Well-established cancer screening programs, such as breast and cervical cancer, have led to a gradual decline in cancer mortality rates over the past few decades (Siegel, Miller, & Jemal, 2018). However, cancer continues to be the second leading cause of death in the United States (Siegel et al., 2018). Breast cancer is the most common type of cancer and the second most common cause of death from cancer among U.S. women (Howlader et al., 2017). In 2018, nearly 266,120 new cases of invasive breast cancers and about 40,920 deaths are estimated to occur in U.S. women (Siegel, Miller, & Jemal, 2017; Siegel et al., 2018). Similarly, cervical cancer is the fourth most common type of cancer in women worldwide, yielding 13,240 new cases annually and 4,170 deaths in U.S. women (Siegel et al., 2018). Notably, cervical cancer is the second leading cause of cancer death in women aged 20 to 39 years (Siegel et al., 2018).
Factors leading to the decline in breast and cervical cancer mortality rates are regular uptake of cancer screening, proper follow-up after abnormal screening results, and appropriate treatment (Chawla, Breen, Liu, Lee, & Kagawa-Singer, 2015; Siegel et al., 2018). The decline is thought to primarily reflect increased use of cancer screening (Siegel et al., 2018). Although there is a slight discrepancy regarding screening guidelines between the American Cancer Society (ACS) and U.S. Preventive Services Task Force, the practice guidelines generally suggest receiving an annual or biennial mammogram for average-risk middle aged women (Oeffinger et al., 2015; Siu & U.S. Preventive Services Task Force, 2016). In the context of cervical cancer control, national guidelines recommend triennial Pap testing for average-risk women aged 21–65 years, defined as women with no history of a high-grade precancerous cervical lesion or cervical cancer, who are not immunocompromised (including those who are HIV positive), and without in utero exposure to diethylstilbestrol (Moyer & U.S. Preventive Services Task Force, 2012; Saslow et al., 2012). However, certain groups such as racial/ethnic minorities and low-socioeconomic-status individuals continue to have low mammogram and Pap testing uptake rates, which then affects increases in cancer mortality rates (Smith et al., 2017). For example, only about 70% of Korean-American women have received triennial Pap testing, which is a rate significantly lower than that of non-Hispanic White women (89%) and non-Hispanic Black women (92%; Chawla et al., 2015).
Health literacy has emerged as a critical factor to preventive healthy behaviors (Berkman, Sheridan, Donahue, Halpern, & Crotty, 2011; Simmons et al., 2017). For example, limited health literacy has been associated with limited awareness and knowledge of cancer and the importance of early detection of cancer and high levels of worry (Kim & Han, 2016; Oldach & Katz, 2014). Evidence also suggests that individuals with limited health literacy tend to face the challenges in comprehending the information shared by healthcare providers relative to cancer screening and, subsequently, had a delayed diagnosis of cancer (Davis, Williams, Marin, Parker, & Glass, 2002). There is a trend for an association between limited health literacy and failing to follow the recommended breast and cervical cancer screening guidelines (Kim & Han, 2016; Oldach & Katz, 2014; Sentell, Braun, Davis, & Davis, 2015). In a systematic review regarding low health literacy and health outcomes, Berkman et al. (2011) asserted the potential role of health literacy as a means of addressing disparities in health outcomes, including self-rated health, and preventive health behaviors, such as cervical cancer screening.
Health literacy is commonly defined as the degree to which individuals have the capacity to obtain, process, and understand health information and services needed for informed health-related decision-making (Ratzan & Parker, 2000). Health literacy is thought to be derived from one's education, culture and society, and experiences related to healthcare system (Institute of Medicine, 2004; Lorini et al., 2018). To this end, researchers conceptualize health literacy as having multiple components such as contextual knowledge of health and healthcare, reading fluency (prose, quantitative, and document), an ability to understand written health information, an ability to orally communicate about health, and an ability to access, analyze, and evaluate media information (Aufderheide, 1993; Baker, 2006). Nevertheless, researchers have often measured only a subset of those dimensions such as reading fluency, which can fail to accurately assess individuals' levels of health literacy (e.g., Lee, Tsai, Tsai, & Kuo, 2012; Schapira et al., 2011). In addition, studies often lacked a prior power analysis to adequately calculate the sample size to determine the association between health literacy and lower cancer screening rates (e.g., Mazor et al., 2012; Roman et al., 2014). Notably, most researchers have recruited study samples from healthcare settings in one geographical area such as northern states, resulting in questionable generalizability of the study findings (e.g., Garbers, Schmitt, Rappa, & Chiasson, 2009; Schapira et al., 2011). Taken together, these findings suggest the need for an investigation of the association between health literacy and breast and cervical cancer screening rates in a nationally representative sample using a multidimensional instrument to assess health literacy.
Studies that used data from the 2007 California Health Interview Survey (CHIS) partially addressed this knowledge gap by using a representative sample. With a representative sample of a single state, studies using data from the 2007 CHIS found that health literacy was positively associated with breast, cervical, or colorectal cancer screening behaviors (Sentell, Braun, Davis, & Davis, 2013; Sentell, Braun, et al., 2015; Sentell, Tsoh, Davis, Davis, & Braun, 2015). However, the health literacy screening items included in the CHIS measured a patient's written health literacy only and thus do not fill a research gap in capturing multiple dimensions of an individual's health literacy. It is unclear if health-related oral and listening literacies in clinical settings are contributing factors to breast and cervical cancer screening behaviors. No study to date has examined the role of oral, listening, and written literacies in adopting breast and cervical cancer screening behaviors in a nationally representative female sample. To this end, the aims of this study were twofold:
- To examine if oral (an ability to ask for medical advice), listening (an ability to understand information that providers offer), or written (an ability to understand printed health information) literacies are associated with breast and cervical cancer screening; and
- To examine which dimension(s) of health literacy are particularly influential in adhering to the recommended breast and cervical cancer screening guidelines.
We anticipate that a higher level of oral, listening, or written health literacy will be associated with breast and cervical cancer screening behaviors.
Design and Data Source
A secondary analysis of the 2016 Behavioral Risk Factor Surveillance System (BRFSS) data was performed. This cross-sectional correlational study used data from the 2016 BRFSS: A random-digit-dialed landline and cellular telephone household survey of the noninstitutionalized civilian U.S. adult (18 years or older) population. The BRFSS is sponsored by the Centers for Disease Control and Prevention (CDC) and is administered in all 50 states as well as the U.S. territories and collects information on health practices and risk behaviors related to chronic conditions, injuries, and preventable infectious diseases. The BRFSS survey uses a disproportionate stratified sample design for landline telephone samples and a random sample design for the cellular telephone survey. The survey consists of three parts: a core component (administered across all states), optional modules such as a health literacy module (selectively administered by states), and state-added questions. The BRFSS uses a verbal consent process. Details on BRFSS methodology are available from the CDC (2016a).
Female respondents within the age groups relevant to the ACS guidelines for breast and cervical cancer screening (i.e., ages 45 years or older for breast [n = 44,241] and ages 21–65 years for cervical [n = 38,956]), and those who completed the health literacy module were included in this study. The ACS recommends receiving a triennial Pap testing for women at average risk aged 21–65 years (Saslow et al., 2012) and an annual mammogram for women at average risk aged 45–54 years and a biennial screening for women aged 55 years or older (Oeffinger et al., 2015). The health literacy module was first administered by 17 states in the 2016 BRFSS: Alabama, Alaska, District of Columbia, Georgia, Illinois, Iowa, Louisiana, Kansas, Maryland, Minnesota, Mississippi, Nebraska, North Carolina, Oklahoma, Pennsylvania, Puerto Rico, and Virginia. Response rates ranged from 30.7% (Louisiana) to 57.0% (Alaska).
In the BRFSS, health literacy was assessed with the following three questions, each addressing oral, listening, and written literacies, respectively: “How difficult is it for you to get advice or information about health or medical topics if you need it?” “How difficult is it for you to understand information that doctors, nurses and other health professionals tell you?” and “In general, how difficult is it for you to understand written health information?” According to the BRFSS data dictionary, possible responses were (1) very easy, (2) somewhat easy, (3) somewhat difficult, (4) very difficult, (5) I don't pay attention to written health information, (7) don't know/not sure, and (9) refused. No responses were assigned to numbers 6 and 8. The responses were grouped into four categories: very easy, somewhat easy, somewhat difficult, and very difficult. We assigned numbers from 1(very easy) to 4 (very difficult) as if the responses were on a 4-point Likert scale, which is then reverse coded (e.g., 1 = very difficult, 4 = very easy). Responses that were ambiguous and did not provide a clear answer (i.e., “I don't pay attention to written health information,” “Don't know/Not sure,” and “Refused”) were dropped and excluded from the analysis.
Breast and Cervical Cancer Screening Status
The main outcome variables were self-reported breast and cervical cancer screening status. A breast cancer screening behavior among U.S. women aged 45 years or older was measured by two questions: “A mammogram is an x-ray of each breast to look for breast cancer. Have you ever had a mammogram?” and “How long has it been since you had your last mammogram?” Possible responses for having received a mammogram included (1) yes, (2) no, (7) don't know/not sure, and (9) refused. Those who reported having the last mammogram within the past year in women aged 45–54 years and within the past 2 years in women aged 55 or older were considered up-to-date for mammogram. Respondents who selected “Don't know/not sure” or “Refused” were collapsed into a category for never-screeners, to be conservative. Women who selected “Don't know/not sure/refused” for the second question were construed as not up-to-date for mammogram.
Adhering to the recommended cervical cancer screening guidelines among U.S. women aged 21–65 years was measured by the following questions: “A Pap test is a test for cancer of the cervix. Have you ever had a Pap test?” and “How long has it been since you had your last Pap test?” Possible responses for having received Pap testing included (1) yes, (2) no, (7) don't know/not sure, and (9) refused. Those who reported having the last Pap testing within the previous 3 years were considered up-to-date for Pap testing. Respondents who selected “Don't know/not sure” or “Refused” were collapsed into a category for never-screeners, to be conservative. Those who selected “Don't know/not sure/refused” were grouped into a category for not up-to-date screeners.
The following demographic characteristics were extracted for this study: age, race and ethnicity, marital status, education attainment, employment status, annual household income, health insurance, and access to a primary care provider because these variables have been associated with cancer screening behaviors and, hence, were controlled for the purpose of the analysis (Kim & Han, 2016; Sentell, Braun, et al., 2015; Sentell, Tosh, et al., 2015). Age in years was grouped into two groups for mammogram: 45 to 64 and ≥65. Because women aged ≥65 years are eligible for Medicare, their accessibility to cancer screening could be different from that of those who are not eligible for Medicare. In addition to eligibility for Medicare, ACS suggests combining Pap testing with a human papillomavirus test for women aged 30 years or older. Therefore, age was categorized into three groups for Pap testing: 21 to 29 years, 30 to 64 years, and 65 years. Self-identified race and ethnicity were categorized into non-Hispanic White, non-Hispanic Black, non-Hispanic American Indian/Alaskan Native, non-Hispanic Asia/Native Hawaiian/Other Pacific Islander, Hispanic, and other. Marital status was grouped into two categories: married/member of an unmarried couple and divorced/widowed/separated/never married. Educational attainment was categorized into four groups: ≤some high school, high school graduate, some college or technical school, and college graduate or above. Self-reported employment status was coded into two groups: yes (employed for wages or self-employed) and no (homemaker/student/retired/unable to work/refused). Annual household income was categorized into seven groups: less than $15,000, $15,000 to $24,999, $25,000 to $34,999, $35,000 to $49,999, $50,000 to $74,999, and ≥$75,000, and don't know/refused. Health insurance status was grouped into two categories: yes and no (no/don't know/not sure/refused). Access to a primary care provider was measured by asking “Do you have one person you think of as your personal doctor or health care provider?” and was categorized into two groups: yes (only one/more than one) and no (don't know/not sure/refused). In the aforementioned variables, missing values were excluded from the analysis.
Descriptive statistics were performed using Stata 14.2 with a two-sided significance level set at p < .05. The following variables were used to specify survey design for dataset: strata (_STSTR), primary sampling units (_PSU), and sample weights (_LLCPWT) for combined landline and cellphone data and (_LCPWTV2) for combined landline and cellphone data, version 2 as informed by the CDC (2016b). The complex sample design ensures an unbiased estimate corresponding to the U.S. general female population. To describe the sample, we used Rao-Scott Pearson χ2 test statistics to calculate weighted percentages and a design-based p value for dichotomous and ordinal variables across screening status. We then conducted weighted multiple logistic regression analyses to examine the independent effect of each of the health literacy items on self-reported breast and cervical cancer screening behaviors, accounting for study covariates including demographics (e.g., age, race/ethnicity, and marital status), socioeconomic status (e.g., education level and income), and healthcare system factors (e.g., health insurance and having a primary care provider). These covariates were chosen based on previous studies (Han et al., 2017; Kim, Xue, Walton-Moss, Nolan, & Han, 2018). Subsequently, we included all three health literacy items in one regression model to assess which health literacy components are particularly influential in adapting breast and cervical cancer screening behaviors after controlling for the study covariates. All analyses were weighted to account for the survey sampling design (CDC, 2016b).
We also performed further analysis using additional responses that offered little information about the level of health literacy (e.g., don't know/not sure and refused to answer), as well as four Likert responses (e.g., very easy, somewhat easy, somewhat difficult, very difficult). The sensitivity analysis provided additional information regarding the association between health literacy and breast and cervical cancer screening among those who were initially excluded from the analysis due to their vague response.
According to the institutional review board standards, this study was exempt because the BRFSS is a public data file prepared for public use. A BRFSS dataset is not individually identifiable and the analysis of the BRFSS data does not involve human subjects.
Tables 1 and 2 provide a sociodemographic description of the study sample by cancer screening status. The majority of the sample were non-Hispanic White, graduated from high school, and had health insurance and a primary care provider. Overall, 73% of the respondents received up-to-date breast cancer screening and 82% were up-to-date for cervical cancer screening. Those with up-to-date breast cancer screening, in comparison to respondents who were not up-to-date on their breast cancer screening, were more likely to be older (≥65 vs. 45–64 years), married, insured, and graduated from college or technical school (27.3% vs. 22.1%); to have higher income (25.4% vs. 20.8% for ≥$75,000); and to have health insurance, a primary care provider, and a higher oral, listening, and written health literacy score.
Up-to-date screeners, compared to those who were not up-to-date on their cervical cancer screening, were more likely to be younger (21–29 or 30–64 years vs. ≥65 years), married, insured, employed, and graduated from college or technical school (35.0% vs. 22.5%); have higher income (31.2% vs. 21.3% for ≥$75,000); and have health insurance (90.0% vs. 82.9%), a primary care provider, and a higher health literacy score. Nearly three in four respondents reported feeling that it was very easy and about one in five reported feeling that it was somewhat easy to get advice or information about health or medical topics (oral literacy). Approximately 60% reported feeling it was very easy and about 30% reported feeling it was somewhat easy to understand information that health care professionals offer (listening literacy). More than three in five respondents reported feeling it was very easy and about one in three reported feeling it was somewhat easy to understand written health information (written literacy).
Table 3 shows the effect of oral, listening, or written literacy on lifetime and up-to-date breast and cervical cancer screenings, respectively. In multivariable logistic regression analyses, after controlling for demographics, health insurance, and access to a primary care provider, we found that for a one-unit increase in oral health literacy score, there was about a 19% increase in the odds of having a lifetime mammogram (odds ratio [OR], 1.19; 95% confidence interval [CI] [1.04, 1.35]) and approximately an 11% increase in the odds of having an up-to-date mammogram (OR, 1.11; 95% CI [1.04, 1.19]). For example, compared to those who indicate very difficult on the oral health literacy scale, there is an 11% increase in their odds of having an up-to-date mammogram among those indicating somewhat difficult. Similarly, for a one-unit increase in oral health literacy score, there was an approximately 33% increase in the odds of receiving a lifetime Pap testing (OR, 1.33; 95% CI [1.17, 1.51]) and about a 21% increase in the odds of receiving up-to-date Pap testing (OR, 1.21; 95% CI [1.13, 1.30]).
Listening health literacy was significantly associated with all but receipt of a lifetime mammogram (OR, 1.04; 95% CI [0.93–1.17]). For a one-unit increase in listening health literacy score, there was about a 12% increase in the odds of having an up-to-date mammogram (OR, 1.12; 95 CI [0.05, 1.19]), approximately a 30% increase in the odds of having a lifetime Pap testing (OR, 1.30; 95% CI [1.15, 1.48]), and about a 10% increase in the odds of having up-to-date Pap testing (OR, 1.10; 95% CI [1.03, 1.20]).
Written health literacy was only associated with receipt of lifetime and up-to-date Pap testing: There was about a 23% increase in the odds of having lifetime Pap testing (OR, 1.23; 95% CI [1.08, 1.40]) and a 10% increase in the odds of having up-to-date Pap testing (OR, 1.10; 95% CI [1.02, 1.18]) for a one-unit increase in the written health literacy score, after controlling for demographics, health insurance, and access to a primary care provider.
Table 4 explains the association between oral, listening, and written literacies and lifetime mammogram and the association between oral, listening, and written literacies and up-to-date mammogram. In multivariable analyses that included all health literacy items, only oral literacy was a contributing factor to being ever screened for mammogram (OR, 1.20; 95% CI [1.03, 1.41]). Table 5 describes the associations between oral, listening, and written literacies and lifetime Pap testing and between oral, listening, and written literacies and up-to-date Pap testing. In the multivariable analyses, oral literacy was an independent factor of lifetime Pap testing (OR, 1.26; 95% CI [1.09, 1.45]) and up-to-date Pap testing (OR, 1.19; 95% CI [1.09, 1.29]). The association between listening literacy and lifetime Pap testing on the one hand and up-to-date mammogram and Pap testing on the other ceased to be significant in the presence of oral and written literacy items.
Overall, the findings of sensitivity analysis were consistent with those from the primary analysis. Oral literacy was a contributing factor to lifetime breast cancer screening. Specifically, individuals who reported that it was very easy to get medical advice from their healthcare providers were about twice as likely to receive lifetime breast cancer screening, compared to those who reporting that it was difficult to get advice (adjusted OR [AOR], 2.02; 95% CI [1.23, 3.33], p = .006). The association was maintained in the presence of all three health literacy items in one model. Oral and listening literacies were associated with cervical cancer screening, and oral and listening literacies maintained significance in the presence of written literacy items. For example, individuals who reported that it was very easy to understand information that healthcare providers offer were twice as likely to receive up-to-date cervical cancer screening, compared to those who reported that it was very difficult to understand health information (AOR, 2.00; 95% CI [1.21, 3.30]; p = .007). Detailed results were provided in supplementary tables, http://links.lww.com/NRES/A313, http://links.lww.com/NRES/A314.
This secondary analysis of the 2016 BRFSS is the first study to investigate the associations between oral, listening, and written health literacy and breast and cervical cancer screening in a nationally representative sample of women. We found that oral health literacy was a contributing factor to adhering to the recommended breast and cervical cancer screening guidelines as well as having ever been screened for mammogram and Pap testing. The study findings are comparable to those of previous studies showing that an ability to ask appropriate questions to find out relevant information was associated with decision-making about cancer screening behaviors (Woudstra et al., 2017). Woudstra et al. (2017) highlighted that asking and answering questions about cancer screening is a critical step toward informed decision-making by offering an opportunity for “knowledge check” to structure the decision options and outcomes, which then leads to adoption of cancer screening. Mazor et al. (2012) offered additional insight into the significant association between oral health literacy and breast and cervical cancer screening. In the study of psychometric analyses of the Cancer Message Literacy Test, the authors found that oral-based measures of health literacy were more likely to reflect information exchange between a patient and a provider (i.e., interactive component), compared with the conventional reading-based measures such as Rapid Estimate of Adult Literacy in Medicine (REALM, a decoding test; Mazor et al., 2012).
Previous studies using data from the 2007 CHIS found a significant association between written health literacy in clinical settings and cancer screening behaviors (Sentell et al., 2013, 2015; Sentell, Tosh, et al., 2015). However, we had mixed results in that we found a significant association between written health literacy and cancer screening only in the context of cervical cancer. One possible explanation is that unlike the study of Sentell, Braun, et al. (2015), we built the statistical model by controlling for having a primary care provider—one of the strongest predictors of cancer screening behaviors—as well as sociodemographic factors, including having health insurance. In addition, it might represent the cheaper cost associated with Pap testing than mammogram for those with limited access to care.
It is worth noting that the distribution of each of the health literacy scores was negatively skewed, with about 60% to 77% of respondents being categorized in the highest score range, yielding relatively little variance in health literacy scores. It should also be noted that the health literacy items used in this study are self-reported, reflecting one's confidence or preferences. One possible explanation for the skewed distribution of health literacy scores is that respondents may not have been aware of their lack of health literacy skills, or they may have overestimated their health literacy levels due to social-desirability bias (McNaughton, Wallston, Rothman, Marcovitz, & Storrow, 2011). In fact, the ceiling effect has been prevalent for the commonly used reading-based, objective health literacy measures, such as the Test of Functional Health Literacy in Adults (a reading comprehension test including numeracy) and REALM due to their lower difficulty. As a modern measurement approach, there has been a trend toward the use of item response theory (IRT) to accurately measure an individual's underlying health literacy skills regardless of the difficulty of health literacy items. In a review of current uses and next steps for health literacy scale development and refinement, Nguyen, Paasche-Orlow, Kim, Han, and Chan (2015) found that only a few studies have used IRT, and its use mainly focused on the strategic removal of items that had low discrimination and aimed the same level of the underlying construct. The authors highlighted that with regard to the spread of current health literacy items, a high density of the items congregated toward the lower difficulty range, thereby offering a rationale for developing more items in higher difficulty ranges (Nguyen et al., 2015).
Disparities in breast cancer screening behaviors exist among those who had worse socioeconomic status, women aged 45–64 years (vs. ≥65 years), and Asian and Pacific islanders (vs. non-Hispanic Whites), whereas disparities in cervical cancer screening behaviors occur in older women (vs. women aged 21–29 years) and those who have lower socioeconomic status. The National Breast and Cervical Cancer Early Detection Program helps low-income, uninsured, or underinsured women aged 21 to 64 years for cervical cancer screening and those aged 40 to 64 years for breast cancer screening to gain access to timely breast and cervical screening and treatment (CDC, 2017). However, it should be noted that women speaking English as a second language and having lower socioeconomic status are the group most likely to exhibit limited health literacy (Mantwill, Monestel-Umaña, & Schulz, 2015; Sentell & Braun, 2012), thereby facing additional challenges in the navigation of complex U.S. healthcare systems. Therefore, strategies to fulfill the unmet needs of culturally and linguistically diverse women should be considered. For example, a grassroots community health worker approach can fill the gap between top–down approaches and unmet needs of culturally and linguistically diverse women (Kim et al., 2016).
Access to a primary care provider was consistently associated with breast and cervical cancer screening in this study, which is comparable to that of previous studies (Chawla et al., 2015; Kim et al., 2018). Providers often prioritize chronic and acute health issues than focusing on preventive health services, such as cancer screening, perhaps due to patients' multiple health problems and appointment scheduling difficulties (Baron et al., 2010; Roman et al., 2014). Aligned with our study findings, we recommend that healthcare providers play a more active role in promoting cancer preventive behaviors by eliminating health literacy demands in medical encounters and offering patient-centered approaches to promote the interactive decision-making process about cancer screening. The reminder and recall systems for healthcare providers have been shown to promote cancer screening (Baron et al., 2010). In addition, the use of shared decision-making models (patient-centered approaches) with opportunities to ask and answer questions rather than paternalistic approaches can help women maintain sustainable breast and cervical cancer screening behaviors. Future research warrants intervention programs to promote providers shared decision-making implementation in the medical encounter.
A first limitation is that due to the nature of cross-sectional data, causality may not be inferred. Second, cancer screening behaviors were assessed by self-reporting and were not confirmed using medical records. A meta-analysis of the accuracy of self-reported cancer screening histories found that the national survey data tend to overestimate relevant cancer screening utilization, including mammograms and Pap testing, as verified by medical record reviews (Rauscher, Johnson, Cho, & Walk, 2008). For example, compared to medical record reviews, studies have demonstrated 70%–87% accuracy of self-reported Pap testing (Caplan et al., 2003). In addition, we endorsed the ACS guidelines for women at average risk. Therefore, we might have underestimated adherence to the ACS guidelines, which warrants further investigation regarding the association between health literacy and cancer screening in a nationally representative high-risk group. Third, there is a discrepancy regarding the national breast cancer screening guidelines between the U.S. Preventive Services Task Force and ACS, which might have affected women's decisions to undergo mammogram and/or Pap testing. Fourth, although this study used data from the national survey, the study sample tended to include more non-Hispanic White and Black women and less Hispanic women. Finally, the 2016 BRFSS has its own limitations. For example, it lacks some key variables such as health beliefs and provider recommendation, which have been associated with cancer screening behaviors. In addition, although the 2016 BRFSS health literacy items addressed three different health literacy domains, the measure consisted of three items that have not been validated against other established health literacy tools. Despite the limitations, this study has a number of strengths. The study sample is a nationally representative female population, which enhances the generalization of our study findings. Unlike the 2007 CHIS health literacy measures, the 2016 BRFSS health literacy measure attempts to capture three different health literacy domains such as oral, listening, and written literacy.
Subjective health-related listening and oral literacies in clinical settings were contributing factors to having ever been screened for breast and cervical cancer and to having followed the recommended breast and cervical cancer screening guidelines. Of the three dimensions of the health literacy construct (oral, listening, and written), subjective oral health literacy was particularly influential in patients' adhering to the recommended breast and cervical cancer screening guidelines. The oral health literacy may be determined based on a patient's ability to ask questions about health or medical topics and a healthcare provider's shared decision-making skill. Thus, healthcare providers should play a crucial role in addressing missed opportunities for breast and cervical cancer screening by creating an atmosphere of effective patient–provider communication and informed decision-making, thus reducing health literacy demands in the medical encounter.
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