Preventive Medicine 28, 418–425 (1999)Article ID pmed.1998.0446, available online at http://www.idealibrary.com on

Breast and Cervix Cancer Screening among Multiethnic Women:Role of Age, Health, and Source of Care1

Jeanne S. Mandelblatt, M.D., M.P.H.*,2 Karen Gold, Ph.D.,† Ann S. O’Malley, M.D., M.P.H.,‡Kathryn Taylor, Ph.D.,§ Kathleen Cagney, M.P.H.,¶ John S. Hopkins,* and Jon Kerner, Ph.D.*

*Department of Medicine, Lombardi Cancer Center and the Institute for Health Care Policy and Research, †Department ofBiomathematics and Biostatistics, ‡Department of Medicine, the Clinical Economics Research Unit, and

§Department of Psychiatry, Georgetown University School of Medicine, Washington, DC 20007; and¶Johns Hopkins School of Hygiene and Pu

Key Words: mass screening; breast cancer; cervical

Objective. The aim of this study was to evaluate therelationships between age, health status, access tocare, and breast and cervical cancer screening amongmultiethnic elderly and nonelderly women.

Methods. A structured telephone survey of a quotasample of 1,420 New York City women from four His-panic groups (Columbian, Dominican, Puerto Rican,Ecuadorian) and three black groups (U.S., Caribbean,and Haitian) was performed. Outcome measures in-cluded “ever” and “recent” self-reported use of mam-mography, clinical breast examination (CBE), and Papsmears. Logistic regression models assessed the pre-dictors of screening use.

Results. Having a regular source of care significantlypredicted all screening use for both elderly and nonel-derly, controlling for ethnicity, sociodemographics,health status, access to care, proportion of life in theUnited States, and cancer attitudes. Elderly women($65 years) were significantly less likely to have everhad (OR 5 0.79, 95% CI 0.65–0.96) and to have recentlyhad (OR 5 0.67, 95% CI 0.57–0.79) Pap smears thanyounger women, controlling for the other variables;being elderly also tended to be an independent pre-dictor of ever and recent mammography and CBE use.Interestingly, there was a trend for health status to actdifferently in predicting Pap smear use for the twoage groups. For younger women, being in poor healthincreased the odds of Pap smear screening, while forelderly women, being in good health increased the oddsof screening.

Conclusions. Elderly women reported being

1 Supported by Grant RO1 CA49573 (J.K.) from the National Can-cer Institute and in part by Grant RO1 AG15430 (J.S.M.) from theNational Institute on Aging, Grant RO1 HS 08395 (J.S.M.) from the

Agency for Health Care Policy and Research, and Grant DAMD 17-97-1-7131 from the Department of the Army (A.S.O.).

2 To whom reprint requests should be addressed.

41

blic Health, Baltimore, Maryland 21205

screened less than younger women; interactions be-tween health status and age need further explora-tion. q1999 American Health Foundation and Academic Press

8

cancer, elderly; ethnicity.

INTRODUCTION

Women ages 65 and over represent 13% of the U.S.population, but constitute the majority of new cases of,and deaths from, breast cancer; this age group alsorepresents a disproportionate 25% of new cases of and43% of the deaths from cervical cancer. [1] The elderlysubgroups most likely to develop and/or die of breastand cervical cancer include socioeconomically disadvan-taged and minority women [2–7].

Effective early detection tests are available for breastand cervical cancer. Unfortunately, despite impressiveoverall gains in use of mammography and Pap smears[8], the elderly [8–11], especially minority elderly [8,9],remain underrepresented in screening programs. Forall age, socioeconomic, and ethnic groups, one of themost powerful predictors of screening is having a physi-cian recommendation [12]. However, physicians are lesslikely to recommend screening to their older, comparedto their younger, patients [13,14]. There are little dataon other determinants of screening in elderly women[15] and still less is known about minority elderly.

Using data from a study of the cancer control needsof three black and four Hispanic urban groups, we ex-amined whether age mediated breast and cervical can-cer screening use in this multiethnic target population.We hypothesized that, despite having a regular sourceof care, elderly women from all ethnic groups would bescreened at lower rates than younger women and that

poorer health status would be associated with lowerscreening use among elderly, but not younger, women.

0091-7435/99 $30.00Copyright q 1999 by American Health Foundation and Academic Press

All rights of reproduction in any form reserved.

M

CANCER SCREENING IN

METHODS

This paper reports on data from an IRB-approved,National Cancer Institute-funded project to describethe smoking, diet, alcohol use, and cancer screeningpractices of Caribbean, Haitian, and U.S. blacks andPuerto Rican, Dominican, Columbian, and EcuadorianHispanics living in New York City (NYC). These groupsrepresented the major ethnic subgroups living in NYCat the time of the study [16]. A structured 20- to 30-minute telephone survey was conducted by trained,multilingual staff between May and November, 1992,to assess health behaviors, cancer knowledge, attitudesand beliefs, screening use, access to medical care, accul-turation, and sociodemographics. This report focuseson the breast- and cervical-cancer-related data.

Study Population

A quota sampling method, stratified by four age cate-gories (18–44, 45–54, 55–64, and 65–74 years), wasused to identify 50 women from each ethnic group (ex-cept Haitians, n 5 25 per age group). This method hasbeen described elsewhere [17]. Briefly, to target house-holds with listed and unlisted numbers, a dual sampledesign was utilized and included a full list frame sampleselected from telephone directories and a sample se-lected by random digit dialing. The random digit dialing(RDD) sample used a two-stage RDD cluster designadapted from Waksberg [18]. First, to obtain the quotasand control screening and interviewing costs, criss-cross directories were used to identify the exchanges inareas where a high percentage of the target populationswere known to reside; next, random samples were se-lected from these exchanges.

Calls were made to 19,300 telephone numbers: 2,514identified eligible subjects (based on age and ethnicity),10,478 had no eligibles, and eligibility status was un-known for 6,308 (due to refusal of screen, languagebarrier, or not contacted once quota was meet). Thus,approximately eight calls were made to identify oneeligible subject. All calls are considered in calculatingthe overall survey response rate. If more than one eligi-ble person lived in the household, the individual withthe most recent birthday was chosen.

To promote participation, advertisements about thestudy were placed on local radio stations, in newspapersand subways, and on community bulletin boards. Theseadvertisements included the fact that participantswould be entered in a lottery with prizes ranging from$100 to $1,000.

Interview

The interview was developed using existing nationalsurvey items and modified for use in the target popula-tions. The interview content areas were reviewed by

ULTIETHNIC ELDERLY 419

focus group participants (n 5 95, participating in 15groups recruited from community groups and clinicsand public hospitals) and community advisors. Partici-pants and advisors were selected to represent all 7 eth-nic groups. Spanish- and Haitian-language trans-lations were developed using translation, back-translation, and resolution of cultural appropriatenessthrough review by community advisors. Computer-assisted telephone interviews were conducted in therespondent’s language of choice. An original call andup to seven call-backs were placed day and evening, 7days a week, before a respondent was considered a non-participant.

Variables

The outcome variables were ever/never and recently/not recently having a mammogram, clinical breast ex-amination (CBE), or Pap smear. Recent was defined formammography as #2 years for women ages 40 to 49years, and #1 year for women 50 years and older; forCBE as #1 year for women 18 years and over; and forPap smear as #3 years for women 18 years and over,based on National Cancer Institute recommendationsin 1992 [12]. Before questions on use were asked, eachscreening modality was described in detail. Indepen-dent variables included age, sociodemographics, health/health care factors, attitudes, and an acculturationindicator.

Age was measured in four groups (18–44, 45–54,55–64, and 651 years); these groups were also col-lapsed to two groups (,65 and $65 years). An age-related screening “rigor” variable was also included,reflecting the fact that the quota ages included groupsof women with differing periodicity of screening recom-mendations. Sociodemographic variables included race/ethnicity (seven groups or collapsed into two groups—black and Hispanic), education (,12 vs $12th grade),marital status (married/living as married vs not), andemployment (working vs nonworking). Health statuswas evaluated using a five-level summary measure(Compared to other people your age, would you sayyour health is . . .? [19]) based on relationships withscreening, this variable was also categorized into twolevels (good, very good, and excellent vs fair and poor).Access to care variables included source of care (noregular source, having a regular source, and having aregular physician at that source), settings of care (pub-lic and hospital out-patient clinics, HMOs, and privateoffices), and insurance (insured vs uninsured).

Cancer attitudes were measured using the Cancer

Attitudes scale [20]. This scale includes an anxiety sub-scale (six items; Kuder–Richardson(KR) [21 5 0.58), ahopelessness subscale (eight items; KR 5 0.72), and adenial subscale (two items; KR 5 0.46); based on similarfactor loadings, distributions, and relationships to

A

not all, models were retained for comparability. Modelgoodness of fit was assessed by the 22 log likelihood

420 MANDELBL

screening, these subscales were combined for thisanalysis into a single measure (KR 5 0.75), where ahigher score reflects having less anxiety and hope-lessness and a lower level of denial. In addition, othercancer “superstitious” beliefs (e.g., not having faith inGod, not being a good person, or having another personwish bad things about you . . . increase your chances ofgetting cancer) and embarrassment about being exam-ined were included.

Acculturation was measured as the proportion of lifespent in the mainland U.S. (,1 year, #25%, 26–50%,51–75%, .75%, and U.S.-born); these data were alsocollapsed into two categories (high as $50% and low as,50% or U.S.-born). Our data included several otherpotential measures of acculturation, including lan-guage of the interview, age of immigration to the UnitedStates, and a validated adaptation of a 26-item linguis-tic acculturation scale [17,22,23], although the lattertwo were only asked of non-U.S.-born and non-Englishspeakers, respectively. Also, many women who immi-grate late in life continue to use their native language,but may assimilate health behaviors over time [24]. Forall of these reasons, we chose the proportion of life inthe United States as a proxy for acculturation in theseanalyses. Regression results were comparable using al-ternative acculturation measures among Hispanics(data not shown).

Data Analysis

Categorical variables were collapsed into two or threelevels, as described above; median values were usedto create dichotomous groups for continuous variables.Bivariate relationships between independent variables

2

and screening use were examined first; x tests wereused to assess statistical significance. To explore inter-

Total 1414 70% 53.0%

Note. NS, not significant; n, sample size.a Recent is defined as the proportion of women having a recent screb In this age group ever and recent mammography only applies to wc x2.

TT ET AL.

of variables and screening use: age and health, age andsource of care, and age and attitudes. Based on initialdata analyses, we also examined interactions betweeneducation and attitudes, ethnicity and attitudes, andethnicity and folk beliefs. If there appeared to be aninteraction in stratified analyses, we next performed alogistic regression including the main effect and theinteraction term. Since we examined many potentialinteractions, we only retained interaction terms for fi-nal analysis that were significant, controlling for themain effect. Finally, we developed a logistic regressionmodel for each screening outcome. Blocks of variableswere entered in the following order: age, sociodemo-graphics, health status, access, attitudes, accultura-tion, and interactions. After the addition of each block,we examined whether added variables changed coeffi-cients of variables already in the model (ie, confounded).Variables that were not significant in any model weredeleted; variables which were important in some, but

e

(the change from the intercept-only model to the fullmodel) and the c values (where 1 indicates perfectprediction) [25].

RESULTS

Response Rate

The overall survey response rate was 62.3% (includ-ing calls made to identify homes with women of theage and ethnic group of interest) and was similar for

younger and older women. Among respondents whoqualified for the survey based on age and ethnicity, the refusal rate was only 2.1%. Among the 1,420 women inactions in screening use by age, data were examinedthe final sample, 43.5% completed the survey instratified by age (,65 and $65). We were specifically

interested in interactions between the following groups English, 51.3% in Spanish, and 5.2% in Creole.

TABLE 1

Unadjusted Screening Rates by Age

Mammogram Clinical breast exam Pap smear

Age group n Ever Recenta Ever Recenta Ever Recenta

18–44b 516 79.8% 70.8% 85.9% 68.8% 88.9% 72.0%45–54 327 64.3 52.0 85.3 62.0 90.7 63.755–64 314 70.6 52.2 87.6 66.1 86.2 54.7$65 275 73.5 48.7 85.4 56.0 81.2 48.5P valuec — 0.012 0.001 NS 0.002 0.004 0.001

86.0% 64.6% 87.3% 62%

n among all women.omen ages 40–44 years (n 5 94).

CANCER SCREENING IN

Bivariate Determinants of Screening Use

Overall 70, 86, and 87.3% of women reported everhaving a mammogram, CBE, and Pap smear, respec-tively, and 53, 64.6, and 62% reported having had thesetests recently. Table 1 summarizes unadjusted screen-ing rates by age. Generally, women 65 and over werethe least likely to not to be recently screened, or in somecases to have ever been screened.

Table 2 presents the relationship between other par-ticipant characteristics and screening. Briefly, eth-

nicity, insurance, reporting a regular source of care,and attitudes were each significantly related to ever

Positive 73.6 P 5 0.02 57.7 P 5 0.001 8Other cancer beliefs

Yes 69.8 47.6No 70.4 P 5 0.85 54.2 P 5 0.06 8

a Regular care includes having a regular place, with or without hav

MULTIETHNIC ELDERLY 421

the United States were significantly more likely to haveever and recently had breast cancer screening thanwomen living in the United States for less time. Inter-estingly, the effects of attitude varied across ethnicgroups (data not shown). While negative attitudes(greater anxiety, hopelessness, and denial) were heldmore often by Hispanic women (range 59.9–65.5% forHispanics; 38–39.5% for blacks), the relationship be-tween screening and attitude was significant more of-ten for black women. For instance, negative attitudessignificantly decreased the rate of ever having mam-

mography and Pap smears only for the black groups, but not for the Hispanic groups (data not shown).and recent use of all three tests, and Haitian women

Education was significantly related to all categoriesreported the lowest screening rates of all ethnic groups.Women who spent a greater proportion of their lives in of test use except ever having had a mammogram.

TABLE 2

Unadjusted Screening Rates by Participant Characteristics Overall (n 5 1,420)

Mammography Clinical breast examination Pap Smear

Ever Recent Ever Recent Ever RecentVariable (n 5 989) (n 5 1414) (n 5 1400)

Age$65 years 73.5% 48.7% 85.4% 56.0% 81.2% 66.7%,65 years 70.0 P 5 0.12 53.1 P 5 0.28 86.7 P 5 0.75 72.6 P 5 0.001 88.2 P 5 0.02 80.8 P 5 0.001

EthnicityColumbian 74.8 53.0 87.6 69.2 79.2 71.8Dominican 74.4 51.7 79.8 66.9 88.3 78.9Ecuadorian 69.1 53.3 85.3 70.1 89.3 80.8Puerto Rican 76.5 61.1 92.3 73.9 86.3 80.6U.S.-born black 73.0 54.2 92.8 78.5 94.7 81.8Haitian 43.5 33.8 79.4 69.7 74.0 68.9Caribbean 68.2 P 5 0.001 47.5 P 5 0.014 83.4 P 5 0.001 62.3 P 5 0.014 92.4 P 5 0.001 83.0 P 5 0.014

Education$12th grade 72.5 56.5 89.3 77.5 90.0 82.7,12th grade 67.5 P 5 0.09 47.3 P 5 0.005 81.1 P 5 0.001 58.5 P 5 0.001 83.0 P 5 0.001 72.3 P 5 0.001

InsuranceYes (public or private) 73.9 55.8 87.7 73.2 88.6 80.8No (uninsured) 54.9 P 5 0.001 37.8 P 5 0.001 79.5 P 5 0.001 57.6 P 5 0.001 82.1 P 5 0.003 70.1 P 5 0.001

Regular source of careYes (regular source)a 73.0 54.6 88.0 72.3 88.4 80.6No (no regular source) 48.2 P 5 0.001 33.6 P 5 0.001 73.7 P 5 0.001 55.3 P 5 0.001 80.1 P 5 0.002 66.3 P 5 0.001

Site of carePublic 78.0 59.3 87.3 73.5 89.5 82.0HMO 80.3 59.0 90.4 73.9 91.6 80.8Private 69.0 P 5 0.005 51.1 P 5 0.067 88.4 P 5 0.567 71.2 P 5 0.648 87.3 P 5 0.234 80.1 P 5 0.75

Health statusExcellent, very good, good 70.2% 55.2% 88.3% 72.8% 88.0% 80.2%Fair, poor 70.1 P 5 0.98 50.2 P 5 0.17 84.6 P 5 0.05 68.2 P 5 0.07 86.8 P 5 0.49 77.6 P 5 0.25

Proportion of life in the U.S.(acculturation)

High ($50% or U.S. born) 76.3 58.5 90.5 80.2 89.3 79.9Low (,50% of life in U.S.) 67.9 P 5 0.01 49.9 P 5 0.019 83.7 P 5 0.001 64.7 P 5 0.001 86.2 P 5 0.10 78.0 P 5 0.40

Cancer attitudesNegative 66.7 46.6 81.3 59.6 83.8 72.0

9.9 P 5 0.001 78.1 P 5 0.001 89.8 P 5 0.001 83.7 P 5 0.001

83.5 60.2 84.7 73.97.0 P 5 0.094 73.5 P 5 0.001 88.2 P 5 0.08 80.0 P 5 0.009

ing a regular doctor at that place.

younger women in poor health more likely to have ever

422 MANDELBL

There was also an interaction between attitudes andeducation that predicted screening use. Women ofhigher education were more likely to be screened ifthey held more positive attitudes; for women with morenegative attitudes, education did not predict screeninguse, indicating that attitudes may interact with educa-tion in predicting screening (data not shown).

In age-stratified analyses, the effect of health statuswas mediated by age for Pap smear screening: amongyounger women, those with poorer health were morelikely than those in better health to have ever (91% vs87%, P 5 0.05) or recently had a Pap (85% vs 79%,P 5 0.01). In contrast, among elderly women, those inpoorer health were less likely to be screened than thosein better health (74% vs 85%, P 5 0.03, for ever and 58%vs 71%, P 5 0.04, for recent Pap). Greater proportionof life in the United States significantly increased allscreening use for younger, but not elderly, women (datanot shown). Lastly, there was an age–cancer belief in-teraction, where younger women with lower “supersti-tious” beliefs were more likely than those with higherlevels of these beliefs to ever have a CBE (87.3% vs82.5, P 5 0.05) and a recent mammogram (56.2% vs47.6, P 5 0.04), CBE (76.3% vs 62.3%, P 5 0.001), andPap smear (82.8% vs 77.5%, P 5 0.05); among elderlywomen there was no relationship between such beliefsand screening. Relationships between age, care, andscreening could not be assessed since, after stratifyingby age, too few women had no source of care.

Logistic Regression Models of Screening Use

Breast cancer screening use. Overall, the strongestsingle independent predictor of breast cancer screeningwas having a usual source of care. Women with a usualsource of care were over 200% more likely to have ever,or recently, had both screening modalities as womenwithout a usual source of care, controlling for the re-maining variables (Table 3). Other measures of accessto care were also significant independent predictors ofever and recent mammography use.

The next most important predictor of breast cancerscreening was attitude. Women with the most negativeattitudes were between 40 and 60% less likely to haveever or recently been screened using either modality.As was seen in the stratified analyses, there was aninteraction between ethnicity and attitude, with blackwomen with negative attitudes less likely to be screenedthan Hispanic women with comparable attitudes.

There was no significant age effect for use of eitherbreast cancer screening modality, although point esti-

mates indicate that elderly women may have beenscreened less often than younger women. Health andinteractions between health and age were also not sig-nificantly related to screening. Goodness of fit calcula-tions indicated strong predictive power for all models:

ATT ET AL.

c 5 0.70, where 1 indicates prefect prediction (evermammography and CBE) and c 5 0.69 (recent mam-mography) and c 5 0.73 (recent CBE).

Cervical cancer screening. Access to care variableswere again the most important predictors of screening,followed by attitudes (Table 3). Elderly women were 21and 33% less likely to have ever had or to have had arecent Pap smear than younger women, controlling forsociodemographics, health, access to care, time in theUnited States, and attitudes. As hypothesized, therewas an interaction between age and health, with

had or to recently have had a Pap smear, and elderlywomen in good health more likely to have beenscreened. Model goodness of fit was very good for bothever (c 5 0.70) and recent Pap use (c 5 0.69).

DISCUSSION

This is one of the first studies to describe the interre-lationships between age, having a usual source of care,and health status on breast and cervical cancer screen-ing use in multiethnic elderly and nonelderly women.For women of all ages, access to care variables, includ-ing having a usual source of care and being insured,were the strongest predictors of screening use. Our firsthypothesis, that elderly women would report lowerscreening rates than younger women, controlling forhaving a usual source of care and other variables, wastrue for Pap smear screening; there was also a weaktrend for lower breast cancer screening among elderlycompared to nonelderly women. We also found an inter-esting effect of age and health on cervical cancer screen-ing use. Younger women with poorer health were morelikely to be screened, while elderly women with goodhealth were most likely to be screened, compared toother groups.

Research on the impact of health status on cancerscreening has been limited and inconsistent. In onestudy, we previously found that elderly women with agreater number of chronic illnesses were more likely toparticipate in breast and cervical cancer screening thanthose with fewer illnesses [26]. We hypothesized thatwomen with more illnesses attended clinic more oftenand were exposed to greater opportunities to bescreened. Alternatively, sicker women may have had agreater sense of susceptibility to illness, including can-cer, that motivated them to be screened. Others havefound chronic disease to both increase [27–29] and de-crease [30–32] cancer screening use. Our finding of aninteraction between health status and age suggests that

physicians (and patients) are behaving differently inresponse to illness level according to the patients’ age.For younger women, it is possible that poor health likelyleads to greater interaction with the system and in-creased opportunity for screening; the physicians may

Note. CI, confidence interval; NS, not significant.a i

The effects of each variable adjusted for ethnicity and the remainb Regular care includes having a regular place, with or without havc The seven ethnic groups are collapsed into two groups—Hispanic* P 5 , 0.05.** 0.10 , P , 0.06.

feel these women should be screened regardless ofhealth condition. For elderly women, there may be sometriage going on, with a decision to screen those in goodhealth more often than those in poorer health. Giventhat physicians consistently underestimate elderlywomen’s life expectancy [13], this triaging may or maynot be appropriate [33–35]. For instance, breast cancer

screening has been noted to save years of life for elderlywomen with comorbid conditions [36]. Given the in-crease in both cancer risk and comorbidity with advanc-ing age, these are important areas for future research.

Our finding that access to care increases screening

ng variables in logistic regression analysis.ing a regular doctor at that place.and black—for testing interaction terms.

use for multiethnic women is consistent with previousresearch in nonelderly [37] and elderly populations [13].Unfortunately, we could not separate the effects of hav-ing a regular source of care and having a physicianrecommendation for screening, since we did not mea-sure perceptions of screening advice.

While not a primary focus of our research, we noted

CANCER SCREENING IN MULTIETHNIC ELDERLY 423

TABLE 3

Adjusted Oddsa of Breast and Cervical Cancer Screening

Mammography Clinical breast examination Pap smear

Ever Recent Ever Recent Ever Recentn 5 989 n 5 945 n 5 1414 n 5 1388 n 5 1400 n 5 1420

Odds ratio Odds ratio Odds ratio Odds ratio Odds ratio Odds ratioVariable (95% CI) (95% CI) (95% CI) (95% CI) (95% CI) (95% CI)

Age$65 years 0.88 (0.65–1.19) 0.99 (0.75–1.31) 0.98 (0.73–1.30) 1.05 (0.86–1.29) 0.79 (0.65–0.96) 0.67 (0.57–0.79),65 years 1.00 — 1.00 — 1.00 — 1.00 — 1.00 — 1.00 —

Education$12th grade 1.18 (0.85–1.64) 1.20 (0.88–1.63) 1.60 (1.13–2.27) 1.59 (1.20–2.11) 1.33 (0.93–1.90) 1.24 (0.92–1.67),12th grade 1.00 — 1.00 — 1.00 — 1.00 — 1.00 — 1.00 —

InsuranceYes (public or private) 1.99 (1.37–2.89) 1.96 (1.35–2.84) 1.25 (0.86–1.82) 1.81 (1.32–2.50) 1.36 (0.91–2.02) 1.55 (1.11–2.16)No (uninsured) 1.00 — 1.00 — 1.00 — 1.00 — 1.00 — 1.00 —

Regular source of careYes (regular source)b 2.53 (1.63–3.93) 2.05 (1.30–3.24) 2.18 (1.46–3.24) 2.10 (1.45–3.02) 1.78 (1.15–2.76) 2.06 (1.43–2.97)No 1.00 — 1.00 — 1.00 — 1.00 — 1.00 — 1.00 —

Site of carePublic 1.82 (1.29–2.56) 1.70 (1.24–2.33) 1.12 (0.79–1.58 1.27 (0.95–1.68) 1.16 (0.81–1.67) 1.23 (0.91–1.66)HMO 1.79 (1.14–2.80) 1.52 (1.02–2.27) 1.31 (0.79–2.18) 1.27 (0.87–1.85) 1.70 (0.97–2.97) 1.28 (0.85–1.94)Private 1.00 — 1.00 — 1.00 — 1.00 — 1.00 — 1.00 —

Health statusExcellent, very good 0.87 (0.62–1.25) 0.74 (0.54–1.03) 0.78 (0.556–1.12) 1.07 (0.80–1.43) 0.98 (0.68–1.43) 0.90 (0.66–1.23)Good, fair, poor 1.00 — 1.00 — 1.00 — 1.00 — 1.00 — 1.00 —

Proportion of life in the U.S.(acculturation)

High ($50% or U.S. born) 1.35 (0.95–1.9 ) 1.40 (1.35–2.84) 1.01 (0.86–1.95) 1.66 (1.56–2.37) 1.18 (0.81–1.73) 1.35 (0.99–1.84)Low (,50% of life in 1.00 — 1.00 — 1.00 — 1.00 — 1.00 — 1.00 —

the U.S.)Cancer attitudes

Negative 0.48 (0.29–0.77) 0.40 (0.24–0.63) 0.69 (0.40–1.19) 0.59 (0.39–0.91) 0.38 (0.20–0.72) 0.45 (0.28–0.71)Positive 1.00 — 1.00 — 1.00 — 1.00 — 1.00 — 1.00 —

Other cancer beliefsYes 1.58 (0.87–2.84) 1.61 (0.91–2.86) 1.11 (0.56–2.21) 1.03 (0.60–1.76) 1.01 (0.48–2.12) 1.13 (0.63–2.03)No 1.00 — 1.00 — 1.00 — 1.00 — 1.00 — 1.00 —

Interaction termsHealth* age NS NS NS NS ** **Acculturation* age NS * * NS NS NSEthnicityc ,* attitude ** * * NS * *Ethnicityc ,* folk beliefs ** * NS NS NS NS

interesting interactions between ethnicity and atti-tudes toward cancer and attitudes and educationallevel. Further research will be important to delineatethe pathways whereby these variables act to produceobserved behaviors; understanding these relationships

tions between age, health, and use of screening. Further

Health Association, Healthwatch, Local 1199 in Manhattan, New

424 MANDELBL

can be an important guide to the development of inter-ventions targeted to specific subgroups of these high-risk multiethnic populations.

There are several caveats that should be consideredin evaluating our results, including use of self-report,external validity, response rate, and secular trends inscreening. Use of screening was determined by self-report. Since women received care from a very largenumber of diverse settings, it was not practical to vali-date self-reports through medical record reviews. Also,we judged that requests for written record releasewould decrease overall survey response rate. Most stud-ies of self-reported Pap smear and mammography usefind self-report to be fairly accurate for crude time peri-ods, even among older and socioeconomically diversegroups [38], although women may overestimate theiruse of screening [38–43]. Since we have no reason toexpect that the validity of self-report varied systemati-cally across age and health groups, any overestimationof screening use should have decreased our ability tofind significant differences.

We used a pragmatic approach to enroll sufficientnumbers of multiethnic women using adaptations ofpopulation-based random-digit dialing telephone sur-vey methods. This, coupled with the focus on householdswith telephones in one urban city, may limit the exter-nal validity of the results. However, the results for oursample are similar to those from national samples ofwomen (Kerner and Breen, unpublished data). More-over, the use of area-probability sampling would nothave yielded a sufficient number of elderly multiethnicwomen for analysis. While it is estimated that the pro-portion of black and Hispanics in NYC owning tele-phones is 78 to 85% (compared to 90% overall) [44],alternatives to telephone interviews (e.g., home inter-views) are difficult to achieve in the economically de-pressed areas of NYC where the majority of the targetpopulation resided.

Our overall response rate of 62% is considered goodfor a survey of this type [45]; among respondents whoqualified for the survey based on age and ethnicity, only2.1% refused to complete the interview. Since we haveno data on nonparticipants, we do not know if womenincluded in the sample differed systematically fromthose not participating. Our sample had high educationlevels, suggesting some self-selection among partici-pants. However, our screening rates were very similarto national rates among underserved black and His-panic women in the same time period as our survey[46–49]. The high rates observed in the early to mid-1990s, compared to historically lower rates [11], may

reflect successes of targeted public health programs[47]; alternatively women completing surveys may bemore likely to participate in screening than nonsurveyparticipants [47]. Since there have been secular in-creases in screening use since this study was conducted,

ATT ET AL.

it will be important to see if age- and health-relatedtrends in use continue to be noted over time.

Overall, our results suggest that among urban, multi-ethnic women there are clinically important interac-

research is needed to delineate these relationships moreprecisely and to define the appropriate uses of cancerscreening services among subpopulations of elderlywomen historically at risk for poor cancer outcomes.

ACKNOWLEDGMENTS

Portions of this work were conducted while Drs. Mandelblatt andKerner and Ms. Cagney were at Memorial Sloan-Kettering CancerCenter, Department of Epidemiology and Biostatistics, New York,New York. The authors thank the women who participated in thisproject for their contributions and Ms. Kathy Summers for manu-script preparation. A special thanks is offered to the communityorganizations who advised us and helped to promote survey participa-tion. These organizations include Bedford Stuyvesant Healthy HeartProgram, Cancer Control Center of Harlem, Caribbean Women’s

York Task Force on Immigrant Health, SHARE-NY, St. NicholasNeighborhood Preservation Corps in Queens, and WashingtonHeights–Inwood Healthy Heart Program. This article is dedicatedto the memory of our friend, colleague, and project advisor, RachelG. Fruchter, Ph.D., MPH, a tireless advocate for women’s health care.

REFERENCES

1. Horm JW, Astire AJ, Young JL, et al. Cancer incidence and mor-tality in the united states. SEER 1973-81. Bethesda (MD): U.S.Department of Health and Human Services, 1985. [NIH publica-tion No. 85-1837]

2. Wells BL, Horm JW. Stage at diagnosis in breast cancer: race andsocioeconomic factors. Am J Public Health 1992;82(10):1383–5.

3. Polednak AP. Breast cancer in black and white women in NewYork state: case distribution and incidence rates by clinical stageat diagnosis. Cancer 1986;58(3):807–15.

4. Farley TA, Flannery JT. Late-stage diagnosis of breast cancer inwomen of lower socioeconomic status: public health implications.Am J Public Health 1989;79(11):1508–12.

5. Saunders LD. Differences in the timeliness of diagnosis, breastand cervical cancer, San Francisco 1974–1985. Am J PublicHealth 1989;79(1):69–70.

6. Bassett MT, Krieger N. Social class and black-white differences inbreast cancer survival. Am J Public Health 1986;76(12):1400–3.

7. McWhorter WP, Mayer WJ. Black/white differences in type ofinitial breast cancer treatment and implications for survival. AmJ Public Health 1987;77(12):1515–7.

8. Breen N, Kessler L. Changes in the use of screening mammogra-phy: evidence from the 1987 and 1990 national health interviewsurveys. Am J Public Health 1994;84:62–7.

9. Calle EE, Flanders D, Thun MJ, Martin LM. Demographic pre-dictors of mammography and pap smear screening in U.S.women. Am J Public Health 1993;83(1):53–60.

10. Hayward RA, Shapiro MF, Freeman HE, Corey CR. Who getsscreened for cervical and breast cancer? Arch Intern Med 1988;148:1177–81.

11. Makuc DM, Freid VM, Kleinman JC. National trends in the useof preventive health care by women. Am J Public Health1989;79:21–6.

12. National Cancer Institute. Cancer screening recommnedations.Bethesda (MD): USDHHS, NIH, NCI, 1992.

M

CANCER SCREENING IN

13. Fox SA, Murata PJ, Stein JA. The impact of physician complianceon screening mammography for older women. Arch InternMed 1991;151:50–6.

14. Caplan LS, Wells BL, Haynes S. Breast cancer screening amongolder racial/ethnic minorities and whites: barriers to early detec-tion. J Gerontol 1992;47:101–10.

15. Mandelblatt JS, Traxler M, Lakin P, Kanetsky P, Kao R. Mam-mography and Papanicolaou smear use by elderly poor blackwomen. J Am Geriatr Soc 1992;40:1001–7.

16. US Department of Commerce. Census statistics, 1985: statisticalabstract of the United States. 105th ed. Washington: U.S. Depart-ment of Commerce, 1985.

17. O’Malley AS, Kerner J, Johnson A, Mandelblatt JS. Accultura-tion and breast cancer screening for urban Hispanic women. AmJ Public Health, 1999. In press.

18. Waksberg J. Sampling methods for ramdom digit dialing. J AmStat Assoc 1978;73:40–6.

19. Stewart AL, Hays RD, Ware JE Jr. The MOS short-form generalhealth survey: reliability and validity in a patient population.Med Care 1988;26(7):724–35.

20. Schottenfeld D, Kerner JF. National Cancer Institute. Final re-port: cancer control development grant (National Cancer Insti-tute, Grant CA16402), Bethesda (MD): 1984.

21. Crocker L, Algina J. Introduction to classical and modern testtheory. New York: Holt, Rinehart & Winston, 1986.

22. Burnam MA. Hough RL, Karno M, Escobar JI, Telles CA. Accul-turation and lifetime prevalence of psychiatric disorders amongMexican-Americans in Los Angeles. J Health Soc Behav 1987;28:89–102.

23. Epstein JA, Botvin GJ, Dusenbury L, Diaz T, Kerner J. Validationof an acculturation measure for Hispanics adolescents. PsycholRep 1996;79:1075–9.

24. Marks G, Solis J, Richardson JL, Collins LM, Birba L, HisserichJC. Health behavior of elderly hispanic women: does culturalassimilation make a difference? Am J Public Health 1987;77:1315–9.

25. SAS Institute Inc. SAS/STAT user’s guide, Version 6. 4th ed. Vol.2. Cary (NC): SAS Institute, 1989.

26. Mandelblatt JS, Traxler M, Lakin P, Kanetsky P, Kao R, HarlemStudy Team. Targeting breast and cervical cancer screening toelderly poor black women: who will participate? Prev Med1993;22:20–33.

27. Chao A, Paganini-Hill A, Ross RK, Henderson BE. Use of preven-tive care by the elderly. Prev Med 1987;16:710–22.

28. Bostick RM, Sprafka JM, Virnig BA, Potter JD. Predictors ofcancer prevention attitudes and participation in cancer screeningexaminations. Prev Med 1994;23:816–26.

29. Grady KE, Lemkau JP, McVay JM, Reisine ST. The importanceof physician encouragement in breast cancer screening of olderwomen. Prev Med 1992;21:766–80.

30. Klassen A. Breast and cervical cancer screening in Baltimore.Presented at the American Public Health Association Meeting,Atlanta: 1991 Nov.

31. Burack RC, Liang J. The acceptance and completion of mammog-raphy by older black women. Am J Public Health 1989;79(6):721–6.

ULTIETHNIC ELDERLY 425

32. Shoen RE, Marcus M, Braham RL. Factors associated with theuse of screening mammography in a primary care setting. J Com-munity Health 1994;19(4):239–52.

33. Satariano WA, Ragland DR. The effect of comorbidity on 3-yearsurvival of women with primary breast cancer. Ann Intern Med1994;120(2):104–10.

34. Grady KE, Lemkau JP, McVay JM, et al. Clinical decision-makingand mammography referral. Prev Med 1996;25:327–38.

35. Boer R, de Koning HJ, van Oortmarssen GJ, van der Maas PJ.In search of the best upper age limit for breast cancer screening.Eur J Cancer 1995;31A(12):2040–3.

36. Mandelblatt JS, Wheat ME, Monane M, Moshief RD, HollenbergJP, Tang J. Breast cancer screening for elderly women with andwithout comorbid conditions. A decision analysis model. AnnIntern Med 1992;116:722–30.

37. NCI Breast Cancer Screening Consortium: Screening mammog-raphy: a missed clinical opportunity? JAMA 1990;264:54–8.

38. Zapka JG, Bigelow C, Hurley T, Ford LD, Egelhofer J, CloudWM, Sachsse E. Mammography use among sociodemographicallydiverse women: the accuracy of self-report. Am J Public Health1996;86(7):1016–21.

39. Zapka J, Chasen-Taber L, Bigelow C, Hurley T. Methodologicalissues for health-related surveys of multicultural older women.Eval Health Professions 1994;17:485–500.

40. Gordon NP, Hiatt RA, Lampert DI. Concordance of self-reporteddata and medical record audit for six cancer screening proce-dures. J Natl Cancer Inst 1993;85:566–70.

41. Sawyer JA, Earp JA, Fletcher RH, Daye FF, Wynn TM. Accuracyof women’s self-report of their last Pap smear. Am J PublicHealth 1989;79:1036–7.

42. Hiatt RA, Perez-Stable EJ, Quesenberry C Jr, Sabogal F, Otero-Sabogal R, McPhee SJ. Agreement between self-reported earlycancer prevention practices and medical audits among Hispaniccompared with non-Hispanic white health plan members inNorthern California. Prev Med 1995;24:278–85.

43. Suarez L, Goldman DA, Weiss NS. Validity of Pap smear andmammogram self-reports in a low-income Hispanic population.Am J Prev Med 1995;11:94–8.

44. 1994 Current Population Survey. Washington: U.S. Bureau ofthe Census, 1995.

45. Frey JH. Survey research by telephone. 2nd ed, Vol 150. Thou-sand Oaks (CA): Sage, 1989.

46. Centers for Disease Contol and Prevention. Self-reported use ofmammography among women aged $40 years—United States,1989 and 1995. JAMA 1997;278(17):1395–6.

47. Hiatt RA, Pasick RJ. Unsolved problems in early breast cancerdetection: focus on the underserved. Breast Cancer Res Treat1996;40:37–51.

48. Hubbell FA, Mishra SI, Chavez LR, Valdez RB. The influence ofknowledge and attitudes about breast cancer on mammographyuse among latinas and anglo women. J Gen Intern Med1997;12:505–8.

49. Anderson LM, May DS. Has the use of cervical, breast, andcolorectal cancer screening increased in the United States? AmJ Public Health 1995;85:840–2.