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Association of Individual and Systemic Barriers to Optimal Medical Care in People Living With HIV/AIDS in Miami-Dade County

Wawrzyniak, Andrew J. PhD*; Rodríguez, Allan E. MD; Falcon, Anthony E. BA, PA-S; Chakrabarti, Anindita MD; Parra, Alexa BS-S; Park, Jane MD; Mercogliano, Kathleen RN; Villamizar, Kira MPH§; Kolber, Michael A. PhD, MD; Feaster, Daniel J. PhD; Metsch, Lisa R. PhD

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: May 1, 2015 - Volume 69 - Issue - p S63-S72
doi: 10.1097/QAI.0000000000000572
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Abstract

INTRODUCTION

Nonengagement in HIV medical care has been associated with deleterious clinical outcomes including delayed antiretroviral therapy initiation, virologic failure, and mortality.1–3 Barriers to the receipt of regular HIV medical care at both the individual and the systemic level have been shown to impede progress along the Continuum of HIV Care4 including the achievement of viral suppression.1,5–9 According to the latest US Antiretroviral Guidelines for Adults and Adolescents, HIV-infected patients should be monitored quarterly, approximately every 3–4 months during the first 2 years of antiretroviral therapy and, in some instances, after 2 years, monitoring can be extended to every 6 months with sustained suppression.10 The HIV/AIDS AIDS Bureau Core Measures developed by HRSA define a regular attender as those with at least 1 medical visit in each 6-month period of a 24-month measurement period with a minimum of 60 days between visits.11 The National HIV/AIDS Strategy (NHAS) and the Institute of Medicine define retention in care as documentation of ≥2 care visits ≥3 months apart during the most recent year.12,13 One of the major goals in the US NHAS is to increase access and retention in primary HIV care to improve HIV outcomes.12

Previous research on barriers to retention to care have predominantly focused on barriers at the individual level that stems from both socioeconomic and psychosocial factors.14 Socioeconomic factors that also reflect individuals' life circumstances15 including food insecurity and poor dietary factors,16–19 low health literacy,20,21 low education,22–24 homelessness,15,25,26 and social stigma27,28 have been identified as important barriers to receipt of adequate HIV care. Additionally, at the individual level, the negative impact of psychosocial barriers on HIV-related health outcomes have been evident in previous research, including factors as substance use,25,29–32 mental illness,31 anxiety,17,33 perceived stress,22,33 depression,17,22,25,33 hopelessness,22 avoidant coping,22,27 personality traits,34 low self-efficacy,35,36 low self-worth,37 lack of social support,27,35,38–40 lack of acceptance of seropositivity,37 and low patient satisfaction.41

On the systemic level, health care costs,37,42 lack of transportation,43–46 lack of child care,45 unstable housing,47,48 and fragmented HIV care services43,44,49,50 have been found to be associated with lower retention in HIV care. Additionally, within the realm of systemic barriers, provider and agency barriers to care can adversely impact health outcomes in HIV patients, such as physicians' communication skills, scheduling systems, clinic access issues,51 and patient trust in their physicians.52,53

A syndemic is defined as the synergistic interaction of 2 or more diseases and the subsequent burden on diseases states.54 In the HIV literature, having multiple negative psychosocial and socioeconomic status issues has been associated with an increased odds of being HIV-infected.55 Syndemics have been more recently applied to the additive nature of psychosocial and socioeconomic health problems and their impact on health outcomes; psychosocial factors treated as a syndemic has served as predictors for detrimental outcomes along the HIV care continuum.56 The existing literature has examined the prospective effects of individual barriers on clinic attendance and viral suppression. Using the syndemic framework is an innovative way of analyzing the association of individual and systemic barriers in HIV-related health outcomes.

Intervention strategies that are effective in promoting retention in care and improving HIV outcomes have recently being identified.57,58 However, which interventions are the most effective, sustainable, and applicable to real world setting is still an evolving field.14 Obtaining a better understanding of the multiple barriers to retention in HIV care can inform which intervention strategies should be directed at individual patients or the system of care to improve retention and reengagement in care resulting in improved outcomes.

Within this context, our study sought to examine how individual and systemic barriers are associated with retention in care based on previous attendance records and virologic suppression among HIV-infected patients from the largest HIV clinic in Miami-Dade County.

METHODS

Participants and Design

Participants were recruited from the University of Miami/Jackson Memorial Medical Center (UM/JMMC) Adult HIV Outpatient Clinic, in Miami, FL; human subjects approval was obtained from the University of Miami Institutional Review Board and the Jackson Health System Office of Research.

As guidelines for retention in care are based primarily on attendance, attendance history was used to classify patients into different groups. To identify patients' attendance to their regularly scheduled HIV care appointments, a database of all patients (n = 3093) who attended the UMH/JMMC Adult HIV Outpatient Clinic was compiled from existing electronic medical records from September 2012 to September 2013. Based on the latest US Antiretroviral Guidelines for Adults and Adolescents monitoring recommendations,10 the NHAS,12 and the Institute of Medicine13 definition of retention in care, patients were categorized as regular (n = 1145), irregular (n = 1208), or nonattenders (n= 487). Based on historical attendance over the previous 12-month span before the day the interview took place, patients were classified as being regular attenders (patients who attended at least 1 appointment in at least 3 of 4 quarters in the previous year which reflects patients who came to their regularly scheduled primary HIV care appointment every 3–6 months, for a total of 3 or 4 appointments per year), irregular attenders (patients who attended at least 1 appointment in 1 or 2 quarters, reflecting those who had, at the most, at least a 6-month span between 2 consecutive primary HIV care appointments), or nonattenders (patients who did not attend a primary HIV care appointment in the previous year).

Procedure

Over the course of the study, from September 2013 to September 2014, patients classified in 1 of the 3 attendance groups who came to a clinic visit were approached by study staff and were asked to participate in the study; all potential participants who spoke and understood English were randomly invited to participate during their clinic visit when study staff was available. Patients were excluded if they were cognitively impaired or unable to understand the consent process; patients without a year of attendance history were also excluded from participating. Nonattenders who did not have an appointment or show up to a scheduled appointment were contacted through phone and were offered the following options: a clinic appointment, a phone interview, or a home visit to have study personnel administer the survey in person. Participants gave full informed consent to participate in the study and were offered a $10 honorarium for their time.

Materials

Barriers Impacting HIV Medical Care

To determine factors associated with clinic attendance, a questionnaire was administered by an interviewer from the study staff. On an individual level, the questionnaire included items that measured depressive symptoms (Center for Epidemiologic Studies Depressive Scale; CES-D),59 stability and predictability in life (Life Chaos Scale),60 perceived health status (12-Item Short Form Health Survey; SF-12),61 quality of life (Medical Outcomes Study HIV Health Survey; MOS-HIV),62 social support (MOS Social Support Survey),63 medication adherence (ACTG Adherence Questionnaire),64 food insecurity (Household Food Insecurity Access Scale; HFIAS),65 drug use (Addiction Severity Index Lite; ASI-Lite),66 and caregiving responsibilities. On a systemic level, barriers that were most relevant to and may have had the largest impact on this patient population were identified by study staff. Specifically, participants were asked about their relationship with their physician, clarity of medical instructions, and the following barriers to care in the past 12 months43,44,50: payment concerns, lack of a telephone, responsiveness of scheduling staff, convenient appointment times, transportation issues, and language spoken with their physician. These systemic-level barriers were considered as environmental factors that were measured on an individual level; patients endorsing these barriers were reporting their symptoms of systemic forces. Demographics and socioeconomic factors including ethnicity, gender, race, sexual orientation, education, and income were measured.

Existence of a Barrier

For each of the measures, we categorized the individual patient as either having a particular barrier or not. Thus, for continuous measures, in addition to mean score, we dichotomized the measure and for categorical measures with more than 1 category, we collapsed into a dichotomy.

Specifically, for the individual-level barriers, any reported substance misuse (cocaine, crack, heroin, crystal methamphetamine, speed, painkillers) in the past 3 months was considered as having a substance misuse barrier. For the continuous individual-level measures, the mean score for each participant was calculated. For depressive symptoms, patients whose mean score was above 2, indicating depressive symptoms were occurring more than “Some or a little of the time” (CES-D),59 were considered as having the depressive symptoms barrier. Patients whose mean score on the life chaos scale60 was greater than 2, which indicated that the patient either somewhat or strongly agreed with statements such as “My life is unstable” and “My daily activities from week to week are unpredictable” were considered as having the chaos barrier. For quality of life, those endorsing 3 or greater, indicating that their quality of life was at least half bad (”Good and bad parts—about equal”) were classified as having a quality of life barrier.

Additionally, at the system level, endorsing the transportation barrier as yes was counted for each patient. For the continuous system-level barriers, for the relationship with their physician, patients with a mean score less than 3 (“Somewhat agree”) were considered to have a relationship with physician barrier. Patients whose mean scores on the medical information clarity were greater than 2 (“Sometimes” having problems understanding) were considered to have the clarity of medical instructions barrier.

HIV-1 Viral Load and CD4 Count

HIV-1 viral loads and CD4 counts were obtained for each patient who consented to participate in the study from existing medical records for up to 2 years before starting the study. The most recent HIV-1 viral loads and CD4 counts were extracted from the medical record; HIV-1 viral loads were log transformed before analysis in that viral loads were not normally distributed but actual viral load averages are reported below. Patients were classified as undetectable if their viral loads were below the lower limit of the assay (<20 copies per milliliter).

Analytic Plan

Retention in Care and Virologic Suppression

In examining how individual and systemic barriers are associated with retention in care and virologic suppression among HIV-infected outpatients, differences in barriers and health outcomes were compared between the 3 attendance groups using an analysis of variance for continuous measures and the χ2 test categorical outcomes. Tests were considered statistically significant if the associated P < 0.05. The assumption of equality of variances was tested using the Brown–Forsythe test statistic and, when violated, the Welch F-test was used. Student t tests were used to compare those who were virologically suppressed to those who were not suppressed for continuous outcome measures; χ2 tests were used to compare proportions of patients in each group who endorsed a particular barrier

Syndemic Analyses

Those barriers whose frequencies or means differed between attendance groups were used in the syndemic analysis. Barriers were examined by developing a count score of the number of individual-level and systemic barriers each client had.

Poisson regression one-way analyses of covariance were used to compare the total number of barriers between both attendance groups and viral suppression. These analyses of covariance models controlled for age, race, gender, education, and income. Contrasts and associated χ2 statistics were calculated to compare the number of barriers between both attendance groups and viral suppression. Finally, logistic regression was used to determine the odds ratio (OR) of the number of barriers on viral suppression controlling for age, race, gender, education, and income.

RESULTS

Sample Characteristics

A total of 444 patients completed the study interview between October 2013 and September 2014. The sample was comprised 187 regular attenders, 191 irregular attenders, and 66 nonattenders. Demographic characteristics of the sample are shown in Table 1. The median age of the sample was 51 years; 80.0% were black and 16.6% were Hispanic or Latino. Just over 40% had not finished high school, 84.0% identified as heterosexual, 60% earned less than $10,000 annually, and nearly 60% of patients had a history of incarceration.

TABLE 1
TABLE 1:
Participant Characteristics (n = 444)

Clinical Characteristics

Using the most recent laboratory results from electronic records, undetectable patients were identified. Laboratory results from within the previous 2 years were available for 427 of the 444 patients [96.2% overall; 185 regular attenders (98.9%), 183 irregular attenders (95.8%), 59 nonattenders (89.4%)]. The median number of days that had elapsed between the most recent laboratory results was 34 days and varied between groups (regular: 34 days; irregular: 73 days; and nonattenders: 101 days).

The clinical characteristics of the sample are shown in Table 1. Overall, 65.1% reported that less than 3 months elapsed between their HIV diagnosis and the time to their first HIV care appointment; 41.4% were told that they were diagnosed with AIDS. Just over two-thirds (75.4%) reported that they were told and were undetectable at some point; for the most recent viral loads, only 38.3% were undetectable. A greater proportion of regular and irregular attenders were undetectable compared with nonattenders (P < 0.001); there was not a significant difference in the proportion of those who were undetectable between regular and irregular attenders. Additionally, nonattenders had significantly higher viral loads (54,576 ± 120,457.2) compared with regular attenders (11,450.08 ± 46,176.5) and irregular attenders (21,541.55 ± 107,183.1; P < 0.001); irregular attenders had higher viral loads than did regular attenders (P = 0.045). Furthermore, nonattenders had significantly lower CD4 counts (375.50 ± 291.0) compared with regular (521.98 ± 337.1) and irregular attenders (489.07 ± 291.0; P = 0.006); regular and irregular attenders did not differ on CD4 counts.

Individual and Systemic Barriers

Variations in barriers to remaining in care were compared between attendance categories; a summary of differences between attendance categories in barrier ratings and endorsement is presented in Table 2.

TABLE 2
TABLE 2:
Individual and Systemic/Structural Barriers by Attendance Group

For the individual-level barriers, nonattenders, compared with regular attenders, had significantly higher scores for depressive symptoms (11.36 ± 4.7 vs. 13.7 ± 6.9; P = 0.022) and chaos (12.02 ± 3.6 vs. 13.59 ± 4.3; P = 0.010) but did not significantly differ from irregular attenders; regular attenders did not differ from irregular attenders. There were no significant differences in perceived health scores between the 3 attendance groups. Nonattenders had significantly lower quality of life ratings (3.41 ± 1.2) compared with regular (3.87 ± 0.9) and irregular attenders (3.82 ± 1.0; P = 0.022). Social support did not differ between attendance groups.

The proportion of patients who self-reported being adherent to their medication regimen did not differ among groups. Compared with both regular (16.0%) and irregular (20.4%) attenders, nonattenders (36.4%) had a significantly higher proportion of reporting that they did not have enough money to eat (P = 0.002); regular and irregular attenders did not differ. None of the groups differed in the proportion of those who ate less because of a lack of funds. In addition, of those reporting drug use, compared with both regular (13.7%) and irregular (11.6%) attenders, a significantly higher proportion of nonattenders (32.4%) had used cocaine in the past 3 months (P = 0.015); regular and irregular attenders did not differ in the proportion using cocaine. Additionally, crack use was higher in the nonattenders (29.0%) compared with the regular attenders (7.1%) in the past 3 months (P = 0.012); crack use differ neither between nonattenders and irregular attenders nor between regular and irregular attenders. No other recent drug use differed between patient attendance categories.

At the systemic level, nonattenders had lower ratings of communication with their physician (11.23 ± 1.9) compared with regular (11.76 ± 0.7) and irregular attenders (11.70 ± 0.7; P = 0.047); regular attenders did not differ from irregular attenders. Additionally, nonattenders had lower ratings of clarity of medical instructions (10.88 ± 3.0) compared with irregular attenders (12.04 ± 2.9; P < 0.001); regular attenders differ neither from irregular nor nonattenders. A greater proportion of nonattenders (40.9%) endorsed having a transportation barrier compared with regular (17.6%) and irregular (22.0%) attenders (P = 0.001).

Barriers were then compared between patients who were virologically suppressed and those who were not virologically suppressed. On an individual level, compared with patients who were undetectable, patients who were detectable had higher depressive symptoms (13.31 ± 6.1 vs. 11.03 ± 4.5, P < 0.001), more chaos (13.47 ± 3.9 vs. 11.86 ± 3.6, P < 0.001), perceived their health between fair and poor (2.96 ± 1.2 vs. 3.63 ± 1.1, P < 0.001), and lower quality of life (3.57 ± 1.1 vs. 3.99 ± 0.9, P < 0.001); social support did not differ in patients based on virologic suppression group. Additionally, those who were detectable had a higher proportion of having a food insecurity barrier (did not eat: 86.0% vs 72.0%, P < 0.001; ate less: 75.7% vs. 65.4%, P = 0.020). Self-reported medication adherence was significantly lower in those who were detectable (85.5% vs. 91.9%, P = 0.042); a greater proportion of those who were detectable had been incarcerated at least once in their life (149 (69.6%) vs. 105 (49.1%), P < 0.001). Finally, a greater proportion of those who were detectable had reported recent use of cocaine (20.5% vs. 9.6%, P = 0.030) and crack (18 (18.0%) vs. 5 (6.8%), P = 0.030).

Regarding systemic-level barriers, patients who were not virologically suppressed had lower ratings of their interactions with their physicians (11.14 ± 3.2 vs. 11.96 ± 2.9, P = 0.046) and lower clarity of medical instructions (11.14 ± 3.2 vs. 11.96 ± 2.9, P = 0.006). None of the other barriers significantly differed between suppression status (Table 3).

TABLE 3
TABLE 3:
Individual and Systemic/Structural Barriers by Virological Suppression

Syndemic Barriers

Viral Suppression and Number of Barriers

Those who were undetectable had significantly fewer individual-level barriers than those who were detectable (1.01, 95% CI: 0.81 to 1.26 vs. 1.39, 95% CI: 1.11 to 1.75; P < 0.001). The number of systemic barriers was not significantly different between those who were undetectable compared with those who were detectable (P = 0.311) (Table 4).

TABLE 4
TABLE 4:
Average Number of Barriers by Type (With 95% CI)

Attendance Groups and Number of Barriers

There was a significant difference in the number of individual-level barriers between attendance groups (P = 0.02); nonattenders had significantly more barriers (1.40, 95% CI: 1.06 to 1.86) compared with regular attenders (1.06, 95% CI: 0.84 to 1.33; P = 0.002) but did not differ from irregular attenders, and regular and irregular attenders did not differ from each other. There was a significant difference in the number of systemic-level barriers between attendance groups (P = 0.009) whereby nonattenders had significantly more barriers than regular attenders (0.45, 95% CI: 0.26 to 0.77 vs. 0.25, 95% CI: 0.15 to 0.39; P = 0.009) and irregular attenders (0.27, 95% CI: 0.17 to 0.44; P = 0.03); irregular attenders did not differ from regular attenders (Table 5).

TABLE 5
TABLE 5:
Multivariate Logistic Regression Analyses of Associations Between the Number of Barriers and Being Detectable

Odds of Viral Suppression

To facilitate the interpretability of the results and that there were only a few patients at the higher counts of each type of barrier (4 individual barriers: n = 6, 1.3%; 3 systemic/structural barriers: n = 1, 0.2%; 4–6 of any barrier type: n = 31, 6.8%), the higher number of barriers was combined into one category. These categories were used in a logistic regression to determine whether the number of barriers was predictive of undetectable status after adjusting for age, gender, race, income, and education. For the individual-level barriers, having one barrier did not significantly increase the odds of being detectable. However, compared with patients not reporting any individual-level barriers, patients were more likely to be detectable if they reported 2 individual-level barriers (OR = 1.82, 95% CI: 1.04 to 3.18) or 3 or more individual-level barriers (OR = 3.60, 95% CI: 1.71 to 7.61). In systemic barriers, there were no increased odds of being detectable between those having 1 or 2 or more barriers compared with those who had no barriers. Examining the combined effects of individual and systemic-level barriers in the same logistic regression model, there was no significant interaction effect between the types of barriers and the likelihood of being detectable (Table 5).

DISCUSSION

This study demonstrated the association of barriers on HIV-infected individuals to attendance patterns. These attendance patterns have a direct association with HIV-viral loads; nonattending patients had higher viral loads and were less likely to be virologically suppressed than both regular and irregular attenders. Aside from the detrimental effect of missing appointments,67 patients not regularly attending their HIV care appointments may increase HIV transmission secondary to poorer viral load suppression.68 Furthermore, this study demonstrates that both individual- and systemic-level barriers impact clinic attendance as distinct singular entities.

Barriers to Attendance

Examined separately, both individual and systemic-level barriers were found to be associated with suboptimal attendance. When barriers were examined synergistically as potential syndemic factors, nonattenders had significantly more individual-level and systemic-level barriers than did regular attenders. This study demonstrates that cooccurring barriers to HIV medical care are additively associated with poorer retention in HIV care and increased viral loads in our patient population. Our findings also suggest that these barriers can cluster and combine synergistically as a syndemic that is associated with poorer outcomes along the HIV continuum of care.

Impact of Barriers on Viral Suppression

When examining viral suppression, the rates of individual-level barriers differed between groups, with detectable patients having higher depressive symptoms, more chaos, lower perceived health status, lower quality of life, more food insecurity, worse medication adherence, more recent substance abuse, and were more likely to have been incarcerated. Systemically, patients not virologically suppressed had lower ratings of their interactions with their physicians and lower clarity of medical instructions. Since systemic-level barriers were considered symptoms of the environment and reflected how individual patients perceived their environment, patients may not have perceived the systemic barriers as true barriers which may have accounted for few differences between attendance groups or virological suppression status. Examining barriers as potential syndemic factors, however, revealed that detectable patients had a significantly higher number of individual-level barriers, but did not differ in the average number of systemic/structural barriers; having 2 or 3 concurrent individual-level barriers increased the odds of being detectable. Taken together, these findings suggest that individual-level barriers operate both on their own and as syndemic barriers, whereas systemic barriers may negatively impact viral suppression as distinct units rather than as syndemic factors. Potentially, systemic-level barriers do not need the additive, cooccurring effects of multiple barriers to negatively impact viral suppression; systemic barriers, as distinct, single entities, may be potent enough to adversely affect attendance and viral suppression on their own. Furthermore, in that the systemic barriers were considered as patients' perception of environmental factors, although these factors may have been present, they may have been underreported if patients did not deem existing systemic barriers as a true barrier.

The results of this study highlight individual-level barriers having both an independent, singular effect, and a syndemic effect compared with systemic barriers. Although a syndemic effect was not demonstrated with systemic-level barriers, the fact that only a single systemic barrier may negatively impact health outcomes indicates that features of the health care system itself function as a barrier and calls for systemic-level changes or interventions if real progress along the care continuum is to be realized.

Differential Impact of Syndemic Barriers

The characteristics of this sample of patients strongly underscores a great need to improve care in inner-city, lower socioeconomic status patients who experience both distinct and syndemic barriers to HIV care. Although these variables themselves were not found to differ between patient attendance categories, they potentially serve as the root cause of why a patient may have experienced a particular barrier or group of barriers. Potentially, fundamental causes theory69 may provide an explanation for how barriers due to socioeconomic factors may serve as the fundamental cause of maintaining poor health outcomes in that the barriers prevent access to health resources and operate through multiple mechanisms. Lower income, less education, and history of incarceration have been demonstrated to be associated with lower health knowledge,70 along with worse coping skills and poorer organizational ability, potentially due to increased allostatic load from chronic stress.71,72 In addition, these socioeconomic status factors fundamentally serve as their own barriers, such as not having enough money to access public transportation. Considering barriers as a syndemic, as fundamental causes framework can help to dictate guidelines for effective interventions.

This study adds to the existing research on syndemics by applying syndemic theory to retention in care in HIV-infected outpatients. The literature has only recently reported on syndemics with health outcomes along the HIV continuum of care. For example, in a multisite study of 1052 HIV-infected injection drug users, having multiple psychosocial problems was associated with an increased odds of not taking HIV medication; participants reporting 3 to 6 psychosocial problems were more than twice as likely to be nonadherent to HIV medicine in the previous day.56 The same study also reported a 2.24 increased odds of being detectable in participants reporting 4 to 6 psychosocial problems. Another study found that psychosocial factors, when considered as syndemic, were associated with nonadherence to antiretroviral therapy; compared with those with no negative psychosocial factors, those with 1 to 5 factors had an increased odds for being nonadherent.73 Our study suggest that barriers to medical care should be considered as a syndemic, with influence on how well HIV-infected patients advance in the HIV continuum of care.

Research has demonstrated that most of the effective interventions to promote retention in care focus on strength-based case management and individual skill-building, which encourages patients to identify and use their own internal abilities to access ancillary resources and problem solve.14,74,75 More recently, clinic-wide interventions and enhanced personal contact has also been shown to improve retention in care.57,58 On an individual level, patients are being equipped to handle the multiple individual-level barriers to care. On a system-level, patients are being assisted in navigating an increasingly complex health care system. It has been suggested that using multiple intervention strategies within a single study may be effective in retaining patients in care.14 Using multiple retention, strategies seem to be necessary to effectively address the multiple syndemic barriers to accessing and consistently using HIV primary care. Potentially, the effect of syndemic factors can be diminished; syndemic psychosocial health conditions and sexual risk behaviors have been buffered by optimism and education in a sample of African American men who have sex with men.76

The effectiveness of multiple interventions is likely due to the underlying syndemic of cooccurring barriers that collectively are more detrimental than a single barrier.14 Until recently, most intervention strategies have been targeted at the individual. Future interventions should ideally be multilevel and focus not only on the individual but also address systemic-level factors associated with barriers to retention in care.

Study Limitations

The study was conducted in English, which limited the participation of monolingual Spanish or Creole-speaking subjects, and Miami has a relatively high prevalence of monolingual Spanish and Creole-speaking individuals. This under representation of non-English speaking patients may have minimized the prevalence of the language structural barrier; potentially, there is a differential effect of the barriers in this study based on the ability to speak English despite Spanish and Creole on public signage in Miami. Furthermore, we did not collect participation refusal rates; this may introduce a potential bias for those who participated compared to those who did not participate, especially with the regular and irregular attenders. Additionally, the study was conducted in one clinic attended by outpatients of a lower socioeconomic status group that may inherently face more barriers than those in a higher socioeconomic status group; this may have hampered our ability to contrast between groups. In addition, there were other potential barriers that could have been measured that were not assessed through the questionnaire, such as health literacy, nutritional deficits, anxiety, coping skills, personality traits, and neurocognitive function, among others. Also, our measurement of barriers was retrospective; however, many, if not most of these barriers are enduring and measures today are likely very similar to measures over the previous year. The fact that viral loads were not obtained in real time is another potential limitation of the study along with the fact that a longer time had elapsed between most recent laboratory results and the date of the interview. Finally, the number of nonattenders were less than the other 2 attendance groups. This population of patients is particularly difficult to reach and include in this type of study.

Future Implications and Recommendations

Despite these limitations, the findings from this study have implications for understanding barriers to care that can guide future interventions. Importantly, this study highlights the association of individual and systemic barriers on HIV clinic engagement. We were able to identify the most salient individual and systemic barriers impacting this inner-city HIV population. In addition, we demonstrated that barriers act in a syndemic pattern whereby those with more barriers experienced worse HIV-related health outcomes. Highlighting the most significant individual-level and systemic-level barriers impacting our HIV-infected patients should help tailor future interventions to retain patients in care. Although multiple interventions, such as enhanced personal contact and basic HIV education, can be done at low costs,77 clinics may need to establish interventions to target those patients experiencing multiple barriers and prioritize those barrier-reduction mechanisms that make the most impact. Notably, some barriers could be alleviated if additional funding was available, such as funds for transportation78 or meal subsidies; potentially interventions incorporating conditional cash transfers for clinic attendance may be successful in lower socioeconomic status patient populations.79 In effect, interventions aimed at barrier reduction should be triaged and considered holistically.80 With these findings in mind, ancillary elements to care, such as directing patients to address mental health, coping skills training, organizational skill training, transportation assistance, physician and staff training in communication, and information delivery should be delivered as a bundle. Furthermore, delivery of these ancillary services should be triaged for those who are in the greatest need and are found to experience multiple barriers in that these patients are the most vulnerable to negative health outcomes.

ACKNOWLEDGMENTS

The authors would like to acknowledge Shelia Findlay and Marcia Vidal for their contributions to this research. The authors gratefully acknowledge use of the services and facilities of the Miami Center for AIDS Research (CFAR) Behavioral/Social Sciences and Community Outreach Core at the University of Miami, funded by NIH grant P30A1073961.

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Keywords:

retention in care; syndemics; barriers to care; psychosocial factors; individual-level barriers; systemic-level barriers

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