Journal of Women & Aging, 27:3–16, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 0895-2841 print/1540-7322 online DOI: 10.1080/08952841.2014.905403

ARTICLES

The Relationship Between Body Mass Index and Cancer Screening Utilization Among Older Women in Latin American and Caribbean Cities STEPHEN MILLER and LESLIE RAFANAN Texas College of Osteopathic Medicine, Fort Worth, TX

SARAH KEIHANY Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA

CARLOS A. REYES-ORTIZ Division of Geriatric Medicine, Oakwood Hospital Medical Center, Dearborn, MI, and Division of Geriatric & Palliative Care, University of Texas Medical School, Houston, TX

To examine the relationship between body mass index and cancer screening utilization, we analyzed data from six cities of the Health, Well-Being and Aging in Latin America and the Caribbean Study on 5,230 women aged 60 and older, from 1999 to 2000. We found that underweight women were less likely to have had a mammogram, a breast self-exam, and a Pap smear, relative to normal-weight women. However, overweight or obese women were more likely to have a breast self-exam and a Pap smear. Thus, being underweight had a role for decreased cancer screening utilization among older women in Latin American cities, but not being overweight or obese, which was associated with decreased cancer screening in most previous studies. KEYWORDS mammography, breast self-examination, pap smear, SABE Study, older women, cancer screening, body mass index, obesity Address correspondence to Carlos A. Reyes-Ortiz, MD, PhD, Division of Geriatric & Palliative Care, University of Texas Medical School, 6431 Fannin St MSB 5.111, Houston, TX, 77030. E-mail: [email protected] 3

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Women who are obese have been found to be at a higher risk for developing postmenopausal breast cancer, endometrial cancer, kidney cancer, and adenocarcinoma of esophagus (Calle & Thun, 2004; Reeves et al., 2007). Paradoxically, obese women seem to receive lower numbers of mammograms as a screening method for breast cancer (Edwards et al., 2010; Ferrante, Chen, Crabtree, & Wartenberg, 2007; Fontaine, Heo, & Allison, 2001; Maruthur, Bolen, Brancati, & Clark, 2009; Østbye, Taylor, Yancy, & Krause, 2005; Zhu, Wu, Jatoi, Potter, & Shriver, 2006). For example, Edwards et al. (2010) reported that obese women were less likely than normal-weight women to have routine, annual as well as biennial mammograms. Also, overweight or obese White women were less likely to use a mammogram compared to normal-weight White women (Fontaine et al., 2001). In a metaanalysis, Maruthur et al. (2009) showed an association between obesity and lower odds for reporting a recent mammogram. Obese women are also less likely to receive Pap smears as a screening method for cervical cancer (Aldrich & Hackley, 2010; Cohen et al., 2008; Ferrante et al., 2007; Fontaine, Faith, Allison, & Cheskin, 1998; Fontaine et al., 2001; Ludman et al., 2010; Maruthur, Bolen, Brancati & Clark, 2008; Mitchell, Padwal, Chuck, &Klarenbach, 2008; Østbye et al., 2005; Park, Park, Park, & Cho, 2012; Tekkel, Veideman, & Rahu, 2011; Wee, Phillips, & McCarthy, 2005). For instance, studies reported that severely obese White women were less likely to undergo Pap smear compared to normal-weight women (Ludman et al., 2010; Mitchell et al., 2008; Park et al., 2012; Tekkel et al., 2011; Wee et al., 2005). Three population-based surveys revealed that obese women were less likely than normal-weight women to be up to date on Pap smear screenings (Ferrante et al., 2007; Fontaine et al., 1998, 2001). The link between obesity and lower rates of cervical cancer screening is further supported by three meta-analyses (Aldrich & Hackley, 2010; Cohen et al., 2008; Maruthur et al., 2008). Thus, body mass index (BMI) may influence breast and cervical cancer screening utilization. The objective of this study is to examine the relationship between BMI with breast and cervical cancer screening utilization among older women residing in Latin American and Caribbean cities. We hypothesize that obesity will be associated with a decreased screening use in the study population.

METHOD Data Source and Sample Data were from the Health, Well-Being and Aging in Latin America and the Caribbean Study (SABE) that consisted of a round of cross-sectional surveys from seven cities in Latin American and Caribbean countries during 1999–2000 (Albala et al., 2005; Pelaez et al., 2005; Wong, Pelaez,

Relationship Between BMI and Cancer Screening

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Palloni & Markides, 2006). These included Havana, Cuba; São Paulo, Brazil; Bridgetown, Barbados; Santiago, Chile; Mexico City, Mexico; Montevideo, Uruguay; and Buenos Aires, Argentina. However, we excluded Buenos Aires from our analyses because anthropometric measures needed to calculate BMI were not taken there. The Pan American Health Organization (PAHO) coordinated the overall SABE study and directed its logistics. The Center for Demography and Ecology, University of Wisconsin–Madison, and PAHO designed the study. Local, country-based teams constituted by a principal investigator and his/her associates trained the interviewers in each city. The response rates were 85% in Bridgetown, 85% in São Paulo, 84% in Santiago, 85% in Mexico City, 95% in Havana, and 66% in Montevideo. The city with the lowest response rate, Montevideo, was compared with its corresponding census information, finding that their sociodemographic distributions were comparable (Instituto Nacional de Estadística, 1996). A classical multistage clustered sampling with stratification of the units at the highest levels of aggregation was used: The primary sampling unit was a cluster of independent households within predetermined geographic areas, grouped into socioeconomic strata and divided into secondary sampling units, each containing a smaller number of households. Finally, the household and target individuals, women 60 years and older, were randomly selected. Then the potential participants were contacted to set an interview at home. The interviews were conducted in English for Bridgetown (Barbados), Portuguese for São Paulo (Brazil), and in Spanish for all other cities, using the same validated questionnaires. Oral and written consent forms—approved by the Human Subjects Committee at each city with the corresponding affiliated Medical Research Institution or University—were obtained from all subjects, and personal identifiers were deleted. If a person having accepted to be interviewed failed the cognitive test, a proxy was selected to respond to some parts of the questionnaire (Albala et al., 2005; Wong et al., 2006). We excluded 664 participants from the 5,894 interviewed because they had missing information on BMI. The excluded participants were older, disabled, or with more medical conditions, and they were less likely to have a Pap smear or a breast self-examination (BSE). The final numbers of women included in the analyses were 1,044 in Havana, 1,064 in São Paulo, 865 in Bridgetown, 806 in Santiago, 626 in Mexico City, and 825 in Montevideo, for a total of 5,230 participants.

Measures The dependent variables were mammography use, clinical breast examination (CBE), breast self-examination (BSE), and Pap smear use. Each screening method was assessed by a question. Mammography use: “In the last 2 years have you had a mammogram that is a test to determine whether you have

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breast cancer?” Clinical breast examination: “In the last 2 years, were your breasts examined by a health professional to see if you have lumps in your breasts?” Breast self-examination: “In the last 2 years, have you regularly examined your breasts for lumps?” Pap smear use: “In the last 2 years have you had a Pap smear that is a test to determine whether you have cervical or uterine cancer?” Responses were yes/no. The main independent variable was body mass index (weight and height measured at interview, kg/m2 ) that was categorized as underweight (

The relationship between body mass index and cancer screening utilization among older women in Latin American and Caribbean cities.

To examine the relationship between body mass index and cancer screening utilization, we analyzed data from six cities of the Health, Well-Being and A...
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