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Page 1: The association of health literacy and socio-demographic factors with medication knowledge

Patient Education and Counseling 78 (2010) 372–376

The association of health literacy and socio-demographic factors withmedication knowledge

Jennifer R. Marks, Joel M. Schectman, Hunter Groninger, Margaret L. Plews-Ogan *

University of Virginia, Division of General Internal Medicine, VA, USA

A R T I C L E I N F O

Article history:

Received 15 January 2009

Received in revised form 27 April 2009

Accepted 11 June 2009

Keywords:

Medication knowledge

REALM

Health literacy

Patient education

Safety

Medication adherence

Underserved populations

A B S T R A C T

Objective: To compare patient demographics and Rapid Estimate of Adult Literacy in Medicine (REALM)

scores with respect to their ability to predict medication comprehension.

Methods: A survey was conducted of 100 patients presenting for follow-up at an academic primary care

clinic serving a low socio-economic status population. The Medication Knowledge Score (MKS) consisted

of knowledge of drug name, dose, indication, and a potential side effect for each of their medications and

then averaged. The REALM (Rapid Estimate of Adult Literacy in Medicine) was administered and socio-

demographic characteristics were recorded. The association of REALM score and patient characteristics

with MKS was evaluated by univariate and multivariable regression analysis.

Results: The subjects’ mean age was 62 with an average of 9.8 years of schooling and 5.9 prescription

medications. Participants identified a correct indication for 78.8% of their medications and correct

dosage for 93.4%. However, they could provide the name for only 55.8% of medications and a known side

effect for only 11.7%. On multivariate analysis without including REALM score, younger age (p = .01),

highest grade completed (p = .001), and female sex (p = .004) remained positively associated with MKS.

When the model included REALM, REALM (p < .0001), age (p = .001), and sex (p = .04) remained

independently associated with MKS.

Conclusion: REALM score predicts medication knowledge as assessed by the MKS. However, age, last

grade completed, and sex were also independently associated with mean MKS with a similar strength of

association to that of REALM. This suggests that simpler cues to screen for medication knowledge deficits

may also be useful. Since the MKS incorporates knowledge of medication indications and side effects, it

may also be useful for quality and safety purposes.

� 2009 Elsevier Ireland Ltd. All rights reserved.

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There is a growing appreciation of the frequency andimportance of medication errors and adverse drug events inhealthcare [1–4]. Patients with low health literacy are likely athigher risk for such adverse outcomes related to misunderstand-ings at the doctor–patient, patient–pharmacist, and/or patient–medication levels. Identifying patients with low health literacywould allow targeting of stronger and more appropriate medica-tion education efforts to promote better treatment outcomes.

The Rapid Estimate of Adult Literacy in Medicine (REALM) is ascreening tool assessing the ability to correctly read 66 commonlyused lay medical terms. It has been validated to help identifypatients at risk for poor health literacy [5]. Since its creation, it hasbeen applied to a variety of populations and clinical situations toexamine the relationship between health literacy and healthoutcomes. For example, lower REALM scores have been shown to

* Corresponding author at: Division of General Internal Medicine, University of

Virginia Health System, PO Box 800744, Charlottesville, VA 22908-0744, USA.

Tel.: +1 434 924 1685; fax: +1 434 924 1138.

E-mail address: [email protected] (M.L. Plews-Ogan).

0738-3991/$ – see front matter � 2009 Elsevier Ireland Ltd. All rights reserved.

doi:10.1016/j.pec.2009.06.017

correlate with poor knowledge of asthma and metered-doseinhaler use by asthmatics [6] and poor knowledge about healtheffects of tobacco abuse in smokers [7]. Low health literacy has alsobeen correlated with increased outpatient visits in rheumatoidarthritis patients [8], hospitalizations in older patients [9], andemergency room visits in congestive heart failure patients [10]. It isalso associated with poor glycemic control in diabetics [11],variable effectiveness in patients taking warfarin [12], and lowadherence to and understanding of medications [13,14].

Lower socio-economic status and older age appear to beindependent risk factors for medication errors and adverse drugevents [15,16]. Health literacy and/or medication knowledge maybe underlying factors. However, surveying actual medicationknowledge is too painstaking and inefficient to be incorporatedinto routine clinical practice. Also no standardized assessment toolto adequately represent medication knowledge exists [17–19].Ideally, a simple surrogate screening tool could quickly identify at-risk patients for further assessment of their medication knowledge.

Other studies seeking a surrogate screening tool have largelyused DRUGS, Drug Regimen Unassisted Grading Scale, which

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Table 1Subject characteristics.

Mean Range Standard Deviation

Age (years) 62 27–90 12.6

Last grade completed 9.8 0–18 3.2

# prescription meds 5.9 1–15 3.1

REALM 43 0–66 24

# of subjects

Sex

Male 47

Female 53

Race

Asian 1

Black 52

White 47

Assistance with medication from others

Yes 18

No 82

Self-reported health literacy

Able to read and understand medication labels 89

Some difficulty reading medication labels 7

Unable to read labels 3

J.R. Marks et al. / Patient Education and Counseling 78 (2010) 372–376 373

assesses patients’ abilities to identify their medications, openmedication containers, provide correct dose, and report correcttiming of dose(s) [22]. This tool does not assess patients’knowledge of medication indications or potential side effects asthe MKS does. The current study evaluates the association of theREALM as well as other socio-demographic characteristics withmedication knowledge including knowledge of medication indica-tions and potential side effects in an at-risk (low socio-economicstatus) patient population. Patients’ understanding of this infor-mation may have implications for medication adherence andsafety.

1. Methods

The study was conducted in the primary care internal medicineclinic at the University of Virginia. The clinic is the primaryambulatory training site for the internal medicine residencytraining program and serves a predominantly low socio-economicstatus population. Forty percent of the 7500 active patients have nohealth insurance and 45% have incomes below the federal povertyline. Patients are asked to bring all of their medications(prescription and non-prescription) to routine office visits.Adherence to this policy among continuity patients is above 50%.

To be included in the present study, each participant had tobring all medications to the appointment, speak English fluently,be at least 18 years old, and not have a diagnosis of dementia ordelirium. In December, 2004, patients waiting to be seen by theirprimary care physician and who fit study criteria were asked if theywould participate in an anonymous quality-improvement surveyregarding medication comprehension. The participation rate was95%. If a given patient’s medications were administered by anotherindividual (e.g., spouse and child), then that person completed thesurvey instead. The survey was orally administered by one of thestudy authors. The first part consisted of demographic items andself-reported health literacy level (‘‘no difficulty reading medica-tion labels’’, ‘‘some difficulty reading medications’’, and ‘‘cannotread’’—the latter two categories were combined in the analysis).For the second part, each participant was asked to pick up eachmedication sequentially and give the following information: (1)name of medication (missed syllables and mispronunciations wereaccepted), (2) dosage (either the milligram strength or the numberof tablets/capsules and the frequency), (3) indication (anycondition for which the medication could be used was accepted),and (4) any known side effect (adjudicated by referencing commonor serious side effects listed in the MicroMedex DrugPointSummary and by the authors). For each medication, a MedicationKnowledge Score (MKS) was created corresponding to the numberof correct answers of a possible four (e.g., knowing a drug’s nameand dosage but not an indication or side effect resulted in a MKS of2 for that drug). Interviewers also observed and noted how patientsidentified medicines (bottle label, color/shape/size, or both). TheMKS took from one to five minutes to complete, depending on thenumber of medications. Following this, each participant wasadministered the REALM test, which took an additional threeminutes, on average. The REALM was chosen due to its brevity andprior experience in numerous other health literacy studies utilizingthis tool as referenced above.

The overall MKS for each participant was calculated as the meanMKS for all their prescription and non-prescription medications.The association of each of the individual components of the MKSwith the overall MKS as well as REALM score of each subject wasassessed by non-parametric correlation analysis (using theSpearman rho). In a subject level analysis utilizing linear modeling,we evaluated the association of MKS with age, sex, schooling, self-reported literacy, and REALM score. The REALM was modeled asboth a continuous and tri-level (0–44, 45–60, 61–66 respectively

indicative of elementary, middle school, and high school levelhealth literacy) categorical variable without substantial impact onthe results. We therefore chose to model REALM as a continuousvariable for the principal analyses. The independent variables witha p < .10 by univariate analysis were entered into a multivariableregression model with MKS as the dependent variable. In themultivariate model, a p-value of less than .05 was required forstatistical significance with a stepwise regression procedureutilized to create the most parsimonious model. Models withand without REALM score as an independent variable werecompared. All analyses were performed with SAS v9.1.

2. Results

Table 1 provides socio-demographic characteristics of thepatient sample. One hundred subjects participated in the surveyinterview and complete data was obtained for 98 of them. Theiraverage age was 62 years with a mean of 9.8 years of formaleducation. They took an average of 5.9 prescription and non-prescription medications on a regular basis. The mean REALMscore was 43 with 41% of subjects scoring less than 45 (readinglevel at or below 6th grade), 18% scoring 45–60 (7–8th gradereading level), and 41% scoring above 60 (9th grade or higherreading). Of the medications taken by all participants, individualswere able to provide the correct drug name for only 55.8%, thecorrect dosage for 93.4%, the correct indication for 78.8%, and atleast one appropriate side effect for only 11.7%. Participantsidentified 89.0% of medications by the bottle label, 6.2% byappearance, and 4.7% by both methods. Overall, the mean MKS was2.40 (SD = 0.78, range 0.2–4.0) with the distribution of scoresshown in Fig. 1. The distribution of each of the 4 components of theMKS was highly skewed, but each was strongly associated with themean MKS as well as statistically significantly associated with theREALM score (Table 2).

By univariate analysis, mean MKS was inversely associated withage and directly associated with schooling, female sex, and REALMscore (Table 3). Administering medications for the patient (i.e.,being a surrogate survey participant) also was associated with alower mean MKS. The associations between mean MKS and totalnumber of medications, race, or self-reported literacy were notstatistically significant.

We initially performed a multivariate analysis without includ-ing the REALM score (Table 4). Patient age, last grade completed,

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Fig. 1. Distribution of MKS Scores.

J.R. Marks et al. / Patient Education and Counseling 78 (2010) 372–376374

and sex independently predicted mean MKS with an overall modelR-square of 0.33 (p < .0001). When REALM was added to themodel, the R-square improved to 0.41 with age, gender, and REALMremaining independently predictive. Though multi-collinearitywas clearly present, tolerance values for all independent variablesin the model were greater than 0.6. Last grade completed (whichdropped from the model including REALM) and REALM were highlycorrelated (Pearson correlation coefficient = 0.55, p < .0001).

The discriminant ability of various thresholds for age, grade,REALM score, as well as sex for identifying patients with MKS belowthe median (2.54 on scale of 0–4) for the sample is shown in Table 5.As suggested by the regression analyses, the REALM score performedwell in this regard but age, grade, and sex also had reasonable

Table 2The association of each of the four components of the Medication Knowledge Score

with their composite (mean MKS) and with the REALM score.

Mean MKS REALM score

ra p-Value ra p-Value

Name of medication 0.87 <.0001 0.69 <.0001

Dosage of medication 0.54 <.0001 0.22 .02

Purpose of medication 0.75 <.0001 0.42 <.0001

Potential side effect 0.54 <.0001 0.31 .001

a Non-parametric correlation coefficient and corresponding statistical signifi-

cance: ‘‘r’’ indicates Spearman rho.

Table 3Patient characteristics associated with mean Medication Knowledge Scor

Variable Parameter estimate

Age �0.02

Last grade completed 0.125

# medications 0.013

REALM 0.019

Sex F

M

Race Asian

Black

White

Assistance with medication Yes

No

Self-reported Yes

Literacya No

a Pts reporting ‘‘some difficulty reading medications’’ and ‘‘cannot read

discriminant power. In our sample, subjects with an eighth gradeeducation or lower had a threefold higher risk of a low MKS (belowmedian) than those with a high school (or higher) education, thoseover age 70 were 2.6 times more likely to have a low MKS thansubjects under 55 years of age, and men were 1.7 times more likelythan women to have an MKS below the median of 2.54.

3. Discussion and conclusion

3.1. Discussion

We found a wide variability in medication knowledge in thispopulation of lower socio-economic status. The REALM score wasthe strongest predictor of medication knowledge (as assessed byMKS) among the variables we examined. This finding notsurprisingly indicates that health literacy plays a major role incomprehending the names, dosages, indications, and potential sideeffects of one’s medications, prescription or otherwise. Ouranalysis also shows that four other parameters – assistance inmedication administration, age, educational attainment, and sex –also are predictive of mean MKS with the latter 3 independentlypredictive in a multivariable regression model. Interestingly,patient self-reporting of health literacy was not predictive ofMKS, although there was a trend toward significance, but samplesize was small. The combination of these three demographicfactors, age, gender, and highest grade level, had similar

e by univariable analyses.

p-Value R-square

.0009 0.11

<.0001 0.24

.57 –

<.0001 0.33a

Mean MKS p-Value R-square

2.64 .0006 0.11

2.19

2.00 .64 –

2.34

2.47

1.90 .005 0.08

2.49

2.43 .11 –

2.00

’’ were grouped into ‘‘no’’ category.

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Table 4Patient characteristics independently associated with mean Medication Knowledge Score by multivariable analyses (one excluding REALM score).

Variable Model 1: without REALM (R-square = 0.33) Model 2: with REALM (R-square = 0.41)

Parameter estimate p-Value Parameter estimate p-Value

Assistance with medications – NSa – NSa

Sex �0.42 .004 �0.29 .04

Self-reported literacy – NSa – NSa

Age �0.015 .01 �0.02 .001

Last grade completed 0.08 .001 – NSa

REALM – – .015 <.0001

a NS: not statistically significant at .05 level—all NS terms were dropped from the model.

Table 5Subject characteristics of those with mean MKS below median value of 2.54.

# of subjects MKS below median (%)

Sex

M 46 63

F 52 38

Age (years)

<55 28 29

55–69 43 49

�70 27 74

Last grade completed

�8th 29 79

9–11th 34 50

�12th 35 26

REALM score

<45 40 80

45–60 18 56

>60 40 18

J.R. Marks et al. / Patient Education and Counseling 78 (2010) 372–376 375

explanatory power (as measured by R-square) of MKS to that of theREALM. Adding the REALM score to the multivariate modelincreases its explanatory power, but the increase is modest (R-square = 0.41 vs. 0.33). This finding suggests two things. First,when taken together, age, sex, and education may be as effective atscreening for medication knowledge deficits as the REALM alone.Second, while including the REALM test can augment prediction ofmean MKS for a given patient, the gain may not be substantialenough to warrant the effort entailed in utilizing the REALM as auniversal screening tool for medication knowledge. However, theuse of patient age, sex, and education level as predictive filters mayhelp to determine high-risk subgroups that might warrant furtherspecific screening with REALM or other measures.

The results of our study are similar to that of another study byKripalani et al. [22]. In a general medical clinic of a large urbanteaching hospital serving an indigent largely African-Americanpopulation, they found that ‘Medication Management Capacity’was associated with health literacy as assessed by the REALM aswell as with educational attainment and patient age (inversely),though not gender. In contrast, their assessment did not includemedication indication or side effect knowledge. It also excludedthose patients who received assistance with their medications. Anumber of other studies in various populations have alsodemonstrated associations between patient demographic factorsand ability to manage medications. Edelberg et al. [23] similarlyfound an inverse association between age and medicationmanagement capacity among geriatric subjects living in aretirement community. Another study by Davis et al. [24] utilizinga standardized medication set (i.e., not the patients’ ownmedications) to assess the ability to understand medication labelsalso found a strong association with REALM score and similartrends with respect to sex and educational attainment to those wereport (their study was limited by very few patients with a lessthan high school education). Lo et al. [19] also found a strong

association between educational background and the ability tounderstand medication labels among parents of pediatric patients.

In our study to assess medication knowledge we developed aMedication Knowledge Score. Previous studies of medicationknowledge have utilized questionnaires aimed at exploringvarious epistemological components (e.g., drug name, indication,side effect profile and pill appearance) without a standardizedsurvey set [9,14,25,26]. Similarly, our 4-component MKS aimed toassess medication knowledge practically and efficiently. Vis-a-viscontent validity, we felt that knowledge of a drug’s name, dosage,and indication were essential components and have been used byprior investigators.

We also believe that patient awareness of potential drug sideeffects is important to medication knowledge by providing awindow on another dimension of medication safety. Indeed, intheir study of adverse drug events in ambulatory care, Gandhi et al.[3] found an overall event rate of 27 events per 100 patients.Twenty-eight percent of these events were judged ameliorable,due to either the patient’s failure to report side effects to theirdoctor or the physician’s failure to respond to medication-relatedadverse effects. Although we had no way of knowing if a patienthad previously been made aware of any side effects we found thatknowledge in this area was quite low.

In addition to side effect knowledge having patient safetyimplications, this knowledge may also affect medication adher-ence. Hill et al. [28] found that patients who received extracounseling regarding their medications including side effects weremore likely to be compliant than those who did not receive sucheducation. The pervasive lack of patient side effect knowledge, asillustrated in our study’s findings as well as those of Hill, underlinethe importance of patient education in this area for purposes ofboth improved safety as well as possibly adherence. In terms ofadherence more recently Gazmararian et al. also found arelationship between health literacy and refill adherence amongstMedicare enrollees [29].

Construct validity for this scale is indicated by close associationwith other variables known to be associated with low medicationknowledge such as low health literacy, education, and advancingage. The fact that the MKS assumed a fairly normal distribution is auseful feature if replicated in other populations where concernexists about medication knowledge. Utilizing a standard scale thatmeasures the important parameters of medication knowledge willbe important in future studies in this area. The MKS may serve thispurpose, but our results require replication in other settings as wellas more socio-economically diverse populations. In addition tobeing a small study conducted in one relatively indigent setting,there are other limitations that may hinder the generalizability ofour findings. First, we utilized a convenience sample of patientswho all spoke English and had brought their medications to clinic.Though the results cannot be extrapolated to non-English speakersor those that did not bring their medications, our participation rate(95%) was very high and our clinic has good adherence (>50%) to astanding policy that patients bring their medications to appoint-

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ments. Furthermore, we used a tool to assess medication knowl-edge that was developed for this study and has not been used byother researchers. However, it has substantial overlap withcomponents of other tools, and we feel it demonstrated gooddistributional qualities as well as reasonable content and constructvalidity.

We view our results as preliminary given the small samplingframe and other methodological limitations. Nevertheless, the factthat we found strong and highly significant associations betweenmedication knowledge and several patient characteristics, similarto those that have been shown by other investigators as notedabove, lends support to our findings.

3.2. Conclusion

In conclusion, our findings suggest that the REALM is aneffective predictor of low medication knowledge as measured bythe MKS and could be used as a screening tool to identify patientsat risk for low medication knowledge. Also, the combination of age,last grade completed, and sex was also a reasonable predictor oflow medication knowledge, with only a moderate added benefitfrom the REALM in our sample. The association seen between thesepatient characteristics and medication knowledge may suggest asimpler and easier algorithm to identify patients who may warrantthe more specific health literacy testing, such as with REALM. Alsogiven the high prevalence of adverse drug events and frequentconcerns regarding medication adherence in ambulatory care,improved patient education particularly regarding medication sideeffects may serve as a simple method for improving medicationsafety as well as adherence.

Acknowledgements

Portions of this paper were presented at the 2005 AnnualMeeting of the Society of General Internal Medicine, New Orleans,Louisiana. Mr. Don Marineau’s work is supported by a grant(5D54HP00040-05-00: Academic Administrative Units in PrimaryCare, Department of Health and Human Services). All investigativework, data assimilation, and writing by other investigators wereaccomplished without funding or grant support.

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