Attitudes towards and sociodemographic determinants of genetic test usage in the USA; data from the Health Information National Trend Survey, 2020

Abstract

The purpose of this study was to examine the trends in who obtains genetic tests, and opinions about how genes affect health. Cross‐sectional survey data from Health Information National Trends Survey (HINTS) 5, Cycle 4 was used. This data was collected from adults 18 years of age or older who completed mailed surveys sent by the National Cancer Institute between January and April 2020. The sample consisted of 2,947 respondents who answered the question ‘Have you ever had a genetic test’? 727 had a test and 2,220 did not have a test. The measures used included survey questions that asked whether respondents obtained certain kinds of genetic tests, who they shared test results with, whether they believed genes affect health status, and their demographic and cancer status information. Multivariate logistic regression models were developed to assess which demographic variables were associated with having different kinds of genetic tests, and whether those who had genetic tests had different opinions about genetic testing and the influence of genes on health. We found that female respondents [OR: 1.9; CI: (1.2–3.1)] had higher odds of having any genetic tests while Hispanic [OR: 0.5; CI: (0.2–1.0)] respondents had lower odds. Our findings indicate that there are demographic disparities in who received genetic tests, and that cancer risk alone does not explain the differences in prevalence of genetic testing.

What is known about this topic

Those who are at high risk of certain genetic conditions are more likely to receive genetic testing, and there are known demographic and socioeconomic disparities in access to genetic testing.

What this paper adds to the topic

Females are more likely to receive genetic testing, while Hispanic people are less likely to receive testing. These effects were seen when controlling for personal history of cancer, indicating that health status alone does not account for differing prevalence of genetic testing in the population.

1. INTRODUCTION

Genetic testing is increasingly used – given the potential benefits for consumers – that include diagnostic or predictive health information, guidance for therapeutics, and identification of ancestry or biological filiation (Franceschini et al., 2018; Phillips et al., 2018; Roberts & Middleton, 2017). Perceptions on genetic tests, mainly direct‐to‐consumer genetic tests (DTC), however, remain equivocal based on issues such as the lack of comprehensive oversight by government (U. S. Food and Drug Administration, 2019; Guerrini et al., 2020; Phillips et al., 2018), inadequate scientific evidence on such tests, and limited utility of genetic tests to support effective healthcare decisions (Pitini et al., 2018).

A qualitative study of interviews with 15 participants by Marzulla and colleagues indicated the potential impact of DTC for their health; albeit, they expressed concerns about understanding the meaning of results and developing a plan of action based on results (Marzulla et al., 2021). DTC consumers may experience physical, psychological, and monetary burdens due to the mere presence of genetic biomarkers, especially when genetic tests are considered outside of the clinical guidelines such as family history of disease (Diamandis & Li, 2016; Horton et al., 2019).

The increasing awareness and use of DTC and any genetic test has been cited as being disproportionately low among various population subgroups that include racial and ethnic minorities and individuals with disadvantaged socioeconomic backgrounds (Agurs‐Collins et al., 2015; Krakow et al., 2017). According to the 2017 Health Information National Trends Survey (HINTS), about half the US general population was not aware of genetic testing, and racial and ethnic minorities, low‐income, and older individuals were among those who were less likely to be aware of genetic testing (Krakow et al., 2017). Although prior iterations of the survey have differentiated DTC genetic testing from any genetic testing, the 2017 and 2020 HINTS did not specify the test types. One study reported that DTC genetic testing awareness was significantly higher among those who reported an annual income of $100,000 or more, and those with high numeracy skills (Agurs‐Collins et al., 2015). Carroll and colleagues noted that, in regard to the use of genetic tests, rate of DTC genetic testing was similar across all race/ethnicities, but individuals who identified as non‐Hispanic (NH) White were more likely to utilize clinical genetic testing and report any abnormal test results to medical professionals compared to racial and ethnic minorities (Carroll et al., 2020). Consistently, a systematic review conducted by Canedo et al. reported that non‐White individuals were more likely to have lower awareness of genetic testing than their White counterparts (Canedo et al., 2019).

With regards to the use of clinical genetic testing, Chapman‐Davis and colleagues reported that NH‐White individuals with family history of cancer are more likely to be referred to undergo cancer screening (e.g., BRCA1/2 mutation), and therefore see a higher uptake of such genetic tests compared to racial and ethnic minorities. Although racial and ethnic minority populations were more likely than NH‐White individuals to use clinical genetic testing upon cancer diagnosis, they are more likely to have advanced‐stage cancers (Chapman‐Davis et al., 2021). Existing sociodemographic disparities, including intersections with racial and ethnic disparities, could widen given the increasing trend of precision medicine coupled with inequitable access to these genetic testing opportunities, and lack of awareness and knowledge about genetic testing in minority populations (Canedo et al., 2019). Historically, access to testing has been limited, as many of the clinical genetic test validations were carried out in predominantly racially White populations that limits our understanding on how these genetic tests perform (prognostic value, sensitivity, specificity, etc.) in diverse populations (Khan et al., 2022). Khan and colleagues point out that the lack of racial and ethnic diversity in clinical genetic test validation studies may correlate with systemic biases. These biases may stem from medical mistrust, financial burden, lack of culturally appropriate educational material on genetic testing, among many other factors that contribute to the significant disparities in clinical genetic test utility among African Americans with family history of hereditary cancer syndromes when compared to their White counterparts (Khan et al., 2022).

The primary aim of our study was to analyze recent national‐level population estimates of opinions of any genetic testing and sociodemographic determinants of any genetic test usage using the publicly available HINTS 2020 data. Our analysis updates the 2017 analyses by Krakow et al. (2017), and provides a view of the rapidly changing realm of genetic testing. However, both HINTS 2017 and 2020 do not differentiate DTC from clinical genetic testing. Despite these limitations comparison between Krakow and colleagues of the HINTS 2017 with our latest HINTS 2020 analysis could potentially give us more insight on how clinical guidelines, ethical paradigms, and cultural pressures could have shifted the perceptions and use of genetic tests between the 2017 and 2020 survey. The most recent HINTS cycle collected information on awareness and receipt of different types of genetic testing (e.g., ancestry tests, genetic health risks, and high‐risk cancer tests), opinions regarding the influence of genes on disease, and how genetic test results were shared. These additional questions on the utility of genetic tests, compared across sociodemographic categories will allow us to critically evaluate the impact of population‐level disparities. Understanding sociodemographic disparities will facilitate targeted interventions to improve genetic literacy, and equitable access to genetic testing among population subgroups.

2. METHODS

2.1. Study population and design

This study was conducted using data from the National Cancer Institute (NCI)'s HINTS 5, Cycle 4. HINTS is a cross‐sectional survey of the non‐institutionalized civilian adult (18+ years) population in the United States. The main goal of this survey is to collect nationally representative data on needs, trends, access, and utilization of cancer‐related information, communication practices, and cancer risk perception. Details of the HINTS survey methods have been previously described in detail (Finney Rutten et al., 2012; National Cancer Institute, 2020). Briefly, respondents were sampled in a two‐phase process; first, addresses were randomly selected using residential addresses from the U.S. Postal Service, and second, an adult within the household was selected with the nearest upcoming birthday. Data were collected between February 24, 2020 and June 15, 2020 via mailed questionnaires. The response rate was 33% (N = 3,865) for the HINTS 5 cycle 4 (HINTS 2020) (National Cancer Institute, 2020).

The current study is a cross‐sectional analysis of data from HINTS 2020. We included respondents only if they answered the HINTS 5 question: ‘Have you ever had any of the following type(s) of genetic tests?’ The purpose of the current analysis is to investigate the sociodemographic determinants of genetic test use and whether beliefs about the effects of genetics on disease affect genetic test use.

2.2. Ethical clearance

HINTS administration was approved by the Institutional Review Boards (IRBs) at Westat. Given that the data are deidentified it was deemed exempt from review by the US National Institutes of Health (NIH) Office of Human Subjects Research.

2.3. Measures

2.3.1. Genetic test use

Responses to the question ‘Have you ever had any of the following type(s) of genetic tests?’ that included the following predefined choices ‘ancestry testing’, ‘genetic health‐risk testing’, ‘high‐risk cancer testing’, ‘other genetic testing’, ‘none of the above’, and ‘not sure’ were made into separate binary variables. Participant who selected any of the ‘ancestry testing, genetic health‐risk testing, high‐risk cancer testing, other genetic tests’ were coded as ‘Had a genetic test’, and those who selected ‘None of the above’ or ‘Not sure’ were coded as ‘Did not have a genetic test or not sure’ for each individual genetic test type. Due to the lack of distinction between clinical and direct‐to‐consumer genetic testing, such classification could not be made in this study. Those who selected ‘Other genetic tests’ were given a textbox to identify the test type, with responses including clotting disorders, prenatal testing, testing for type 1 diabetes, and testing for sensitivities. A binary variable was created if respondents said yes to any of the genetic test types versus having had no tests or were not sure, and another binary variable was made to assess whether respondents had any genetic tests excluding the ancestry category for evaluation of health‐related genetic testing.

2.3.2. Opinions and sharing results of genetic testing

If participants had any genetic tests, they were asked the question ‘If you had a genetic test, with whom did you personally share the results?’ and could select multiple options, including healthcare professionals, genetic counselors, spouses, parents, siblings, children, friends, other, or not shared with anyone. We combined spouses, parents, siblings, and children into the category ‘Family’ for analysis, and combined healthcare professionals and genetic counselors into ‘HCP/Genetic Counselor’. Respondents were asked questions about their opinions regarding genetic testing in regard to cancer, including ‘How important is knowing a person's genetic information for preventing cancer’, ‘How important is knowing a person's genetic information for detecting cancer’, and ‘How important is knowing a person's genetic information for treating cancer’ with the choices ‘Not at all, A Little, Somewhat, and Very’. Additionally, respondents were asked their opinions regarding the extent to which genes determine risk for different chronic diseases with the question ‘How much do you think genetics, that is characteristics passed from one generation to the next, determine whether or not a person will develop… (cancer/diabetes/obesity/heart disease)?’ with the responses ‘Not at all, A Little, Some, and Very’. Responses were classified into ‘Not at all/A little’ and ‘Somewhat/Very’ for analysis.

2.3.3. Sociodemographic variables

Sociodemographic and health characteristics used as independent variables in our analyses included sex (male/female), age (18–34/35–49/50–64/65–74/75+), race/ethnicity (non‐Hispanic White/non‐Hispanic Black/Hispanic/Other), education (high school graduate or less/some college/bachelor's degree or more), income (under $35,000/$35,000–$75,000/over $75,000), insurance status (insured/uninsured), ever had cancer (yes/no), and family members ever had cancer (yes/no).

2.4. Statistical analysis

All data were analyzed using survey weights based on population estimates from the American Community Survey to account for non‐response and coverage error, making the results more generalizable to the population‐based on HINTS protocols (National Cancer Institute, 2020). As is best practice with the HINTS data, jackknife replicate weights were used to provide bias‐corrected variance estimates.

2.4.1. Descriptive analysis

Descriptive statistics were used to summarize the sample demographics, the proportion having each test type, and the proportion sharing test results with each type of relationship (family, friends, healthcare professional or other). Chi‐square tests were used to assess demographic differences between those who did or did not have genetic tests in the sample.

2.4.2. Regression analysis

Weighted multivariable logistic regression models were used to estimate odds that respondents had any genetic test, ancestry tests, genetic health‐risk tests, high‐risk cancer tests, or other genetic tests by demographics and health status variables. The health status variables included personal and family history of cancer, as the U.S. Preventive Services Task Force recommends cancer risk testing for those with personal or family cancer history (Owens et al., 2019). Demographic and socioeconomic variables chosen reflected past literature regarding genetic testing disparities. Finally, we estimated weighted multivariable logistic regression models to examine the association between having any genetic test or having high‐risk cancer tests by each of the questions regarding genetic testing opinions, dichotomizing the opinion responses to ‘Not at all/A little’ vs. ‘Somewhat/Very’, controlled for demographics. Multivariable models were adjusted for sex, age, race/ethnicity, education, income, insurance status, family history of cancer, and personal history of cancer. For multivariate tests we applied Bonferroni corrections to maintain the overall Type I error rate at 0.05 (alpha of 0.05/8 = 0.00625). All logistic models were estimated with Bonferroni‐corrected confidence intervals (99.38). All analyses were completed using Stata version 17 (2021, StataCorp LP).

3. RESULTS

3.1. Participant characteristics

A total of 2,947 respondents were included who answered the question regarding whether they had ever had a genetic test. Table 1 summarizes the demographic characteristics of those who did and did not have genetic tests. The majority of the sample was female, non‐Hispanic White, was insured, never had cancer, and had family history of cancer. There were differences in who did or did not ever have a genetic test by sex, race, education, income, insurance status, personal history of cancer, and family history of cancer. The adjusted percentage of those who had any genetic test was 21.6%, and the most frequently reported tests among respondents were ancestry tests (15.8%), followed by genetic health‐risk tests (7.7%), high‐risk cancer tests (4.2%), and other genetic tests (1.2%) (data not shown). Within those who had any genetic tests, an adjusted percentage of 51.4% had only ancestry testing, 9.7% genetic health‐risk testing only, 10.2% high‐risk cancer testing only, 4.5% other only, and 24.2% selected multiple genetic test types (data not shown).

TABLE 1.

Sample population from HINTS 5 cycle 4 (2020) data

Characteristic	Total (n = 2,947)	Had a genetic test (n = 727)	Did not have a genetic test, or not sure (n = 2,220)	p‐value
		N (%)	N (%)
Sex				<0.001
Male	1,126	245 (16.5)	881 (83.5)
Female	1,623	431 (26.4)	1,192 (73.6)
Age (yrs)				0.076
18–34	417	92 (17.3)	325 (82.7)
35–49	580	137 (20.6)	443 (79.4)
50–64	920	223 (24.6)	697 (75.5)
65–74	637	170 (25.2)	467 (74.8)
75+	327	92 (24.6)	235 (75.4)
Race/Ethnicity				<0.001
Non‐Hispanic White	1,794	499 (25.6)	1,295 (74.4)
Non‐Hispanic Black	357	63 (13.5)	294 (86.5)
Hispanic	377	69 (11.1)	308 (88.9)
Other	211	53 (23.8)	158 (76.2)
Education				0.013
High school or less	597	110 (17.3)	487 (82.7)
Some college	846	184 (20.1)	662 (79.9)
Bachelor's or more	1,432	421 (27.5)	1,011 (72.5)
Income				0.002
Less than $35,000	707	117 (14.4)	590 (85.6)
$35,000–$75,000	832	210 (22.6)	622 (77.4)
Greater than $75,000	1,153	344 (25.5)	809 (74.5)
Insurance				<0.001
Insured	2,775	709 (23.2)	2,066 (76.8)
Not insured	135	11 (5.4)	124 (94.6)
Ever had cancer				0.002
No	2,439	562 (20.7)	1,877 (79.4)
Yes	473	160 (32.7)	313 (67.3)
Family member had cancer				0.006
No	516	103 (16.1)	413 (83.9)
Yes	2,159	566 (23.8)	1,593 (76.2)

Note: Not every total adds up to 2,947. Missing responses to demographic questions were excluded.

3.2. Opinions of genetic testing

There were no significant relationships between opinions about genetic testing and having received genetic tests or high‐risk cancer tests. Respondents who thought genetics were somewhat or very important for preventing or detecting cancer had odds approaching significance (Prevention OR = 3.8 (CI 0.8–19.2), p = 0.02 and Detection OR = 15.0 (CI 0.4–580.4), p = 0.039) for having high‐risk cancer tests than those who thought they were not at all or a little important (Table 2).

TABLE 2.

Odds of getting genetic test by opinions about genetic testing

	Any genetic test (569 had test)	High‐risk cancer test (109 had test)
	OR (Adjusted CI)	OR (Adjusted CI)
Importance of a person's genetic information for
Cancer Prevention (n = 2,216)
Not at all/A little	Ref.	Ref.
Somewhat/Very	1.1 (0.6–2.0)	3.8 (0.8–19.2)
Detecting cancer (n = 2,220)
Not at all/A little	Ref.	Ref.
Somewhat/Very	1.2 (0.6–2.3)	15.0 (0.4–580.4)
Treating cancer (n = 2,208)
Not at all/A little	Ref.	Ref.
Somewhat/Very	1.1 (0.6–2.1)	1.4 (0.4–5.5)
How much do you think person's genetics determine
Development of cancer (n = 2,209)
Not at all/A little	Ref.	Ref.
Somewhat/Very	1.2 (0.6–2.6)	2.4 (0.3–21.4)
Development of diabetes (n = 2,216)
Not at all/A little	Ref.	Ref.
Somewhat/Very	0.9 (0.4–1.8)	1.3 (0.4–4.5)
Development of obesity (n = 2,200)
Not at all/A little	Ref.	Ref.
Somewhat/Very	0.9 (0.5–1.5)	1.4 (0.6–3.4)
Development of heart disease (n = 2,209)
Not at all/A little	Ref.	Ref.
Somewhat/Very	1.0 (0.5–2.0)	1.0 (0.3–3.7)

Note: All models control for all sociodemographic and health characteristics (i.e., sex, age, race/ethnicity, education, income, insurance status, had cancer, and family history of cancer). Bonferroni corrected p < 0.00625 for α of 0.05 (99.38).

3.3. Sociodemographic determinants of genetic testing

Table 3 shows the odds of receiving any genetic test, any genetic test excluding ancestry, and receiving each type of genetic test by demographic characteristics. Of all the sociodemographic characteristics investigated (sex, age, race/ethnicity, education, income, insurance, ever had cancer, family member had cancer), we observed significant differences in genetic test use by sex, race/ethnicity, insurance, and personal history of cancer.

TABLE 3.

Odds of having genetic tests by demographic characteristic (n = 2,249)

	Any genetic test (569 had test)	Any genetic test excluding ancestry (283 had test)	Ancestry test (420 had test)	Genetic health‐risk test (185 had test)	High‐risk cancer test (109 had test)	Other genetic test (30 had test)
	OR (Adjusted CI)	OR (Adjusted CI)	OR (Adjusted CI)	OR (Adjusted CI)	OR (Adjusted CI)	OR (Adjusted CI)
Sex
Male	1.0	1.0	1.0	1.0	1.0	1.0
Female	1.9 (1.2–3.1)	2.4 (1.4–4.1)	1.3 (0.7–2.4)	1.8 (1.0–3.4)	4.1 (1.5–11.3)	1.6 (0.3–8.6)
Age (yrs)
18–34	1.0	1.0	1.0	1.0	1.0	1.0
35–49	1.1 (0.5–2.3)	1.4 (0.7–3.0)	0.8 (0.3–1.9)	1.0 (0.4–2.5)	3.0 (0.7–12.9)	1.1 (0.2–5.5)
50–64	1.1 (0.6–2.1)	1.3 (0.6–2.8)	1.0 (0.5–2.1)	0.9 (0.4–2.3)	2.1 (0.4–10.6)	1.1 (0.1–17.7)
65–74	1.3 (0.6–2.7)	0.9 (0.3–2.4)	1.4 (0.6–3.0)	0.9 (0.3–2.8)	0.9 (0.1–6.5)	1.9 (0.2–14.8)
75+	1.0 (0.4–2.5)	0.6 (0.2–2.4)	1.5 (0.6–3.8)	0.9 (0.2–4.3)	0.7 (0.1–7.6)	1.6 (0.4–6.3)
Race/ethnicity
Non‐Hispanic White	1.0	1.0	1.0	1.0	1.0	1.0
Non‐Hispanic Black	0.5 (0.2–1.2)	0.6 (0.2–1.9)	0.6 (0.2–1.6)	0.8 (0.2–3.4)	0.7 (0.1–5.4)	0.3 (0.1–1.0)
Hispanic	0.5 (0.2–1.0)	0.6 (0.2–1.6)	0.6 (0.3–1.3)	0.5 (0.1–2.3)	0.5 (0.1–2.2)	0.4 (0.2–0.9)
Non‐Hispanic Other	0.8 (0.3–2.2)	1.3 (0.4–3.9)	0.8 (0.3–2.2)	1.2 (0.4–3.7)	0.5 (0.1–3.2)	3.2 (0.1–98.5)
Education
High school or less	1.0	1.0	1.0	1.0	1.0	1.0
Some college	0.9 (0.4–1.9)	0.7 (0.2–2.5)	1.2 (0.6–2.4)	0.6 (0.1–2.9)	0.8 (0.2–2.7)	0.7 (0.01–35.4)
Bachelor's or more	1.2 (0.6–2.4)	1.1 (0.4–3.4)	1.7 (0.9–3.2)	1.5 (0.4–6.1)	0.5 (0.1–1.7)	0.8 (0.02–44.6)
Income
Less than $35,000	1.0	1.0	1.0	1.0	1.0	1.0
$35,000–$75,000	1.8 (0.9–3.7)	2.1 (0.7–5.8)	1.5 (0.6–3.8)	1.7 (0.5–6.4)	1.9 (0.4–8.8)	8.5 (0.1–835.2)
Greater than $75,000	1.8 (0.9–3.5)	1.8 (0.8–4.2)	1.6 (0.6–4.0)	1.7 (0.5–5.6)	1.8 (0.5–6.8)	11.1 (0.1–1,288.9)
Insurance
Insured	1.0	1.0	1.0	1.0	1.0	1.0
Not insured	0.4 (0.1–1.7)	0.4 (0.04–4.5)	0.4 (0.1–2.7)	0.8 (0.1–10.6)	0.02 (0.003–0.1)	0.6 (0.2–1.7)
Ever had cancer
No	1.0	1.0	1.0	1.0	1.0	1.0
Yes	1.6 (0.8–3.3)	2.4 (1.1–5.1)	1.0 (0.4–2.0)	1.2 (0.5–2.8)	4.4 (1.8–10.8)	0.7 (0.3–1.4)
Family member had cancer
No	1.0	1.0	1.0	1.0	1.0	1.0
Yes	1.4 (0.7–2.6)	1.2 (0.4–3.2)	1.4 (0.8–2.6)	0.8 (0.2–2.3)	3.1 (0.3–27.6)	1.6 (0.1–17.6)

Note: Due to complete case analysis, n is lower than in Table 1 because participants who did not answer every question were excluded from the analysis. Bonferroni corrected p < 0.00625 for α of 0.05 (99.38).

Bold indicates a significant Bonferroni corrected p value.

3.3.1. Sex

Respondents who identified as female had a significantly higher odds of obtaining any genetic test (OR 1.9, CI 1.2–3.1), any genetic test excluding ancestry (OR 2.4, CI 1.4–4.1), and high‐risk cancer tests (OR 4.1, CI 1.5–11.3) when compared with male respondents. Female respondents had higher odds of receiving genetic health‐risk tests than men (p = 0.011), but after Bonferroni correction this was not significant.

3.3.2. Race/ethnicity

Respondents that identified as Hispanic had significantly lower odds of taking any genetic tests (OR 0.5, CI 0.2–1.0) and both NH Black and Hispanic respondents had significantly lower odds of taking other genetic tests (OR 0.3, CI 0.1–1.0 and OR 0.4, CI 0.2–0.9, respectively) when compared with their NH White counterparts. NH Black respondents had lower odds of taking any tests, but this was not significant after Bonferroni correction (p = 0.026).

3.3.3. Education

College graduates had higher odds of having ancestry tests than those with high school degrees or less, but this was not significant after Bonferroni correction (p = 0.032).

3.3.4. Income

Respondents with incomes of $35,000–$75,000 (p = 0.019) or > $75,000 (p = 0.021) had higher odds of taking genetic tests than those with incomes <$35,000, but these were not significant when Bonferroni‐corrected.

3.3.5. Insurance

Although there were no significant differences across respondents who used any genetic tests, any tests excluding ancestry, ancestry tests, general health‐risk tests and other genetic tests, those who were uninsured had a significantly lower odds of having high‐risk cancer tests (OR 0.02, CI 0.003–0.1) when compared to insured individuals.

3.3.6. Personal and/or family history of cancer

Respondents who reported as ever having cancer had higher odds of receiving any genetic test excluding ancestry tests (OR 2.4, CI 1.1–5.1) and high‐risk cancer tests (OR 4.4, CI 1.8–10.8) compared to their cancer free counterparts. There were no significant relationships between family history of cancer and receiving genetic tests.

3.3.7. Patterns of genetic test result sharing among survey respondents

Among those who had obtained any genetic tests, respondents were most likely to share genetic test results with family and healthcare professionals (Figure 1). Those who did not share their results with anyone made up 15% of those who had any genetic tests.

FIGURE 1.

Proportion of respondents who obtained any genetic tests reporting that they shared genetic test results with different individuals (n = 710)

4. DISCUSSION

Genetic testing may allow people at risk for certain conditions to obtain personalized care and better plans for their treatment (Torkamani et al., 2018). There are, however, differences in who is more likely to receive genetic testing. Our results indicated that women were more likely to receive testing, while Hispanic respondents (compared to Non‐Hispanic White) were less likely to receive any kind of testing. Socioeconomic and racial disparities in genetic testing have been extensively documented, where previous studies indicate that disparities in obtaining testing could exacerbate differences in timing and intensity of care for various chronic diseases (Armstrong et al., 2015; Dillon et al., 2022; Koeller et al., 2017). It may be important to raise awareness of genetic testing in racial and ethnic minority groups, as awareness of genetic testing followed similar trends in prior research, indicating that less awareness could lead to reduced testing seen among minority groups in our results (Canedo et al., 2019; Hann et al., 2017; Krakow et al., 2017). There is evidence that there may be distrust of genetic testing among minority groups, due to concerns about confidentiality, stigma, and discrimination (Hann et al., 2017). These negative attitudes, if present, will need to be addressed by healthcare professionals and genetic counselors to decrease disparities in genetic testing among high‐risk groups.

For specific test types, nothing was significantly associated with ancestry testing; female sex neared significance for having higher odds of genetic health‐risk tests, and lower odds of other genetic tests (e.g., prenatal testing, testing for clotting disorders, testing for diabetes) were associated with respondents who identified as Non‐Hispanic Black or Hispanic. People may not have known whether certain tests fell into the category of genetic health‐risk tests or other, encouraging further specification in future iterations of HINTS. Many of the kinds of tests specified by participants who chose ‘Other’ included various chronic diseases that may have been classified as genetic health‐risk tests by others, like clotting disorders and tests for rare diseases or carrier genes for rare disorders that may affect children. These results indicating that females may receive more genetic testing are similar to prior research showing that women may be more likely to get genetic testing than men because of prenatal testing (Norwitz & Levy, 2013).

Female sex and those with a history of cancer had higher odds of having had high‐risk cancer tests, while lower odds of high‐risk cancer tests were associated with being uninsured. In prior research, women were more likely to receive genetic testing for cancer risk, as many of the high‐risk cancer tests are for BRCA1/2‐related hereditary breast and ovarian cancer (LaDuca et al., 2020). The high‐risk cancer testing results mirror national clinical guidelines, as women with breast or ovarian cancer at young ages are encouraged to be tested (Owens et al., 2019), and those who are uninsured are less likely to receive care. Guidelines also encourage women with family history of breast cancer at young ages to be tested (Owens et al., 2019); however, family history was not a significant variable in our results. This may be due to the question being too vague to parse out the degree of relatedness, kind of cancer, or age at diagnosis among family members. Though not corroborated by our results, other research has shown lower rates of high‐risk cancer testing among races other than Non‐Hispanic White (Chapman‐Davis et al., 2021; Cragun et al., 2017; Kurian et al., 2019) and lower income individuals (Armstrong et al., 2015; Martin et al., 2019). Individuals who identify as racial and ethnic minorities and are of a low‐income group already have disparities in ovarian and breast cancer death rates that may persist without preventive testing (Chornokur et al., 2013; Dietze et al., 2015). Prior research has shown vast increases in the rates of BRCA1/2 testing, partially due to media attention and public awareness campaigns in the past decade (Chen et al., 2018; Martin et al., 2019). Increases in testing, however, persisted among higher income individuals, and many who are at high risk for cancer are still underutilizing clinical testing (Frey et al., 2022; Levy et al., 2011). Though increases in testing among those at risk for cancer can help some people take preventive measures, the trendiness of cancer testing due to media influence and increased availability of direct‐to‐consumer testing may increase the potential for over‐testing among those who are not at risk (Guo et al., 2017; Juthe et al., 2015).

The adjusted percent who had any test was 21.6% in the HINTS 2020 data, which is not a large increase since the reported 20.95% from the HINTS 2017 data (Krakow et al., 2017), despite other prior research showing increases in genetic testing rates (U.S. Department of Health & Human Services, 2021; Phillips et al., 2018). Only ancestry tests were included in both HINTS iterations, and in HINTS 2020 genetic health‐risk tests and high‐risk cancer tests were included but are less specific than the options in HINTS 2017, so direct comparisons about who received genetic tests cannot be made between iterations. In the HINTS 2017 survey, the types of genetic tests included in the survey question were ancestry tests, paternity tests, DNA fingerprinting, Cystic Fibrosis carrier tests, BRCA1/2 hereditary breast and ovarian cancer tests, and Lynch Syndrome tests. The exclusion of paternity tests and DNA fingerprinting in HINTS 2020 but not in HINTS 2017 may partly explain the lack of increase in genetic testing rates. Moving forward, the HINTS survey has the opportunity to better allow assessment of trends over time if they keep the categories in the genetic test variable stable, and possibly provide definitions and examples for genetic health‐risk tests and high‐risk cancer tests so that people do not misclassify their tests as ‘Other’ or answer ‘Unsure’. In the analyses by Krakow et al., only personal cancer history was associated with having genetic tests, while sex and race were significant in our analyses. The types of tests included in the question may have led to sex shifts, as paternity testing was no longer included, and women were more likely to receive genetic health‐risk testing and high‐risk cancer testing in 2020, but individual test types were not assessed in the Krakow et al. analyses. As future HINTS iterations are conducted, the question may become more standardized and comparable across years.

While health‐risk tests and cancer tests have clinical implications and are useful tools for preventive care, ancestry tests and direct‐to‐consumer genetic tests may lead to unnecessary worry and misinterpretation due to lower reliability (Middleton et al., 2017; Tandy‐Connor et al., 2018). In addition, many tests have clinical guidelines regarding the risk for certain conditions that should be considered before testing. The number of genetic tests is rapidly increasing, with estimates that there are 10 new tests on the market daily, but most of these are DTC and not clinically performed (Phillips et al., 2018). The growing popularity of genetic testing has implications for medical professionals, as more doctors may need the training to help interpret results received from DTC tests or may need to encourage follow‐up testing with more clinically relevant tests (Douma et al., 2016; Haga et al., 2019; Hamilton et al., 2017). Our results showed that those who had genetic testing most often shared results with family, including spouses, parents, and siblings, with 35.3% sharing with healthcare professionals or genetic counselors, while 15% of respondents shared results with no one. These results are likely due to respondents sharing ancestry test results with family and not medical professionals, however, 51.4% had only ancestry tests, and 48.6% had at least one kind of health‐related test, indicating over 10% of those with a health‐related genetic test did not share results with healthcare professionals. As campaigns increase awareness of genetic testing, people should be encouraged to share results with medical professionals and genetic counselors to ensure a proper understanding of results. Evidence indicates that interventions supporting knowledge translation regarding genetic testing have improved patient knowledge, risk perception, anxiety, decisional conflict, and even improved disease‐prevention behaviors like smoking cessation (Légaré et al., 2016).

One of the main strengths of using HINTS data is the large and diverse respondent population that would be hard to achieve using a local survey, along with the included survey weights to make the sample nationally representative. However, there are limitations to this data. Changes in the wording of HINTS questions and inclusion of questions in only some iterations make it difficult to compare across years and make pooled samples for certain topics. There are limited questions regarding types of genetic tests and vague wording for some of these questions that may lead to underreporting or misclassification. There was a lack of questions regarding eligibility or experiencing clinical recommendations for genetic testing, making it hard to assess whether those receiving health risk and cancer risk tests were appropriately following guidelines. Future iterations of HINTS survey must differentiate clinical versus direct‐to‐consumer testing, information on personal and family history of disease conditions specific to genetic testing, and standardized verbiage/classification of genetic tests. Despite these limitations, it is important to assess trends and disparities in genetic testing as it becomes increasingly popular.

5. CONCLUSION

Our study showed that there are racial and sex differences in who receives genetic tests, and that those who receive high‐risk cancer tests have stronger opinions about their utility for preventing and detecting cancer. As more genetic tests become publicly available and encouraged for disease prevention, it is important to raise awareness about how to interpret results and to share results with trained professionals who can help people take appropriate next steps. In addition, disadvantaged populations may need to be targeted to increase their uptake of health risk and cancer risk tests to alleviate disparities in chronic disease morbidity and mortality. The potential for improved personal health knowledge through genetic testing has created many great opportunities for disease prevention and treatment, but these benefits are disproportionally distributed.

AUTHOR CONTRIBUTIONS

Christine M Swoboda: Conceptualization; data curation; formal analysis; investigation; methodology; writing – original draft; writing – review and editing. Akemi T. Wijayabahu: Investigation; methodology; supervision; writing – original draft; writing – review and editing. Naleef Fareed: Conceptualization; investigation; methodology; supervision; writing – original draft; writing – review and editing. Authors Swoboda and Fareed confirm that they had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All of the authors gave final approval of this version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

ACKNOWLEDGEMENTS

Jehannine Austin served as action editor for this manuscript.

COMPLIANCE WITH ETHICAL STANDARDS

Conflict of interest

Christine Swoboda, Naleef Fareed, and Akemi Wijayabahu declare that they have no conflicts of interest.

Human studies and informed consent

The HINTS 5 survey was approved by the Westat Institutional Review Board. No informed consent was required from subjects as deidentified data were extracted from the National Cancer Institute. All procedures followed were in accordance with US Federal Policy for the Protection of Human Subjects.

Animal studies

No non‐human animal studies were carried out by the authors for this article.

Data sharing and data accessibility

All data used in this paper are publicly available data from the National Cancer Institute. Data can be downloaded at hints.cancer.gov.

Supporting information

Table S1

Swoboda, C. M. , Wijayabahu, A. T. , & Fareed, N. (2023). Attitudes towards and sociodemographic determinants of genetic test usage in the USA; data from the Health Information National Trend Survey, 2020. Journal of Genetic Counseling, 32, 57–67. 10.1002/jgc4.1620