Differences in predictors of cervical and breast cancer screening by screening need in uninsured Latina women

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<ul><li><p>Differences in Predictors of Cervical and BreastCancer Screening by Screening Need in UninsuredLatina Women</p><p>Lydia P. Buki, PhD1</p><p>Jorja Jamison, MS2</p><p>Carolyn J. Anderson, PhD2</p><p>Anai M. Cuadra, PhD3</p><p>1 Department of Kinesiology and CommunityHealth, University of Illinois, Champaign, Illinois.</p><p>2 Department of Educational Psychology, Univer-sity of Illinois, Urbana-Champaign, Illinois.</p><p>3 Department of Pediatrics, Miller School of Med-icine, University of Miami, Miami, Florida.</p><p>BACKGROUND. Latina women experience higher mortality for cervical cancer andlower 5-year survival for breast cancer than non-Latina White women. Adherence</p><p>with screening recommendations can increase chances of survival, yet the factors</p><p>that influence screening behaviors in uninsured women are not well documen-</p><p>ted.</p><p>METHODS. Uninsured Latina women (N 5 467) recruited in four US cities partici-pated in the study. Logistic regression was used to model adherence to recom-</p><p>mendations by screening type (cervical or breast cancer) and screening need</p><p>(needs to obtain initial screening, overdue for rescreening, up-to-date with</p><p>rescreening).</p><p>RESULTS. Predictors differed by type of screening and screening need. Womenwho reported exposure to cancer education were more likely to have had a mam-</p><p>mogram and to be up-to-date with Pap smear screening than women without</p><p>such exposure. Women who were younger, had more than a sixth grade educa-</p><p>tion, and/or had children were more likely to have had a Pap smear. Older</p><p>women who had been in the US the longest were more likely to be overdue for a</p><p>Pap smear. Women with incomes $5000 to $7000 were more likely to have</p><p>obtained a mammogram. Regional differences were found with respect to mam-</p><p>mography screening and maintenance behaviors.</p><p>CONCLUSIONS. Exposure to cancer education is an important predictor of screen-ings among uninsured urban Latina women. The potential of creating educa-</p><p>tional interventions that can increase screening rates among women who</p><p>evidence health disparities is encouraging. Recruitment strategies to reach</p><p>women in need of screenings are provided. Cancer 2007;110:157885. 2007American Cancer Society.</p><p>KEYWORDS: Latinas, cervical cancer, breast cancer, screening, health insurance.</p><p>L atina women experience an unequal burden of cervical andbreast cancer.14 They face numerous barriers to early screeningssuch as lack of access to healthcare and culturally responsive pro-</p><p>grams, lack of transportation and childcare, and linguistic isolation</p><p>from information.59 As a result, these women do not obtain screen-</p><p>ing exams according to the timing recommended by the US Preven-</p><p>tive Task Force,10,11 and cancers are detected at more advanced</p><p>stages, when prognosis is more guarded.1214 Consequently, Latinas</p><p>have a lower 5-year survivorship rate for breast cancer and a higher</p><p>mortality rate for cervical cancer compared with non-Latina</p><p>Whites.1,14,15</p><p>The authors thank Diana Jeffery, PhD, for insightand advice on the article, and Marta Sotomayor,DSW, for her support of this project. The helpfulcomments on earlier versions of this article pro-vided by Viviana Pitton, MEd, and Allison Grupski,MA, are acknowledged. Data collection was sup-ported by Cooperative Agreement U57/CCU310174-03 from the Centers for DiseaseControl and Prevention.</p><p>Address for reprints: Lydia P. Buki, PhD, Depart-ment of Kinesiology and Community Health, Uni-versity of Illinois, 224 Huff Hall, 1206 S. FourthSt., MC 588, Champaign, IL 61820; Fax: 217-333-2766; E-mail: buki@uiuc.edu</p><p>Received October 10, 2006; revision receivedMay 18, 2007; accepted May 23, 2007.</p><p> 2007 American Cancer SocietyDOI 10.1002/cncr.22929Published online 14 August 2007 in Wiley InterScience (www.interscience.wiley.com).</p><p>1578</p></li><li><p>Health insurance status and primary language</p><p>have been found to predict screening rates among</p><p>Latina women; these findings result from the use of</p><p>samples that are diverse with respect to these fac-</p><p>tors.1626 A focus on uninsured and Spanish-speaking</p><p>Latinas, however, is warranted,27 as they comprise</p><p>a substantial proportion of the adult Latino/a</p><p>population (47% and 34%50%, respectively).9,28</p><p>Among Latinos who are primarily Spanish-speaking,</p><p>61% do not have health insurance,9 placing them</p><p>at risk for nonadherence to screening recommenda-</p><p>tions.9,13,27,29</p><p>For uninsured women, different screening pre-</p><p>dictors may emerge. A few studies have examined</p><p>this population, with a focus on farmworkers.30 Find-</p><p>ings suggest that knowledge about screenings may</p><p>increase screening rates in that population,30 which</p><p>is consistent with data from Latina samples pooled</p><p>according to health insurance status and primary</p><p>language.1926 However, to our knowledge, no study</p><p>has examined screening predictors among urban</p><p>populations without health insurance. Such an inves-</p><p>tigation is reasonable because the majority of Latinos</p><p>live in urban areas.31 Therefore, the purpose of this</p><p>study was to examine how demographic factors and</p><p>exposure to cancer education are related to cancer</p><p>screening in a large national community sample of</p><p>urban, Spanish-speaking Latina women without pri-</p><p>vate health insurance coverage.</p><p>Consistent with previous studies, we analyzed</p><p>cervical and breast cancer screening data separately,</p><p>as the predictors may differ from one another.5,3237</p><p>Finally, we investigated predictors at 3 levels of</p><p>screening need: 1) needs to obtain initial screening</p><p>(ie, never screened); 2) overdue for rescreening (ie, a</p><p>time longer than that recommended by screening</p><p>guidelines has passed since the last screening exam);</p><p>or 3) up-to-date with rescreening (ie, will need a</p><p>maintenance repeat screening when it becomes due).</p><p>This is consistent with recent conceptualizations in</p><p>the literature that focus not only on factors that</p><p>affect initial screening but also on factors that can</p><p>help discriminate women who have had a recent</p><p>repeat screening versus those who are over-</p><p>due.26,32,34,38</p><p>Specifically, we sought to answer the following</p><p>research questions: 1) are the predictors of breast</p><p>and cervical cancer screening different?, and 2) do</p><p>screening predictors differ according to screening</p><p>need? Answers to these questions will further our</p><p>understanding of the context in which urban</p><p>women obtain screenings and may help optimize</p><p>interventions aimed at increasing their screening</p><p>rates.</p><p>MATERIALS AND METHODSParticipantsA total of 467 self-identified Latina women partici-</p><p>pated in the study. Data were collected at baseline as</p><p>part of a larger national project designed to increase</p><p>cervical and breast cancer screening rates among</p><p>medically underserved Latinas. We collected this</p><p>cross-sectional, purposeful sample at 4 sites across</p><p>the US, chosen because of their urban setting and</p><p>their sizable population of medically underserved</p><p>Latinas over the age of 40. The sites were: Dallas</p><p>(TX), Hartford (CT), Newark (NJ), and Washington</p><p>(DC). An additional outreach was conducted in</p><p>Washington DC during Minority Cancer Awareness</p><p>Week (MW), which was treated as a separate site</p><p>given that the recruitment strategy relied on radio</p><p>advertising rather than face-to-face outreach, poten-</p><p>tially resulting in samples that differed along demo-</p><p>graphic factors.</p><p>MeasuresA sociodemographic survey included 20 questions</p><p>about demographic characteristics (eg, age), cultural</p><p>variables (eg, country of origin), risk factors (eg, can-</p><p>cer history), and screening behaviors. Exposure to</p><p>cancer education was measured with the question</p><p>Have you ever attended an educational presentation</p><p>about the importance of early detection of breast</p><p>and cervical cancer? A draft of the survey was</p><p>reviewed by 1 peer health worker (promotora de</p><p>salud) from each site and their feedback was incor-</p><p>porated into the final version. The questionnaire was</p><p>administered in Spanish and took approximately 10</p><p>minutes to complete.</p><p>ProcedureParticipants were recruited through community out-</p><p>reach efforts that included at least 1 promotora de</p><p>salud at each site. These promotoras were bilingual,</p><p>bicultural women who were trusted members of the</p><p>community and who tailored their culturally respon-</p><p>sive recruitment efforts to the needs of each local</p><p>site. The benefit of using promotoras to reach medi-</p><p>cally underserved populations has been estab-</p><p>lished.39,40 The promotora de salud in Dallas was</p><p>affiliated with a senior center and a local Catholic</p><p>church and she recruited participants mainly</p><p>through these organizations, at health fairs, and at</p><p>daylong Spanish language health conferences orga-</p><p>nized for the entire family. The promotora de salud</p><p>in Hartford was affiliated with a community health-</p><p>care organization and she recruited participants from</p><p>their pool of past patients, at health fairs, and</p><p>Predictors of Screening in Latina Women/Buki et al. 1579</p></li><li><p>through extensive door-to-door outreach in the com-</p><p>munity. The promotora de salud in Newark worked</p><p>at a community organization that provided social</p><p>services. Women at this site were recruited at the</p><p>waiting area of the organization, as well as through</p><p>media announcements and visits to senior housing</p><p>complexes. The area covered by the 2 promotoras de</p><p>salud in Washington DC included northern Virginia</p><p>and adjacent counties in Maryland as well. Outreach</p><p>at this site included recruitment at beauty salons,</p><p>churches, community clinics, Hispanic festivals,</p><p>laundromats, libraries, shopping malls, on the street,</p><p>and at the senior center with which 1 health pro-</p><p>moter was affiliated. The recruitment method was</p><p>different for MW than for the remainder of the DC</p><p>sample, as it relied heavily on radio announcements</p><p>made at a popular Spanish radio station in a con-</p><p>certed effort associated with Minority Cancer Aware-</p><p>ness Week. Women listening to the announcements</p><p>were provided with a toll-free telephone number to</p><p>call for participation in the program.</p><p>Promotoras administered the survey by reading</p><p>each question in person or over the phone and re-</p><p>cording each participants response. Participants</p><p>recruited in person also had the option of filling out</p><p>the questionnaire themselves. Participation in the</p><p>study was voluntary and without compensation. All</p><p>information obtained was kept confidential.</p><p>Data AnalysesLogistic regression was used to model womens ad-</p><p>herence to screening recommendations for cervical</p><p>(Pap smear) and breast cancer (mammogram).</p><p>Because there were 3 sequential levels of the adher-</p><p>ence variables (needs to obtain initial screening,</p><p>overdue for rescreening, up-to-date with rescreen-</p><p>ing), we used continuation ratios to model 1)</p><p>whether a woman has or has not been screened for</p><p>cancer (ever vs never screened), and 2) whether a</p><p>woman is up-to-date or not with repeat screening</p><p>(up-to-date vs overdue repeat screening).41,42</p><p>On the basis of results from past studies con-</p><p>ducted with Latina samples,18,22,23,26,30,33,37,4345 the</p><p>following predictors were considered: demographic</p><p>variables, exposure to cancer education, and interac-</p><p>tions among these variables. Demographic variables</p><p>were age, formal education (less than 6, 6, 711, 12,</p><p>more than 12 years completed), annual household</p><p>income ($01,500; 1,5015,000; 5,0017,000; 7,001</p><p>10,000; 10,00114,000; 14,00125,000; more than</p><p>25,000), recruitment site (TX, CT, NJ, DC, MW),</p><p>country of origin (US, Mexico, Puerto Rico, South</p><p>America, Central America and the Caribbean, other),</p><p>proportion of life in US (.10, .25 and .43, which cor-</p><p>respond to the 25th, 50th, and 75th percentiles,</p><p>respectively), and having had a child. When parame-</p><p>ter estimates for levels of a variable were nearly</p><p>identical (or not statistically different), we forced</p><p>them to be equal, thus creating dichotomous cate-</p><p>gories. To deal with missing observations in the</p><p>data, most models were fit to the data 3 times: 1)</p><p>women with no missing observations, 2) women</p><p>with complete data for a particular model, and 3)</p><p>multiple imputation using an experimental imple-</p><p>ment for categorical variables using SAS v. 9.1 (Cary,</p><p>NC). These 3 methods yielded the same basic find-</p><p>ings; therefore, we report the analyses of women</p><p>who had complete data available for a particular</p><p>model. We believe this information is of the highest</p><p>quality because it is based on data provided by the</p><p>participants themselves.</p><p>RESULTSDescriptive InformationDemographic characteristics and participants cancer</p><p>screening behaviors are presented in Table 1. Partici-</p><p>pants ages ranged from 40 to 87 (M 5 53 years;SD 5 11). Most women were born outside of the USmainland (94%), including almost two-thirds from</p><p>Central America, the Caribbean, and South America.</p><p>Women born outside the US had lived in this coun-</p><p>try, on average, 14 years (SD 5 11 years), or just overa quarter (27%) of their lives. The average household</p><p>income was $10,579 (SD 5 $6,601). Although 70% ofparticipants reported 6 or more years of formal edu-</p><p>cation, only 10% reported education beyond high</p><p>school. The majority of participants had children.</p><p>Just over one-third reported exposure to a presenta-</p><p>tion on the importance of early detection of cervical</p><p>or breast cancer. Time since last screening ranged</p><p>from 021 years and 022 years for Pap smear and</p><p>mammography screenings, respectively. The percent-</p><p>age of women who had never had a Pap smear or</p><p>were overdue for one was 61%; among women over</p><p>50, three-fourths had never had one or were overdue</p><p>for a mammogram. All participants reported having</p><p>no private health insurance coverage.</p><p>Cervical Cancer ScreeningEver versus never screenedA total of 427 women were used in modeling the</p><p>probability of a woman ever having had a Pap smear.</p><p>As shown in Table 2, which includes odds ratio (OR)</p><p>and 95% confidence interval (CI) data for all the</p><p>models, age, formal education, and having had a</p><p>child were the only statistically significant predictors.</p><p>This model provided a good representation of the</p><p>1580 CANCER October 1, 2007 / Volume 110 / Number 7</p></li><li><p>data (Hosmer-Lemeshow statistic 5 2.90, df 5 8,P 5 .94). With respect to age, the odds that a womanhad a Pap smear were 1.47 times larger than the</p><p>odds for a woman 10 years older. The odds that a</p><p>woman had a Pap smear given that she completed at</p><p>least 6th grade were 1.94 times larger than the odds</p><p>for a woman with less than a 6th-grade education.</p><p>The odds that a woman had a Pap smear given that</p><p>she had a child were 1.69 times larger than the odds</p><p>for a woman who has never had a child.</p><p>Up-to-date versus overdue repeat screeningWe now turn to distinguishing between women who</p><p>needed a maintenance repeat screening as opposed</p><p>to a rescreening that is overdue. A relatively complex</p><p>logistic regression model was chosen that included</p><p>age (Wald chi-square 5 5.45, df 5 1, P 5 .02), pro-portion of life in the US (Wald chi-square 5 5.87,df 5 1, P 5 .01), cancer education (Wald chi-square 5 4.53, df 5 1, P 5 .03), and the interactionbetween age and proportion of life in the US (Wald</p><p>chi-square 5 5.39, df 5 1, P 5 .02). This model was fitto 393 women with complete data on all variables and</p><p>provided a good representation of the data (Hosmer-</p><p>Lemeshow statistic 5 6.28, df 5...</p></li></ul>