Share this article on:

Effects of a Laboratory Health Information Exchange Intervention on Antiretroviral Therapy Use, Viral Suppression, and Racial/Ethnic Disparities

Cunningham, William E. MD, MPH*,†; Ford, Chandra L. PhD, MPH, MLIS; Kinsler, Janni J. PhD, MPH*,‡; Seiden, Danielle MPP*; Andrews, Laral MS§; Nakazono, Terry MS*,†; Bell, Douglas S. MD, PhD*

JAIDS Journal of Acquired Immune Deficiency Syndromes: July 1st, 2017 - Volume 75 - Issue 3 - p 290–298
doi: 10.1097/QAI.0000000000001385
Implementation Science

Background: Although antiretroviral therapy (ART) is available to treat HIV+ persons and prevent transmission, ineffective delivery of care may delay ART use, impede viral suppression (VS), and contribute to racial/ethnic disparities along the continuum of care. This study tested the effects of a bi-directional laboratory health information exchange (LHIE) intervention on each of these outcomes.

Methods: We used a quasi-experimental, interrupted time-series design to examine whether the LHIE intervention improved ART use and VS, and reduced racial/ethnic disparities in these outcomes among HIV+ patients (N = 1181) in a comprehensive HIV/AIDS clinic in Southern California. Main outcome measures were ART pharmacy fill and HIV viral load laboratory data extracted from the medical records over 3 years. Race/ethnicity and an indicator for the intervention (after vs. before) were the main predictors. The analysis involved 3-stage, multivariable logistic regression with generalized estimating equations.

Results: Overall, the intervention predicted greater odds of ART use (odds ratio [OR] = 2.50; 95% confidence interval: 2.29 to 2.73; P < 0.001) and VS (OR = 1.12; 95% confidence interval: 1.04 to 1.21; P < 0.05) in the final models that included sociodemographic, behavioral, and clinical covariates. Before the intervention, there were significant black/white disparities in ART use OR = 0.75 (0.58–0.98; P = 0.04) and VS OR = 0.75 (0.61–0.92; P = 0.001). After the intervention, the black/white disparities decreased after adjusting for sociodemographics and the number of HIV care visits, and Latinos had greater odds than whites of ART use and VS, adjusting for covariates.

Conclusions: The intervention improved overall ART treatment and VS, and reduced black/white disparities. LHIE interventions may hold promise if implemented among similar patients.

*Division of General Internal Medicine and Health Services Research, Department of Medicine, School of Medicine, University of California, Los Angeles, CA;

Departments of Health Policy and Management;

Community Health Sciences, School of Public Health, University of California, Los Angeles, CA; and

§St. Mary's Medical Center, Long Beach, CA.

Correspondence to: William E. Cunningham, MD, MPH, Division of General Internal Medicine, Department of Medicine, UCLA, 911 Broxton Avenue, Los Angeles, CA 90024 (e-mail:

Supported by a grant from the Health Resources and Services Administration (HRSA-07-046). W.E.C. also received partial support for his time on this manuscript from NIH/NIA (P30-AG021684), NIH/NIMHD (P20-MD000182), NCATS (TL1TR001883), and NIH/NIDA (R01-DA030781). D.S.B., W.E.C., and T.N had partial support from the National Center for Advancing Translational Science (UL1TR000124). C.L.F. received partial support from NIH/NINR (1R01-N4014789-01) and the UCLA California Center for Population Research (CCPR) through support from the Shriver National Institute of Child Health and Human Development (5R24HD041022).

The authors have no funding or conflicts of interest to disclose.

Received October 05, 2016

Accepted March 22, 2017

Back to Top | Article Outline


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.

Back to Top | Article Outline


Study Design

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.

Back to Top | Article Outline

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.

Back to Top | Article Outline


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.

Back to Top | Article Outline


Primary Outcomes

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.

Back to Top | Article Outline


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.

Back to Top | Article Outline

Survey Methods

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.

Back to Top | Article Outline

Data Analysis

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.

Back to Top | Article Outline


Sample Characteristics

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.



Back to Top | Article Outline


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).



Back to Top | Article Outline

Viral Suppression

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.



Back to Top | Article Outline

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.



Back to Top | Article Outline


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.

Back to Top | Article Outline


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.

Back to Top | Article Outline


1. Magnus M, Herwehe J, Gruber D, et al. Improved HIV-related outcomes associated with implementation of a novel public health information exchange. Int J Med Inform. 2012;81:e30–e38.
2. Shade SB, Chakravarty D, Koester KA, et al. Health information exchange interventions can enhance quality and continuity of HIV care. Int J Med Inform. 2012;81:e1–e9.
3. Rosenberg ES, Millett GA, Sullivan PS, et al. Understanding the HIV disparities between black and white men who have sex with men in the USA using the HIV care continuum: a modeling study. Lancet HIV. 2014;1:e112–e118.
4. Sullivan PS, Rosenberg ES, Sanchez TH, et al. Explaining racial disparities in HIV incidence in black and white men who have sex with men in Atlanta, GA: a prospective observational cohort study. Ann Epidemiol. 2015;25:445–454.
5. Simard EP, Fransua M, Naishadham D, et al. The influence of sex, race/ethnicity, and educational attainment on human immunodeficiency virus death rates among adults, 1993–2007. Arch Intern Med. 2012;172:1591–1598.
6. Mahle Gray K, Tang T, Shouse L, et al. Using the HIV surveillance system to monitor the national HIV/AIDS strategy. Am J Public Health. 2013;103:141–147.
7. Cunningham W. HIV racial disparities: time to close the gaps. Arch Intern Med. 2012;172:1599–1600.
8. Centers for Disease Control and Prevention (CDC). Diagnoses of HIV Infection in the United States and Dependent Areas, 2011. HIV Surveillance Report. Atlanta, GA: US Department of Health and Human Services; 2013.
9. Schwarcz S, Hsu L, Dilley JW, et al. Late diagnosis of HIV infection: trends, prevalence, and characteristics of persons whose HIV diagnosis occurred within 12 months of developing AIDS. J Acquir Immune Defic Syndr. 2006;43:491–494.
10. Hall HI, Gray KM, Tang T, et al. Retention in care of adults and adolescents living with HIV in 13 U.S. areas. J Acquir Immune Defic Syndr. 2012;60:77–82.
11. Chen NE, Gallant JE, Page KR. A systematic review of HIV/AIDS survival and delayed diagnosis among Hispanics in the United States. J Immigr Minor Health. 2012;14:65–81.
12. Greenberg AE, Purcell DW, Gordon CM, et al. Addressing the challenges of the HIV continuum of care in high-prevalence cities in the United States. J Acquir Immune Defic Syndr. 2015;69(suppl 1):S1–S7.
13. The White House Office of National AIDS Policy. National HIV/AIDS Strategy for the United States. The White House Office of National AIDS Policy; 2015. Available at: Accessed September 10, 2016.
14. Virga PH, Jin B, Thomas J, et al. Electronic health information technology as a tool for improving quality of care and health outcomes for HIV/AIDS patients. Int J Med Inform. 2012;81:e39–e45.
15. NORC. Understanding the Impact of Health IT in Underserved Communities and those With Health Disparities. Bethesda, MD: University of Chicago; 2010.
16. Baron RJ. Quality improvement with an electronic health record: achievable, but not automatic. Ann Intern Med. 2007;147:549–552.
17. Persell SD, Kaiser D, Dolan NC, et al. Changes in performance after implementation of a multifaceted electronic-health-record-based quality improvement system. Med Care. 2011;49:117–125.
18. Kaushal R, Kern LM, Barron Y, et al. Electronic prescribing improves medication safety in community-based office practices. J Gen Intern Med. 2010;25:530–536.
19. Gibbons MC. A historical overview of health disparities and the potential of eHealth solutions. J Med Internet Res. 2005;7:e50.
20. Bell DS, Cima L, Seiden DS, et al. Effects of laboratory data exchange in the care of patients with HIV. Int J Med Inform. 2012;81:e74–e82.
21. DHHS Panel on Antiretroviral Guidelines for Adults and Adolescents, A Working Group of the Office of AIDS Research Advisory Council (OARAC). Guidelines for Using Antiretroviral Agents in HIV-1 Infected Adults and Adolescents. Bethesda, MD: National Institutes of Health; 2002.
22. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Personal Soc Psychol. 1986;51:1173–1182.
23. MacKinnon DP. Introduction to Statistical Mediation Analysis (Multivariate Applications Series). 1 Pap/Cdr edition ed. New York, NY: Routledge; 2008.
24. MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol. 2007;58:593–614.
25. Stata for Windows [computer Program]. Version 12/SE. College Station, TX; 2011.
26. Steel RGD, Torrie JH. Duncan's New Multiple Range test. Principles and Procedures of Statistics. New York, NY: McGraw-Hill Book Co; 1980.
27. Hall HI, Frazier EL, Rhodes P, et al. Differences in human immunodeficiency virus care and treatment among subpopulations in the United States. JAMA Intern Med. 2013;173:1337–1344.
28. Simoni JM, Huh D, Wilson IB, et al. Racial/Ethnic disparities in ART adherence in the United States: findings from the MACH14 study. J Acquir Immune Defic Syndr. 2012;60:466–472.
29. Bell DS, Cretin S, Marken RS, et al. A conceptual framework for evaluating outpatient electronic prescribing systems based on their functional capabilities. J Am Med Inform Assoc. 2004;11:60–70.
30. Bogart LM, Kelly JA, Catz SL, et al. Impact of medical and nonmedical factors on physician decision making for HIV/AIDS antiretroviral treatment. J Acquir Immune Defic Syndr. 2000;23:396–404.
31. Hall WJ, Chapman MV, Lee KM, et al. Implicit racial/ethnic Bias among health care professionals and its influence on health care outcomes: a systematic review. Am J Public Health. 2015;105:e60–e76.
32. Harawa NT, Williams JK, McCuller WJ, et al. Efficacy of a culturally congruent HIV risk-reduction intervention for behaviorally bisexual black men: results of a randomized trial. AIDS. 2013;27:1979–1988.
33. Williams JK, Ramamurthi HC, Manago C, et al. Learning from successful interventions: a culturally congruent HIV risk-reduction intervention for African American men who have sex with men and women. Am J Public Health. 2009;99:1008–1012.
34. Wilton L, Herbst JH, Coury-Doniger P, et al. Efficacy of an HIV/STI prevention intervention for black men who have sex with men: findings from the Many Men, Many Voices (3MV) project. AIDS Behav. 2009;13:532–544.
35. Blumenthal D. Launching HITECH. N Engl J Med. 2010;362:382–385.
36. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363:501–504.
37. Nachega JB, Uthman OA, del Rio C, et al. Addressing the Achilles' heel in the HIV care continuum for the success of a test-and-treat strategy to achieve an AIDS-free generation. Clin Infect Dis. 2014;59(suppl 1):S21–S27.
38. Maulsby C, Millett G, Lindsey K, et al. A systematic review of HIV interventions for black men who have sex with men (MSM). BMC Public Health. 2013;13:625.
39. Martin EG, Schackman BR. What does U.S. health reform mean for HIV clinical care? J Acquir Immune Defic Syndr. 2012;60:72–76.
40. Cunningham WE, Markson LE, Andersen RM, et al. Prevalence and predictors of highly active antiretroviral therapy use in patients with HIV infection in the United States. J Acquir Immune Defic Syndr. 2000;25:115–123.
41. Gant Z, Bradley H, Hu X, et al; Centers for Disease C, Prevention. Hispanics or Latinos living with diagnosed HIV: progress along the continuum of HIV care - United States, 2010. MMWR Morb Mortal Wkly Rep. 2014;63:886–890.
42. Sheehan DM, Trepka MJ, Fennie KP, et al. Rate of new HIV diagnoses among Latinos living in Florida: disparities by country/region of birth. AIDS Care. 2015;27:507–511.
43. Turner BJ, Cunningham WE, Duan N, et al. Delayed medical care after diagnosis in a US national probability sample of persons infected with human immunodeficiency virus. Arch Intern Med. 2000;160:2614–2622.
44. Solorio RM, Currier J, Cunningham WE. HIV health care Services for Mexican Migrants. J Acquir Immune Defic Syndr. 2004;37(suppl 4):S240–S251.
45. Sheehan DM, Trepka MJ, Dillon FR. Latinos in the United States on the HIV/AIDS care continuum by birth country/region: a systematic review of the literature. Int J STD AIDS. 2015;26:1–12.
46. Centers for Disease Control and Prevention (CDC). HIV in the United States: the Stages of care. 2012. Available at: http:// Accessed June 2, 2013.
47. Tobias C, Cunningham WE, Cunningham CO, et al. Making the connection: the importance of engagement and retention in HIV medical care. AIDS Patient Care STDS. 2007;21(suppl 1):S3–S8.
48. Naar-King S, Bradford J, Coleman S, et al. Retention in care of persons newly diagnosed with HIV: outcomes of the Outreach Initiative. AIDS Patient Care STDS. 2007;21(suppl 1):S40–S48.
49. Health Resources and Services Administration. HIV/AIDS Bureau performance measures. 2015. Available at: Accessed August 3, 2017.

health information exchange; health information technology (IT) intervention; racial/ethnic disparities; antiretroviral therapy; HIV viral load; HIV outcomes; electronic medical record; electronic health record

Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.