Psychological impact of receiving genetic risk information for breast cancer, with and without lifestyle information

Abstract

The psychological impact of receiving hypothetical genetic risk information for breast cancer, with and without lifestyle information, was investigated. The psychological responses included in the study were drawn from three theories of behaviour change and included perceived risk, beliefs in health behaviours, motivations to change health behaviours, and use of coping strategies. Vignettes were used to present hypothetical risk information to 198 female university students. Results indicated that lifestyle information had an impact on psychological measures, in particular, increased beliefs in health behaviours, increased motivation for exercise, and decreased rational problem solving. Suggestions for future research are discussed.

Breast cancer is the most frequently diagnosed cancer in women, with 1 in 11 Australian women affected over their lifetime (Giles & Thursfield, 2002). Both behavioural and genetic factors have been implicated as risk factors for breast cancer. Health behaviours such as high levels of alcohol consumption and reduced physical activity are associated with the disease (Byers et al., 2002). Measures of weight (obesity, adult weight gain and central adiposity) have been identified as risk factors for breast cancer (McTiernan, 2003), yet associations between breast cancer risk and diet are inconclusive (McPherson, Steel, & Dixon, 2000).

A family history of breast cancer is also associated with an increased risk of the disease. Genes BRCA1 and BRCA2 have been identified and predispose the carrier to a high-risk of developing the disease when mutated (McPherson et al., 2000). However, the majority of women with a family history of breast cancer are given risk estimates based on their family history of the disease. These estimates can range from low, moderate to high risk. Although both genetic and behavioural risk factors for breast cancer have been identified, the interaction between these is not yet understood.

Genetic counselling aims to convey relevant genetic risk information to an affected or potentially affected population, thus those who experience a genetic disorder in their family often attend a genetic consultation to gain a better understanding of their genetic risk (Biesecker & Marteau, 1999). There is benefit in allowing individuals to understand their genetic risk, so a plan of management, prevention and risk reduction can be employed (Trepanier et al., 2004). However, lifestyle risk factors (with the exception of screening) are generally not discussed in genetic counselling for breast cancer, and little is known about the impact of receiving information based on genotype, on behavioural outcomes (Marteau & Croyle, 1998). Qualitative research has indicated health professionals hold differing and often opposing views regarding the presentation of risk information to women at increased risk of breast cancer (Rees, Young, Gaff & Martin, 2006).

Being aware of a possible genetic risk may encourage a patient to engage in positive health-related behaviours associated with that disease. For example, women with a first-degree relative with breast cancer can be motivated to improve their health-related behaviours in a variety of ways if they believe those behaviours can be risk factors for breast cancer (Lemon, Zapka & Clemow, 2004). Women presenting for genetic testing for breast cancer susceptibility have been shown generally to have better health behaviours than the general population (Emmons et al., 2000).

Although this evidence suggests that being aware of a family history of disease may motivate an individual to engage in health-related behaviours, little is known about behavioural responses following the provision of genetic risk information. Senior, Marteau and Weinman (2000) investigated risk perceptions of heart disease and arthritis using vignettes to present hypothetical genetic risk information. Half the participants were told that they were at risk due to a genetic test result, and the other half were told they were at risk due to an unspecified test. When participants believed they were at increased susceptibility due to a genetic test result, they were more likely to attribute the cause of the risk to genes, and less likely to attribute it to lifestyle (Senior et al., 2000). This was accompanied by a perception that a disease could not be prevented or treated, if genetic (Marteau & Croyle, 1998; Senior et al., 2000). This finding has aroused concern that providing genetic risk information may result in a reduced sense of control and an increased sense of fatalism, which may result in a decrease in intentions and motivations to improve health behaviour. This hypothesis has not been tested in a clinical population at risk of cancer. However, qualitative research has found that some women at increased risk of breast cancer because of their family history of the disease engage in a healthy lifestyle, stress reduction and regular screening, whilst others adopt a fatalistic view (Kenen, Ardern-Jones, & Eeles, 2003).

The variability in responses to genetic risk information can be understood in terms of three theories of behaviour change, which have been used to guide the outcome measures: Protection-Motivation theory (Rogers, 1975), Theory of Planned Behaviour (Ajzen, 1991), and the Self-Regulatory model (Leventhal, Meyer & Nerenz, 1980). These theories of behaviour change suggest the importance of several psychological responses when assessing the impact of genetic risk information.

Specifically, beliefs in the efficacy of health behaviours are relevant to psychological response and have been shown to be an important aspect of behavioural change. Higher motivation levels have been shown when accompanied by a belief that the behaviour change will be successful in reducing risk (Courneya & Hellsten, 2001). Likewise, intention to change behaviour is an important psychological measure, and heavily influential on behaviour change (Marteau & Lerman, 2001). In addition, the employment of coping strategies is an important psychological response to assess. The adoption of negative coping strategies, such as fatalism and hopelessness, are unlikely to lead to a positive behaviour change (Senior, Marteau & Peters, 1999). Finally, perceived risk is a vital outcome as it is has been shown to be associated with positive behaviour changes (Emmons et al., 2000). Research has reported that women do have difficulty estimating their risk of breast cancer accurately (Lloyd et al., 1996). Women at different levels of risk or who hold different perceptions of risk may vary in their responses to genetic risk information. For example high risk perception has been associated with psychological distress, both general and cancer-specific (Hopwood et al., 1998; Van Dooren et al., 2004; Watson et al., 1999), and anxiety (Cull et al., 1999; Meiser et al., 2001).

It is not current practice for genetic counsellors to routinely present lifestyle information to their patients, and the effects of presenting lifestyle information in genetic counselling sessions, have not been addressed. This study aims to assess the impact of providing different levels of hypothetical genetic risk information (high risk and moderate risk; as an exploratory variable), with and without lifestyle information, using vignette methodology. It is hypothesised that those participants who receive lifestyle information in conjunction with genetic risk information, compared with those who only receive genetic risk information, will show: stronger beliefs in the efficacy of health-related behaviours; stronger intentions to engage in health-related behaviours; more active coping strategies, including less fatalism and hopelessness, and increased rational problem solving; and lower perceived risk.

Method

Design

This experiment was a 2×2 between-subjects design, where the two factors were type of genetic risk information received (high or moderate risk), and the presence or absence of lifestyle information.

Participants

Power analysis indicated a minimum of 180 participants were required for ANOVA with 4 groups with a medium effect size (p < .05, power = 0.8). Inclusion criteria specified females over 18 years of age with sufficient English to read the questionnaire independently. Five hundred and twenty-nine questionnaires were distributed in undergraduate classes at RMIT University. One hundred and ninety eight were returned, resulting in a response rate of 37.4%. Students did not receive any inducement to participate.

Measures

Five measures were included in the questionnaire booklet.

Beliefs about the efficacy of health behaviours were measured by items similar to those used by Courneya and Hellsten (2001). Items for this measure were adapted from colon cancer to breast cancer, to suit this study. There were five items per health behaviour, all using a 7-point Likert scale. Health behaviours assessed were diet, alcohol consumption and exercise, with internal consistency scores in this sample of .73, .86 and .76, respectively.

Intention and motivation to change behaviour were assessed using three items per health behaviour (diet, alcohol consumption and exercise). These items were developed by Courneya and Hellsten (2001), and are assessed on a 7-point Likert scale ranging from “extremely unlikely” to “extremely likely”. These items were modified from colon to breast cancer. Cronbach alpha scores obtained for this sample were .82 for motivation to exercise, .88 for motivation for less alcohol consumption, and .90 for motivation for a healthy diet.

Coping was assessed through items for fatalism, hopelessness and rational problem solving, taken from Prentice-Dunn et al. (2001). Three extra questions were taken from Abraham, Sheeran, Abrams and Spears (1994). A 7-point Likert scale was used for these items. There were six items for fatalism, four for hopelessness and five for rational problem solving. All three measures of coping reported good internal consistency in this sample, with .80 for fatalism items, .80 for hopelessness, and .84 for rational problem solving.

Perceived risk items were adapted from Courneya and Hellsten (2001) to suit breast cancer. Four items were assessed on a 7-point Likert scale. These items had a Cronbach's alpha of .92 in this sample.

Materials

Four vignettes were constructed by the experimenters in accordance with the National Health and Medical Research guidelines on familial aspects of cancer (NHMRC, 1999). Vignettes have been demonstrated to be suitable research tools that avoid practical or ethical issues (Gould 1996; Hughes & Huby, 2002). Vignettes differed on the risk of breast cancer (moderate or high), and the presence or absence of lifestyle information (see Appendix).

Each participant received a questionnaire booklet comprising 54 items including demographics and one of four vignettes. Demographics collected included age, marital status, and university course. In addition, participants were asked whether they were familiar with genetic counselling, and whether they knew anyone with breast cancer. Pilot work was conducted to ensure the vignettes were realistic and accurate, and the questionnaire booklet was easy to understand (see below).

Piloting vignettes and questionnaires

Genetic counsellors

Vignettes were piloted by genetic counsellors for their accuracy and reality. Twelve genetic counsellors were contacted and ten responded. Each vignette was assessed at least twice. Three-point rating scales for accuracy and reality were completed by the genetic counsellors, and open-ended questions allowed their input for improvement. Sixty per cent reported the vignettes as being very realistic and 30% reported very accurate, with 40% reporting fairly realistic and fairly accurate. Minor changes were made to the vignettes on the basis of common responses to the open-ended questions.

Potential participants

As items from previous questionnaires were altered to be relevant for breast cancer, the questionnaire booklet was also piloted by 11 potential participants for understanding and comprehension. The vignettes were also piloted in this sample to assess whether participants could easily believe and imagine themselves in the presented scenarios. An open-ended question was also included allowing for suggestions to improve either the vignette or the questionnaire booklet. All participants in this sample believed that the items in the questionnaire booklet were both easy to understand and to comprehend. Eighty-two per cent believed that they could very easily imagine themselves in the scenario detailed by the vignette, and 18% reported it was fairly easy. One hundred per cent thought that the scenario was very believable. All four vignettes were piloted at least twice. On the basis of the open-ended feedback, one change was made to the wording of the vignettes.

Procedure

Using a randomisation generator, vignettes numbered 1 to 4 within questionnaire packs were randomly allocated to individual students. Questionnaires took between 10 and 15 minutes to complete. They were completed and returned to a sealed drop box or directly to the researchers.

Results

Preliminary analyses

The numbers of vignettes returned per group and some participant demographics relating to each vignette group, are shown in Table I. The differences between numbers of questionnaires returned for each vignette was not significant, χ² (N = 198) = 3.39, p = .27.

Table I. Vignette numbers and participants demographics across vignette groups, including age and experience with breast cancer in frequency (and standardised residuals)

Measures	Moderate risk		High risk
	No lifestyle information	With lifestyle information	No lifestyle information	With lifestyle information
Vignettes returned	50	56	38	54
Mean age (SD)	21.2 (4.62)	23.2 (7.10)	21.2 (5.93)	21.5 (5.14)
Have heard of genetic counselling	18 (0)	22 (.4)	12 (−.4)	19 (−.1)
Did not know anyone with breast cancer	14 (0)	9 (−1.7)	10 (−.2)	22 (1.8)
Did know someone:	35 (0)	46 (1.1)	27 (.1)	31 (−1.1)
A first degree relative	4 (.6)	3 (−.2)	2 (−.2)	3 (−.2)
Another family member	18 (.6)	24 (1.5)	9 (−.8)	11 (−1.4)
A friend	19 (−.2)	25 (.6)	16 (.3)	18 (−.7)
I have been diagnosed	1 (.7)	1 (.6)	0 (−.6)	0 (−.7)
Other	5 (−.3)	6 (−.2)	4 (−.2)	8 (.7)

Demographic variables were tested for even distribution across the four groups. A one-way analysis of variance (ANOVA) showed that age was evenly distributed across the four vignette groups F(3,193) = 1.33, p = .27. The sample was positively skewed with a skewness value (standard error) of 2.64 (.18).

Contingency table analysis was used to examine response rate across vignette groups for categorical demographic variables. There was no difference across groups in marital status, (χ² (N = 192) = 5.00, p = .84), nor fields of university study (χ² (N = 194) = 0.56, p = .91).

Variables having a first degree relative with breast cancer (χ² (N = 194) = 0.45, p = 0.93), having a friend with breast cancer (χ² (N = 194) = 1.67, p = .64), personally being diagnosed with breast cancer (χ² (N = 194) = 1.76, p = .62), knowing someone else with breast cancer (χ² (N = 194) = 0.75, p = 0.86), were not significant against vignette group. There was, however, a significant relationship between not knowing anyone with breast cancer and vignette group (χ² (N = 194) = 8.44, p = .04). Examination of standardised residuals indicated that the high risk with no lifestyle information vignette group had a high proportion of women who did not know anyone with breast cancer (standardised residual = 1.8, Table I), which contributed most to this significant result. Likewise, there was a significant relationship between having another family member with breast cancer and vignette group (χ² (N = 194) = 8.01, p = .046), mostly due to a high response to having another family member with breast cancer within the moderate risk with no lifestyle information group (standardised residual = 1.5, Table I). Independent t-tests indicated that having another family member with breast cancer was not significant for the main outcomes. However the variable not knowing anyone with breast cancer was significant for the coping variable hopelessness. Specifically, participants who did not know anyone with breast cancer (M = 3.26, SD = 1.25) had higher hopelessness than those who did (M = 2.82, SD = 1.29), t(192) = 2.15, p = .03. Therefore, ANCOVA was adopted for the main analysis with the variable not knowing anyone with breast cancer as the covariate.

Main analyses

Missing data was estimated on SPSS using missing data analysis and included in all analyses. Means and standard deviations of responses to the measures are presented in Table II.

Table II. Means (standard deviations) for each measure across the four vignette groups

Measures	Moderate risk		High risk
	No lifestyle information	With lifestyle information	No lifestyle information	With lifestyle information
Risk Perception	5.25 (1.19)	5.28 (1.32)	5.44 (1.47)	5.62 (1.18)
Beliefs in Health Behaviours
1. Exercise	4.18 (1.15)	4.87 (0.94)	3.82 (0.99)	4.63 (0.91)
2. Alcohol Consumption	4.34 (1.37)	4.82 (0.94)	4.18 (1.14)	4.92 (0.87)
3. Healthy Diet	5.06 (1.09)	5.15 (0.88)	4.81 (1.00)	5.25 (0.85)
Motivation to Improve Health
1. Exercise	5.56 (1.15)	5.67 (1.18)	5.36 (1.28)	6.04 (0.81)
2. Alcohol Consumption	5.29 (1.26)	5.29 (1.47)	5.30 (1.36)	5.32 (1.35)
3. Healthy Diet	6.01 (0.89)	5.57 (1.28)	5.66 (1.25)	6.18 (0.90)
Coping Strategies
1. Rational Problem Solving	5.86 (0.94)	5.28 (1.09)	5.65 (0.87)	5.62 (0.81)
2. Fatalism	3.62 (1.27)	3.76 (1.20)	3.95 (0.94)	3.89 (0.98)
3. Hopelessness	2.66 (1.35)	3.06 (1.26)	3.12 (1.26)	3.01 (1.26)

A 2×2 between subjects Multivariate Analysis of Variance (MANOVA) was carried out with level of risk (moderate or high) and lifestyle information (present or absent) as the independent variables. Risk information had no effect, Wilks Λ = .94, F(11,184) = 1.07, p = 33. The impact of lifestyle information was significant, Wilks Λ = .73, F(11,184) = 6.05,p < .001. There was an interaction effect between risk effect and lifestyle effect, Wilks Λ = .89, F(11,184) = 2.14, p = .02. The ANCOVA results for each of the dependent variables used in the MANOVA are presented in Table III.

Table III. Results of 2×2 analysis of variance for each measure

Measures	Risk effect	Lifestyle effect	Risk × Lifestyle
Risk Perception	F(1,194) = 2.78, p = .10	F(1,194) = 0.85, p = .36	F(1,194) = 0.17, p = .68
Beliefs in Health
1. Exercise	F(1,194) = 4.06, p = .04^☆	F(1,194) = 25.47, p < .001^☆☆	F(1,194) = 0.17, p = .67
2. Alcohol Consumption	F(1,194) = 0.08, p = .78	F(1,194) = 13.77, p < .001^☆☆	F(1,194) = 0.44, p = .51
3. Healthy Diet	F(1,194) = 0.37, p = .54	F(1,194) = 3.31, p = .0.71	F(1,194) = 1.38, p = .24
Motivation for Health
1. Exercise	F(1,194) = 0.48, p = .49	F(1,194) = 6.58, p = .011^☆	F(1,194) = 2.184 p = .09
2. Alcohol Consumption	F(1,194) = 0.11, p = .74	F(1,194) = 0.04, p = .95	F(1,194) = 0.01, p = .98
3. Healthy Diet	F(1,194) = 0.76, p = .38	F(1,194) = 0.09, p = .77	F(1,194) = 7.48, p = .007^☆
Coping Strategies
1. Problem Solving	F(1,194) = 0.25, p = .61	F(1,194) = 4.94, p = .03^☆	F(1,194) = 3.44, p = .07
2. Fatalism	F(1,194) = 1.71, p = .19	F(1,194) = 0.6, p = .81	F(1,194) = 0.15, p = .70
3. Hopelessness	F(1,194) = .88, p = .35	F(1,194) = 0.48, p = .49	F(1,194) = 1.07, p = .30
^☆p < .05; ^☆☆p < .001.

Inspection of Table III indicates that the lifestyle information variable had an effect on two of the beliefs in health behaviours variables and a trend on the third. This demonstrated that providing lifestyle information resulted in an increase in beliefs about efficacy of health behaviours for exercise and alcohol consumption, and a trend for healthy diet. Significant main effects for lifestyle information were also found for motivation for health with respect to exercise, and for problem solving as a coping strategy. The former effect indicated that providing lifestyle information resulted in increased motivation for increasing exercise, and the later effect indicated that providing lifestyle information resulted in decreased use of problem solving as a coping strategy.

Significant interaction effects occurred for motivation to have a healthy diet. This interaction is displayed in Figure 1. It can be seen that providing lifestyle information increased motivation for dietary change in the high-risk group and decreased motivation in the moderate-risk group.

Figure 1. The interaction between level of risk and lifestyle information on motivation to have a healthy diet

Although the multivariate main effect for risk level was not significant, the main effect for risk level was significant for the variable belief in efficacy of exercise. This main effect indicated that belief in the efficacy of exercise was higher in the moderate-risk group than the high-risk group.

No effects were significant or even tended towards significance on the variables risk perception, motivation to change alcohol consumption, fatalism as a coping strategy, or hopelessness as a coping strategy.

Discussion

This study aimed to assess the impact of receiving hypothetical genetic risk information for breast cancer, with and without the provision of lifestyle information. Specifically, it was hypothesised that participants who received lifestyle information would have lower perceived risk, higher beliefs in the efficacy of health behaviours, higher motivations to improve behaviours, less negative coping strategies of fatalism and hopelessness, and increased use of the positive coping strategy of rational problem solving.

The multivariate analysis indicated that providing lifestyle information did make a difference (p < .001). The most noticeable findings were in the domain of beliefs about the efficacy of health behaviours. Providing lifestyle information resulted in highly significant increases in efficacy beliefs for exercise and alcohol consumption, and a trend for an increase for healthy diet. Interestingly, whilst changes in health beliefs pertaining to exercise were accompanied by changes in motivation to increase exercise, changes in health beliefs pertaining to alcohol consumption were not accompanied by changes in motivation to decrease alcohol consumption. This result is comparable to that achieved by Lemon et al. (2004) who demonstrated that women at increased risk of breast cancer can improve their health behaviours if they believe those behaviours are a risk factor, however this result was not consistent across women or across behaviours. Lemon et al. (2004) found women did not change smoking behaviours, similarly the women in this study displayed little motivation to reduce alcohol consumption.

Providing lifestyle information did not lead to the increased use of coping strategies generally considered maladaptive, namely fatalism and hopelessness, but it did lead to a decreased use of the adaptive coping strategy of rational problem solving. The reduction of rational problem solving was in contrast to predictions and was also inconsistent with the finding by Senior et al. (2000). As this study demonstrated the provision of genetic information can lead to a perception of a less preventable disease, it was unexpected that this study showed the provision of lifestyle information reduced rational problem solving.

The multivariate analysis resulted in a significant interaction between lifestyle information and level of risk (p = .02), and this effect was mainly apparent on the motivation for healthy diet variable. This interaction indicated that for diet, the impact of providing lifestyle information depended on whether the recipient was at high or only moderate risk as motivation for change was increased in the former group but decreased in the latter group.

In summary, the results offer mixed support for providing lifestyle information in genetic counselling. It led to: (i) positive changes in beliefs and motivation with respect to exercise; (ii) positive changes in beliefs but not motivation for alcohol consumption; and (iii) marginal positive changes in beliefs regarding diet, and positive changes in motivation for high-risk individuals but negative changes in motivation for moderate-risk individuals.

A limitation of the study is that it was carried out with university students. This group showed that it was prepared to increase exercise in response to the lifestyle information but not change its alcohol consumption. It seems likely that a middle aged or older group may have different priorities. Indeed, a recent qualitative study identified that life stage influences women's behavioural response to genetic risk particularly concerning alcohol consumption and diet (Rees et al., 2006). Also, over two thirds of the participants were psychology or nursing students who may have known more about how lifestyle relates to health than the general population, so that providing lifestyle information to this group may have had less impact than it would have had on other groups. The results are also limited with a low recruitment rate of 37.4%. Although the vignettes were randomised, there were still differences between the groups.

Another limitation pertains to the use of vignettes. A meeting with a genetic counsellor provides the opportunity to clarify and discuss concerns, which is not possible with hypothetical scenarios. Also, it is always debateable the degree to which participants are able to respond in terms of the vignettes rather than themselves. However, although the results of this study are speculative due to the use of hypothetical information, they are an indication that the information presented by genetic counsellors has the ability to influence responses to genetic risk information.

Future research needs to investigate behavioural responses to breast cancer risk and factors that determine these responses. Longitudinal studies that assess what information is provided during a genetic consultation and the impact of genetic counselling on beliefs and behaviour would be valuable in assessing responses to these consultations. Associations between behavioural change and psychological outcomes such as perceived risk and coping strategies, are also relevant to the assessment of providing information to promote the most positive responses.

An important finding of this study is that the provision of lifestyle information is not accompanied by significant negative psychological responses, so future research could address a similar research question in a clinical population. A longitudinal study to examine the psychological and behavioural impacts of genetic counselling, would contribute to this body of knowledge. This study rests on the theoretical assumption that the measured outcomes do influence behaviours and it would be worthwhile to take this line of research one step further by seeing whether behaviour change actually does take place.

Conclusion

In conclusion, knowledge of genetics is increasing very rapidly and genetic counselling is a growth profession. More research is needed, however, to understand the impact of providing lifestyle information within the context of genetic counselling, to maximise positive responses.

Acknowledgements

This research was conducted as part of an honours project at RMIT University Melbourne. The authors wish to thank Dr John Reece, RMIT University Melbourne, and Kathryn Thomson, Peter MacCallum Cancer Centre, for statistical advice. The authors would also like to thank the genetic counsellors in Victoria for their help with the evaluation of the vignettes.

Appendix

Vignette 1

Your grandmother died from breast cancer a number of years ago, and your mother has recently been diagnosed with the disease. You are concerned that this means you too may develop the disease. You make an appointment to go to a familial cancer clinic to find out more about your risk. At your appointment, a genetic counsellor assesses specific details of your family history of cancer. The genetic counsellor tells you that your family history is likely to be a result of a combination of both genetic and environmental factors. The genetic counsellor tells you that you are at moderately increased risk of breast cancer, compared to women in the general population. A surveillance programme involving regular mammography screening and clinical breast examination is recommended to begin between the ages of 40 – 50. In addition, you are advised to maintain breast awareness and visit your GP promptly if you notice any changes.

Vignette 2

Your grandmother died from breast cancer a number of years ago, and your mother has recently been diagnosed with the disease. You are concerned that this means you too may develop the disease. You make an appointment to go to a familial cancer clinic to find out more about your risk. At your appointment, a genetic counsellor assesses specific details of your family history of cancer. The genetic counsellor tells you that your family history is likely to be a result of a combination of both genetic and environmental factors. The genetic counsellor tells you that you are at moderately increased risk of breast cancer, compared to women in the general population. A surveillance programme involving regular mammography screening and clinical breast examination is recommended to begin between the ages of 40 – 50. In addition, you are advised to maintain breast awareness and visit your GP promptly if you notice any changes. The genetic counsellor also tells you about recent research, which suggests that certain health habits may influence breast cancer risk. In particular being overweight, high alcohol consumption and lack of physical activity have been associated with an increased risk of breast cancer.

Vignette 3

Your mother died from breast cancer a number of years ago, and your older sister has recently been diagnosed with the disease at a young age. You are concerned that this means that you too may develop the disease. You make an appointment to go to a familial cancer clinic to find out more about you risk. At your appointment, a genetic counsellor assesses specific details of your family history of cancer. Based on your family history, the genetic counsellor tells you that it is likely there is a genetic alteration in your family that puts you at risk for breast cancer. The genetic counsellor tells you that you are at potentially high risk of breast cancer, compared to women in the general population. You make another appointment to discuss the option of genetic testing in your family. A surveillance programme involving regular mammography screening and clinical breast examination is recommended to begin at around age 35 – 40. In addition, you are advised to maintain breast awareness and visit your GP promptly if you notice any changes.

Vignette 4

Your mother died from breast cancer a number of years ago, and your older sister has recently been diagnosed with the disease at a young age. You are concerned that this means that you too may develop the disease. You make an appointment to go to a familial cancer clinic to find out more about you risk. At your appointment, a genetic counsellor assesses specific details of your family history of cancer. Based on your family history, the genetic counsellor tells you that it is likely there is a genetic alteration in your family that puts you at risk for breast cancer. The genetic counsellor tells you that you are at potentially high risk of breast cancer, compared to women in the general population. You make another appointment to discuss the option of genetic testing in your family. A surveillance programme involving regular mammography screening and clinical breast examination is recommended to begin at around age 35 – 40. In addition, you are advised to maintain breast awareness and visit your GP promptly if you notice any changes. The genetic counsellor also tells you about recent research, which suggests that certain health habits may influence breast cancer risk. In particular being overweight, high alcohol consumption and lack of physical activity have been associated with an increased risk of breast cancer.