Patterns of Communicating About Family Health History: Exploring Differences in Family Types,Age,and Sex

Abstract

Family communication environments can be a facilitator or barrier to family cooperation and communication in collecting family health history (FHH) information, which can facilitate disease prevention. This study examined the direct and indirect effects of family communicative environments on whether individuals actively collected FHH information, as well as how age and sex differences complicate this relationship. Participants (N = 203) completed online surveys, answering close-ended questions about their family’s communication patterns, how open their family is to communicating about FHH, and whether they have actively collected FHH information. Results show there was a direct effect between open family communicative environments and active collection, and found FHH communication openness was a positive partial mediator. Conversely, family environments stressing hierarchy and homogeneity of beliefs inhibit open communication about and collection of a FHH. Analysis of age and sex as moderators in the models showed a significant conditional indirect effects, which grew stronger as participants’ age increased. Furthermore, results showed open family communicative environments lead to active collection of FHH for women, but not for men. Results confirm the importance of family communicative environments in facilitating or inhibiting FHH collection. Findings from the current study provide intervention points for practitioners to advise patients on the importance of collecting a FHH and guide behaviors to collect FHH information based on the family communicative environment.

Keywords

age differences communication family communication patterns family health history patient counseling sex differences

Advances in science and genomic medicine are leading to more discoveries of genetic variations associated with specific diseases, making family health history (FHH) an important diagnostic tool for clinicians (Rolland & Williams, 2006). FHH, or a family’s medical history, can provide genetic risk information about a patient’s relatives, which can alert health care providers to potential health risks they may be able to monitor, prevent, and treat (Parrott & Hong, 2014). Specifically, a useful FHH includes age of death of three generations, disease information, and age of onset (Centers for Disease Control and Prevention, 2016). However, for physicians to use this information, individuals must first communicate about health and collect FHH information from relatives (Gaff, Galvin, & Bylund, 2010). Although FHH can be an important tool in health care, Welch, O’Connell, and Schiffman (2015) found the percentage of Americans who seek FHH information went from 29% in 2004 to only 37% in 2014. Thus, researchers and clinicians must explore facilitators and barriers to family cooperation and communication in collecting and disseminating FHH information.

Although research has focused on how family members share or block genetic risk information (Forrest et al., 2003; Koehly et al., 2003), far less research examines the persistent influence family communication exercises on health attitudes and behaviors regarding FHH communication. Family communication environment can be a pivotal factor to conversations about FHH. The environment family creates through communication forms rules, influencing family functioning, norms, and roles (Koerner & Fitzpatrick, 2002). One way of exploring family communicative environment’s influence on health behaviors is to explore the family’s conversation and conformity orientations. Conversation orientation refers to the extent to which families create a relational climate encouraging family members to participate in unrestrained interactions about a myriad of topics (Koerner & Fitzpatrick, 2002). It stands to reason families high on this dimension may openly and frequently talk about FHH and members are more likely to have actively collected a FHH. Indeed, Rauscher, Hesse, Miller, Ford, and Youngs (2015) found conversation orientation had a positive, significant relationship with communication about genetic risk. Alternatively, conformity orientation refers to the degree to which family communication stresses a climate of homogeneity of worldviews, values, and belief systems (Koerner & Fitzpatrick, 2002). In intergenerational conversations of families high in conformity orientation, communication behaviors usually reflect obedience to parents and other adults (Koerner & Fitzpatrick, 2006). Families high in conformity orientation may block FHH conversations and avoid FHH information as these conversations may violate norms or create conflict (Kenen, Arden-Jones, & Eeles, 2004). These findings suggest families high on conformity orientation may be less likely to openly communicate about FHH and have not actively collected FHH information.

Beyond communicative differences in family environments, variables such as age and sex are also important factors to consider in FHH communication research. Oftentimes, families view older generations as responsible for communicating FHH to younger generations to facilitate preventive health behaviors (Ashida, Kaphingst, Goodman, & Schafer, 2013). However, Yamasaki and Hovick (2015) found older generations tended to hide information about the FHH because they were afraid of stigma, or because they viewed FHH as adult-only information. Interestingly, previous research finds younger generations tend to talk more frequently about FHH with family members (Claes et al., 2003; Kaphingst et al., 2012). Sex too is likely an influential factor in FHH communication as female family members are traditionally seen as gatekeepers of health information (Jones, Beach, & Jackson, 2004). In previous research, women have been found to be more likely to discuss FHH with both family members and physicians (Kaphingst et al., 2012; Koehly et al., 2009). Indeed, the roles women take on as information disseminators and support providers seem to be a guiding force in facilitating FHH conversations (Jones et al., 2004; Thompson et al., 2015). Thus, both age and sex are likely to influence family communicative environments and FHH collection.

This study explores how family communicative environments may facilitate or impede FHH communication and collection. As communicating about FHH is a family system–level problem, how members communicate plays an important role in how individuals within the family make sense of and disclose FHH (Galvin & Young, 2010). The primary goal of this study is to examine the direct and indirect effects family communicative environments have on whether individuals actively collect FHH information. Second, this study also examines the moderating effects of age and sex that may complicate the effects of family communication environments on individuals’ FHH collection behaviors. Overall, understanding the role of family communication environments in predicting FHH collection can help practitioners develop tailored interventions to facilitate FHH communication.

Methods

Procedures

On institutional review board approval, U.S.-based participants were recruited from Amazon’s Mechanical Turk website and received a payment of $1.00 for completing the online Qualtrics survey. The survey was set up to not allow multiple responses from the same IP address, ensuring no duplicate responses (Goodman & Paolacci, 2017). Additionally, the survey was estimated to take respondents an average of 13 to 17 minutes, and participants from Mechanical Turk took an average of 15 minutes to complete the survey, suggesting participants did not speed through the study (Sheehan, 2018). Participants were required to input a random code provided from Qualtrics at the end of the survey into the Turk system to ensure validity of responses. Mechanical Turk has been an acceptable and high-quality source of data for research in the social sciences (Buhrmester, Kwang, & Gosling, 2011; Sheehan, 2018). All survey measures used a 7-point Likert-type scale ranging from (1) strongly disagree to (7) strongly agree.

Measures

Family Communication Patterns

Family communication patterns were measured using Ritchie and Fitzpatrick’s (1990) Revised Family Communication Patterns instrument. The 26-item instrument is divided into the subscales of conversation (15 items) and conformity (11 items) orientation. Questions included “I usually tell my parents what I am thinking about things” and “I can tell my parents almost anything.” Reliabilities for conversation (α = .94) and conformity (α = .91) orientation were acceptable. Correlations and descriptive statistics for all variables can be found in Table 1.

Table 1.

Correlations and Descriptive Statistics.

Variable	M	SD	1	2	3	4	5	6
1. Conversation orientation	4.33	1.31	—
2. Conformity orientation	3.60	1.33	−0.22***	—
3. Openness about FHH	4.37	1.29	0.55***	−0.42***	—
4. Active collection			0.29***	−0.04	0.34***	—
5. Self-health	5.23	1.40	−0.05***	−0.05**	0.21**	0.10	—
6. Family health	5.13	1.26	−0.02***	−0.02***	0.01	0.13	0.55***	—

Note. FHH = family health history.

p < .05. **p < .01. ***p < .001.

FHH Communication Openness

FHH communication openness was measured using an altered version of Mesters et al.’s (1997) Openness to Communicate about Cancer in the Nuclear Family scale, in which higher scores indicate more openness about FHH. Questions included “My family doesn’t like me to talk about family health history with them.” These items formed a reliable scale (α = .91).

Active Collection of FHH Information

Collection of FHH information was measured with the question, “Have you actively collected health information from your relatives for purposes of developing a family health history?” where participants responded yes or no. Over half of participants (64%) reported not having actively collected FHH information.

Demographic Information

Participants self-reported their ethnicity by choosing from a list of options including Caucasian, African American, Asian, Native American, Hispanic, and other, please specify. Only one participant chose the other category and specified Middle Eastern as their ethnic identity. Participants also responded to items measuring perceptions of their own health (“I consider myself healthy”) as well as perceptions of their family’s overall health (“To my knowledge, my family is healthy”). Finally, participants identified if anyone in their family was diagnosed with a chronic disease using one item (“Have you, your siblings, your parents, or your grandparents had any of the following illnesses? Choose all that apply”). Response choices for this item included cancer, diabetes, heart disease, Alzheimer’s/dementia, depression, high blood pressure, high cholesterol, and substance abuse.

Age and Sex

Participants were given the choice to select sex from male (n = 91), female (n = 112), or other, please specify (n = 0). Participants put their age (in years) into an open box in the survey.

Data Analyses

Using G*Power 3.1 (Erdfelder, Faul, & Buchner, 1996), we calculated the power of the sample post hoc and found the sample size did not limit the significance of statistical comparisons detailed below, at 0.70 power (1 − β) and α = .05, two-tailed (Cohen, 1992). We controlled for both the perceived healthiness of the participant and the participant’s perception of the healthiness of their family as both variables strongly correlated with main study variables (see Table 1). Both self-health and family health were one-item questions, with higher numbers indicating greater perceptions of healthiness. SPSS 24 was used to analyze direct effects. To test indirect effects of FHH communication openness on conversation and conformity orientations and active collection of FHH information, Hayes’s (2013) PROCESS macro (Model 4) was used. For models testing age and sex differences, Hayes’s (2013) PROCESS macro (Model 59) was used to test for moderated mediation. It should be noted that using cross-sectional data to test mediation can be a limitation. However, this study examines the effect of family communication environments, which have long-term effects on how individuals communicate (Koerner & Fitzpatrick, 2002). Indeed, these patterns of family communication have been shown to repeat over time and across generations as well as be trait-like rather than state-like (Ritchie & Fitzpatrick, 1990; Vangelisti, 2003). Thus, due to measurement constructs for family communication environments, using these data to test a mediation model is appropriate.

Results

Participants (N = 203) ranged in age from 18 to 68 years (M = 35.6, SD = 10.72). The sample included 112 women and 91 men. Most participants self-identified as Caucasian (78.3%), 5.4% as African American, 12.8% as Asian, 2% as Native American, 3.9% as Hispanic, and 0.5% as Middle Eastern. Most participants (n = 130) reported not having actively collected FHH information. Participants reported considering themselves (M = 5.23, SD = 1.40) and their families (M = 5.13, SD = 1.26) healthy on a 7-point scale. Participants also reported whether anyone in their family had ever been diagnosed with heart disease (n = 116), diabetes (n = 104), and cancer (n = 74).

Effects of Conversation Orientation

Results from logistic regression show significant positive direct effects between conversation orientation and active collection (see all regression results in Table 2). Indirect effects between conversation orientation and whether individuals actively collected FHH information through FHH communication openness were also significant. Furthermore, FHH communication openness partially mediated the relationship between conversation orientation and whether individuals actively collect FHH information. See indirect effects results for conversation orientation in Table 3.

Table 2.

Summary of Logistic Regressions for Variables Predicting Whether Individuals Actively Collected FHH.

Predictor	β	SE	e^B	χ²	df	R ²
Conversation orientation	0.50***	0.14	1.65	17.85	3	.12
Conformity orientation	−0.06	0.11	0.94	3.89	3	.03

Note. FHH = family health history; e^B = exponentiated B; SE = standard error; df = degrees of freedom. Controls are self-health and family health (omitted from table).

p < .05. **p < .01. ***p < .001.

Table 3.

Indirect Effects of Conversation Orientation and Conformity Orientation on Openness About FHH Communication and Collection of FHH Information.

Paths	β	SE	95% CI
Convo → OpenFHH	0.54***	0.06	[1.30, 2.76]
OpenFHH → FHHCollect	0.48**	0.15	[0.19, 0.78]
Convo → FHHCollect	1.63	0.16	[−0.05, 0.57]
Convo → OpenFHH → FHHCollect	0.26***	0.10	[0.09, 0.47]
Confo → OpenFHH	−0.40***	0.44	[3.81, 5.53]
OpenFHH → FHHCollect	0.70**	0.15	[0.40, 1.00]
Confo → FHHCollect	0.22	0.14	[−0.05, 0.50]
Confo → OpenFHH → FHHCollect	−0.28***	0.10	[−0.50, 0.13]

Note. FHH = family health history; Convo = conversation orientation; Confo = conformity orientation; OpenFHH = openness about FHH communication; FHHCollect = active collection of FHH information; SE = standard error; CI = confidence interval. Controls are self-health and family health (omitted from table).

p < .05. **p < .01. ***p < .001.

Results from a test of moderated mediation demonstrate that age did not fully moderate the mediating relationship of FHH communication openness on conversation orientation and whether individuals actively collected FHH information. However, age did significantly moderate the relationship between conversation orientation and FHH communication openness. In examining age groups, the conditional indirect effect of FHH communication openness tended to get stronger for younger participants in the model. Furthermore, age did moderate the positive direct effect of conversation orientation on active collection of FHH information for older participants. See all moderated mediation results for conversation orientation in Table 4.

Table 4.

Moderated Mediation of Sex and Age for Conversation Orientation on Openness About FHH Communication and Collection of FHH Information.

Paths	OpenFHH			FHHCollect
Paths	b	SE	CI [LL, UL]	b	SE	CI [LL, UL]
Conversation Orientation/Sex
Convo	−0.13	0.20	[−0.53, 0.27]	0.79	0.53	[−0.25, 1.83]
Convo × Sex	0.40**	0.16	[0.17, 0.63]	−0.35	0.32	[−0.97, 0.27]
OpenFHH	—	—	—	−0.22	0.55	[−1.29, 0.85]
OpenFHH × Sex	—	—	—	0.44	0.33	[−0.21, 1.09]
Male (direct effects)	—	—	—	0.44	0.25	[−0.05, 0.93]
Female (direct effects)	—	—	—	0.09	0.21	[−0.32, 0.50]
Male (indirect effects)	—	—	—	0.06	0.11	[−0.11, 0.36]
Female (indirect effects)	—	—	—	0.44**	0.16	[0.15, 0.77]
Index of Moderated Mediation	—	—	—	0.38	0.19	[−0.02, 0.74]
Conversation Orientation/Age
Convo	0.47*	0.21	[0.06, 0.88]	−0.48	0.54	[−1.54, 0.58]
Convo × Age	0.00	0.01	[−0.01, 0.01]	0.02	0.02	[−0.04, 0.02]
OpenFHH	—	—	—	0.91	0.52	[−0.11, 1.94]
Open FHH × Age	—	—	—	−0.01	0.01	[−0.04, 0.02]
24.89 (direct effects)	—	—	—	0.07	0.21	[−0.34, 0.48]
35.61 (direct effects)	—	—	—	0.31	0.17	[−0.02, 0.64]
46.33 (direct effects)	—	—	—	0.55*	0.26	[0.03, 1.06]
24.89 (indirect effects)	—	—	—	0.31*	0.18	[0.08, 0.82]
35.61 (indirect effects)	—	—	—	0.25*	0.11	[0.06, 0.50]
46.33 (indirect effects)	—	—	—	0.18	0.17	[−0.11, 0.58]

Note. FHH = family health history; CI = confidence interval; LL = lower limit; UL = upper limit; SE = standard error; Convo = conversation orientation; OpenFHH = openness about FHH communication; FHHCollect = active collection of FHH information. Controls are self-health and family health (omitted from table).

p < .05. **p < .01. ***p < .001.

Results testing sex in a moderated mediation model also showed that sex did not fully moderate the mediating relationship of FHH communication openness on conversation orientation and active collection of FHH information. However, sex did significantly moderate the relationship between conversation orientation and FHH communication openness. Furthermore, when the relationship between conversation orientation and FHH communication openness was moderated by sex, there was a stronger, positive relationship especially for women. In examining difference based on sex groups, conditional indirect effects of FHH communication openness were only significant for women, but not for men.

Effects of Conformity Orientation

Results from logistic regression did not show significant direct effects between conformity orientation and active collection. However, results found significant indirect effects between conformity orientation and active collection through FHH communication openness. Thus, FHH communication openness fully mediated the relationship between conformity orientation and active collection. See indirect effects for conformity orientation in Table 3.

Results from a test of moderated mediation demonstrate that age did not significantly moderate the mediating relationship of FHH communication openness on conformity orientation and whether individuals actively collected FHH information. Only in examining differences among age groups do results demonstrate significant negative conditional indirect effects between conformity orientation and active collection through the FHH communication openness. The conditional indirect negative effect of FHH communication openness tended to get stronger for older participants as negative effect sizes steadily increased with participants’ ages. See all moderated mediation results for conformity orientation in Table 5.

Table 5.

Moderated Mediation of Sex and Age for Conformity Orientation on Openness About FHH Communication and Collection of FHH Information.

Paths	OpenFHH			FHHCollect
Paths	b	SE	CI [LL, UL]	b	SE	CI [LL, UL]
Conformity Orientation/Sex
Confo	−0.50***	0.22	[−0.93, −0.07]	0.44	0.49	[−0.54, 1.42]
Confo × Sex	0.08	0.13	[−0.17, 0.33]	−0.13	0.29	[−0.70, 0.44]
OpenFHH	—	—	—	0.22	0.55	[−0.85, 1.30]
OpenFHH × Sex	—	—	—	0.28	0.32	[−0.36, 0.91]
Male [direct effects]	—	—	—	0.31	0.23	[−0.15, 0.77]
Female [direct effects]	—	—	—	0.18	0.17	[−0.16, 0.52]
Male [indirect effects]	—	—	—	−0.21	0.12	[−0.54, 0.01]
Female [indirect effects]	—	—	—	−0.26**	0.14	[−0.60, −0.07]
Index of Moderated Mediation	—	—	—	−0.05	0.20	[−0.44, 0.35]
Conformity Orientation/Age
Confo	−0.23	0.22	[−0.66, 0.20]	0.17	0.50	[−0.82, 1.15]
Confo × Age	−0.00	0.02	[−0.02, 0.00]	0.00	0.01	[−0.03, 0.03]
OpenFHH	—	—	—	0.75	0.54	[−0.31, 1.81]
OpenFHH × Age	—	—	—	−0.00	0.01	[−0.03, 0.03]
24.89 (direct effects)	—	—	—	0.21	0.20	[−0.18, 0.59]
35.61 (direct effects)	—	—	—	0.22	0.14	[−0.05, 0.49]
46.33 (direct effects)	—	—	—	0.24	0.21	[−0.17, 0.65]
24.89 (indirect effects)	—	—	—	−0.25*	0.14	[−0.58, −0.06]
35.61 (indirect effects)	—	—	—	−0.28*	0.10	[−0.49, −0.12]
46.33 (indirect effects)	—	—	—	−0.31*	0.15	[−0.65, −0.09]

Note. FHH = family health history; CI = confidence interval; LL = lower limit; UL = upper limit; SE = standard error; Confo = conformity orientation; OpenFHH = openness about FHH communication; FHHCollect = active collection of FHH information. Controls are self-health and family health (omitted from table).

p < .05. **p < .01. ***p < .001.

Finally, results from a test of moderated mediation demonstrate that sex did not fully significantly moderate the mediating relationship of FHH communication openness on conformity orientation and whether individuals actively collected FHH information. Conformity orientation did significantly, negatively predict FHH communication openness, but none of the other paths in the model were significantly moderated. Furthermore, only females had a significant negative conditional indirect effect from the relationship of conformity orientation to active collection through FHH communication openness.

Discussion

The focus of the current study was to explore direct and indirect effects of family communicative environments on active collection of FHH information. Active collection in this study was operationalized as an individual collecting the family’s medical history for the purposes of establishing a FHH. Findings demonstrate how family communicative environments foster or inhibit open FHH conversations, which can affect whether individuals actively collect their FHH information. Furthermore, this study also examined how age and sex may complicate the effects of family communicative environments on individuals’ FHH collection behaviors. Overall, findings provide opportunities for practitioners to tailor messages to patients about FHH collection based on the patient’s family type, age, and sex.

Using conversation and conformity orientations provides practitioners with dimensions for family communication categorization, which can be used to develop interventions motivating families to communicate about FHH (Koerner, LeRoy, & Veach, 2010). Findings show there was a direct effect between conversation orientation and active collection, and found FHH communication openness was a positive partial mediator. This finding supports previous research that open family communicative environments support open health-related conversations such as FHH information (Holt, 2006; Kenen et al., 2004; Rauscher et al., 2015). As these families are more communicatively open, they may have casual conversations about the FHH, which provides a clear intervention point for practitioners. Providers can give patients from these types of families resources such as the Surgeon General’s “My Family Health Portrait” to help patients more deliberately collect and organize their FHH information (Department of Health and Human Services, n.d.). These types of families may not need as much help from practitioners as families from more closed communicative environments. When the family environment is conducive to FHH communication, practitioners can bring up the importance of having this information to motivate patients’ active collection and provide direction on what a useful FHH includes to guide FHH conversations (Koerner et al., 2010). Thus, practitioners can act as information providers or experts to guide family conversations about FHH as patients in open family communicative environments already foster open FHH communication.

Patients coming from family environments stressing hierarchy and homogeneity of beliefs may need more counseling on how to have FHH conversations without causing family conflicts (Koerner et al., 2010). Results from this study show these families may be less likely to openly communicate about FHH or actively collect a FHH. Families high in conformity orientation tend to avoid conflicts, which suggests these families may avoid health topics such as FHH since this information is often seen as private or adult-only information (Koerner et al., 2010; Yamasaki & Hovick, 2015). Individuals from families high in conformity orientation are socialized to respect authority figures, which places providers in an influential position to direct patient behavior and provide communicative tools (Koerner et al., 2010; Koerner & Fitzpatrick, 2002). For instance, practitioners may need to stress the importance of gathering this information while also providing example questions patients might ask. Practitioners can also role play with patients to find productive ways of starting FHH conversations in high conformity families. In these ways, providers take on roles as medical authority figures by providing directives to collect FHH while also acting as counselors by helping patients strategize FHH conversations. Overall, findings from the study highlight the importance of family communication environments as factors influencing open FHH communication and collection.

Exploring how age differences complicate effects of family communicative environments on FHH communication provides further insight for practitioners on tailoring their messages. Although age did not fully moderate the mediation of FHH communication openness, age did moderate the relationship between conversation orientation and FHH communication openness. Furthermore, the conditional indirect effect of FHH communication openness was stronger for younger participants. These findings demonstrate younger age groups may be more likely to openly communicate about FHH information as opposed to older age groups, which is problematic as younger generations tend to rely on older family members to talk about the FHH and provide key health information about the medical history of older generations (Ashida et al., 2013). Thus, younger family members may be limited by the cooperation of older generations to share information (Yamasaki & Hovick, 2015). To further encourage younger patients’ active collection of FHH information, practitioners might provide advice on what questions to ask to gather a useful FHH and help younger patients target members of the older generation who might help with their FHH collection.

Findings demonstrate significant negative conditional indirect effects of age, which was stronger for older participants. These findings suggest that older adults may be higher in conformity orientation as they come from generations in which health was a deeply private topic and individuals were often concerned about stigma and judgement, which may explain part of the reluctance to communicate openly about FHH (Fisher, 2011; Yamasaki & Hovick, 2015). Although age did not fully moderate the mediating relationship of FHH communication openness, results still suggest that age can play a noteworthy role in inhibiting or facilitating active collection of FHH information. As individuals get older and move through different life stages, they can prioritize subjects such as health differently, changing the ways they communicate about health to be more protective and private (Hovick, Yamasaki, Burton-Chase, & Peterson, 2015). Practitioners can use older adults’ goals to protect their family to stress why communicating about FHH information can be important for other family members’ health and provide suggestions on what specific information they should be sharing (Ashida et al., 2013). Furthermore, practitioners might also explore the reasons why older patients may not want to have these conversations and provide counseling resources to help families work through emotional FHH information or family dysfunction (Gaff et al., 2010).

Finally, this study found sex did not fully moderate the mediating relationship of FHH communication openness on family communicative environment and active collection. However, for both conversation and conformity orientation models there were significant conditional indirect effects for women, but not men. Results exploring the effects of females’ family communication environments on their FHH communication and collection largely support the gendered nature of health communication in the family as women tend to take on roles as gatekeepers of information and support providers when managing the FHH (d’Agincourt-Canning, 2001; Koehly et al., 2009). However, men in this area have frequently been understudied and what research has been done finds men rarely take on active roles in communicating about FHH (Daly, 2009; Hesse-Biber & Chen, 2017). Findings from the current study show that general openness about FHH can facilitate active collection of FHH information, which was moderated by sex with a stronger positive relationship for women. Although the family may be open about FHH information, men may not be participating in or attending closely to these conversations (Claes et al., 2003). Practitioners may need to first stress to men the importance of collecting a FHH and becoming actively involved in these family discussions (Rauscher, Dean, & Campbell-Salome, 2018). Furthermore, practitioners might also encourage female family members as gatekeepers to invite men to take a more active role in these conversations. Overall, findings from this study and previous research suggest practitioners need to take a family-centered approach to encouraging FHH collection by examining factors motivating or constraining FHH communication based on family types as well as age and sex differences of family members.

There are several limitations in this study that should be noted such as sample limitations. Due to the overwhelming Caucasian makeup of the participant population, results cannot be generalized to samples with varying racial or ethnic composition. Second, participants considered themselves and their families to be healthy, which suggests these results may not transfer to individuals managing more significant hereditary risks. Furthermore, information on family communication environments is based on participant self-reports rather than objectively observed differences among families. Next, there are limitations related to using Amazon Mechanical Turk for study recruitment. These data were collected among an online group who have access to Internet and feels comfortable using online tools, which limits also the study’s generalizability (Bunge et al., 2018). Finally, due to the cross-sectional design of this study’s data collection, no causal claims can be made from these findings. Although these limitations exist, this study provides a foundation to continue exploring the effects of family communication environments on FHH collection.

As genomic medicine advances, more links are being drawn between genetic variants and disease, which suggests FHH communication will be evermore important in the future even for families who currently have no significant hereditary disease risks (Centers for Disease Control and Prevention, 2017; Guttmacher, Collins, & Carmona, 2004). Future research might use findings from this study to tailor interventions aimed at helping individuals gather a FHH based on family types. For example, an intervention tool might include broad questions to start FHH conversations in a nonthreatening way for high conformity families, such as “What traits run in our family?” Future research might also explore how FHH information is used, or often not used, in clinical contexts (Acheson, 2003; Williams, Collingridge, & Williams, 2011). Often physicians do not have the resources to store FHH information, which suggest research should explore how to better support providers when patients provide FHH information.

Conclusions

Collecting a FHH is the first step in understanding and managing hereditary health risks and physicians frequently cite the incomplete nature of patient’s FHH information as a reason for not using such information in clinical care (Williams et al., 2011; Wilson et al., 2009). Although identifying and targeting family communication patterns may not be a panacea for FHH communication and collection, understanding the ways families interact and shape individual behaviors is an important step in facilitating FHH conversations. Findings from this study provide intervention points and tailoring options to help practitioners educate patients on the importance of collecting a FHH and guide patient behaviors to collect a FHH based on their family communication environment.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Gemme Campbell-Salome

References

Acheson

(2003). Fostering applications of genetics in primary care: What will it take? Genetics in Medicine, 5, 63-65.

Ashida

Kaphingst

K. A.

Goodman

Schafer

E. J.

(2013). Family health history communication networks of older adults: Importance of social relationships and disease perceptions. Health Education & Behavior, 40, 612-619.

Buhrmester

Kwang

Gosling

S. D.

(2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3-5.

Bunge

Cook

H. M.

Bond

Williamson

R. E.

Cano

Barrera

A. Z.

. . . Muñoz

R. F.

(2018). Comparing Amazon Mechanical Turk with unpaid internet resources in online clinical trials. Internet Interventions, 12, 68-73.

Centers for Disease Control and Prevention. (2016). Family health history: The basics. Retrieved from https://www.cdc.gov/genomics/famhistory/famhist_basics.htm

Centers for Disease Control and Prevention. (2017). Knowing is not enough: Act on your family health history. Retrieved from https://www.cdc.gov/features/familyhealthhistory/index.html

Claes

Evers-Kiebooms

Boogaerts

Decruyenaere

Denayer

Legius

(2003). Communication with close and distant relatives in the context of genetic testing for hereditary breast and ovarian cancer in cancer patients. American Journal of Medical Genetics. Part A, 116(1), 11-19.

Cohen

(1992). A power primer. Psychological Bulletin, 112, 155-159.

d’Agincourt-Canning

. (2001). Experiences of genetic risk: Disclosure and the gendering of responsibility. Bioethics, 15, 231-247.

10.

Daly

M. B.

(2009). The impact of social roles on the experience of men in BRCA1/2 families: Implications for counseling. Journal of Genetic Counseling, 18, 42-48.

11.

Department of Health and Human Services. (n.d.). “My Family Health Portrait”: A tool from the Surgeon General. Retrieved from https://phgkb.cdc.gov/FHH/html/index.html

12.

Erdfelder

Faul

Buchner

(1996). GPOWER: A general power analysis program. Behavior Research Methods, Instruments, & Computers, 28(1), 1-11.

13.

Fisher

C. L.

(2011). “Her pain was my pain”: Mothers and daughters sharing the breast cancer journey. In Miller-Day

(Ed.), Family communication, connections, and health transitions (pp. 56-76). New York, NY: Peter Lang.

14.

Forrest

Simpson

S. A.

Wilson

B. J.

Van Teijlingen

E. R.

McKee

Haites

Matthews

(2003). To tell or not to tell: Barriers and facilitators in family communication about genetic risk. Clinical Genetics, 64, 317-326.

15.

Gaff

C. L.

Galvin

K. M.

Bylund

C. L.

(2010). Facilitating family communication about genetics in practice. In Gaff

C. L.

Bylund

C. L.

(Eds.), Family communication about genetics: Theory and practice (pp. 243-272). Oxford, England: Oxford University Press.

16.

Galvin

K. M.

Young

M. A.

(2010). Family system theory. In Gaff

C. L.

Bylund

C. L.

(Eds.), Family communication about genetics: Theory and practice (pp. 102-119). Oxford, England: Oxford University Press.

17.

Goodman

Paolacci

(2017). Crowdsumers take over: Towards valid crowdsourcing of consumer research. Retrieved from https://u.osu.edu/goodman/files/2016/12/JCR-MTurk-Tutorial-1dnc063.pdf

18.

Guttmacher

A. E.

Collins

F. S.

Carmona

R. H.

(2004). The family history—More important than ever. New England Journal of Medicine, 35, 1-22.

19.

Hayes

A. F.

(2013). An introduction to mediation, moderation, and conditional process analysis. New York, NY: Guilford Press.

20.

Hesse-Biber

Chen

(2017). Within-gender differences in medical decision making among male carriers of the BRCA genetic mutation for hereditary breast cancer. American Journal of Men’s Health, 11, 1444-1459.

21.

Holt

(2006). What do we tell the children? Contrasting the disclosure choices of two HD families regarding risk status and predictive genetic testing. Journal of Genetic Counseling, 15, 253-265.

22.

Hovick

S. R.

Yamasaki

J. S.

Burton-Chase

A. M.

Peterson

S. K.

(2015). Patterns of family health history communication among older African American adults. Journal of Health Communication, 20, 80-87.

23.

Jones

D. J.

Beach

S. R.

Jackson

H. O. P. E

. (2004). Family influences on health: A framework to organize research and guide intervention. In Vangelisti

A. L.

(Ed.), Handbook of family communication (pp. 647-672). Mahwah, NJ: Lawrence Erlbaum.

24.

Kaphingst

K. A.

Goodman

Pandya

Garg

Stafford

Lachance

(2012). Factors affecting frequency of communication about family health history with family members and doctors in medically underserved population. Patient Education and Counseling, 88, 291-297.

25.

Kenen

Arden-Jones

Eeles

(2004). We are talking, but are they listening? Communication patterns in families with a history of breast/ovarian cancer (HBOC). Psycho-Oncology, 13, 335-345.

26.

Koehly

L. M.

Peters

J. A.

Kenen

Hoskins

L. M.

Ersig

A. L.

Kuhn

N. R.

. . . Greene

M. H.

(2009). Characteristics of health information gatherers, disseminators, and blockers within families at risk of hereditary cancer: Implications for family health communication interventions. American Journal of Public Health, 99, 2203-2209.

27.

Koehly

L. M.

Peterson

S. K.

Watts

B. G.

Kempf

K. K.

Vernon

S. W.

Gritz

E. R.

(2003). A social network analysis of communication about hereditary nonpolyposis colorectal cancer genetic testing and family functioning. Cancer Epidemiology, Biomarkers & Prevention, 12, 304-313.

28.

Koerner

A. F.

Fitzpatrick

M. A.

(2006). Family communication patterns theory: A social cognitive approach. In Engaging theories in family communication: Multiple perspectives (pp. 50-65). Thousand Oaks, CA: Sage. doi:10.4135/9781452204420.n4

29.

Koerner

A. F.

LeRoy

Veach

(2010). Family communication patterns. In Gaff

C. L.

Bylund

C. L.

(Eds.), Family communication about genetics: Theory and practice (pp. 184-198). Oxford, England: Oxford University Press.

30.

Koerner

F. A.

Fitzpatrick

M. A.

(2002). Understanding family communication patterns and family functioning: The roles of conversation orientation and conformity orientation. Annals of the International Communication Association, 26, 36-65.

31.

Mesters

van den Borne

McCormick

Pruyn

de Boer

Imbos

(1997). Openness to discuss cancer in the nuclear family: Scale, development, and validation. Psychosomatic Medicine, 59, 269-279.

32.

Parrott

Hong

S. J.

(2014). Personalized medicine. In Thompson

T. L.

(Ed.), Encyclopedia of health communication (pp. 1051-5054). Thousand Oaks, CA: Sage.

33.

Rauscher

E. A.

Dean

Campbell-Salome

G. M.

(2018). “I am uncertain about what my uncertainty even is”: Men’s uncertainty and information management of their BRCA-related cancer risks. Journal of Genetic Counseling, 27, 1417-1427. doi:10.1007/s10897-018-0276-y

34.

Rauscher

E. A.

Hesse

Miller

Ford

Youngs

E. L.

(2015). Privacy and family communication about genetic cancer risk: Investigating factors promoting women’s disclosure decisions. Journal of Family Communication, 15, 368-386.

35.

Ritchie

L. D.

Fitzpatrick

M. A.

(1990). Family communication patterns: Measuring intrapersonal perceptions of interpersonal relationships. Communication Research, 17, 523-544.

36.

Rolland

J. S.

Williams

J. K.

(2006). Toward a psychosocial model for the new era of genetics. In Miller

S. M.

McDaniel

S. H.

Rolland

J. S.

Feetham

S. L.

(Eds.), Individuals, families, and the new era of genetics (pp. 36-75). New York, NY: W. W. Norton.

37.

Sheehan

K. B.

(2018). Crowdsourcing research: Data collection with Amazon’s Mechanical Turk. Communication Monographs, 85, 140-156.

38.

Thompson

Seo

Griffith

Baxter

James

Kaphingst

K. A.

(2015). The context of collecting family health history: Examining definitions of family and family communication about health among African American women. Journal of Health Communication, 20, 416-423.

39.

Vangelisti

A. L.

(Ed.). (2003). The Routledge handbook of family communication. London: Routledge.

40.

Welch

B. M.

O’Connell

Schiffman

J. D.

(2015). 10 Years later: Assessing the impact of public health efforts on the collection of family health history. American Journal of Medical Genetics. Part A, 167A, 2026-2033.

41.

Williams

J. L.

Collingridge

D. S.

Williams

M. S.

(2011). Primary care physicians’ experience with family history: An exploratory qualitative study. Genetics in Medicine, 13, 21-25.

42.

Wilson

B. J.

Qureshi

Santaguida

Little

Carroll

J. C.

Allanson

Raina

(2009). Systematic review: Family history in risk assessment for common diseases. Annals of Internal Medicine, 151, 878-885.

43.

Yamasaki

Hovick

S. R.

(2015). “That was grown folks’ business”: Narrative reflection and response in older adults’ family health history communication. Health Communication, 30, 221-230.