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. Author manuscript; available in PMC: 2021 Jun 1.
Published in final edited form as: AIDS Care. 2019 May 28;32(6):694–700. doi: 10.1080/09540121.2019.1622641

Critical, and not functional, health literacy is associated with missed HIV clinic visits in adults and older adults living with HIV in the Deep South

Pariya L Fazeli a, Steven Paul Woods b, C Ann Gakumo a, Michael J Mugavero c, David E Vance a
PMCID: PMC6881544  NIHMSID: NIHMS1534520  PMID: 31137958

Abstract

Engagement in care is a key component of the HIV treatment cascade and is influenced by a host of biopsychosocial factors. Little is known about the association of health literacy with this impactful outcome in people living with HIV (PLWH). Ninety-five PLWH completed a comprehensive battery including neuropsychological assessments and health literacy measures covering several domains (i.e., numeracy, reading, self-efficacy, and ability to appraise and access health information). Engagement in care was operationalized as missed HIV clinic visits (i.e., the proportion of scheduled routine HIV care visits in the prior 24 months in which the participant did not attend and did not cancel or reschedule). The ability to appraise health information (as measured by the Newest Vital Sign [NVS]) was the only significant health literacy predictor of missed clinic visits. Hierarchical linear regression including demographic variables, mood, drug use, neurocognitive functioning, and all health literacy domain variables showed that age, depression, neurocognitive functioning, and NVS were all significant (p<0.05) independent correlates of missed clinic visits. The ability to appraise health information was a strong and independent predictor of missed clinic visits in PLWH, even in the context of other traditional correlates. Such measures may be useful in identifying PLWH with low health literacy who may be at risk for poorer engagement in care. Future research developing interventions targeting this dimension of health literacy are warranted.

Keywords: health literacy, visit adherence, retention in care

Introduction

The HIV treatment cascade (also referred to as continuum) depicts a relevant and impactful population-level spectrum in people living with HIV (PLWH)(Gardner, McLees, Steiner, Del Rio, & Burman, 2011) that consists of five interlinked stages: 1) screening and initial diagnosis; 2) linkage to care; 3) engagement in care; 4) prescription of and adherence to antiretroviral therapy (ART); and 5) achieving viral suppression. This framework facilitates identifying and understanding gaps in such areas in order to ultimately develop strategies to improve engagement in care, adherence, and sustained viral suppression to improve outcomes for PLWH. Of the 1.2 million PLWH in the US, it is estimated that about 70% are not virally suppressed, and of those, 66% are diagnosed but not engaged in care (Bradley et al., 2014). Engagement in care is crucial for consistently accessing ART and ultimately towards achieving viral suppression, which in addition to improving the health and longevity of PLWH can help to reduce HIV transmission (Cohen et al., 2011; Giordano et al., 2007; Mugavero et al., 2009; Park et al., 2007). Akin to other complex health behaviors and outcomes, engagement in HIV care is predicted by a host of biopsychosocial factors, including sociodemographics (e.g., race/ethnicity), psychiatric comorbidity (e.g., depression) (Zuniga, Yoo-Jeong, Dai, Guo, & Waldrop-Valverde, 2016), neurocognitive impairment (e.g., Jacks et al., 2015), social support (Waldrop-Valverde, Guo, Ownby, Rodriguez, & Jones, 2014), and substance use (Kipp et al., 2017).

Although the HIV treatment cascade is multi-determined, health literacy is a potentially important and modifiable contributor to gaps in one or successive steps. PLWH are at a greater risk for low levels of health literacy than the general population, with estimates of health literacy deficits as high as 50% in PLWH (Kalichman & Rompa, 2000; Rivero Mendez, Suarez-Perez, & Solis Baez, 2015). In the setting of HIV disease, low levels of health literacy are related to lower socioeconomic status (SES), less educational attainment, and race/ethnicity (e.g., Walker, Hong, Talavera, Verduzco, & Woods, 2018). African Americans and those residing in the Deep South are at particularly high risk for poor health literacy (Baumann, Phillips, & Arya, 2015). Lower health literacy is also associated with poorer health outcomes, including viremia (Kordovski et al., 2017; Rebeiro et al., 2018), poor self-efficacy for health behaviors, higher rates of neurocognitive impairment (Morgan et al., 2015), as well as treatment cascade outcomes such as non-adherence to ART (Kalichman, Ramachandran, & Catz, 1999; Osborn, Paasche-Orlow, Davis, & Wolf, 2007). While there is a wide body of research examining health literacy and its association with ART adherence, less is known about the association of health literacy with other treatment cascade outcomes, most notably engagement in care/visit adherence. In one HIV study, lower health literacy (i.e., numeracy, reading comprehension, and health-related appraisal) was associated with deficits in PLWH’s ability to access their electronic medical records, read and understand a message from their provider, and schedule a follow-up appointment (Woods et al., 2016).

Sørensen et al.’s (2012) theoretically driven integrated model of health literacy offers a comprehensive approach to examine the dimensions of health literacy. In this framework, health literacy is operationalized by two related, yet distinct, core components—functional and critical capacities (Sorensen et al., 2012). These functional components (knowledge, motivation, and competencies), underlie the critical competencies of accessing, understanding, appraising, and applying health information. While there is consistent evidence of both components predicting health outcomes and behaviors, these health literacy components are separable and may disparately affect health outcomes in HIV (e.g., Walker et al., 2018). Thus, understanding how both functional and critical components of health literacy affect HIV treatment cascade outcomes may ultimately inform targets for interventions.

The purpose of the current study was to gain a greater understanding of correlates of missed clinic visits in a sample of adults and older adults living with HIV in the Deep South, with a focus on dimensions of health literacy. The specific aim was to examine whether health literacy domains predicted missed clinic visits above and beyond demographic factors, mood, and cognitive functioning. We broadly used Sørensen’s integrated conceptual model of health literacy to operationalize our health literacy domains (Sorensen et al., 2012). For the current study, depressive symptoms, education level, and neurocognitive functioning represent the functional components of Sørensen’s model. Our health literacy measures capture both the functional (e.g., numeracy, reading) and critical competencies of this model, which are evaluated in the context of other clinicodemographic predictors of healthcare engagement (e.g., demographics, mood, and neurocognition).

Method

Participant and Procedure

One hundred and one PLWH were recruited from an urban University HIV/AIDS clinic located in the Southeastern US that treats approximately 3,500 active patients. A telephone screen was used to determine eligibility, which included that participants must be: 1) aged 40 or older; 2) HIV+ for at least one year; 3) free of major comorbidities that may affect neurocognitive functioning (e.g., schizophrenia, traumatic brain injury, Alzheimer’s disease); 4) without major visual or auditory impairments that would preclude neurocognitive testing; and 5) not currently undergoing chemo or radiation therapy. After completing the University Institutional Review Board (IRB) approved consent process and form, participants completed comprehensive assessments of neurocognitive functioning and health literacy. Of the 101 participants enrolled in the current study, four had missing data for clinic visit attendance, one had missing data on the Self-Efficacy composite, and one had missing urine toxicology data, thus the final N for analyses was 95 (see Table 1 for sample characteristics).

Table 1.

Demographic and Clinical Characteristics of the Study Sample (N=95)

Variable M (SD) or %
Demographics
 Age 51.59 (7.03)
 Gender (% men) 58%
 Education (years) 12.42 (1.85)
 Race (% Black) 83%
Clinical
 Estimated Duration of Infection 18.46 (8.35)
 Current CD4* 698.42 (386.53)
 Nadir CD4*(Median [IQR]) 144.00 (18.00-465.00)
 Plasma Viral Load (% Undetectable) * 70%
 Missed Clinic Visits (%) 15.84 (18.58)
 Center for Epidemiological Studies-Depression Scale (of 60) 17.90 (11.01)
 Positive Urine Drug Screen 41%
 Global Neurocognitive T-Score 45.00 (5.59)
 Global Neurocognitive Impairment 53%
Health Literacy Domains & Measures
 Self-Efficacy Composite 0.22 (0.16)
  SILS (of 5) 1.62 (0.95)
  3 Brief (of 4) 0.49 (0.67)
 Reading Composite 0.85 (0.15)
  REALM (of 66) 56.12 (10.96)
  TOFHLA Reading (of 50) 42.87 (7.97)
 Numeracy Composite 0.63 (0.19)
  Expanded Numeracy (of 7) 3.26 (1.75)
  TOFHLA Numeracy (of 17) 13.39 (2.99)
 Apply
  UBACC-T (of 19) 12.74 (3.72)
 Appraise
  Newest Vital Sign (of 6) 2.75 (1.61)
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Notes.

*

N for Current CD4=74, Nadir CD4=95, Plasma Viral Load=80; REALM=Rapid Estimate of Adult Literacy Measure; SILS=Single Item Literacy Screener; TOFHLA=Test of Functional Health Literacy in Adults; UBACC-T=Modified UCSD Brief Assessment of Capacity to Consent Test.

Measures

Missed clinic visits.

Data on missed clinic visits were extracted from clinic records. Specifically, this variable represents the proportion of scheduled routine HIV care visits in the prior 24 months in which the participant did not attend (Mugavero, Davila, Nevin, & Giordano, 2010). Consistent with the literature, cancellations and reschedules were not counted as missed visits. Thus, our missed visit variable was calculated by dividing the number of no showed visits (sample range 0 – 18) by the total number of visits scheduled (i.e., “no shows” plus completed visits) (sample range = 1 – 22), which yielded a range of 0 to 81.8% missed visits (median = 9.6%).

Health literacy measures

Knowledge & Understanding.

Three composites were created to assess knowledge and the ability to understand health information: Self-Efficacy, Reading, and Numeracy. For each composite an average was created by first converting each of the two underlying measures to the same scale (0-1) by dividing each individual test score by the total possible score, summing these values, then dividing this sum by two. The Self-Efficacy composite consisted of the Single Item Literacy Screener (SILS)(Morris, MacLean, Chew, & Littenberg, 2006) and the 3-Brief Health Literacy Screening Questions (3 Brief) (inter-correlation between measures rho=0.69, p<0.001) (Chew, Bradley, & Boyko, 2004). The Reading composite consisted of the Rapid Estimate of Adult Literacy Measure (REALM)(Davis et al., 1993) and the Test of Functional Health Literacy in Adults (TOFHLA) Reading test (Parker, Baker, Williams, & Nurss, 1995) (inter-correlation between measures rho=0.75, p<0.001). The numeracy composite consisted of the Expanded Numeracy (Lipkus, Samsa, & Rimer, 2001) and the TOFHLA numeracy (Parker et al., 1995) (inter-correlation between measures rho=0.57, p<0.001). Higher values reflect better performance for the Reading and Numeracy composites, while lower values reflect higher levels of self-efficacy. The correlation between the Numeracy composite with Reading and Self-Efficacy composites, was rho=0.71 and rho=−0.30, respectively. The correlation between the Reading and Self-Efficacy composites was rho=−0.42.

Appraise.

The ability to appraise health information was measured with the Newest Vital Sign (NVS)(Weiss et al., 2005). This 6-item measure includes an ice cream label stimulus to which the participant must answer literacy questions (e.g., ‘‘Pretend that you are allergic to the following substances: Penicillin, peanuts, latex gloves, and bee stings. Is it safe for you to eat this ice cream?’’) as well as numeracy items (e.g., “If you eat the entire container, how many calories will you eat?”). The total score, ranging from 0 to 6 was used in the current study.

Apply.

The Modified UCSD Brief Assessment for Capacity to Consent (UBACC-T) (Burton et al., 2012; Doyle et al., 2016) was used to assess health-related decision making abilities. In this task, participants are read a hypothetical vignette in which their friend who is terminally ill is diagnosed with pneumonia and must decide whether or not to treat the pneumonia with antibiotics, with side effects and other information presented. Participants are presented with 10 questions related to both assessing (e.g., “How will the choice affect Pat’s health?”) and comprehending (e.g., “Please describe some of the risks or discomforts of the treatment.”) the health information.

Covariates

Demographic variables (i.e., age, education, ethnicity/race, gender) were gathered via a self-report demographic questionnaire. Depressive symptoms were measured with the Center for Epidemiological Studies Depression Scale (CES-D)(Radloff, 1977). Active drug use was assessed via urine toxicology for the following substances: amphetamine, meth, cocaine, opiates, and THC. Finally, neurocognitive functioning was assessed via a comprehensive, seven-domain (i.e., verbal fluency, executive function, speed of information processing, learning, recall, working memory, motor) battery of clinical neurocognitive measures. Individual neurocognitive test scores were corrected for age, education, gender, and race/ethnicity when available and then used to calculate global neurocognitive T-scores (i.e., average of domain T scores), which were used in analyses. Detailed information on specific cognitive measures and normative data can be found elsewhere (Fazeli et al., 2017; Woods et al., 2004).

Statistical Analyses

We first examined bivariate associations between missed clinic visits and potential covariates, including demographics (i.e., age, education, race/ethnicity, and gender), depressive symptoms, active drug use (i.e., positive urine drug screen), and neurocognition (i.e., global cognitive T-scores) using Pearson’s correlations or t-tests (or their non-parametric counterparts as appropriate). All variables that were associated with missed clinic visits at the critical alpha level of .10 were then included as covariates in our primary model. Next, we conducted hierarchical linear regression with missed clinic visits as the criterion, with block one containing our selected demographic, depression, and neurocognitive covariates. In block two we included the health literacy variables (i.e., Reading composite, Numeracy composite, Self-Efficacy composite, NVS, and UBACC-T) and employed a critical alpha of .05 to define statistically significant associations. A posthoc power calculation revealed that with a critical alpha of 0.05 a sample size of 95 had 80% power to detect an effect size of at least 0.25. Analyses were conducted in JMP version 13.

Results

Proportion of missed clinic visits were not associated with gender, race/ethnicity, or neurocognitive functioning (all ps > .10), but were significantly correlated with fewer years of education, younger age, active drug use, and higher levels of depressive symptoms (ps < .05). Regarding health literacy indices, bivariate analyses showed that missed clinic visits were significantly associated with poorer NVS performance only (p = .02), while no associations emerged with the UBACC-T and the Reading, Numeracy, or Self-Efficacy Composites (ps > .10). Table 2 displays bivariate correlations between missed clinic visits and continuous variables.

Table 2.

Pairwise Correlations with Missed Clinic Visits

Variable rho p value
Age −0.21 0.04
Education −0.30 <0.01
Depression Score (CES-D) 0.26 0.01
Global Neurocognitive T-Score 0.07 0.52
Self-Efficacy Composite −0.12 0.25
Reading Composite −0.15 0.15
Numeracy Composite −0.11 0.29
UBACC-T −0.17 0.11
Newest Vital Sign −0.24 0.02
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Note. CES-D = Center for Epidemiological Studies- Depression Scale; UBACC-T = Modified UCSD Brief Assessment of Capacity to Consent Test.

Next we conducted a hierarchical linear regression and forced all demographic variables, mood, drug use, and neurocognitive functioning in block one of the regression and all health literacy domain variables into block 2. See Table 3 for complete regression results. Model 1 was statistically significant (F(7, 87)=4.53, p<0.01) as was Model 2 with all variables (F(12, 82)=4.26, p<0.01). Results showed that younger age, greater levels of depressive symptoms, poorer neurocognitive functioning, and poorer NVS performance were all significant (p<0.05) independent correlates of a greater proportion of missed clinic visits.

Table 3.

Hierarchical Regression Results Predicting Missed Clinic Visits From Clinicodemographic Factors and Health Literacy (N = 95)

Model 1 Model 2
Variable β Upper and Lower CI β Upper and Lower CI
Age −0.18 [−0.99]—[0.04] −0.22* [−1.14]—[−0.04]
Gender −0.07 [−10.03]—[4.98] −0.02 [−8.19]—[6.86]
Race −0.03 [−11.50]—[9.06] 0.10 [−7.33]—[17.14]
Education −0.24* [−4.58]—[−0.33] −0.20 [−4.39]—[0.40]
Depression 0.23* [0.07]—[0.70] 0.30** [0.20]—[0.81]
Active Drug Use 0.19* [0.04]—[14.54] 0.08 [−4.51]—[10.32]
Cognitive Function 0.18 [−0.04]—[1.20] 0.26* [0.10]—[1.65]
Self-Efficacy −0.20 [−45.11]—[0.83]
Reading 0.10 [−20.00]—[45.45]
Numeracy 0.08 [−22.78]—[38.37]
UBACC-T −0.04 [−1.50]—[1.14]
Newest Vital Sign −0.46** [−8.71]—[−1.86]
R2 0.27 0.36
Adjusted R2 0.21 0.27
F 4.53 3.92
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Note. Modified UCSD Brief Assessment for Capacity to Consent Test (UBACC-T).

*

p<0.05

**

p<0.001

Discussion

Given the importance of engagement in HIV care in the broader HIV treatment cascade, the goal of the current study was to understand the role of health literacy in retention in care by evaluating HIV clinic visit attendance in PLWH in the Deep South. Bivariate tests showed that while gender, race/ethnicity, and neurocognitive functioning were not associated with missed HIV clinic visits, those with fewer years of education, younger age, active drug use, and higher levels of depression had a higher rate of missed clinic visits. This is consistent with existing literature on predictors of engagement in care in HIV. Of the health literacy domains examined, we found that the ability to appraise health information (as measured by the NVS) was the strongest correlate of missed HIV clinic visits. Furthermore, this ability remained a strong and independent predictor of missed clinic visits even in the context of traditional correlates of this outcome, including demographics, mood, neurocognitive function, and substance use.

The most novel and robust finding of this study was that poorer ability to appraise health-related information was associated with a higher proportion of missed clinic visits in both the bivariate and multivariate analyses. These findings were associated with small, but significant effect sizes and suggest that persons with weak prose and document health literacy may have difficulty navigating the complex ecosystem of an HIV clinic. Specifically, those with poor NVS scores (<4) had nearly three times the odds of missing a clinic visit in the past 24 months (p=0.02; OR=2.80; CI = 1.17 – 6.66) than those with adequate NVS scores. Furthermore, those who missed a clinic visit were more likely (p=0.02; OR=3.22; CI = 1.11 – 9.32) to have detectable viral load in plasma, which demonstrates the importance of visit adherence in the HIV treatment cascade.

Although the NVS also measures numeracy and reading abilities, we believe that it is the prose and document aspects as well as abstract reasoning of this task that were driving the findings because the numeracy and reading composites comprised of traditional functional capacity-based measures were not significant predictors, at the bivariate or multivariable level. Thus, these results suggest that the unique and non-overlapping higher-order ability of appraisal of health-related information assessed via NVS is a particularly useful tool in predicting an important treatment cascade parameter. NVS is quick (approximately 3 minutes) and inexpensive to administer, making it suitable for healthcare settings. The NVS suffers less from the ceiling effects commonly seen in other literacy tests (e.g., Kordovski et al., 2017), which facilitates its ability to measure higher-levels of health literacy. The NVS has also shown evidence of reliability and validity in PLWH (Kordovski, Woods, Avci, Verduzco, & Morgan, 2017), as lower scores are associated with HAND and associated dependence in activities of daily living (Morgan et al., 2015), viremia and poor medication management (e.g., Kordovski et al. 2017), lower self-efficacy for healthcare provider interactions (e.g., Kordovski et al., 2017), and difficulties navigating electronic medical records (Woods et al., 2016).

Although a limitation of this study is its use of retrospective data for missed clinic visits, the data are nevertheless novel because few studies have examined the role of health literacy on engagement in HIV care. Furthermore, our operationalization using missed clinic visits is unique and meaningful, in that it not only represents engagement/retention in care, but goes further in capturing those who did not show up for their visit (and did not call to cancel or reschedule). Thus, using such an outcome allows for identifying and understanding this unique subset of PLWH. While there are likely factors not measured in the current study (e.g., prospective memory, apathy, social support) that may affect clinic attendance, our findings help to explain why perhaps some PLWH simply do not show up for visits, and therefore may have no intention of rescheduling. Such reasons could be forgetting the visit, being too depressed or impaired by substance use to attend the visit, and having poorer ability to appraise health-related information and thus not understanding the importance of attendance. Another limitation of this study is that the majority of the sample was African American, which likely limited our power to detect racial/ethnic differences in clinic attendance, especially given the relatively small sample size of the overall sample. While this is representative of the epidemiology of HIV/AIDS in the Deep South, and indeed African Americans are especially vulnerable to poor treatment cascade outcomes, nonetheless future work should examine the generalizability of the current findings to other vulnerable populations, including older adults and Hispanics (e.g., (Jacks et al., 2015).

In conclusion, these findings highlight that the health literacy skill of critically appraising health information, as measured by the NVS, was independently associated with an important clinical outcome in PLWH in the Deep South. Such brief and sensitive measures capturing this health literacy domain may be particularly useful for signaling PLWH that may be at risk for poorer retention in care. Future work should use longitudinal, prospective approaches to examine the role of health literacy earlier in the treatment cascade to understand how it may impact future engagement in care. Furthermore, future work is needed to develop interventions to improve the ability to appraise health information in PLWH which may ultimately improve retention in care and visit attendance. For example, psychoeducational approaches targeting appraisal of health information that incorporate mnemonic techniques grounded in applied cognitive psychology paradigms may offer a valuable avenue for intervention (Avci et al., 2017).

Acknowledgements, Funding, & Disclosures:

This study was funded by NIH/NIMH (1R01MH106366-01A1; Vance, P.I.) and NIH/NIA (R00AG048762; Fazeli, P.I.). The research was conducted via the NIH/NIA Edward Roybal Center (5P30AG02283814). HIV clinic data was extracted via the UAB site of the CFAR Network of Integrated Clinical Systems (CNICS) (P30AI027767; R24AI067039). Dr. Fazeli is also supported by L30AG045921. The authors have no conflicts of interest to disclose.

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