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Functional health literacy mediates the relationship between socio-economic status, perceptions and lifestyle behaviors related to cancer risk in an Australian population
Patient Education and Counseling 91 (2013) 206–212
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Health literacy
Functional health literacy mediates the relationship between socio-economic status, perceptions and lifestyle behaviors related to cancer risk in an Australian population Robert J. Adams a, Cynthia Piantadosi a,*, Kerry Ettridge b, Caroline Miller c, Carlene Wilson b,d, Graeme Tucker e, Catherine L. Hill a a
Discipline of Medicine, The University of Adelaide, Adelaide, Australia Cancer Council SA, Adelaide, Australia c Department of Public Health, University of Adelaide, Adelaide, Australia d Flinders Centre for Innovation in Cancer, Flinders University, Adelaide, Australia e Health Statistics, SA Health Department, Adelaide, Australia b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 1 July 2012 Received in revised form 20 November 2012 Accepted 1 December 2012
Objective: To determine if functional health literacy (FHL) mediates the relationship between socioeconomic status, and perception of the risk of lifestyle behaviors for cancer. Methods: Cross-sectional, random population survey, 2824 people aged 15 years, September–October 2008, included newest vital sign measure of FHL. Results: Less than adequate FHL occurred in 45.1%. People who perceived behavioral factors (smoking, diet, obesity, alcohol, physical activity) to be not important, or did not know if they were important cancer risks, were more likely to have inadequate FHL. In a logistic regression model adjusted for age, gender, education, income, occupation, country of birth and area of residence, inadequate FHL was associated with 2–3 (OR = 1.9; 95% CI: 1.2–3.0) and 4 or more self-reported lifestyle risk factors (OR = 2.8; 95% CI: 1.6–5.0). In a structural equation model of the relationship of socio-economic status, perceptions of risk and behaviors there was significant mediation effect of FHL on the path from SES to health perceptions, estimated 29.4% of the total effect. Conclusion: A specific focus on the literacy demands made on individuals from health promotion and materials with a view to improving health communication is indicated. Practice implications: Health literacy is important for health promotion. ß 2012 Elsevier Ireland Ltd. All rights reserved.
Keywords: Health literacy Cancer risk Structural equation model
1. Introduction Lifestyle factors, particularly smoking, poor diet, physical inactivity and obesity, are the leading causes of premature death in Western societies [1,2], and have been estimated to account for a 78% variance in the risk of chronic diseases, including cancer [3]. Avoidance of weight gain and adequate physical activity has been shown to reduce the risk of a number of cancers, including breast and colon [4]. Achieving cancer prevention guideline recommendations for obesity, diet, physical activity, and alcohol consumption is associated with lower risk of death from cancer, CVD, and all causes in nonsmokers [5]. The combined impact of unhealthy
* Corresponding author at: The Health Observatory, Discipline of Medicine, The University of Adelaide, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia. Tel.: +61 8 82228818; fax: +61 8 82226042. E-mail address: [email protected] (C. Piantadosi). 0738-3991/$ – see front matter ß 2012 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pec.2012.12.001
behaviors appears multiplicative and affects multiple outcomes [3]. A number of studies across different countries have also shown that only small percentages of the population, usually less than 10%, exhibit all of the 4 healthy behaviors listed above [3,5]. A number of possible factors may contribute to the discrepancy between longstanding scientific knowledge and the health behavior of populations. Motivation to perform certain behaviors is thought to increase with the perception that a health risk exists [6], although the evidence for this proposition is inconsistent [6]. Although a number of studies have found that behavior is related to risk perception, this has not been a universal finding [7,8]. Social cognition models of health behavior also postulate that self-efficacy relating to personal capacity and perceptions of the efficacy of the action are important influences on behavior [9]. Awareness of a link between lifestyle behaviors and conditions such as cancer is a necessary precondition for these cognitive processes to occur [10]. Promoting public awareness of such a link becomes a necessary but not sufficient step toward lifestyle changes [10].
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Awareness of the contribution of lifestyle factors to cancer and heart disease has been reported to be low, particularly amongst younger people and those from more socio-economically deprived backgrounds [11]. This reduced level of awareness may be more pronounced for risk factors such as weight and exercise for cancer, than it is for heart disease [10]. An Australian survey reported that only around one-third of respondents recognize excess weight or inadequate physical activity as potential cancer risks [12]. Of the variables usually studied, education appears to be the most strongly related to awareness of risk [10], although the reasons for this remain speculative [10,13]. Health literacy is the range of skills and competencies that people develop to seek out, comprehend, evaluate, and use health information and concepts to make informed choices, reduce health risks, and increase quality of life [14]. Individuals with adequate literacy and numeracy may have inadequate health literacy [15], although research has established a strong, positive longitudinal relationship between intelligence and health [16]. The link between low or limited health literacy and adverse health outcomes is well established [14,15,17–19]. Health literacy has been investigated in acute and chronic health care [14,17,18,20,21] but less is known at a population level about the associations with health-linked behaviors such as dietary choices, smoking or activity levels [22]. Von Wagner et al. found an association between health literacy, smoking and vegetable intake in a UK population. However, the prevalence of marginal health literacy in this sample was much lower than that found in other international surveys [23,24], raising the possibility of bias. The way in which individuals process and contextualize information will vary, and few studies have examined the links between healthy behaviors, perceptions of the importance of lifestyle factors for cancer, and health literacy. This study examines the association of health literacy with self-reported lifestyle behaviors and perceptions of the risk of these behaviors for cancer in a representative Australian population sample. We hypothesized that FHL would mediate the relationship between socioeconomic status, and perception of the risk of lifestyle behaviors for cancer, with consequent effects on lifestyle behaviors. 2. Methods Data were obtained from the South Australian Health Omnibus Survey (SAHOS) between September and October 2008. Australian Bureau of Statistics collector districts (CD) were chosen with a probability proportional to their size, and within each a starting point was randomly selected and 10 households were sampled using a fixed skip interval. In a non-replacement sample, one interview was conducted per household. Where more than one person aged 15 or over resided in the household, the chosen respondent was the person who was last to have their birthday. The SAHOS methodology has been described in detail elsewhere [15,25]. 2.1. Functional health literacy Respondents completed the Newest Vial Sign (NVS) as a measure of FHL [26]. The NVS is based on a nutrition label from an ice cream container, which people are asked to read and then answer six questions about their interpretation and how they would use the information on the label. The NVS assesses reading, numeracy and document skills. Compared with the most commonly used functional health literacy instrument, the Test of Functional Health Literacy in Adults (TOFHLA) [27] the NVS has high sensitivity for detecting limited FHL [28], beyond that of education and age alone [26]. The NVS is scored out of 6, with a score of 4–6 almost always indicating adequate FHL (described as
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‘‘adequate’’ in this article), 2–3 indicating the possibility of limited FHL (‘‘at-risk’’), and 0–1 indicating a high likelihood (50% or greater) of limited FHL (‘‘inadequate’’) [26,28]. Data collected included sex, education level, before-tax household income, occupation, country of birth and area of residence (metropolitan or regional), and self reported lifestyle choices; diet, exercise participation, smoking status and extent of alcohol consumption. Occupation was classified by the broad categories of occupation for most of an individual’s life according to the Australian and New Zealand Standard Classification of Occupations (ANZSCO) system of the Australian Bureau of Statistics [29]. Height and weight were measured by interviewers for calculation of body mass index. 2.2. Risk perceptions Respondents rated items related to lifestyle behaviors (smoking, alcohol, diet, weight, exercise) on how much of a health risk they perceived these to be in developing cancer for all Australians, on a scale from ‘not important’, ‘slightly/moderately important’ to ‘very/extremely important’ including ‘do not know/cannot answer’. Perceived cancer risk was measured by asking respondents, ‘‘What do you think the percentage chance is of developing cancer in your lifetime?’’, with 0% being no chance of getting cancer and 100% being will definitely get cancer. 2.3. Health behaviors For health behaviors, 4 items asking fruit and vegetable intake on a usual day and the previous day were used. Usual and actual consumption of fruit and vegetables were recorded and categorized according to whether or not participants ate at least 5 serves of vegetables and 2 serves of fruit a day [30]. Definitions of a ‘‘serve’’ were provided to respondents. Participants were asked to recall the number of times (sessions), duration (hours/minutes) and level (vigorous/moderate/walking) of physical activity they had undertaken in the previous week. Sufficient activity was defined by having engaged in at least 150 min/wk and 5+ sessions/ wk. At-risk alcohol consumption was recorded as more than 1 standard drink/day for females and more than 2 standard drinks/ day for males. Participants at low risk drank alcohol but did not exceed recommendations. 2.4. Statistical analysis Data were analyzed using SPSS version 17.0 (SPSS Inc., Chicago, USA) and weighted to the individual’s probability of selection and to the Australian Bureau of Statistics population estimates [31]. Bivariate associations of FHL with demographics and self reported behaviors were determined using the chi-squared test. Multivariable logistic regression models were developed for outcome variables of inadequate (NVS score 0–1) FHL compared with adequate (NVS score 4–6) FHL. Odds ratios for inadequate health literacy with 95% confidence intervals (CIs) are presented and adjusted where specified. Structural equation modeling was used to assess the mediating effect of FHL on the relationship between socio-economic status and perceptions of lifestyle behaviors and cancer risk. Fig. 1 sets out the pathway theoretical structure, where exogenous indicator or manifest variables (education, income, occupation, aboriginality) of the exogenous latent variable (socio-economic status) are shown in relation to the endogenous latent variables (FHL, perceptions of lifestyle behaviors and cancer risk) and the endogenous manifest variables of behavior (smoking, fruit and vegetable intake, physical activity, BMI). The assumption of the model is that the correlations observed between manifest variables
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HLSCORE
0.00
OVERWEIGHT
0.46
1.00 0.90
AB FHL 0.73
0.32 0.46 0.43
EDN
0.73
0.16 0.75
0.82
SES
0.18
PERC
INC
0.47
NEVEG
0.33
NEFRUIT
0.41
EXERCISE
0.37
VEGAC
0.85
FRUITAC
0.75
ACTIVITY
0.91
SMOKER
0.76
0.79
0.18 0.97
0.77
CIGS
0.32 0.67
0.29
0.39
BEH 0.50 0.55
-0.30
OCC
-0.49 -0.15
OBESE
0.98
Chi-Square=801.47, df=86, P-value=0.00000, RMSEA=0.054 Fig. 1. Structural equation modeling of the pathway of socio-economic status, perceptions of risk and lifestyle behaviors.
of the same latent factor are caused by the latent factor. The model was fit using polychoric correlations in LISREL Version 8.71. Unweighted least squares was the method used, as recommended for the analysis of ordinal indicators by Forero et al. [32]. 3. Results From 4614 households contacted there were 2824 respondents (61.2% participation rate), with the sociodemographic distribution corresponding to population estimates [31]. There were 1358 males (48.1%) and 2158 (76.4%) resided in the metropolitan area. We have previously reported that 24.1% were classified at-risk of limited FHL, and 21.0% had a high likelihood of inadequate FHL [15], increasing with age (inadequate, 65 years 49.7% vs. 25–44 years 11.4%). Less than adequate FHL was significantly associated with less education and lower income. Table 1 shows associations between lifestyle behaviors and demographic categories, from multivariable logistic regression models. Participants aged between 45 and 64 yrs were more likely than younger participants (<45 years) to be obese (OR = 1.4; 95% CI: 1.1–1.9) and undertake insufficient exercise (OR = 1.4; 95% CI: 1.1–1.7). Having an age of over 65 years was also associated with not enough physical activity (OR = 1.9; 95% CI: 1.3–2.7). Lower income earners and those with less education had increased odds of engaging in a number of health risk behaviors; smoking, not meeting adequate fruit requirements and not participating in sufficient physical activity (Table 1).
Table 2 shows the prevalence of perceptions of cancer risk for various lifestyle risk factors according to FHL category. The percentage of respondents who perceived these to be either not at all important, did not know, or were unable to answer if they were a cancer risk, varied between risk factors. Participants were more likely to perceive alcohol (14%) or lack of exercise (12%) as not a cancer risk than smoking (5%) or being overweight (9%). Across risk factors, people who perceived these factors to be not important or did not know if they were important were more likely to have inadequate health literacy, after adjusting for age, gender, income and education. Table 3 shows the prevalence of self-reported health behaviors in relation to health literacy categories, and adjusted odds-ratios for inadequate versus adequate FHL from multiple logistic regression models. Those reporting sufficient physical activity, actual recommended fruit intake or usual recommended vegetable intake, were more likely to have adequate FHL, as were at-risk alcohol consumers. In multivariable logistic regression models adjusted for age, gender, education, income, occupation category, country of birth and area of residence, inadequate health literacy was associated with having 2–3 (OR = 1.9; 95% CI: 1.2–3.0) and 4 or more reported risk factors (OR = 2.8; 95% CI: 1.6–5.0), compared with 0 or 1. Fig. 1 shows the results of structural equation modeling of the pathway from socio-economic status, to perceptions of risk and lifestyle behaviors. The model shows adequate specification with a Satorra–Bentler chi-square of 801.47 on 86 df, p < 0.000, RMSEA of
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Table 1 Factors associated with health behaviors, odds ratios and 95% confidence intervals.
Age (years) <45 45–64 65+ Sex Male Female Education University Trade Left school, Still study School >15 years old School <15 years old Still school Income ($) >100,000 50–100,000 20–49,000 <20,000
Risks (4–5)
BMI > 30
Smoking
Vegetables insufficient
Fruit insufficient
Exercise insufficient
1.0 0.8 (0.5–1.2) 0.2 (0.1–0.4)
1.0 1.4 (1.1–1.9) 0.8 (0.5–1.2)
1.0 0.6 (0.4–0.8) 0.1 (0–0.2)
1.0 0.6 (0.4–0.8) 0.5 (0.3–0.8)
1.0 0.6 (0.4–0.7( 0.2 (0.2–0.4)
1.0 1.4 (1.1–1.7) 1.9 (1.3–2.7)
1.0 0.5 (0.3–0.7)
1.0 1.2 (0.9–1.5)
1.0 0.6 (0.5–0.8)
1.0 0.5 (0.4–0.8)
1.0 0.6 (0.5–0.7)
1.0 1.0 (0.9–1.3)
1.0 2.5 2.5 3.8 4.8 –
1.0 1.7 1.5 1.5 2.0 0.4
1.0 2.1 2.3 2.3 4.4 0.8
1.0 0.8 0.6 1.5 1.6 0.9
1.0 1.2 0.9 1.7 1.8 1.1
1.0 1.4 1.1 1.5 1.6 0.5
(1.4–4.4) (1.0–6.4) (1.9–7.4) (2.0–11.6)
1.0 3.4 (2.0–5.9) 2.7 (1.4–5.1) 6.5 (2.7–15)
(1.2–2.5) (0.8–2.9) (1.0–2.3) (1.2–3.2) (0.1–1.8)
1.0 1.5 (1.1–2.1) 1.4 (1.0–2.1) 1.6 (1.0–2.5)
(1.4–3.2) (1.2–4.5) (1.5–3.6) (2.6–7.8) (0.3–2.4)
1.0 1.7 (1.2–2.5) 2.3 (1.5–3.5) 4.0 (2.4–6.7)
(0.5–1.3) (0.3–1.4) (0.8–2.7) (0.8–3.3) (0.2–3.6)
1.0 1.2 (0.8–1.8) 1.0 (0.6–1.7) 1.7 (0.8–3.4)
(0.9–1.5) (0.5–1.5) (1.2–2.4) (1.2–2.8) (0.5–2.1)
1.0 1.4 (1.1–1.8) 1.4 (1.0–2.0) 1.8 (1.2–2.8)
(1.0–1.8) (0.6–1.8) (1.1–2.0) (1.1–2.4) (0.3–1.1)
1.0 1.5 (1.1–1.9) 1.3 (1.0–1.8) 1.6 (1.1–2.4)
Numbers and percentages may not add to n or 100%, respectively, as data are weighted. *Inadequate: newest vital sign score, 0–1 yadequate: newest vital sign score, 4–6. **Any of the following: BMI > 30, smoker, does not meet vegetable intake requirements, does not meet fruit intake requirements, not sufficient activity.
Table 2 Prevalence of perceptions of importance of lifestyle risk factors for cancer within functional health literacy categories and odds ratios adjusted for age for risk of inadequatea compared with adequateb functional health literacy. FHL (%)
Passive smoking Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Smoking Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Being overweight Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Lack of exercise Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Not eating enough fruit Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Not eating enough vegetables Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Drinking alcohol Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Chance of developing cancer over lifetime <50% 50% >50%
N
Adequate
At-risk
Inadequate
OR (95% CI)a
79 965 1718 55
24 61 54 13
33 20 26 24
43 18 20 64
1.0 0.2 (0.1–0.3) 0.2 (0.1–0.4) 2.3 (0.8–6.7)
47 172 2567 30
23 30 58 10
34 23 24 23
43 47 18 67
1.0 0.8 (0.3–2.0) 0.2 (0.1–0.4) 2.4 (0.5–10.6)
165 807 1762 83
38 57 57 22
23 23 25 23
39 20 18 55
1.0 0.4 (0.3–0.6) 0.4 (0.2–0.6) 2.2 (1.1–4.2)
264 1111 1368 73
38 58 58 23
24 24 25 21
38 19 17 54
1.0 0.4 (0.3–0.5) 0.4 (0.3–0.5) 1.7 (0.9–3.5)
213 1351 1151 103
36 60 56 22
24 24 24 23
40 16 20 54
1.0 0.3 (0.2 – 0.4) 0.3 (0.2 -, 0.4) 1.5 (0.8 – 2.9)
144 1191 1391 91
38 60 55 23
26 22 25 26
37 18 20 51
1.0 0.3 (0.2–0.5) 0.3 (0.2–0.5) 1.8 (0.9–3.7)
262 1371 1061 122
39 62 54 25
26 23 25 27
34 16 21 48
1.0 0.4 (0.3–0.5) 0.6 (0.4–0.9) 2.1 (1.2–3.7)
852 761 744
58 56 65
22 27 23
20 18 12
1.0 1.0 (0.7–1.3) 0.6 (0.5–0.9)
Numbers and percentages may not add to n or 100%, respectively, as data are weighted. a Inadequate: newest vital sign score, 0–1. b Adequate: newest vital sign score, 4–6.
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Table 3 Prevalence of lifestyle risk factors for cancer within functional health literacy categories and multiple logistic regression models for comparison of inadequate* with adequatey functional health literacy. FHL (%) N Cumulative risk factorsa 0–1 risk factors 457 1490 2–3 risk factors 4–5 risk factors 384 BMI (kg/m2) <25 1074 25–30 791 >30 559 Current smoker No 2271 Yes 553 Sufficient physical activity Sufficient activity 1148 Some activity 1127 No activity 539 Serves of vegetables usually consumed 5+ 308 1827 2–4 0–1 689 Serves of vegetables actually consumed yesterday 5+ 237 2–4 1500 0–1 1059 Serves of fruit usually consumed 3+ 521 2 826 0–1 1476 Serves of fruit actually consumed 3+ 475 2 774 0–1 1484 a
Adequate
At-risk
Inadequate
OR (95% CI)*
66 56 51
21 22 28
13 21 21
1.0 1.9 (1.2–3.0) 2.8 (1.6–5.0)
56 59 56
25 21 24
19 20 21
1.0 1.0 (0.8–1.3) 1.1 (0.8–1.4)
56 51
23 29
21 20
1.0 1.1 (0.8–1.4)
61 54 44
23 25 25
16 21 31
1.0 1.2 (0.8–1.7) 2.2 (1.5–3.2)
59 58 45
21 23 28
21 21 28
1.0 0.9 (0.6–1.3) 1.7 (1.0–2.8)
59 57 51
21 24 26
20 19 24
1.0 0.7 (0.4–1.2) 0.7 (0.4–1.2)
56 58 53
25 24 24
19 19 23
1.0 0.7 (0.4–1.0) 0.7 (1.4–1.1)
60 58 52
24 22 25
21 20 22
1.0 1.7 (1.1–2.7) 1.5 (0.9–2.6)
BMI > 30, smoker, does not meet daily vegetable requirements, does not meet daily fruit intake and not engaging in sufficient activity.
0.054 (NNFI is 0.9464, and the CFI is 0.9561). The indirect effect of SES on perceptions is 0.0740, with a standard error of 0.0103. The total effect of SES on perceptions is 0.2519, with a standard error of 0.0267. This equates to an indirect effect of SES on perceptions through FHL of 0.0740/0.2519 = 29.4%. The Sobel test for this is z = ab/sab = 0.0740/0.0103 = 7.1845 (p < 0.001). There is a statistically significant mediation effect of FHL on the path SES to health perceptions, with the mediation effect estimated to be 29.4% of the total effect. 4. Discussion and conclusion 4.1. Discussion Limited FHL has a strong independent association with the cumulative number of lifestyle risk factors, and was significantly associated with physical activity, fruit and vegetable consumption, and alcohol use. People with limited FHL were significantly less likely to perceive lifestyle factors as cancer risks. Strong associations were also seen between lifestyle risk factors and markers of socio-economic status, including age, gender, education and income. We hypothesized that health literacy would mediate the relationship between socio-economic status, perception of cancer risk related to lifestyle factors and lifestyle behaviors. A significant partial mediation effect was found, estimated at 29% of the variance of the total effect. Consistent with previous research, those with lifestyle risk factors were from relatively socially disadvantaged backgrounds and were the least likely to recognize that cancer risk may be modified by lifestyle changes [6,10,11]. Our results have extended previous research by pointing to the critical influence of health literacy on the relationships between
perception and behaviors. Low health literacy is more common among groups with less formal education but is prevalent across social groups, and our results suggest it may be low health literacy that is of importance rather than less education per se. In order to fully understand what is required to engage in healthy behavior considerable demands are made of individual’s literacy skills. Ideas such as a ‘‘serve’’ of vegetables and a ‘‘standard drink’’ of alcohol require the application of reading and numeracy skills in ways that are not necessarily intuitive. Furthermore, health information is often inaccessible because of poorly written materials or use of jargon that frequently defy ease of comprehension [33,34]. This situation is not necessarily improved in the new media [35]. Although a number of communication strategies have demonstrated efficacy when working with patients with limited health literacy [36], the use of these by healthcare professionals is not universal. Interventions to improve the health of people with low health literacy can be effective [37]. Thus, as stated by the Institute of Medicine, ‘‘the skills of health professionals, the media, and government and private sector agencies to provide health information in a manner appropriate to their audiences are as equally important as an individual’s skills [38]. How these messages are promoted will be important in avoiding negative attitudes toward people with cancer, as recognition increases of the possibilities of lifestyle factors in cancer prevention [39]. Avoiding the negative consequences of blame attribution while developing a positive message around lifestyle and cancer highlights the potential of a health literacy approach, where issues of numeracy and risk have a place along with reading and understanding. Differential access to information and reduced informationseeking behaviors with lower health literacy may also influence
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perception and behavior. Limited FHL has been associated with a tendency to avoid health-related information [40], which may be an important contributor to reduced awareness of risk factors for cancer. Although there are few studies examining the effect of health literacy on lifestyle behaviors and perceived risk in the context of cancer, low FHL has been associated with lower uptake of cancer screening such as Pap tests or colorectal cancer screening [41]. Cancer-related knowledge and perceived risk have also been reported to have a small effect on participation in cancer screening programmes [42]. However, few studies have examined the influence of low FHL on these relationships, and whether there are differences in screening behavior with differing cancer knowledge or perceived risk dependent on health literacy levels. Other variables such as self-efficacy have been used to predict health behaviors [40,42]. We did not measure participant’s selfefficacy in relation to lifestyle behaviors and cancer risk. In one study self-efficacy was a significant predictor of attendance for colonoscopy, although others have found no association with participation in other forms of colorectal cancer screening [43,44]. Further work could examine if measured self-efficacy adds value to our understanding of the pathways from FHL to perceptions of risk and behavior [40,45]. 4.2. Conclusion The NVS [26] like the TOFHLA [27] measures reading, interpretation skills and the ability to use numbers, rather than all aspects of health literacy, such as decision-making [14]. Its brevity permits use in clinical settings and population surveys with acceptable respondent burden. Our estimates of the prevalence and demographic correlates of limited FHL in the population are very similar to those obtained by other Australian surveys using much lengthier and more comprehensive instruments [23,24]. The cross-sectional and observational nature of the study means we are describing associations rather than causal relationships. Nevertheless, the use of SEM contributes to our understanding of the relationships between these variables. The self-report measures of diet and physical activity were simple, and more robust measures may provide better discrimination. However, it is likely that more precise measures of dietary quality and exercise are likely to increase the relationship between SES, FHL, perceptions of risk and behavior [46] suggesting any bias is likely minimizing the effect seen in this study. 4.3. Practice implications Developing effective risk communication messages requires understanding the role health literacy plays in the link between perceptions of risk and specific behaviors [6]. Taking health literacy into account in the design and dissemination of health information regarding healthy behaviors and cancer risks should be a priority of public health policymakers and clinical service designers [47]. Conflict of interest statement The authors declare that there are no conflicts of interest. References [1] McGinnis JM, Foege WH. Actual causes of death in the United States. J Am Med Assoc 1993;270:2207–12. [2] Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. J Amer Med Assoc 2004;291:1238–45. [3] Ford ES, Bergmann MM, Kro¨ger J, Schienkiewitz A, Weikert C, Boeing H. Healthy living is the best revenge: findings from the European Prospective Investigation into cancer and nutrition-potsdam study. Arch Intern Med 2009;169:1355–62.
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Patient Education and Counseling journal homepage: www.elsevier.com/locate/pateducou
Health literacy
Functional health literacy mediates the relationship between socio-economic status, perceptions and lifestyle behaviors related to cancer risk in an Australian population Robert J. Adams a, Cynthia Piantadosi a,*, Kerry Ettridge b, Caroline Miller c, Carlene Wilson b,d, Graeme Tucker e, Catherine L. Hill a a
Discipline of Medicine, The University of Adelaide, Adelaide, Australia Cancer Council SA, Adelaide, Australia c Department of Public Health, University of Adelaide, Adelaide, Australia d Flinders Centre for Innovation in Cancer, Flinders University, Adelaide, Australia e Health Statistics, SA Health Department, Adelaide, Australia b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 1 July 2012 Received in revised form 20 November 2012 Accepted 1 December 2012
Objective: To determine if functional health literacy (FHL) mediates the relationship between socioeconomic status, and perception of the risk of lifestyle behaviors for cancer. Methods: Cross-sectional, random population survey, 2824 people aged 15 years, September–October 2008, included newest vital sign measure of FHL. Results: Less than adequate FHL occurred in 45.1%. People who perceived behavioral factors (smoking, diet, obesity, alcohol, physical activity) to be not important, or did not know if they were important cancer risks, were more likely to have inadequate FHL. In a logistic regression model adjusted for age, gender, education, income, occupation, country of birth and area of residence, inadequate FHL was associated with 2–3 (OR = 1.9; 95% CI: 1.2–3.0) and 4 or more self-reported lifestyle risk factors (OR = 2.8; 95% CI: 1.6–5.0). In a structural equation model of the relationship of socio-economic status, perceptions of risk and behaviors there was significant mediation effect of FHL on the path from SES to health perceptions, estimated 29.4% of the total effect. Conclusion: A specific focus on the literacy demands made on individuals from health promotion and materials with a view to improving health communication is indicated. Practice implications: Health literacy is important for health promotion. ß 2012 Elsevier Ireland Ltd. All rights reserved.
Keywords: Health literacy Cancer risk Structural equation model
1. Introduction Lifestyle factors, particularly smoking, poor diet, physical inactivity and obesity, are the leading causes of premature death in Western societies [1,2], and have been estimated to account for a 78% variance in the risk of chronic diseases, including cancer [3]. Avoidance of weight gain and adequate physical activity has been shown to reduce the risk of a number of cancers, including breast and colon [4]. Achieving cancer prevention guideline recommendations for obesity, diet, physical activity, and alcohol consumption is associated with lower risk of death from cancer, CVD, and all causes in nonsmokers [5]. The combined impact of unhealthy
* Corresponding author at: The Health Observatory, Discipline of Medicine, The University of Adelaide, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia. Tel.: +61 8 82228818; fax: +61 8 82226042. E-mail address: [email protected] (C. Piantadosi). 0738-3991/$ – see front matter ß 2012 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pec.2012.12.001
behaviors appears multiplicative and affects multiple outcomes [3]. A number of studies across different countries have also shown that only small percentages of the population, usually less than 10%, exhibit all of the 4 healthy behaviors listed above [3,5]. A number of possible factors may contribute to the discrepancy between longstanding scientific knowledge and the health behavior of populations. Motivation to perform certain behaviors is thought to increase with the perception that a health risk exists [6], although the evidence for this proposition is inconsistent [6]. Although a number of studies have found that behavior is related to risk perception, this has not been a universal finding [7,8]. Social cognition models of health behavior also postulate that self-efficacy relating to personal capacity and perceptions of the efficacy of the action are important influences on behavior [9]. Awareness of a link between lifestyle behaviors and conditions such as cancer is a necessary precondition for these cognitive processes to occur [10]. Promoting public awareness of such a link becomes a necessary but not sufficient step toward lifestyle changes [10].
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Awareness of the contribution of lifestyle factors to cancer and heart disease has been reported to be low, particularly amongst younger people and those from more socio-economically deprived backgrounds [11]. This reduced level of awareness may be more pronounced for risk factors such as weight and exercise for cancer, than it is for heart disease [10]. An Australian survey reported that only around one-third of respondents recognize excess weight or inadequate physical activity as potential cancer risks [12]. Of the variables usually studied, education appears to be the most strongly related to awareness of risk [10], although the reasons for this remain speculative [10,13]. Health literacy is the range of skills and competencies that people develop to seek out, comprehend, evaluate, and use health information and concepts to make informed choices, reduce health risks, and increase quality of life [14]. Individuals with adequate literacy and numeracy may have inadequate health literacy [15], although research has established a strong, positive longitudinal relationship between intelligence and health [16]. The link between low or limited health literacy and adverse health outcomes is well established [14,15,17–19]. Health literacy has been investigated in acute and chronic health care [14,17,18,20,21] but less is known at a population level about the associations with health-linked behaviors such as dietary choices, smoking or activity levels [22]. Von Wagner et al. found an association between health literacy, smoking and vegetable intake in a UK population. However, the prevalence of marginal health literacy in this sample was much lower than that found in other international surveys [23,24], raising the possibility of bias. The way in which individuals process and contextualize information will vary, and few studies have examined the links between healthy behaviors, perceptions of the importance of lifestyle factors for cancer, and health literacy. This study examines the association of health literacy with self-reported lifestyle behaviors and perceptions of the risk of these behaviors for cancer in a representative Australian population sample. We hypothesized that FHL would mediate the relationship between socioeconomic status, and perception of the risk of lifestyle behaviors for cancer, with consequent effects on lifestyle behaviors. 2. Methods Data were obtained from the South Australian Health Omnibus Survey (SAHOS) between September and October 2008. Australian Bureau of Statistics collector districts (CD) were chosen with a probability proportional to their size, and within each a starting point was randomly selected and 10 households were sampled using a fixed skip interval. In a non-replacement sample, one interview was conducted per household. Where more than one person aged 15 or over resided in the household, the chosen respondent was the person who was last to have their birthday. The SAHOS methodology has been described in detail elsewhere [15,25]. 2.1. Functional health literacy Respondents completed the Newest Vial Sign (NVS) as a measure of FHL [26]. The NVS is based on a nutrition label from an ice cream container, which people are asked to read and then answer six questions about their interpretation and how they would use the information on the label. The NVS assesses reading, numeracy and document skills. Compared with the most commonly used functional health literacy instrument, the Test of Functional Health Literacy in Adults (TOFHLA) [27] the NVS has high sensitivity for detecting limited FHL [28], beyond that of education and age alone [26]. The NVS is scored out of 6, with a score of 4–6 almost always indicating adequate FHL (described as
207
‘‘adequate’’ in this article), 2–3 indicating the possibility of limited FHL (‘‘at-risk’’), and 0–1 indicating a high likelihood (50% or greater) of limited FHL (‘‘inadequate’’) [26,28]. Data collected included sex, education level, before-tax household income, occupation, country of birth and area of residence (metropolitan or regional), and self reported lifestyle choices; diet, exercise participation, smoking status and extent of alcohol consumption. Occupation was classified by the broad categories of occupation for most of an individual’s life according to the Australian and New Zealand Standard Classification of Occupations (ANZSCO) system of the Australian Bureau of Statistics [29]. Height and weight were measured by interviewers for calculation of body mass index. 2.2. Risk perceptions Respondents rated items related to lifestyle behaviors (smoking, alcohol, diet, weight, exercise) on how much of a health risk they perceived these to be in developing cancer for all Australians, on a scale from ‘not important’, ‘slightly/moderately important’ to ‘very/extremely important’ including ‘do not know/cannot answer’. Perceived cancer risk was measured by asking respondents, ‘‘What do you think the percentage chance is of developing cancer in your lifetime?’’, with 0% being no chance of getting cancer and 100% being will definitely get cancer. 2.3. Health behaviors For health behaviors, 4 items asking fruit and vegetable intake on a usual day and the previous day were used. Usual and actual consumption of fruit and vegetables were recorded and categorized according to whether or not participants ate at least 5 serves of vegetables and 2 serves of fruit a day [30]. Definitions of a ‘‘serve’’ were provided to respondents. Participants were asked to recall the number of times (sessions), duration (hours/minutes) and level (vigorous/moderate/walking) of physical activity they had undertaken in the previous week. Sufficient activity was defined by having engaged in at least 150 min/wk and 5+ sessions/ wk. At-risk alcohol consumption was recorded as more than 1 standard drink/day for females and more than 2 standard drinks/ day for males. Participants at low risk drank alcohol but did not exceed recommendations. 2.4. Statistical analysis Data were analyzed using SPSS version 17.0 (SPSS Inc., Chicago, USA) and weighted to the individual’s probability of selection and to the Australian Bureau of Statistics population estimates [31]. Bivariate associations of FHL with demographics and self reported behaviors were determined using the chi-squared test. Multivariable logistic regression models were developed for outcome variables of inadequate (NVS score 0–1) FHL compared with adequate (NVS score 4–6) FHL. Odds ratios for inadequate health literacy with 95% confidence intervals (CIs) are presented and adjusted where specified. Structural equation modeling was used to assess the mediating effect of FHL on the relationship between socio-economic status and perceptions of lifestyle behaviors and cancer risk. Fig. 1 sets out the pathway theoretical structure, where exogenous indicator or manifest variables (education, income, occupation, aboriginality) of the exogenous latent variable (socio-economic status) are shown in relation to the endogenous latent variables (FHL, perceptions of lifestyle behaviors and cancer risk) and the endogenous manifest variables of behavior (smoking, fruit and vegetable intake, physical activity, BMI). The assumption of the model is that the correlations observed between manifest variables
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208
HLSCORE
0.00
OVERWEIGHT
0.46
1.00 0.90
AB FHL 0.73
0.32 0.46 0.43
EDN
0.73
0.16 0.75
0.82
SES
0.18
PERC
INC
0.47
NEVEG
0.33
NEFRUIT
0.41
EXERCISE
0.37
VEGAC
0.85
FRUITAC
0.75
ACTIVITY
0.91
SMOKER
0.76
0.79
0.18 0.97
0.77
CIGS
0.32 0.67
0.29
0.39
BEH 0.50 0.55
-0.30
OCC
-0.49 -0.15
OBESE
0.98
Chi-Square=801.47, df=86, P-value=0.00000, RMSEA=0.054 Fig. 1. Structural equation modeling of the pathway of socio-economic status, perceptions of risk and lifestyle behaviors.
of the same latent factor are caused by the latent factor. The model was fit using polychoric correlations in LISREL Version 8.71. Unweighted least squares was the method used, as recommended for the analysis of ordinal indicators by Forero et al. [32]. 3. Results From 4614 households contacted there were 2824 respondents (61.2% participation rate), with the sociodemographic distribution corresponding to population estimates [31]. There were 1358 males (48.1%) and 2158 (76.4%) resided in the metropolitan area. We have previously reported that 24.1% were classified at-risk of limited FHL, and 21.0% had a high likelihood of inadequate FHL [15], increasing with age (inadequate, 65 years 49.7% vs. 25–44 years 11.4%). Less than adequate FHL was significantly associated with less education and lower income. Table 1 shows associations between lifestyle behaviors and demographic categories, from multivariable logistic regression models. Participants aged between 45 and 64 yrs were more likely than younger participants (<45 years) to be obese (OR = 1.4; 95% CI: 1.1–1.9) and undertake insufficient exercise (OR = 1.4; 95% CI: 1.1–1.7). Having an age of over 65 years was also associated with not enough physical activity (OR = 1.9; 95% CI: 1.3–2.7). Lower income earners and those with less education had increased odds of engaging in a number of health risk behaviors; smoking, not meeting adequate fruit requirements and not participating in sufficient physical activity (Table 1).
Table 2 shows the prevalence of perceptions of cancer risk for various lifestyle risk factors according to FHL category. The percentage of respondents who perceived these to be either not at all important, did not know, or were unable to answer if they were a cancer risk, varied between risk factors. Participants were more likely to perceive alcohol (14%) or lack of exercise (12%) as not a cancer risk than smoking (5%) or being overweight (9%). Across risk factors, people who perceived these factors to be not important or did not know if they were important were more likely to have inadequate health literacy, after adjusting for age, gender, income and education. Table 3 shows the prevalence of self-reported health behaviors in relation to health literacy categories, and adjusted odds-ratios for inadequate versus adequate FHL from multiple logistic regression models. Those reporting sufficient physical activity, actual recommended fruit intake or usual recommended vegetable intake, were more likely to have adequate FHL, as were at-risk alcohol consumers. In multivariable logistic regression models adjusted for age, gender, education, income, occupation category, country of birth and area of residence, inadequate health literacy was associated with having 2–3 (OR = 1.9; 95% CI: 1.2–3.0) and 4 or more reported risk factors (OR = 2.8; 95% CI: 1.6–5.0), compared with 0 or 1. Fig. 1 shows the results of structural equation modeling of the pathway from socio-economic status, to perceptions of risk and lifestyle behaviors. The model shows adequate specification with a Satorra–Bentler chi-square of 801.47 on 86 df, p < 0.000, RMSEA of
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Table 1 Factors associated with health behaviors, odds ratios and 95% confidence intervals.
Age (years) <45 45–64 65+ Sex Male Female Education University Trade Left school, Still study School >15 years old School <15 years old Still school Income ($) >100,000 50–100,000 20–49,000 <20,000
Risks (4–5)
BMI > 30
Smoking
Vegetables insufficient
Fruit insufficient
Exercise insufficient
1.0 0.8 (0.5–1.2) 0.2 (0.1–0.4)
1.0 1.4 (1.1–1.9) 0.8 (0.5–1.2)
1.0 0.6 (0.4–0.8) 0.1 (0–0.2)
1.0 0.6 (0.4–0.8) 0.5 (0.3–0.8)
1.0 0.6 (0.4–0.7( 0.2 (0.2–0.4)
1.0 1.4 (1.1–1.7) 1.9 (1.3–2.7)
1.0 0.5 (0.3–0.7)
1.0 1.2 (0.9–1.5)
1.0 0.6 (0.5–0.8)
1.0 0.5 (0.4–0.8)
1.0 0.6 (0.5–0.7)
1.0 1.0 (0.9–1.3)
1.0 2.5 2.5 3.8 4.8 –
1.0 1.7 1.5 1.5 2.0 0.4
1.0 2.1 2.3 2.3 4.4 0.8
1.0 0.8 0.6 1.5 1.6 0.9
1.0 1.2 0.9 1.7 1.8 1.1
1.0 1.4 1.1 1.5 1.6 0.5
(1.4–4.4) (1.0–6.4) (1.9–7.4) (2.0–11.6)
1.0 3.4 (2.0–5.9) 2.7 (1.4–5.1) 6.5 (2.7–15)
(1.2–2.5) (0.8–2.9) (1.0–2.3) (1.2–3.2) (0.1–1.8)
1.0 1.5 (1.1–2.1) 1.4 (1.0–2.1) 1.6 (1.0–2.5)
(1.4–3.2) (1.2–4.5) (1.5–3.6) (2.6–7.8) (0.3–2.4)
1.0 1.7 (1.2–2.5) 2.3 (1.5–3.5) 4.0 (2.4–6.7)
(0.5–1.3) (0.3–1.4) (0.8–2.7) (0.8–3.3) (0.2–3.6)
1.0 1.2 (0.8–1.8) 1.0 (0.6–1.7) 1.7 (0.8–3.4)
(0.9–1.5) (0.5–1.5) (1.2–2.4) (1.2–2.8) (0.5–2.1)
1.0 1.4 (1.1–1.8) 1.4 (1.0–2.0) 1.8 (1.2–2.8)
(1.0–1.8) (0.6–1.8) (1.1–2.0) (1.1–2.4) (0.3–1.1)
1.0 1.5 (1.1–1.9) 1.3 (1.0–1.8) 1.6 (1.1–2.4)
Numbers and percentages may not add to n or 100%, respectively, as data are weighted. *Inadequate: newest vital sign score, 0–1 yadequate: newest vital sign score, 4–6. **Any of the following: BMI > 30, smoker, does not meet vegetable intake requirements, does not meet fruit intake requirements, not sufficient activity.
Table 2 Prevalence of perceptions of importance of lifestyle risk factors for cancer within functional health literacy categories and odds ratios adjusted for age for risk of inadequatea compared with adequateb functional health literacy. FHL (%)
Passive smoking Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Smoking Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Being overweight Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Lack of exercise Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Not eating enough fruit Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Not eating enough vegetables Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Drinking alcohol Not at all important Slightly/moderate important Very/extremely important Do not know/cannot answer Chance of developing cancer over lifetime <50% 50% >50%
N
Adequate
At-risk
Inadequate
OR (95% CI)a
79 965 1718 55
24 61 54 13
33 20 26 24
43 18 20 64
1.0 0.2 (0.1–0.3) 0.2 (0.1–0.4) 2.3 (0.8–6.7)
47 172 2567 30
23 30 58 10
34 23 24 23
43 47 18 67
1.0 0.8 (0.3–2.0) 0.2 (0.1–0.4) 2.4 (0.5–10.6)
165 807 1762 83
38 57 57 22
23 23 25 23
39 20 18 55
1.0 0.4 (0.3–0.6) 0.4 (0.2–0.6) 2.2 (1.1–4.2)
264 1111 1368 73
38 58 58 23
24 24 25 21
38 19 17 54
1.0 0.4 (0.3–0.5) 0.4 (0.3–0.5) 1.7 (0.9–3.5)
213 1351 1151 103
36 60 56 22
24 24 24 23
40 16 20 54
1.0 0.3 (0.2 – 0.4) 0.3 (0.2 -, 0.4) 1.5 (0.8 – 2.9)
144 1191 1391 91
38 60 55 23
26 22 25 26
37 18 20 51
1.0 0.3 (0.2–0.5) 0.3 (0.2–0.5) 1.8 (0.9–3.7)
262 1371 1061 122
39 62 54 25
26 23 25 27
34 16 21 48
1.0 0.4 (0.3–0.5) 0.6 (0.4–0.9) 2.1 (1.2–3.7)
852 761 744
58 56 65
22 27 23
20 18 12
1.0 1.0 (0.7–1.3) 0.6 (0.5–0.9)
Numbers and percentages may not add to n or 100%, respectively, as data are weighted. a Inadequate: newest vital sign score, 0–1. b Adequate: newest vital sign score, 4–6.
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Table 3 Prevalence of lifestyle risk factors for cancer within functional health literacy categories and multiple logistic regression models for comparison of inadequate* with adequatey functional health literacy. FHL (%) N Cumulative risk factorsa 0–1 risk factors 457 1490 2–3 risk factors 4–5 risk factors 384 BMI (kg/m2) <25 1074 25–30 791 >30 559 Current smoker No 2271 Yes 553 Sufficient physical activity Sufficient activity 1148 Some activity 1127 No activity 539 Serves of vegetables usually consumed 5+ 308 1827 2–4 0–1 689 Serves of vegetables actually consumed yesterday 5+ 237 2–4 1500 0–1 1059 Serves of fruit usually consumed 3+ 521 2 826 0–1 1476 Serves of fruit actually consumed 3+ 475 2 774 0–1 1484 a
Adequate
At-risk
Inadequate
OR (95% CI)*
66 56 51
21 22 28
13 21 21
1.0 1.9 (1.2–3.0) 2.8 (1.6–5.0)
56 59 56
25 21 24
19 20 21
1.0 1.0 (0.8–1.3) 1.1 (0.8–1.4)
56 51
23 29
21 20
1.0 1.1 (0.8–1.4)
61 54 44
23 25 25
16 21 31
1.0 1.2 (0.8–1.7) 2.2 (1.5–3.2)
59 58 45
21 23 28
21 21 28
1.0 0.9 (0.6–1.3) 1.7 (1.0–2.8)
59 57 51
21 24 26
20 19 24
1.0 0.7 (0.4–1.2) 0.7 (0.4–1.2)
56 58 53
25 24 24
19 19 23
1.0 0.7 (0.4–1.0) 0.7 (1.4–1.1)
60 58 52
24 22 25
21 20 22
1.0 1.7 (1.1–2.7) 1.5 (0.9–2.6)
BMI > 30, smoker, does not meet daily vegetable requirements, does not meet daily fruit intake and not engaging in sufficient activity.
0.054 (NNFI is 0.9464, and the CFI is 0.9561). The indirect effect of SES on perceptions is 0.0740, with a standard error of 0.0103. The total effect of SES on perceptions is 0.2519, with a standard error of 0.0267. This equates to an indirect effect of SES on perceptions through FHL of 0.0740/0.2519 = 29.4%. The Sobel test for this is z = ab/sab = 0.0740/0.0103 = 7.1845 (p < 0.001). There is a statistically significant mediation effect of FHL on the path SES to health perceptions, with the mediation effect estimated to be 29.4% of the total effect. 4. Discussion and conclusion 4.1. Discussion Limited FHL has a strong independent association with the cumulative number of lifestyle risk factors, and was significantly associated with physical activity, fruit and vegetable consumption, and alcohol use. People with limited FHL were significantly less likely to perceive lifestyle factors as cancer risks. Strong associations were also seen between lifestyle risk factors and markers of socio-economic status, including age, gender, education and income. We hypothesized that health literacy would mediate the relationship between socio-economic status, perception of cancer risk related to lifestyle factors and lifestyle behaviors. A significant partial mediation effect was found, estimated at 29% of the variance of the total effect. Consistent with previous research, those with lifestyle risk factors were from relatively socially disadvantaged backgrounds and were the least likely to recognize that cancer risk may be modified by lifestyle changes [6,10,11]. Our results have extended previous research by pointing to the critical influence of health literacy on the relationships between
perception and behaviors. Low health literacy is more common among groups with less formal education but is prevalent across social groups, and our results suggest it may be low health literacy that is of importance rather than less education per se. In order to fully understand what is required to engage in healthy behavior considerable demands are made of individual’s literacy skills. Ideas such as a ‘‘serve’’ of vegetables and a ‘‘standard drink’’ of alcohol require the application of reading and numeracy skills in ways that are not necessarily intuitive. Furthermore, health information is often inaccessible because of poorly written materials or use of jargon that frequently defy ease of comprehension [33,34]. This situation is not necessarily improved in the new media [35]. Although a number of communication strategies have demonstrated efficacy when working with patients with limited health literacy [36], the use of these by healthcare professionals is not universal. Interventions to improve the health of people with low health literacy can be effective [37]. Thus, as stated by the Institute of Medicine, ‘‘the skills of health professionals, the media, and government and private sector agencies to provide health information in a manner appropriate to their audiences are as equally important as an individual’s skills [38]. How these messages are promoted will be important in avoiding negative attitudes toward people with cancer, as recognition increases of the possibilities of lifestyle factors in cancer prevention [39]. Avoiding the negative consequences of blame attribution while developing a positive message around lifestyle and cancer highlights the potential of a health literacy approach, where issues of numeracy and risk have a place along with reading and understanding. Differential access to information and reduced informationseeking behaviors with lower health literacy may also influence
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perception and behavior. Limited FHL has been associated with a tendency to avoid health-related information [40], which may be an important contributor to reduced awareness of risk factors for cancer. Although there are few studies examining the effect of health literacy on lifestyle behaviors and perceived risk in the context of cancer, low FHL has been associated with lower uptake of cancer screening such as Pap tests or colorectal cancer screening [41]. Cancer-related knowledge and perceived risk have also been reported to have a small effect on participation in cancer screening programmes [42]. However, few studies have examined the influence of low FHL on these relationships, and whether there are differences in screening behavior with differing cancer knowledge or perceived risk dependent on health literacy levels. Other variables such as self-efficacy have been used to predict health behaviors [40,42]. We did not measure participant’s selfefficacy in relation to lifestyle behaviors and cancer risk. In one study self-efficacy was a significant predictor of attendance for colonoscopy, although others have found no association with participation in other forms of colorectal cancer screening [43,44]. Further work could examine if measured self-efficacy adds value to our understanding of the pathways from FHL to perceptions of risk and behavior [40,45]. 4.2. Conclusion The NVS [26] like the TOFHLA [27] measures reading, interpretation skills and the ability to use numbers, rather than all aspects of health literacy, such as decision-making [14]. Its brevity permits use in clinical settings and population surveys with acceptable respondent burden. Our estimates of the prevalence and demographic correlates of limited FHL in the population are very similar to those obtained by other Australian surveys using much lengthier and more comprehensive instruments [23,24]. The cross-sectional and observational nature of the study means we are describing associations rather than causal relationships. Nevertheless, the use of SEM contributes to our understanding of the relationships between these variables. The self-report measures of diet and physical activity were simple, and more robust measures may provide better discrimination. However, it is likely that more precise measures of dietary quality and exercise are likely to increase the relationship between SES, FHL, perceptions of risk and behavior [46] suggesting any bias is likely minimizing the effect seen in this study. 4.3. Practice implications Developing effective risk communication messages requires understanding the role health literacy plays in the link between perceptions of risk and specific behaviors [6]. Taking health literacy into account in the design and dissemination of health information regarding healthy behaviors and cancer risks should be a priority of public health policymakers and clinical service designers [47]. Conflict of interest statement The authors declare that there are no conflicts of interest. References [1] McGinnis JM, Foege WH. Actual causes of death in the United States. J Am Med Assoc 1993;270:2207–12. [2] Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. J Amer Med Assoc 2004;291:1238–45. [3] Ford ES, Bergmann MM, Kro¨ger J, Schienkiewitz A, Weikert C, Boeing H. Healthy living is the best revenge: findings from the European Prospective Investigation into cancer and nutrition-potsdam study. Arch Intern Med 2009;169:1355–62.
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