Psychosocial factors associated with genetic testing status among African American women with ovarian cancer: Results from the African American Cancer Epidemiology Study

Introduction

Since 2007, the National Comprehensive Cancer Network has recommended that all women diagnosed with high-grade ovarian, fallopian tube, or peritoneal cancer be offered genetic counseling for BRCA1/2.1, 2 Racial disparities in the uptake of genetic counseling and testing are well documented, with notable and consistently lower uptake among African American (AA) women compared with White women.3-5

When referred, AA women are less likely than White women to seek genetic counseling and testing when they are determined eligible.5, 6 Interventions aimed at increasing access to and uptake of genetic counseling and testing services (eg, telegenetic counseling) have not uniformly shown improvements for AA women.7, 8 Studies of genetic testing uptake among minority groups conclude that despite their generally positive attitudes toward genetic services, a number of concerns (eg, confidentiality, discrimination, and health care–related trust) inhibit their uptake of genetic counseling and testing.6, 9, 11-13, 10 However, to date, the relative contributions of multiple psychosocial factors to the uptake of genetic services among AA women have yet to be explored.

Two conceptual frameworks can be applied to consider multivariable factors inhibiting AAs from seeking genetic services. Critical race theory14 suggests that AAs' lived experiences within social systems characterized by endemic structural racism inhibit access and shape their expectations regarding the benefits and harms of genetic services.15 Applying this thinking to genetic service uptake, we posit that AA women who have experienced discrimination perceive that the risks of genetic services outweigh the potential benefits. Indeed, previous evidence suggests that individuals reporting more everyday discrimination experiences also report lower levels of health care utilization, including genetic services.16 In the context of genetic testing, as described previously, AA women cite distrust of how genetic information might be used against them as a deterrent to uptake.4 Additionally, structural racism contributes to fewer income and education opportunities, and this in turn limits access to health care and inhibits the uptake of genetic testing. Social assets, including strong religiosity and available social support, have had a mixed association with the uptake of genetic testing.12 For example, religiosity has been both negatively and positively associated with genetic service uptake among AAs.12, 13 Similarly, Andersen's Access to Care Model17, 18 is widely applied in health services contexts to conjointly consider multiple influences on health care uptake. Andersen's conceptualization holds that the perceived need for genetic testing (eg, a cancer diagnosis or family history) is driven by predisposing factors that impede the uptake of genetic testing (eg, education and distrust due to discrimination experiences) and personal and community factors that enable uptake (eg, religiosity and social support).

We analyzed survey data from a prospective, observational study of AA women diagnosed with ovarian cancer. The observational study included assessments of a complement of psychosocial factors that align with critical race theory and the Access to Care Framework (Fig. 1).

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Conceptual model of factors associated with the uptake of genetic testing.

Materials and Methods Study Population

The African American Cancer Epidemiology Study (AACES) is a multisite, population-based, case-control study of ovarian cancer. Detailed study methods have been reported elsewhere.19 Briefly, study sites were selected on the basis of geographic regions with large AA populations and included Alabama, Georgia, Louisiana, metropolitan Detroit, Michigan, New Jersey, North Carolina, Ohio, South Carolina, Tennessee, and Texas. Women were eligible to participate if they self-identified as AA or Black, were 20 to 79 years old at diagnosis, had histologically confirmed epithelial ovarian cancer, and had the ability to complete an interview in English. The protocol and study materials were approved by the institutional review boards at all participating sites and at Duke University, the lead institution.

AACES Recruitment and Data Collection

Rapid case ascertainment was used to identify cases through state cancer or Surveillance, Epidemiology, and End Results registries and through gynecologic oncology departments at individual hospitals. Participants were recruited between December 2010 and December 2015. Once enrolled, participants completed a baseline telephone survey that collected information on the following: sociodemographic characteristics, medical history, self-reported family history of cancer, lifestyle characteristics, perceived daily and lifetime discrimination, religiosity, health insurance status, health care provider access, and trust in health care providers. Participants completed a 12-month follow-up survey that reassessed topics from the baseline survey with some additional measures, including ever having genetic testing.

A total of 546 cases completed the full baseline survey. One hundred twenty-seven of these cases were not approached for follow-up because of late recruitment to the study, 51 participants completed a shortened survey without psychosocial measures, and 276 were not included. The final analytic sample comprised the 270 cases who completed the full baseline and follow-up surveys and responded to the genetic testing question. There were no statistically significant differences in predisposing, enabling, or need factors between those excluded (n = 276) and those included (n = 270; Table 1).

TABLE 1. Participant Characteristics by Genetic Testing Status Andersen Access to Care Construct Excluded AACES Cases (n = 276) Self-Reported Genetic Testing P Yes (n = 94) No (n = 176) Predisposing Highest level of education, No. (%) .136 High school or less 122 (44.2) 33 (35.1) 88 (50.0) Some college 69 (25.0) 28 (29.8) 40 (22.7) College graduate 54 (19.6) 20 (21.3) 28 (15.9) Graduate degree 31 (11.2) 13 (13.8) 20 (11.4) Perceived Everyday Discrimination Score, mean (SD) 2.21 (3.36) 2.54 (3.17) 2.23 (3.42) .460 For unfair reasons, have you ever been not hired for a job?, No. (%) 39 (14.2) 7 (7.5) 27 (15.5) .094 Enabling Annual family income, No. (%) .001 <$10,000 66 (24.5) 7 (7.4) 44 (25.4) $10,000-$24,999 58 (21.6) 20 (21.3) 48 (27.7) $25,000-$49,999 61 (22.7) 30 (31.9) 42 (24.3) $50,000-$74,999 42 (15.6) 16 (17.0) 21 (12.1) $75,000-$100,000 24 (8.9) 12 (12.8) 13 (7.5) >$100,000 18 (6.7) 9 (9.6) 5 (2.9) Marital status, No. (%) .035 Single 76 (27.5) 11 (11.7) 44 (25.0) Partnered 89 (32.2) 37 (39.4) 58 (33.0) Widowed/divorced 111 (40.2) 46 (48.9) 74 (42.0) Have a primary care provider, No. (%) 220 (79.7) 85 (90.4) 145 (82.4) .111 Have health insurance, No. (%) 253 (91.7) 93 (98.9) 160 (90.9) .020 Insurance type, No. (%) .002 Medicaid or none 91 (33.1) 19 (20.2) 69 (39.2) Other 184 (66.9) 75 (79.8) 107 (60.8) Total Trust in Physician score, mean (SD) 38.9 (13.6) 40.22 (11.5) 39.53 (10.8) .627 Very religious or spiritual (3 categories), No. (%) .489 Not/slightly 13 (4.75) 4 (4.3) 13 (7.4) Moderately 81 (29.6) 30 (31.9) 48 (27.3) Very 180 (65.7) 60 (63.8) 115 (65.3) Religious service attendance, No. (%) .195 ≤1 time a month 70 (25.4) 15 (16.0) 46 (26.1) 2-3 times a month 46 (16.7) 13 (13.8) 23 (13.1) 1 time a week 107 (38.8) 45 (47.9) 65 (36.9) Several times a week 53 (19.2) 21 (22.3) 42 (23.9) Frequency of prayer, No. (%) .006 ≤1 time a week 9 (3.3) 1 (1.1) 7 (4.0) Several times a week 6 (2.2) 0 (0.0) 8 (4.5) 1-2 times a day 79 (28.6) 32 (34.0) 33 (18.8) Many times a day 182 (65.9) 61 (64.9) 128 (72.7) Religion/spirituality involved in dealing with stressful situations, No. (%) .790 Not at all/not very 7 (2.5) 2 (2.1) 6 (3.4) Somewhat 41 (14.9) 9 (9.6) 19 (10.8) Very 228 (82.6) 83 (88.3) 151 (85.8) Perceived Social Support, mean (SD) 4.20 (0.70) 4.36 (0.56) 4.22 (0.65) .078 Need Family history risk, No. (%) .096 Low risk 182 (74.0) 55 (61.8) 119 (73.0) Medium risk 32 (13.0) 12 (13.5) 21 (12.9) High risk 32 (13.0) 22 (24.7) 23 (14.1) Age at diagnosis ≤ 55 y, No. (%) 119 (44.9) 40 (43.5) 61 (35.7) .268 Abbreviation: AACES, African American Cancer Epidemiology Study. a The P value compares the values between genetic testing groups. Statistical Analyses

Univariate analyses were conducted to characterize variable distributions and missing data. In the sample of 270 people, there was less than 10% missing data for any of the study variables. Bivariate analyses were conducted to test associations of the predisposing, enabling, and need factors with genetic testing uptake. Because of the binary outcome variable and continuous/interval covariates and predictors, a point-biserial correlation analysis was conducted. The point-biserial correlation is mathematically equivalent to the Pearson correlation function20; therefore, Pearson correlation coefficients were used to assess the associations between the study variables. To consider potential confounding effects on the outcome variable, a multivariable logistic regression model including all covariates and predictors was conducted. For this analysis, the variance inflation factor values were significantly under the recommended value of 10, and this indicated little evidence for confounding.

Multivariable logistic regression was performed to calculate odds ratios and 95% confidence intervals (CIs) for factors found in bivariate analyses to be associated with uptake of genetic testing at the P < .10 level of significance for inclusion in the multivariable logistic regressions. For the multivariable logistic regression analyses, conceptually aligned response options for these covariates were collapsed to facilitate interpretation. Odds ratios and 95% CIs were computed and reported when the CI did not include 1. All analyses were performed with SAS version 9.4.

Measures Outcome

Genetic testing was assessed with the following question: “Have you ever had genetic testing to determine a gene mutation for ovarian or breast cancer?” Participants were asked to respond to this question at each follow-up that they completed. If the participants ever reported genetic testing, they were coded as having genetic testing, and if at no follow-up the participants reported genetic testing, they were coded as not having genetic testing.

Predisposing factors

Education was assessed with the following question: “What is the highest level of school you have completed?” Eight response categories, ranging from ≤8th grade to a graduate or professional degree, were offered.

Perceived discrimination was assessed with the 5-item Williams Everyday Discrimination Scale (Cronbach α = 0.76). The following is an example of an item: “In your day-to-day life, how often have any of the following things happened to you: you are treated with less courtesy or respect than other people?” Response options ranged from never to almost every day. Responses were averaged with a range of 0 to 5. The 6-item Perceived Major Experiences of Discrimination Scale was also assessed.21, 22 The Cronbach α for the scale was low at 0.56. Item analysis showed that 1 item—“For unfair reasons, have ever not been hired for a job?” (yes/no)—differed significantly when it was crossed with ever have genetic testing versus not being tested (7.5% and 15.5%, respectively; P = .09), and it was included as a predisposing factor.

Enabling factors

Annual family income was assessed categorically with 5 categories ranging from “less than $10,000” to “more than $100,000.”

Health insurance was assessed with the following question: “Do you currently have health insurance or some form of health coverage?” The response options were yes and no. Given that virtually all participants reported having health insurance (93%), we also considered the type of insurance in analyses as “Medicaid or no insurance” versus any other insurance source.

The health care provider status was assessed with the following question: “Do you have a person that you consider your regular doctor or health care provider?” The response options were yes and no.

Trust in physicians was measured with the Trust in Physician Scale.23 An example of a question is “I trust my doctor so much I always try to follow his/her advice.” Five response options ranging from 1 (strongly disagree) to 5 (strongly agree) were offered. Responses were reverse-coded as appropriate and summed across 11 questions in the survey about trust with a range of 0 to 55, where higher scores indicated higher levels of trust. In this analytic sample, the Cronbach α was 0.91.

Social Support: Perceived social support was measured with the 12-item Multidimensional Scale for Perceived Social Support24 (eg, an example of an item is “I get the emotional help and support I need from my family,” with response options ranging from strongly disagree to strongly agree). The Cronbach α for the scale was 0.94. Responses were summed and averaged with a range of 1 to 5; higher scores indicated greater perceived social support.

Religious and spiritual beliefs were assessed with the 4-item Brief Multidimensional Measure of Religiousness/Spirituality25 (eg, an example of an item is “How often do you attend religious services?”).

Need factors

The age at diagnosis was assessed on the basis of the difference between the date of diagnosis and the date of birth.

The family history was calculated on the basis of the reported number of first-degree female relatives (parents, siblings, and daughters) who had been diagnosed with BRCA1/2-related cancers (ie, breast and ovarian cancers) and the age of diagnosis (<50 vs ≥50 years). Familial risk was ranked as 1) low risk (those with no first-degree relatives with a history of breast or ovarian cancer), 2) medium risk (those with at least 1 first-degree relative diagnosed with breast cancer at an age ≥ 50 years), or 3) high risk (those with at least 1 first-degree relative diagnosed with breast cancer under the age of 50 years and/or any family history of ovarian cancer). These classifications were adapted from previous studies that used National Health Interview Survey data.26

Results

Of the 270 women, 94 (35%) reported ever having had genetic testing. Thirty-eight percent of the women reported being diagnosed with ovarian cancer at an age younger than 55 years, and 30% were at medium or greater risk for carrying a BRCA1/2 mutation on the basis of their family history. The women had a mean age of 58.4 years, with 61.6% being older than 55 years. The majority were not partnered (67% were single or widowed/divorced), and most had health insurance and a health care provider (92% and 84%, respectively). One-third of the women reported a college degree or greater, and 28% reported a family income greater than $50,000.

With respect to predisposing factors, differences in education by genetic testing status were not significant (P = .14). Experiences of everyday discrimination also did not differ by genetic testing status (P = .46; Table 1). However, those who reported job discrimination experiences were marginally less likely to report having genetic testing than those who had not experienced such discrimination (15.5% vs 7.5%; P = .09).

As shown in Table 1, for enabling factors, those who had genetic testing reported significantly higher annual household income (P = .001) and were more likely to have health insurance other than Medicaid (P = .002) than those who had not been tested. Women who reported being single were less likely to have had genetic testing than those who had been or were partnered (P = .035). Women who reported more frequent praying were less likely to have had genetic testing than those who prayed less frequently (P = .006). Additionally, women who perceived greater social support were marginally more likely to have genetic testing than those with lower levels of perceived support (mean level of support, 4.36 vs 4.22; P = .078). There were no differences in the reported uptake of genetic testing by having a primary care provider or trust in a health care provider.

With respect to need factors, in bivariate analyses, AA women who had undergone genetic testing were marginally more likely to have a family history indicating a medium or high risk for a BRCA1/2 mutation based on family history (P = .096). There were no differences in genetic service uptake based on the age at diagnosis alone.

Multivariable Model

The predisposing, enabling, and need factors associated with genetic testing uptake at the P < .10 level of significance were tested in a multivariable logistic regression. These included experiences of not being hired for unfair reasons, income, marital status, Medicaid or no insurance, frequency of prayer, social support, and family history. Results showed that the odds of having genetic testing for those who had experienced not being hired for a job for unfair reasons were 0.39 (95% CI, 0.14-0.95) times the odds for those who had not had the discriminatory experience (Table 2). Women in the highest family income category had a 2.3 times greater likelihood of being tested in comparison with those with the lowest family income. Family history, Medicaid or no-insurance status, marital status, frequency of prayer, and perceived social support were not significant contributors to uptake of genetic testing in the multivariable model.

TABLE 2. Multivariable Logistic Regression Predicting Genetic Testing Status Characteristic Β (SE) Odds Ratio 95% CI P Predisposing For unfair reasons, have you ever been not hired for a job? No — 1.0 Referent — Yes –0.94 (0.47) 0.39 0.14-0.95 .048 Highest level of education High school or less — 1.0 Referent — More than high school 0.15 (0.33) 1.16 0.60-2.22 .660 Enabling Annual family income < $24,999 — 1.0 Referent — $25,000-$74,999 0.71 (0.34) 2.04 1.06-3.99 .034 ≥ $75,000 0.84 (0.47) 2.32 0.92-5.94 .076 Marital status Partnered — 1.0 Referent — Single –0.51 (0.40) 0.60 0.26-1.28 .199 Insurance type Medicaid or none — 1.0 Referent — Other 0.44 (0.36) 1.56 0.77-3.21 .220 Frequency of prayer Many times a day — 1.0 Referent — Not many times a day 0.42 (0.30) 1.52 0.84-2.76 .162 Perceived Social Support 0.25 (0.26) 1.28 0.77-2.18 .345 Need Family history risk Low — 1.0 Referent — Medium/high 0.52 (0.31) 1.68 0.92-3.10 .093 Abbreviation: CI, confidence interval. The χ2 likelihood ratio was 29.273; the overall model P value was .0006. Discussion

To our knowledge, this is the first study of the uptake of genetic testing among AA women with ovarian cancer. Thirty-five percent of AA women with ovarian cancer who completed the follow-up AACES survey reported having had genetic testing. In accordance with differing recommendations, these genetic testing rates were higher than those reported in the context of breast cancer; 26.7% was reported by McCarthy and colleagues

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