The Landscape of Perception: Racial Differences in How Disability Is Viewed Among Older-Adult Cancer Survivors

Abstract

This paper compares disability perceptions of Black with white older cancer survivors’ to document racial disparities in these perceptions and the factors that contribute to them. The data are from a randomly selected tumor registry sample of 321 older adult cancer survivors from an NCI funded study. OLS regression models of disability perceptions, nested by race, examined the effects cancer and non-cancer health factors along with important covariates. Black older adult cancer survivors perceived themselves to be more disabled than did white survivors. Multivariate analyses showed a strong relationship between functional difficulties and disability perceptions for both Black and white survivors. However cancer-related factors such as continuing symptoms of the illness or treatment were relatively more important for Blacks. The findings suggest that race and cancer are both important factors in our understanding of disability in later life. These findings can then inform clinical best practices among minority older adults.

Keywords

cancer disability race cancer stigma survivor identity older-adult cancer survivors

Introduction

This paper examines the role cancer and other health problems play in the perceptions of disability of older adult, long–term survivors of breast, colorectal and prostate cancers. It specifically focuses on racial differences (Black compared with white survivors) in disability perceptions as well as the role that racial differences in both cancer and non-cancer health factors play in these survivors’ perceptions.

The culture surrounding inclusivity has become much more of a focus on health-related vulnerability over the past two decades. Not only is there more conversation about what it means to be disabled in today’s sociopolitical climate, but there is also more acceptance and understanding surrounding people with disabilities.

History of Disability Research

Disability research was largely absent from the sociological literature until the 1980s. Green and Barnartt (2016, p. 42) discuss that “During the period from 1950 to 1980, sociologists developed many concepts which could have relevance to disability, but in many cases, they were not actually applied to disability.” These authors note Parsons (1951) work on the sick role as an early example of this, although his work did not explicitly examine how we understand “disability.” They go on to remark that his work did lay the groundwork for “the medical model of disability” (19). It was not until Goffman’s work on stigma that a conceptualization of disability without being viewed as “sick” via the sick role, that sociologist really started to embark on this work (Green & Barnartt, 2016, p. 21).

Cancer and Disability

It has been well documented that cancer and its treatment have the potential to create significant long-term disability (Hewitt et al., 2003; Rowland & Yancik, 2006). In a population-based study comparing cancer survivors with those without a history of cancer, Yabroff et al. (2004) found that cancer survivors were more likely to report functional limitations and were more likely to have multiple comorbid conditions than were control subjects. Similarly, Short et al. (2008) discovered an elevated disability rate for those who are cancer survivors, in comparison to adults with no chronic conditions. Among cancer survivors, nearly one-third report having two or more co-morbid health conditions (Ogle et al., 2000) which have been linked to increased functional limitations (Catalá-López et al., 2014). Hewitt et al. (2003) point out that this interaction of cancer-related factors and co-morbidities is an essential element of geriatric oncology.

While research has documented the additional vulnerability cancer can confer in terms of comorbidities and functioning/disability, what is less well known, as Parry et al. (2011) note, is the nature of the interaction of cancer and chronic illness among survivors. This is especially important as survivors age and comorbidities are likely to increase. They further suggest that this area may shape cancer survivorship research and service delivery in the future, especially services designed for older adults. Research by Deimling et al. (2019) in this area examined the relationship between cancer-related factors along with co-morbidities on the functioning of older adult cancer survivors. They reported that the functional difficulties among older adult long-term survivors report are the result of both their continuing cancer-related health problems as well as other health conditions that they were simultaneously experiencing

Perceptions of Disability

Even though cancer survivors often experience long-term effects and comorbidities that are implicated in their ability to function, as Iezzoni et al. (2000) state, “Whether people view themselves as ‘disabled’ is a multilayered personal assessment with societal ramifications.” According to the ADA, the term “disability” is defined in three ways: as a physical or mental impairment that can limit functioning in life activities, having a recorded history of disability, or being seen as having an impairment that would be considered a disability (Department of Justice, 2010). However, individual’s perceptions of their own disability, even though they may not meet the specific ADA criteria, can still have important consequences in their adjustment, illness in general, and cancer more specifically. Rather, as the illness literature suggests, self-perceptions held by those who face functional challenges involve a good deal of individual interpretation and contextual issues. This was recognized decades ago by Zola (1993) who discussed the complicated language that disability and related perception involve, including illness labels and aspects of identity as part of this interpretive process. More recently, Darling and Heckert (2010) described how people determine their “disability orientation” and how self-identifying as disabled is only one part of their disability orientation.

Kelley-Moore et al. (2006) explored the social and health related factors that influence the oldest old to define themselves as disabled. They concluded that older adults use many different criteria to define their functioning which includes recent changes in health status and social networks, as well as health anxiety. Langlois et al. (1996) found that over half of older adults who could not perform or had problems performing one ADL did not consider themselves disabled. In a study by Iezzoni et al. (2000) which focused on mobility difficulties, the researchers found that while most of the respondents with mobility difficulties perceived themselves as disabled, some did not—including some respondents who used wheelchairs. Particularly relevant to our research is that they also found that women, racial minorities, and Hispanic respondents were less likely to perceive themselves as disabled, whereas low-income persons were more likely to hold this perception. The above research speaks to the importance of examining how functional difficulties translate into perceptions of disability. Deimling et al. (2019) found that in addition to functioning, the number of co-morbid health conditions, and current illness symptoms (both those related to cancer and those not related to cancer) each were each significantly associated with perceptions of disability.

“Raced” Disability

A number of studies have investigated the racial differences in health-related quality of life (Carreon & Noymer, 2011; Hu, 2007; Jang et al., 2009; Pergolotti et al., 2017; Skarupski et al., 2007). On average, African Americans have higher rates of disability and levels of functional impairment (Fuller-Thomson et al., 2009; Kelley-Moore & Ferraro, 2004, 2005). A study by Taylor (2008) looks at racial inequality over time, and how timing may influence disability trajectories between Black and white adults. That research (2008) suggests that “weathering” or a cumulative disadvantage may play a role in race-related vulnerability (e.g., having poorer access to health care early on, has an impact on disability and quality of life in older adults later in life).

Race and Age: Double Jeopardy

A study by Carreon and Noymer (2011) found a much larger difference in the amount of physically unhealthy days reported by African American and Hispanics in comparison to whites, particularly among older adults. Importantly, this difference could not be fully explained by other social characteristics, suggesting the importance of considering “double jeopardy.” The double jeopardy hypothesis argues that minority older adults and elderly experience a double disadvantage to their health due to the consequences of both aging, and racial minority status (Dowd & Bengtson, 1978; Markides, 1983). Consequently, the barriers created by systematic race-related differences in access to care place an additional disadvantage on older adult African Americans, and that these in turn may result in poorer health outcomes¹.

Focusing on older cancer survivors, Siegel et al. (2012) note that being Black and lower socioeconomic status were each associated with poorer physical functioning and that age at cancer diagnosis is often associated with poorer physical functioning. Additional research has shown that African Americans cancer survivors overall have worse health related quality of life and survivorship outcomes in comparison to whites (Ashing-Giwa et al. (2004); Campbell et al., 2016; Chavers et al., 2002; Hayes et al., 2011; Huang & Park, 2015). A recent study by Ornstein et al. (2020) of cancer survivors showed that non-white older adults tended to have worse overall health, higher rates of depression, more unmet care needs, and fewer financial resources even prior to cancer.

Beyond the differences in relatively “objective” measures of physical and mental health, cancer has the potential to complicate perceptions of disability that may have different meanings among different racial or cultural groups. As Atkin (1991) argues, health, illness and disability are not stagnant, objective realities but are shaped by social context and quotes the work of Kiple and King (1981)

“in health matters Black people suffer twice, once from the disease itself and secondly because their health problems are turned against them. Black ‘health’ problems are often, therefore, identified as arising from cultural practice and the cause becomes located in the minorities themselves. Rather than beginning to understand and accept Black people’s ways of life, the assumed ‘solution’ is to change them (42).”

The above suggests that there may be “different models” in terms of how disability is perceived by Black cancer survivors when compared with white survivors. More specifically, it suggests that among older cancer survivors, race-linked health factors such as level of co-morbidity, functional difficulties, and illness symptoms may be important in the construction of perceptions of disability. To our knowledge there has been no research that compares race-specific models of how other later life illnesses and functioning combine with cancer-related factors to affect perceptions of disability among older cancer survivors.

Research Questions

The above discussion suggests three research questions that were used to guide our analyses.

RQ-1: Is there a significant difference between Black older adult cancer survivors and white older adult cancer survivors in their perceptions of disability?

RQ-2: Are there racial differences in the general health/and physical functioning of older-adult cancer survivors that may affect perceptions of disability?

RQ-3: Are there racial differences in the relative importance of cancer and noncancer health factor as predictors of perceptions of disability?

The conceptual model in Figure 1 below portrays the key variables in our analyses and the anticipated relationship among them.

Figure 1

Factors affecting perceptions of disability among older-adult cancer survivors.

Methods

The data analyzed in this paper were derived from a larger longitudinal study of 471 older-adult, long-term cancer survivors funded by the National Cancer Institute (Grant # R01-CA-78975). These data were collected in six waves of in-person interviews conducted over a ten-year period (1999 to 2009). The sample was randomly selected from the tumor registry at an NCI designated Comprehensive Cancer Center in a major mid-western city. The study received human studies approval on March 18, 1998 and has had continuous approval to date (CWRU IRB #2995).

The sampling design selected survivors of breast, colorectal, or prostate cancer as the focal cancer for this research. These were selected because they are the three most common survivable cancers among older adults. Given that the study design focused on older adults, only survivors age 60 years or older were included in the sampling frame. The study design also focused on long-term survivors as defined by NCI, that is those who had been diagnosed a minimum of 5 years prior to study enrollment.

Note that our study is one of the few research samples that oversamples Black survivors to allow empirical comparison of differences in cancer outcomes for these two racial groups (Black survivors and white survivors). Unfortunately, the original study design was not able to include survivors from other racial/ethnic groups as there were too few who met the primary design criteria (age, cancer type, survival duration) in any one group in the tumor registry for meaningful statistical analysis. The data reported here are cross-sectional in nature and selected from the first wave of interviews (N = 321). This wave was included as it contained all of the variables required for our analyses. The sample used in the analyses presented here includes 121 Black survivors and 200 white survivors.

Measures

The measures that operationalize the concepts included in our model are described below as they were used in the data analyses presented (See also Table 1).

Dependent Variable

Perceived Disability was operationalized using a single item indicator originally used by Kelley-Moore et al. (2006) in their research on older adults at the Elder Care Research Center at Case Western Reserve University. Respondents answered the following question: “Do you consider yourself disabled?” Responses were scored on a five-point Likert-like scale ranging from “0 = Not at all to 5 = Very Much. The mean in our sample on this item was 1.36.

Predictor Variables

Demographic and Personal Characteristics

Table 1 provides data on the sample characteristics. Race and gender were documented using tumor registry information and verified at the time of interview. In this analysis they are treated as dichotomous variables (1 = Black, 0 = white; 1 = female, 0 = male). Age was operationalized as continuous variables based on tumor registry information (M = 72.3) and the number of years of education was based on respondent’s self-report in the interview (M = 13.9)

Table 1

Sample Description (N = 321)

Variables	Mean	SD	Percentage	Coding
Personal characteristics
Age	72.3	7.5	–	Ranges from 58 to 95
Female	–	–	59.2%
African American	–	–	37.7%
Education	13.9	3.5	–	Ranges from 3 to 30
General health
Co-morbidities	3.7	2.4	–	Ranges from 0 (none) to 27 (high)
Noncancer symptoms	2.6	2.4	–	Ranges from 0 (none) to 22 (high)
Functional difficulties	5.2	5.4	–	Ranges from 0 (none) to 33 (high)
Cancer-related health
Number of treatment types	1.6	0.8	–	Ranges from 0 (none) to 4 (high)
Cancer stage	2.8	1.9	–	Ranges from 1 (in stitu) to 4 (distant)
Current cancer symptoms	0.8	1.5	–	Ranges from 0 (none) to 22 (high)

General Health Variables

Three measures were used to operationalize the respondent’s general health and functioning. These were the number of comorbidities, the level of reported functional difficulties and the number of current non-cancer symptoms (Table 1). For the comorbidities measure respondents were asked to indicate the number of diagnosed health conditions from among 23 possible conditions they were experiencing at the time of the interview. The total number reported ranged from 0, no comorbidities, to 11 with a mean of 3.7. The comorbidities included in the measure were those common in later life such as diabetes, hypertension, osteoporosis, cataracts, arthritis, and chronic obstructive pulmonary disease.

The level of functional difficulties was operationalized using the Nagi (1976) index. It assesses the respondent’s ability to perform specific physical activities. These were activities such as difficulty balancing, buttoning their shirts, and standing for long periods of time amongst others. The respondents were asked how much difficulty they have performing these activities: no difficulty, some difficulty, a great deal of difficulty or tell me if you are unable to perform the activity. The mean score on this index in our sample was 5.2.

To assess the level of respondent’s current illness symptoms not attributed to cancer they were asked to indicate from a list of 22 possible illness symptoms which they were currently experiencing. The indicator was calculated as the simple sum of those items reported. The mean number of symptoms reported in the total sample was 2.6.

Cancer-Related Variables

A number of the key predictors in this study document the nature of treatment the respondent received and the sequelae they continue to experience. The first, cancer stage at diagnosis, is a potential surrogate measure of cancer severity. This was derived from tumor registry data and was coded 0 – in situ, 2- local, 3- regional and 4-distal. The mean for this variable was 2.8.

Another indicator of cancer severity was the number of types of treatment they received and represents a surrogate measure for the complexity of treatment received (M = 1.6). The types of treatment included in this summary measure were radiation therapy, chemotherapy and other therapies such as hormonal therapy. The specific treatment types are not included in the analysis presented here as our preliminary analyses did not find them to be significant correlates of disability perceptions. The indicator of current cancer-related symptoms was measured by the summing the total number of different symptoms the respondents attributed to cancer at the time of the interview. The same list of 22 symptoms described above was used and the mean number of symptoms reported in the total sample was 0.8.

Analysis Plan

The first step in the analysis examined differences in mean scores on all of the model variables by race. This was followed by bivariate correlation analysis of the total sample and the Black as well as white subgroups. These finding were used as the basis for three Ordinary Least-Squares Regression (OLS) analyses. The first was conducted with the total sample (N = 321), including race as a predictor as portrayed in Figure 1. This was followed by separate regression analyses of the Black subgroup (N = 121) and the white subgroup (N = 200) using the same model predictors. Statistical analyses were conducted using both SPSS and STATA.

Results

Looking first at the results that compare the two racial subgroups (Table 2), a number of important differences are evident. First, Black survivors report significantly higher levels of perceived disability (M = 1.54 vs 1.25), our key outcome. They also report substantially greater functional difficulties (M = 6.83 vs 4.30). This may be due, at least in part, to greater numbers of co-morbid health conditions (M = 3.97 vs 3.60), and higher levels of current illness symptoms not attributed to cancer (M = 2.97 vs 2.56). They also reported more continuing symptoms attributed to cancer when compared to white survivors (M = .89 vs .75). While these latter differences did not achieve statistical significance, they are part of a pattern of greater vulnerability among Black survivors when compared to whites. One important finding was the significant difference in education by race (M = 12. 40 yrs. vs 14.82 yrs.). This supports the narrative of discrimination as it is linked to fewer educational opportunities for Black Americans that may translate into poorer health.

Table 2

Comparison of Black with White Cancer Survivors (Means Scores)

	Total sample	Black	White	F-ratio
Female	0.59	0.70	0.53	10.08*
Age	72.26	71.91	72.47	0.41
Education	13.91	12.40	14.82	39.05***
Comorbidities	3.73	3.97	3.60	1.91
Functional difficulties	5.24	6.83	4.30	17.29***
Non-cancer symptoms	2.55	2.97	2.56	0.02
Number of treatment types	1.61	1.61	1.61	0.00
Current cancer symptoms	0.80	0.89	0.75	0.59
Cancer stage	2.33	2.17	2.26	4.47*
Perceived disability	1.36	1.54	1.25	9.55**

Note. p < .05, p < .01, p < .001.

The analysis next turned to the bivariate correlations for all variables in the study (Table 3). The significant association of race with perceived disability shown in the means comparison above is replicated in the correlations (r = .17). Other significant correlates of perceived disability are the number of co-morbid health conditions (r = .25) and reported functional difficulties (r = .59). Examining the correlations within each racial group, the correlation between functional difficulties and perceptions of disability was relatively strong for both for Black survivors (r = .60) and white survivors (r = .54). Current symptoms, both those attributed to cancer (r = .32 for Blacks and .20 for whites) and those not contributed to cancer (r = .27 for Blacks and .18 for whites), were also important correlates of disability perceptions.

Table 3

Perception of Disability Regression Estimates by Race

Variables	Total sample			Black			White
Variables	Beta	B (SE)	r	Beta	B (SE)	r	Beta	B (SE)	r
Race	.06	.10 (.08)	.17*	–	–	–	–	–	–
Gender	−.11*	−.18 (.09)	.09	.00	.01 (.19)	.15	−.17**	−.24 (.09)	−.01
Age	−.04	−.00 (.01)	.05	−.04	−.01 (.01)	−.01	−.04	−.00 (.01)	.12
Education	−.02	−.00 (.01)	−.09	−.00	−.00 (.01)	−.07	−.03	−.00 (.02)	−.05
Comorbidities	−.00	−.1 1(.02)	.25***	−.08	−.03 (.04)	.23**	.02	.01 (.02)	.26***
Functional difficulties	.59***	.09 (.01)	.59***	.54***	.09 (.02)	.60***	.60***	.09 (.01)	.54***
Current non-cancer symptoms	.02	.01 (.02)	.27***	.11	.05 (.04)	.32***	−.06	−.02 (.02)	.20**
Number of treatment types	−.01	−.02 (.05)	−.00	−.13	−.16 (.10)	−.06	.06	.05 (.06)	.05
Cancer stage at diagnosis	.04	.02 (.02)	.08	.17*	.08 (.04)	.20*	−.07	−.03 (.02)	−.04
Current cancer-related symptoms	.09	.01 (.44)	.23***	.15	.09 (.05)	.27**	.06	.03 (.03)	.18*
R²		.37	–		.43	–		.33

With the bivariate analyses as a basis, the focus of the analyses turns to the regression findings (Table 3). In the regression model estimates, the best predictor of respondents’ perceptions of disability is the level of functional difficulty in the total model (beta = .59) and also in the separate model estimates for Blacks and whites (beta = .54 and .60, respectively). However, the statistically significant association of race and perceptions of disability found in the bivariate analyses do not persist in the multivariate model for the total sample. This appears to be largely the result of the powerful effect that the functional difficulties variable has on disability perceptions in the model. This suggests that the impact of race is largely the result of their greater level of functional impairment.

Comparing the regression estimates for the two subsamples, cancer stage at diagnosis was a significant predictor for Blacks (beta = .17), indicating that more advanced disease before treatment was associated with stronger perceptions of disability. In contrast this was not a significant predictor of white survivors’ perceptions of disability. While comorbidities and the cancer-related and non-cancer symptoms were all shown to be relatively strong correlates in the bivariate analyses for both Blacks and whites, the strength of these relationships did not persist in the multivariate regressions. Again, the power of functional impairment as a predictor appears to decrease the net effects of these other health variables. All three models were statistically significant and explained considerable total variance; R² = 37 % in the total sample, 43 % in the Black survivor regression model and 33 % in the model for white survivors.

Summary and Discussion

The findings presented above clearly document that Black cancer survivors are more likely to view themselves as disabled compared to white survivors. Also important is the fact that Black survivors report substantially greater levels of functional difficulty and this was shown to be the key factor in their disability perceptions. The analysis also documented that Blacks also reported a greater number of comorbidities and greater numbers of cancer and non-cancer illness symptoms compared to white survivors. Even though the net effect of these is reduced in the multivariate analysis due to the relative power of functional difficulties, they are significant correlates of older survivor’s perceptions of disability.

Comparing the separate multivariate regression models of disability perceptions for Blacks and whites indicates that the two groups are nearly identical in terms of the relative importance of functional difficulties, comorbidities and reported non-cancer symptom as predictors. The only statistically difference was on the importance of cancer stage at diagnosis with this predictor significant for Black survivors but not for whites. Other differences include the greater importance of continuing cancer/treatment related symptoms for Black survivors compared to whites, although these coefficients did not reach statistical significance. Finally, as with the total sample, it appears that the effect of comorbidities and illness symptoms (cancer-related and non-cancer) documented so clearly in the bivariate analysis are reduced dramatically in their independent effects when the relatively strong impact of functional difficulties is considered.

The above findings support other research in aging and cancer that functioning plays a central role in determining the health quality of life of older adults (Kelley-Moore et al., 2006). The research reported here indicates that this is especially true for Blacks who, on average, report considerably more functional difficulty. Short et al. (2008) suggest that the elevated disability rate among minority cancer survivors indicates that a broader scope of resources and services need to be available to minority survivors. The results also suggest that both primary care and cancer follow up care providers need to be aware of the powerful effect of physical functioning on the health quality of life of survivors.

Even though cancer-related factors are somewhat secondary to other health challenges, the higher levels of these among Black survivors suggests these need to be part of the health conversation with minority survivors. Our findings replicates previous research Campbell et al., 2016; Huang & Park, 2015) who point to the need for both geriatric and cancer care practitioners to assess the levels of these factors as they treat older cancer survivors. A closer look at the health disparities Black older adult cancer survivors endure is necessary to better understand and prevent “double jeopardy” or cumulative disadvantage (Dowd & Bengtson, 1978; Markides, 1983; Taylor, 2008.

Footnotes

Acknowledgments

The authors want to acknowledge the very important contributions by Erin K. Phelps MA to prior drafts of this paper. We also wish to thank Gabrielle Beck for her extensive assistance in preparation and review of our manuscript drafts.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Support for this research was provided by grant R01-CA-78975 funded by the National Cancer Institute.

Note

Author Biographies

Spencier Ciaralli is a doctoral candidate in the Department of Sociology and a senior researcher at the Cancer Survivor Research Program at Case Western Reserve University.

Gary T. Deimling is professor of Sociology and Medical Research (Oncology) at Cases Western Reserve University. He is the founder and director of the Cancer Survivor Research Program.

Dyanna L. Burnham is a doctoral student and research assistant at the Cancer Survivor Research Program at Case Western Reserve University

References

Ashing-Giwa

K. T.

Padilla

Tejero

Kraemer

Wright

Coscarelli

Clayton

Williams

Hills

. (2004). Understanding the breast cancer experience of women: A qualitative study of African American, Asian American, Latina and Caucasian cancer survivors. Psycho-Oncology, 13(6), 408–428.doi:10.1002/pon.750

15188447

Atkin

. (1991). Health, illness, disability and black minorities: A speculative critique of present day discourse. Disability, Handicap & Society, 6(1), 37–47.doi:10.1080/02674649166780031

Campbell

L. J.

Cai

Gao

. (2016). Racial/ethnic disparities in nursing home quality of life deficiencies, 2001 to 2011. Gerontology and Geriatric Medicine, 2, 233372141665356.doi:10.1177/2333721416653561

27819015

Carreon

Noymer

. (2011). Health-related quality of life in older adults: Testing the double jeopardy hypothesis. Journal of Aging Studies, 25(4), 371–379.doi:10.1016/j.jaging.2011.01.004

Catalá-López

Suárez-Pinilla

Valderas

J. M.

Gómez-Beneyto

Martinez

Balanzá-Martínez

Climent

Valencia

McGrath

Crespo-Facorro

Sanchez-Moreno

Vieta

Tabarés-Seisdedos

. (2014). Inverse and direct cancer comorbidity in people with central nervous system disorders: A meta-analysis of cancer incidence in 577,013 participants of 50 observational studies. Psychotherapy and Psychosomatics, 83(2), 89–105.doi:10.1159/000356498

24458030

Chavers

L. S.

Gilbert

G. H.

Shelton

B. J

. (2002). Racial and socioeconomic disparities in oral disadvantage, a measure of oral health-related quality of life: 24-month incidence. Journal of Public Health Dentistry, 62(3), 140–147.doi:10.1111/j.1752-7325.2002.tb03435.x

12180041

Darling

R. B.

Heckert

D. A

. (2010). Orientations toward disability: Differences over the lifecourse. International Journal of Disability, Development and Education, 57(2), 131–143.doi:10.1080/10349121003750489

Deimling

G. T.

Pappada

Nalepa

Ciaralli

Phelps

Burant

C. J

. (2019). Factors affecting perceptions of disability and self-rated health among older adult, long-term cancer survivors. Journal of Aging and Health, 31(4), 667–684.doi:10.1177/0898264317745745

29254449

Department of Justice . (2010). Americans with disabilities act title iII regulations. United States Department of Justice Civil Rights Division.

10.

Dowd

J. J.

Bengtson

V. L

. (1978). Aging in minority populations. An examination of the double jeopardy hypothesis. Journal of Gerontology, 33(3), 427–436.doi:10.1093/geronj/33.3.427

748438

11.

Fuller-Thomson

Nuru-Jeter

Minkler

Guralnik

J. M

. (2009). Black-White disparities in disability among older Americans: Further untangling the role of race and socioeconomic status. Journal of Aging and Health, 21(5), 677–698.doi:10.1177/0898264309338296

19584411

12.

Green

S. E.

Barnartt

S. N.

. (Eds.). (2016). Sociology looking at disability: What did we know and when did we know it? Emerald Group Publishing.

13.

Hayes

D. K.

Greenlund

K. J.

Denny

C. H.

Neyer

J. R.

Croft

J. B.

Keenan

N. L

. (2011). Racial/ethnic and socioeconomic disparities in health-related quality of life among people with coronary heart disease, 2007. Preventing Chronic Disease, 8(4), A78.21672402

14.

Hewitt

Rowland

J. H.

Yancik

. (2003). Cancer survivors in the United States: Age, health, and disability. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 58(1), M82–M91.doi:10.1093/gerona/58.1.M82

12560417

15.

. (2007). Health-related quality of life in low-income older African Americans. Journal of Community Health Nursing, 24(4), 253–265.doi:10.1080/07370010701645901

18092917

16.

Huang

D. L.

Park

. (2015). Socioeconomic and racial/ethnic oral health disparities among US older adults: Oral health quality of life and dentition. Journal of Public Health Dentistry, 75(2), 85–92.doi:10.1111/jphd.12072

25234710

17.

Iezzoni

McCarthy

E. P.

Davis

R. B.

Siebens

. (2000). Mobility problems and perceptions of disability by self-respondents and proxy respondents. Medical Care, 38(10), 1051–1057.doi:10.1097/00005650-200010000-00009

11021678

18.

Jang

Chiriboga

D. A.

Borenstein

A. R.

Small

B. J.

Mortimer

J. A

. (2009). Health-related quality of life in community-dwelling older Whites and African Americans. Journal of Aging and Health, 21(2), 336–349.doi:10.1177/0898264308329001

19114608

19.

Kelley-Moore

J. A.

Ferraro

K. F

. (2004). The black/white disability gap: Persistent inequality in later life? The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 59(1), S34–S43.doi:10.1093/geronb/59.1.S34

14722342

20.

Kelley-Moore

J. A.

Ferraro

K. F

. (2005). A 3-D model of health decline: Disease, disability, and depression among black and white older adults. Journal of Health and Social Behavior, 46(4), 376–391.doi:10.1177/002214650504600405

16433282

21.

Kelley-Moore

J. A.

Schumacher

J. G.

Kahana

. (2006). When do older adults become “disabled”? Social and health antecedents of perceived disability in a panel study of the oldest old. Journal of Health and Social Behavior, 47(2), 126–141.doi:10.1177/002214650604700203

16821507

22.

Kiple

K. F.

King

V. H

. (1981). Another Dimension to the Black Diaspora. Cambridge University Press.

23.

Langlois

J. A.

Maggi

Harris

Simonsick

E. M.

Ferrucci

Pavan

Sartori

Enzi

. (1996). Self-report of difficulty in performing functional activities identifies a broad range of disability in old age. Journal of the American Geriatrics Society, 44(12), 1421–1428.doi:10.1111/j.1532-5415.1996.tb04065.x

8951310

24.

Markides

K. S

. (1983). Ethnicity, aging and society: Theoretical lessons from the United States experience. Archives of Gerontology and Geriatrics, 2(3), 221–228.doi:10.1016/0167-4943(83)90025-0

6651394

25.

Nagi

S. Z

. (1976). An epidemiology of disability among adults in the United States. The Milbank Memorial Fund Quarterly. Health and Society, 54(4), 439–467.doi:10.2307/3349677

137366

26.

Ogle

K. S.

Swanson

G. M.

Woods

Azzouz

. (2000). Cancer and comorbidity. Cancer, 88(3), 653–663.doi:10.1002/(SICI)1097-0142(20000201)88:3<653::AID-CNCR24>3.0.CO;2-1

27.

Ornstein

K. A.

Liu

Schwartz

R. M.

Smith

C. B.

Alpert

Taioli

. (2020). Cancer in the context of aging: Health characteristics, function and caregiving needs prior to a new cancer diagnosis in a national sample of older adults. Journal of Geriatric Oncology, 11(1), 75–81.doi:10.1016/j.jgo.2019.03.019

30952516

28.

Parry

Kent

E. E.

Mariotto

A. B.

Alfano

C. M.

Rowland

J. H

. (2011). Cancer survivors: A booming population. Cancer Epidemiology Biomarkers & Prevention, 20(10), 1996–2005.doi:10.1158/1055-9965.EPI-11-0729

21980007

29.

Parsons

. (1951). Illness and the role of the physician. In Kluckhohn

(Ed.), Personality in nature, society and culture. Knopf.

30.

Pergolotti

Deal

A. M.

Williams

G. R.

Bryant

A. L.

Bensen

J. T.

Muss

H. B.

Reeve

B. B

. (2017). Activities, function, and health-related quality of life (HRQOL) of older adults with cancer. Journal of Geriatric Oncology, 8(4), 249–254.doi:10.1016/j.jgo.2017.02.009

28285980

31.

Rowland

J. H.

Yancik

. (2006). Cancer survivorship: The interface of aging, comorbidity, and quality care. JNCI: Journal of the National Cancer Institute, 98(8), 504–505.doi:10.1093/jnci/djj154

16622113

32.

Short

P. F.

Vasey

J. J.

Belue

. (2008). Work disability associated with cancer survivorship and other chronic conditions. Psycho-Oncology, 17(1), 91–97.doi:10.1002/pon.1194

17429835

33.

Siegel

DeSantis

Virgo

Stein

Mariotto

Smith

Cooper

Gansler

Lerro

Fedewa

Lin

Leach

Cannady

R. S.

Cho

Scoppa

Hachey

Kirch

Jemal

Ward

. (2012). Cancer treatment and survivorship statistics, 2012. CA: A Cancer Journal for Clinicians, 62(4), 220–241.doi:10.3322/caac.21149

22700443

34.

Skarupski

K. A.

de Leon

C. F. M.

Bienias

J. L.

Scherr

P. A.

Zack

M. M.

Moriarty

D. G.

Evans

D. A

. (2007). Black-white differences in health-related quality of life among older adults. Quality of Life Research, 16(2), 287–296.doi:10.1007/s11136-006-9115-y

17033898

35.

Taylor

M. G

. (2008). Timing, accumulation, and the Black/White disability gap in later life. Research on Aging, 30(2), 226–250.doi:10.1177/0164027507311838

36.

Warner

D. F.

Brown

T. H

. (2011). Understanding how race/ethnicity and gender define age-trajectories of disability: An intersectionality approach. Social Science & Medicine, 72(8), 1236–1248.doi:10.1016/j.socscimed.2011.02.034

21470737

37.

Yabroff

K. R.

Lawrence

W. F.

Clauser

Davis

W. W.

Brown

M. L

. (2004). Burden of illness in cancer survivors: Findings from a population-based national sample. JNCI Journal of the National Cancer Institute, 96(17), 1322–1330.doi:10.1093/jnci/djh255

15339970

38.

Zola

I. K

. (1993). Self, identity and the naming question: Reflections on the language of disability. Social Science & Medicine, 36(2), 167–173.doi:10.1016/0277-9536(93)90208-L

8421793