Preventive Medicine 31, 481–493 (2000)doi:10.1006/pmed.2000.0747, available online at http://www.idealibrary.com on

A Systems Model of Clinical Preventive Care: The Case of BreastCancer Screening among Older Women1

Dorothy S. Lane, M.D., M.P.H.,*,2 Jane Zapka, Sc.D.,† Nancy Breen, Ph.D.,‡ Catherine R. Messina, Ph.D.,*and David J. Fotheringham§

For the NCI Breast Cancer Screening Consortium

S

*Department of Preventive Medicine, School of Medicine,†Division of Preventive and Behavioral Medicine, University of

‡National Cancer Institute, Bethesda, Maryland 20892; and §Infor

Background. In older women covered by Medicare,relationships among physician recommendation,mammography in the past 2 years, and clinical breastexamination (CBE) in the past year were systemati-cally explored with a variety of predisposing, enabling,and situational factors identified in the Systems Modelof Clinical Preventive Care.

Methods. A population-based survey of women age 65years and older was conducted in five National CancerInstitute’s Breast Cancer Screening Consortium geo-graphic areas. Analyses focused on women with a regu-lar physician and site of care (n 5 5318).

Results. Physician recommendation and mammogra-phy use declined with women’s increasing age andincreased with income, education, and insurance. CBEand mammography increased with number of physi-cians and breast cancer family history; mammographyuse decreased with worsening health status. Recom-mendations were higher among physicians who wereyounger, female, and internists. Family practitionerswere older and male; women who saw family prac-titioners reported characteristics associated withdecreased screening—lower income, education, andinsurance—and seeing only one physician.

Conclusions. Public policy and health systemchanges that create a uniform system of finance andservice performance expectations may reduce the per-sistent discrepancy in physician recommendation and

1 The authors acknowledge the National Cancer Institute for spon-soring the survey (Contract N01-CN-85122-01). The NCI grants forthe individual sites were CA44990 (University of Massachusetts Med-ical School), CA45003 (University of California at Los Angeles Schoolof Medicine), CA45022 (University of North Carolina at Chapel Hill),CA45034 (Research Foundation of the State University of New York,SUNY at Stony Brook), and CA45834 (Fox Chase Cancer Center).

2 To whom correspondence and reprint requests should be ad-dressed. Fax: (621) 444-2202. E-mail: dlane@dean.som.sunysb.edu.

48

UNY at Stony Brook, Stony Brook, New York 11794-8036;Massachusetts Medical School, Worcester, Massachusetts 01655;mation Management Services, Inc., Silver Spring, Maryland 20904

mammography use due to sociodemographics and phy-sician specialty. q 2000 American Health Foundation and Academic

Press

Key Words: mammography; utilization; health serviceaccessibility; socioeconomic factors; physician’s prac-tice patterns; physicians; aged.

INTRODUCTION AND PURPOSE

The proportion of American women screened bymammography has increased steadily since 1982, whenclinical trial results reported that mammography re-duced mortality from breast cancer [1]. While mammog-raphy utilization doubled between 1987 and 1990 [2],the growth in use slowed considerably after 1990 [3,4].

Walsh and McPhee [5] propose a model to conceptual-ize the many factors involved in the provision and re-ceipt of clinical preventive care. This Systems Model ofClinical Preventive Care focuses on the patient–physician interaction. It details categories of factorswhich promote or inhibit completing preventive careactivities. The categories include patient and physicianpredisposing factors, such as sociodemographics, healthbeliefs, and attitudes; enabling factors, such as skillsand resources; and reinforcing factors, such as socialsupport. Other enabling factors include health care sys-tem organizational factors, such as access or availabil-ity; characteristics of the preventive activity, such ascost; and cues to action, such as symptoms or reminders.Numerous studies have been conducted over the pastdecade to better understand the complex set of factorswhich influence patient and provider screening behav-iors. These are reflected in Fig. 1, which we haveadapted from Walsh and McFee [5]. Our research con-

siders and extends this body of evidence.

Having a regular doctor is associated with improvedaccess to health care [30,31], including higher mam-mography participation [22,23]. Women with a regular

1 0091-7435/00 $35.00Copyright q 2000 by American Health Foundation and Academic Press

All rights of reproduction in any form reserved.

insurance coverage (more coverage) [11,21]; health care delivery system organizational factors—having a regular physician [9,22,23], andsite of care [8,11,22]. Situational factors/cues to action trigger breast cancer screening behavior and include health status (absence of

y

,

comorbidity) [11,24,25], having a family history of breast cancer [15], t(greater) [7,26]. Prevention behavior includes having a physician recomfactors include geographic area because practice patterns, insurancegeographic region [4,11].

doctor were significantly more likely than those withouta regular site of care to have a clinical breast examina-tion (CBE) [22]. Older women report lack of a physicianrecommendation as a critical barrier to breast cancerscreening [27,29]. Our study more closely examinesolder women with a regular physician and source of carein order to explore whether other factors are associatedwith screening participation. This study is importantbecause most older American women have a regularsource of care, yet screening for breast cancer re-

mains inadequate.

Even though a woman’s age is a consistent predictorof breast cancer screening, use of both CBE and mam-mograms drops off after age 64. Physicians reportscreening older women less than younger women [6].

pe of physician seen [17,20], and the number of visits to the physicianmendation for screening [27–29] and prior utilization [15]. Reinforcingand HMO penetration, which can influence screening, may vary by

Although obstetrician/gynecologists most frequentlyrecommend mammography and perform CBE, olderwomen are less likely to visit them [7]. Women 65 yearsand older are almost universally covered by basic Medi-care health insurance (97%) but they still have screen-ing rates that are less than optimal. Thus, the age-eligible Medicare population is especially appropriatefor this study. This paper systematically explores therelationships between screening recommendation andparticipation with patterns of provider utilization, phy-

482 LANE ET AL.

FIG. 1. Study measures within the Systems Model of Clinical Preventive Care. Outcome: detection at an earlier stage is associated withdecreased morbidity/mortality due to breast cancer among women 50–74 [1]. Predisposing factors motivate breast cancer screening utilization.Patient predisposing factors include sociodemographic characteristics: women’s age (younger) [6–8], race (white) [8–15], education andincome (higher) [9,14,15]. Physician predisposing factors include physician socio-demographics: age (younger) [16], years since graduationfrom medical school (recent) [17], gender (female) [17–19], and specialty: obstetrician/gynecologists followed by internists and family andgeneral practitioners [11,17,19,20]. Enabling factors include the woman’s resources necessary to facilitate screening. These include level of

sician characteristics, and older women’s characteris-tics in a sample of geographically diverse older womenwith access to health care (Medicare) who report havinga regular physician. The specific research questions areas follows:

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A SYSTEMS MODEL OF BR

1. In view of the critical role of physician recommen-dation, how are recommendation and actual screeningparticipation related to physician predisposing fac-tors—(a) specialty of the regular physician, and (b) phy-sician sociodemographics?

2. Given that health problems and need for care arerelated to realized access, is screening recommendationand participation related to women’s enabling factors—(a) level of health insurance and (b) usual place of care;and to situational factors—(c) health status, (d) familyhistory of breast cancer, (e) number and type of physi-cians seen, and (f) number of visits?

3. Further, is the relationship of the physician’s spe-cialty to breast cancer screening confounded by differ-ences in the characteristics of the patient population for

gorized women as having used the Medicare benefit ifthey reported that Medicare reimbursed any part oftheir most recent mammogram. Another enabling vari-able women were asked about was the place where theyusually receive their medical care (e.g., doctor’s office,

whom they care, and do physician predisposing factorsvary by the specialty of the regular physician?

4. Last, which factors are independently associatedwith getting a screening recommendation or actualscreening participation?

METHODS

Study Sample and Survey Methods

The five participating study sites for this project werepart of the NCI-funded Breast Cancer Screening Con-sortium which was composed of population-based re-search projects designed to investigate ways to increasemammography and CBE utilization [2]. All projects re-ceived institutional human subjects review board ap-proval. These sites included suburban Los Angeles,eastern Massachusetts, eastern North Carolina, east-ern Long Island, and metropolitan Philadelphia[3,15,32].

The sample of women age 65 and older was randomlyselected from the Health Care Financing Administra-tion’s (HCFA) file of Medicare beneficiaries for each ofthe five study sites. The upper age limit for the sites,except Los Angeles, was 79 years—4.4% of the LosAngeles women were age 80 and older. Women wereeligible if they did not have a history of breast cancer,were not institutionalized, and were able to completea half-hour interview. Surveys were conducted fromApril through mid-July 1993. Participants were sent aletter introducing the survey and then contacted bytelephone for an interview. When participants could notbe reached by telephone, follow-up questionnaires bymail or in-person interviews were attempted. Em-ploying these three different survey methods did notinfluence the pattern of results [15].

The samples were weighted to adjust for oversam-pling by race and age within some sites. Sample weights

were normalized within each site to sum to the samplesize of that site rather than to the population fromwhich each sample was drawn. This insured that areaswith large populations would not overwhelm areas with

AST CANCER SCREENING 483

small populations. A geographic site variable was in-cluded in the regression analyses to adjust for differ-ences among them. Inferences from this research arelimited to the study site populations from which theindividuals were sampled and do not constitute a ran-dom sample of communities. They do, however, consti-tute a geographically and sociodemographically diversestudy population. The overall response rate across geo-graphic sites was 79% (range: 62 to 85%) [3]. Of 6502women surveyed, 5318 reported having a usual site formedical care and a regular physician. These womenconstitute the study sample.

Measures

Figure 1 depicts the study variables according to theSystems Model of Clinical Preventive Care [5]. Threedependent screening participation variables were stud-ied: (a) having received a recommendation for mammo-gram from a physician, (b) having a mammogram inthe past 2 years, and (c) having a CBE in the pastyear.3 We selected the 2-year window for having had amammogram, because at the time of the survey, Medi-care benefits stipulated a 2-year interval.

Women reported their personal sociodemographic in-formation, including income, education, and race. Giventhe importance of the physician’s recommendation, datawere collected on several measures to explore in greaterdepth the relationship of patient and clinician predis-posing factors. Women reported on the age, gender, andrace of their regular physician and the specialty (basedupon provided definitions) of the “regular physician.”Since practice norms may vary because of geographicvariations [4], this potentially reinforcing factor wascontrolled for in the analysis.

Figure 1 shows that insurance access is an importantenabling variable within the health care delivery sys-tem. Nearly everyone in the age group under study hasMedicare. What varies in this population is supplemen-tal coverage, which relates to differences in income andeducation. Supplemental insurance is less likely amongwomen with lower income and education [21]. We inves-tigated whether the amount of insurance coverage in-fluenced a physician’s recommendation and a woman’suse of screening. Insurance categories included Medi-care, Medicare supplemented by private insurance,Medicaid, or another type of health insurance. We cate-

3 Data on women who reported “ever having a mammogram” andwomen who had one “in the past year” can be obtained from the au-thors.

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484 LANE

clinic, health center), because the organizational con-text, including structure and processes of services, canimpact physician and patient behavior [33,34].

Data were collected on measures related to the clini-cian–patient interaction. In this study we characterizeself-reported health status and family history as situa-tional factors because a woman’s health status and fam-ily history are determinants of the number of doctorsseen, the frequency of visits, and/or how a physiciansets service priorities. A woman’s health status andunderstanding of her family’s breast cancer historycould also be conceptualized as predisposing factors.Research on the relationship of health care utilizationin general to breast cancer screening suggests thatmammography use for Medicare enrollees increaseswith the number of physician visits, but levels off aftertwo visits [24]. Among Medicare enrollees, however,the number of primary care visits did less to “boost”mammography use for black women than white women[25]. Because of the limited research about utilizationfactors, we asked questions to further explore such rela-tionships: the number of visits they made to their regu-lar physician and other physicians and the number ofphysicians they visited (regardless of the frequency ofvisits) in the 2 years prior to the interview.

Statistical Analyses

x2 tests of significance were performed to identifydifferences among characteristics. Tests of colinearityamong predictor variables were preformed. Predictorsof screening use and physician recommendation weredetermined by logistic regression analyses. The controlvariable for site was entered first into each model, fol-lowed by women’s and physician’s predisposing factorsand enabling factors (entered simultaneously). Theglobal significance level of predictor variables (ratherthan the significance of each level of each category),odds ratios (OR), and 95% confidence intervals (CI)

were obtained using SUDAAN (Survey Data Analyses)[35]. This software package was designed for the statis-tical analysis of correlated data and employs general-ized estimating equations methods to estimate regres-sion parameters.

RESULTS

Sixty-four percent of women with a regular physicianand site of care had a mammogram in the past 2 years,57% had CBE in the past year, and 81% reported receiv-ing a recommendation for mammography from theirphysician. Table 1 presents the study sample character-

istics grouped by the categories shown in Fig. 1. Theeffect of these characteristics on the three dependentmeasures of clinical preventive behavior were first ex-amined using univariate analyses (data not shown).4

4 Tables of univariate analyses can be obtained from the authors.

T AL.

TABLE 1

Characteristics of Study Population (n 5 5318)

Characteristics n (%)

Predisposing factors: Women’s sociodemographiccharacteristics

Age65–69 2373 (44.6)70–74 2127 (40.0)75–79 505 (9.5)801 233 (4.4)Don’t know 13 (0.2)Refused 69 (1.3)

Income,$10,000 1063 (20.0)$10,000–20,000 1427 (26.8)$20,0001 1649 (31.0)Don’t know 663 (12.5)Refused 517 (9.7)

Education,8th grade 386 (7.2)8th–11th grade 934 (17.6)High school graduate 2077 (39.0)Some college/trade 1175 (22.1)College graduate 1 704 (13.2)Don’t know 14 (0.3)Refused 30 (0.6)

RaceWhite/non-Hispanic 4510 (84.8)Black/non-Hispanic 506 (9.5)Hispanic 170 (3.2)Other/non-Hispanic 64 (1.2)Refused 71 (1.3)

Predisposing factors: Physician specialty andsociodemographic characteristics

Specialty of regular physicianInternist 2169 (40.8)Family medicine/general practitioner 2558 (48.1)Othera 542 (10.0)Don’t know 48 (0.9)Refused 1 (0.0)

Age of regular physician,40 672 (12.9)40–49 2420 (46.5)50–59 1440 (27.7)$60 475 (9.1)Unspecified/don’t knowb 241 (4.6)Refused 5 (,0.1)Missing 65 (1.2)

Gender of regular physicianMale 4578 (87.2)Female 605 (11.5)Unspecified/don’t knowc 65 (1.2)Refused 5 (,0.1)Missing 65 (1.2)

Race of regular physicianWhite/non-Hispanic 4347 (81.7)Black/non-Hispanic 173 (3.3)Hispanic 110 (2.1)

Other/non-Hispanic 485 (8.9)Unspecified/don’t knowd 121 (2.3)Refused 17 (0.3)Missing 64 (1.2)

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f Denominator varies because of missing responses.

g Includes 37 hospital emergency room, 77 hospital outpatient de-

partment, 2 work health center, and 19 general “other” category.h Includes 621 combined no and don’t know responses for mother,

sister, and other and 2 don’t know for all.

Physician recommendation, mammography, and CBEscreening were significantly higher (P’s , 0.001) amongwomen using internists (88, 73, and 65%, respectively)vs family practitioners (75, 56, and 50%, respectively).

AST CANCER SCREENING 485

Table 2 displays predisposing sociodemographic char-acteristics of women and physicians by physician spe-cialty. Women who were younger and poorer, had lesseducation, and were non-white more frequently sawfamily practitioners than internists (P , 0.02). Com-pared to internists, family practitioners were describedas older (P , 0.001) and more frequently were male,black or Hispanic (P , 0.001).

Table 2 also shows women’s enabling factors by physi-cian specialty. Women covered by Medicare plus Medic-aid or Medicare-only health insurance coverage weremore likely to use family practitioners than internists.Women who reported poorer health, those who less fre-quently reported a family history (mother or sister orother relative) of breast cancer, and those whose usualplace of health care was not a physician’s private officewere more likely to see family practitioners than intern-ists (P’s , 0.001). Women who reported less utilization(i.e., one or more visits to their regular physician onlyand only saw one physician in the past year) were alsomore likely to see a family practitioner than an internist(P’s , 0.001). The results of an additional logisticregression analyses confirmed that women who sawfamily practitioners were more likely to be poorer,less educated, and have less insurance coverage;(P’s , 0.004).

Multivariate Analyses

The results of multivariate modeling of self-reportedbreast cancer screening use and physician recommen-dation for mammography are shown in Table 3. Eachmultivariate analysis was conducted using the entirestudy sample of 5318 women. Tests of colinearity amongthe predictor variables indicated that only two vari-ables, number of visits to the physician and number ofphysicians seen, were highly correlated (Kendall Taucorrelation coefficient 5 0.70). The number of visits tothe physician was excluded from the regression modelsbecause it was not as strongly correlated with the out-come variables as the number of physicians seen. Geo-graphic site, which may relate to practice norms invarious parts of the country, significantly predicted phy-sician recommendation for and mammography use(P’s , 0.001) but not CBE use.

Predisposing women’s sociodemographic characteris-tics. Older women were less likely to report affirma-tively on all three dependent measures (P’s , 0.001).Income was related to mammography use (P’s , 0.001)and physician recommendation (P , 0.03). Comparedto women with incomes of more than $20,000, womenwith lower annual incomes were less likely to have a

A SYSTEMS MODEL OF BR

TABLE 1—Continued

Characteristics n (%)

Enabling factors: Insurance and place of careInsurance coverage

Medicare plus supplemental 3754 (70.2)Medicare only 1091 (20.5)Medicare plus Medicaid 376 (6.8)Othere 25 (0.4)Don’t know 71 (1.3)

Women’s usual place of care f

Neighborhood health center 115 (6.7)Office of HMO 151 (8.6)Physician’s office 1314 (74.8)Otherg 135 (7.6)Don’t know 39 (2.2)Refused 1 (0.1)

Enabling factors: SituationalHealth status

Excellent 706 (13.3)Very good 1331 (25.0)Good 1834 (34.5)Fair 1105 (20.8)Poor 314 (5.9)Don’t know 30 (0.6)

Family history of breast cancerMother/sister/only 560 (10.5)Mother/sister/other 450 (8.5)Other only 111 (2.1)No history 3574 (67.2)Don’t knowh 623 (11.7)Refused 1 (0.0)

Number of visits to physician1 or more to regular MD, no other MDs seen 1862 (35.0)1–5 to regular MD, with other MDs seen 1793 (33.7)61 to regular MD, with other MDs seen 1530 (28.8)Don’t know 62 (1.2)Refused 2 (0.0)Missing 68 (1.3)

Number of physicians seenOne 1869 (35.2)Two 1884 (35.4)Three or more 1564 (29.4)

Note: Totals sum to weighted n’s.a Includes 228 cardiologists, 138 obstetrician/gynecologists, 6 sur-

geons, and 170 general “other” category.b Includes 52 who saw a different physician each visit (unspecified)

and 189 don’t know.c Includes 52 who saw a different physician each visit (unspecified)

and 13 don’t know.d Includes 52 who saw a different physician each visit (unspecified)

and 69 don’t know, 17 refused, 64 missing.e Obtained Medicare benefits through HMO.

physician recommendation or a mammogram in thepast 2 years. While income was significantly related toCBE in the univariate analyses, it no longer signifi-cantly predicted CBE use after controlling for site and

486 LANE ET AL.

TABLE 2

Women’s and Physician’s Predisposing Factors and Women’s Enabling and Situational Factors by Physician Specialty

Physician specialty

Internist Family medicine(n 5 2169) (n 5 2558)

Factors n (%) n (%)

Predisposing factorsWomen’s sociodemographic characteristics

Age*65–69 968 (46.0) 1136 (54.0)70–74 850 (44.8) 1047 (55.5)75–79 209 (46.2) 243 (53.8)801 116 (56.6) 89 (43.4)Unknowna 26 (37.7) 43 (62.3)

Income**,$10,000 267 (29.0) 653 (71.0)$10,000–20,000 597 (46.7) 681 (53.3)$20,0001 855 (58.0) 620 (42.0)Unknowna 449 (42.6) 604 (57.7)

Education**,8th grade 58 (17.7) 270 (82.3)8th–11th grade 237 (28.8) 578 (70.1)High school graduate 861 (46.1) 1006 (53.9)Some college/trade 589 (56.4) 456 (43.6)College graduate 1 414 (65.5) 218 (34.5)Unknowna 10 (25.6) 29 (74.4)

Race**White/non-Hispanic 1943 (46.5) 2065 (51.5)Black/non-Hispanic 131 (28.4) 315 (70.6)Hispanic 53 (33.7) 104 (66.2)Other (NH)/unknowna 41 (35.3) 75 (64.6)

Physician’s sociodemographic characteristicsAge of regular physician**

,40 309 (50.1) 308 (49.9)40–49 1032 (47.5) 1141 (52.5)50–59 607 (46.9) 687 (53.1)$60 164 (27.8) 268 (61.7)Unknowna 56 (26.7) 154 (73.3)

Gender of regular physician**Male 1862 (45.4) 2241 (54.6)Female 292 (51.8) 272 (48.2)Unknowna 16 (26.7) 44 (73.3)

Race of regular physician**White/non-Hispanic 1826 (46.8) 2079 (53.2)Black/non-Hispanic 50 (31.8) 107 (68.1)Hispanic 35 (35.7) 63 (64.3)Other (NH)/unknowna 258 (45.6) 308 (54.4)

Enabling factorsInsurance

Insurance coverage**Medicare plus supplemental 1654 (49.7) 1671 (50.2)Medicare plus Medicaid 73 (23.7) 235 (76.3)Medicare only 386 (39.5) 590 (60.4)

Otherb/unknowna 56 (47.9) 61 (52.1)

Place of careWomen’s usual place of carec**

Neighborhood health center 33 (37.1) 56 (62.9)Office of HMO 75 (52.4) 68 (47.5)Physician office 537 (47.3) 597 (52.6)Otherd 59 (42.1) 81 (57.9)

a Unknown categories 5 categories of don’t know, refused, missing, etc.; see numbers in Table 1.b

pa

Obtained Medicare benefits through HMO.c Denominator varies because of missing responses.d Includes 37 hospital emergency room, 77 hospital outpatient de* P , 0.02.

** P , 0.001.

women’s age. Education positively influenced mam-mography use only (P , 0.001) although it was alsosignificantly related to physician recommendation andCBE in the univariate analyses. Compared to womenwho completed 4 or more years of college, women withless education were less likely to have had a mammo-gram in the past 2 years. Race was significantly relatedto all three dependent measures in the univariate anal-yses but only significantly impacted physician recom-mendation (P , 0.02) in the multivariate model.

Predisposing physician sociodemographic character-istics. Compared to women whose regular physicianwas an internist, women who saw a family practitionerwere less likely to affirmatively report all three mea-sures (P’s , 0.001). Physician recommendation andmammography use in the past 2 years decreased asphysician age increased (P , 0.001 and P 5 0.03, respec-tively). Physician age, which was significantly related

to CBE in the univariate analysis, did not significantlypredict CBE after controlling for site and women’s pre-disposing factors. Women with female physicians weremore likely to affirmatively report all three measures

rtment, 2 work health center, and 19 general “other” category.

compared to women who saw male physicians (P’s ,0.02). Although significantly related to physician rec-ommendation and mammography use in the univariateanalyses, physician race did not significantly predictphysician recommendation or screening use in the mul-tivariate model.

Enabling factors. Compared to women who re-ported having Medicare plus supplemental insurance,women who had Medicare or Medicare plus Medicaidwere less likely to report receiving a physician recom-mendation for mammography or having mammo-graphy in the past 2 years (P’s , 0.001). The usualsite of health care was not significantly associated withreports of screening use or physician mammographyrecommendation.

Situational factors. When the physician who recom-mended a mammogram was not her regular physician,

A SYSTEMS MODEL OF BREAST CANCER SCREENING 487

TABLE 2—Continued

Physician specialty

Internist Family medicine(n 5 2169) (n 5 2558)

Factors n (%) n (%)

Enabling factors—ContinuedSituational

Health status**Excellent 327 (52.5) 296 (47.5)Very good 585 (49.7) 617 (51.3)Good 728 (44.2) 919 (55.8)Fair 417 (42.8) 556 (57.1)Poor 103 (39.8) 156 (60.2)Unknowna 8 (38.1) 13 (61.9)

Family history**Mother/sister/only 239 (48.7) 252 (51.3)Other only 206 (52.7) 185 (47.3)Mother/sister/other 58 (59.2) 40 (40.8)No history/unknowna 1666 (44.5) 2080 (55.5)

Number of visits to physician**1 or more to regular MD, no other MDs seen 615 (36.9) 1052 (63.1)1–5 to regular MD, with other MDs seen 872 (54.7) 723 (45.3)61 to regular MD, with other MDs seen 668 (47.4) 741 (52.6)Unknowna 14 (25.0) 42 (75.0)

Number of physicians seen**One 606 (36.4) 1059 (63.6)Two 819 (48.9) 857 (51.1)Three or more 744 (53.7) 641 (46.3)

Note. Total sum to weighted n’s. NH, non-Hispanic.

women were less likely to report mammography in thepast 2 years or CBE (all P’s , 0.001). Self-perceivedhealth status predicted mammography use only (P ,0.001) (although it was also significantly associated

488 LANE ET AL.

TABLE 3

Predictors of Women’s Breast Cancer Screening Utilization and Physician Recommendation (n 5 5318)

MD recommendation Mammo. past 2 years CBE in past year

Predictor Global P OR (95% CI) Global P OR (95% CI) Global P OR (95% CI)

Reinforcing factorSite ,0.001 ,0.001 ns

North Carolina 0.54 (0.41, 0.71) 0.84 (0.67, 1.06) 1.09 (0.89, 1.33)California 0.60 (0.44, 0.81) 0.88 (0.69, 1.13) 0.85 (0.68, 1.06)Philadelphia, PA 0.56 (0.40, 0.79) 0.68 (0.51, 0.90) 0.81 (0.64, 1.04)Long Island, NY 0.42 (0.33, 0.53) 0.60 (0.50, 0.73) 0.93 (0.79, 1.10)Massachusetts 1.00a 1.00a 1.00a

Predisposing factorsWomen’s sociodemographics

Age ,0.001 ,0.001 0.03070–74 0.86 (0.73, 1.02) 0.88 (0.76, 1.00) 0.94 (0.83, 1.06)75–79 0.69 (0.53, 0.88) 0.96 (0.76, 1.22) 1.08 (0.88, 1.34)$80 0.35 (0.22, 0.56) 0.28 (0.17, 0.44) 0.55 (0.36, 0.83)Unspecified/refused 0.74 (0.41, 1.33) 0.90 (0.53, 1.52) 1.04 (0.63, 1.73)65–69 1.00a 1.00a 1.00a

Income 0.028 ,0.001 ns,$10,000 0.75 (0.59, 0.96) 0.63 (0.51, 0.78) 0.83 (0.68, 1.00)$10,000–20,000 0.84 (0.68, 1.04) 0.83 (0.69, 1.00) 0.96 (0.81, 1.12)Unspecified/refused 1.02 (0.81, 1.29) 0.91 (0.75, 1.10) 0.94 (0.79, 1.12)$$20,000 1.00a 1.00a 1.00a

Education ns ,0.001 ns.8th grade 0.75 (0.51, 1.10) 0.50 (0.36, 0.70) 0.79 (0.58, 1.06)8th to 11th grade 0.82 (0.60, 1.13) 0.59 (0.45, 0.78) 0.84 (0.66, 1.07)High school grad 0.76 (0.58, 1.01) 0.70 (0.55, 0.89) 0.88 (0.72, 1.08)Some college/trade 0.95 (0.70, 1.29) 0.84 (0.65, 1.09) 0.94 (0.76, 1.17)Unspecified/refused 0.65 (0.27, 1.55) 0.68 (0.33, 1.39) 1.03 (0.44, 2.42)College grad 1 1.00a 1.00a 1.00a

Race 0.017 ns nsBlack (NH) 0.84 (0.65, 1.09) 0.97 (0.77, 1.23) 1.17 (0.95, 1.45)Hispanic 1.42 (0.90, 2.25) 0.96 (0.65, 1.41) 0.81 (0.57, 1.18)Other (NH)/unspecified 0.58 (0.37, 0.91) 0.76 (0.50, 1.15) 0.98 (0.66, 1.48)White (NH) 1.00a 1.00a 1.00a

Physician specialty and sociodemo-graphics

Regular MD ,0.001 ,0.001 ,0.001Family practitioner/GP 0.54 (0.45, 0.64) 0.68 (0.58, 0.78) 0.66 (0.58, 0.75)Other MD 0.55 (0.42, 0.73) 0.87 (0.68, 1.10) 0.86 (0.70, 1.06)Internist 1.00a 1.00a 1.00a

MD age ,0.001 0.033 ns40–49 0.74 (0.57, 0.97) 0.77 (0.62, 0.96) 0.92 (0.76, 1.12)50–59 0.67 (0.51, 0.89) 0.76 (0.60, 0.97) 0.96 (0.78, 1.18)$60 0.50 (0.36, 0.69) 0.65 (0.49, 0.87) 0.97 (0.75, 1.26)

Unknown 0.53 (0.35, 0.80) 0.62 (0.41, 0.94) 0.96 (0.66, 1.39),40 1.00a 1.00a 1.00a

MD gender ,0.003 0.044 0.028Female 1.55 (1.15, 2.08) 1.34 (1.06, 1.68) 1.27 (1.04, 1.55)Unknown 1.99 (1.08, 3.66) 1.06 (0.61, 1.82) 0.75 (0.45, 1.26)Male 1.00a 1.00a 1.00a

MD race ns ns nsBlack (NH) 0.72 (0.49, 1.05) 0.86 (0.61, 1.21) 0.85 (0.62, 1.17)Hispanic 0.63 (0.39, 1.02) 1.35 (0.84, 2.16) 0.95 (0.62, 1.45)Other/unknown 0.81 (0.64, 1.04) 0.83 (0.66, 1.03) 0.98 (0.80, 1.19)White (NH) 1.00a 1.00a 1.00a

Two MDs seen 1.68 (1.41, 1.99) 2.26 (1.94, 2.63) 1.59 (1.38, 1.82)Three1 MDs seen 2.70 (2.20, 3.33) 3.56 (2.98, 4.25) 2.33 (2.00, 2.72)

a

en

one MD seen 1.00

Note. Global P, global significance value; OR, odds ratio; CI, confida Fixed reference category.

with physician recommendation and CBE in the univar-iate analysis). Compared to women who reported excel-lent health status, mammography use declined ashealth status worsened. A positive family history ofbreast cancer predicted CBE and mammography use(P’s , 0.005), but was not significantly related to physi-

A SYSTEMS MODEL OF BREAST CANCER SCREENING 489

TABLE 3—Continued

MD recommendation Mammo. past 2 years CBE in past year

Predictor Global P OR (95% CI) Global P OR (95% CI) Global P OR (95% CI)

Enabling factorsInsurance ,0.001 ,0.003 ns

Medicare only 0.62 (0.46, 0.82) 0.77 (0.58, 0.97) 0.88 (0.68, 1.13)Medicare 1 Medicaid 0.78 (0.65, 0.94) 0.80 (0.68, 0.94) 0.99 (0.85, 1.15)Other 1.55 (0.85, 2.81) 1.77 (0.94, 3.35) 1.00 (0.64, 1.58)Medicare 1 supplement 1.00a 1.00a 1.00a

Place of health care ns ns nsNeighborhood health center 1.04 (0.87, 1.25) 0.93 (0.79, 1.08) 0.99 (0.86, 1.14)HMO 0.93 (0.55, 1.58) 1.15 (0.72, 1.86) 1.08 (0.71, 1.64)Other 1.39 (0.81, 2.35) 0.97 (0.63, 1.51) 0.92 (0.64, 1.33)Physician’s office 1.00a 1.00a 1.00a

SituationalMD who rec. mammo — ,0.001 ,0.001

Was not the reg. MD — — 0.28 (0.24, 0.32) 0.60 (0.53, 0.67)Was the reg. MD — — 1.00a 1.00a

Health status ns ,0.001 nsVery good 0.98 (0.75, 1.28) 0.96 (0.76, 1.22) 0.92 (0.75, 1.12)Good 0.96 (0.74, 1.23) 0.73 (0.59, 0.91) 0.83 (0.68, 1.01)Fair 1.11 (0.83, 1.47) 0.68 (0.53, 0.86) 0.84 (0.68, 1.04)Poor 0.73 (0.50, 1.05) 0.49 (0.36, 0.69) 0.81 (0.60, 1.10)Unknown 1.00 (0.42, 2.35) 0.23 (0.09, 0.61) 0.46 (0.19, 1.11)Excellent 1.00a 1.00a 1.00a

Family history ns ,0.001 0.003Mother/sister only 1.09 (0.84, 1.41) 1.58 (1.27, 1.97) 1.36 (1.11, 1.65)Mother/sister/other 1.33 (0.99, 1.79) 1.31 (1.02, 1.69) 1.19 (0.96, 1.48)Other only 1.54 (0.85, 2.79) 1.32 (0.81, 2.14) 1.54 (1.00, 2.38)No history/dk/ref 1.00a 1.00a 1.00a

Number of MDs seen ,0.001 ,0.001 ,0.001

cian recommendation after adjusting for the previous

variables. Compared to women who saw only one physi-cian in the past 2 years, odds ratios describing all threedependent measures increased with the number of phy-sicians seen (P’s , 0.001).

DISCUSSION

Models of health behavior provide a useful frameworkfor understanding how a variety of factors influence a

desired behavior or outcome. Here we apply one suchframework specifically to breast cancer screening in alarge, geographically diverse sample of women 65 yearsof age and older with known access to some care.

1.00a 1.00a

ce interval; ns, not significant; NH, non-Hispanic.

One purpose of our study was to examine whetherphysician recommendation and women’s screening usewere related to physician predisposing factors. We havecontributed to the prior literature the finding that, evenamong those who have a regular source and site of care,physician recommendation and mammography usewere higher among women who saw younger and fe-male physicians and internists vs family practitioners.Fewer sociodemographic variables were related to CBEthan mammography, perhaps because CBE is per-formed during an office visit and does not require theadditional initiative of following through with a recom-mendation and obtaining a mammogram. When recom-mendation for mammography came from the women’sregular physician vs another physician, both mammog-raphy and CBE were more likely to be obtained. While

this suggests a potential benefit of continuity of care,on the other hand a predictor of physician recommenda-tion for mammography was the use of multiple physi-cians (see below).

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The reinforcing factor of geographic site was a signifi-cant predictor, suggesting that over and above patientand physician sociodemographics, practice patternsmay vary geographically. Our study further suggeststhat they may vary according to the screening methoditself. We found no significant regional differences forCBE, a method requiring only a single visit, performedas part of a routine medical examination and thereby ofcomparatively lower cost. However, we did find regionaldifferences in terms of recommendation for and comple-tion of mammography.

A second purpose of our study was to determinewhether screening recommendation and participationwere related to women’s enabling factors. We found thata physician’s recommendation and mammography usewere strongly associated with level of insurance.Women with Medicare coverage plus a supplement weremore likely than women with Medicare alone, or Medi-care and Medicaid, to have had a physician recommen-dation for mammogram and to have had one in the past2 years. Blustein had similar findings regarding theinfluence of Medicare coverage plus supplemental in-surance on women’s use of mammography [21]. Thefinding that insurance and income did not indepen-dently influence CBE use probably relates to the factthat this is directly provided by the regular physicianand does not require referral or a separate charge orcopayment against a deductible.

Previous studies have documented the higher levelsof screening participation by women who receive medi-cal care in HMOs [36,37] and this trend appeared tobe true for older women enrolled in HMO senior plans[38,39]. This is usually attributed to more formal clini-cal protocols and reminder systems in HMOs. Our studydoes not mirror these findings for several reasons.There is some suggestion that this insurance advantageis decreasing [29] as plan benefits for screening becomemore uniform and mandated [39,40]. With the growingcomplexity of insurance systems, many women receivedcare in places which served heterogeneous mixes ofinsured patients and may be coded as HMOs, thus dilut-ing the validity of the category. In our study, the propor-tion reporting care within HMOs was small (9%). Thus,in the current period of transition in insurance coverageand reimbursement, reported place of care may not bean adequate measure of level of insurance coverage. Infuture studies it deserves special attention in terms ofboth data collection and analysis.

Utilization patterns were important for screening

recommendation and use. Women who see three or morephysicians were more likely to get a mammographyrecommendation and to have had one in the past 2years. Women with more doctors use more health care,

T AL.

have more opportunity for in-reach, and seek care, in-cluding preventive services and checkup visits. Al-though the survey was conducted on a noninstitutional-ized sample of older women, poor health status wasassociated with lower screening rates. This is consistentwith other findings that show lack of physician recom-mendation in the presence of comorbidity associatedwith shortened life expectancy [24,25] and is reinforcedby our data which indicate that women reporting poorhealth were more likely to see three or more physiciansand, despite this, less likely to use screening.

A third objective for this study was to determinewhether the relationship of physician specialty to rec-ommendation and screening use was confounded bydifferences in the characteristics of the patient popula-tion for whom they care and whether physician predis-posing factors vary by the specialty of the regular physi-cian. We found that family practitioners have a greaterproportion of patients with characteristics associatedwith lower screening and recommendation rates(women who are poorer, less educated, and have lessinsurance coverage). Additionally, family physiciansmore frequently had the characteristics associated withlower recommendations and screening rates, that is,being older and male. This suggests that the specialtydifference is explained by these same characteristicsthat are related to within-specialty variations in screen-ing rates and recommendation and would be consistentwith a greater concentration of family physician prac-tices within communities (often urban) serving low-in-come populations. We further found that this differencein patient characteristics is not limited to the subset ofthe typically older general practitioners, since thesedifferences between family practitioners and internistspersisted when restricted to physicians in the age groupwhere residency training in family practice is the norm.The poorer, less educated women who more frequentlyused family practice (or older, male) physicians werealso more likely to use only one physician. In additionto the women’s socioeconomic status, the less frequentuse of other physicians by patients of family prac-titioners may also relate to possibly lower referral ratesto subspecialists by family practitioners then intern-ists, due to a more generalist orientation.

A limitation of this study is that the physician charac-teristics were reported by the women. While sex andrace are likely to be accurate, age (at 5-year intervals)may be prone to error. Physician’s specialty was prede-fined for women in the questionnaire to improve theaccuracy of responses for that variable. One could arguethat the perception of these characteristics may be moremeaningful in terms of their influence on patient behav-

ior (although not on physician behavior), as describedin the social psychology literature [41].

Another limitation is that the validity of women’sreported physician recommendation is unclear and may

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be correlated with mammography use. Previous re-search has identified lack of physician recommendationas a major reason reported by women for not obtainingmammograms [27,29]. Increased reports of physicianmammography recommendation have accompanied theincreased national trend in mammography use [7].Prior studies have established that self-reported dataare generally valid for population surveillance of mam-mography utilization [42–47]. The accuracy of self-re-ported dates of mammography range from 82 to 86%,however, and among those whose self-report is inaccu-rate, the majority underestimate how long it has beensince the mammogram [42,44,46]. Women’s estimationsof the actual date of a mammogram decline in accuracyover time [42]. This effect of time on recall of screeningcould have influenced the accuracy of reporting CBEin the past year vs mammography in the past 2 years.However, there is evidence that older women’s self-re-ports of screening are more accurate for mammographythan for CBE [49]. With respect to the potential impactof patient characteristics on the accuracy of self-reportsof our predominantly white, age 65 and older studypopulation, prior research has shown that age is not apredictor of accuracy [46,50], and accuracy has beenfound to be higher among white (compared to Hispanicand black) women [46].

Women in our sample reported a number of physi-cians similar to national utilization patterns; however,a greater percentage of women in our sample haveMedicare only, and a smaller percentage are coveredby Medicare and Medicaid, and by Medicare and a sup-plement, than the national average [21]. Although ourstudy is based upon 1993 data, we know that the rateof increase in the use of mammography has slowedconsiderably after 1990 and that the overall impact ofMedicare coverage of mammography, at least initially,showed a very small increase despite the rise in theuse of that public payment to partially reimbursescreening [3]. HCFA, in its sixth scope of work for con-tracts with peer review organizations (PROs), has madebreast cancer detection in the Medicare population oneof six priority areas for health care quality improvementby PROs, indicating that mammography use amongolder women is a continued concern and highlightingthe relevance of our data for targeting of these qualityimprovement efforts.

The multivariate analysis underscores that a com-plex set of factors is related to whether women getpreventive services and that decisions occur within aconstantly changing health care environment. This re-search highlights that mammography use is the mostsensitive among the three measures of breast screening

services, to the influence of almost every independentvariable we studied. Unlike the other screening partici-pation measures, it involves a two-step process whichrequires a second appointment at a different location

ST CANCER SCREENING 491

and additional payment. These make mammographyvulnerable to lack of either physician recommendationor patient follow-through. This has implications forother preventive services which may not be provideddirectly by the primary care physician, for example, theuse of endoscopic tests for colorectal cancer screening.

Physician recommendation was associated with thesociodemographic characteristics of the women, theirlevel of insurance coverage, and the sociodemographicand specialty characteristics of the physician; but wasnot independently related to most of the situationalfactors studied (health status, family history). The find-ing that family history is not a determinant of screeningrecommendation may reflect physician recognition thatmammography guidelines refer to all women in thisage group regardless of risk status. This is in keepingwith reports that demonstrate a decline in reportinglack of physician recommendation as a reason for notobtaining a mammogram [7]. On the other hand, CBEuse was in general not influenced by the sociodemo-graphic variables of women (except modestly by age,particularly over 80 years) or to the sociodemographicsof physicians (except modestly for gender), but ratherwas predicted by the situational factors of family his-tory and number of physicians.

Public policy and health system changes that createa uniform system of financing and service delivery per-formance expectations would likely reduce the persis-tent discrepancies we found due to sociodemographicsand physician specialty. Several investigators, whilestressing the importance of insurance coverage and eco-nomic access, point out the inadequacies of those strate-gies alone [3,14,51,52]. Our analysis included many, butnot all of the variables encompassed by the SystemsModel of Clinical Preventive Care. In particular, we didnot address those situational factors that relate to theuse of specific office strategies, which serve as cues toaction. There is evidence in the literature, however, ofthe effectiveness of strategies that address the organi-zation of the immediate environment where the clini-cian practices [53–55] and direct approaches to re-minding women (outreach) or their physicians (in-reach) [56–62], thereby encouraging patients to makeadditional (preventive) visits or to follow-through onrecommendations and assuring that providers performscreening (e.g., CBE) while the patient is in the office.

We also did not address predisposing factors relatingto the beliefs and attitudes of women and physicianswhich have been shown to influence motivation andreadiness for the receipt of preventive services [63] andto misperception of patient preferences and information

needs [64,65]. These also deserve scrutiny in an elderlypopulation with access to care through future researchand deliberate attention in order to ultimately reducethe burden of breast cancer within an age group of

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women with a high incidence of the disease and thelowest screening rates.

ACKNOWLEDGMENTS

The authors acknowledge Mathematica Policy Research, Inc. forcollecting the data. We appreciate the assistance of Allison Lang,programmer/analyst at Information Management Services, Inc., withcomputer graphics. The other members of the NCI Breast CancerScreening Consortium participating in this study are Mary E. Cos-tanza, M.D., University of Massachusetts Medical School, Worcester,Massachusetts; Eric J. Feuer, Ph.D., National Cancer Institute,Bethesda, Maryland; Sarah A. Fox, Ed.D., RAND Corp., Santa Mon-

ica, California; Russell Harris, M.D., M.P.H., University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Barbara Rimer,Dr. P.H., Director of Division of Cancer Control and Population Sci-ences, Rockville, Maryland (Dr. Rimer was at the Fox Chase CancerCenter in Philadelphia, Pennsylvania, at the time this Medicare studywas initiated).

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