfully influence both access to and adequacy of HCBS. Using PBFs to guide resource allocation and targeting modifiable PBFs could improve HCBS access and efficiency.

Keywords: neighborhood; environment; social determinants of health; long-term services and supports; aging in place; home healthcare (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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