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Research
Communicating prognosis in early breast cancer: do women understand the language used?
Elizabeth A Lobb, Phyllis N Butow, Dianna T Kenny and Martin H N
Tattersall
MJA 1999; 171: 290-294
For related articles see Maguire, Prince & Naganathan et al
Abstract -
Introduction -
Methods -
Results -
Discussion -
Acknowledgements -
References -
Authors' details
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More articles on Oncology
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Abstract |
Objectives: To determine the degree to which women
with early breast cancer understand the prognostic information
communicated by clinicians after breast cancer diagnosis, and their
preferences for how this information is presented.
Design: Cross-sectional survey conducted within two months
of breast cancer diagnosis, using a self-administered written
questionnaire.
Participants and setting: One hundred women attending
five Sydney teaching hospitals and one country hospital, who were
diagnosed with early stage breast cancer between January and
December 1997.
Results: The 100 respondents represented 70% of the 143
women originally approached to participate. Many
respondents did not fully understand the language typically used by
surgeons and cancer specialists to describe prognosis: 53% could not
calculate risk reduction (with adjuvant therapy) relative to
absolute risk; 73% did not understand the term "median" survival; and
33% believed a cancer specialist could predict an individual
patient's outcome. Women in professional/ paraprofessional
occupations understood more prognostic information than
non-professional women. There was no agreement on the descriptive
equivalent of a "30%" risk, nor the numerical interpretation of a
"good" chance of survival. Forty-three per cent of women preferred
positively framed messages (eg, "chance of cure"), and 33%
negatively framed messages (eg, "chance of relapse"). The
information women most wanted was that relating to probability of
cure, staging of their cancer, chances of treatment being
successful, and 10-year survival figures with and without adjuvant
therapy.
Conclusions: Our results suggest that misunderstanding
is responsible for women's confusion about breast cancer prognosis.
Clinicians should use a variety of techniques to communicate
prognosis and risk, and need to verify that the information has been
understood.
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| | Introduction |
To make informed decisions women with breast cancer must understand
what their prognosis is without systemic treatment, and the likely
advantages and disadvantages of treatment.
While most Australian doctors now tell cancer patients their
diagnosis,1 prognosis is less commonly
discussed.2 Reticence to provide
prognostic information is often based on concerns that the
information will be overwhelming, not understood or will destroy
hope.3,4
However, it is not clear whether the information
itself, or the language used, is the critical feature.
Many patients have a poor understanding of their disease and
their prognosis,5,6 or have difficulty
recalling the information they have been given about their
disease.7,8 Similarly, women at risk
of developing breast cancer commonly misreport individual and
population risk.9 Denial and minimisation of
risk are also common psychological reactions to cancer risk
notification after screening procedures.10 If it were possible to
determine whether patients cannot understand the terminology or
mathematics of risk information, or, rather, prefer not to be told or
do not absorb the information, clearer directions for best clinical
practice in discussing prognosis could be established.
Most previous studies on risk communication in cancer have not dealt
specifically with issues pertinent to women with breast cancer. In
two studies that did, the women surveyed were well down the treatment
path and their experience of learning their prognosis was long
past.5,11 To our knowledge, there
are no reports of women's understanding of specific prognostic
information in early breast cancer.
We investigated women's understanding of prognostic information
and their preferences for the way the information on the risk of their
breast cancer recurring after surgery is presented to them.
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Methods |
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Survey subjects | |
Women were recruited through their treating physician. To ensure
input from a range of women, five urban centres attracting referrals
from populations with different socioeconomic profiles (Royal
Prince Alfred, Royal North Shore, Prince of Wales, St George, and
Westmead hospitals, all in Sydney, New South Wales) and one rural
centre (Tamworth Hospital, Tamworth, NSW) were approached to
participate in the study. Thirteen breast surgeons and 13 medical
oncologists from these centres were invited to participate in the
study and all agreed.
One hundred and forty-three consecutive women newly diagnosed with
stage I or II breast cancer at any of the six treatment centres between
January and December 1997 were contacted by letter to request their
participation. The women received the letter within 2-4 weeks of
making their own decisions about adjuvant treatment and within 2
months of their initial diagnosis. (The timing of questionnaire
administration was carefully considered to maximise the saliency of
the issues while avoiding distressing women making their own
treatment decisions.) The letter was followed up by a phone call from
the research coordinator, who obtained verbal consent for
participation and then sent out the questionnaire by mail. One centre
opted to send women a letter signed by their oncologist inviting them
to participate in the study.
The survey sample included patients of surgeons and medical
oncologists in both private and public practice. Women from a
non-English-speaking background with insufficient knowledge of
English to complete the questionnaire, and women presenting with a
second cancer, were excluded.
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Questionnaire |
We gathered the data using a self-administered written 17-item
questionnaire, designed on the basis of a review of the literature; an
analysis of 20 audiotapes of initial oncology consultations with
breast cancer patients (collected from two centres -- Royal Prince
Alfred Hospital and Westmead Hospital -- during another study
undertaken between 1995 and 199712); and expert
consultation (a working party set up by the National Breast Cancer
Centre). The 20 audiotapes were transcribed and the contents
analysed to identify the range of ways in which prognosis was conveyed
to patients (eg, absolute and relative risk, cumulative risk,
numerical or non-numerical probability, and individual versus
population risk).
The questionnaire investigated women's understanding of and
preferences for these different formats used by doctors for
disclosing prognosis and risk information. In addition, a standard
hypothetical scenario of adjuvant therapy in early stage breast
cancer was included, and women responded to questions applying to
that scenario (Box 1).
Six of the questions explored women's understanding of different
ways in which the risk of breast cancer recurring after surgery could
be presented. Two sample questions are shown in Box 2. A "don't know"
option was not offered in the items relating to "understanding" in
order to force a choice and allow an analysis of common errors in
interpretation.
The remaining questions focused on the importance of different
prognostic information to women's decision making, and on their
preferences for presentation of risk - for example:
- percentages versus numbers (eg, "70%" v. "7 in 10");
- numerical versus verbal descriptions of risk (eg, "30%" v.
"small"); and
- positively framed versus negatively framed statements (eg, "70%
chance of remaining free of cancer" v. "30% chance of the cancer coming
back").
The questionnaire also elicited the women's demographic data and
details of their breast cancer diagnosis and treatment (Box 3).
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Statistical analysis | |
Appropriate sample sizes were calculated using the SAM sample size
software package.13 Sample size calculations
were based on effect sizes from related studies in patients' level of
recall after a variety of interventions. In an Australian study
measuring understanding of information presented in an oncology
consultation, a sample size of 47 per group was sufficient to detect
statistically significant differences of 7% (P < 0.005)
in recall between groups. Thus, in a comparison of two patient
subgroups (eg, young v. old), a total sample size of 100 would allow us
to detect a similar difference in responses in the two groups. A sample
size of 100 would also allow detection of a difference of 30% or more
(felt to be clinically significant) between subgroups in the
proportion of women preferring one presentation of risk versus
another, with a power of 0.8 and a significance level of 0.05.
A "total understanding" score was calculated by summing correct
responses to the six items assessing "understanding". The summary
score was normally distributed (K-S Lilliefors .0514).
Descriptive statistics were used to identify the percentage of
patients understanding and preferring different risk information.
Analysis of variance (ANOVA), Student's t tests and
χ2
tests of association were used to examine the relationship between
demographic variables and outcomes, as appropriate; two-sided
tests were used.15 | |
Ethical approval | |
Approval was granted for this study by the Ethics Committee of the
University of Sydney, the Central Sydney Area Health Service, the
Southern Sydney Area Health Service, and individual hospital ethics
committees at Westmead, Royal North Shore and Tamworth hospitals.
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Results |
Of the 118 women who agreed to participate in the survey, 100 returned
questionnaires (70% of the original 143 women contacted).
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Demographic data | |
Demographic characteristics of the participants are presented in
Box 3. Their mean age was 56 years and most were city dwellers. Just over
half had completed the Higher School Certificate, university or some
form of tertiary training. The percentage of women with tertiary
qualifications was 42% (compared with 37% in the general Australian
population16)..Nearly two-thirds worked (or had worked) in
professional or paraprofessional occupations, and 22% were working
in occupations related to medicine (eg, doctor, nurse, medical
receptionist, technician).
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Questionnaire responses | |
A summary of the women's responses to the questionnaire is given in Box
4.
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Discussion |
We have identified some of the problems women with breast cancer
experience when trying to interpret prognostic information
presented by their doctors. Our results support the hypothesis that
it is misunderstanding, not denial, that causes confusion. A
considerable number of women in our study did not clearly understand
some of the language used to describe the risk of breast cancer
recurrence after surgery or how additional treatment might benefit
them. Moreover, the response from this group of relatively highly
educated women probably represents a "best case" scenario, and, if
anything, one might expect understanding to be poorer in the general
population of women with breast cancer.
These findings have implications for informed consent. Clinicians
need to explain what type of prognostic information can be given, and
enquire how much of this information women want to hear. They should
check very carefully how women have interpreted the information
presented to them, and must not assume that, because a woman has
already consulted a number of specialists, her prognosis has been
conveyed to her and clearly understood. This applies to all patients
with breast cancer, but especially those who work in unskilled
occupations.
It might be argued that our sample was not truly representative, as (i)
the women surveyed, having recently been told their diagnosis, may
not have been in the best frame of mind to answer the questionnaire
clearly and impartially; and (ii) we did not include a similar group of
women who had never had breast cancer. Furthermore, patient
responses may have been different had the questions concerned
personal experience rather than a hypothetical case
scenario.5,9,10,19
However, data from Degner et al20 suggest that views
expressed by people diagnosed with cancer differ considerably from
those of the well population, which underscores the importance of
surveying those who have actually been diagnosed with cancer. In
addition, we felt that a typical case vignette was the most
appropriate tool to control for the influence of individual disease
variables and treatment protocols; to reduce the positive bias
associated with evaluating one's own treatment team; and to examine
all aspects of risk communication in adjuvant therapy.
Creative measures to assist women in understanding risk statistics
are needed. Bunker et al have recently proposed that a life table
constructed from published statistics on national morbidity and
mortality may be used to display the likelihood of developing or dying
of a disease at any given moment.21 A similar approach could
be used to display the likelihood of disease recurrence and premature
death after cancer diagnosis. We believe that these and other
information aids may contribute to informed patients' involvement
in treatment decisions, and a more realistic understanding of
prognosis.
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Acknowledgements | |
We thank Dr Afaf Girgis, Dr Lyn Mann, Ms Kate White, Ms Joan Wilson and Ms
Kim Hobbs for their assistance and advice; also the 26 clinicians who
participated in this project, and the women who so willingly filled
out the questionnaire. The research was funded by the National Health
and Medical Research Council National Breast Cancer Centre of
Australia.
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References |
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Butow PN, Kazemi J, Beeney LJ, et al. When the diagnosis is cancer:
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Oken D. What to tell cancer patients: a study of medical attitudes.
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Beisecker AE, Helmig I, Graham D, et al. Attitudes of oncologists,
oncology nurses and patients from a women's clinic regarding medical
decision making with older and younger breast cancer patients.
Gerontologist 1994; 34: 505-512.
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Siminoff LA, Fetting JH, Abeloff MD. Doctor-patient
communication about breast cancer adjuvant therapy. J Clin
Oncol 1989; 7: 1192-1200.
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Sheldon JM, Fetting JH, Siminoff LA. Offering the option of
randomized clinical trials to cancer patients who overestimate
their prognoses with standard therapies. Cancer Invest
1993, 11: 57-62.
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Dunn SM, Butow PN, Tattersall MHN, et al. General information tapes
inhibit recall of the cancer consultation. J Clin Oncol 1993;
11: 2279-2285.
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Mackillop WJ, Stewart WE, Ginsburg AD, Stewart SS. Cancer
patients' perceptions of their disease and its treatment. Br J
Cancer 1988; 58: 355-358.
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Evans DR, Blair V, Greenhalgh R, et al. The impact of genetic
counselling on risk perceptions in women with a family history of
breast cancer. Br J Cancer 1994; 70: 934-938.
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Lerman C, Rimer BK, Engstrom PF. Cancer risk notification:
psychosocial and ethical implications. J Clin Oncol 1991; 9:
1275-1282.
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Hughes KK. Decision making by patients with breast cancer: the
role of information in treatment decision selection. Oncol Nurs
Forum 1993; 20: 623-628.
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Brown R, Dunn S, Butow P. Meeting patient expectations in the
cancer consultation. Ann Oncol 1997; 8: 877-882.
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Glasziou P. SAM 2.1: a sample size calculator [computer program].
Sydney: NHMRC Clinical Trials Centre, University of Sydney, 1992.
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Armitage P, Berry G. Statistical methods in medical research.
Oxford: Blackwell Scientific, 1994: 397.
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SPSS Advanced Statistics, TM6.1. Chicago; SPSS Inc, 1994.
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Australian women's year book. Canberra: Australian Bureau of
Statistics, 1997.
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Degner LF, Kristjanson LJ, Bowman D, et al. Information needs and
decisional preferences in women with breast cancer. JAMA
1997; 277: 1485-1492.
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Bilodeau BA, Degner LF. Informational needs, sources of
information, and decisional roles in women with breast cancer.
Oncol Nurs Forum 1996; 23: 691-696.
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Marteau TM. Framing of information: its influence upon decisions
of doctors and patients. Br J Soc Psychol 1989; 28: 89-94.
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Degner LF, Sloan JA. Decision making during serious illness: what
role do patients really want to play? J Clin Epidemiol 1992;
45: 941-950.
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Bunker JP, Houghton J, Baum M. Putting the risk of breast cancer in
perspective. BMJ 1998; 317: 1307-1309.
(Received 4 Jan, accepted 11 May, 1999)
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| | Authors' details |
University of Sydney, Sydney, NSW.
Elizabeth A Lobb, BAdEd, MAppSci, Associate Lecturer,
Medical Psychology Unit.
Phyllis N Butow, PhD, MPH,
Executive Director, Medical Psychology Unit; and Research
Co-ordinator, Department of Psychological Medicine, Royal North
Shore Hospital.
Dianna T Kenny, PhD, MA, Associate Professor
of Psychology, Faculty of Health Sciences.
Martin H N
Tattersall, MD, FRACP, Professor of Cancer Medicine,
Department of Medicine.
Reprints: Ms E A Lobb, Associate Lecturer, Medical
Psychology Unit, Department of Psychological Medicine, University
of Sydney, NSW 2006.
Email: lizlobbATblackburn.med.usyd.edu.au
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| 1: Hypothetical breast cancer scenario used in the questionnaire
Sheila is a 54-year-old woman with breast cancer. Sheila has gone through the menopause. Sheila chose to have her breast cancer (tumour) removed by a lumpectomy, but she also had some of the lymph glands in her armpit removed.
Sheila was advised to have radiotherapy after her lumpectomy. Sheila's breast cancer was small, and the lymph nodes under her arm were not affected with cancer. Her tumour contained receptors to oestrogen, suggesting that it may be sensitive to the effects of hormones. Sheila's doctor uses this information to decide if she would benefit from additional treatment.
Sheila understands that any additional treatment other than surgery is called "adjuvant" therapy. Adjuvant therapy means giving treatment now after her surgery to try to prevent the cancer returning in the future.
Following her breast cancer surgery, Sheila was told of her risk of having her cancer return (her prognosis) if she has no further treatment. This risk can be expressed in different ways.
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2: Examples of questions to assess women's understanding of relative risk reduction
Example A:
Sheila's doctor told her that 30% of women with a cancer similar to hers will have their cancer come back within 5 years. If Sheila has additional treatment, the risk of her cancer coming back will be reduced by 30%.
Tick one only
- If Sheila has additional treatment this means the risk of her cancer coming back within 5 years is zero.
- If Sheila has additional treatment this means the risk of her cancer coming back within 5 years is 21%.
- If Sheila has additional treatment this means the risk of her cancer coming back within 5 years is 30%.
Example B:
If Sheila's doctor says the median time for her breast cancer to return without further treatment is about 5 years, he/she means:
- The average time for Sheila's cancer to return is 5 years
- That 50% of women with breast cancer like Sheila's will have their cancer return within 5 years
- That the women whose cancer will come back will have it come back within
5 years
- I don't understand the word "median"
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3: Demographic characteristics of the respondents (n = 100) to a questionnaire about provision of information on breast cancer prognosis* | | Category | Number of participants |
| | Age (mean, 56 years; range, 35-88 years) | | | Postcode | | City | 82 | | Country | 18 | | Educational level | | Non-tertiary | 58 | | Tertiary | 42 | | Occupation | | Professional/paraprofessional | 63 | | Non-professional | 36 | | Marital status | | Married | 58 | | Other | 41 | | English as first language | 84 | | Working in medicine-related occupation | 22 | | Time since diagnosis | | 1-2 months | 71 | | ≥3 months | 26 | | Treatment for breast cancer | | Lumpectomy only | 14 | | Mastectomy only | 17 | | Lumpectomy + R | 38 | | Lumpectomy +R + C | 15 | | Mastectomy + C | 15 | | Family member/friend with breast cancer | | Yes | 61 | | No | 38 |
| | * Not all categories sum to 100 because of missing data.
R = radiotherapy; C = chemotherapy. |
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4: Summary of questionnaire responses
Understanding of risk
Risk concepts tested
- Absolute risk of relapse:
-- 86% of respondents gave a correct
response.
- 30% relative risk reduction, with therapy, of an absolute risk of 30% (sample question A, Box 2):
-- 47% gave a correct response;
-- 28% thought additional treatment would reduce the risk of relapse to
zero; and
-- 25% thought the risk would remain at 30%.
- Median 5-year survival (sample question B, Box 2):
-- 27% answered correctly;
-- 43% thought it meant "average" survival;
-- 10% thought that half the women not having adjuvant therapy would have their breast cancer return within 5 years; and
-- 20% did not understand the term "median".
- Interpretation of a graphical representation of risk:
-- 80% of respondents answered correctly.
- Distinguishing individual risk from population risk:
-- 66% gave correct response.
-- The remaining women believed that their cancer specialist knew whether or not they would respond to treatment.
Association with demographic variables
- Mean number of correct responses, 3.4 (95% CI, 3.08-3.6); only one woman answered all six questions correctly.
- Women in professional employment (mean number of correct responses, 3.6 [95% CI, 3.2-4.0]) or paraprofessional employment (mean number of correct responses, 3.4 [95% CI, 3.0-3.8]) understood more prognostic information than women in non-professional employment (mean number of correct responses, 2.5 [95% CI, 1.7-3.4] [F2,87 = 4.24, P = 0.02]).
- No other variables were found to be associated with understanding of risk (eg, working in a medically related field; having tertiary qualifications; having had surgery, radiotherapy and/or chemotherapy for breast cancer; or time elapsed since consultation in which prognosis was discussed).
Interpretation of words versus statistics
- There was no consistency in respondents' interpretation of
"a good chance of remaining free of cancer" in statistical terms, nor agreement on the non-numerical interpretation of "a 30% risk" (17% thought it was a very high or high risk, 34% that it was a medium risk, and 49% that it was a low risk).
Preferences for language
- 44% of respondents preferred "a 70% chance of cure", 13% preferred "a 7 in 10 chance", and the remainder had no preference.
- 53% of women preferred "a 30% chance of cancer coming back", 38% preferred "a small chance of cancer coming back", and the remainder had no preference.
- 49% of non-tertiary-educated women versus 24% of tertiary-educated women preferred the descriptive option (a "small" chance) (χ22 = 8.17, P = 0.02).
- 43% of women preferred the wording "70% chance of cure", 33% preferred "30% chance of the cancer coming back", and 25% had no preference.
Preferences for framing of information
- Reasons given for choosing positively framed prognostic information: "a more positive/optimistic statement" and "encourages determination to manage treatment positively".
- Reasons given for choosing negatively framed prognostic information: "it emphasises the importance of additional treatment" and "more specific/precise".
| Importance attributed to prognostic information (Table)- Over 90% of respondents regarded information about their chances of being cured, the staging of their cancer, and the chances that the recommended treatment would work as very important to their decision making.
- Nearly two-thirds of women regarded the 10-year survival rate with adjuvant therapy as very important information, and 45% wanted to know this rate without adjuvant therapy.
- These percentages are considerably higher than documented in previous studies.17,18
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Women's ratings of the importance of different types of prognostic information | | Prognostic information | Very important | Somewhat important | Not important |
| | My chances of being cured | 94% | 2% | 4% | | What things about my cancer influence my chances of being cured (eg, size of my cancer, whether lymph nodes are involved, etc) | 92% | 6% | 2% | | The chances that the recommended treatment will work | 91% | 7% | 2% | | How many women in my situation choosing to have the recommended treatment would be alive in 10 years | 60% | 29% | 11% | | Statistics about long term outcome of breast cancer | 50% | 32% | 18% | | How many women not choosing to have the recommended treatment are alive in 10 years | 45% | 35% | 20% | | The longest anyone in my situation has lived | 34% | 19% | 47% | | The shortest anyone in my situation has lived | 30% | 14% | 56% | | The risk of my cancer shortening my life compared with other life events (eg, heart disease, old age) | 45% | 23% | 32% | | The average time people in my situation have lived | 44% | 28% | 28% |
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