Abstract
Objective.
This study investigated how self-reported numeracy ability and preferences predict preferences for the amount and types of information provided about genome sequencing results among 1,080 women diagnosed with breast cancer at age 40 or younger.
Methods.
Participants reported their level of interest in 14 topics related to genome sequencing results on a survey. We calculated a Participant Information Needs (PIN) value based on the number of topics for which a participant wanted “a lot” of information. Numeracy was assessed using the Subjective Numeracy Scale. Analyses examined associations between the numeracy ability and preferences subscales, information needs for individual content topics, and PIN.
Results.
Higher preference for numeric data was correlated with increased PIN (β=0.60, p<0.01), while numeric ability was not correlated (β=0.16, p=0.22). Family composition and knowledge about sequencing benefits were also significant covariates. Patients most preferred information on topics related to disease risk and health implications.
Conclusion.
There may be utility in separating numeracy ability and preferences into two components in future research in order to investigate how numeracy impacts the return of genetic testing results.
Practice Implications.
These data suggest that numeracy preferences may be important to inform strategies for the return of genetic results.
1. Introduction
Effective healthcare delivery involves not only the development and administration of diagnostic tests and therapies but the personalized communication of medical information generated by these technologies from providers to patients. The effectiveness of patient-provider communication depends on both provider competency and the health literacy skills of the patient [1]. Health numeracy is a key component of patient health literacy and is defined as ‘the degree to which individuals have the capacity to access, process, interpret, communicate, and act on numerical, quantitative, graphical, biostatistical, probabilistic health information needed to make effective health decisions’ [2, 3]. Numeracy encompasses not only a patient’s competencies or skill but their preferences for how quantitative information is presented to them [4]. The patient’s ability to understand and reason through mathematical concepts is critical when they are presented with data regarding disease risk and prevention, prognoses, and the efficacies or risks of therapies and procedures. When patients understand quantitative data, their medical decisions may be more evidence-based. While numeracy is largely considered as a skill associated with mathematic assessment, patient preference for numeric data has also been shown to be positively correlated with objective numeracy [5]. As healthcare models shift their focus to shared decision making between patients and providers, understanding how to implement this patient preference is an important consideration.
The 2017 Program for the International Assessment of Adult Competencies (PIAAC) found that only 12% of U.S. adults scored in the highest literacy proficiency levels, and only 9% scored in the highest numeracy proficiency levels with lower average literacy and numeracy in underserved populations [6]. These concerning findings are a challenge to the data-rich field of genetics and genomics where information is not only highly probabilistic but often requires understanding of abstract concepts of biology. Understanding the genetic basis of common diseases is hindered by their multifactorial nature, with attributable genetic risk being only a portion of total risk, complicating a patient’s understanding of their own risk, prevention, and intervention [7, 8]. Furthermore, with increasing access to direct-to-consumer genetic testing, patients are uncovering more questions about genetic identity and disease risks outside of clinical guidance or recommendations [9, 10]. The return of these various genetic testing results is quickly becoming a ubiquitous feature of healthcare delivery. However, despite the likely importance of numeracy in communication of genetic or genomic information, few research studies have investigated this area. In a scoping review of 513 papers examining communication of cancer genetic and genomic information with patients and the public that were published from 2010–2017, we found that only 21 (4%) assessed or described numeracy [11]. Of these, only 3 papers examined numeracy preferences. Research is therefore critically needed regarding how both numeracy ability and preferences impact communication processes and outcomes in the context of genetics and genomics.
Genome sequencing can provide a patient with various types of results, each with associated clinical or biologic information. The information available for each result may include a variant’s effect on disease risk or treatment, the biological function of the gene, laboratory sequencing methods, or current and evolving research around the variant. There is a body of research suggesting what types of results patients would like returned [12]. However, few studies have investigated what information a patient would prefer to receive about each result. One study in oncology showed that patient preferences for molecular testing centered around topics related to clinical treatment, family implications, and the severity of disease [13]. In another study of patients diagnosed with Lynch Syndrome, nearly all patients reported a strong preference for the return of whole exome sequencing delivered by expert clinicians, believing benefits outweighed undesirable effects [14]. No prior studies have considered how a patient’s numeracy preferences or ability affects their preferences for information provided about a genome sequencing result.
In this study, we investigated the impact of numeracy on preferences for the type and amount of information desired regarding a genome sequencing result among women diagnosed with breast cancer at a young age. We examined self-reported numeracy ability and preferences and how these were related to preferences for various types of information associated with genome sequencing results among 1,080 women diagnosed with breast cancer at a young age. The results of this study help to shed light on how patient numeracy could affect strategies for communicating results generated by sequencing technologies.
2. Methods
2.1. Study Population and Survey Procedures
The survey procedures have been published previously [15]. In brief, study participants were women diagnosed with breast cancer at the age of 40 or younger recruited from a national cohort (Young Women’s Breast Cancer Program; YWBCP). In total, a 60% response rate yielded 1080 participants who elected to participate in the study via survey completed online, by mail, or by phone with 91% completing the online version. All participants reviewed a consent information sheet or were read information by phone before giving consent to participate. Participants received a $10 gift card in appreciation for their time. The data collection for this study was approved by the Washington University in St. Louis review board with secondary data analysis approved by the University of Utah institutional human subjects review board.
2.2. Measures
2.2.1. Content Topics of Interest
The outcome variable was participants’ interest in various content topics that could be provided regarding a result generated by genome sequencing. Each participant was asked how much information they would prefer to receive about fourteen different topics related to a genome sequencing result (i.e. “If you were to learn about a gene variation that you carry, how much information would you want regarding…?”), adapted from Lillie et al [17]. Participants reported their interest related to each content topic on a four-point scale ranging from “None” to “A lot”. We created an “a lot” indicator variable for each item. For each participant, we calculated a Participant Information Needs (PIN) value ranging from 0–14, comprised of the number of topics for which a participant wanted “a lot” of information.
2.2.2. Numeracy Ability and Preferences
Participant numeracy was assessed using a validated eight-item subjective numeracy scale comprised of two four-item subscales [5]. One subscale related to perceived ability to perform mathematical tasks (i.e. “How good are you at working with percentages?”) with the second subscale related to preferences toward numeric versus verbal information (i.e. “When people tell you the chance of something happening, do you prefer that they use words or numbers?”). Responses were given on a six-point Likert-type scale (e.g. “not at all good” to “extremely good” and “always prefer words” to “always prefer numbers/percentages”). Average scores were generated for each subscale and treated as continuous variables in analysis. Higher subscale scores are representative of higher perceived ability and stronger preferences for numeric information.
2.2.3. Covariates
Covariates were selected based on factors shown or hypothesized to be related to information preferences in our work and other prior research described above [15, 16]. We collected data from the YWBCP database regarding participant age, BRCA1/2 genetic testing and mutation status, and family history of breast cancer. In the survey, we assessed participant knowledge about genomic sequencing (benefits and limitations subscales), [18] whether they had prior genetic testing or a formal diagnosis of a genetic condition or more than one primary cancer. We also gathered information via survey regarding family composition including having biological children, biological siblings, and living parents, and sociodemographic characteristics including age at diagnosis, race/ethnicity, educational attainment, and marital status.
2.3. Analysis
We conducted analyses using SAS/STAT version 9.4 (SAS Institute, Inc., Cary, NC) and R (R Core Team, 2017). We examined descriptive statistics for all variables. We conducted bivariate analyses between PIN and the numeracy subscales and covariates using analysis of variance and correlation coefficients. We then examined bivariate associations between the numeracy subscales and each content topic, based on whether participants wanted “a lot” of information for the topic vs. any other amount. We then created a multivariable linear regression model to examine prediction of PIN by the numeracy subscales. In the multivariable model, we controlled for those covariates that had a bivariate association with p < .10 [19], and Akaike information criterion was used to determine the best multivariable model [20]. Time since diagnosis was not tested for entry into the model as this variable covaried with age at diagnosis and age at survey completion. Statistical significance was assessed as p < .05.
3. Results
3.1. Participant Characteristics
As shown in Table 1, the average age when completing the survey was 46 years, and mean age at diagnosis was 35 years. The majority of participants were Caucasian (96%). Most participants were married or living as married (78%). Participants reported their family composition; 93% reported having biological siblings, 69% reported biological children, 64% with a living father and 77% with a living mother. About 44% had some graduate education.
Table 1.
Survey respondent characteristics (n=1080)
| Characteristic | M (SD) | Range |
| Current age | 45.9 (9.1) | 26–82 |
| Age at diagnosis | 35 (4.2) | 21–40 |
| Numeracy ability | 5.0 (1.1) | 1–6 |
| Numeracy preferences | 4.9 (0.9) | 1–6 |
| Knowledge of genome sequencing benefits | 6.1 (2.2) | 0–10 |
| Knowledge of genome sequencing limitations | 5.0 (2.2) | 0–8 |
| N | % | |
| Caucasian | 1,037 | 96 |
| Some graduate school | 481 | 45 |
| Married/living as married | 839 | 78 |
| Have biological children | 740 | 69 |
| Have biological siblings | 999 | 93 |
| Living mother | 831 | 77 |
| Living father | 688 | 64 |
| BRCA1/2 mutation carrier | 118 | 11 |
| Prior genetic testing | 899 | 83 |
| Strong family history of breast cancer | 303 | 28 |
| More than one primary cancer | 156 | 15 |
About 15% had more than one primary cancer; most second primary cancers were a second breast cancer. A majority of participants had prior genetic testing (83%). Participants had a mean score of 6.1 on knowledge of the benefits of genome sequencing and 5.0 on knowledge of genome sequencing limitations. There were 11% of participants who carried a deleterious BRCA1/2 gene mutation. A strong family history of breast cancer was reported by 28% of participants. The mean score for participant numeracy ability was 5.0 while numeracy preferences was 4.9.
3.2. Content Topics
The percentages of participants interested in receiving “a lot” of information about each topic are shown in Table 2. Participants reported interest in a mean of 8 topics with a standard deviation of 4. Overall, participants were most interested in the health implications of the variant such as for prevention or treatment (86%) and the effect of the gene variant on disease risk for self (79%) and family (75%). Other topics for which participants wanted a lot of information were the certainty of the result (74%) and the accuracy of the laboratory performing the test (64%). The content topics for which the fewest participants wanted a lot of information were technical information such as how genome sequencing is done (22%), the prevalence of the variant in the U.S. (30%), or when the variant was identified (31%).
Table 2.
Percentage of participants interested in “a lot” of information regarding topics related to genome sequencing results (n=1080).
| Information Topic | % of Participants |
|---|---|
| Implications for prevention and treatment | 86% |
| Effect of gene variant on disease risk | 79% |
| Effect of gene variant on disease risk for family | 75% |
| Certainty of results | 74% |
| Information about disease/condition | 69% |
| Current research about variant | 65% |
| Accuracy of lab | 64% |
| Prevalence of variant in young breast cancer patients | 54% |
| Cause of variant | 45% |
| How gene works in the body | 44% |
| Cost of having genome sequenced | 41% |
| When the variant was identified | 31% |
| Prevalence of variant in the US | 30% |
| How sequencing is done | 22% |
3.3. Relationship between Information Needs and Numeracy
In bivariate analysis of the associations between the numeracy subscales and individual content topics (Table 3), those with higher numeracy ability were significantly more likely to say that they wanted “a lot” of information on nine of the fourteen topics examined. These topics included the effect of the variant on individual and family disease risk and implications for treatment as well as information about the disease of interest, current research regarding the variant, variant frequency in women with breast cancer, gene function, lab accuracy, and test certainty. We also found that those with higher numeracy preferences were significantly more likely to say that they wanted “a lot” of information on twelve of the fourteen topics. In addition to the topics mentioned above, there was significantly higher interest in topics related to cause of the variant, history of gene identification, and prevalence of the variant in the US population.
Table 3.
Bivariate associations between subjective numeracy subscales and participant information needs related to gene variant content topic items
| Question | Ability (mean) | Subscale | p-value* | Preference (mean) | Subscale | p-value* |
|---|---|---|---|---|---|---|
| A lot | Other categories | A lot | Other categories | |||
| How much the gene variation affects your risk of a disease | 5.1 | 4.6 | <0.001 | 5.1 | 4.5 | <0.001 |
| How the gene variation affects the risk of a disease for your family members | 5.1 | 4.7 | <0.001 | 5.1 | 4.7 | <0.001 |
| What the gene variation means for prevention or treatment of disease | 5.0 | 4.6 | <0.001 | 5.0 | 4.5 | <0.001 |
| Information about the disease or condition related to the gene variation | 5.1 | 4.8 | 0.001 | 5.1 | 4.7 | <0.001 |
| What current research shows about the gene variation | 5.1 | 4.8 | <0.001 | 5.1 | 4.7 | <0.001 |
| What caused the variation | 5.0 | 5.0 | 0.49 | 5.0 | 4.9 | 0.019 |
| When the gene variation was identified | 5.0 | 5.0 | 0.66 | 5.0 | 4.9 | 0.015 |
| How the gene works in the body | 5.1 | 4.9 | 0.032 | 5.0 | 4.9 | 0.001 |
| How common the gene variation is in the U.S. | 5.1 | 4.9 | 0.080 | 5.0 | 4.9 | <0.001 |
| How common the gene variation is among women diagnosed with breast cancer ≤ 40 | 5.1 | 4.9 | <0.001 | 5.0 | 4.9 | <0.001 |
| How sequencing is done | 5.0 | 5.0 | 0.36 | 5.0 | 4.9 | 0.16 |
| How much it costs to have your genome sequenced | 5.0 | 5.0 | 0.75 | 5.0 | 4.9 | 0.097 |
| How accurate the lab is in the sequencing process | 5.0 | 4.9 | 0.002 | 5.0 | 4.8 | <0.001 |
| How certain the meaning of the results are | 5.0 | 4.8 | 0.003 | 5.0 | 4.7 | <0.001 |
p-value by Wilcoxon Rank Sum Test
The multivariable linear regression model with prediction of the PIN by the numeracy subscale scores is shown in Table 4. We found that numeracy preference was positively associated with PIN (β=0.60, Standard Error (SE)=0.15, p<0.01). However, perceived numeracy ability was not significantly associated with PIN (β=0.16, SE=0.13, p=0.22). For the covariates, we observed that knowledge of genome sequencing benefits was positively associated with PIN (β=0.37, SE=0.06, p<0.01). Covariates related to family composition were also found to be significant. Having biological siblings was positively associated with PIN (β=0.99, SE=0.48, p=0.04), while participants having a living mother was negatively associated with PIN (β=−1.04, SE=0.30, p<0.01). Clinical variables such as BRCA1/2 mutation status and diagnosis with a genetic condition were not significantly associated with PIN.
Table 4.
Multivariable linear regression model for content topic score
| Variable Name | β | SE | p-value |
|---|---|---|---|
| Numeracy – ability | 0.16 | 0.13 | 0.22 |
| Numeracy – preferences | 0.60 | 0.15 | <0.01 |
| Knowledge about genome sequencing benefits | 0.37 | 0.06 | <0.01 |
| Positive BRCA mutation status | −0.47 | 0.49 | 0.34 |
| Unknown BRCA mutation status | −2.07 | 2.38 | 0.38 |
| Have a genetic condition | −1.46 | 2.39 | 0.16 |
| Have biological siblings | 0.99 | 0.48 | 0.04 |
| Mother living | −1.04 | 0.30 | <0.01 |
4. Discussion and Conclusion
4.1. Discussion
Our study found that among over 1,000 women diagnosed with breast cancer at a young age, participant preference for numeric data was positively associated with an interest in a higher number of content topics related to genome sequencing results. These findings were in contrast to self-reported numeric ability, which was not associated with PIN. We also found that knowledge about genome sequencing benefits and having biological siblings was positively associated with PIN while having a living mother was negatively associated.
Few research studies have examined how numeracy is related to genetic communication processes or outcomes. In the small group of existing studies, most researchers have focused on measures of objective or subjective numeric abilities, but few have examined how numeric preferences affect decision making [21, 22] and research on how numeric preferences affect information needs is lacking. Numeracy is often assessed as a combined measure that encompasses both reported ability and preferences of participants. These data suggest there may be utility in separating these two components using a scale such as the one utilized here with subscales for ability and preferences.
The findings presented here suggest that while a patient may feel comfortable with their skills related to understanding and working with numeric data, this does not necessarily mean they desire to gather a great deal of genetic information to assist them in making healthcare decisions. This information seeking may be based more in patient personality, health history, values, or other psychosocial factors [23]. Previous research has shown that the importance individuals place on health information is associated with seeking health information [24]. We suggest that, in practice, patients with higher preferences for numbers and close family relationships may find value in more detailed information on topics related to genetic testing, while those with lower numeracy preferences may find value in more focused discussions with data presented mainly for personal health or decision-making implications.
It is possible that some individuals may believe that numeric data is an important tool for making decisions, independent of their perceived numeric abilities [25, 26, 27]. This may explain why a stronger preference for numeric data was associated with an increased information need. However, in our bivariate analysis we observed that this difference in need was also related to non-numeric topics such as gene function and historical discovery, which may relate to an interest in scientific topics and data generally. Because the vast majority of participants in this sample had had prior genetic testing, they may have had previous experience with numeric presentations of genetic risk. Those individuals with stronger preferences for verbal information may have felt that this information was less useful for their decision making. Future research should utilize separate numeracy subscales for ability and preferences in order to help investigate the contributions of these related but different factors to preferences for genetic communication, including decision making. Additionally, future research should help to develop and test practical methods or techniques for communicating genome sequencing results to patients with low numeracy preferences or abilities. The need to understand these topics will only grow as genomic testing results become an increasingly ubiquitous component of healthcare delivery and patient decision making [28].
We found that covariates related to family relationships contribute to differences in information need for genome sequencing results. In participants with living biological siblings there was an increased need for more genetic information. Conversely, participants with living mothers had markedly decreased information needs. These findings may reflect differences in how participants view ownership and responsibility of genetic information within a family unit [29, 30]. A negative test result in a child could influence the care or perceived risks for a living mother. In those with a living mother, there may be some drive to avoid concern, fear, or preoccupation with risk and a feeling that the decision to obtain this information is outside their stewardship within the family [31]. The influence of living siblings may not carry this weight if those relationships are perceived as more equal in terms of responsibility and proximity [32]. Another possibility is that persons who have a deceased mother are more cognizant of the utility of preventative screening such as genetic sequencing that may prevent fatal disease. This may lead to an increased need for information related to preventative care and clinical decision making. Future research may consider how parental age, number of siblings, birth order of siblings, or other aspects of family composition affect these preferences and decisions.
The findings from this study should be considered in light of its limitations. This sample of breast cancer patients had all been diagnosed at age 40 or younger and the findings likely do not generalize to all breast cancer patients, in part because of the greater likelihood of a gene mutation and greater familiarity with genetic testing and the prior receipt of genetic information among the former group. Participants were not offered sequencing as a part of this study and most were a number of years past their cancer diagnosis. While we reported the percentage of participants who had received prior genetic testing, future studies may also investigate the relationship between information preferences and type of genetic testing obtained by participants as well as when participants received this testing. Women diagnosed with breast cancer at a young age today may express different preferences at the time of sequencing than the women in this study. However, being generally past the time of diagnosis and active treatment allowed the participants to reflect on what types of information about sequencing results might be most useful. Future studies may also explore the effect of disease stage on preferences. Participants were mainly Caucasian with high educational attainment and it is critical to examine how race, ethnicity, and socioeconomic status may affect these preferences [33, 34].
4.2. Conclusion
Numeracy has typically been considered as a combined measure that encompasses both reported ability and preferences of participants. Our data suggest that there may be utility in separating these two components in future research in order to fully understand how numeracy impacts the return of genetic testing results, patient education, and shared decision making in healthcare. Preference for numeric information was positively correlated with an increased interest in more information about genetic test results, while numeric abilities had no significant correlation. Family relationships also impact the amount of information that individuals desire regarding genetic testing results.
4.3. Practice Implications
We observed that participants preferred to receive information from content topics related to immediate clinical decision making, such as implications for treatment and prevention, effect of variant on disease risk, and the disease risk to family members. This finding may seem intuitive but it highlights the importance of focusing on these types of information in patient education. Our findings also indicate that increased knowledge about the benefits of genome sequencing, such as uncovering actionable or heritable variants, is associated with higher needs for genome sequencing information. This is consistent with findings from other genetic contexts that have shown that higher knowledge and educational attainment contributes to lower preference for the return of results seen as less clinically actionable [9]. Future research may further consider what types of genetics knowledge or education affect these information needs and how this knowledge influences decision making after genetic testing. Clinicians and science educators should consider avoiding involved discussions of sequencing methods, variant prevalence, gene function, and scientific history. These topics may be of professional interest to clinicians and scientists but of more limited concern to patients facing difficult decisions related to a new diagnosis. These findings also are important for clinical practice where patient numeracy preferences and abilities are likely not routinely assessed prior to focused discussions of genetic disease.
From these data we suggest that numeracy preferences may be an important variable to inform strategies for the return of genetic results. For example, it may be important to develop ways to convey genetic risk information in words rather than numbers for patients with low numeric preference [35]. Short numeracy assessments may have potential as clinical tools that assist providers and patients to communicate about genomic information in order to make more informed and evidence-based decisions. Family relationships and values should be considered when counseling patients regarding the return of genetic results, with a particular emphasis on the role of living parents. Our survey results also suggest that clinical encounters related to the return of genetic results should reflect patient preferences by prioritizing discussion of clinical risk, actionability, and certainty of results rather than that of sequencing methods, genetic biology, or history.
Highlights.
Numeracy was assessed using the Subjective Numeracy Scale with subscales distinguishing ability and preferences.
Patient interest in information related to genetic testing results was quantified by a content topic score.
Multivariable linear regression predicted content topic score based on numeracy and covariates.
High patient preference for numeric data was correlated with increased desire for genetic results.
High subjective numeracy ability was not correlated with increased desire for genetic results.
Acknowledgments:
The authors thank the women who agreed to participate in the study and the research staff. This work was supported by the National Cancer Institute, National Institutes of Health (R01CA168608).
Footnotes
Conflict of Interest: The authors have no conflicts of interest to declare.
Compliance with Ethical Standards
Ethical Approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
Informed Consent: Informed consent was obtained from all individual participants included in the study.
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References
- [1].Kaphingst KA, Blanchard M, Milam L, Pokharel M, Elrick A, Goodman MS. (2016) Relationships Between Health Literacy and Genomics-Related Knowledge, Self-Efficacy, Perceived Importance, and Communication in a Medically Underserved Population. Journal of Health Communication, 21:58–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2].Goldbeck AL, Ahlers-Smith CR, Paschal AM. (2005) A definition and operational frame-work for health numeracy. Am J Prev Med, 29:375–6. [DOI] [PubMed] [Google Scholar]
- [3].Lea DH, Kaphingst KA, Bowen D, Lipkus I, Hadley DW. (2011) Communicating Genetic and Genomic Information: Health Literacy and Numeracy Considerations. Public Health Genomics, 14:279–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Lipkus IM. (2007) Numeric, verbal, and visual formats of conveying health risks: suggested best practices and future recommendations. Med Decis Making, 27:696–713. [DOI] [PubMed] [Google Scholar]
- [5].Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. (2007) Measuring numeracy without a math test: development of the Subjective Numeracy Scale. Med Decis Making, 27:672–80. [DOI] [PubMed] [Google Scholar]
- [6].U.S. Department of Education. (2017) National Center for Education Statistics, Program for the International Assessment of Adult Competencies (PIAAC), U.S. PIAAC; 2017. [Google Scholar]
- [7].Bjork J, Andersson T, Ahlbom A. (2019) Commonly used estimates of the genetic contribution to disease are subject to the same fallacies as bad luck estimates. Eur J Epidemiol, Epub ahead of print, doi: 10.1007/s106-54-00573-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Trinidad SB, Burke W. (2016) The Deceptive Appeal of Direct-to-Consumer Genetics. Ann Intern Med, 164:564–5. [DOI] [PubMed] [Google Scholar]
- [9].Roberts JS, Gornick MC, Carere DA, Uhlmann WR, Ruffin MT, Green RC. (2017) Direct-to-Consumer Genetic Testing: User Motivations, Decision Making, and Perceived Utility of Results. Public Health Genomics, 20:36–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Guan Y, Condit CM, Escoffery C, Bellcross CA, McBride CM. (2019) Do Women who Receive a Negative BRCA1/2 Risk Result Understand the Implications for Breast Cancer Risk? Public Health Genomics, 9:1–8. [DOI] [PubMed] [Google Scholar]
- [11].Kaphingst KA, Peterson E, Chou WS. Addressing health literacy and numeracy in cancer-related genetic communication studies. International Conference on Communication in Healthcare, Porto, Portugal, September 3, 2018. [Google Scholar]
- [12].Burke W, Antommaria AH, Bennett R, et al. (2013) Recommendations for returning genomic incidental findings? We need to talk! Genet Med, 15:854–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Pinheiro APM, Pocock RH, Switchenko JM, et al. (2017) Discussing molecular testing in oncology care: comparing patient and physician information preferences. Cancer, 123:1610–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Hitch K, Joseph G, Guiltinan J, Kianmahd J, Youngblom J, Blanco. (2014) A. Lynch syndrome patients’ views of and preferences for return of results following whole exome sequencing. J Genet Couns, 23:539–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Kaphingst KA, Ivanovich J, Lyons S, Biesecker B, Dresser R, Elrick A, Matsen C, Goodman M. (2018) Preferences for learning different types of genome sequencing results among young breast cancer patients: Role of psychological and clinical factors. Translational Behavioral Medicine, 8:71–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Kaphingst KA, Ivanovich J, Biesecker B, Dresser R, Seo J, Dressler LG, Goodfellow PJ, Goodman MS. (2015) Preferences for return of incidental findings from genome sequencing among women diagnosed with breast cancer at a young age. Social and Behavioural Research in Clinical Genetics, 89(3). [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Lillie SE, Brewer NT, O’Neill SC, et al. (2007) Retention and use of breast cancer recurrence risk information from genomic tests: the role of health literacy. Cancer Epidemiol. Biomarkers Prev, 16:249–55. [DOI] [PubMed] [Google Scholar]
- [18].Kaphingst KA, Facio FM, Cheng MR, et al. (2012) Effects of informed consent for individual genome sequencing on relevant knowledge. Clin Genet, 82:408–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Hidalgo B, Goodman M. (2013) Multivariate or multivariable regression? Am J Public Health, 103:39–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20].Bozdogan H (1987) Model selection and Akaike’s Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika, 52:345–70. [Google Scholar]
- [21].Miron Shatz T, Hanoch Y, Doniger GM, Omer ZB, Ozanne EM. (2014) Subjective but not objective numeracy influences willingness to pay for BRCA1/2 genetic testing. Judgment and Decision Making, 9: 152–8. [Google Scholar]
- [22].Wynn J, Martinez J, Duong J, Chiuzan C, Phelan JC, Fyer A, Klitzman RL, Appelbaum PS, Chung WK. (2017) Research Participants’ Preferences for Hypothetical Secondary Results from Genomic Research. J Genet Counsel, 26:841–51. [DOI] [PubMed] [Google Scholar]
- [23].Waters EA, Wheeler C, Hamilton JG. (2016) How Are Information Seeking, Scanning, and Processing Related to Beliefs About the Roles of Genetics and Behavior in Cancer Causation? Journal of Health Communication, 21:6–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [24].Dutta-Bergman MJ. (2004) Health attitudes, health cognitions, and health behaviors among Internet health information seekers: population-based survey. J Med Internet Res, 6:e15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Schapira MM, Fletcher KE, Gilligan MA, King TK, Laud PW, Matthews BA, Neuner JM, Hayes E. (2008) A Framework for Health Numeracy: How Patients Use Quantitative Skills in Health Care. Journal of Health Communication, 13:501–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Goodyear-Smith F, Kenealy T, Wells S, Arroll B, Horsburgh M. (2011) Patients’ Preferences for Ways to Communicate Benefits of Cardiovascular Medication. Ann Fam Med, 9:121–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27].Perzynski AT, Terchek JJ, Blixen CE, Dawson NV. (2013) Playing the numbers: how hepatitis C patients create meaning and make healthcare decisions from medical test results. Sociol Health Illn, 35:610–27. [DOI] [PubMed] [Google Scholar]
- [28].Malloy-Weir LJ, Schwartz L, Yost J, McKibbon KA. (2016) Empirical relationships between numeracy and treatment decision making: A scoping review of the literature. Patient Education and Counseling, 99:310–25. [DOI] [PubMed] [Google Scholar]
- [29].Elrick A, Ashida S, Ivanovich J, Lyons S, Biesecker BB, Goodman MS, Kaphingst KA. (2017) Psychosocial and Clinical Factors Associated with Family Communication of Cancer Genetic Test Results Among Women Diagnosed with Breast Cancer at a Young Age. J Genet Couns, 26:173–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Arribas-Ayllon M, Sarangi S, Clarke A. (2008) Managing self-responsibilities through other-oriented blame: family accounts of genetic testing. Soc Sci Med, 66:1521–32. [DOI] [PubMed] [Google Scholar]
- [31].Forrest LE, Curnow L, Delatycki MB, Skene L, Aitken M. (2008) Health first, genetics second: exploring families’ experiences of communicating genetic information. Eur J Hum Genet, 16:1329–35. [DOI] [PubMed] [Google Scholar]
- [32].McGivern B, Everett J, Yager GG, Baumiller RC, Hafertepen A, Saal HM. (2004) Family communication about positive BRCA1 and BRCA2. Genet Med, 6:503–9. [DOI] [PubMed] [Google Scholar]
- [33].Yu JH, Crouch J, Jamal SM, Bamshad MJ, Tabor HK. (2014) Attitudes of non-African American focus group participants toward return of results from exome and whole genome sequencing. Am J Med Genet A, 164A:2153–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Lea DH, Kaphingst KA, Bowen D, Lipkus I, Hadley DW. (2011) Communicating genetic and genomic information: health literacy and numeracy considerations. Public Health Genomics, 14:279–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [35].Petrova D, Kostopoulou O, Delaney BC, Cokely ET. (2018) Strengths and Gaps in Physicians’ Risk Communication: A Scenario Study of the Influence of Numeracy on Cancer Screening Communication. Med Decis Making, 38:355–65. [DOI] [PubMed] [Google Scholar]