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

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    Patient Education and Counseling 78 (2010) 372376


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    journa l homepage: www.e lseThere is a growing appreciation of the frequency andimportance of medication errors and adverse drug events inhealthcare [14]. Patients with low health literacy are likely athigher risk for such adverse outcomes related to misunderstand-ings at the doctorpatient, patientpharmacist, and/or patientmedication 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

    correlate with poor knowledge of asthma and metered-doseinhaler use by asthmatics [6] and poor knowledge about healtheffects of tobacco abuse in smokers [7]. Lowhealth literacy has alsobeen correlated with increased outpatient visits in rheumatoidarthritis patients [8], hospitalizations in older patients [9], andemergency roomvisits 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 inefcient to be incorporatedinto routine clinical practice. Also no standardized assessment toolto adequately represent medication knowledge exists [1719].Ideally, a simple surrogate screening tool could quickly identify at-risk patients for further assessment of theirmedication knowledge.

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

    Received 15 January 2009

    Received in revised form 27 April 2009

    Accepted 11 June 2009


    Medication knowledge


    Health literacy

    Patient education


    Medication adherence

    Underserved populations

    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. TheMedication 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 identied 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 meanMKS with a similar strength of

    association to that of REALM. This suggests that simpler cues to screen formedication knowledge decits

    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.

    * 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: (M.L. Plews-Ogan).

    0738-3991/$ see front matter 2009 Elsevier Ireland Ltd. All rights reserved.doi:10.1016/j.pec.2009.06.017The association of health literacy and somedication knowledge

    Jennifer R. Marks, Joel M. Schectman, Hunter Groni

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

    A R T I C L E I N F O

    Article history:

    A B S T R A C T

    Objective: To compare patieio-demographic factors with

    ger, Margaret L. Plews-Ogan *

    demographics and Rapid Estimate of Adult Literacy in Medicine (REALM)

    and Counseling

    vier .com/ locate /pateducou

  • 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 signicance 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 4560 (78th grade

    J.R. Marks et al. / Patient Education and Counseling 78 (2010) 372376 373assesses patients abilities to identify their medications, openmedication containers, provide correct dose, and report correcttiming of dose(s) [22]. This tool does not assess patientsknowledge 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 ofce 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 uently,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 andwhot study criteriawere asked if theywould participate in an anonymous quality-improvement surveyregarding medication comprehension. The participation rate was95%. If a given patients 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 rst part consisted of demographic items andself-reported health literacy level (no difculty reading medica-tion labels, some difculty reading medications, and cannotreadthe 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 ofmedication (missed syllables andmispronunciationswereaccepted), (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 drugs nameand dosage but not an indication or side effect resulted in a MKS of2 for that drug). Interviewers also observed and noted howpatientsidentied medicines (bottle label, color/shape/size, or both). TheMKS took from one to ve minutes to complete, depending on thenumber of medications. Following this, each participant wasadministered the REALM test, which took an additional threeminutes, on average. The REALMwas chosen due to its brevity andprior experience in numerous other health literacy studies utilizingthis tool as referenced above.

    The overallMKS for each participantwas calculated as themeanMKS 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 linearmodeling,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 (044, 4560, 6166 respectivelyreading 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%. Participantsidentied 89.0% of medications by the bottle label, 6.2% byappearance, and 4.7% by bothmethods. Overall, themeanMKSwas2.40 (SD = 0.78, range 0.24.0) with the distribution of scoresshown in Fig. 1. The distribution of each of the 4 components of theMKSwas highly skewed, but eachwas strongly associatedwith themean MKS as well as statistically signicantly associated with theREALM score (Table 2).

    By univariate analysis, meanMKSwas inversely associatedwithage 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 signicant.

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

    Table 1Subject characteristics.

    Mean Range Standard Deviation

    Age (years) 62 2790 12.6

    Last grade completed 9.8 018 3.2

    # prescription meds 5.9 115 3.1

    REALM 43 066 24

    # of subjects


    Male 47

    Female 53


    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 difculty reading medication labels 7

    Unable to read labels 3

  • and sex independently predicted meanMKSwith an overall modelR-square of 0.33 (p < .0001). When REALM was added to themodel, the R-square improved to 0.41with 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 themodel including REALM) and REALMwere highlycorrelated (Pearson correlation coefcient = 0.55, p < .0001).

    The discriminant ability of various thresholds for age, grade,REALM score, as well as sex for identifying patients withMKS belowthemedian (2.54 on scale of 04) for the sample is shown in Table 5.As suggestedby the regression analyses, the REALMscoreperformed

    Fig. 1. Distribution of MKS Scores.

    J.R. Marks et al. / Patient Education and Counseling 78 (2010) 372376374well 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

  • e Sco


    e m

    J.R. Marks et al. / Patient Education and Counseling 78 (2010) 372376 375explanatory power (asmeasured by R-square) ofMKS to that of theREALM. Adding the REALM score to the multivariate model

    Table 4Patient characteristics independently associated with mean Medication Knowledg

    Variable Model 1: without REALM (R-squ

    Parameter estimate

    Assistance with medications

    Sex 0.42Self-reported literacy

    Age 0.015Last grade completed 0.08


    a NS: not statistically signicant at .05 levelall NS terms were dropped from th

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

    # of subjects MKS below median (%)


    M 46 63

    F 52 38

    Age (years)

  • 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 signicant associations betweenmedication knowledge and several patient characteristics, similarto those that have been shown by other investigators as notedabove, lends support to our ndings.

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    J.R. Marks et al. / Patient Education and Counseling 78 (2010) 3723763763.2. Conclusion

    In conclusion, our ndings 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 lowmedication knowledge. Also, the combination of age,last grade completed, and sex was also a reasonable predictor oflow medication knowledge, with only a moderate added benetfrom the REALM in our sample. The association seen between thesepatient characteristics and medication knowledge may suggest asimpler and easier algorithm to identify patients whomaywarrantthe more specic 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 regardingmedication sideeffects may serve as a simple method for improving medicationsafety as well as adherence.


    Portions of this paper were presented at the 2005 AnnualMeeting of the Society of General Internal Medicine, New Orleans,Louisiana. Mr. Don Marineaus 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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    The association of health literacy and socio-demographic factors with medication knowledgeMethodsResultsDiscussion and conclusionDiscussionConclusion