Abstract
Introduction
This study aimed to determine the frequency of genetic testing awareness, the number of individuals who have undergone genetic testing, and the subsequent behavior changes following testing.
Methods
The analysis utilized recent data from the Health Information National Trends Survey (HINTS) 6, collected between March and September 2022, from a diverse sample of adults aged 18 and older. Logistic regressions were applied to assess predictors of outcome variables. A p value of < 0.05 was considered statistically significant.
Results
Among the 4,631 respondents, 81.6% reported being aware of genetic testing, 28.7% (n = 1,327) had undergone some form of testing, and 16.3% of those tested reported making behavioral changes based on their results. Ancestry-related genetic testing was the most widely recognized and frequently utilized. However, behavioral changes were most commonly reported among individuals who underwent disease-specific genetic testing, especially those who perceived themselves to be at high risk, were motivated to take preventive measures, and received assistance in understanding their results. Within this subgroup, lifestyle modification was the most frequently cited change, followed by adjustments in dietary supplement use, increased health screenings, and changes to medications. Additionally, individuals from racial and ethnic minority groups were more likely than non-Hispanic white respondents to undergo specific types of genetic testing and to report behavior changes in response to the findings.
Conclusion
The study highlights an increasing awareness and involvement in genetic testing, though a smaller percentage of individuals have altered their behavior based on the test results. Additionally, the study identifies genetic literacy as a key factor in predicting behavior changes.
Keywords: Changing lifestyle, Genetic testing, Genetic testing services, Health Information National Trends Survey, Health promotion
Introduction
Since the completion of the Human Genome Project in 2003, genetic testing has made remarkable strides [1]. This marked the beginning of the “Era of the Genome,” characterized by significant advancements in the interpretation of genetic data and the creation of new testing technologies [2]. In response to the rapid expansion of genetic testing services and the growing number of available tests, the Genetic Testing Registry (GTR) was established in the USA. The GTR aimed to provide transparent results and foster collaboration with laboratories nationwide [3].
Genetic testing is “the analysis of human deoxyribonucleic acid (DNA), ribonucleic acid (RNA), chromosomes, proteins, and specific metabolites to identify heritable disease-related genotypes, mutations, phenotypes, or karyotypes for clinical purposes” [4]. Over time, genetic testing has become more affordable and widely accessible to the general population [5]. As a result, experts predict that by 2030, some countries may be able to sequence the DNA of every newborn at birth [2, 6]. The increasing public exposure to genetic testing technology and its potential impact on healthcare, disease prediction, and familial conditions has placed more significant pressure on the scientific community to demonstrate its value [7]. The global expansion of genetic testing could lead to a new era of personalized medicine, with preventive and therapeutic approaches tailored to individuals’ unique genetic profiles, ushering in the age of precision medicine [2].
As of November 2022, the GTR listed a total of 129,624 genetic tests in the USA and 197,779 worldwide. These figures include newly developed tests as well as updated versions of previously submitted tests, reflecting those that have been registered and are potentially available for clinical or research use [3]. These figures highlight the rapid growth of genetic testing technologies. Public attitudes toward genetic testing have become increasingly positive, with more people opting for testing to improve their healthcare outcomes [2]. In one cross-sectional study, approximately 88.6% of participants expressed interest in undergoing genetic testing to access better healthcare [8]. This growing interest stems from a greater understanding of genetics’ role in disease and well-being, with higher testing rates for treatable conditions and those with clear familial transmission [2].
Genetic tests serve various purposes, including newborn screening, prenatal diagnostics, carrier screening, and predictive or predisposition testing. Among these, newborn screening is the most widely conducted in the USA, with nearly every newborn tested for various genetic conditions [9]. Predictive or predisposition testing is performed to detect the potential onset of a disease in an individual before any symptoms appear. This type of test is crucial for identifying genetic variations in individuals who are predisposed to certain diseases, especially useful for individuals with a family history of genetic disorders. In such cases, rehabilitation services can be provided to reduce severity or prevent disease. Genetic tests are typically categorized based on their specific purpose, such as diagnostics, carrier detection, or prenatal screening [9, 10].
Diagnostic genetic testing is performed to accurately identify or confirm a suspected genetic condition in individuals, which can, in some cases, inform prognosis and guide treatment decisions [11]. Carrier genetic testing identifies individuals carrying a gene for a disease that could be passed on to their children [12]. Prenatal genetic testing screens fetuses for genetic conditions, helping identify any diseases that may affect them. Similarly, newborn genetic screening is conducted one to 2 days after birth to detect any conditions hindering a newborn’s growth and development [13]. Predictive and presymptomatic genetic testing aims to identify gene changes or mutations that could lead to the later development of genetically inherited diseases, empowering individuals to make lifestyle changes to mitigate disease onset. Lastly, genetic testing for research investigates genetically inherited diseases and explores how genes influence a person’s health and susceptibility to diseases, considering the roles of mutations and environmental factors [4].
Genetic literacy is essential for interpreting and understanding genetic testing results, whether conducted by healthcare providers or through direct-to-consumer testing services. It is defined as “an individual, society, or nation’s knowledge level, or understanding of genetics and genomics” [7]. Genetic literacy is increasingly important as people recognize that analyzing DNA can provide valuable insights into behavioral outcomes [8]. This understanding enhances individuals’ ability to comprehend their genetic information and its implications, making genetic literacy a critical component of informed decision-making in healthcare and personal health management [14].
In the USA, the Health Information National Trends Survey (HINTS) is a research initiative conducted by the National Cancer Institute. This program periodically gathers and evaluates the knowledge and awareness of the general population regarding health information access and usage. The 2020 HINTS survey analysis revealed that 75% of the US population was aware of genetic testing, with 19% having undergone testing themselves. Among those aware, the most common type of genetic testing recognized was ancestry testing, reported by 71% of participants, with 14% having received such testing [15]. This represents a significant increase compared to the 2017 HINTS survey, where only 57% of respondents were aware of genetic testing [16]. These findings indicate a remarkable rise in awareness of genetic testing among the US population over time.
Previous cross-sectional and nationally representative surveys have focused on public awareness of genetic testing and the prevalence of individuals who have undergone such testing. However, limited attention has been given to the behavioral responses that may follow the receipt of genetic test results. Understanding these responses is critical, particularly in the context of disease-related genetic testing, which has direct implications for medical decision-making and risk-reducing behaviors. The present study addresses this gap by conducting a secondary analysis of the most recent HINTS. Specifically, we aim to assess the overall and specific awareness of genetic testing and the proportion of individuals who have undergone different types of tests and to explore the extent to which individuals report behavior change in response to their results. In addition, while previous studies have generally examined predictors of overall awareness or testing uptake, this study uniquely explores predictors for specific types of genetic testing (e.g., ancestry, personal trait, disease-specific, and prenatal), offering a more nuanced understanding of the factors influencing engagement with different forms of genetic testing.
Based on the existing literature, this study proposes three research questions: (1) what is the overall level of awareness regarding genetic testing, including awareness of specific types, and what factors are associated with this awareness? (2) How many participants have undergone any form of genetic testing – including specific types – and what factors predict testing uptake? (3) How many participants have changed their behavior in response to disease-related genetic testing and what are the predictors of these changes?
Materials and Methods
Data for this study were extracted from the most recent HINTS cycle 6, conducted from March 7 to November 2, 2022. HINTS is a cross-sectional survey conducted annually by the National Cancer Institute (NCI), targeting US civilians aged 18 and older, using a modified Dillman approach (Dillman et al., 2009), i.e., sending mail, with all selected households receiving a total of four mailings as an initial, a reminder, and two follow-ups. Unlike previous surveys, HINTS 6 implemented an embedded methodological approach using both concurrent (control) and sequential (treatment) groups to enhance data quality. In total, 6,252 participants responded to HINTS 6, yielding a 28.1% response rate. While the overall response rate for HINTS 6 was 28%, the data were weighted to adjust for sampling design and nonresponse bias using demographic benchmarks from the American Community Survey. Although not all respondents completed items related to genetic testing, the application of calibrated weights allows for generalization to the broader US adult population, within the limits of survey-based research. More details about the methodology report of HINTS 6 can be accessed by visiting https://hints.cancer.gov/data/download-data.aspx#H6. This paper’s sample includes 4,631 participants who responded to at least one question related to awareness of genetic testing (section G) on the HINTS 6.
This study includes three primary outcome variables. The first focuses on participants’ awareness of genetic testing, assessed through the question: “Which of the following types of genetic tests have you heard of?” HINTS presented several options, including ancestry testing (to learn about family origins), personal trait testing (to understand specific traits), testing for specific diseases (e.g., breast cancer, colon cancer, and diabetes), and prenatal genetic testing (to assess the risk of a genetic disorder in a baby), along with an option for respondents who had not heard of genetic testing. Participants could select more than one type of test. This variable was measured dichotomously: those who reported awareness of at least one type of genetic test were categorized as “heard.” At the same time, those who selected none were classified as “not heard” or unaware.
The second outcome variable asked participants, “Which of the following types of genetic testing have you had?” Response options included various types of genetic testing (as described above) or “I'm not sure.” Participants were allowed to select multiple types of genetic tests if applicable. Those who selected “not sure” were excluded from the regression analysis. For the purpose of regression, the variable was treated as dichotomous, distinguishing between participants who had undergone at least one type of genetic testing and those who had not.
The third outcome variable asked participants whether they had changed their behavior based on the results of genetic testing. Reported behavior changes included lifestyle modifications (such as increased physical activity, dietary changes, or quitting smoking), initiation or adjustment of dietary supplements, changes in medication, changes in frequency of health screenings, or no behavior change. Although some participants indicated changing medications, it is important to note that the HINTS survey does not specify whether these changes were made in response to pharmacogenetic testing. Therefore, this item was treated as a general indicator of behavior change following any type of genetic testing. For analysis, the variable was dichotomized, with any reported behavior change classified as “yes” and no change as “no.” It is important to note that behavior changes are typically expected only in response to specific types of genetic tests, such as those indicating disease risk. Therefore, the regression analysis was limited to participants who reported behavior changes in response to genetic for specific diseases only.
All sociodemographic variables collected from HINTS 6 were included in the analysis. These variables are gender at birth (male/female), age (18–34, 35–49, 50–64, 65–74, 75 and older), race/ethnicity (non-Hispanic white, non-Hispanic African American, Hispanic, non-Hispanic Asian, and other), education level (less than high school, high school graduate, some college, bachelor’s degree, and post-bachelor’s degree), annual income (less than USD 20,000, USD 20,000 to <USD 35,000, USD 35,000 to <USD 50,000, USD 50,000 to <USD 75,000, USD 75,000 or more), and marital status (single/never married, married, and no longer married, which combines divorced/widowed/separated). To adjust for frequency distributions, categories within race/ethnicity, education, and marital status were combined for the analysis.
Additional variables from section (G) of HINTS included sources of information, reasons for obtaining genetic testing, confidence in the accuracy of genetic testing, and whether participants sought help understand the results. For sources of information, responses captured various options such as the Internet (e.g., social media, Google searches), other media (e.g., TV, news, radio, magazines), healthcare providers or counselors, family or friends, and “I have not heard.” The reasons for obtaining genetic testing were assessed with multiple response options, including recommendations from healthcare providers, understanding family history, learning personal traits, assessing disease risk, prenatal reasons, or receiving the test as a gift. Confidence in the accuracy of genetic testing was measured on a 5-point Likert scale (not at all, a little, somewhat, very, completely) and was later classified into three categories for analysis (not at all, somewhat/a little/completely, and very). Lastly, participants were asked if they sought help understand genetic testing results, with options including healthcare providers, laboratory assistants, family, friends, or siblings.
Statistical Analysis
We conducted all analyses using SPSS version 20. All analyses were conducted using survey weights provided in the HINTS dataset to ensure national representativeness of the results. Descriptive statistics were used to summarize categorical variables through frequencies and percentages. To examine bivariate associations between demographic characteristics and outcome variables, we applied the chi-square test of independence. To identify predictors of awareness and undergoing genetic testing, we performed multivariable binary logistic regression analyses. Additionally, we employed hierarchical binary logistic regression to assess predictors of behavioral change following disease-related genetic testing only. In hierarchical regression, variables entered into the model in conceptually informed blocks, allowing us to evaluate the incremental explanatory power of each block. For all regression models, we reported odds ratios and 95% confidence intervals. Model fit was evaluated using the change in −2 log likelihood and Nagelkerke R2 to assess the added contribution of each block. Statistical significance was defined as p < 0.05.
Results
The majority were female (59.6%), married (46.6%), aged 50–64 years (29.5%), with a postbaccalaureate education (29.3%), non-Hispanic white (58.8%), an annual income of USD 75,000 or above (41.7%), and employed (55.6%) (Table 1). Among the 4,631 participants included in the study, 3,777 (81.6%) reported having heard about genetic testing from at least one source. Among the various types of genetic testing, the majority of respondents (74.8%) were aware of ancestry testing, followed by tests for specific diseases (58.3%), prenatal testing (40.4%), personal trait testing (27.2%), and other types (0.6%). The internet was the most common source of information on genetic testing (62.5%), followed by electronic media (TV, radio, newspapers, magazines) (60.4%), family or friends (52.8%), and healthcare providers or counselors (33.6%).
Table 1.
Descriptive characteristics of study participants (N = 4,631)
| Variables | N (%) |
|---|---|
| Gender | |
| Male | 1,871 (40.4%) |
| Female | 2,760 (59.6%) |
| Age | |
| 18–34 years | 754 (16.3%) |
| 35–49 years | 1,002 (21.6%) |
| 50–64 years | 1,367 (29.5%) |
| 65–74 years | 973 (21%) |
| Above 75 years | 535 (11.6%) |
| Marital status | |
| Single | 1,238 (26.7%) |
| Married | 2,160 (46.6%) |
| Not married anymore | 1,233 (26.6%) |
| Employment status | |
| Unemployment | 2,058 (44.4%) |
| Employment | 2,573 (55.6%) |
| Education status | |
| < School and graduate school | 1,195 (25.8%) |
| Some college | 769 (16.6%) |
| Bachelors | 1,309 (28.3%) |
| Postbaccalaureate | 1,358 (29.3%) |
| Race | |
| Non-Hispanic white | 2,722 (58.8%) |
| Non-Hispanic African American | 716 (15.5%) |
| Hispanic | 791 (17.1%) |
| Non-Hispanic Asian and other | 402 (8.7%) |
| Annual income | |
| Less than 20,000 | 696 (15%) |
| 20,000 to < 35,000 | 576 (12.4%) |
| 35,000 to < 50,000 | 600 (13%) |
| 50,000 to < 75,000 | 826 (17.8%) |
| Above 75,000 | 1,933 (41.7%) |
| Heard about genetic testing | |
| No | 854 (18.4%) |
| Yes | 3,777 (81.6%) |
| Had genetic testing | |
| No | 2,314 (50%) |
| Yes | 1,327 (28.7%) |
| Not Sure | 70 (1.5%) |
| Not applicable | 920 (19.9%) |
| Change of behavior | |
| No | 1,111 (24%) |
| Yes | 216 (4.7%) |
| Not applicable | 3,304 (71.3%) |
All 4,631 participants were asked whether they had undergone genetic testing, regardless of their reported awareness of it. Among the total sample, 1,327 participants (28.7%) reported having undergone genetic testing, while 2,314 (50.0%) reported they had not, 70 (1.5%) were unsure, and 920 (19.9%) selected “not applicable.” Notably, 3,777 participants (81.6%) had previously indicated that they were aware of genetic testing. It is possible that a small number of participants who reported undergoing genetic testing but not having heard of it may reflect recall issues or misunderstanding of the question. Among those who underwent genetic testing (1,327), the most common was ancestry genetic testing (57.2%), followed by specific disease testing (42%), prenatal testing (23.6%), personal trait testing (17.6%), and other types of testing (1.1%). The most common reason for undergoing genetic testing was understanding family history (43.9%), followed by a doctor’s recommendation (34.7%), assessing disease risk (28.9%), prenatal reasons (20.2%), finding family (17.1%), learning about personal traits (14.9%), receiving the test as a gift (9.9%), developing strategies (5.8%), and other reasons (3.6%).
Of the 1,327 participants who reported undergoing genetic testing, only 216 (16.3%) indicated that they made behavioral changes based on their test results. The most commonly reported change was lifestyle modification, mentioned by all individuals who reported any change (100%). Other reported changes included adjusting the use of dietary supplements (32.9%), undergoing additional health screenings (25%), modifying medications (18%), and, to a lesser extent, reducing the frequency of health screenings (2.3%).
Behavior change was most prevalent among individuals who underwent genetic testing for specific diseases, with 167 participants (77.3%) reporting changes. Among this group, lifestyle modification was again the most common (100%), followed by initiating or changing dietary supplements (35.3%), increasing the frequency of health screenings (26.3%), altering medications (18.6%), and reducing health screenings (1.8%). In comparison, behavior changes were less frequently reported by those tested for ancestry (35.6%), personal traits (24.5%), prenatal conditions (19.9%), and other types of genetic tests (0.9%).
Around 61% of participants were very or completely confident in the accuracy of the test results, 30% were somewhat confident, and 9% had little or no confidence. Overall, 61.1% sought assistance in understanding their genetic test results, with healthcare providers being the most common source of help (44.1%), followed by genetic counselors (14%), spouses or partners (7.4%), siblings (5%), parents (3.8%), children (3%), others (2.6%), and friends (1.5%).
Table 2 shows the difference between demographic variables and the three dependent variables: awareness of genetic testing, having undergone genetic testing, and changes in behavior following genetic testing. All demographic variables showed statistically significant associations with awareness of genetic testing. However, employment status and race were not significantly associated with having undergone genetic testing. Lastly, no significant associations were found between gender, age, or employment status and changes in behavior.
Table 2.
Chi-square test for genetic testing awareness, genetic testing experience, and change in behavior
| Variables | Awareness of genetic testing (N = 4,631) | Had genetic testing (N = 3,777) | Change of behavior (1,327) | |||
|---|---|---|---|---|---|---|
| no (N = 854) | yes (N = 3,777) | no (N = 2,314) | yes (N = 1,327) | no (N = 1,111) | yes (N = 216) | |
| Gender | ||||||
| Male | 383 (20.5%) | 1,488 (79.5%) | 969 (67.7%) | 463 (32.3%) | 377 (81.4%) | 86 (18.6%) |
| Female | 471 (17.1%) | 2,289 (82.9%) | 1,345 (60.9%) | 864 (39.1%) | 734 (85%) | 130 (15%) |
| χ2 (1) = 8.59, p value <0.01 | χ2 (1) = 17.24, p value <0.01 | χ2 (1) = 2.75, p value = 0.09 | ||||
| Age | ||||||
| 18–34 years | 95 (12.6%) | 659 (87.4%) | 434 (68%) | 204 (32%) | 180 (88.2%) | 24 (11.8%) |
| 35–49 years | 178 (17.8%) | 824 (82.2%) | 459 (57.3%) | 342 (42.7%) | 285 (83.3%) | 57 (16.7%) |
| 50–64 years | 224 (16.4%) | 1,143 (83.6%) | 702 (63.5%) | 404 (36.5%) | 325 (80.4%) | 79 (19.6%) |
| 65–74 years | 174 (17.9%) | 799 (82.1%) | 511 (66.9%) | 253 (33.1%) | 215 (85%) | 38 (15%) |
| Above 75 years | 183 (34.2%) | 352 (65.8%) | 208 (62.7%) | 124 (37.3%) | 106 (85.5%) | 18 (14.5%) |
| χ 2 (4) = 109.85, p value <0.01 | χ 2 (4) = 22.79, p value <0.01 | χ2 (4) = 6.84, p value = 0.14 | ||||
| Marital status | ||||||
| Single | 210 (17%) | 1,028 (83%) | 668 (68.5%) | 307 (31.5%) | 242 (78.8%) | 65 (21.2%) |
| Married | 355 (16.4%) | 1,805 (83.6%) | 1,052 (59.9%) | 703 (40.1%) | 608 (86.5%) | 95 (13.5%) |
| Not married anymore | 289 (23.4%) | 944 (76.6%) | 594 (65.2%) | 317 (34.8%) | 261 (82.3%) | 56 (17.7%) |
| χ 2 (2) = 28.05, p value <0.01 | χ 2 (2) = 21.30, p value <0.01 | χ 2 (2) = 9.78, p value <0.01 | ||||
| Employment status | ||||||
| Unemployment | 468 (22.7%) | 1,590 (77.3%) | 976 (64.5%) | 537 (35.5%) | 455 (84.7%) | 82 (15.3%) |
| Employment | 386 (15%) | 2,187 (85%) | 1,338 (62.9%) | 790 (37.1%) | 656 (83%) | 134 (17%) |
| χ 2 (1) = 45.52, p value <0.01 | χ2 (1) = 1.01, p value = 0.31 | χ2 (1) = 0.67, p value = 0.41 | ||||
| Education status | ||||||
| <School and graduate school | 205 (17.2%) | 990 (82.8%) | 568 (58.7%) | 400 (41.3%) | 350 (87.5%) | 50 (12.5%) |
| Some college | 235 (306%) | 534 (69.4%) | 354 (69.3%) | 157 (30.7%) | 121 (77.1%) | 36 (22.95) |
| Bachelors | 246 (18.8%) | 1,063 (81.2%) | 661 (65.4%) | 350 (34.6%) | 287 (82%) | 63 (18%) |
| Postbaccalaureate | 168 (12.4%) | 1,190 (87.6%) | 731 (63.5%) | 420 (36.5%) | 353 (84%) | 67 (16%) |
| χ 2 (3) = 109.77, p value <0.01 | χ 2 (3) = 18.61, p value <0.01 | χ 2 (3) = 10.08, p value = 0.01 | ||||
| Race | ||||||
| Non-Hispanic white | 355 (13%) | 2,367 (87%) | 1,435 (62.4%) | 863 (37.6%) | 771 (89.3%) | 92 (10.7%) |
| Non-Hispanic African American | 174 (24.3%) | 542 (75.7%) | 337 (65.9%) | 174 (34.1%) | 111 (63.8%) | 63 (36.2%) |
| Hispanic | 226 (28.6%) | 565 (71.4%) | 350 (64.8%) | 190 (14.3%) | 148 (77.9%) | 42 (22.1%) |
| Non-Hispanic Asian and other | 99 (24.6%) | 303 (75.4%) | 192 (65.8%) | 100 (34.2%) | 81 (81%) | 19 (19%) |
| χ 2 (3) = 133.30, p value <0.01 | χ2 (3) = 3.46, p value = 0.32 | χ 2 (3) = 75.97, p value <0.01 | ||||
| Annual income | ||||||
| Less than 20,000 | 251 (36.1%) | 445 (63.9%) | 275 (68.1%) | 129 (31.9%) | 93 (72.1%) | 36 (27.9%) |
| 20,000 to <35,000 | 141 (24.5%) | 435 (75.5%) | 297 (71.2%) | 120 (28.8%) | 96 (80%) | 24 (20%) |
| 35,000 to <50,000 | 132 (22%) | 468 (78%) | 285 (63.6%) | 163 (36.4%) | 138 (84.7%) | 25 (15.3%) |
| 50,000 to <75,000 | 131 (15.9%) | 695 (84.1%) | 456 (67.8%) | 217 (32.2%) | 175 (80.6%) | 42 (19.4%) |
| Above 75,000 | 199 (10.3%) | 1,734 (89.7%) | 1,001 (58.9%) | 698 (41.1%) | 609 (87.2%) | 89 (12.8%) |
| χ 2 (4) = 251.66, p value <0.01 | χ 2 (4) = 35.04, p value <0.01 | χ 2 (4) = 22.00, p value <0.01 | ||||
Predictors of Genetic Testing Awareness
We conducted multivariable logistic regression analysis to identify predictors of awareness of both overall and specific types of genetic testing (Table 3). After adjusting for all sociodemographic variables, participants who identified as female had significantly higher odds of being aware of any genetic testing, but lower odds of being aware of ancestry-related testing. Participants aged 35–49 years were significantly more likely to be aware of ancestry and prenatal genetic testing, yet less likely to report general awareness of genetic testing overall. Respondents aged 50–64 and 65–74 years demonstrated greater awareness of specific types of genetic testing – including ancestry-related, personal trait, disease-specific, and prenatal tests – but showed lower odds of overall awareness. Similarly, individuals aged 75 and older were significantly more likely to be aware of specific categories, such as ancestry-related genetic testing, personal traits, disease-specific, and prenatal genetic testing, yet had lower odds of general awareness of genetic testing.
Table 3.
Multivariable logistic regression analysis for awareness of genetic testing
| Variables | Any genetic testing | Ancestry testing | Personal trait | Specific disease | Prenatal | Other |
|---|---|---|---|---|---|---|
| adjusted OR (CI) | adjusted OR (CI) | adjusted OR (CI) | adjusted OR (CI) | adjusted OR (CI) | adjusted OR (CI) | |
| Gender | ||||||
| Male | Reference | Reference | Reference | Reference | Reference | Reference |
| Female | 1.52 (1.29–1.79) | 0.76 (0.66–0.89) | 1.01 (0.87–1.16) | 0.70 (0.61–0.79) | 0.62 (0.54–0.71) | 0.81 (0.37–1.73) |
| Age | ||||||
| 18–34 years | Reference | Reference | Reference | Reference | Reference | Reference |
| 35–49 years | 0.60 (0.45–0.81) | 1.75 (1.35–2.27) | 1.16 (0.94–1.44) | 1.11 (0.90–1.38) | 1.57 (1.27–1.94) | 0.99 (0.32–3.02) |
| 50–64 years | 0.63 (0.47–0.85) | 1.67 (1.29–2.16) | 1.61 (1.30–2.01) | 1.25 (1.02–1.55) | 2.69 (2.17–3.32) | 1.31 (0.41–4.18) |
| 65–74 years | 0.55 (0.40–0.77) | 1.95 (1.45–2.62) | 1.96 (1.51–2.54) | 1.57 (1.23–2.00) | 3.94 (3.07–5.07) | 1.87 (0.42–8.28) |
| Above 75 years | 0.20 (0.14–0.29) | 5.09 (3.65–7.10) | 3.11 (2.23–4.33) | 2.97 (2.23–3.96) | 7.10 (5.17–9.73) | 1.04 (0.21–5.16) |
| Marital status | ||||||
| Single | Reference | Reference | Reference | Reference | Reference | Reference |
| Married | 0.85 (0.68–1.06) | 1.28 (1.05–1.56) | 1.30 (1.09–1.55) | 1.26 (1.07–1.49) | 0.85 (0.72–1.01) | 1.44 (0.57–3.63) |
| Not married anymore | 0.93 (0.73–1.18) | 0.96 (0.77–1.20) | 1.16 (0.94–1.44) | 1.17 (0.96–1.41) | 0.91 (0.75–1.12) | 1.33 (0.43–4.10) |
| Employment status | ||||||
| Unemployment | Reference | Reference | Reference | Reference | Reference | Reference |
| Employment | 0.84 (0.68–1.03) | 1.19 (0.99–1.44) | 1.11 (0.93–1.32) | 1.05 (0.90–1.23) | 1.12 (0.95–1.31) | 0.97 (0.36–2.57) |
| Education status | ||||||
| <School and graduate school | Reference | Reference | Reference | Reference | Reference | Reference |
| Some college | 0.63 (0.50–0.80) | 1.65 (1.32–2.05) | 2.64 (2.05–3.40) | 1.87 (1.53–2.28) | 2.73 (2.19–3.41) | 2.11 (0.55–8.10) |
| Bachelors | 1.06 (0.85–1.32) | 0.88 (0.72–1.07) | 1.49 (1.24–1.80) | 1.30 (1.09–1.54) | 1.76 (1.47–2.09) | 1.74 (0.62–4.87) |
| Postbaccalaureate | 1.32 (1.04–1.68) | 0.76 (0.62–0.94) | 1.05 (0.89–1.25) | 1.02 (0.86–1.21) | 1.26 (1.07–1.49) | 1.21 (0.51–2.91) |
| Race | ||||||
| Non-Hispanic white | Reference | Reference | Reference | Reference | Reference | Reference |
| Non-Hispanic African American | 0.48 (0.38–0.60) | 2.66 (2.17–3.25) | 1.92 (1.55–2.39) | 1.79 (1.49–2.13) | 2.09 (1.72–2.54) | 2.14 (0.61–7.45) |
| Hispanic | 0.36 (0.29–0.44) | 3.13 (2.58–3.81) | 1.92 (1.57–2.36) | 2.32 (1.95–2.76) | 2.04 (1.70–2.46) | 7.53 (0.99–57.31 |
| Non-Hispanic Asian and other | 0.36 (0.27–0.47) | 3.60 (2.81–4.61) | 2.10 (1.62–2.73) | 2.37 (1.89–2.96) | 2.25 (1.78–2.86) | 1.34 (0.39–4.58) |
| Annual income | ||||||
| Less than 20,000 | Reference | Reference | Reference | Reference | Reference | Reference |
| 20,000 to <35,000 | 1.76 (1.35–2.29) | 0.54 (0.42–0.69) | 0.72 (0.53–0.97) | 0.68 (0.54–0.87) | 0.59 (0.45–0.78) | 2.57 (0.50–13.1) |
| 35,000 to <50,000 | 1.90 (1.45–2.48) | 0.49 (0.38–0.63) | 0.62 (0.46–0.83) | 0.56 (0.44–0.71) | 0.46 (0.35–0.61) | 3.03 (0.58–15.8) |
| 50,000 to <75,000 | 2.66 (2.03–3.50) | 0.37 (0.28–0.47) | 0.59 (0.44–0.78) | 0.51 (0.40–0.64) | 0.48 (0.37–0.62) | 3.18 (0.72–14.0) |
| Above 75,000 | 4.04 (3.09–5.27) | 0.24 (0.19–0.31) | 0.45 (0.34–0.59) | 0.40 (0.32–0.50) | 0.34 (0.26–0.43) | 1.50 (0.47–4.81) |
Bold: significant findings. Outcome variables adjusted with demographic variable.
OR, odds ratio; CI, confidence interval.
Married participants were more likely to be aware of ancestry genetic testing, personal trait testing, and genetic testing for specific diseases. Regarding education, those with some college education had higher odds of being aware of ancestry testing, personal trait testing, specific disease testing, and prenatal genetic testing. Participants with a bachelor’s degree were more likely to be aware of personal trait testing, specific disease testing, and prenatal genetic testing. Furthermore, individuals with postbaccalaureate education showed increased odds of being aware of any genetic testing, particularly prenatal genetic testing. This pattern suggests that both marital status and higher levels of education are associated with greater awareness of various types of genetic testing.
Participants who had race other than non-Hispanic white had significantly higher odds for ancestry, personal trait, specific disease, and prenatal genetic testing and lower odds of being aware of any genetic testing. Respondents with annual income of more than USD 20,000 reported a significant positive association with awareness of any genetic testing, whereas there was a significant negative association with awareness of ancestry, personal traits, specific diseases, and personal genetic testing. There was no significant predictor identified for awareness of other types of genetic testing.
Predictors of Undergoing Genetic Testing
We attempted to identify the predictors of undergoing genetic testing among respondents who were aware of genetic testing by adjusting to sociodemographic variables (Table 4). We found that female participants reported significantly greater odds of undergoing overall or any genetic testing, whereas they had lower odds of undergoing genetic testing for any specific disease and prenatal genetic testing than male participants. Respondents aged 35–49 years were more likely to undergo any genetic testing and were less likely to obtain genetic testing for specific diseases than 18–34. Respondents with age (50–64 years) and (65–74 years) showed a significant negative association with specific disease genetic testing and a significant positive association with prenatal genetic testing. Participants above 75 reported significantly lower odds of ancestry genetic and specific diseases. In contrast, they had higher odds of having prenatal genetic testing.
Table 4.
Multivariable logistic regression analysis for predictors of undergoing genetic testing
| Variables | Any genetic testing | Ancestry testing | Personal trait | Specific disease | Prenatal | Other genetic testing |
|---|---|---|---|---|---|---|
| adjusted OR (CI) | adjusted OR (CI) | adjusted OR (CI) | adjusted OR (CI) | adjusted OR (CI) | adjusted OR (CI) | |
| Gender | ||||||
| Male | Reference | Reference | Reference | Reference | Reference | Reference |
| Female | 1.42 (1.23–1.65) | 0.98 (0.81–1.14) | 0.85 (0.64–1.13) | 0.62 (0.51–0.76) | 0.31 (0.23–0.42) | 1.15 (0.39–3.33) |
| Age | ||||||
| 18–34 years | Reference | Reference | Reference | Reference | Reference | Reference |
| 35–49 years | 1.40 (1.12–1.76) | 0.85 (0.64–1.12) | 0.88 (0.59–1.32) | 0.53 (0.38–0.74) | 0.87 (0.63–1.21) | 0.36 (0.03–3.46) |
| 50–64 years | 1.09 (0.87–1.37) | 0.81 (0.61–1.12) | 0.99 (0.65–1.51) | 0.50 (0.36–0.70) | 3.25 (2.21–4.78) | 0.31 (0.03–2.98) |
| 65–74 years | 0.93 (0.71–1.22) | 0.77 (0.56–1.07) | 1.36 (0.79–2.34) | 0.55 (0.38–0.82) | 11.05 (5.83–20.95) | 0.94 (0.06–13.60) |
| Above 75 years | 1.09 (0.78–1.53) | 0.60 (0.40–0.88) | 1.18 (0.58–2.38) | 0.58 (0.36–0.93) | 8.59 (3.81–19.36) | 1.04 (0.04–22.74) |
| Marital status | ||||||
| Single | Reference | Reference | Reference | Reference | Reference | Reference |
| Married | 1.33 (1.11–1.60) | 1.12 (0.90–1.40) | 1.17 (0.83–1.64) | 0.96 (0.74–1.23) | 0.26 (0.18–0.38) | 1.101 (0.22–4.62) |
| Not married anymore | 1.17 (0.94–1.46) | 1.20 (0.92–1.56) | 1.54 (0.97–2.43) | 1.04 (0.78–1.32) | 0.34 (0.22–0.53) | 0.50 (0.10–2.37) |
| Employment status | ||||||
| Unemployment | Reference | Reference | Reference | Reference | Reference | Reference |
| Employment | 0.94 (0.79–1.12) | 1.09 (0.88–1.34) | 0.85 (0.59–1.22) | 1.00 (0.79–1.26) | 0.90 (0.65 (1.26) | 1.34 (0.37–4.79) |
| Education status | ||||||
| <School and graduate school | Reference | Reference | Reference | Reference | Reference | Reference |
| Some college | 0.70 (0.55–0.90) | 1.50 (1.11–2.03) | 2.11 (1.17–3.79) | 0.95 (0.70–1.29) | 2.24 (1.38–3.64) | 1.75 (0.32–9.49) |
| Bachelors | 0.82 (0.68–0.99) | 0.99 (0.79–1.24) | 1.21 (0.83–1.77) | 1.02 (0.79–1.32) | 1.78 (1.25–2.53) | 1.25 (0.36–4.31) |
| Postbaccalaureate | 0.83 (0.69–0.99) | 1.02 (0.82–1.25) | 0.89 (0.84–1.24) | 1.14 (0.89–1.45) | 1.47 (1.09–1.98) | 3.26 (0.65–16.35) |
| Race | ||||||
| Non-Hispanic white | Reference | Reference | Reference | Reference | Reference | Reference |
| Non-Hispanic African American | 0.95 (0.77–1.17) | 1.94 (1.45–2.58) | 1.85 (1.11–3.08) | 0.70 (0.54–0.90) | 0.81 (0.55–1.21) | 2.93 (0.35–23.98) |
| Hispanic | 0.94 (0.77–1.16) | 1.13 (0.89–1.45) | 0.90 (0.62–1.32) | 0.96 (0.73–1.27) | 0.91 (0.65–1.28) | 1.23 (0.25–5.90) |
| Non-Hispanic Asian and other | 0.86 (0.66–1.12) | 1.35 (0.98–1.87) | 1.35 (0.80–2.29) | 1.21 (0.82–1.78) | 0.95 (0.62–1.45) | 0.59 (0.12–2.81) |
| Annual income | ||||||
| Less than 20,000 | Reference | Reference | Reference | Reference | Reference | Reference |
| 20,000 to <35,000 | 0.85 (0.62–1.15) | 1.16 (0.78–1.73) | 1.27 (0.60–2.67) | 1.42 (0.96–2.11) | 1.18 (0.62–2.25) | 3.41 (0.36–32.41) |
| 35,000 to <50,000 | 1.19 (0.88–1.60) | 0.83 (0.57–1.20) | 0.80 (0.41–1.54) | 1.18 (0.80–1.74) | 0.70 (0.39–1.24) | 3.58 (0.37–34.37) |
| 50,000 to <75,000 | 0.96 (0.72–1.28) | 0.86 (0.60–1.23) | 1.00 (0.53–1.90) | 1.28 (0.89–1.85) | 1.18 (0.66–2.10) | 2.67 (0.41–17.11) |
| Above 75,000 | 1.30 (0.99–1.71) | 0.59 (0.42–0.82) | 0.68 (0.37–1.23) | 0.99 (0.70–1.39) | 0.87 (0.51–1.49) | 1.97 (0.38–10.02) |
Bold: significant findings. Outcome variables adjusted with demographic variable.
OR, odds ratio; CI, confidence interval.
Married participants were significantly more likely to receive any genetic testing than single participants and less likely to obtain prenatal genetic testing. Respondents who were not married anymore (divorced/widow/separated) showed a significant negative association with prenatal genetic testing when compared with single. Some college-level education showed higher odds for ancestry, personal traits, and prenatal genetic testing, whereas others had lower significant odds for any genetic testing. Bachelor’s degree holders reported significantly lower odds for any genetic testing and higher odds for prenatal genetic testing. Respondents who postbaccalaureate reported significantly lower odds for any genetic testing and higher odds for prenatal genetic testing. Respondents who identified as non-Hispanic African American had significantly higher odds for ancestry and personal trait genetic testing, whereas they reported significantly lower odds for specific disease genetic testing.
It is important to note that behavior change is most appropriately associated with disease-specific genetic testing. Other test types, such as ancestry or personal trait testing, are less likely to yield information that would reasonably prompt behavioral modifications. Thus, to better understand the relationships between undergoing genetic testing and behavior change, we conducted additional analyses for specific disease risk only.
Predictors of Behavior Change in Response to Disease-Related Genetic Testing
We conducted a hierarchical binary logistic regression analysis to examine the predictors of behavior change following genetic testing for specific diseases (Table 5). This focus reflects the behavioral response to actionable disease risk information.
Table 5.
Predicting behavior change following genetic testing for a specific disease (N = 1,327)
| Variables | Model 1 | Model 2 | Model 3 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| B (standard error)a | ORb | χ2c | R 2d | B (standard error)a | ORb | χ2 | R 2d | B (standard error)a | ORb | χ2c | R 2d | |
| Block 1 | 85.04 | 0.11** | 265.22 | 283.87 | ||||||||
| Gendere | ||||||||||||
| Female | −0.15 (0.18) | 0.86 | 0.65 | −0.33 (0.21) | 0.71 | 2.42 | −0.36 (0.21) | 0.69 | 2.82 | |||
| Agee | ||||||||||||
| 35–49 years | 0.66 (0.32)* | 1.93 | 4.12 | 0.67 (0.36) | 1.95 | 3.42 | 0.71 (0.36) | 2.03 | 3.74 | |||
| 50–64 years | 0.95 (0.31)** | 2.59 | 8.89 | 0.95 (0.36) | 2.59 | 6.95 | 0.94 (0.36)* | 2.57 | 6.63 | |||
| 65–74 years | 0.96 (0.38)* | 2.61 | 6.33 | 1.04 (0.43) | 2.84 | 5.67 | 0.97 (0.44)* | 2.63 | 4.77 | |||
| Above 75 years | 1.22 (0.44)** | 3.39 | 7.50 | 1.28 (0.51) | 3.61 | 6.17 | 1.23 (0.52)* | 3.43 | 5.57 | |||
| Marital statuse | ||||||||||||
| Married | −0.37 (0.23) | 0.69 | 2.59 | −0.38 (0.25) | 0.68 | 2.20 | −0.48 (0.26) | 0.61 | 3.40 | |||
| Not married anymore | −0.34 (0.26) | 0.70 | 1.75 | −0.36 (0.29) | 0.69 | 1.56 | −0.35 (0.29) | 0.70 | 1.44 | |||
| Employment statuse | ||||||||||||
| Employment | 0.19 (0.22) | 1.21 | 0.70 | 0.24 (0.25) | 1.27 | 0.88 | 0.15 (0.25) | 1.16 | 0.34 | |||
| Education statuse | ||||||||||||
| Some college | 0.67 (0.28)* | 0.02 | 5.35 | 0.70 (0.32) | 2.02 | 4.68 | 0.70 (0.32)* | 2.02 | 4.58 | |||
| Bachelors | 0.23 (0.24) | 0.34 | 0.91 | 0.35 (0.27) | 1.42 | 1.69 | 0.37 (0.27) | 1.45 | 1.90 | |||
| Postbaccalaureate | 0.34 (0.23) | 0.15 | 2.06 | 0.36 (0.26) | 1.43 | 1.85 | 0.34 (0.37) | 1.40 | 1.60 | |||
| Racee | ||||||||||||
| Non-Hispanic African American | 1.36 (0.22)** | 3.91 | 37.69 | 1.27 (0.25) | 3.56 | 25.62 | 1.22 (0.25)** | 3.41 | 23.50 | |||
| Hispanic | 0.77 (0.24)** | 2.17 | 10.10 | 0.80 (0.27) | 2.23 | 8.73 | 0.75 (0.27)** | 2.12 | 7.43 | |||
| Non-Hispanic Asian and other | 0.59 (0.33) | 1.81 | 3.25 | 0.51 (0.36) | 1.67 | 1.97 | 0.51 (0.37) | 1.67 | 1.90 | |||
| Annual incomee | ||||||||||||
| 20,000 to <35,000 | −0.32 (0.34) | 0.72 | 0.89 | −0.03 (0.39) | 0.96 | 0.00 | 0.01 (0.39) | 1.01 | 0.00 | |||
| 35,000 to <50,000 | −0.72 (0.36)* | 0.48 | 4.01 | −0.48 (0.40) | 0.61 | 1.45 | −0.41 (0.40) | 0.65 | 1.06 | |||
| 50,000 to <75,000 | −0.08 (0.32) | 0.91 | 0.07 | 0.31 (0.36) | 1.36 | 0.71 | 0.37 (0.37) | 1.44 | 1.00 | |||
| Above 75,000 | −0.62 (0.31)* | 0.53 | 3.92 | −0.42 (0.36) | 0.65 | 1.39 | −0.32 (0.36) | 0.72 | 0.77 | |||
| Block 2 | 0.34** | |||||||||||
| Reasons to get genetic testingf | ||||||||||||
| Doctor’s recommendation | 1.23 (0.21) | 3.44 | 32.88 | 0.92 (0.22)** | 2.52 | 16.91 | ||||||
| Understanding family | −0.58 (0.26) | 0.56 | 4.92 | −0.30 (0.27) | 0.73 | 1.26 | ||||||
| Finding family | −0.44 (0.36) | 0.64 | 1.46 | −0.30 (0.38) | 0.73 | 0.63 | ||||||
| Personal trait | 0.17 (0.32) | 1.19 | 0.28 | 0.28 (0.33) | 1.32 | 0.72 | ||||||
| Risk to develop disease | 1.26 (0.20) | 3.55 | 39.15 | 1.15 (0.20)* | 3.17 | 31.53 | ||||||
| Learn strategies | 1.75 (0.34) | 5.79 | 26.35 | 1.76 (0.35)* | 5.82 | 25.24 | ||||||
| Prenatal | −0.49 (0.28) | 0.61 | 2.99 | −0.61 (0.28)* | 0.54 | 4.60 | ||||||
| Received a gift | −1.51 (0.74) | 0.22 | 4.11 | −1.39 (0.75) | 0.24 | 3.40 | ||||||
| Block 3 | 0.36** | |||||||||||
| Confidentg: somewhat | −0.04 (0.42) | 0.96 | 0.00 | |||||||||
| Completely/very | 0.21 (0.40) | 1.23 | 0.28 | |||||||||
| Seek help for understandingh | 1.31 (0.34)** | 3.72 | 14.95 | |||||||||
Outcome variable change in behavior in response to any genetic testing (no) serve as a reference category.
aEstimated regression (beta) coefficients and standard error and so on for effects when they were entered into the models.
bEstimated regression (odds ratio) coefficients and so on for effects in the models.
cLikelihood ratio chi-square test statistics are reported for tests of blocks and models; Wald chi-square test statistics are reported for tests of predictors.
dNagelkerke R Square.
eGender (male), age (18–24), marital status (single), employment status (unemployment) education (< school and high school), race (non-Hispanic white), and annual income (<USD 20,000) were considered as reference.
fReasons to get genetic testing (no) serve as reference category.
gConfident about test accuracy (not at all/little bit) coded as reference.
hSeek help for understanding (no) serve as a reference category.
*p < 0.05.
**p < 0.01.
In block 1, demographic variables were entered and compared to the null model. The model was statistically significant, χ2 (18) = 82.04, p < 0.01, explaining 11% of the variance in behavior change (Nagelkerke R2) and correctly classifying 87.3% of cases. The Hosmer-Lemeshow test indicated a good model fit, χ2 (8) = 5.59, p = 0.69. Significant positive predictors included age above 24 years, having some college education, and identifying as non-Hispanic African American or Hispanic. In contrast, having an annual income between USD 20,000 and USD 34,999 or above USD 75,000 was negatively associated with behavior change.
In block 2, we added participants’ reasons for undergoing disease-specific genetic testing. Compared to the block 1 model, this block significantly improved model fit, Δχ2 (8) = 183.18, p < 0.01, with a total model chi-square of χ2 (26) = 265.22, p < 0.01. The variance explained increased to 34%, and classification accuracy improved slightly to 88.4%. The Hosmer-Lemeshow test continued to support a good fit, χ2 (8) = 8.15, p = 0.41. Significant positive predictors of behavior change included testing due to a doctor’s recommendation, perceived risk of developing a genetic condition, and a desire to learn strategies to manage one’s health. Testing for family history or receiving a genetic test as a gift were negatively associated with behavior change. Demographic predictors such as age above 24 years, some college education, and identifying as non-Hispanic African American or Hispanic remained significant.
In block 3, we included confidence in the accuracy of genetic testing and whether participants sought help interpret their results. This addition significantly improved the model fit compared to block 2, Δχ2 (3) = 18.65, p < 0.01, with a final model chi-square of χ2 (29) = 283.87, p < 0.01. The model explained 36% of the variance in behavior change and correctly classified 88.6% of cases. The Hosmer-Lemeshow test confirmed a good fit, χ2 (8) = 12.45, p = 0.13. Among the new predictors, only seeking help understand the genetic test results was significantly associated with increased likelihood of behavior change. Confidence in test accuracy was not a significant predictor. Reasons for testing – doctor’s recommendation, perceived disease risk, and learning strategies – remained strong positive predictors, and the same demographic predictors continued to show significance.
Discussion
Our study, utilizing a robust sample of 4,631 respondents, revealed several important insights. A substantial majority (81.6%) reported being aware of genetic testing, and 28.7% had undergone some form of it. Among those who had undergone genetic testing (n = 1,327), only 216 individuals (16.3%) reported making any behavioral changes in response to their test results. This finding highlights a critical gap and suggests the need for further investigation into the extent to which genetic testing influences health-related behavior change.
In response to research question 1, we found that 81.1% of participants were aware of genetic testing – a figure notably higher than those reported in studies conducted in other contexts, including Rwanda (29.5%) and prior US-based analyses of HINTS cycles 1 and 5, where awareness ranged from 57% to 75%. [15–17]. Interestingly, only one study from Malaysia reported levels of awareness (79.8%) similar to our findings [18]. Although the HINTS cycles 1 and 5 studies were conducted in similar national contexts, they were based on smaller sample sizes (n = 3,484 and 3,568, respectively), which may limit the generalizability of their findings. The inclusion of global comparisons, such as those from Rwanda and Malaysia, is not intended as a direct equivalence but to underscore the substantial variability in awareness across diverse healthcare systems and cultural settings. These findings suggest a promising upward trend in public awareness of genetic testing, particularly in the USA and highlight the growing relevance of specific forms of testing – such as ancestry testing, which was the most widely recognized type among our participants.
Similar to findings from HINTS cycle 5 [15] and a study conducted in Philadelphia [19], the current results show that postbaccalaureate education and higher annual income are significant positive predictors of awareness of general genetic testing. However, the relationship between gender and awareness yielded mixed results. While the Philadelphia study found a significant positive relationship between gender and awareness, other studies did not show any significant association [15, 18]. This discrepancy could be due to the inclusion of additional factors, such as a history of cancer and medical literacy, in some studies. Differences in sample size might also explain this variation. Additionally, race other than non-Hispanic white was negatively associated with awareness of genetic testing, consistent with prior research [20, 21]. This suggests that disparities in awareness persist among minority ethnic groups, possibly due to the predominance of non-Hispanic white participants in research studies and lower levels of participation and interest among minority groups.
Interestingly, individuals identifying as races other than non-Hispanic white demonstrated significantly higher awareness of specific types of genetic testing – an observation that contrasts with earlier findings from the 2020 HINTS survey, where non-Hispanic white respondents were more likely to report genetic testing awareness. Additionally, higher annual income was negatively associated with awareness of specific types of genetic testing in our analysis, which also diverges from previous HINTS findings showing a positive relationship between income and awareness [15]. These discrepancies may reflect evolving patterns of awareness or shifting population dynamics since the earlier survey cycles. Differences in sample size, the reclassification of independent variables, and the inclusion of additional predictors in the multivariable regression models may also account for these variations. Moreover, community-based initiatives have played a pivotal role in raising awareness among minority groups. Collaborative efforts between public health organizations and community leaders have led to the development of culturally tailored educational resources, which have been effective in improving genetic literacy and awareness in these communities [22]. Despite these differences, the strong positive association between educational attainment and awareness of specific types of genetic testing remains consistent with prior research, reinforcing the critical role of education in shaping public understanding of genetic technologies [15, 23].
In response to research question 2, we examined the proportion of individuals who underwent genetic testing and identified its predictors. While existing literature has documented adequate awareness of genetic testing, few studies have assessed actual screening behavior, with most focusing on individuals’ willingness to undergo genetic testing once aware [2, 24]. A previous HINTS survey [15] found that only 19% of respondents had undergone genetic testing, which is significantly lower than the 28.7% reported in our study. This suggests that the likelihood of individuals in the USA opting for genetic testing has increased over time.
In contrast to previous HINTS survey findings [15], this study found that participants who identified as female and non-Hispanic African American were not associated with ancestry testing additionally, showed a negative association with awareness of specific disease-related genetic testing. However, education levels demonstrated a consistent positive association with ancestry testing, aligning with the earlier HINTS survey. The divergence in findings could be attributed to the current study’s inclusion of a limited range of predictors. In contrast, the previous research focused on changes related to participants’ personal and family histories of cancer and genetic diseases.
To address research question 3, findings revealed that among individuals who underwent genetic testing for specific diseases, those who perceived themselves to be at high risk and demonstrated a strong motivation to change were more likely to report behavioral changes following their test results. This pattern aligns with findings from previous studies, which suggest that perceived risk and motivation are key drivers of health-related behavior change in response to genetic risk information [14, 25–27]. Genetic literacy refers to having adequate knowledge and understanding of genetic principles, enabling individuals to make informed decisions that promote personal well-being and allow for meaningful participation in societal discussions about genetic issues [28]. Individuals with high genetic literacy can comprehend their genetic testing results, effectively communicate with healthcare providers about genetic testing options, and make informed decisions regarding their risk for gene-related diseases [28, 29]. In line with this, the current study found a positive association between seeking help understand specific types of genetic testing and behavior change, consistent with findings from a previous study conducted with patients undergoing genetic testing for diabetes [14]. This highlights the crucial role of genetic literacy in improving individuals’ understanding and promoting behavior changes based on genetic testing results.
The novel findings of the current study regarding behavior change reveal critical differences between general awareness, genetic testing, and behavior change across racial groups. Non-Hispanic white participants were found to be more aware of and more likely to undergo genetic testing. In contrast, participants from racial groups other than non-Hispanic whites were more likely to exhibit behavior changes following testing. Specifically, there was a positive association between Hispanic participants and ancestry testing, non-Hispanic African American and Hispanic participants with specific disease testing, and non-Hispanic Asian participants with prenatal genetic testing. The fact that racial minorities were more likely to change their behavior post-testing, despite lower overall testing awareness, suggests that these groups may place greater importance on actionable health information when it becomes available. This aligns with literature on health disparities, where historically marginalized groups may be more responsive to health interventions due to previous underutilization of healthcare services.
The study observed a significant increase over time in both awareness and the proportion of people undergoing genetic testing. Awareness and genetic screenings were especially high for ancestry tests compared to other types of genetic testing. These findings underscore the need for targeted campaigns to raise awareness about the importance of various genetic tests and to address barriers that limit access to genetic testing services. The small number of participants reporting behavior changes after receiving their genetic testing and its association with seeking help understand suggests a gap between receiving information and translating it into actionable health behaviors. This may indicate the need for better communication strategies to help individuals understand how genetic testing results can inform lifestyle changes or medical decisions [14, 30]. Post-test counseling and follow-up services should be integrated into genetic testing frameworks to help individuals interpret and apply results effectively. Considering disparities, this study also advocates for enhancing participation in genetic testing and emphasizes the importance of continued efforts by researchers to develop strategies that effectively engage underrepresented populations in research surveys.
Limitation
This study offers valuable insights into the level of awareness regarding genetic testing, sources of information influencing testing behavior, and associated behavioral changes. However, several limitations should be acknowledged when interpreting the findings. First, cross-sectional design limits the ability to draw causal inferences, as associations observed may not reflect temporal relationships. Separately, the use of self-reported data introduces the possibility of social desirability bias, wherein participants may respond in ways they believe are more socially acceptable, as well as recall bias, given the potential difficulty in accurately remembering past behaviors or decisions. Another important limitation involves sample bias. The analytic sample was predominantly composed of female, white, older adults with higher income and education levels, limiting the generalizability of findings to men, racial/ethnic minorities, and individuals from lower socioeconomic backgrounds. Although the large sample size enabled detailed subgroup analysis, the need to collapse certain demographic categories due to small cell sizes reduced the granularity of the analysis, potentially obscuring important differences between subgroups. Additionally, the use of multiple statistical tests across various models raises the possibility of type I error (false positives). While these analyses were guided by research questions, we acknowledge that multiple testing increases the likelihood of identifying statistically significant results by chance. Despite these limitations, the findings contribute to a growing body of literature on public engagement with genetic testing. Future research employing longitudinal designs and more representative sampling strategies is recommended to strengthen the validity and generalizability of results.
Conclusion
Awareness of genetic testing and the number of individuals opting for such testing have grown over time; however, a smaller proportion of participants indicated a willingness to modify their behavior based on test results. Awareness and participation in genetic testing were more prevalent among females, those with higher education, and individuals with higher incomes. In contrast, behavior change was more frequently observed among racial groups other than non-Hispanic white individuals. These findings, particularly regarding the need for assistance in understanding genetic testing results and subsequent behavior changes, underscore the importance of targeted interventions aimed at improving genetic literacy to foster actionable health responses across diverse populations.
Statement of Ethics
Ethical approval and consent were not required as this study was based on publicly available data.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
This study was not supported by any sponsor or funder.
Author Contributions
Athar Memon: conceived, designed, conducted statistical analysis, methodology, discussion, supervision, and final approval of the manuscript. Hiba Hamid: designed, introduction, and editing of the manuscript. Ayesha Mehboob: discussion, conclusion, and statistical analysis. Muhammad Ovais: statistical analysis and data interpretation. Zahid Wali: abstract and discussion. Emma Khayat-Mishne: introduction.
Funding Statement
This study was not supported by any sponsor or funder.
Data Availability Statement
The data that support the findings of this study are openly available in National Cancer Institute, Health Information National Trends Survey (HINTS), at https://hints.cancer.gov/data/download-data.aspx#H6 [HINTS 6 (2022) dataset, updated May 2024].
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are openly available in National Cancer Institute, Health Information National Trends Survey (HINTS), at https://hints.cancer.gov/data/download-data.aspx#H6 [HINTS 6 (2022) dataset, updated May 2024].
