Age-Friendly Communities, Housing Tenure, and Affordability: An Ecological Analysis of Older Adult Health

Anthony Traver, Holly Dabelko-Schoeny, & Marisa Sheldon

OSU College of Social Work

Background

The Aging World

The number of people worldwide aged 60 years and older (United Nations 2022)

Year Total Percentage
2021 1 billion 13.7
2050 2.1 billion 22

Age-Friendly Communities Framework

Launched by the World Health Organization in 2007.

Recognized the impact of the built and social environment on healthy aging.

Community Features and Older Adult Health

  • Housing, outdoor spaces, and social participation were associated with a decreased likelihood of any functional limitation (Choi 2020)

  • Outdoor spaces and transportation were positively associated with high self-rated health (Choi 2020)

  • Outdoor spaces, health/community services, and social participation were positively associated with self-rated health (Kim, Buckley, Burnette, Kim, et al. 2022)

  • AARP Livability Index was positively associated with self-rated health(Kim, Buckley, Burnette, Huang, et al. 2022)

  • Healthcare service proximity, fewer neighborhood problems, social support, and community engagement were positively associated with self-rated health (Lehning, Smith, and Dunkle 2014)

Housing and Older Adult Health

Conceptual Models of community, housing, & health

Conceptual Models

Housing functions as an intermediary between community-level factors and individual health.


  • Social Determinants of Health
  • Social Ecology

Conceptual Models

Housing as a Social Determinant of Health (Swope and Hernández 2019)


Conceptual Models

Housing as a Social Determinant of Health (Swope and Hernández 2019)


The Four C’s

  • Cost: the financial burden of acquiring and maintaining housing including rent, utilities, mortgage, maintenance, and modification.

  • Conditions: the structural soundness, environmental hazards, and safety features of a home.

  • Consistency: the resident’s ability to remain in their home while avoiding harassment and dispossession.

  • Context: the qualities of the build and social environments and the availability of resources.

Conceptual Models

Ecological Model (Mehdipanah et al. 2017)


{fig-align=“center” width = 800}

Conceptual Models

Mehdipanah et al. (2017) tested their model using census tract data.

Home ownership partially mediated the relationship between neighborhood racial composition and mortality, but not neighborhood racial composition and disability.

Home value did not moderate relationship between neighborhood racial composition and either health outcome.

Ecological Model of Community Age-Friendliness, Housing, and Health Among Older Adults

Structural forces shape the age-friendliness and housing system within a community.

Policies, programs, and features that support aging may not provide equitable benefit across the range of housing circumstances.

Ecological Model


  • Hypotheses

    • H1: Rating of community as a place to age is positively associated with self-rated health.

    • H2: Housing tenure modifies the association between community rating and health.

    • H3: Being concerned about housing affordability modifies the association between community rating and health.

Methods

Data Source

2021 Central Ohio Regional Assessment on Aging.

  • Eligible households were identified using household data managed by Data Axle.

  • Random sample stratified by county.

  • 15% response rate resulted in 1417 surveys.

  • Removed 37 participants who reported their housing tenure as “other”.

  • Final analytic sample of 1380.

Sample Characteristics

Variable N Percent Mean St. Dev. Range
Age 1285 70.0 8.80 50-95
Exercise 1324 4.1 1.40 1-5
Social Network 1259 16.0 6.10 0-30
Health 1329 3.4 1.00 1-5
Comm. Rating 1274 3.7 0.95 1-6
Gender 1325
Male 578 44%
Female 743 56%
Other 4 < 1%
Race 1317
Asian 26 2%
Black 140 11%
Latine 31 2%
Multiracial 18 1%
White 1102 84%
Income 1060
Under $10K 94 9%
$10 - $25K 182 17%
$25K - $40K 157 15%
$40K - $60K 172 16%
$60K - $75K 127 12%
$75K - $100K 146 14%
Over $100K 182 17%
Housing Tenure 1340
Owner 620 46%
Owner with Mortgage 522 39%
Renter 198 15%
Affordability Concern 1244
No 1058 85%
Yes 186 15%

Missing Data

  • 5% of all data was missing.

  • 37% of respondents had incomplete data.

  • MCAR Test (χ2 = 601.35, df = 427, p < 0.001) indicated non-random missingness.

  • Imputed 37 data sets using multiple imputation by chained equations (Azur et al. 2011).

Analytic Approach

Linear Regression Models


Hypothesis 1

\(\hat{Health} = B_0 + B_cX_c + B_1Comm.\)

 

Hypothesis 2

\(\hat{Health} = B_0 + B_cX_c + B_1Comm. + B_2Tenure_{om} + B_3Tenure_r + B_4Comm.*\ Tenure_{om} +B_5Comm.*\ Tenure_r\)


Hypothesis 3

\(\hat{Health} = B_0 + B_cX_c + B_1Comm. + B_2Aff. Concern_{yes} + B_3Comm.*\ Aff. Concern_{yes}\)


where:

\(Tenure_{om}\) = owner with mortgage
\(Tenure_r\) = renter
\(B_c\) is the coefficient vector associated with the covariates \(X_c\) {age, excerise, social network, gender, race, income}

Analytic Approach

Moderation Effect Size

\(\Delta R^2_{mo}\) procedure to measure the proportion of variance of self-rated health jointly explained by community rating and the interaction term that is uniquely attributed to the interaction term (Liu and Yuan 2021).


where:

\(\Delta R^2_{mo} = \frac{R^2_2 - R^2_1}{R^2_2- R^2_0}\)

\(R^2_0\) corresponds to the moderator only model

\(R^2_1\) corresponds to the main effects model

\(R^2_2\) corresponds to the interaction model

Results

Model Summary

Model 1 Model 2 Model 3
B (SE) B (SE) B (SE)
Variables
(Intercept) 1.46(0.31) *** 1.53(0.34) *** 1.63(0.32) ***
Age 0.00(0.00) 0.00(0.00) -0.01(0.00) +
Exercise 0.22(0.02) *** 0.22(0.02) *** 0.21(0.02) ***
Social Network 0.01(0.00) *** 0.01(0.00) ** 0.01(0.00) **
Gender (ref = Male)
Female 0.12(0.05) * 0.12(0.05) * 0.12(0.05) *
Other -0.57(0.44) -0.51(0.44) -0.48(0.43)
Race (ref = Asian)
Black -0.16(0.19) -0.11(0.19) -0.09(0.19)
Latine -0.17(0.24) -0.14(0.24) -0.10(0.24)
Multiracial 0.08(0.27) 0.09(0.27) 0.11(0.27)
White -0.10(0.18) -0.08(0.18) -0.04(0.18)
Income (ref = Under $10k)
$10 - $25K 0.39(0.11) *** 0.34(0.12) ** 0.38(0.11) ***
$25K - $40K 0.74(0.11) *** 0.67(0.11) *** 0.72(0.11) ***
$40K - $60K 0.78(0.11) *** 0.70(0.12) *** 0.72(0.11) ***
$60K - $75K 0.87(0.12) *** 0.78(0.12) *** 0.81(0.12) ***
$75K - $100K 1.05(0.11) *** 0.97(0.12) *** 0.96(0.11) ***
Over $100K 1.20(0.11) *** 1.12(0.12) *** 1.11(0.12) ***
Comm. Rating 0.10(0.03) *** 0.14(0.04) *** 0.12(0.03) ***
Interactions
Housing Tenure (ref = Owner)
Owner w/ Mortgage 0.02(0.21)
Renter 0.02(0.29)
Comm. Rating × Owner w/ Mortgage -0.04(0.06)
Comm. Rating × Renter -0.07(0.08)
Affordability Concern (Yes) 0.15(0.27)
Comm. Rating × Affordability Concern (Yes) -0.13(0.07) +
Model Summary
Num.Obs. 1380 1380 1380
Num.Imp. 37 37 37
R2 0.309 0.316 0.322
R2 Adj. 0.301 0.306 0.313
F Statistic 3.31 10.91
(df = 4, 1359) * (df = 2, 1361) ***

p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. F statistic reflects the goodness of fit of the model compared to Model 1.

Housing Tenure Interaction Plot

Affordability Concern Interaction Plot

Moderation Effect Size



Moderator \(\Delta R^2_{mo}\)
Housing Tenure 0.07
Affordability Concern 0.22

Conclusion

Hypothesis 1 Subjective rating of one’s community as a place to age was positively associated with self-rated health.


Hypothesis 2 Housing tenure did not modify the relationship between community rating and health.


Hypothesis 3 There is weak evidence that housing affordability concerns modified the relationship between community rating and health.

Conclusion

  • The positive connection between community rating and older adult health is strong, but unaffordable housing may the by undermine this relationship.

  • Older adults living in unaffordable housing are perceiving the desirable aspects of their community but not experiencing the health benefits of them.

  • Increasing access to affordable housing may allow the health benefits of age-friendly initiatives to be distributed more equitably.

Limitations

  • Coarse measurements of health (1-5), community as a place to age (1-5), and affordability concerns (Y/N).

  • Subjective rating is not well established in the literature of age-friendly communities and health.

  • Regional sample excludes other types of housing markets (i.e. high density or shrinking city).

References

Azur, Melissa J., Elizabeth A. Stuart, Constantine Frangakis, and Philip J. Leaf. 2011. “Multiple imputation by chained equations: what is it and how does it work?” International Journal of Methods in Psychiatric Research 20 (1): 40–49. https://doi.org/10.1002/mpr.329.
Bhat, Aarti C., David M. Almeida, Andrew Fenelon, and Alexis R. Santos-Lozada. 2022. “A Longitudinal Analysis of the Relationship Between Housing Insecurity and Physical Health Among Midlife and Aging Adults in the United States.” SSM - Population Health 18 (May): 101128. https://doi.org/10.1016/j.ssmph.2022.101128.
Choi, Yeon Jin. 2020. “Age-Friendly Features in Home and Community and the Self-Reported Health and Functional Limitation of Older Adults: The Role of Supportive Environments.” Journal of Urban Health : Bulletin of the New York Academy of Medicine 97 (4): 471–85. https://doi.org/10.1007/s11524-020-00462-6.
Haurin, Donald, and Stephanie Moulton. 2017. “International Perspectives on Homeownership and Home Equity Extraction by Senior Households.” Journal of European Real Estate Research 10 (3): 245–76. https://doi.org/10.1108/JERER-06-2017-0020.
Jenkins Morales, Meghan, and Stephanie A. Robert. 2022. “Housing Cost Burden and Health Decline Among Low- and Moderate-Income Older Renters.” The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences 77 (4): 815–26. https://doi.org/10.1093/geronb/gbab184.
Kantz, Mary E., Comfort Enah, and Lisa M. Abdallah. 2023. “The relationship between health and housing in low-income older adults: A secondary analysis of survey data.” Public Health Nursing (Boston, Mass.) 40 (6): 931–39. https://doi.org/10.1111/phn.13248.
Kim, Kyeongmo, Thomas D. Buckley, Denise Burnette, Jin Huang, and Seon Kim. 2022. “Age-Friendly Communities and Older Adults Health in the United States.” International Journal of Environmental Research and Public Health 19 (15): 9292. https://doi.org/10.3390/ijerph19159292.
Kim, Kyeongmo, Tommy Buckley, Denise Burnette, Seon Kim, and Sunghwan Cho. 2022. “Measurement Indicators of Age-Friendly Communities: Findings From the AARP Age-Friendly Community Survey.” The Gerontologist 62 (1): e17–27. https://doi.org/10.1093/geront/gnab055.
Lehning, Amanda J., Richard J. Smith, and Ruth E. Dunkle. 2014. “Age-Friendly Environments and Self-Rated Health:: An Exploration of Detroit Elders.” Research on Aging 36 (1): 72–94. https://doi.org/10.1177/0164027512469214.
Liu, Hongyun, and Ke-Hai Yuan. 2021. “New Measures of Effect Size in Moderation Analysis.” Psychological Methods 26 (6): 680–700. https://doi.org/10.1037/met0000371.
Mehdipanah, Roshanak, Jaclyn Martin, Alexa K. Eisenberg, Amy J. Schulz, Lewis B. Morgenstern, and Kenneth M. Langa. 2022. “Housing Status, Mortgage Debt and Financial Burden as Barriers to Health Among Older Adults in the u.s.” Housing and Society 49 (1): 58–72. https://doi.org/10.1080/08882746.2021.1881373.
Mehdipanah, Roshanak, Amy J. Schulz, Barbara A. Israel, Graciela Mentz, Alexa Eisenberg, Carmen Stokes, and Zachary Rowe. 2017. “Neighborhood Context, Homeownership and Home Value: An Ecological Analysis of Implications for Health.” International Journal of Environmental Research and Public Health 14 (10): 1098. https://doi.org/10.3390/ijerph14101098.
Meltzer, Rachel, and Alex Schwartz. 2016. “Housing Affordability and Health: Evidence from New York City.” Housing Policy Debate 26 (1): 80–104. https://doi.org/10.1080/10511482.2015.1020321.
Swope, Carolyn B., and Diana Hernández. 2019. “Housing as a Determinant of Health Equity: A Conceptual Model.” Social Science & Medicine 243 (December): 112571. https://doi.org/10.1016/j.socscimed.2019.112571.
United Nations. 2022. “World Population Prospects 2022.” New York. https://population.un.org/wpp/Download/Standard/MostUsed/.