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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2022 Sep 26;78(2):253–263. doi: 10.1093/geronb/gbac149

Differential Associations Between the Area Deprivation Index and Measures of Physical Health for Older Black Adults

Alexa C Allan 1,✉, Alyssa A Gamaldo 2, Regina S Wright 3, Adrienne T Aiken-Morgan 4, Anna K Lee 5, Jason C Allaire 6, Roland J Thorpe Jr 7, Keith E Whitfield 8
Editor: Robert B Wallace
PMCID: PMC9938923  PMID: 36161476

Abstract

Objectives

This study explored the association between place-based characteristics (e.g., neighborhood socioeconomic deprivation) and physical health within older Black adults, a critical gap in the literature as identified by the National Institute on Minority Health and Health Disparities.

Methods

The sample was from Wave 1 data of Baltimore Study of Black Aging: Patterns of Cognitive Aging (N = 450; Mage = 68.34). Variables included the area deprivation index (ADI), objective (e.g., average blood pressure) and subjective (e.g., self-rated health) measures of physical health. Multiple linear regression models were conducted controlling for key sociodemographic characteristics.

Results

Participants reporting better self-rated health and less likely to need help with activities of daily living were significantly more likely to be living in more disadvantaged neighborhoods based on national and state ADI, respectively, even after adjusting for covariates. A significant age and ADI interaction revealed better self-rated health was associated with a more disadvantaged neighborhood particularly for individuals ≤66 years. There was no significant association between ADI and objective physical health measures.

Discussion

The findings suggest that national- and state-level place-based characteristics should be considered along with individual-level factors, which can enrich the scientific understanding of how neighborhood characteristics relate to varying health indicators among older Black adults.

Keywords: Neighborhood, Physical health measurements, Place-based characteristics

Significance of Exploring Contextual Influences (e.g., Area Deprivation Index) on Physical Health

Low socioeconomic status (SES) contributes to chronic stress, which is consequently linked to poor health outcomes (Gallo & Matthews, 2003; Smyth et al., 2013). Further, individuals with lower SES or who live in poor or marginalized communities have a higher risk of early mortality (Di Cesare et al., 2013; Kind & Buckingham, 2018), particularly from noncommunicable diseases (e.g., diabetes, heart disease, high blood pressure). Ludwig et al. (2012) suggest nearly 9 million people in the United States live in extreme-poverty neighborhoods. This number has undoubtedly grown given national increases in poverty according to recent 2020 poverty rates (United States Census Bureau, 2021). This is a major societal concern given extreme-poverty neighborhoods increase risk for worse educational, economic, and health outcomes relative to less disadvantaged neighborhoods (Ludwig et al., 2011). Some studies have suggested that life experiences for individuals with a household income below the poverty threshold can vary based upon the socioeconomics of their residential neighborhood (high disadvantage vs low disadvantage; Kind & Buckingham, 2018; Ludwig et al., 2011). In other words, there may be factors at the neighborhood level (e.g., food accessibility, safety, access to education) that distinctly affect health outcomes above and beyond an individual’s socioeconomic characteristics (e.g., income, financial strain). Indeed, Finlay et al. (2021) found that neighborhood factors, such as number of accessible parks, fitness amenities, and walkable destinations, motivated physical activity and were associated with increased longevity, higher levels of functional health, lower risk of falling, better cognitive function, and increased social integration (Gulsvik et al., 2012). This evidence highlights the potential independent role of neighborhoods on health outcomes. In fact, the National Institute on Minority Health and Health Disparities (NIMHD) research framework, which incorporates the socioecological model (Kilanowski, 2017), recognizes that physical and built environments and neighborhood- and community-level factors are critical to our understanding of health and health disparities (Alvidrez et al., 2019). However, despite this recognition, Alvidrez and colleagues (2019) have noted that limited studies include both these domains of influence to gain a more comprehensive understanding of their impact on health. Therefore, this study aims to address the gap identified by the NIMHD.

Longstanding research has identified that neighborhood socioeconomic deprivation is associated with health behaviors and outcomes (Pampel et al., 2010; Stafford & Marmot, 2003). Despite these associations, only single-measure variables of Census data at differing geographic levels (e.g., county, census tract, block group) are widely used to assess neighborhood socioeconomic context rather than a comprehensive composite index that would capture the multiple dimensions of socioeconomic resources (Lian et al., 2016). Contextual variables for assessing neighborhood deprivation risk have varied from study to study. However, prior studies have typically measured one or more of the following variables: poverty/income, racial/ethnic composition, education, employment, and occupation, while the latter variables, housing/crowding and residential stability, have only appeared in a handful of studies (Messer et al., 2006). Considering neighborhood SES consists of multiple aspects of socioeconomic resources (Lian et al., 2016), an area deprivation index (ADI), which is a multidimensional evaluation of a region’s socioeconomic conditions, is a more comprehensive tool to assess neighborhood SES environment (Kind & Buckingham, 2018). Unlike the typically contextual variables used in prior studies, an ADI is an algorithmic metric estimated for an array of state and federal data regarding neighborhood income, education, employment, and housing quality. The ADI enhances the accuracy of detecting area deprivation, which is useful in improving the scientific understanding of how poor neighborhood quality influences poor health outcomes and more specifically health disparities (Kind & Buckingham, 2018).

Significance of Exploring Association of Contextual Influences on Physical Health Within Older Black Adults

Much of the research on race and health has focused on using White/Caucasian adult samples as the comparison group to understand poor health in Black adults (Whitfield & Baker-Thomas, 1999). To date, many studies have found that there are racial group differences, particularly differences between Black and White adults, across health conditions (e.g., diabetes, hypertension, physical function, social support, and cognition; Schoenbaum & Waidmann, 1997). However, given the changing demographic makeup of the United States (i.e., racially and ethnically), it is important to address the heterogeneity commonly observed within minority populations in terms of health awareness, behaviors, access, and/or outcomes (Whitfield & Baker-Thomas, 1999). Numerous studies have identified higher rates and poorer outcomes of cardiovascular disease (CVD), diabetes, hypertension, and functional limitations in older Black adults (Rooks et al., 2008; Sundquist et al., 2001). Manolio et al. (1995) compared clinical and subclinical CVD and its risk factors in Black and White men and women aged 65 years and older and found the prevalence of CVD and its risk factors was generally higher in Black adults. Kanchi et al. (2018) examined gender and racial/ethnic disparities in CVD risk factors and found that although women had lower prevalence of CVD risk factors compared to men, when stratified by race/ethnicity, non-Latino Black women had a higher burden of CVD risk factors than other gender and racial/ethnic groups.

Racial and ethnic minorities have various life experiences that are commonly influenced by social and structural inequalities (Brondolo et al., 2009). These structural forces are what then create the social disparities, which lead to disadvantage across the life course (Forrester et al., 2020). Poor self-rated health and several cardiovascular outcomes are linked to cumulative disadvantage (Shuey & Willson, 2008). Furthermore, a study by Chamberlain et al. (2020) found that higher ADI was not only associated with increased risk for multimorbidity, but associations were strengthened when adjusting for sociodemographic factors like education, age, and sex. Some empirical evidence has also shown significant interactions between sociodemographic factors (e.g., age, gender, education) and neighborhood deprivation as measured by indexes such as ADI for health behaviors and outcomes. For example, the magnitude of the association between greater deprivation health behaviors and low consumption of healthy foods (fruits and vegetables) was more pronounced in individuals that identify as male or with low levels of education (Shohaimi et al., 2004). Another study observed that greater neighborhood deprivation was associated with greater mortality risk from cancer, particularly within individuals younger than 65 years of age (Yu et al., 2022). These prior findings further support the possibility that the association between ADI and health indicators, beyond the indicators explored in the prior literature, may vary by sociodemeographic characteristics (e.g., age, sex, education) in level of significance and/or magnitude of association. These findings have implications for how the association between health and neighborhood quality is explored. However, limited research has explored the association between health and disadvantage using measures of contextual disadvantage (e.g., neighborhood quality) that would extend beyond the common approach focused on measures of individual characteristics of disadvantage (e.g., income and financial strain) for older Black adults.

Older racial minority groups have been affected by unique emotional, social, and psychological experiences that can affect aging both negatively and positively (Forrester et al., 2020). Without accounting for unique experiences of aging minorities, we cannot appropriately capture the aspects that can potentially lead to healthy aging. More specifically, Black adults have health problems across the life course and generally report higher levels of morbidity with pronounced prevalence in illnesses likes diabetes and hypertension (Farmer & Ferraro, 2005). Much of the research examining this inequality has examined the association between individual or personal socioeconomic characteristics and poor health outcomes for Black adults (Farmer & Ferraro, 2005). However, recent studies that examined area deprivation while adjusting for individual socioeconomic characteristics found that neighborhood disadvantage accounts for disease risk above and beyond traditional singular socioeconomic characteristics (Akwo et al., 2018; Lang et al., 2008). While these findings are promising, limited research has explored the association between disadvantaged neighborhoods and physical health among older Blacks adults.

Distinction Between Objective and Subjective Physical Health Measurements

Physical health is often measured through subjective and objective assessments. Subjective or self-rated physical health is typically the initial approach to measure an individual’s health awareness and change in health (Cleary, 1997). Subjective physical health assessments are typically rapid and easy to administer (Kaplan & Baron-Epel, 2003). More importantly, subjective physical health assessments are useful in identifying the potential need for additional health assessments, particularly objective measurements of health. While subjective physical health assessments can be a beneficial initial indicator of poor health, the underlying mechanisms for poor subjective health assessments are not always clear considering poor subjective health could be related to various factors (e.g., personality, mood, disease; Moor et al., 2006). Thus, objective physical health measures (e.g., blood draws to detect inflammation) can better disentangle underlying mechanisms of poor health (Henson et al., 2013). Despite the unique advantages of both measures, limited studies have included both types of health measures when exploring determinants of health, particularly contextual determinants (e.g., neighborhood quality). The inclusion of both subjective and objective health measures in the current study allowed observation of potential differential associations with ADI, which contributes to the understanding of how neighborhood deprivation relates to varying health indicators.

The purpose of this study was threefold. The first study aim examined the relationship between subjective measures of physical health (e.g., activities of daily living [ADLs], instrumental activities of daily living [IADLs], and self-rated health) and ADI rankings (national and state) in older Black adults. It was hypothesized that worse subjective physical health reports would be associated with a higher ADI (i.e., greater disadvantage). The second study aim examined the relationship between objective measures of physical health (e.g., cardiovascular risk factor and average blood pressure) and ADI rankings (national and state). It was hypothesized that higher scores on objective physical health measures (worse health) would be associated with a higher ADI. Finally, the third study aim explored whether the relationship between measures of health and ADI varies by sociodemographic characteristics (e.g., age, sex, and education). It was hypothesized that the relationship between the health outcomes and ADI would vary by sociodemographic characteristics.

Method

Participants

The current study leveraged the 2005 sample of African American adults who were enrolled in the Baltimore Study of Black Aging–Patterns of Cognitive Aging (BSBA-PCA), which specifically explored changes in the associations among cognition, health, and psychosocial factors in community-dwelling older African Americans (Aiken-Morgan et al., 2014; Gamaldo et al., 2014) across two waves approximately 3 years (33 months) apart. The BSBA-PCA baseline (Wave 1) total sample consisted of 602 participants and the follow-up Wave 2 sample consisted of 450 participants. The BSBA-PCA was approved by the Duke University Institutional Review Board. The current study focused on the baseline (Wave 1) data, which included the metrics of interest to test the proposed study aims.

Measures

This study utilized measures of national and state economic disadvantage ADI rankings, objective and subjective assessments of health, and demographic characteristics of the sample.

Area deprivation index

The ADI is a metric of socioeconomic disadvantage within a neighborhood developed by researchers at the University of Wisconsin–Madison (Kind & Buckingham, 2018) that was modified from a scale from the Health Resources and Services Administration. It includes data regarding neighborhood income, education, employment, and housing quality from various state and federal sources (e.g., 2010 U.S. Census American Community Survey Data) and measures disadvantage at the level of the Census block group, the closest approximation to a neighborhood. The ADI is a validated measure for classifying neighborhood adversity and has been shown to be associated with worse health outcomes (e.g., obesity, diabetes, hospital readmission; Kind et al., 2014; Ludwig et al., 2011). The 2010 national and Maryland state ADI percentile rankings were obtained for all participants using the 12-digit Federal Information Processing Standards (FIPS) code at the time of Wave 1 BSBA data collection via University of Wisconsin Neighborhood Atlas (Kind & Buckingham, 2018; University of Wisconsin School of Medicine and Public Health, 2019). The national percentile rankings range from 1 to 100 indicating the lowest and highest ADI, respectively. Similarly, deciles range from 1 to 10 for each state indicating lowest and highest ADI, respectively. ADI at the state level is without consideration of national ADI.

Objective health

Measures of blood pressure and cardiovascular risk were included in the current study. First, as an objective assessment of cardiovascular health, two assessments of orthostatic blood pressure readings were collected three times and averaged (i.e., systolic blood pressure [SBP] and diastolic blood pressure [DBP]). As recommended by the American Heart Association, normal SBP and DBP are equivalent to less than 120 mm/Hg and less than 80 mm/Hg, respectively (American Heart Association, 2021). Participants without SBP and DBP values were coded as missing. A cardiovascular risk factor (CVRF) composite score was calculated following methodology by Gamaldo et al. (2014). CVRF was summed using participants’ self-report of whether a physician had diagnosed any of the following conditions: CVD, heart attack, angina, circulation problems, high blood pressure, diabetes, and stroke. CVRF scores ranged from 0 (no risk) to 6 (most risk factors).

Subjective health

ADLs and self-rated health compared to the past were included in the current study. First, the study included a commonly used scale assessing difficulty in performing basic ADLs and IADLs (Katz et al., 1963). The scale consisted of 17 items where participants answered questions about how well they can perform various daily activities. Participants indicated their proficiency on a scale of 1 (No, I never need help) to 4 (Never do the activity). This scale has been shown to be sensitive in detecting decline in health status (Edemekong et al., 2021). Second, the study included an item from the Short Form 36 Health Survey Questionnaire (Ware & Sherbourne, 1992). The scale item asked participants, “Compared to 1 year ago, how would you rate your health in general now?” Participants were given response options to this item that ranged from 0 (Much poorer now) to 4 (Much better now).

Demographics

A self-reported questionnaire was used to collect demographic data. The items from the questionnaire included age, sex, education (in years), and income level. Because the distribution of monthly gross family income was positively skewed, we created a dichotomous income variable with two levels (<$1,700 and ≥$1,700) following the methodology outlined in Gamaldo et al. (2014) to include as covariates in the analyses.

Statistical Analyses

Descriptive statistics were conducted to explore the demographic, objective and subjective health, and ADI characteristics of the sample. A Pearson correlation was run to explore potential relationships between objective and subjective health, ADI rankings, and sample characteristics. A regression model was conducted to test whether ADI rankings were uniquely associated with CVRF, average blood pressure, ADL, IADL, or self-rated health after adjusting for sociodemographic characteristics. All continuous variables (ADI, age, and education) entered into the regression models as predictors were centered. Correlation and regression analyses were performed using SAS 9.4. An online computational tool for multiple linear regression two-way interactions was used to evaluate the simple slopes (Preacher et al., 2006).

Results

Description of Sample Characteristics (Analytic Sample Only)

This study included a total of 577 Black adults with complete address data that could be linked to the national and Maryland state ADI rankings. However, ADI rankings could not be identified for several participants (n = 96) due to a block group falling into one or more suppression criteria (i.e., low population and/or housing, high group quarters population, or both) leading to missing ADI ranks (University of Wisconsin School of Medicine and Public Health, 2019). Additional participants were removed for missingness in the dependent variables totaling the sample to 450 Black adults. Participants ranged in age from 48 to 95 years old (M = 68.34, SD = 9.72). A high percentage of the sample identified as women (n = 332; 72%). The participant sample had 3–20 years of education (M = 11.65, SD = 3.00) and 84% of the participants reported a monthly gross income of $100–$1,700. Participants lived in neighborhoods with a national ADI ranging from 5 to 100 (M = 58.74, SD = 29.12) and state ADI ranging from 1 to 10 (M = 8.08, SD = 2.50). State ADI values indicate that the majority of participants were living in disadvantageous neighborhoods compared to other geographic locations in the state of Maryland. In contrast, ADI values at the national level indicate greater heterogeneity of the neighborhood quality within the BSBA sample when compared to other residents across the nation. The majority of the sample had one or two CVRFs (M = 2.08, SD = 1.41) and most of the participants were found to have an average SBP value ranging from 92 to 227 (M = 146.51, SD = 23.82), with an average DBP value ranging from 53 to 137 (M = 86.80, SD = 13.01). For the subjective measurements of physical health, participants ranged from a total of 0 to 14 for ADL (M = 1.70, SD = 2.15), while they ranged from 0 to 11 for IADL (M = 0.94, SD = 1.87). Lastly, participants were typically found to rate their health as being “About the same” as compared to 1 year ago (n = 224; 50%). See Table 1 for more details.

Table 1.

Demographic Characteristics of Analytic Sample

N (%) Mean Range SD
Age, years 450 68.34 48–95 9.72
Sex
 Males 128 (28.44)
 Females 332 (71.56)
Education 449 11.65 3–20 3.00
Income level
 <1,700 361 (84.15)
 ≥1,700 68 (15.85)
ADI national rank 450 58.74 5–100 29.12
ADI state rank 450 8.08 1–10 2.50
CVRF 450 2.08 0–6 1.41
Systolic blood pressure 450 146.51 92–227 23.82
Diastolic blood pressure 450 86.80 53–137 13.01
Self-rated health 450 2.13 0–4 0.98
 Much worse 12
 Somewhat worse 97
 About the same 224
 Somewhat better now 55
 Much better now 62
ADL 450 1.70 0–14 2.15
IADL 450 0.94 0–11 1.87
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Notes: ADI = area deprivation index; ADL = activities of daily living; CVRF = cardiovascular risk factor; IADL = Instrumental Activities of Daily Living.

Subjective Health Measures Significantly Associated With ADI, But Objective Health Measures Not Significantly Associated With ADI

A significant association was observed between national ADI ranking and self-ratings of health compared to a year ago (r = 0.11, p < .05; see Table 2). Participants who rated their health as being better than compared to a year ago were living in an area with a higher ADI, which reflects those participants having lived in a more disadvantaged neighborhood. Linear regression results remained significant when covarying for age, sex, education, and income (β = 0.10, SE = 0.00, p < .05; see Table 3). A significant association was observed between ADL performance and state ADI ranking (r = −0.12, p < .01; Table 2). Specifically, participants who reported less difficulty performing ADLs were more likely to be living in a place with a higher ADI. Linear regression results indicated this finding remained significant after adjusting for covariates in the model that included state ADI (β = −0.13, SE = 0.04, p < .01; Table 4) and remained nonsignificant for national ADI (β = −0.09, SE = 0.00, p > .05). National and state ADI rankings were not significantly associated with IADL (Tables 2 and 5) or any of the objective measures of health (i.e., average SBP, average DBP, and CVRF; Table 2 and Supplementary Tables 1–3).

Table 2.

Correlations Among Area Deprivation Index (ADI) and Subjective and Objective Physical Health Measures

ADI national rank ADI state rank Age Sex Education Income
Subjective health
 Self-rated health 0.11 (0.02)* 0.08 (0.09) −0.08 (0.09) −0.12 (0.01)* 0.04 (0.44) −0.01 (0.84)
 Activities of daily living −0.08 (0.09) −0.12 (0.01)** 0.05 (0.27) 0.02 (0.68) −0.00 (0.98) −0.08 (0.09)
 Instrumental activities of daily living 0.00 (0.92) −0.03 (0.56) 0.03 (0.53) 0.04 (0.44) −0.01 (0.91) −0.10 (0.03)*
Objective health
 Cardiovascular risk factor −0.00 (0.94) −0.01 (0.83) 0.13 (0.01)** 0.06 (0.23) −0.03 (0.54) −0.12 (0.01)*
 Average systolic blood pressure −0.02 (0.64) −0.04 (0.37) 0.21 (0.00)*** −0.03 (0.55) −0.18 (0.00)*** −0.04 (0.36)
 Average diastolic blood pressure −0.01 (0.90) 0.01 (0.79) −0.18 (0.00)*** −0.03 (0.57) −0.04 (0.37) −0.00 (0.97)
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Note: Bold values indicate significance levels denoted as *p < .05. **p < .01. ***p < .001.

Table 3.

Multiple Linear Regression Models (With Interactions) to Examine Relationship Between Area Deprivation Index (ADI) and Self-Rated Health

Self-rated health
Model 1 Model 2 Model 1 Model 2
ADI
 National rank 0.10 (0.00)* 0.06 (0.00)
 State rank 0.07 (0.02) 0.05 (0.02)
Demographic covariates
 Age −0.05 (0.01) −0.06 (0.01) −0.05 (0.01) −0.06 (0.01)
 Sex 0.10 (0.11)* 0.09 (0.11) 0.10 (0.11)* 0.10 (0.11)*
Socioeconomic covariates
 Education 0.06 (0.02) 0.07 (0.02) 0.06 (0.02) 0.06 (0.02)
 Income 0.03 (0.14) 0.04 (0.14) 0.03 (0.14) 0.04 (0.14)
Interactions
 Age × ADINationalRank −0.13 (0.00)**
 Sex × ADINationalRank 0.04 (0.00)
 Education × ADINationalRank −0.08 (0.00)
 Age × ADIStateRank −0.12 (0.00)*
 Sex × ADIStateRank 0.01 (0.04)
 Education × ADIStateRank −0.07 (0.01)
Obs 429 429 429 429
R 2 0.03 0.05 0.02 0.04
Akaike Information Criterion (AIC) 426.68 422.70 428.65 427.21
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Notes: Model 1 is linear regression models testing for main effects. Model 2 is linear regression models testing for two-way interactions with ADI (national or state) rankings. All continuous variables were mean-centered. Obs represents number of observations/participants included in the model.

Bold values indicate significance levels denoted as *p < .05. **p < .01.

Table 4.

Multiple Linear Regression Models (With Interactions) to Examine Relationship Between Area Deprivation Index (ADI) and Activities of Daily Living (ADL)

ADL
Model 1 Model 2 Model 1 Model 2
ADI
 National rank −0.09 (0.00) −0.09 (0.00)
 State rank −0.13 (0.04)** −0.13 (0.05)*
Demographic covariates
 Age 0.05 (0.01) 0.06 (0.01) 0.06 (0.01) 0.06 (0.01)
 Sex 0.01 (0.23) 0.01 (0.23) 0.01 (0.23) 0.01 (0.23)
Socioeconomic covariates
 Education 0.04 (0.04) 0.03 (0.04) 0.04 (0.04) 0.04 (0.04)
 Income 0.11 (0.30)* 0.10 (0.30) 0.10 (0.30) 0.10 (0.30)
Interactions
 Age × ADINationalRank 0.02 (0.00)
 Sex × ADINationalRank 0.01 (0.01)
 Education × ADINationalRank 0.08 (0.00)
 Age × ADIStateRank 0.03 (0.00)
 Sex × ADIStateRank 0.02 (0.09)
 Education × ADIStateRank 0.04 (0.01)
Obs 429 429 429 429
R 2 0.02 0.03 0.03 0.03
Akaike Information Criterion (AIC) 1,090.74 1,093.68 1,086.89 1,091.71
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Notes: Model 1 is linear regression models testing for main effects. Model 2 is linear regression models testing for two-way interactions with ADI (national or state) rankings. All continuous variables were mean-centered. Obs represents number of observations/participants included in the model.

Bold values indicate significance levels denoted as *p < .05. **p < .01.

Table 5.

Multiple Linear Regression Models (With Interactions) to Examine Relationship Between Area Deprivation Index (ADI) and Instrumental Activities of Daily Living (IADL)

IADL
Model 1 Model 2 Model 1 Model 2
ADI
 National rank 0.01 (0.00) 0.01 (0.00)
 State rank −0.03 (0.04) −0.04 (0.04)
Demographic covariates
 Age 0.02 (0.01) 0.03 (0.01) 0.02 (0.01) 0.03 (0.01)
 Sex −0.04 (0.20) −0.03 (0.20) −0.03 (0.20) −0.03 (0.20)
Socioeconomic covariates
 Education 0.02 (0.03) 0.02 (0.03) 0.02 (0.03) 0.03 (0.03)
 Income 0.11 (0.26)* 0.10 (0.26) 0.11 (0.26)* 0.11 (0.26)*
Interactions
 Age × ADINationalRank 0.09 (0.00)
 Sex × ADINationalRank 0.02 (0.01)
 Education × ADINationalRank 0.09 (0.00)
 Age × ADIStateRank 0.08 (0.00)
 Sex × ADIStateRank 0.05 (0.08)
 Education × ADIStateRank 0.05 (0.01)
Obs 429 429 429 429
R 2 0.01 0.03 0.01 0.02
Akaike Information Criterion (AIC) 971.71 971.23 971.37 973.50
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Notes: Model 1 is linear regression models testing for main effects. Model 2 is linear regression models testing for two-way interactions with ADI (national or state) rankings. All continuous variables were mean-centered. Obs represents number of observations/participants included in the model.

Bold values indicate significance levels denoted as *p < .05.

Association Between Subjective Health and ADI Varies by Age

For self-rated health, significant interactions were observed between age and the ADI rankings, national ADI (β = −0.13, SE = 0.00, p < .01) and state ADI (β = −0.12, SE = 0.00, p < .05; Table 3). The online computational tool to estimate simple slopes indicated better self-rated health compared to a year ago was associated with a more disadvantaged neighborhood, particularly for participants ≤66 years (b = 0.02–0.03, p < .05; Supplementary Table 4) and ≤54 years (b = 0.21–0.24, p < .05; Supplementary Table 5) for national and state ADI, respectively. There were no significant interactions between age, sex, or education for the objective physical health measures (i.e., average SBP, average DBP, and CVRF; Supplementary Tables 1–3) and the other subjective physical health measures (IADL and ADL; Tables 4 and 5).

Discussion

This study examined the associations between national and state ADI and subjective and objective reports of physical health. Study results indicated that participants living in more disadvantaged neighborhoods, based on state ADIs, were more likely to report being less likely to need help with ADLs, even after adjusting for covariates. Significant age and ADI (national and state) interactions were observed for self-rated health. There was no significant association between ADI and IADL or objective physical health measures (i.e., CVRF and average SBP and DBP). The discrepant findings for subjective and objective health further support different explanations for poor ratings across these types of health measures (Henson et al., 2013; Moor et al., 2006), which warrants future research consideration of inclusion of both types of health indicators. Interestingly, we did not observe consistent significant findings with the ADI using national rankings or state rankings. Descriptive statistics indicated less variance in ADI state scores and a high majority of the participants being identified as residing in a disadvantaged neighborhood. However, there was more variance in ADI national rankings compared to other national locations, which suggests participants were not necessarily living in more disadvantaged neighborhoods compared to individuals across the nation. This may be indicative of the limited generalizability of state ADI findings given that overall economics and resources within some states may be higher than other states. For example, a study by Hu et al. (2018) highlighted how detecting the effect of the ADI may differ across contexts when comparing their analyses to a national distribution. This further supports unique parameters at the state and national level must be considered.

Contrary to our hypotheses, higher national and state ADI (greater neighborhood disadvantage) were significantly associated with better self-rated health as compared to a year ago, particularly among participants 48–66 years of age. These findings suggest that participants may be overestimating their health. Self-rated health status typically predicts outcomes such as mortality and declines in functional ability (Lee, 2000). However, according to a study that included a sample of older African Americans, aged 58–95, participants who were more likely to overestimate their health were also likely to base their health status on social activities and relationships rather than biomedical criteria (Idler et al., 1999). The models for this study did not include social activities or relationships that would account for this overestimation of health. Furthermore, participants living in more disadvantageous neighborhoods tended to report less likely needing help with ADLs, which also did not support the Aim 1 hypothesis. A possible explanation for this finding is that the ADI accounts for neighborhood factors of income, education, employment, and housing quality, which may not capture potential neighborhood or household resources (e.g., transportation services, home assistance, etc.) that would be attributed to assisting the functional needs of older Black adults in this study. Cagney et al. (2005) employed collective efficacy theory to suggest that some part of the racial disparity in self-assessed health in older persons can be attributed to the variation in neighborhood-level factors both at the economic and social resource level. Collective efficacy theory is a perspective that emphasizes the role of neighborhood-level economic and social resources in enhancing individual health (Cagney et al., 2005). Moreover, given that the ADI does not account for social resources believed to strengthen individual health, this may affect the association between ADI and this subjective measure of physical health. The importance of social resources, particularly networks, is further supported by Campbell et al. (1986), who explain that although high SES individuals will have access to greater socioeconomic resources, their social networks are less closely knit. In contrast, low SES individuals may likely have more closely knit social networks to accommodate for their limited availability of socioeconomic resources. This pathway is additionally supported by a study where Nguyen et al. (2016) found that family and friend networks make unique contributions to the well-being of older Black adults. Given our participant sample was recruited from Baltimore, MD, a metropolitan city with a prevailing history of racial segregation and socioeconomic inequities (LaVeist et al., 2011), our sample may be uniquely leveraging social network resources to compensate for limited available socioeconomic resources. Lastly, reports of greater life satisfaction (e.g., daily life/leisure, family life, personal finances, and health), a measure of well-being, were associated with higher positive ratings on subjective health measures within a sample of Black adults, which may further support those perceptions of social resources are related to health self-evaluations (Gamaldo et al., 2021).

Because the significant association between self-rated health and ADI was strictly observed in middle-aged participants, these findings further support the unique health characteristics and patterns, as well as meaningful factors related to health status, that have been observed in middle-aged Black adults but not in older Black adults (Tan et al., 2021). More research, however, is warranted to understand the underlying reasons for the observed factors related to health among middle-aged Black adults as is the case with the current study’s observations.

The current study did not observe a significant association between ADI and cardiovascular risk (e.g., blood pressure readings and diagnosed cardiovascular risk factors) even though past research has observed an association between increased incidence of CVD in disadvantaged neighborhoods after controlling for individual SES (Diez Roux et al., 2001). However, inconsistent findings in prior literature have been observed regarding the association between objective measures of physical health and neighborhood quality. Nordstrom et al. (2004) found that neighborhood characteristics was inversely related to prevalence of subclinical CVD; however, after adjusting for individual SES, the association was no longer statistically significant. Our findings not aligning with previous studies may be due to the way cardiovascular health and neighborhood quality were assessed. For example, Diez Roux and colleagues (2001) ascertained cardiovascular health by determining if a participant had a coronary event by either validated or probable myocardial infarction, a death due to coronary heart disease, or an unrecognized new myocardial infarction. Moreover, the socioeconomic environment of the neighborhood was constructed using a summary score of wealth and income, education, and occupation, which differs from the ADI used in our study. Additionally, the participants sampled for the aforementioned studies were from longitudinal studies which collected data from multiple sites across the United States (e.g., North Carolina, California, Pennsylvania, etc.) with participants’ ages falling between 45–64 years of age (Diez Roux et al., 2001) and 65 years and older (Nordstrom et al., 2004). Instead, the present study had participants aged 48–95 years and more importantly only collected data strictly within older Black adults.

Although our study revealed interesting findings about the associations between subjective health and ADI, a few study limitations should be noted. Primarily, all multiple regression models were run using cross-sectional data, which limits the ability to detect whether ADI relates to changes in physical health. Secondly, our participants specifically completed measurements related to cardiovascular health; however, area deprivation may affect other objective measurements of health (e.g., cognition, sleep, and weight). Future studies should explore both objective (e.g., ADI) and subjective (e.g., dimensions of aesthetic quality, social cohesion, violence, etc.) assessments of neighborhood quality and whether this objective and subjective reporting of area deprivation has implications for our understanding on the reporting of physical health. Additionally, although these findings may advance our understanding of the associations between neighborhood quality and health for older Black adults, these study findings may not be generalizable to the U.S. population or populations outside of the United States. Furthermore, the BSBA specifically collected data from older Black adults who were generally living in more disadvantaged neighborhoods within Maryland. Lastly, our calculation of cardiovascular risk was not a comprehensive objective assessment, and our study did not include complementary variables (e.g., health insurance variability, visits to doctor) that could further explain the current study’s observations. A follow-up study could use the multifactor calculator developed by the American College of Cardiology/American Heart Association to calculate 10-year risk of heart disease or stroke (Goff et al., 2014).

Despite substantial work documenting the impact of persisting socioeconomic disparity beginning in early life to adulthood, methodology for determining objective neighborhood quality has been inconsistent. This study focused on advancing our understanding of how older Black adults assess their subjective and objective physical health while using a standardized ADI as a correlate for health outcomes. The utilization of a standardized metric for ADI is thought to be valuable, especially when comparing our results to other studies. Many of these studies used an inconsistent objective measure of neighborhood disadvantage and/or specifically assessed health disparities while using White adults as the comparison sample. In comparing our results to the studies using the aforementioned methodology, our findings differed. This difference in results has implications for how health is examined in older Black adults. As research has indicated, older Black adults face unique experiences that can affect aging, and thus health, positively and negatively, so it will be important for researchers to initiate studies that not only specifically assess older Black adults but utilize a consistent measure for neighborhood disadvantage. It may also be important for healthcare professionals to know the quality of a patient’s neighborhood when assessing and tracking their subjective and objective health, as well as the potential for discrepancy between self-rated and actual health among older Black patients. Enhancement of healthcare policies to include richer sociodemographic information about patients and formally assess their subjective health would further support these efforts. With the older Black population increasing, it is important that we address how neighborhood quality affects the physical health of older Black adults.

Supplementary Material

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Acknowledgments

Special thanks to the BSBS-PCA staff for data collection and data entry. The data will be made available upon request. The study reported in this manuscript was not preregistered.

Contributor Information

Alexa C Allan, Department of Human Development and Family Studies, The Pennsylvania State University, University Park, Pennsylvania, USA.

Alyssa A Gamaldo, Department of Human Development and Family Studies, The Pennsylvania State University, University Park, Pennsylvania, USA.

Regina S Wright, School of Nursing, University of Delaware, Newark, Delaware, USA.

Adrienne T Aiken-Morgan, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Anna K Lee, Department of Psychology, North Carolina Agricultural and Technical State University, Greensboro, North Carolina, USA.

Jason C Allaire, Department of Psychology, North Carolina State University, Cary, North Carolina, USA.

Roland J Thorpe, Jr., Hopkins Center for Health Disparities Solutions, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

Keith E Whitfield, Department of Psychology, University of Nevada, Las Vegas, Las Vegas, Nevada, USA.

Funding

The Baltimore Study of Black Aging–Patterns of Cognitive Aging (BSBA-PCA) was supported by the National Institute on Aging (R01 AG24108 and AG24108-S1—K. E. Whitfield and J. C. Allaire; 02AG059140—R. J. Thorpe Jr.; R01 AG054363—R. J. Thorpe Jr. and K. E. Whitfield; P30AG059298—R. J. Thorpe Jr.) and the National Institute on Minority Health and Health Disparities (U54MD000214—R. J. Thorpe Jr.). There are no additional financial conflicts to disclose.

Conflict of Interest

None declared.

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