Potent antiretroviral therapy (ART) medications are widely available to treat people living with HIV (PLWH) and prevent transmission to partners in the community; however, ineffective communication between physicians, laboratories, and pharmacies may delay the delivery of ART, hamper viral suppression (VS), and contribute to racial disparities along the continuum of care.1,2 Blacks have the highest incidence of HIV infections, the highest prevalence of undiagnosed HIV, and the greatest mortality among racial/ethnic groups.3–5 They are least likely to be linked to and retained in care, to receive ART, and to achieve HIV RNA VS.6,7 Latinos are disproportionately affected, as well. The annual incidence of HIV among Latinos is approximately 3 times that of whites.8 Latinos are also more likely than whites to be diagnosed with advanced disease9 and less likely to receive ART.10,11
Although the National HIV/AIDS Strategy emphasizes the need for innovative interventions to reduce HIV-related disparities, increase access to ART, and improve outcomes along the continuum of care for PLWH,12,13 few interventions or policies have been shown to reduce racial/ethnic gaps in HIV treatment and outcomes. Facilitating the delivery of ART and achieving VS are critical to achieving these goals. Increasingly, health care providers adopt HIE systems to help them maintain clinical information, laboratory test results, and ART prescription filling associated with HIV care visits.14,15 These systems work on top of the existing electronic medical records (EMRs).
Although some evidence suggests that EMRs help improve the quality of health care in general,16–18 the applicability of HIE to HIV care and the extent to which it can reduce disparities are less well studied.15,19 One recent study used a public health HIE system to alert providers of patients who had been out of HIV care when they presented to emergency departments in Louisiana.1 A quality improvement project in New Jersey used a serial cross-sectional design to document improvement in 4 of the 7 quality improvement indicators for HIV care and health status over 2 years after implementation of a web-based health information support system that included alerts to providers about indicated tests and treatment.14
Bell and colleagues previously reported on process of care changes after adding a laboratory health information exchange (LHIE) system to an existing EMR in a HIV care clinic. They found that when patients had clinically important increases in viral load (VL) (n = 171), providers responded by changing the ART regimen an average of 6 days earlier after LHIE implementation than before.20 Despite these findings, there is limited evidence that HIE systems improve ART use and VS outcomes. Furthermore, whether an HIE system intervention designed to improve care and outcomes for overall populations can also reduce racial/ethnic disparities in these outcomes has not yet been established.
This study had 2 main goals. First, we examined whether a novel, bi-directional LHIE intervention would increase the rates of ART use and VS. Second, we examined the effect of the intervention on racial/ethnic disparities in these outcomes, and identified factors contributing to the disparities among a stable cohort from a large HIV clinic in Southern California. We hypothesized that the LHIE intervention would increase the rates of ART use and VS overall, and reduce the magnitude of black/white and Latino/white disparities in ART use and VS over the 3-year study period.
To evaluate the effect of our bi-directional LHIE intervention on ART use and VS among a cohort of patients in HIV care, we used a quasi-experimental, interrupted time-series design over a 3-year period. At the time of the study, no clinic of similar patient size and EMR system maturity was available to serve as a comparison clinic. Therefore, neither a randomized controlled trial (RCT), nor a 2-sample interrupted time series was feasible. To diminish the possibility of spurious results from a pre–post design, we used an extended observational period. We collected baseline data prospectively 1 year preintervention and followed up for 2 years postintervention.
Setting and Participants
This study was conducted from December 2007 through November 2011 among PLWH receiving care at an HIV clinic in Southern California. Beginning from December 2008 and continuing through January 2009, we developed and implemented a multilevel (ie, operating at the system and provider levels) LHIE intervention and linked clinical, administrative, and pharmacy data from the EMR to create the analytic data set. The data set contained detailed information on patients' sociodemographic characteristics, HIV risk factors, clinical factors (CD4 count and HIV RNA VL), ART medications, and HIV care visits. Eligibility criteria were (1) ≥18 years of age, (2) documented HIV-positive status, and (3) at least 1 face-to-face visit with an HIV provider during the 1 year preintervention period, and at least 1 visit during the postintervention study period. The final cohort included 1181 PLWH who visited one of the following types of provider during the study period: physician, nurse practitioner, social worker, case manager, or adherence counselor. This study was approved by the Institutional Review Boards of UCLA and the participating clinic.
The multilevel LHIE intervention study featured implementation of a bi-directional exchange of laboratory information (between ordering physician and laboratory staff) through an existing EMR system.20 In the LHIE system, the provider uses the EMR to order laboratories. On receiving the electronic requisitions, the laboratory performs the tests and deposits the results into the EMR. A color-coded system cues providers for action regarding any abnormal results.
Trainings accompanied the LHIE system enhancements. During the initial 3 months, physicians and staff underwent weekly trainings on the system and workflow changes. After launching the intervention, they moved to online tutorials; physician and administrative leaders continued meeting with staff bi-weekly for 2 additional months. The system programmer continued problem solving as needed.
We measured monthly ART use using pharmacy data on prescription fills, and VS, using laboratory data from VL tests. All ART regimens were potent combinations, according to standard criteria.21 We obtained all pharmacy records of ART prescriptions filled each month to construct a variable indicating ART use. We obtained all VL results conducted during the study period, to construct a variable for undetectable VL. Based on the detection threshold of the clinic's laboratory assay, undetectable VL, or VS, was operationalized as ≤75 copies μ/L.
Sociodemographic characteristics, HIV risk group, and clinical factors were assessed as covariates. Sociodemographic characteristics included race/ethnicity, sex, age category, income level (federal poverty level), and insurance status (private, Medicare, Medicaid, and uninsured). Race/ethnicity comprised 4 categories: non-Hispanic/Latino white (referent), non-Hispanic/Latino black, Hispanic/Latino, and other race/ethnicity, which included persons reporting non-Hispanic Asian/Pacific Islander and non-Hispanic Native American backgrounds. Additional covariates were HIV risk group, CD4 cell count level, and number of HIV care visits in the study period.
The survey was described in detail previously.20 Eligible participants were 18 years of age or older, documented HIV+, and received care onsite. We conducted face-to-face, anonymous, cross-sectional interviews before (November 2008; n = 100) and after (February 2011; n = 126) the intervention with consecutive patients recruited from clinic waiting rooms. The instrument included 4 secondary outcome measures on the patient–physician relationship: a 4-item general communication scale, a similar 4-item scale assessing communication about HIV-related laboratory tests (VL and CD4 count), a 4-item provider trust scale, and a 2-item overall satisfaction with care scale.
There were 2 sets of analyses: (1) longitudinal examination of EMR data with mediation analysis of intervention effects and (2) cross-sectional, pre/postsurvey data analysis of the 4 secondary survey measures.
In the longitudinal analysis, we examined the effects of the intervention on ART use, VS and racial/ethnic disparities in these outcomes. We conducted 2 parallel series of staged, multivariable logistic regression analyses with generalized estimating equations and an exchangeable matrix to assess whether the intervention helped mediate the longitudinal relationship of race/ethnicity with (1) ART use and (2) VS outcomes.22–24 Generalized estimating equation adjusts for the clustering of variance that results from both repeated assessments of patients over time and similarities among patients of the same providers. The forward model-building process began with race/ethnicity as the sole predictor. To this baseline bivariate model, model A added the other main predictor, the LHIE intervention indicator, and we assessed changes in the race/ethnicity adjusted odds ratio (AOR) and 95% confidence interval (95% CI). Model B added sociodemographic factors, HIV risk group, and CD4 count. The final model, model C, added the number of HIV care visits. Analyses were completed using STATA Version 11.0.25 Given a baseline sample size of 1181, assuming 30% attrition, and 80% power (type I error 0.05), the minimal detectable difference was 3.1% for ART and 8.5% for VL.
For the pre/postsurvey data analysis, we used 1-way analysis of variance with Duncan multiple range adjustment26 to compare racial/ethnic groups on the baseline and final interview scores, respectively, for each measure. Then, using 2 sample t tests we compared the baseline and final interview scores within each racial/ethnic group. Finally, we examined multivariable linear regressions of each measure on race/ethnicity, risk group, income, homelessness, insurance status, and CD4 count, preintervention and postintervention.
Non-Hispanic blacks (22%) and Hispanic/Latinos (28%) made up more than half the sample (N = 1181) (Table 1). Most participants were men (89%), between ages 35–49 (59%), and men who have sex with men (MSM) (68%). Approximately 47% had incomes at or below 100% of the federal poverty level and 30% were uninsured. Two-thirds (66%) had a CD4 cell count less than 350 cells per milliliter, and 17% reported 3 or fewer HIV care visits. Baseline levels of ART and VS did not differ significantly by race/ethnicity. Over the 3-year period, ART use increased from 79% at baseline to 93%, and VS increased from 39% to 49%. The bivariate correlation between ART use and VL at baseline was 0.50, P < 0.0001.
The intervention was associated with more than twice the odds of ART use in the baseline bivariate analysis (OR) = 2.22; 95% CI: 2.07 to 2.39; P = 0.0001; Table 2. The magnitude of the association increased steadily across each model stage (A–C) that adjusted for additional sample characteristics (in the fully adjusted model, AOR = 2.50; 95% CI: 2.29 to 2.73; P = 0.0001). Regarding racial/ethnic disparities, blacks had 25% lower odds of ART use than whites in the bivariate analysis (OR = 0.75, 95% CI: 0.58 to 0.98, P < 0.05). In the next stage (model A), which added the intervention indicator, the AOR for blacks moved to AOR = 0.83 with a CI crossing the null (95% CI: 0.65 to 1.07; P = 0.22), indicating the intervention mediated the decrease in the black/white disparity. The final model (model C) revealed a dose–response relationship between the number of HIV care visits and ART use: 1–3 visits (AOR = 0.34; 95% CI: 0.23 to 0.51; P < 0.001) and 4–5 visits (AOR = 0.42; 95% CI: 0.31 to 0.59; P < 0.001) compared with those with ≥8 visits. Moreover, the intervention remained a significant predictor of ART use in the fully adjusted model. The bivariate association with ART use was not significant for Latinos; however, the point estimates increased and the 95% CIs narrowed with each subsequent model containing sociodemographic characteristics. In the fully adjusted model, the odds of ART use were 77% higher for Latinos than for whites (model C: AOR = 1.77; 95% CI: 1.36 to 2.31; P < 0.001).
Overall, the odds of VS increased 16% and 12% in the bivariate and fully adjusted models, respectively, on implementation of the intervention (model C: AOR = 1.12; 95% CI: 1.04 to 1.21; P < 0.01; Table 3). In the bivariate analysis of racial/ethnic disparities, blacks had 25% lower odds of VS compared with whites (OR = 0.75, 95% CI: 0.61 to 0.92; P < 0.01). Although the intervention variable did not initially eliminate this disparity, after adjustment for sociodemographic characteristics and CD4 count, the magnitude of the black/white disparity in VS decreased to AOR = 0.81 (95% CI: 0.66 to 1.00; P = 0.05). Furthermore, in the final model that adjusted for the number of HIV care visits (model C AOR = 0.85; 95% C I: 0.69 to 1.05; P = 0.14), the black/white difference in VS was eliminated. A dose–response relationship between the number of visits and VS was also evident in this model (model C): 1–3 visits in the previous year (AOR = 0.26; 95% CI: 0.17 to 0.41; P < 0.001), 4–5 visits (AOR = 0.38; 95% CI: 0.28 to 0.50; P < 0.001), and 6–7 visits (AOR = 0.73; 95% CI: 0.62 to 0.86; P < 0.001), compared with those with ≥8 visits. As with the findings for ART use, the odds of VS were greater for Latinos than for whites in the fully adjusted model (model C: AOR = 1.33; 95% CI: 1.11 to 1.59; P < 0.001), but not in the bivariate model. Furthermore, the intervention remained a significant predictor of VS in the fully adjusted model.
Patient–Physician Relationship Survey Results
We compared cross-sectional survey responses to the 4 patient–physician relationship measures between groups at baseline and within the racial/ethnic groups before and after the LHIE intervention (Table 4). At baseline, blacks reported the lowest scores on each of the 4 dimensions, but the disparity was only significant (P < 0.05) for physician communication. After the intervention, blacks' scores were similar to the other groups' on every dimension. Correspondingly, blacks' had the greatest improvement in scores for every dimension, although the only significant increase in mean scores was for whites on the laboratory test communication scores (P = 0.01). Multivariable analyses, preintervention and postintervention, produced similar findings: preintervention compared with whites, blacks had significantly lower scores for physician communication and overall satisfaction, whereas Latinos had lower trust (Table 4). After intervention, there were no differences by race/ethnicity on any measure.
One of the most vexing problems in the HIV epidemic is the persistence of racial/ethnic disparities along multiple steps of the care continuum. Most relevant to this study, blacks are less likely than whites to receive ART medications, adhere to them, and have suppressed virus—the essential goals of care.7,27,28 We observed odds of ART use and VS that were approximately 25% lower among blacks than whites before implementation of our LHIE intervention, and a significant attenuation of the disparities after its implementation. In addition to closing the gap between blacks and whites, the intervention significantly increased ART use and VS over the 3-year study period for all racial/ethnic groups. These findings were supported by the survey findings; although at baseline blacks reported lower quality communication with physicians than others did, their scores increased after the intervention, eliminating black/white differences in the scores. We submit that the intervention delivered key test results such as VL and CD4 count to the EMR more efficiently and facilitated communication about ART prescriptions and adherence, which together led to better outcomes.29 Improved communication and satisfaction helped reduce disparities as the EMR delivered more timely, objective data enabling patients, especially black patients, to follow recommendations.30,31
These are important and unique findings because virtually no other intervention designed to improve overall HIV care has also helped reduce disparities in the outcomes. The findings raise important questions about how to achieve the combined goals of (1) fostering ART delivery and adherence to suppress VL for all patients, while (2) reducing racial/ethnic disparities in ART use and VS. Until now, many interventions have sought to achieve either one or the other of these 2 major goals of the National HIV/AIDS Strategy.13 For instance, interventions that are successful among blacks typically were designed for this population and, therefore, may be less effective for others.32 Our study shows that an intervention designed to improve care for everyone can have the added benefit of reducing black/white disparities in HIV care. This is a promising finding as race-specific interventions face challenges that practice-wide interventions such as ours do not face. The potential benefits of race-specific efforts may also depend on the groups being compared. Few interventions have focused on disparities in HIV care for blacks. Some have reduced HIV risk behavior among black MSM or men who have sex with men and women (MSMW)33,34; however, their dissemination and sustainability may be hampered by the limited financial support available for such interventions.7 The potential benefits of race-specific efforts may also depend on the groups being compared. As discussed below, the finding of better ART use and VS for Latinos than whites in the final models is particularly salutary and remarkable.
Our LHIE intervention reflects broader changes in medical practice promulgated by the HITECH Act of 2009 (during this project's study period), which was part of the Obama administration's American Recovery and Reinvestment Act of 2009 (the so-called “Stimulus” legislation).35 The HITECH legislation was enacted to incentivize “meaningful use”36 of EMR-based technologies in clinical care, such as the LHIE. This interplay of effects may represent a rare example of the implementation of a technology policy—the LHIE intervention—having coincidental positive effect within the same clinical population on another policy goal: the National HIV/AIDS Strategy policy goals of improving outcomes along the care continuum, and reducing HIV care disparities.12,37,38
Although the intervention helped close the black/white gap in ART use and VS, sociodemographic characteristics helped explain much of the remaining gap. Notably income (for ART use) and insurance status remained significant predictors in the final models. Thus, addressing social determinants of health—income inequality and insurance reform39—may be needed to eliminate these disparities. This suggestion is consistent with at least 1 recent modeling study of HIV incidence among black MSM.3,4
In our final regression models, the number of HIV care visits was a significant predictor of both outcomes; it helped mediate the gap between blacks' and whites' ART use and VS. Thus, interventions that improve engagement and retention in care may also help improve these outcomes and reduce disparities over and above a LHIE intervention. Somewhat surprisingly, ART use and VS increased over the study period to 77% and 33% greater, respectively, among Latinos compared with whites in the 2 model stages that included sociodemographic characteristics. In the bivariate analyses, the odds of each outcome did not differ significantly between Latinos and whites. This suggests that addressing social and economic factors may boost ART use and VS to a greater degree for Latinos than for whites. The finding of greater improvements after the intervention for Latinos compared with whites contrasts that of national studies,40 as well as that of other studies in Los Angeles and California.41 Compared with whites, Latinos generally have greater delays in HIV diagnosis42 and treatment,43 lower ART use,44 and lower VS rates than whites.45
The implications of our findings notwithstanding, there were several limitations. Although an RCT is the strongest design for causal inference, it was not possible to randomly assign participants to the LHIE intervention vs. control in this study; furthermore, no appropriate group existed to serve as a control. Therefore, we used the strongest available design, a time-series intervention with repeated measures and mediation analysis. As a single-site study, this investigation may have limited generalizability to other clinics or geographic areas; however, the clinic is one of the largest in the second largest epicenter of the US epidemic (Los Angeles metropolitan area). Like any longitudinal study, power to detect differences in outcomes comparing subgroups diminished with attrition over time. The design also cannot exclude the possibility that secular trends in combination and daily dosing ART medications affected the findings, although it is unclear that these would affect race/ethnic groups differentially. Our measure of ART use was based on filled prescriptions rather than direct data on medications consumed, such as electronic medication monitoring bottle caps.28 Moreover, although the correlation between ART use and VS was high, there was a difference of about 40 percentage points between the absolute rates of each. This gap likely represents nonadherence to prescribed ART medications. The rates of ART use and VS observed here are very similar to those reported in a recent HIV HIE study, adding support for the validity and generalizability of our estimates.14 Although data collection was completed more than 5 years ago, low rates of adherence to ART and low rates of VS continue to be major problems, especially among blacks.28,46 HIE systems may be useful ways to improve care and outcomes that have not yet reached many clinical settings. Finally our measure of HIV care frequency should not be considered a strict measure of the frequency of physician care visits, generally known as retention in care, as we were unable to disaggregate physician visits from visits for supportive care, such as case–manager appointments. Although our frequency categories do not correspond to those of well-known standards in the field, which now consider as few as 2 physician visits per year adequate retention in care,47–49 our measure is closely related to them. That is because our measure includes supportive care visits, which standard measures of retention in care usually exclude.
Important strengths of our study design include its 1-year preintervention observation period, 3-year follow-up, which is longer than a typical 1-year RCT, and our sample size of more than 1000 participants, which is considerably larger than many single-site RCTs.
In summary, improving the efficiency and accuracy of EMR-coordinated information exchanged between physicians, laboratories, and pharmacies may facilitate timely ART use and VS. This study provides evidence that an LHIE intervention can improve HIV care and health outcomes and reduce disparities in an urban HIV clinic population. Future research should assess its effectiveness in other populations and settings.
We are grateful to Dr. Marcia Alcouloumre, Medical Director, St Mary's Care Center, Dr. Lubabah Ben-Ghaly for presenting a poster of an earlier version, to Dr. Chi-hong Tseng for assistance with the power analysis, and to Jimmy Ngo for assistance in preparing the manuscript.
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