INTRODUCTION
Low health literacy (LHL) remains a formidable barrier to reducing gaps in health care quality and improving outcomes. Approximately one-third of the population (36%) is estimated to have basic or below basic health literacy,
1 defined as the “degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions”.
2 Individuals with LHL find it difficult to understand directions for taking medicine, to calculate a dose of an over-the-counter medication for a child or comprehend a consent form.
3,
4 LHL may also contribute to suboptimal care and outcomes through lower participation in screening programs,
5 reduced ability to act on and understand the advice of a health professional,
6 and limited ability to access and navigate the health care system.
7,
8
Although health literacy and general literacy may be linked, researchers contend that the complexity of the health care system, the medical jargon used by many providers,
9 and the exposure to novel health concepts (many times while under a great deal of stress), have the potential to negatively impact one’s health literacy skills, even among those with adequate literacy.
10 Therefore, the prevalence of limited literacy is even higher when considered within a health context.
11
Despite the availability of direct measures of health literacy including the
Rapid Estimate of Adults’ Literacy (REALM), the
Test of Functional Health Literacy in Adults (TOFHLA), and the
Newest Vital Sign.
12‐
15, the role of LHL in health outcomes remains largely unaddressed in public health and clinical practice. While these measures and other screening questions can be used by providers to identify individual patients at higher risk for LHL, administering such measures is logistically complex, and the measures themselves are limited largely to assessing reading ability and medical vocabulary and do not provide much, in any, information on other skills integral to health literacy.
16,
17 Such measures were also designed for individual, rather than community-level assessments, and provide little information about the level of health literacy within one’s patient population or community overall. Thus, there is a need for a predictive model that can use currently available data to help medical and public health practitioners, researchers, and health centers identify whether LHL may be a significant problem in the community or population they serve. The development of such a model may help set the stage for the development of community level measures, thus advancing action and facilitating efforts to target health literacy interventions in the practice setting and in areas of greatest need in the community at large.
Absent a predictive model, some providers use level of educational attainment or income as a proxy for health literacy, a practice that may lead to under- or over-estimation of the roles of each. While studies have identified individual characteristics associated with health literacy such as lower educational attainment, older age, lower income, and minority race or ethnicity
12,
13,
15,
18‐
22,
23 it is not clear whether using a combination of predictive factors in the form of a multivariable model is significantly more accurate than relying on a single variable.
Only recently have studies attempted to examine how these social factors work together
11,
21,
24,
25 and few, if any, of these models have included other constructs hypothesized to predict health literacy such as marital status, rurality,
26 language spoken in home,
1 and length of residence in the U.S. While the existing multivariable models demonstrate the utility, feasibility, and validity of such predictive models of health literacy, each has limitations. In Canada, for example, a multivariable model predicting health literacy included constructs such as daily reading at home and at work in addition to demographic characteristics.
11 The amount of daily reading at home was the strongest predictor of health literacy in this model, yet such measures are not readily available in administrative or census data, reducing the model’s utility to generate community-level estimates. Recent U.S. models of health literacy have also been developed using data from elderly and/or Medicare populations. However, these models may have limited applicability to the general population in that the association of health literacy with demographic characteristics may vary with the age of the population of interest. Some have argued, for example, that among the elderly, income is not a strong predictor of health literacy, as many are no longer employed.
27
Unfortunately, there have not been attempts to examine combinations of known predictors of LHL in a nationally representative sample of U.S. adults. Developing such a predictive model has significant potential to advance efforts to address action on poor quality care and outcomes related to health literacy by providing practitioners and public health officials with information on the average health literacy of the community they serve. Individuals and organizations serving communities with lower average health literacy may then target and implement a range of additional supports and strategies to increase individuals’ access to and understanding of health information.
As a first step toward overcoming the limitations of existing multivariable models, and to provide clinicians and health care providers with a means to estimate the health literacy of the community they serve, we developed two related models of health literacy that can be applied to widely available census data. Both used an identical set of demographic factors to predict health literacy as measured by the National Assessment of Adult Literacy (NAAL), a large, nationally-representative survey of adults in the United States. The first model estimates a mean health literacy score; the second estimates the probability of having health literacy skills in the “above basic” range (i.e., intermediate or proficient).
1 We also examine whether such models are better predictors of community health literacy as compared to commonly used proxies such as education or income.
DISCUSSION
Using a nationally representative sample, we developed two predictive models of health literacy: one estimating mean health literacy, and one estimating the probability of having health literacy skills in the ‘above basic’ (intermediate or proficient) range. Lower educational attainment, racial/ethnic minority status, older age, lower income, and recent immigration to the U.S. were associated with lower estimated health literacy. Individuals who were not married also had a lower health literacy, on average, although the association was much weaker (p < 0.05).
While the results of these models are consistent with previous work in this area, several findings merit further comment. First, despite controlling for a host of related characteristics, race and ethnicity were strongly associated with health literacy. The strength of this association was somewhat surprising, although it may be explained in part by unmeasured factors such as quality of education, which are correlated with both race/ethnicity and health literacy. Schools serving a high proportion of minority students, for example, are less likely to offer advanced placement courses and to have effective teachers in terms of years of experience and number of teachers with certifications in their primary teaching field.
36 Given that racial/ethnic minorities tend to cluster in both inner-cities and rural areas where the quality of education may be lower, this may help to explain the observed racial/ethnic differences in health literacy.
Somewhat surprising was the lack of association between language spoken in the home and health literacy. Results from our models suggest that recent immigration to the U.S., rather than language spoken at home per se, is a stronger predictor of health literacy. Note, however, that our models were based on NAAL data, which assesses health literacy in the English language. Therefore, one’s health literacy skills may be higher in their native language than estimated by our models.
Another unexpected finding was that no difference in health literacy was found between those living in rural and urban locations. Results, however, may be limited by the only available measure of rurality in the NAAL: a dichotomous measure of MSA. It is more likely that health literacy follows an inverse U-shaped curve, where health literacy is lower, on average, among individuals residing in rural or urban areas, with individuals in suburban areas having higher health literacy, on average. Finally, the multivariate model was a stronger predictor of health literacy and explained substantially more of the variance than commonly used health literacy proxies such educational attainment or income.
Several limitations to these models are worth noting. First, the NAAL assessed health literacy using only printed materials. As a result, our models focus on the ability to read materials to accomplish health related tasks. They do not predict oral language (speaking) or aural language (listening) skills, which have been cited as important components of health literacy.
17,
16 Consequently, predictions of health literacy based on our models will not fully capture a broader conceptualization of health literacy. Second, although characteristics in our model were selected based on existing research findings and theoretical justification, there are likely other unmeasured characteristics that contribute to health literacy that were not included in the model, such as quality of education and state of residence. To assess the potential for regional variation in the models we did conduct stratified analyses for the four US Census regions (north, south, east, west; results not shown). Models predicting the mean health literacy score for each region explained between 27% and 31% of the variance in health literacy scores. While there were minor regional differences in the models, none of these were statistically significant (F test = 0.48, p-value = 0.99 for linear models; F test = 0.28, p-value = 1.0 for probit models).
These predictive models of health literacy expand our understanding of factors that contribute to low health literacy in the general population, and allow us to estimate the average health literacy of communities. In so doing, individuals and organizations serving communities with lower average health literacy may then target and implement a range of additional supports and strategies to increase individuals’ access to and understanding of health information. This includes, for example, offering in-depth patient counseling with nursing staff or health educators, where important information related to diagnosis, treatment and follow-up can be discussed using plain language and in a less intimidating environment. A significant advantage of such models is that, when applied to census data and well-defined geographic areas such as census tracts, the average health literacy of a region can be mapped, providing visual insight into local areas or “hot spots” of lower average health literacy within the community, further helping promote effectively and appropriately targeted action to reduce disparities, poor quality care and poor outcomes related to limited health literacy.