Effects of health literacy on health status and health service utilization amongst the elderly

Social Science & Medicine 66 (2008) 1809e1816 www.elsevier.com/locate/socscimed

Effects of health literacy on health status and health service utilization amongst the elderly* Young Ik Cho a,*, Shoou-Yih D. Lee b, Ahsan M. Arozullah a,c, Kathleen S. Crittenden a a University of Illinois, Chicago, IL, USA University of North Carolina, Chapel Hill, NC, USA c Jesse Brown VA Medical Center, Chicago, IL, USA

b

Available online 4 March 2008

Abstract Amid increased concerns about the adverse consequences of low health literacy, it remains unclear how health literacy affects health status and health service utilization. With a sample of 489 elderly Medicare patients in a Midwestern city in the USA, we explored the intermediate factors that may link health literacy to health status and utilization of health services such as hospitalization and emergency care. We expected to find that individuals with higher health literacy would have better health status and less frequent use of emergency room and hospital services due to (1) greater disease knowledge, (2) healthier behaviors, (3) greater use of preventive care, and (4) a higher degree of compliance with medication. Using path analysis, we found, however, that health literacy had direct effects on health outcomes and that none of these variables of interest was a significant intermediate factor through which health literacy affected use of hospital services. Our findings suggest that improving health literacy may be an effective strategy to improve health status and to reduce the use of expensive hospital and emergency room services among elderly patients. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Health literacy; Health; Health service utilization; Health status; USA; Elderly

Introduction *

The study was supported by a grant (R01 HS13004) from the Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services. Dr. Arozullah was supported by a Career Development Award from the Veterans Affairs Health Services Research and Development Service. * Corresponding author. University of Illinois, Survey Research Laboratory, 412 S. Peoria Street, Chicago, IL 60607-7069, USA. Tel.: þ1 312 996 5271; fax: þ1 312 996 4117. E-mail addresses: [email protected] (Y.I. Cho), sylee@email. unc.edu (S.-Y.D. Lee), [email protected] (A.M. Arozullah), kcritt@ uic.edu (K.S. Crittenden). 0277-9536/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2008.01.003

Over the past decade, many studies have reported linkages between health literacy and health outcomes, such as health status, chronic illnesses, and hospitalization. For example, Baker et al. showed that among 2659 patients from emergency care centers and walk-in clinics, those with low health literacy were more likely to report their health as poor and had a higher rate of hospitalization (Baker et al., 1997; Baker, Parker, Williams, & Clark, 1998; Williams, Baker, Parker, & Nurss, 1998). Using a sample of Medicare beneficiaries

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enrolled in a managed care organization, Gazmararian et al. (1999) found that individuals who rated their health as ‘‘fair/poor’’ were twice as likely to have low health literacy compared with individuals who rated their health as ‘‘good/excellent.’’ Baker et al. (2004) found that participants with inadequate and marginal health literacy were more likely to have an emergency department visit. Similarly, Friedland (1998) discovered that patients with lower health literacy tended to have longer hospital stays but fewer outpatient physician visits. Findings of these studies raise the issue of how health literacy affects individual health and health care utilization. Experts suggest that the effects of health literacy on health status and utilization may be indirect, possibly through conditions such as disease knowledge, health behavior, and use of preventive care (Ad Hoc Committee on Health Literacy for the Council on Scientific Affairs, AMA, 1999; NeilsenBohlman, Panzer, & Kindig, 2004). As Berkman et al. (2004) has cautioned, potential confounders may lie in the causal pathway between health literacy and health outcomes. However, prior research tended to examine the consequences of low health literacy in a singular fashion and did not consider the inter-relationships among the various health outcomes (Lee, Arozullah, & Cho, 2004). In this study, we explored four potential intermediate factors that may link health literacy and health status and utilization: (1) disease knowledge, (2) health behavior, (3) preventive care, and (4) compliance. The relevance of these potential intermediary variables is reviewed below. Disease knowledge Research has shown that individuals with lower health literacy are less knowledgeable about diseases and less capable of properly caring for themselves (Arnold et al., 2001; Davis et al., 1996; Gazmararian, Williams, Peel, & Baker, 2003; Kalichman, Ramachandran, & Catz, 1999; Kalichman et al., 2000; Knight, 1999; Lindau et al., 2002; Miller et al., 2003; Schillinger et al., 2002; Williams, Baker, Honig, Lee, & Nowlan, 1998; Williams et al., 1998; Wolf et al., 2005). The areas of medical knowledge and self-care documented to be associated with health literacy include the knowledge of tobacco effects, diabetes, hypertension, chronic heart failure, asthma, HIV/AIDS, and the knowledge of prostate cancer screening and management, mammography screening, and cervical cancer prevention. Disease knowledge may be treated as a subcomponent of health

literacy, which may include general health background knowledge (Neilsen-Bohlman et al., 2004). However, since it is not clear what constitutes ‘‘background’’ health knowledge and to what extent health literacy should encompass disease knowledge, we are treating disease knowledge as a separate construct in this study. Health behavior Research evidence linking health behavior specifically to health literacy is limited. Several studies, however, have reported associations between general literacy level and substance use (e.g., Arnold et al., 2001; Fredrickson et al., 1995; Hawthorne, 1996). It is conceivable that individuals with lower health literacy are more likely to engage in negative health behaviors, such as smoking, drinking, abuse of illegal substances, and living a sedentary lifestyle. This may be in part because of their limited access to and ability to understand health and medical information. Preventive care Limited ability to comprehend information about the importance and methods of early disease detection and treatment may lead to a lower rate of preventive care utilization among people with lower health literacy. Having problems following physician instructions and understanding information on an appointment slip or referral form (Davis et al., 2002; Williams et al., 1995) also may limit access to preventive care and routine physician visits. Some empirical evidence supports these arguments. Scott, Gazmararian, Williams, and Baker (2002) demonstrated a positive relationship between health literacy and preventive health care use among enrollees of a managed care organization’s Medicare plan. Friedland (1998) found a significant association between lower health literacy and fewer physician visits. Compliance Lorenc and Branthwaite (1993) conducted a study to understand factors leading to better medication compliance. Five of seven factors were potentially related to patients’ health literacy level: accurate knowledge of regimen, belief in taking tablets exactly as prescribed, less fear of illness, ability to read the label on the bottle, and understanding what the doctor had said. Low health literacy was a significant predictor of two-day treatment adherence among HIV patients, after controlling for

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factors such as age, education, ethnicity, income, HIV symptoms, substance abuse, and emotional distress (Kalichman et al., 1999). A more recent study, however, found that low literacy was not associated with adherence to antiretroviral therapy among the HIV-infected patients (Paasche-Orlow et al., 2006). Among patients with cardiovascular risk factors, Gazmararian et al. (2006) found that patients with inadequate health literacy had an odds ratio of 1.4 for low medication refill adherence compared to those with adequate health literacy. We anticipated that individuals with lower health literacy would have poorer disease knowledge, worse health behaviors, less preventive care, fewer physician visits, and poorer compliance with routine clinical visits and medications. These factors, in turn, may delay seeking timely and appropriate care, leading to worse health status and increased use of emergency and hospital services. Methods Data collection A total of 489 elderly Medicare patients completed face-to-face interviews at their home or at the medical center between March 2003 and February 2004. Subjects were drawn from patients who had at least one outpatient clinic visit at the Mercy Hospital and Medical Center (MHMC) in Chicago or the Mercy Family Health Center between 1999 and 2003. The Mercy Family Health Center is a MHMC-affiliated federally qualified community health center located on the near south side of Chicago and serves predominantly African American patients. The Center also maintains facilities on the southwest side of Chicago which serves predominantly White patients. The Institutional Review Boards of the University of Illinois at Chicago, MHMC, and the University of North Carolina at Chapel Hill approved the study protocol. Informed consent was obtained from each respondent prior to the face-toface interview. Potential respondents were screened by telephone for eligibility and willingness to participate and selected by the following criteria: (1) age  65 years; (2) being a Medicare recipient; (3) having at least one visit to a Mercy Hospital and Medical Center-affiliated outpatient clinic between 1999 and 2003; (4) being mentally competent (determined by a cognitive test); (5) having good vision (determined by vision questions during telephone screening and ability to see well enough during consent form signing); (6) currently living at

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home in Illinois; (7) having good hearing (determined by responses to the screener without the use of a text telephone, or TTY); and (8) being able to conduct the interview in English. The interview assessed the respondent’s socio-demographics, health literacy, social support, physical and mental health status, medical comorbidities, functional status, disease knowledge, attitudes towards health care, risk and healthy behaviors, medication compliance, health care utilization and access. On average, respondents took 60 min to complete the interview. The overall response rate of the survey was 50.6%. Measurement Health literacy We measured health literacy using the Short Test of Functional Health Literacy in Adults (S-TOFHLA) (Baker, Williams, Parker, & Gazmararian, 1999). The S-TOFHLA assesses functional health literacy using a modified Cloze-type procedure in which respondents complete a reading comprehension test by filling in missing words from two medically related passages. Note that reading ability assessed by S-TOFHLA does not measure all domains of a multifaceted concept of health literacyddefined as the capacity of individuals to obtain, process, and understand basic health information needed to make appropriate health decisions (Selden, Zorn, Ratzan, & Parker, 2000; see Baker, 2006, for a review of health literacy measurement issues). However, the S-TOFHLA has excellent correlations with another health literacy test, the Rapid Estimate of Adult Literacy in Medicine or REALM (r ¼ 0.80) (Davis et al., 1993), and the original TOFHLA (r ¼ 0.91) (Baker et al., 1999). The test scores of S-TOFHLA range from 0 to 36 and can be classified into three health literacy levels: inadequate (0e16, unable to read and interpret health texts), marginal (17e22, has difficulty reading and interpreting health texts), and adequate (23e36, can read and interpret most health texts) (Nurss, Parker, & Baker, 2001). A dummy variable was created by coding 1 for ‘‘adequate’’ and 0 for ‘‘inadequate’’ or ‘‘marginal’’ health literacy based on our preliminary findings of bimodal distribution with only a small number of marginally health literate respondents. Disease knowledge We assessed disease knowledge by asking respondents about cardiovascular disease risk factors, potential complications of diabetes mellitus, and hypertension (Pearlman, 1995). For example,

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respondents were asked, ‘‘Do you think a person with diabetes or high blood sugar is more likely to develop kidney failure in the future?’’ A total of 17 questions were asked, and disease knowledge was estimated by the total number of correct answers to these questions. Health behavior Health behavior was measured by nine Likert-scale items selected from the Health Promoting Lifestyle Profile (Walker, Sechrist, & Pender, 1987). The items were related to exercise, nutrition, and health responsibility, and they were shown to have adequate reliability (alpha ¼ 0.692). The mean of the nine items was computed as an indicator of health behavior.

study only included respondents who were African American (¼1) or Caucasian (¼0), because the numbers of respondents in the other racial/ethnic groups were too small to be included in the analysis. An ordinal variable was created to indicate the respondent’s highest level of education completed (1 ¼ grade/ elementary school, 2 ¼ some high school, 3 ¼ high school diploma/GED, 4 ¼ some college, 5 ¼ college graduate, and 6 ¼ graduate degree). In our preliminary analysis, age and household income also were included. However, they were found to have no significant effects on other variables of interest among our sample and therefore were not included in our final analysis. Statistical analysis

Preventive care We assessed preventive care utilization based on a dichotomy of whether the respondent had either fecal occult blood testing or prostate cancer screening (if male) or mammography or Pap smear tests (if female) during the past two years. Medication compliance Medication compliance was measured by asking how often respondents forgot to fill prescriptions on time. Respondents who always filled prescriptions on time were assigned 1 and others 0 to indicate medication compliance. Health status A self-rated five-point Likert-scale (poor, fair, good, very good, and excellent; coded 1e5, respectively) item was used to assess health status. Health care utilization We assessed self-reported health care utilization by asking participants the number of emergency room visits and the number of hospital admissions in the previous year. Respondents who visited an emergency room one or more times during the past 12 months for treatment were coded 1 and 0 otherwise. Similarly, for hospital admissions, one or more admissions last year was coded 1 and no admissions 0. Socio-demographic variables Three socio-demographic variablesdrace/ethnicity, gender, and educational attainmentdwere included in the analysis as controls. Respondents reported their own ethnicity as American Indian or Alaskan Native, Asian or Pacific Islander, Black or African American, White or Caucasian, multiracial, or other. The present

Using LISREL 8, we conducted path analyses with observed variables to examine the effects of health literacy on health status and health care utilization. The advantage of this analytical model is its simultaneous consideration of interrelated pathways in explaining the effects of health literacy on health status and health care utilization. Because the analysis included categorical and ordinal variables, we used the weighed leastsquared method (Jo¨reskog, 1990) with asymptotic covariance matrix to adjust non-normal distribution of the variables and to obtain less biased estimates (Curran, West, & Finch, 1996). To assess the fitness of model, we used several fit statistics: the relative Chi-square Model-Fit Statistic, Adjusted Goodness-of-Fit Index (AGFI), Normed Fit Index (NFI), and Root Mean Squared Error of Approximation (RMSEA). The Chi-square statistic, which is known to be sensitive to sample size, provides a general guideline for overall fit, and a value with p > 0.05 indicates a good model fit (Carmines & McIver, 1981; Kline, 1998). The other three model-fit statisticsdAGFI, NFI, RMSEAdare less dependent on sample size but more sensitive to the number of estimated parameters (Fan, Thompson, & Wang, 1999). While AGFI and RMSEA are based on Chi-square assumptions, NFI does not require Chi-square assumptions. RMSEA is favored over the other measures because it is unaffected by the estimation method and least affected by the size of the sample. An AGFI greater than 0.90, RMSEA less than 0.05, and NFI greater than 0.90 are considered ‘‘good fit’’ (Fan et al., 1999; Jo¨reskog & So¨rbom, 1981). Given the complementary features of these measures, we evaluated the model based on all four indexes.

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Findings The majority of our respondents were female (78.7%) and African American (59.1%), with an average education level of 2.95 (SD ¼ 1.49), which was equivalent to a high school diploma (see Table 1). About half of the respondents (49.1%) were found to have an adequate health literacy level. In terms of disease knowledge, respondents scored an average of 10 (SD ¼ 2.94) out of 17. Eighty-six percent of the respondents were identified as users of preventive care. Eighty-two percent of the respondents were categorized as being compliant to medication. The average level of self-rated health status in our sample was 2.63 (SD ¼ 1.05), which falls in-between ‘‘fair’’ and ‘‘good.’’ Thirty-six percent of respondents reported having had ER visits, and 32% reported one or more hospital admissions. The zero order correlation matrix shows (Table 2) that health literacy was significantly related to all variables included in the model, especially level of education (r ¼ 0.65), African American race (r ¼ 0.76) and self-rated health status (r ¼ 0.50). Health literacy was positively correlated with all four intermediate factors: disease knowledge (r ¼ 0.38), health behavior (r ¼ 0.42), preventive care (r ¼ 0.21), and medication compliance (r ¼ 0.20). As depicted in Fig. 1, respondents’ gender, race/ ethnicity (African American) and education are treated as a set of exogenous variables that are assumed to influence health literacy. The model then permits health literacy and exogenous variables to influence the measures of four potential mediating factors: disease knowledge, health behavior, preventive health care and compliance. Effects of health literacy and the four mediating factors are estimated on the three health outcomes: health status, hospitalization, and ER visits. The three exogenous background variables could also be Table 1 Descriptive statistics of the variables

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permitted to influence the health outcomes directly. However, our preliminary analysis indicated that these pathways were statistically non-significant and therefore they are not included in the model. Error terms of the four mediating factors and the three outcome variables are permitted to be correlated, and statistically non-significant correlations among them are set to be zero in the final model. Contrary to our expectation, the linkages of health literacy with health status and utilization tended to be direct rather than through intermediate factors. Health literacy was directly and positively related to self-rated health status (b ¼ 0.48), and it had direct and negative effects on hospitalization (b ¼ 0.24) and ER visits (b ¼ 0.35), for which errors were correlated. We found that health literacy had a positive relationship with disease knowledge (b ¼ 0.61) and preventive care (b ¼ 0.42). Aside from health literacy, health behavior was the only variable found to be significantly correlated with perceived health status (b ¼ 0.13) (see Fig. 1). All three socio-demographic variablesdgender, race/ethnicity and education leveldwere associated with health literacy. Male respondents compared to females (b ¼ 0.14), and African Americans than Whites (b ¼ 0.58) had lower health literacy; higher educational attainment was positively associated with the level of health literacy (b ¼ 0.33). Education also had a positive relationship with health behavior (b ¼ 0.26), which was in turn positively correlated with perceived health status (b ¼ 0.13). It is noteworthy that educational attainment did not affect health outcomes directly but indirectly through health literacy. Compared to females, male respondents were more knowledgeable about disease (b ¼ 0.13). African Americans were also less likely to engage in health behaviors (b ¼ 0.21). All four model fit statistics indicated adequate fit: the model-fit Chi-square is 15.26 (p ¼ 0.291), and NFI, AGIF, and RMSEA are 0.99, 0.97 and 0, respectively. Discussion

Variable

Mean or percent

Gender (male) Race/ethnicity (African American) Education Adequate health literacy Disease knowledge Health behavior Preventive care Medication compliance Self-rated health status Hospitalization ER visits

21.33% 59.11% 2.95 49.11% 10.07 2.31 85.78% 82.22% 2.63 32.22% 36.44%

SD

1.49 2.94 0.57

1.05

In this study, we explored four potential mediating factorsddisease knowledge, health behavior, preventive care, and compliance with medicationsdthat may link health literacy and health status and utilization. Contrary to our expectations, we found that health literacy tended to have direct rather than indirect effects on health outcomes, and none of these variables of interest was found to be significant mediating factor through which health literacy indirectly affect health status, hospitalization and/or ER visits.

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Table 2 Correlation matrix of the variables in the model (1) (1) (2) (3) (4) (5)

Adequate health literacy Disease knowledge Health behavior Preventive care Compliance with medication (6) Health status (7) Hospitalization (8) ER visits (9) Gender (10) African American (11) Education

(2)

(3)

(4)

0.38* 0.42* 0.21* 0.20*

0.31* 0.09* 0.02

0.30* 0.20*

0.50* 0.24* 0.32* 0.20* 0.76* 0.65*

0.12* 0.16* 0.07 0.02 0.21* 0.21*

0.30* 0.07 0.10* 0.14* 0.41* 0.43*

(5)

(6)

(7)

(8)

(9)

(10)

0.16* 0.04 0.07 0.14* 0.28* 0.21*

0.27* 0.30* 0.15* 0.44* 0.36*

0.72* 0.04 0.17* 0.12*

0.02 0.27* 0.14*

0.03 0.14*

0.53*

0.06 0.10* 0.01 0.06 0.17* 0.06 0.06

*p < 0.05.

Educational attainment often is used as a proxy measure of health literacy and has been associated with health status and utilization in prior studies. However, we found that health literacy tended to have direct effects on health status and utilization, while educational attainment had indirect effects that were mediated via increased

health literacy. Our findings suggest that improving health literacy may be the most effective and direct approach for improving the health status and reducing hospital and emergency room use among elderly patients. To minimize the adverse effects of low health literacy on health, hospitalizations and ER visits, efforts

-.10

.13 .21 -.06

Disease Knowledge [R2=.18]

.61

.26

Male

Health Behavior [R2=.24]

-.58

.03

Health Status [R2=.27] -.17

.48 .24

Health Literacy [R2=.68]

-.24

.33

Education

.13 .04

.07 -.14

African American

.05 .18

-.09 -.21

-.08

Hospitalization [R2=.08]

-.35 .42 -.11 .19

Preventive Care [R2=.09]

-.17 -.15 -.34

.64 -.04

ER Visit [R2=.10]

.03 -.01

-.12

.04 Compliance [R2=.11]

.08 -.01

.12 Model Fit Chi-square=15.26 (df=13, p=.291) Normed Fit Index (NFI) =.99 Adjusted Goodness of Fit Index (AGFI) =.97 Root Mean Square Error of Approximation (RMSEA) =.00

-.14

Significant at p< .05 Non-significant at p=.05

Fig. 1. An exploratory path model of health literacy and health: standardized coefficients.

Y.I. Cho et al. / Social Science & Medicine 66 (2008) 1809e1816

should be directed for making the health care system more accessible to adults with low health literacy by designing more reader-friendly media with simple illustrations and culturally sensitive examples (Ad Hoc Committee on Health Literacy for the Council on Scientific Affairs, AMA, 1999; Davis, Berkel, Arnold, Nandy, & Jackson, 1998; Lee, 1999; Ley, 1998; Mayeaux et al., 1996; Roter & Stableford, 1999) and enhancing patients’ understanding of health information by communicating in simpler language and with simpler instructions (Mayeaux et al., 1996). Several limitations of the study should be noted. Although our findings are original and significant, the cross-sectional nature of the data made it difficult to establish temporal order among the variables. Thus, it must be emphasized that the causal inferences should be made with great caution. Future studies with longitudinal data are strongly recommended. Note also that the health care variablesdhospitalization and ER visitsdwere not very well explained by health literacy and the other variables included in the model. For instance, only 8% of the variance in hospital admissions was explained by health literacy and other intermediate variables included in the model. This suggests that either these variables were inadequately measured or unknown factors, yet to be explored, contribute to ER visits and hospitalization among the elderly. Future research should examine other intermediate variables, such as patientephysician communication. Further, the measurement of some of the variables could be enhanced in future research. Medication compliance, for instance, was based on a single self-report item on how often the respondent forgot to fill prescriptions on time, a measurement that suffers from the problem of validation and measurement error due to social desirability bias. This problem can be minimized with questions that are less threatening and less direct and with multiple measures of medication adherence (MacLaughlin et al., 2005). More importantly, the S-TOFHLA is an approximate measure of reading comprehension of medically related passages, not a comprehensive measure of the broad range of skills and capacities that health literacy entails (Baker, 2006; Neilsen-Bohlman et al., 2004). Hence, this study’s findings must be interpreted in terms of the health-related reading fluency element of health literacy rather than overall health literacy. We also recognize that health literacy should be understood in a broader health context that encompasses the health system, the education system, and society and culture (Neilsen-Bohlman et al., 2004). It is our recommendation that future studies transcend

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treating health literacy as an individual issue and explore mechanisms that individuals use to overcome or improve health illiteracy within the medical and social systems within which they are embedded, systems that vary in the amount of health literacy they demand from patients and in the degree of health literacy support they provide. Acknowledgement We would like to thank Timothy Johnson and Richard Campbell for their valuable comments and consultation, and Lisa Kelly-Wilson for her kind help with manuscript preparation. We also want to acknowledge four anonymous reviewers of the journal for their valuable comments. One of the reviewers contributed significantly to the statistical analysis of the paper. References Ad Hoc Committee on Health Literacy for the Council on Scientific Affairs, AMA. (1999). Health literacy: report of the Council on Scientific Affairs. JAMA, 281(6), 552e557. Arnold, C. L., Davis, T. C., Berkel, H. J., Jackson, R. H., Nandy, I., & London, S. (2001). Smoking status, reading level, and knowledge of tobacco effects among low-income pregnant women. Preventive Medicine, 32, 313e320. Baker, D. W. (2006). The meaning and the measure of health literacy. Journal of General Internal Medicine, 21(8), 878e883. Baker, D. W., Gazmararian, J. A., Williams, M. V., Scott, T., Parker, R. M., & Green, D., et al. (2004). Health literacy and the use of outpatient physician services by Medicare managed care enrollees. Journal of General Internal Medicine, 19, 215e220. Baker, D. W., Parker, R. M., Williams, M. V., Clark, W. S., & Nurss, J. (1997). The relationship of patient reading ability to self-reported health and use of health services. American Journal of Public Health, 87(6), 1027e1030. Baker, D. W., Parker, R. M., Williams, M. V., & Clark, W. S. (1998). Health literacy and the risk of hospital admission. Journal of General Internal Medicine, 13(12), 791e798. Baker, D., Williams, M., Parker, R., & Gazmararian, J. A. (1999). Development of a brief test to measure functional health literacy. Patient Education and Counseling, 38, 33e42. Berkman, N. D., DeWalt, D. A., Pignone, M. P., Sheridan, S. L., Lohr, K. N., & Lux, L., et al. (2004). Literacy and health outcomes. Evidence Report/Technology Assessment No. 87. AHRQ Publication No. 04-E007-2. Rockville, MD: Agency for Healthcare Research and Quality. Carmines, E. G., & McIver, J. P. (1981). Analyzing models with unobserved variables: analysis of covariance structures. In G. W. Bohmstedt, & E. F. Borgatta (Eds.), Social measurement (pp. 65e115). Thousand Oaks, CA: Sage Publications. Curran, P. J., West, S. G., & Finch, W. J. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16e29. Davis, T. C., Arnold, C., Berkel, H. J., Nandy, I., Jackson, R. H., & Glass, J. (1996). Knowledge and attitude on screening mammography among low-literate, low-income women. Cancer, 78(9), 1912e1920.

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