The Effects of Social and Behavioral Determinants of Health on the Relationship Between Race and Health Status in U.S. Breast Cancer Survivors

Abstract

Background:

To examine the association between race and health outcomes among U.S. breast cancer (BC) survivors and explore to what extent do social and behavioral factors contribute to racial disparities for these associations.

Materials and Methods:

Four hundred forty-one female participants diagnosed with BC in the National Health and Nutrition Examination Survey from 2007 to 2016 were included in this study. Poisson regression with robust variance was used to estimate the prevalence ratio (PrR) and 95% confidence intervals for the associations between race, diabetes, hypertension, and other cancers.

Results:

The PrR for co-occurrence of diabetes and hypertension was 2.21 (p < 0.001) and 1.62 (p < 0.001) times, respectively, among African Americans (AA) compared with non-Hispanic whites. Body mass index (BMI) explained 17.5% of the association between AA race and diabetes prevalence; a smaller reduction (7.8%) was observed adjusting for type of health insurance coverage, only. A 23.5% reduction was observed in the association between AA race and diabetes prevalence with adjustment for BMI and insurance. The association between AA race and hypertension prevalence was reduced by <6% with addition of individual risk factors, including education, insurance, poverty, obesity, smoking, and physical activity, and with adjustment of the combination of these factors.

Conclusions:

The association between AA race and diabetes prevalence among BC survivors may be partially explained by BMI and insurance coverage to a lesser extent. Interventions to improve outcomes among AA survivors should focus on weight management strategies.

Introduction

The number of breast cancer (BC) survivors is projected to reach over 4.5 million in the next 10 years due to improvements in BC screening methods and treatment.¹ With an overall 5-year survival rate of 89.7%,² BC survivors are living longer and effects of age-related comorbidities among this population is of public health concern. The three major comorbidities faced by BC survivors are diabetes, hypertension, and secondary cancer.³

It is estimated that roughly 32.2% of BC survivors have one or more comorbidities, which is similar to the national population average of 31.8%; however, the effect of the comorbidity is greater in BC survivors and leads to worse quality of life and decreased survival.⁴ Second cancers are also of great concern, as they are a leading cause of morbidity and increased mortality among cancer survivors.⁵

BC mortality rates are 42% higher among African American (AA) women compared with whites.⁶ AA women suffer disproportionately from other chronic diseases, including type 2 diabetes and hypertension.⁷ Comorbidities may account for nearly half of the survival disparities among AA and white women with BC.⁸ Hypertension alone, has been shown to account for 30% of the racial disparity in BC survival.⁹

Poor physical health in the form of increased body mass and low levels of physical activity is correlated with increased risk of BC recurrence as well as mortality.¹⁰ BC survivors are more likely to lead sedentary lifestyles and engage in insufficient levels of physical activity.¹¹ Furthermore, more than 60% of BC survivors may gain weight after treatment due to effects of chemotherapy, resulting in 22% being obese.¹²

When considering racial differences, AA women report worse physical health compared with non-Hispanic white (NHW) BC survivors.^13,14 AA survivors are also more likely to be obese compared with NHW BC survivors.¹⁵ Obesity is a well-known modifiable risk factor for type 2 diabetes. It is also a prognostic risk factor for mortality among AA women with BC.^16
–18 Considering these associations, we hypothesize that obesity may partially explain the association between race and health outcomes among BC survivors.

Tobacco smoking is another modifiable behavioral risk factor associated with increased risk of overall mortality among women diagnosed with BC.¹⁹ Studies have shown that 10%–20% of BC survivors are current smokers at the time of their BC diagnosis.^20,21 Although AA women, in general, start smoking later in life when compared with white women, they are more likely to die from smoking-related diseases.²² To our knowledge, the examination of the relationship between smoking history and health status among BC survivors has not evaluated the potential effects of racial disparities.

Racial disparities in BC survival have also been attributed to differences in social factors, including insurance coverage and geographical access to care.^23
–25 There is substantial evidence that improved health care coverage predicts the early diagnosis of BC across different race/ethnicity groups.²⁶ Socioeconomic status measured by level of income has been reported to be strongly associated with BC survival for women in the United States.²⁷ Interestingly, for BC risk, unlike many other health conditions, a positive association has been observed between the incidence of BC and the level of education attained among women.²⁸ It could be hypothesized that differences in social factors, such as insurance status, income levels, and education might also contribute to racial disparities in health status among BC survivors.

In this study, we sought to examine the racial disparity in the prevalence of major comorbidities, as a proxy for health status, and explored to what extent do specific social and behavioral determinants of health contribute to these associations. Using nationally representative data from National Health and Nutrition Examination Survey (NHANES), we aimed to provide a population-wide understanding of the problem and to identify a specific target of intervention.

Materials and Methods

We combined the data from NHANES from 2007 to 2016, which assesses the health and nutritional status of civilian, noninstitutionalized U.S. population by selecting representative samples using a complex multistage probability strategy.²³ The survey consists an in-home interview collecting information on demographics, socioeconomic, health conditions and health behaviors, and physical examination (e.g., body mass index or BMI) performed at a mobile exam center (MEC). Subpopulation analysis was performed on female participants with a self-reported history of BC.

Race and ethnicity was self-reported by the participants as NHW, non-Hispanic black (NHB), or others, including Mexican American, other Hispanic, non-Hispanic Asian, and multiracial groups. Comorbidities, such as diabetes, high blood pressure (HBP) or hypertension, and history of other cancers, were treated as the proxy measurement of health status. Diabetes is defined as answering yes to question “have you ever been told by a doctor or health professional that you have diabetes or sugar diabetes.” Hypertension is defined as answering yes to the question “Have you ever been told by a doctor or other health professional that you had hypertension, also called high blood pressure?.” Self-reported history of cancer was used to define whether the individual had only BC or additional cancers. Socioeconomic status and health behaviors, including education (<high school or 12th grade, high school graduate, more than high school), income poverty level (defined as family monthly poverty level index, a ratio of family monthly income to federal poverty line (FPL) specific to family size according to the Department of Health and Human Services), type of health insurance (categorized as private, Medicare and Medicaid, other types of insurance, and uninsured), measured BMI (weight, kg/height²), physical activity (defined as whether or not participants involved in at least moderate-intensity work), smoking history (smoked at least 100 cigarettes over lifetime), were analyzed as potential confounders. BMI ≥30 kg/m² was considered obese based on the World Health Organization (WHO) criteria.²⁹

Statistical methods

All statistical analyses were performed using Stata/IC 15.0 (StataCorp LLC, College Station, TX) and accounted for the NHANES survey design and population weights.³⁰ The survey data were released at every 2-year cycle, thus the sample weights for the combined 10-year data were constructed as 1/5 × (2-year weight) for interview variables and MEC variables, respectively. Descriptive statistics were reported as weighted mean (svy: mean) and proportion with SE (svy option) to estimate the distribution of characteristics stratified by races among the represented population (svy prefix). We calculated the Pearson's chi-square tests for independence to determine whether there was a significant association between each of the categorical covariates included in the analyses. All models were adjusted for age at interview and age at diagnosis for BC (≤ or ≥65 years old). Weighted glm Poisson regression was used to estimate the prevalence ratio (PrR) of the health outcomes [svy: glm family (Poisson) link (log)] as log-binomial regression failed to converge. We first modeled each of the three health outcomes (diabetes, HBP, and other cancers) with race as the only predictor (adjusted for ages). Then other risk factors were added one at a time to each model to check for the association between the variable and the outcome, and the change in coefficient for race. Model 3 included all social and behavioral variables in this study. The percentage change in PrR of race was calculated by following formula (PrR_{model 1} − PrR_{model 2 or 3})/PrR_{model 2 or 3} × 100%, where PrR_{model 1} denotes the effect of race on health outcomes in model 1 adjusted for age at interview and age at BC diagnosis, and PrR_{model 2} denotes the PrR for the effect of race after adjusting for one other risk factor.³¹ Interactions between race and risk factors included in Model 3 were assessed using the likelihood ratio test comparing the model, including an interaction term with a reduced model without the term. p Values <0.05 were considered statistically significant.

Results

Baseline characteristics of the study population are shown in Table 1. Among the over 3 million individuals represented by 441 women in the dataset diagnosed with BC, 81.8% were NHW, 7.7% were NHB/AA, and the rest other races. NHB/AA cases had higher average BMI (32.6 vs. 28.8), less proportion of nonobese (42.0% vs. 62.4%), and more likely to be physically inactive (79.0% vs. 60.8%) than NHW BC survivors. There was a higher proportion of education less than 12th grade (24.8% vs. 10.7%) and income below 1.3 FPL (25.0% vs. 15.2%) among NHB/AA survivors compared with NHW survivors. While the uninsured rate was low for the overall population, the percentage of people insured by both Medicaid and Medicare was higher in NHB/AA than NHW (10.0% vs. 3.2%). There was a higher percentage of NHB/AA with only BC (94.3% vs. 79.7%). In contrast, both diabetes and HBP were more prevalent among NHB/AA as there were fewer proportions of women free from diabetes (60.9% vs. 80.1%) and HBP (19.3% vs. 44.2%), respectively. We also observed significant associations between obesity and type of insurance (p = 0.03) and for obesity and poverty level (p = 0.0004). We did not find any significant associations between the other categorical risk factors/covariates (data not shown).

Table 1.

Characteristics of U.S. Women Diagnosed with Breast Cancer, National Health and Nutrition Examination Survey, 2007–2016

	Total population	NHW	NHB/AA	Other races
Sample, n (%)	441	253 (57.4)	72 (16.3)	116 (26.3)
Weighted, N (%)	3.4 × 10⁶	2.7 × 10⁶ (81.8)	2.6 × 10⁵ (7.7)	3.9 × 10⁵ (11.5)
Risk factors

Mean (SE)
Age at interview	65.8 (0.74)	66.7 (0.82)	62.5 (1.56)	61.7 (1.31)	<0.001
Years since BC diagnosis	10.0 (0.48)	10.0 (0.57)	9.3 (1.17)	10.2 (1.09)	0.869
BMI	29.1 (0.45)	28.8 (0.54)	32.6 (0.86)	28.5 (0.79)	<0.001

% (SE)
Age at diagnosis
% <65	73.7 (2.5)	71.7 (2.9)	78.9 (5.3)	83.7 (3.9)
Missing, n		3		1
Education level
<12	13.9 (1.9)	10.7 (2.2)	24.8 (6.1)	28.9 (5.1)	<0.001
12	21.8 (2.6)	23.8 (3.2)	16.3 (4.8)	11.6 (2.7)
>12	64.3 (3.2)	65.5 (3.9)	58.9 (5.8)	59.5 (5.5)
Missing, n				1
Insurance
Private	35.6 (3.3)	35.6 (3.7)	44.7 (6.3)	29.8 (6.4)	<0.01
Medicare + medicaid	4.9 (1.2)	3.2 (1.2)	10.0 (3.8)	11.4 (4.0)
Others	55.6 (3.0)	58.1 (3.5)	43.4 (5.7)	46.4 (5.7)
Uninsured	4.1 (1.4)	3.1 (1.7)	1.9 (1.8)	12.3 (4.5)
Poverty level
≤1.3 FPL	17.9 (2.0)	15.2 (2.3)	25.0 (6.3)	32.5 (4.7)	<0.05
1.3–1.85 FPL	15.9 (2.1)	16.1 (2.5)	15.9 (6.2)	14.8 (4.1)
>1.85 FPL	60.0 (2.7)	63.3 (3.1)	47.2 (5.9)	45.8 (6.5)
Unknown	6.1 (1.1)	5.3 (1.4)	11.9 (4.2)	7.8 (2.4)
Obesity
% Nonobese	60.7 (2.5)	62.4 (3.1)	42.5 (6.9)	61.5 (4.9)	<0.05
Missing, n	28	20	3	5
Smoked 100 cigarettes
% Not smoking	54.8 (3.5)	54.3 (4.5)	50.5 (7.1)	61.4 (6.4)	0.56
Missing, n				1
Physically active
% Not active	64.5 (2.9)	60.8 (3.2)	79.0 (4.9)	81.5 (5.2)	<0.001
Health outcomes
Other cancers
% with only BC	81.6 (2.6)	79.7 (3.2)	94.2 (2.6)	86.9 (5.2)	0.07
Diabetes
% No diabetes	78.3 (2.5)	80.1 (3.0)	61.6 (5.8)	76.6 (4.6)	<0.05
Missing, n				1
High blood pressure
% No HBP	44.4 (3.2)	44.2 (3.8)	19.5 (5.4)	63.1 (5.9)	<0.001

Data are weighted mean and SE or percentage (%) and SE as indicated. NHW, NHB/AA, other races = Mexican American, other Hispanic, non-Hispanic Asian, and multiracial groups. Dichotomous variables: age at diagnosis (<65 or ≥65), smoked 100 cigarettes (no or yes), physically active (no or yes), other cancers (with only BC or with other cancers), diabetes (no or yes/borderline), and high blood pressure (no or yes). Education was indexed to 12th grade/high school graduate. Poverty level was indexed as the ratio to FPL. Obesity was defined as BMI ≥30 kg/m². The numbers of missing values for variables were indicated in each race category and individuals with missing values were excluded from the analysis. p Values based on adjusted Wald test for continuous variables and Pearson's corrected χ² for categorical variables. p Values <0.05 were considered statistically significant.

BMI, body mass index; FPL, federal poverty line; HBP, high blood pressure; NHB/AA, non-Hispanic Black/African American; NHW, non-Hispanic White; SE, standard error.

The overall associations between race and health outcomes were assessed in models (Model 1), including race as the main predictor adjusted with age at interview and age at diagnosis for each health outcome, respectively (Table 2). The prevalence of diabetes (PrR 2.21, 95% confidence interval [CI] 1.43–3.42) and HBP (PrR 1.62, 95% CI 1.36–1.93) was significantly higher among NHB/AA BC survivors compared with NHW. In contrast, NHB/AA BC survivors were 70% (PrR 0.3, 95% CI 0.12–0.77) less likely to have multiple cancers (two to four types) compared with white women. The prevalence of health outcomes was not significantly different between other race groups and NHW group (p > 0.05).

Table 2.

Association Between Race and Health Outcomes: Diabetes, High Blood Pressure, and Other Cancers

	Model 1 ^a
	PrR	95% CI	p
Diabetes
NHW	1	Referent
NHB/AA	2.21	1.43–3.42	0.000
Others	1.36	0.80–2.32	0.249
HBP
NHW	1	Referent
NHB/AA	1.62	1.36–1.93	0.000
Others	0.75	0.53–1.05	0.095
Other cancers
NHW	1	Referent
NHB/AA	0.30	0.12–0.77	0.013
Others	0.67	0.29–1.64	0.393

Model 1: adjusted for age at interview and age at diagnosis of BC.

p Values <0.05 were considered statistically significant.

BC, breast cancer; CI, confidence interval; PrR, prevalence ratio.

Next, we examined the effects of other risk factors on health outcomes by adding an additional risk factor to model 1 for each health outcome (Table 3). In the new model (Model 2), the prevalence of diabetes was 49% higher in obese women than nonobese women (PrR 1.49, 95% CI 1.02–2.16), adjusted for race and age. The prevalence of diabetes in women insured by both Medicare and Medicaid was 3.73 times higher than women with private insurance only (95% CI 1.56–8.91). Similarly, the prevalence of HBP was higher among women with obesity (PrR 1.3, 95% CI 1.02–1.65) and women insured by both Medicare and Medicaid (PrR 1.67, 95% CI 1.17–2.39). Higher education (>12) was associated with lower prevalence of HBP (PrR 0.75, 95% CI 0.60–0.94) compared with lower education (<12). All other risk factors, including education, poverty level, smoking, and physical activity, were not significantly associated with the health outcomes, and no association between any risk factors and other cancers was observed.

Table 3.

Association Between Social and Behavioral Risk Factors and Health Outcomes: Diabetes, High Blood Pressure, and Cancers

	Model 2 ^a
	Diabetes		HBP		Other cancers
	PrR (95% CI)	p	PrR (95% CI)	p	PrR (95% CI)	p
Education level
<12	1 (Ref)		1 (Ref)		1 (Ref)
12	0.85 (0.47–1.54)	0.592	0.86 (0.67–1.09)	0.208	1.01 (0.40–2.52)	0.976
>12	0.76 (0.46–1.28)	0.299	0.77 (0.62–0.95)	0.016
Insurance
Private	1 (Ref)		1 (Ref)		1 (Ref)
Medicare + Medicaid	3.73 (1.56–8.91)	0.003	1.67 (1.17–2.39)	0.005	0.75 (0.20–2.82)	0.676
Others	2.14 (0.97–4.70)	0.059	1.02 (0.70–1.49)	0.912	0.61 (0.28–1.31)	0.206
Uninsured	0.87 (0.19–4.01)	0.860	0.95 (0.39–2.31)	0.911	1.08 (0.27–4.24)	0.907
Poverty level
≤1.3 FPL	1 (Ref)		1 (Ref)		1 (Ref)
1.3–1.85 FPL	0.94 (0.56–1.59)	0.821	1.04 (0.76–1.43)	0.777	1.21 (0.54–2.68)	0.629
>1.85 FPL	0.65 (0.37–1.14)	0.133	0.84 (0.64–1.09)	0.194	1.25 (0.60–2.58)	0.536
Unknown	0.68 (0.32–1.44)	0.315	0.8 (0.59–1.31)	0.525	0.74 (0.19–2.77)	0.654
Obesity
No	1 (Ref)		1 (Ref)		1 (Ref)
Yes	1.49 (1.02–2.16)	0.036	1.29 (1.01–1.65)	0.037	0.99 (0.50–1.94)	0.985
Smoked 100 cigarettes
No	1 (Ref)		1 (Ref)		1 (Ref)
Yes	1.01 (0.65–1.58)	0.954	1.00 (0.79–1.27)	0.974	1.17(0.70–1.97)	0.529
Physically active
No	1 (Ref)		1 (Ref)		1 (Ref)
Yes	0.97 (0.59–1.60)	0.911	1.15 (0.91–1.45)	0.230	1.40 (0.77–2.54)	0.258

p Values <0.05 were considered statistically significant.

Model 2: adjusted for Model 1 plus one social or behavioral risk factor as indicated.

We further explored the confounding effects accounted for by individual risk factors on the relationship between race and health outcomes (Table 4). The change in estimates (PrR) was adopted in our study to quantify the effects.³² As shown in Table 4, in assessing the effect of an individual risk factor, the change in estimate between race and diabetes was the greatest when obesity was added to model 1 that it attenuated the PrR yet retained the statistical significance (% change in PrR: 17.5%). The addition of insurance to model 1 attenuated the PrR by 7.8% for diabetes and the combining effects of obesity and insurance attenuated the PrR up to 23.5%. However, the change in estimates was not as prominent for association between race and HBP or other cancers when additional risk factors were added to model 1 for HBP and other cancers, respectively. In fully adjusted Model 3, the change in estimates were 25.6% and 16.7% for diabetes and other cancers, and no change was observed for HBP (% change in PrR: 0).

Table 4.

Change in Prevalence Ratios (95% Confidence Interval) for Health Outcomes Comparing Non-Hispanic Whites to Non-Hispanic Black/African American Race Across Different Models

	NHW	NHB/AA, PrR 95% CI	% Change in PrR ^a
Diabetes
Model 1	1 (Ref)	2.21 (1.43–3.42)
Model 2
Education	1 (Ref)	2.13 (1.37–3.31)	3.7
Insurance	1 (Ref)	2.05 (1.29–3.29)	7.8
Poverty	1 (Ref)	2.12 (1.39–3.25)	4.2
Obesity	1 (Ref)	1.88 (1.19–2.99)	17.5
Smoked 100 cigarettes	1 (Ref)	2.20 (1.43–3.40)	0.4
Physically active	1 (Ref)	2.20 (1.39–3.48)	0.4
Obesity + insurance	1 (Ref)	1.79 (1.11–2.89)	23.5
Model 3	1 (Ref)	1.76 (1.05–2.94)	25.6
HBP
Model 1	1 (Ref)	1.62 (1.36–1.93)
Model 2
Education	1 (Ref)	1.55 (1.31–1.84)	4.5
Insurance	1 (Ref)	1.54 (1.30–1.83)	5.2
Poverty	1 (Ref)	1.59 (1.33–1.89)	1.9
Obesity	1 (Ref)	1.59 (1.29–1.94)	1.9
Smoked 100 cigarettes	1 (Ref)	1.61 (1.35–1.93)	0.6
Physically active	1 (Ref)	1.65 (1.40–1.95)	−1.8
Obesity + insurance	1 (Ref)	1.54 (1.27–1.86)	5.2
Obesity + education	1 (Ref)	1.54 (1.26–1.88)	5.2
Model 3	1 (Ref)	1.63 (1.37–1.93)	0
Other cancers
Model 1	1 (Ref)	0.30 (0.29–1.64)
Model 2
Education	1 (Ref)	0.31 (0.12–0.79)	−3.2
Insurance	1 (Ref)	0.30 (0.11–0.80)	0
Poverty	1 (Ref)	0.31 (0.12–0.78)	−3.2
Obesity	1 (Ref)	0.30 (0.11–0.78)	0
Smoked 100 cigarettes	1 (Ref)	0.30 (0.11–0.75)	0
Physically active	1 (Ref)	0.32 (0.12–0.83)	−6.2
Model 3	1 (Ref)	0.36 (0.15–0.89)	−16.7

% Change in PrR = [(PrR_{model 1} − PrR_{model n})/PrR_{model n}] × 100%. Model 1: adjusted for age at interview and age at diagnosis of BC. Model 2: adjusted for Model 1 plus one social or behavioral risk factor as indicated. Model 3: Fully adjusted for all social and behavioral factors mentioned in this study. BC, breast cancer.

Lastly, we examined statistical interactions between race and all risk factors assessed. No significant interactions (p < 0.05) were identified with the present data analysis (data not shown).

Discussion

In the present study, we assessed three types of chronic health outcomes as proxies for health status among BC survivors: diabetes, irrespective of type 1 or type 2, HBP, and history of other cancers. NHB/AA race, compared with NHW survivors, was significantly associated with the co-occurrence of diabetes and HBP. BMI explained almost 18% of the association between NHB/AA race and prevalence of diabetes, whereas insurance type alone accounted for this association to a lesser extent. BMI and type of health care insurance, together, however, accounted for ∼24% of the association between NHB/AA race and prevalence of diabetes. In estimating the co-occurrence of HBP, a small reduction (≤5%) in prevalence was observed when only insurance or education was included. Similarly, the association between race and other cancers changed slightly when an additional risk factor was added to the model, suggesting these social and behavioral factors were not major confounders for the relationship between race and HBP or other cancers.

Studies have shown that women with diabetes have poorer prognosis than women without diabetes, partially explained by metabolic factors or estrogenic effect of obesity, suggesting that the coexistence of diabetes may contribute to worse prognosis in BC survivors.³³ According to the CDC, the prevalence of diagnosed diabetes was almost doubled in black women than white women, and nearly one-third of women currently live with the prediabetes.³⁴ Consistent in our study, the prevalence of diabetes/borderline among NHB/AA BC survivors was 38.4%, doubling the percentage in white women (19.9%). Minorities, especially NHBs have been reported to be burdened with diabetes and worse outcomes, and black women might also have worse glycemic control, which may be due to the eating behaviors in response to stress.³⁵

HBP increases significantly with age, as the prevalence in women over 60 was more than double the prevalence in women under 60 and was higher in black women than white women 18 years of age and over.³⁶ In our study, more than half of the black or white population with BC had HBP, and the percentage HBP was 25% higher in black (80.5%) than white (55.8%), as the average age was over 60 years of age. HBP was shown to be explaining the survival disparity between younger and older patients by over 40%, which served as the evidence that HBP adversely affects a patient diagnosed with BC.³⁷ Previous studies have identified the mechanisms why HBP preferentially impacts black population, among which obesity is particularly important for the difference between black women and white women.^38,39 However, in the present analysis, obesity only slightly accounted for the association between NHB/AA race and co-occurrence of HBP.

In this NHANES study, NHB/AA BC survivors were 70% less likely to have a history of additional cancers compared with NHW (PrR 0.3, p = 0.012), which is consistent with other studies showing NHW women have a higher incidence rate for all cancer sites combined when compared with black women.⁴⁰

A higher percentage of black women (58%) was obese compared with white women (38%). BMI exceeding obese level had the largest effect on the relationship between race and prevalence of diabetes. Along with previous evidence that type 2 diabetes preferentially impacts black women,³⁵ and a study clearly indicated that obesity is associated with higher overall and BC-specific mortality.⁴¹ Although our current study does not address the causal relationship between obesity and diabetes, the association is consistent with the findings between BMI and diabetes from other investigators.⁴²

Other behavioral factors were examined in the context of racial disparity and health status. The prevalence of being physically active was lower in black survivors compared with whites, inversely related to the prevalence of obesity, which was in alignment with previous studies suggesting lack of physical activity being a leading contributor to obesity among black women.⁴³ Other studies have found that black women are typically less involved in physical activity when compared with white women.⁴⁴ While there has been evidence for the beneficial effects of physical activities and outcomes of diabetes,⁴⁵ our study did not show a significant contribution of merely being physically active to the relationship between race and diabetes, suggesting other aspects of physical activities (e.g., aerobic exercise) may explain the effect. We also examined the effects of smoking on the associations between race and health outcomes. Although the prevalence of smoking is similar between black and white women in the general population, blacks tend to initiate smoking at an older age than whites.^41,42 Our results did not demonstrate significant associations between smoking history and health outcomes among BC survivors in our study.

The overall uninsured proportion was low, and the percentage was similar between NHB/AA and NHW. Since most of the individuals reported having some type of health insurance coverage and were over 65 years of age, we were interested in the subgroup covered by Medicare and Medicaid, as this subpopulation may also reflect a lower socioeconomic status. Our results showed a significant association between coverage by Medicare and Medicaid and higher prevalence of diabetes and HBP, and it also contributed ∼8% of the racial disparities. The effects could be unique to the structure of insurance rather than the eligibility of insurance as income poverty level was not associated with any of the health outcomes. None of the SES or health behavioral factors had an association with history of other cancers, suggesting factors other than social and behavioral factors, such as genetics or diet, were more important in determining co-occurrence of other type of cancers among women with BC.⁴⁶

There are strengths and limitations with the present study. NHANES is designed to be representative of the entire U.S. population, thus results from NHANES data are generalizable to other BC survivors not included in the study. We assessed multiple health outcomes and investigated the effects of demographic, social, and behavioral factors. Our findings among AA women are similar to those reported in a previous study; White et al. investigated racial disparities on health status and health behaviors among BC survivors and found that more black survivors reported obesity than white survivors.⁴⁷ Our study, however, might be the first study to examine the association between race and indicators of health status (e.g., diabetes) among BC survivors.

We combined 10 years of data to increase the number of study participants for statistical purposes. We acknowledge that changes in social factors and improvement in disease management might have occurred during this timeframe; however, we cannot account for these factors with the current data. An additional limitation in the study was the small sample size in the subpopulations as there were only 72 NHB/AA BC survivors, which rendered insufficient statistical power to perform multiple stratification analyses. Since NHANES is a cross-sectional study, the information of variables only represents at the time of interview and not necessarily reflecting the course of change in health outcomes. Additionally, we were unable to assess specific types of physical activity due to missing data. We adjusted for age at diagnosis of BC and age at interview to control the confounding by age. There was no information on the severity of the diseases or BC prognostic variables (i.e., stage, tumor receptor status) available to adjust for in modeling these associations. In addition, there was a staggering pattern for which medical condition was diagnosed first, for example, BC as the primary or secondary cancer, or diabetes diagnosed prior or posterior to BC, thus the risk factors and disease progression could be very different. Lastly, we used only complete data for analysis as the missing data was less than 10%.

In conclusion, obesity accounts for the most important modifiable factor in the association between race and diabetes prevalence in our study of female BC survivors in the U.S. Therefore, weight management is critical and should be prioritized as a primary intervention for NHB/AA BC survivors with diabetes. We acknowledge that there are potentially other biological factors that could account for the racial disparities we have observed; however, the primary objective of this study was to provide a population-wide assessment of the problem by examining the effects of behavioral and social factors to propose a specific target of intervention. Larger longitudinal studies may be required to better characterize the effects of other behavioral and social factors in BC survivors with comorbidities, with survival or quality of life as outcomes of interests.

Data Availability

The NHANES is conducted by the National Center for Health Statistics, U.S. Centers for Disease Control and Prevention (CDC). The datasets generated during and/or analyzed during the current study are available in the National Center for Health Statistics repository, https://wwwn.cdc.gov/nchs/nhanes/Default.aspx

Footnotes

Author Disclosure Statement

No competing financial interests exist.

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