Tailored Risk Notification for Women with a Family History of Breast Cancer

Preventive Medicine 29, 355–364 (1999)Article ID pmed.1999.0556, available online at http://www.idealibrary.com on

Tailored Risk Notification for Women with a FamilyHistory of Breast Cancer

Roshan Bastani, Ph.D.,1 Annette E. Maxwell, Dr.P.H., Clarence Bradford, Ph.D.,Irene Prabhu Das, M.P.H

s
Division of Cancer Prevention and Control Research, UniverJonsson Comprehensive Cancer Cente
Background. Evidence indicates that although first-degree relatives of breast cancer cases are at increasedrisk of developing the disease themselves, they maybe underutilizing screening mammography. Therefore,interventions to increase the use of mammography inthis group are urgently needed.

Methods. A randomized two-group design was usedto evaluate an intervention to increase mammographyuse among women (N 5 901) with at least one first-degree relative with breast cancer. A statewide cancerregistry was used to obtain a random sample of breastcancer cases who identified eligible relatives. Themailed intervention consisted of personalized risk no-tification and other theoretically driven materials tai-lored for high-risk women.

Results. An overall significant intervention effectwas observed (8% intervention group advantage) inmammography at post-test. There was an interactionof the intervention with age such that there was noeffect among women ,50 years of age and a fairly large(20% advantage) effect among women 501 and 651.Health insurance, education, and having had a mam-mogram in the year before baseline assessment werepositive predictors of mammography at post-test. Per-ceived risk, calculated risk, and relationship to indexcancer case were not associated with mammographyreceipt.

Conclusion. The intervention was successful in in-creasing mammography rates among high-risk women
501 years of age. Further work is needed to determinewhy it was ineffective among younger women. q 1999
American Health Foundation and Academic Press

Key Words: breast cancer; high risk; risk notification.

1 To whon reprint requests should be addresssed at the Divisionof Cancer Prevention and Control Research, 650 Charles Young DriveSouth, A2-125 CHS, Box 956900, Los Angeles, CA 90095-6900. Fax:(310) 206-3566. E-mail: [email protected].

35

., and Kang X. Yan, M.A.

ity of California at Los Angeles School of Public Health andr, Los Angeles, California 90095-6900

INTRODUCTION

Breast cancer is the most common neoplasm amongwomen in the United States and the second leadingcause of cancer deaths. There were 178,700 new breastcancer cases and 43,900 breast cancer deaths estimatedin 1998 [1]. One of the most important risk factors forthis disease is having a history of breast cancer in afirst-degree relative. Women with a cancer history infirst-degree relatives have an approximate 2- to 10-foldincreased risk of the disease. Having only one first-degree relative with the disease results in a 2-fold in-creased risk. Risk increases substantially with multipleaffected family members and pre-menopausal diagnosisor bi-lateral disease in the index case [2–4].

There is general consensus that such high-riskwomen should be especially adherent to mammographyscreening guidelines [5]. Despite this, screening ratesamong such women are not substantially higher thanin the general population of women with similar demo-graphic characteristics [6–9]. For example, Vogel et al.[9] reported that mammography screening rates wereno different between a group of 501 women with a fam-ily history of breast cancer and a matched group ofcontrols. Bastani et al. [8] found that women with afamily history had only slightly higher mammographyrates compared with women without this risk factor.Estimates of the prevalence of mammography screen-ing in the previous year among high-risk women varywidely, from 28 to 94%, depending upon the characteris-tics of the sample and the time period in which the studywas conducted [10–12,6]. However, evidence indicatesthat even among select samples of high-risk women,such as those recruited from urban cancer centers [e.g.,13], mammography rates may be sub-optimal. There-
fore, interventions specifically targeting these womenare an important component of a comprehensive strat-egy for increasing and maintaining rates of screeningmammography nationwide.
5 0091-7435/99 $30.00Copyright q 1999 by American Health Foundation and Academic Press

All rights of reproduction in any form reserved.

I
356 BASTAN
We report the results of a randomized experimentdesigned to increase mammography utilization in apopulation-based sample of first-degree relatives ofbreast cancer cases, recruited through a statewide can-cer registry. Most of the available literature on womenat high risk for breast cancer is based on relativelysmall cross-sectional samples of women recruited fromspecific clinics or cancer centers, and most interventionstudies targeting this population have focused on risk

gible due to a variety of reasons such as living outside

perception and psychological distress. Our study addsto the literature by recruiting subjects through a state-wide cancer registry and by evaluating a risk notifica-tion intervention designed to specifically increasescreening mammography in this high-risk group.

METHODS

Overview of Research Design

A random sample of 2,500 breast cancer cases, diag-nosed in 1988, was obtained from the statewide Califor-nia Cancer Registry. The cases were contacted by mailto obtain information regarding their first-degree fe-male relatives (mothers, sisters, daughters). Eligiblefemale relatives were contacted by telephone to obtainbaseline information regarding risk factors, knowledge,attitudes, and behaviors regarding breast cancer andmammography. Nine hundred two women completedthe baseline survey and were thus subjects for thisstudy. Following the baseline survey women were ran-domized into an intervention (49.3%) or control group(50.7%). Women from the same family were randomizedas a unit rather than as individuals. Intervention sub-jects received a mail-out intervention consisting of apersonalized risk assessment as well as other materialstailored for high-risk women and a message regardingthe importance of obtaining regular screening mam-mography. Control subjects received existing NCI mate-rials on breast cancer and mammography. Approxi-mately 1 year after the baseline survey the originalgroup of 902 women was recontacted to obtain informa-tion that would help determine whether the interven-tion was successful in increasing screening mammogra-phy rates in the intervention group. Completed follow-up interviews were obtained from 84% (N 5 753) ofthe original sample. This article focuses on these 753women for whom we have both baseline and follow-up information.

Recruitment of Subjects

Under the Statewide Cancer Reporting Act of 1985,all newly diagnosed cancer cases in California are re-
ported to the California Cancer Registry (CCR). Rou-tinely available information from the CCR was used torecruit subjects into the study. Using their computer-ized database, the CCR identified a random sample of
ET AL.

2,500 female breast cancer cases (excluding in situcases) diagnosed in 1988 that met the following criteria:alive at time of case selection and address and/or tele-phone number available. Although the cases were diag-nosed in 1988, data were actually obtained from theregistry in late 1990. In November 1990, physicians ofthese cases (if available in the database) were sent aletter to obtain permission to contact their patients.Consent was assumed if we did not receive a responsefrom a physician. Next, cases with physician consentand cases for whom no physician was listed were con-tacted by mail to inform them of the study and elicitinformation on their female first-degree relatives, .30years of age. Cases were asked to first check with theirrelatives before releasing information about them. Eli-gible relatives identified in the above step were sentan informational letter regarding the study and told toexpect a telephone call in the next few weeks. A returnform was included with the letter to allow subjects toindicate good times for the telephone interview andtheir language preference (English or Spanish). Two tothree weeks following the mailed notification, eligiblerelatives were contacted by telephone to recruit theminto the study and to obtain baseline information oneligibility and risk factors. Eligibility criteria includedbeing the mother, sister, or daughter of the index case,being 30 years of age or older, residing in the UnitedStates or Canada, and having no personal history ofbreast cancer. Relatives identified as ineligible beforethe telephone call were sent a letter thanking them fortheir interest and explaining why they could not beincluded in the study. A similar telephone script wasfollowed for relatives identified as ineligible at the startof the baseline telephone interview.

As indicated in Table 1, the number of cases thatwere actually sent the recruitment letter is less thanthe number of cases received from the tumor registry(14% attrition). The most common reasons for this attri-tion were incomplete or inaccurate addresses and pa-tient death (information obtained from physicians).Five physicians asked us not to contact their patientsdue to the existence of severe comorbid conditions. Onephysician refused consent because of concern that thepatient may choose to obtain future treatment atUCLA. Of the 2,013 cases for whom we had valid ad-dresses and physician consent, 62% responded with in-formation on relatives. It is possible that some casesdid not respond at all because they did not have anyrelatives or did not have age-eligible relatives. The re-sponding cases provided information on a total of 1,846relatives. About a quarter of these relatives were ineli-

of the United States or Canada, being too young, andhaving breast cancer. In a small number of instancesthe case provided information on a relative but did notgive us permission to contact her. About 24% of eligible

baseline interview " '

Mail TelephoneRandomization 451 451

fied as being at slightly higher risk. Eleven percent of

↓ ↓ 78% ↓ 89%No. of relatives completing 1-year 352 401

follow-up interview

relatives declined participation in the study at the timeof the initial mailing to them. We were able to completebaseline interviews with 87% of consenting relatives,resulting in a total baseline sample of 902 women. Theinterviews were conducted using a computer-assistedtelephone interviewing (CATI) system. Approximately1 year following the baseline survey, this original cohortwas recontacted to obtain outcome information. At thisstage, the sample was randomly divided into twogroups. One group received the follow-up survey viamail and the other received it via telephone (usingCATI). As Table 1 indicates, the 12-month follow-upresponse rates were 78% by mail and 89% by telephonefor a total response rate of 84% (N 5 753). In order todetermine the yield of relatives from the initial sampleof cases obtained from the registry, we calculated a
relative-to-case ratio by dividing the number of rela-tives interviewed at baseline (902) by the number ofcases initially obtained from the registry (2,500). Ourstudy yielded 36 eligible relatives interviewed at base-line for every 100 cases obtained from the registry.
ER RISK NOTIFICATION 357

Intervention

The intervention materials consisted of a personal-ized risk notification, an educational booklet, a notepad,and a bookmark.

Risk Notification. Information obtained from thebaseline interview was used to provide each interven-tion subject with a personalized risk notification in theform of a letter listing her personal risk factors andclassifying her as being at slightly, moderately, or sub-stantially higher risk of developing breast cancer com-pared with other women her age. This classificationrepresented an adaptation of the risk algorithm devel-oped by Gail et al. [14]. The Gail model uses age atmenarche, age at first live birth, number of breast biops-ies, and number of first-degree relatives with breastcancer to calculate individual probability of developingbreast cancer. We incorporated the following additionalvariables in our algorithm: age at first biopsy, age ofthe index case with cancer, age of the subject, and age atmenopause. Rather than providing specific probabilityfigures, we classified women into three categories(slightly, moderately, or substantially higher risk)based on the presence of at least one risk factor in theappropriate category (see Table 2). Specific probabilityestimates were not provided because we felt that it wasnot appropriate to provide such information via mailwithout giving the recipient the opportunity to discussit with a trained professional. By definition, all inter-vention subjects in our sample had at least one first-degree relative with breast cancer. If the relative wasdiagnosed post-menopausally (over the age of 50 years)and the subject had no other risk factors, she was classi-

TAILORED BREAST CANC

TABLE 1

Accrual of First-Degree Relatives of Breast Cancer CasesIdentified through the California Cancer Registry (CCR)

No. of patients obtained from CCR 2,500 Attrition due to: 86.2% Physician nonconsent Death Duplicate cases ↓ Undeliverable letters ↓

No. of cases sent letters 2,013 Attrition due to: 61.8% No response after three letters No living female relatives 301 ↓ Death ↓

No. of cases (w/relatives) responding 1,244↓ ↓

No. of relatives provided by these cases 1,846 Attrition due to: 74.3% No permission to contact Lived overseas Too young ↓ Had breast cancer ↓

No. of eligible relatives (age 301 and 1,371no breast cancer)

↓ ↓ 76.1%No. of eligible relatives not declining 1,043

participation↓ ↓ 86.5%

No. of eligible relatives completing 902

the sample fell into this category. The majority of sub-jects (50%) fell into the moderately higher risk category.These women, in addition to having a first-degree rela-tive with post-menopausally diagnosed breast cancer,

TABLE 2

Classification into Risk Categories Based upon Presence of RiskFactors Cited under Each Category (N 5 747)

%

Slightly Higher Risk (N 5 86) 11.5Postmenopausal breast cancer in first-degree relative

Moderately Higher Risk (N 5 376) 50.3Over 50 years of age 36.8One breast biopsy by age 55 1.0Breast cancer diagnosis in second-degree relative 16.6Menarche before age 12 15.3Menopause after age 55 3.2
Nulliparity 10.8First child after age 30 5.2
Substantially Higher Risk (N 5 285) 38.1Premenopausal breast cancer in first-degree relative 36.8Two1 breast biopsies by age 55 2.7

I
358 BASTAN
had at least one other risk factor from those listed underthis category in Table 2. Examples of these additionalrisk factors include early menarche, late menopause,or having a second-degree relative with breast cancer.Subjects were classified as being at substantially higherrisk if they had a first-degree relative with pre-meno-pausally diagnosed breast cancer and/or two or morebreast biopsies before age 55 years. Thirty-eight per-cent of subjects fell into this category. Details of subjectrecruitment and risk calculation are reported else-where [15].

Other intervention materials. A booklet, entitled“Taking Action . . . When you have a family history ofbreast cancer,” was especially developed for this study.This booklet provided information on risk factors forbreast cancer, including family history, and stressedthe importance of early detection via mammography.Using a mix of graphics and text, it provided informa-tion on how the procedure is performed and highlightedthe associated benefits while minimizing the risks.Common barriers to screening were presented togetherwith credible counter-arguments. The notepad con-sisted of 25 sheets of paper with the word mammogra-phy printed across the top and a message, to obtain amammogram soon, printed along one side. The book-mark urged women to ask their physicians for a referralto a mammography facility and addressed some of thebarriers to obtaining screening by describing mammog-raphy as lifesaving, safe, fast, painless, affordable, etc.The booklet was mailed together with the risk notifica-tion. The notepad was mailed 1 month after the booklet,and the bookmark was mailed 1 month after that.

Women in the attention control group received a notethanking them for participating in the baseline surveytogether with a general information booklet on breastcancer developed by the National Cancer Institute.

The intervention was based on the Adherence Model[8,16–20], which is a comprehensive theoretical frame-work designed to understand adherence to, or participa-tion in, health behaviors. Decades of research have pro-duced a multitude of theoretical perspectives on theissue of adherence to health recommendations. Al-though each theory has its own unique features, it ispossible to see several common themes among them.The Adherence Model (Fig. 1) synthesizes several ofthese complementary theoretical perspectives in orderto ensure that all the major factors that may influenceadherence are addressed. In particular, the model incor-porates the Health Belief Model [21], the Theory of
Reasoned Action/Planned Behavior [22,23], the Trans-theoretical Model of Change [24,25], components of thePrecede Model [26], and the Social Influence Theory[27,29]. It also includes other important constructs sucha self-efficacy [30,31], social support [32,33], perceived
ET AL.

control [23], coping style [33,34], and psychological dis-tress. The intervention (risk notification letter, booklet,notepad, bookmark) was based upon applicable compo-nents of this theoretical framework. The risk notifica-tion letter was designed mainly to influence perceivedsusceptibility to developing breast cancer. The book-mark and notepad were designed to serve as remindersand cues to schedule and obtain a mammogram. Thebooklet, due to its format, was able to more comprehen-sively address model constructs. For example, detailedinformation on breast cancer risk factors, the benefitsof mammography, and age guidelines for obtaining theprocedure was provided. In addition, specific barriersto obtaining screening were addressed, accompanied by
strategies for overcoming them.
Although all interview and intervention materialswere available in Spanish, no subjects chose to be inter-viewed in Spanish or receive the Spanish language in-tervention package.

RESULTS

Subjects

Table 3 shows the demographic characteristics of the753 subjects for whom we had both baseline and follow-up data. The mean age of the sample was 51 years(range 5 30–90 years). Subjects were predominantlywhite, with relatively high levels of income and educa-tion. In order to determine whether subjects re-con-tacted at follow-up (N 5 753) differed from the drop-outs (N 5 148), we compared these two groups on demo-graphic characteristics and risk factors. Bivariate x2

tests (using P , 05) indicated that drop-outs were morelikely to be non-white (20% vs 13%) and less likely tohave an education beyond high school (59% vs 69%) orincomes of $30,000 or more (57% vs 67%). There wereno differences between drop-outs and those retainedwith respect to risk status. The drop-out rate was equalin the intervention (16.06%) and control (16.04%) arms.To assess the effectiveness of the randomization proc-ess, the intervention and control groups were comparedat both baseline and follow-up. At both time points,intervention subjects were significantly less likely tohave any college education (62% at both time points)compared with control subjects (73% at baseline, 76%at follow-up). This would be expected to underestimatean intervention effect, since lower education levels aregenerally associated with lower screening rates [35].No other statistically significant differences betweenthe two groups were observed.

Over a third of the women in the sample were relatedto one another. Therefore, their responses cannot beconsidered truly independent. However, the distribu-tion of related individuals was equal in the interventionand control groups.

359

d
FIG. 1. The A
Mammography Screening

The major outcome of interest was receipt of a screen-ing mammogram between the baseline and the follow-up surveys. Data on the outcome were based on self-reported behavior at the follow-up interview. Only
mammograms obtained for screening purposes were
20,000–29,999 18.630,000–39,999 16.740,000–49,999 14.7$50,000 35.9

Insurance (yes) 92.8

herence Model.

Table 4 provides data on screening rates at baselineand follow-up, broken down by intervention and controlgroup status and age. To assess intervention effective-ness, change scores in screening mammography be-tween baseline and follow-up were calculated sepa-rately for the intervention and control groups, and thechange scores for the two groups were directly com-pared using the Mann–Whitney U test, which is de-signed for testing in such situations. Direct comparisonof change scores controls for any pre-existing differ-ences between groups. First, two indicator variableswere created for each woman to note whether she hadhad a mammogram in the 12 months preceding baselineand in the 12 months between baseline and follow-up.For each variable, a “0” indicated no mammogram anda “1” indicated receipt of a mammogram. For eachwoman, a change score was created by subtracting thevalue of the indicator for baseline from the value forfollow-up. The control and intervention groups wereconsidered independent samples, and a Mann–WhitneyU test was computed to assess differences between thetwo groups in change scores. The test was significant( p , .05), indicating that, across all age groups, theintervention was successful in increasing screeningmammography rates. Overall, 55% of women in boththe intervention and the control groups reported havinghad a screening mammogram in the 12 months priorto the baseline interview. In the 12-month interval be-tween baseline and follow-up, 58% of the control groupand 65% of the intervention group had obtained ascreening mammogram, showing a 10.2 percentage

TAILORED BREAST CANCER RISK NOTIFICATION

counted. Since no statistically significant differences inoutcome were observed between the mail (64%) and thetelephone (68%) follow-up surveys, data from these twomodalities were combined in all subsequent analyses.

TABLE 3

Characteristics of Respondents (N 5 753)

%

Age30–39 27.140–49 26.250–64 25.0$65 21.7

EthnicityWhite 90.2Hispanic 3.2African-American 2.3Other 4.3

EducationLess than high school 4.0High school diploma 27.21–3 years college 34.8College degree or higher 34.0

Married or living as married 71.4Income

,20,000 14.1
point increase in the intervention group compared witha 2.5 percentage point increase in the control group,suggesting a moderate intervention effect.
Table 4 also enables us to assess the effectiveness ofthe intervention among high risk women of different

Control 94 57.4 67.0 9.6 0.2650–64 years 175

group. The modest odds ratio of 1.45 reflects the 7%point difference in outcome between the two groups at

Intervention 95 58.9Control 80 68.8

a Mann–Whitney U test.

age groups. The pattern of significant results in thetable suggests that the intervention was most effectiveamong women over the age of 50 years. For women inthe 30- to 40-, and 40- to 50-year age groups, there wasno significant intervention effect. On the other hand,for women 50–64 years of age and women 65 yearsand older, there was a fairly large intervention groupadvantage.

The above analysis of differences in change scoresanswers the question of whether there was an interven-tion effect, after controlling for pre-existing differencesin outcome. Another approach to assessing interventioneffectiveness is to test the independent effect of theintervention after controlling for factors, other thanthe intervention, that could influence the outcome ofscreening mammography. In an experimental study inwhich randomization achieves equality betweengroups, this approach essentially answers the questionof the extent to which the intervention influences out-come, in the context of other factors that also influenceoutcome. In order to examine this question, a logisticregression analysis was performed with screeningmammography as the outcome. Predictor variables in-cluded the following: age (continuous), ethnicity (white/non-white), education, health insurance, risk status,
mammogram in year preceding baseline, mail/tele-phone follow-up, intervention group assignment, andinteractions of group with demographics (age, ethnicity,education, insurance). As indicated in Table 5, for allages combined, group emerged as a significant variable,
76.8 17.968.8 0.0 0.02

indicating that the intervention was successful in in-creasing mammography rates in the experimentalgroup, beyond the secular trend observed in the control

360 BASTANI ET AL.

TABLE 4

Change in Mammography Screening by Group Status

Baseline Follow-up Change Significance ofN (%) (%) (%) difference in changea

All ages (301 years) 753Intervention 382 55.0 65.2 10.2Control 371 54.9 57.7 2.5 0.05

401 years 546Intervention 291 59.1 70.4 11.3Control 255 65.5 67.5 2.0 0.04



30–40 years 202Intervention 87 41.4 49.4 8.0Control 115 31.4 35.7 4.3 0.66

40–50 years 208Intervention 114 61.4 62.3 0.9

follow-up. Table 5 also indicates that older age, havinghealth insurance, and receipt of a recent prior mammo-gram significantly increased the chances of obtaining

TABLE 5

Logistic Regression Analysis Predicting the Likelihood of Receiptof Mammogram during the 12-Month Follow-up Period

95% CI for odds ratio

Odds ratio Lower Upper P value

Women, all agesGroup 1.45 1.04 2.01 0.03MM in year before baseline 4.04 2.91 5.62 0.0001Age (continuous) 1.02 1.00 1.03 0.0003Health insurance 3.02 1.56 5.84 0.001

Women under 40MM in year before baseline 2.09 1.11 3.96 0.03Age (continuous) 1.18 1.05 1.32 0.004

Women 40 and olderMM in year before baseline 4.63 3.09 6.95 0.0001
Health insurance 3.87 1.74 8.58 0.0008Education (college, no/yes) 1.53 1.00 2.33 0.05Group by age (51–64 vs 2.26 1.26 4.05 0.006
40–50)Group by age (651 vs 40–50) 1.80 0.99 3.28 0.06


a mammogram during the study period. Additional re-gression analyses, stratified by age, replicated the pat-tern of results shown in Table 4. There was no interven-tion effect among women under 40 years of age. Forwomen 40 years and older, there was a significant inter-action effect between age and group such that the inter-vention had a significant effect in the 50–65 and 651age groups, but was not effective in the 40- to 50-year-old group. As before, having had a recent mammogramat baseline and health insurance were also significant.
Perceived risk and the relationship to index cancer case
with those found by Lerman and by us.

(e.g., sister, mother, daughter) were not related toscreening. Also, exploring knowledge and attitudinalpredictors of mammography was beyond the scope ofthis paper and will be discussed in a future article.

DISCUSSION

Overall, our intervention was able to produce a mod-est 8% increase in mammography screening in the in-tervention compared with the control group. This is inkeeping with the literature which indicates that mailedinterventions are not likely to achieve large effects [36,37]. For example, Richardson et al. [38], in a random-ized experiment with 369 twin sisters of breast cancercases, were able to achieve a 10% higher annual mam-mography rate in the intervention compared with thecontrol group. However, examination of overall effectsin our study masks the fact that the intervention wasnot equally effective in all age groups. In fact, therewas no intervention effect among women under the ageof 50 years. On the other hand, there was a fairly largeeffect among women over 50 years of age, includingamong elderly women over 65 years. The size of thiseffect is comparable to the 24% intervention group ad-vantage found by Curry et al. [39], among high-riskwomen, over age 50 years, who were members of ahealth maintenance organization. The lack of effectamong women under 40 years in our study is under-standable, since there are no specific mammographyscreening guidelines for this age group. The interven-tion materials informed women of the existing guide-lines and encouraged them to talk with their physiciansregarding their risk status and the issue of mammo-grams. It is likely that most women under the age of40 years were discouraged from having mammogramsby their physicians. The reasons the intervention wasnot successful among women in the 40- to 50-year agegroup are unclear. Perhaps the materials were moreappealing to older women. Also, the screening guide-lines at the time recommended mammography every 1to 2 years in the 40- to 49-year group, and this may
have influenced some women and their physicians topostpone screening.
Although the intervention when effective, consideredin the context of other predictors of screening, it did


not have a large effect. For all ages, the odds ratio forgroup effect was a very modest 1.45. For women overthe age of 40 years, the odds ratios for the interactionof group and age were around 2.0. In contrast, the oddsof obtaining a mammogram at follow-up were four timeshigher among women who had had a recent mammo-gram at baseline, compared with women who had not.This finding is consistent with theory and research inthe area of adherence behavior [36,18], which indicatethat past behavior is one of the best predictors of futurebehavior. Consistent with the literature [18,40], healthinsurance also emerged as a strong predictor althoughthe vast majority of our sample was insured. This sug-gests that in the small subgroup of uninsured women,this factor was a very powerful barrier to obtainingscreening. Lack of insurance may also be a proxy forother variables such as income and education whichconsistently predict mammography use in the litera-ture [35,40,41].

An important finding is that although the women inthis study were all first-degree relatives of breast can-cer cases, their usage of mammography at baseline,although somewhat higher than that found in the gen-eral population of women with similar demographiccharacteristics, was far from optimal. The averagescreening rate in our sample, at baseline, was 62.1%for women over the age of 40 years and 63.6% for womenover the age of 50 years. Figures from the BehavioralRisk Factor Surveillance System for California indicatethat in 1991, 56% of women over the age of 40 yearshad obtained a screening mammogram in the past 12months [42]. In a general population random digit-di-aled survey in Los Angeles County, we found mammog-raphy rates of 42 and 53% in 1989 and 1990, respec-tively, among women over the age of 40 years [8,18].Therefore, the screening rates among the high-riskwomen in our study were only about 10% higher com-pared with women in the general population. This isparticularly troublesome considering that 50% of oursample was classified as being at moderately high riskfor breast cancer and 38% was classified as being atsubstantially higher risk. Further, risk status was notrelated to receipt of mammography. Lerman et al. [13]found similarly low (63%) mammography rates amonga sample of mostly white high-risk women recruitedthrough a large urban cancer center. These results un-derscore the need for continuing to test interventionsto increase screening rates among high-risk women.Particular attention should be focused on high-riskwomen from under-represented minority groups, whoare likely to have even lower screening rates compared

With respect to the risk classification, we found thatthe 89% of our sample had at least one risk factor inaddition to a first-degree relative with breast cancer.The most common risk factor that put women in the

I

work is needed to determine why this may be and to

362 BASTAN

moderately higher risk category was age over 50 years.However, 17% of the sample reported a breast cancerdiagnosis in a second-degree relative and 15% reportedearly menarche. A large proportion of women were clas-sified as being at substantially high risk, based mostlyon the fact that their first-degree relative had beendiagnosed pre-menopausally. In fact, 37% of the totalsample had this risk factor. This distribution of riskfactors in our sample, with large proportions fallinginto higher risk categories, may not accurately reflecttheir occurrence in the population. It is quite plausiblethat higher risk women self-selected themselves intothe study.

There are several methodological issues in this study,which bear discussion. First, there were no significantdifferences in screening rates reported in the mail ver-sus the telephone follow-up surveys. This runs contraryto the prevailing notion that survey modality can sub-stantially influence the results obtained [43,44]. How-ever, these results may not replicate in other popula-tions that are more ethnically diverse and from lowerincome and education groups compared with our studyparticipants. This is also likely to be true of the rela-tively high and very similar 12-month response ratesthat we achieved for the mail (78%) and telephone (89%)surveys of subjects.

This raises another methodological issue, related tothe observation that 90% of our sample was white, de-spite the fact that whites represent only 57% of Califor-nia’s population [45]. However, our sample is quite rep-resentative of the ethnic distribution of women who arediagnosed with breast cancer in California. This is dueto the combination of differences in breast cancer inci-dence rates among the various ethnic groups and theethnic distribution of California’s population. Whites,who have the highest incidence rates, comprise 57% ofthe population. Hispanics and Asians, who have thelowest rates, comprise 26 and 9% of the population,respectively. African Americans, who have the secondhighest incidence rate, make up only 7% of the popula-tion. Thus, data from the California Cancer Registryindicate that for the year in which our cases were ob-tained, 80% of breast cancers diagnosed in the statewere among white women [46]. Since we took a randomsample of the incident cases for that year, we wouldexpect that about 80% of cases and their relatives wouldbe white, assuming that there were no differences inresponse rates between whites and non-whites. Our90% white sample slightly over-represents the propor-tion of whites that should have resulted from a trulyrandom sample, reflecting that response rates among
non-whites were slightly lower than among whites.Also, 62% of the cancer cases initially contacted re-sponded to our mailed request to provide informationon their relatives, and 66% of the identified relativeseventually completed the baseline telephone survey.
ET AL.

These are respectable response rates and comparableto those achieved in other studies using similar method-ology [e.g., 6]. It should be noted that, since no subjectschose Spanish language materials and interview andintervention materials were not available in any Asianlanguage, non-English-speaking Hispanics and Asiansare likely to be under-represented in our sample.

Although our sampling strategy was used as a meansof obtaining a truly population-based, representativesample of high-risk women, it is not necessarily the beststrategy for delivery of the intervention. An appropriatevenue for the intervention would be a physician prac-tice, for example, in which women with a family historyof breast cancer could easily be identified from routineintake forms. Cancer centers and other treatment facili-ties could also identify potential intervention recipientsthrough their cancer patients. High-risk clinics areanother venue, although screening rates among thewomen who seek such services are likely to already bequite high [11].

Last, due to wide geographic dispersion of our subjectpopulation throughout the United States, it was notpossible to validate self-reports of mammography. Thissituation is not unique to the present study, and muchof the published work on mammography screening uti-lizes self-reported data. However, over the years, ampleevidence has accumulated to indicate that self-reportstend to be fairly reliable reflections of mammographystatus validated through chart reviews or other medicalrecords [e.g., 47–49].

In conclusion, our personalized, tailored risk notifica-tion intervention was modestly successful in increasingscreening mammography rates in the overall sample.However, closer inspection of the data indicated thatthis overall modest result reflected no effect amongwomen under the age of 50 years and a fairly largeeffect among women over the age of 50 years. Further

develop potent interventions for women ages 40–50years. The efficacy of this type of intervention for high-risk women from diverse ethnic groups also needs tobe investigated.

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Documents

Tailored Risk Notification for Women with a Family History of Breast Cancer