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Benchmarking HIV Quality Measures in the US OPERA HIV Cohort

Benchmarking HIV Quality Measures in the US OPERA HIV Cohort Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 1 2 3 2 2 Robert Heglar , Rodney Mood , Julie L. Priest , Kathy L. Schulman , Gregory P. Fusco Author affiliations: 1 2 Familiy Medicine, AIDS Healthcare Foundation, Fort Lauderdale, FL, USA; Health Economics and Outcomes Research, Epividian Inc., Durham, NC, USA; US Health Outcomes, ViiV Healthcare, Durham, NC, USA Corresponding author: Kathy L. Schulman Epividian Inc., 4505 Emperor Blvd, Suite 220, Durham, NC 27703, USA Office: (919) 827-0010 kathy.schulman@epividian.com Alternative corresponding author: Robert Heglar AIDS Healthcare Foundation, 1164 E. Oakland Pk Blvd, Fort Lauderdale, FL 33334 Office: (954) 561-6900 Robert.Heglar@aidshealth.org © The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution- NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Target journal: Clinical Infectious Disease Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Summary of key points: Trends in National Quality Forum quality measure performance have been established, with notable subgroup variability. Only the antiretroviral therapy prescription measure reached the 90% target; viral suppression rates lagged the 80% target. Engagement and retention in care declined over 2014−2016. Keywords: antiretroviral therapy, benchmarking, national quality forum, retention in care, quality measures. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Abstract: Background: Quality measures are effective tools to improve patient outreach, retention in care, adherence, and outcomes. This study benchmarks National Quality Forum-endorsed human immunodeficiency virus (HIV) quality measures in a United States clinical cohort. Methods: This observational study utilized prospectively-captured data from the Observational Pharmaco-Epidemiology Research and Analysis (OPERA) database over 2014−2016 to assess quality measure achievement among patients with HIV in terms of medical visit frequency (#2079), medical visit gaps (#2080), viral suppression (#2082), and antiretroviral therapy (ART) prescriptions (#2083). The proportion of patients meeting each measure was calculated. Generalized estimating equations assessed trends in measure achievement. Results: The OPERA sample included 23,059−42,285 patients with similar demographics and characteristics across measurement periods. Overall, 62%−66% of patients met the visit frequency measure (#2079), 81%−85% had no gaps between visits (#2080), 71%−73% achieved viral suppression (#2082), and 92%−94% were prescribed ART (#2083). The adjusted odds of achieving viral suppression and being prescribed ART increased over time by 3% and 19%, respectively, despite a significant decline in patient engagement (16% for #2079; 25% for #2080). Patients <30 years of age were significantly less likely to meet all measures than older patients (p<0.0001), with particularly low levels of engagement. Measure achievement also varied by gender, ethnicity, region and select clinical characteristics. Conclusion: Despite gains in the rate of ART prescription and viral suppression, there remains room for improvement in the care of patients with HIV. Strategies for quality improvement may be more effective if tailored by age group. Study funding: ViiV Healthcare (Study: HO-17-18909). Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Introduction Quality improvement is an essential component in human immunodeficiency virus (HIV) care models, but effective implementation requires a set of common and consistent measures to accompany an evaluation strategy, which can assess process and outcomes as they relate to the goals of care. The Health Resources and Services Administration, in conjunction with the National Quality Forum (NQF) and other organizations, developed and endorsed a standardized set of HIV quality indicators beginning in 2008 [1-4]. These indicators, used to evaluate provider and practice adherence to HIV care standards, are an integral part of the National HIV/Acquired Immune Deficiency Syndrome (AIDS) Strategy (NHAS) [3, 5]. A central goal of the NHAS strategy is for 90% of people living with HIV (PLWH) to know their diagnosis, 90% to be retained in care, and 80% of those with a diagnosis to be virologically suppressed by 2020 [5]. Additionally, although they differ slightly, the NHAS strategy is to align the United States (US) policy with the United Nations AIDS (UNAIDS) initiatives, commonly referred to as the 90−90−90 target [5, 6]. The UNAIDS goals, also to be achieved by 2020, are for 90% of all PLWH to know their HIV status, 90% of all people with diagnosed HIV infection to receive sustained antiretroviral therapy (ART), and 90% of all people receiving ART to be virologically suppressed [6]. However, achieving these targets requires a standardized monitoring and evaluation framework as well as the availability of these data in the public domain. Measures endorsed by the NQF are used by hospitals, healthcare systems, and government agencies for public reporting and quality improvement [7, 8]. Select measures are currently used to assess HIV care in federal programs, such as the Merit-based Incentive Payment System, the Ryan White HIV/AIDS Program, and the Medicaid Adult Core Set. However, though US healthcare providers are often required to calculate and submit HIV-specific quality measure achievement results related to their clinical practice, these measures are not consistently required across payer types or practices and as such, limited benchmarking data are available [1, 9, 10]. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Providing a point of reference or benchmark on four of the NQF-endorsed HIV quality measures, specifically those characterizing retention/engagement in care, ART prescription, and viral suppression, may enhance identification of populations that will benefit from quality improvement programs, especially when such benchmarking characterizes variability in measure achievement by select demographic and clinical characteristics. Benchmarking these measures using a large, geographically diverse, cohort of HIV patients, from a real-world setting in the US, may also help to assess progress to date nationally to both the NHAS and UNAIDS targets. Methods Study design and population This observational analysis of a US clinical cohort utilized prospectively-captured electronic health records (EHR) data from the Observational Pharmaco-Epidemiology Research & Analysis (OPERA) database and included data from 85 clinics across 54 cities (Figure 1). OPERA-participating physicians and ancillary healthcare providers have documented the care of over 77,108 PLWH (16.2% women), representing 8% of all PLWH linked to care in the U.S. Forty-six percent of the cohort was diagnosed with HIV on or after 2010; 22% prior to 2000. One third of patients are followed for at least 5 years, 13% for 10 or more years. Eighty-nine percent were ART experienced at their last follow-up. The OPERA database is refreshed from each clinic’s individual EHR daily. Proprietary algorithms are used to sort, classify, and aggregate the data pulled from each system. The process includes automated classification of clinical terms into common clinical terms with review by trained medical staff. The patient health data gathered, classified and aggregated includes medical and social history, visit dates, vital signs, lab orders and results, medications, problems & diagnoses, and procedures. Through their membership in OPERA, medical practices meet the Centers for Medicare & Medicaid Services EHR Incentive Program for Integration with a Specialized Registry. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 PLWH, who were seen at least once at an OPERA-participating clinic between January 1, 2013, and December 31, 2016, were included in the study. Diagnosis of HIV‐1 was confirmed with evidence of a positive HIV‐1 Western Blot, enzyme-linked immunosorbent assay, or viral load test. Study endpoints NQF-endorsed quality measures Four NQF-endorsed measures were included as primary outcomes (Table 1). Two measures were used as markers of engagement and/or retention; measure #2079 HIV medical visit frequency and measure #2080 No gaps in HIV visits measure. The latter measure was inverted from the original NQF-endorsed Gap in HIV medical visits measure so that all measures would consistently correlate higher performance rates with the targeted positive treatment and outcomes. The #2082 HIV viral load suppression and #2083 Prescription of ART measures assessed the proportion of patients with an HIV viral load <200 copies/mL or with ≥1 prescription for any ART during the measurement period, respectively. The measurement periods for all measures were each of the full calendar years within the study (i.e., 2014, 2015, and 2016), except for the #2079 HIV medical visit frequency measure, where a 24-month measurement period was required. Patient characteristics Unless otherwise specified, all patient demographics and clinical characteristics were captured as of January 1 of each measurement year. AIDS-defining events (ADE) were defined according to the 1993 Centers for Disease Control and Prevention AIDS case definition [11]. The Veterans Aging Cohort Study (VACS) mortality index scores were determined by summing pre-assigned points for age, CD4 count, HIV-1 RNA, hemoglobin, platelets, aspartate and alanine transaminase, creatinine, and viral hepatitis C infection [12], and were only calculated for patients with test results for all score components in the 12 months prior to each measurement period. Comorbidities were based on documented diagnoses (either code based or extracted as per text strings with logic applied to Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 exclude rule out diagnoses). The only exception was renal impairment and chronic kidney disease which were based on estimated glomerular filtration rates. Statistical analysis The proportion of patients meeting the criteria for each measure was calculated for each measurement period (2014, 2015, and 2016). For NQF measure #2079 (HIV medical visit frequency), where a 24-month evaluation period was required, the displayed calendar year was the period in which the last 12 months were measured. Measure achievement ratios were not standardized to account for changes in OPERA demographics. Patient characteristics were summarized using medians and interquartile ranges for continuous data and percentages for categorical data. Patient characteristics were presented in aggregate at the population level for each measure and contrasted at measure level by criteria achievement (met vs. not met) using Pearson’s chi-square or Fisher exact tests for categorical variables and Wilcoxon rank-sum test for continuous variables. The Sidak correction was applied to account for multiple comparisons between groups. Generalized estimating equations were used to fit four repeated measures logistic regression models to assess trends in measure achievement over time while controlling for changes in demographics and HIV-specific clinical characteristics (adjusted analyses). OPERA complies with all Health Insurance Portability and Accountability Act and Health Information Technology for Economic and Clinical Health requirements and receives annual institutional review board approval by Advarra, including a waiver of informed consent and authorization for use of protected health information. Results Description of the OPERA population Through 2016, there were 75,579 patients with HIV in the OPERA database, representing approximately 8% of patients with HIV diagnosed and linked to care in the US. The number of patients for each measurement period ranged from 23,059 patients for the #2079 HIV medical visit Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 frequency measure in 2014 to 42,285 patients for the #2082 HIV viral load suppression and #2083 Prescription of ART measures in 2016 (Table 2). The median age across 2014−2016 was 45 years; 15%−18% of patients were <30 years of age, 46%−51% were 30−49 years of age, and 35%−37% were ≥50 years of age. Although median age was constant across the measuring period, the proportions of patients <30 and ≥50 years of age significantly increased over time (p<0.0001). Overall, 83% of patients were male and 45%−49% were classified as men who have sex with men (MSM). Over 2014−2016, 35%−40% of patients were African-American, and 24%−25% were Hispanic or Latino. There was a moderate increase in the proportion of African-American patients over time (Table 2). Over half of patients (54%−56%) resided in the Southern US (Table 2). The proportion of patients residing in the Northeast, South, and Midwest increased over time, and there was a corresponding decrease in those residing in the West (p<0.0001). Viral load and VACS score remained consistent across the measurement periods, whereas CD4 cell count increased over 2014−2016 (p<0.0001). Across the measurement periods, the proportion of patients who had a history of syphilis, diabetes mellitus, mild renal impairment, and moderate/severe chronic kidney disease increased (p<0.0001) whereas ADE, hyperlipidemia, and chronic hepatitis C significantly decreased (p<0.0001). Patients meeting NQF-endorsed measures Figure 2 depicts the percentage of patients who met the criteria for each quality measure. Patient engagement diminished over 2014−2016, as evidenced by decreases in the percentage of patients with visits at least every 6 months over both a 12-month (#2080 No gaps in HIV visits) and a 24- month (#2079 HIV medical visit frequency) period. In contrast, there were modest increases in the proportion of patients meeting the #2082 HIV viral load suppression measure or the #2083 Prescription of ART measure. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 The unadjusted trends (Figure 2) varied by age group, with younger patients significantly less likely to meet each of the NQF measures. While levels of engagement in patients <30 years of age was largely unchanged across performance years (~50% measure #2079, ~76% #2080), the proportion of patients prescribed ART increased from 85.1% to 91.0% and the proportion achieving viral suppression increased from 57.8% to 64.5%. Levels of engagement/retention were highest among patients ≥50 years of age (~73% measure #2079, ~87% #2080), although still below the 90% NHAS target. Moreover, levels of engagement/retention in this population appeared to decrease over time in the unadjusted analysis. Despite decreasing levels of engagement across performance years, the proportion of patients ≥50 years of age prescribed ART increased from 94.4% to 95.1% and the proportion achieving viral suppression increased from 78.4% to 79.4%. Measure achievement also varied by gender, ethnicity, census region, and risk of infection. There were significant differences in the distribution of key covariates in the underlying OPERA population across performance years (Table 2). Since rates of measure achievement varied notably by age, gender, ethnicity, census region, risk of infection and several other clinical characteristics, no conclusion could be drawn regarding trends in measure achievement in the unadjusted analysis. Figure 3 presents adjusted odds ratios for measure achievement by select demographic and clinical characteristics. Similar to the unadjusted analyses, patients <30 years of age were significantly less likely to achieve each of the four quality measures compared with patients 30−49 years of age, whereas patients ≥50 years of age were significantly more likely to meet all measure criteria versus patients 30−49 years of age (both p<0.0001). In addition, female (vs. male), MSM (vs. not MSM) and Northeastern US (vs. Southern US) patients were significantly more likely to meet all measures (all p<0.0001). Hispanic/Latino patients were significantly more likely to meet all measure criteria compared with non-Hispanic/Latino patients (p<0.0001), except for the #2083 Prescription of ART measure, which was not significant (p=0.93). Patients with a history of ADE had lower odds of meeting the #2080 No gaps in HIV visits measure; however, they were significantly more likely to Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 achieve the #2079 HIV medical visit frequency, #2082 HIV viral load suppression, and #2083 Prescription of ART measures (p≤0.0001). Compared with non-African-American patients, African- American patients had greater odds of meeting the #2079 HIV medical visit frequency, #2080 No gaps in HIV visits, and #2083 Prescription of ART measures (all p≤0.02); however, they were significantly less likely to achieve the #2082 HIV viral load suppression measure (p<0.0001). Table 3 presents both the unadjusted and adjusted odds of measure achievement associated with each measurement year. The adjusted odds of meeting #2079 HIV medical visit frequency and #2080 No gaps in HIV visits measures decreased by 25% and 17% per calendar year, respectively (both p<0.0001). Conversely, the adjusted odds of meeting measures #2082 HIV viral load suppression and #2083 Prescription of ART were increased by 3% and 19%, per calendar year, respectively (both p<0.0001). These data suggest that even after adjusting for changes in the underlying OPERA population, there was an increase in the odds of meeting the ART prescription measure despite decreases in the engagement/retention measures. Moreover, comparatively large increases in the odds of meeting the ART prescription measure (19%) did not translate to the same magnitude of increase in achievement of viral suppression (3%). Discussion This study provides important real-world data on rates of measure achievement for four NQF- endorsed HIV-specific quality measures over 2014−2016 in a large, geographically diverse cohort of PLWH in the US. These data not only establish trends in measure achievement for engagement and retention in care, ART prescription, and viral suppression over time, but also demonstrate the variability in measure achievement across a wide array of demographic and clinical characteristics. This type of benchmarking is critical for helping payers, providers, and state governments identify areas to improve patient care, facilitate achievement of NHAS and UNAIDS targets, and develop future impactful measures. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 In this analysis, only the #2083 Prescription of ART measure reached the 90% NHAS and UNAIDS threshold across all measurement periods, with the adjusted odds of meeting this measure increasing by 19% over 2014−2016. Despite improvement in ART prescription measure achievement, the proportion of virally suppressed patients continues to fall short of the NHAS 80% (UNAIDS 90%) target (73% in 2016), with the odds of achieving this measure increasing by only 3% between 2014 and 2016. It is important to note that the ART prescription measure only assesses whether a patient was prescribed ART during a measurement period [8] but does not evaluate complete regimens or consider multiple prescriptions over time; therefore, it does not measure treatment adherence. In addition to early linkage and retention in care, adherence to ART is essential for both achieving and maintaining virologic suppression. [13-15]. Poor or non-adherence to ART in PLWH has been widely associated with higher morbidity and mortality, increased risk of HIV transmission, and ART drug resistance [16]. Despite the negative outcomes associated with suboptimal adherence, adherence rates continue to be low, ranging from 27% to 80% across different populations. [17, 18] still generally below the threshold targeted (80-95%) to achieve viral suppression [17-20]. Study finding are similar to those reported by Bradley et al. in a 2016 study which used data from the Medical Monitoring Project (MMP) to assess trends in ART prescription and viral suppression. [21] The authors conclude that rates of ART prescription rose significantly (p<.01) from 89% in 2009 to 94% in 2013, slightly higher than the 92-94% (2014-2016) we report in the current study. Rates of viral suppression in the MMP based study rose from 72% in 2009 to 80% in 2013, higher than the 71%-73% viral suppression rate observed in the current study. Differences in the rate of viral suppression between the studies may be an artifact of population selection as patients with any encounter during a performance year are included in the NQF performance measure even if that encounter occurs towards the end of the year. In contrast, the Bradley study restricted eligibility to those with an encounter in the first four months of the year evaluated, allowing time for newly diagnosed patients prescribed ART to achieve suppression. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Rates of retention observed in the current study are similar to those reported by Rebeiro and colleagues in a 2016 study. The authors used data from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) to evaluate geographic variation in retention among patients who had been successfully linked to, and established, in-care.[22] Retention rates, which rose significantly (p<.01) during the course of the study (2000-2010), were highest in the Midwest (87%) and lowest in the West (72%). Retention rates in the current study, using a comparable retention definition (NQF #2080), ranged from 80-85%. In contrast to the NA-ACCORD study, the odds of meeting this retention metric in the current study decreased significantly (p<.0001). The decreasing levels of care engagement observed in our study may be an artifact of increasing ART prescription, as better drugs translate into guidelines that recommend starting treatment early with less frequent monitoring. Given the potency of newer, first line, therapies, many providers are seeing patients more frequently. Accordingly, the decrease observed in patient engagement between 2014 and 2016 in the current study may therefore be the product of changes in practice patterns and NQF measure requirement that patients be seen once every six months. While the definition of retention remains in flux, early engagement in care following diagnosis and subsequent retention in care remain critical elements of the HIV care continuum and have been associated with increased viral suppression and reduced viral load burden in PLWH [23-26]. This study highlights a clear opportunity for improvement in achieving various HIV quality care measures, particularly for viral suppression. While the odds of achieving viral suppression increased significantly between 2014 and 2016, only 73% of patients met the measure, despite ART prescription rates above both NHAS and UNAIDS targets. In this analysis, measure achievement varied by key patient characteristics including age, gender, race, and ethnicity. The most notable variation was observed in different age groups, whereby older patients (≥50 years of age) had the greatest levels of achievement across all measures and Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 measurement periods evaluated. Younger patients (<30 years of age) had the lowest levels of measure achievement and were significantly less likely to meet each of the four measures compared with patients 30−49 years of age. This finding is in line with previous studies, where the proportions of patients meeting HIV quality care measures of ART adherence and care retention were also lower in younger versus older patients [10, 27, 28]. Keeping younger people engaged in care is of concern, especially among patients <30 years of age; this study showed that only half of younger patients met the visit frequency measure, and a quarter had a gap in their visit schedule. In addition, while ART prescription in younger patients rose above the 90% threshold for the first time in 2016, levels of viral suppression remained low at 65%. This may be indicative of an overall lack of engagement with care for younger PLWH. Younger PLWH have been reported to have lower performance than older adults in all steps of the cascade of care, resulting in less than 6% maintaining viral suppression [29]. Moreover, younger people in the US continue to represent an active population for HIV, with people 13–24 years of age representing 21% of new HIV diagnoses in 2016 [30]. Therefore, effective interventions tailored for younger PLWH are needed to successfully engage them in the cascade of HIV care programs [29]. Our study is not without limitations and results should therefore be interpreted with caution. Like other observational analyses, this study may be subject to potential information- and confounding- bias due to missing data and unknown confounders that were not included in the EHR data. Additionally, although the OPERA database includes complete patient health records managed in EHR systems, several issues confronting population-level assessments need to be highlighted. These include the effects of differential medical care by practice size and specialty, the academic and research orientation of the healthcare practitioner, gender- and ethnic-based attitudes, and geographical regional healthcare practices. As data are collected at the point-of-care, they are also subject to the record-keeping practices of each healthcare provider and the standards of each clinic or organization. Patients may also consult multiple physician practices for various conditions, which might result in incomplete case ascertainment. To mitigate information bias, OPERA employs a host Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 of quality assurances processes, most notably the use of proprietary algorithms to sort, classify and aggregate patient diagnoses and procedures from all major coding systems in addition to text based natural language processing. This process includes automated classification of clinical terms which are reviewed by trained medical staff. Finally, data are collected for the medical management of patients and are not directly intended for research purposes. Key strengths of the study include the large sample size and a geographically diverse cohort of PLWH, which expands the current knowledge gained from smaller, geographically-centered studies of HIV quality care measures. Although this study identified various areas for improvement across the evaluated measures, further research is required to investigate the underlying reasons for these identified gaps in HIV care. Conclusions This real-world evaluation of four NQF-endorsed measures of HIV quality care demonstrates that improvement is needed in the care of PLWH, with care engagement and viral suppression levels continuing to fall short of both NHAS and UNAIDS targets. Strategies for quality improvement may be more effective if tailored by age group. Funding This work was supported by ViiV Healthcare (HO-17-18909). The funders of the study had a role in study design, data analysis, data interpretation, and writing of the report and are also funding the article processing charges and open access fee. Authorship All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this manuscript, take responsibility for the integrity of the work, contributed to the writing and reviewing of the manuscript, and have given final approval of the version to be published. RH, RM, KLS, and GPF had full access to all the data in this study and take complete responsibility for the integrity of the data and accuracy of the data analysis. RH, RM, KLS, JLP, and Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 GPF were involved in the conception and design of the study and data interpretation. RH, RM, and GPF were involved in the acquisition of data. KLS and RM were involved in the data analysis. Conflicts of interest RH is an employee of AIDS Healthcare Foundation. RM, KLS and GPF are the employees of Epividian Inc. JLP is an employee of ViiV Healthcare and holds stocks and shares in GSK as part of her employment. Acknowledgments Editorial support (in the form of writing assistance during development of the initial draft, assembling tables and figures, collating authors’ comments, grammatical editing, and referencing) was provided by Meghan Betts, PhD, and Eshvendar Reddy, PhD, of Fishawack Indicia Ltd, UK, and was funded by ViiV Healthcare. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 References: 1. Backus LI, Boothroyd DB, Phillips BR, et al. 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Yehia BR, Stewart L, Momplaisir F, et al. Barriers and facilitators to patient retention in HIV care. BMC Infect Dis 2015; 15: 246. 25. Gardner EM, McLees MP, Steiner JF, Del Rio C, Burman WJ. The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clin Infect Dis 2011; 52(6): 793-800. 26. Reveles KR, Juday TR, Labreche MJ, et al. Comparative value of four measures of retention in expert care in predicting clinical outcomes and health care utilization in HIV patients. PLoS One 2015; 10(3): e0120953. 27. Yehia BR, Fleishman JA, Metlay JP, et al. Comparing different measures of retention in outpatient HIV care. AIDS 2012; 26(9): 1131–9. 28. Silverberg MJ, Leyden W, Horberg MA, DeLorenze GN, Klein D, Quesenberry CP, Jr. Older age and the response to and tolerability of antiretroviral therapy. Arch Intern Med 2007; 167(7): 684–91. 29. Zanoni BC, Mayer KH. The adolescent and young adult HIV cascade of care in the United States: exaggerated health disparities. AIDS Patient Care STDS 2014; 28(3): 128–35. 30. Center for Disease Control and Prevention. HIV Among Youth. Available at: https://www.cdc.gov/hiv/group/age/youth/index.html. Accessed 03 December 2018. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Figure 1: HIV-infected population and OPERA clinic locations in the US HIV-infection based on CDC 2008–2010 data. CDC, Center for Disease Control and Prevention; HIV, human immunodeficiency virus; OPERA, Observational Pharmaco‐ Epidemiology Research and Analysis; US, United States. Figure 2: Unadjusted proportion of patients meeting NQF-endorsed quality measures by measure and measurement period. ART, antiretroviral therapy; HIV, human immunodeficiency virus; NQF, National Quality Forum. Figure 3: Adjusted odds ratios for NQF-endorsed quality measures by select demographics over 2014–2016 (GEE model) *The history of syphilis covariate was removed from the #2079 HIV medical visit frequency quality measure model as it neither improved model fit nor was significant. NQF-endorsed measure definitions: #2079 HIV medical visit frequency; #2080 No gaps in HIV visits; #2082 HIV viral load suppression; #2083 Prescription of ART. ADE, AIDS-defining event; ART, antiretroviral therapy; CI, confidence interval; GEE, generalized estimating equations; HIV, human immunodeficiency virus; MSM, men who have sex with men; NQF, National Quality forum; OR, odds ratio. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Tables and figures Table 1: NQF-endorsed quality measures NQF-endorsed quality measure Measure description #2079 Percentage of patients with HIV with ≥1 medical visit in each 6-month period of the 24-month measurement period HIV medical visit frequency (minimum of 60 days between visits), used to gauge engagement or retention of care. Percentage of patients with HIV who had a medical visit in both the first and last 6 months of the 12-month #2080 measurement period, also used to gauge engagement or retention of care. This measure was inverted from the No gaps in HIV visits original NQF-endorsed Gap in HIV medical visits measure. #2082 Percentage of patients with a HIV viral load <200 copies/mL at their last HIV viral load test during the measurement HIV viral load suppression period. #2083 Percentage of patients with HIV with ≥1 prescription for ART at any point during the measurement period. Prescription of ART ART, antiretroviral therapy; HIV, human immunodeficiency virus; NQF, National Quality Forum. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Table 2: Demographics and clinical characteristics of the OPERA study population Measurement period Demographics 2014 2015 2016 p-value for trend* (N=34,657) (N=37,187) (N=42,285) Age, years, n (%) <0.0001 <30 5051 (14.6) 6020 (16.2) 7421 (17.5) 30–49 17,581 (50.7) 17,852 (48.0) 19,433 (46.0) ≥50 12,025 (34.7) 13,315 (35.8) 15,431 (36.5) Median (IQR) 45.1 (35.1, 52.1) 45.1 (34.1, 53.1) 45.1 (33.1, 53.1) 0.0003 Gender 0.9537 Male 28,847 (83.2) 30,914 (83.1) 35,187 (83.2) Female 5800 (16.7) 6254 (16.8) 7069 (16.7) Race <0.0001 African-American 12,183 (35.2) 13,726 (36.9) 16,735 (39.6) Not African-American 20,741 (59.8) 21,850 (58.8) 23,757 (56.2) Unknown 1733 (5.0) 1611 (4.3) 1793 (4.2) Ethnicity <0.0001 Hispanic/Latino 8539 (24.6) 9287 (25.0) 10,302 (24.4) Not Hispanic/Latino 24,743 (71.4) 26,693 (71.8) 30,586 (72.3) Unknown 1375 (4.0) 1207 (3.2) 1397 (3.3) Region <0.0001 South 18,875 (54.5) 20,239 (54.4) 23,862 (56.4) West 12,515 (36.1) 13,090 (35.2) 13,579 (32.1) Northeast 2982 (8.6) 3373 (9.1) 4136 (9.8) Midwest 285 (0.8) 484 (1.3) 708 (1.7) Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Payer Commercial 7,768 (22.4) 9410 (25.3) 11,712 (27.7) <0.0001 Medicaid 7044 (20.3) 7667 (20.6) 7730 (18.3) <0.0001 Medicare 3531 (10.2) 3772 (10.1) 4068 (9.6) 0.0071 Ryan White 7304 (21.1) 7640 (20.5) 7616 (18.0) <0.0001 Other payer 98 (0.3) 139 (0.4) 113 (0.3) 0.5830 No Payer Information 11,733 (33.9) 11,743 (31.6) 13,918 (32.9) 0.0154 Risk of infection <0.0001 MSM 16,855 (48.6) 17,267 (46.4) 19,164 (45.3) Not MSM 17,802 (51.4) 19,920 (53.6) 23,121 (54.7) History of syphilis 7918 (22.8) 8999 (24.2) 10,343 (24.5) <0.0001 History of ADE 6240 (18.0) 6289 (16.9) 6472 (15.3) <0.0001 Viral load, copies/mL <0.0001 Median (IQR) 19.0 (19.0, 88.0) 19.0 (19.0, 40.0) 19.0 (19.0, 35.0) CD4 count, cells/μL <0.0001 Median (IQR) 564.0 (386.0, 578.0 (390.0, 592.0 (403.0, 770.0) 786.0) 810.0) Common comorbidities Hyperlipidemia 11,039 (31.9) 11,753 (31.6) 12,706 (30.0) <0.0001 Hypertension 8905 (25.7) 9,915 (26.7) 10,996 (26.0) 0.4188 Mild renal impairment 6159 (17.8) 7296 (19.6) 8753 (20.7) <0.0001 Anxiety disorders 5924 (17.1) 6611 (17.8) 7330 (17.3) 0.4551 Depression 3630 (10.5) 3896 (10.5) 4251 (10.1) 0.0482 Diabetes mellitus 2660 (7.7) 3088 (8.3) 3569 (8.4) 0.0002 Chronic Hepatitis C 2365 (6.8) 2391 (6.4) 2348 (5.6) <0.0001 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Moderate/Severe CKD 799 (2.3) 1047 (2.8) 1394 (3.3) <0.0001 VACS score Median (IQR) 13.0 (6.0, 24.0) 13.0 (6.0, 25.0) 13.0 (6.0, 24.0) 0.1816 *Sidak Correction was applied (adjusted α<0.001) to adjust for multiple comparisons and statistically significant results are bolded. Data are presented as n (%) unless otherwise stated. Renal status is based on the last two consecutive CKD-EPI calculated eGFR results prior to the start of the calendar year. ADE, AIDS-defining event; AIDS, acquired immunodeficiency syndrome; CD4, cluster of differentiation 4; CKD-EPI, chronic kidney disease epidemiology collaboration; eGFR, estimate glomerular filtrate rate; IQR, interquartile range; MSM, men who have sex with men; OPERA, Observational Pharmaco-Epidemiology Research and Analysis; VACS, veterans aging cohort study. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Table 3: Odds ratios for NFQ-endorsed measures over 2014–2016 Unadjusted Adjusted* NFQ-endorsed quality measure Per Calendar Year Per Calendar Year Odds ratio (95% Cl) Odds ratio (95% Cl) #2079 0.77 (0.76, 0.78) 0.75 (0.74, 0.77)§ HIV medical visit frequency #2080 0.84 (0.82, 0.86) 0.83 (0.81, 0.85)§ No gaps in HIV visits #2082 1.03 (1.02, 1.05) 1.03 (1.02, 1.05)§ HIV viral load suppression #2083 1.18 (1.15, 1.20) 1.19 (1.16, 1.22)§ Prescription of ART *Adjusted for changes in demographics and HIV-specific clinical characteristics. § p<0.0001 ART, antiretroviral therapy; CI, confidence interval; HIV, human immunodeficiency virus; NQF, National Quality Forum. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Figure 1 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Figure 2 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Figure 3 Accepted Manuscript http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Forum Infectious Diseases Oxford University Press

Benchmarking HIV Quality Measures in the US OPERA HIV Cohort

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Oxford University Press
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© The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America.
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2328-8957
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10.1093/ofid/ofz418
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Abstract

Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 1 2 3 2 2 Robert Heglar , Rodney Mood , Julie L. Priest , Kathy L. Schulman , Gregory P. Fusco Author affiliations: 1 2 Familiy Medicine, AIDS Healthcare Foundation, Fort Lauderdale, FL, USA; Health Economics and Outcomes Research, Epividian Inc., Durham, NC, USA; US Health Outcomes, ViiV Healthcare, Durham, NC, USA Corresponding author: Kathy L. Schulman Epividian Inc., 4505 Emperor Blvd, Suite 220, Durham, NC 27703, USA Office: (919) 827-0010 kathy.schulman@epividian.com Alternative corresponding author: Robert Heglar AIDS Healthcare Foundation, 1164 E. Oakland Pk Blvd, Fort Lauderdale, FL 33334 Office: (954) 561-6900 Robert.Heglar@aidshealth.org © The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution- NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Target journal: Clinical Infectious Disease Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Summary of key points: Trends in National Quality Forum quality measure performance have been established, with notable subgroup variability. Only the antiretroviral therapy prescription measure reached the 90% target; viral suppression rates lagged the 80% target. Engagement and retention in care declined over 2014−2016. Keywords: antiretroviral therapy, benchmarking, national quality forum, retention in care, quality measures. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Abstract: Background: Quality measures are effective tools to improve patient outreach, retention in care, adherence, and outcomes. This study benchmarks National Quality Forum-endorsed human immunodeficiency virus (HIV) quality measures in a United States clinical cohort. Methods: This observational study utilized prospectively-captured data from the Observational Pharmaco-Epidemiology Research and Analysis (OPERA) database over 2014−2016 to assess quality measure achievement among patients with HIV in terms of medical visit frequency (#2079), medical visit gaps (#2080), viral suppression (#2082), and antiretroviral therapy (ART) prescriptions (#2083). The proportion of patients meeting each measure was calculated. Generalized estimating equations assessed trends in measure achievement. Results: The OPERA sample included 23,059−42,285 patients with similar demographics and characteristics across measurement periods. Overall, 62%−66% of patients met the visit frequency measure (#2079), 81%−85% had no gaps between visits (#2080), 71%−73% achieved viral suppression (#2082), and 92%−94% were prescribed ART (#2083). The adjusted odds of achieving viral suppression and being prescribed ART increased over time by 3% and 19%, respectively, despite a significant decline in patient engagement (16% for #2079; 25% for #2080). Patients <30 years of age were significantly less likely to meet all measures than older patients (p<0.0001), with particularly low levels of engagement. Measure achievement also varied by gender, ethnicity, region and select clinical characteristics. Conclusion: Despite gains in the rate of ART prescription and viral suppression, there remains room for improvement in the care of patients with HIV. Strategies for quality improvement may be more effective if tailored by age group. Study funding: ViiV Healthcare (Study: HO-17-18909). Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Introduction Quality improvement is an essential component in human immunodeficiency virus (HIV) care models, but effective implementation requires a set of common and consistent measures to accompany an evaluation strategy, which can assess process and outcomes as they relate to the goals of care. The Health Resources and Services Administration, in conjunction with the National Quality Forum (NQF) and other organizations, developed and endorsed a standardized set of HIV quality indicators beginning in 2008 [1-4]. These indicators, used to evaluate provider and practice adherence to HIV care standards, are an integral part of the National HIV/Acquired Immune Deficiency Syndrome (AIDS) Strategy (NHAS) [3, 5]. A central goal of the NHAS strategy is for 90% of people living with HIV (PLWH) to know their diagnosis, 90% to be retained in care, and 80% of those with a diagnosis to be virologically suppressed by 2020 [5]. Additionally, although they differ slightly, the NHAS strategy is to align the United States (US) policy with the United Nations AIDS (UNAIDS) initiatives, commonly referred to as the 90−90−90 target [5, 6]. The UNAIDS goals, also to be achieved by 2020, are for 90% of all PLWH to know their HIV status, 90% of all people with diagnosed HIV infection to receive sustained antiretroviral therapy (ART), and 90% of all people receiving ART to be virologically suppressed [6]. However, achieving these targets requires a standardized monitoring and evaluation framework as well as the availability of these data in the public domain. Measures endorsed by the NQF are used by hospitals, healthcare systems, and government agencies for public reporting and quality improvement [7, 8]. Select measures are currently used to assess HIV care in federal programs, such as the Merit-based Incentive Payment System, the Ryan White HIV/AIDS Program, and the Medicaid Adult Core Set. However, though US healthcare providers are often required to calculate and submit HIV-specific quality measure achievement results related to their clinical practice, these measures are not consistently required across payer types or practices and as such, limited benchmarking data are available [1, 9, 10]. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Providing a point of reference or benchmark on four of the NQF-endorsed HIV quality measures, specifically those characterizing retention/engagement in care, ART prescription, and viral suppression, may enhance identification of populations that will benefit from quality improvement programs, especially when such benchmarking characterizes variability in measure achievement by select demographic and clinical characteristics. Benchmarking these measures using a large, geographically diverse, cohort of HIV patients, from a real-world setting in the US, may also help to assess progress to date nationally to both the NHAS and UNAIDS targets. Methods Study design and population This observational analysis of a US clinical cohort utilized prospectively-captured electronic health records (EHR) data from the Observational Pharmaco-Epidemiology Research & Analysis (OPERA) database and included data from 85 clinics across 54 cities (Figure 1). OPERA-participating physicians and ancillary healthcare providers have documented the care of over 77,108 PLWH (16.2% women), representing 8% of all PLWH linked to care in the U.S. Forty-six percent of the cohort was diagnosed with HIV on or after 2010; 22% prior to 2000. One third of patients are followed for at least 5 years, 13% for 10 or more years. Eighty-nine percent were ART experienced at their last follow-up. The OPERA database is refreshed from each clinic’s individual EHR daily. Proprietary algorithms are used to sort, classify, and aggregate the data pulled from each system. The process includes automated classification of clinical terms into common clinical terms with review by trained medical staff. The patient health data gathered, classified and aggregated includes medical and social history, visit dates, vital signs, lab orders and results, medications, problems & diagnoses, and procedures. Through their membership in OPERA, medical practices meet the Centers for Medicare & Medicaid Services EHR Incentive Program for Integration with a Specialized Registry. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 PLWH, who were seen at least once at an OPERA-participating clinic between January 1, 2013, and December 31, 2016, were included in the study. Diagnosis of HIV‐1 was confirmed with evidence of a positive HIV‐1 Western Blot, enzyme-linked immunosorbent assay, or viral load test. Study endpoints NQF-endorsed quality measures Four NQF-endorsed measures were included as primary outcomes (Table 1). Two measures were used as markers of engagement and/or retention; measure #2079 HIV medical visit frequency and measure #2080 No gaps in HIV visits measure. The latter measure was inverted from the original NQF-endorsed Gap in HIV medical visits measure so that all measures would consistently correlate higher performance rates with the targeted positive treatment and outcomes. The #2082 HIV viral load suppression and #2083 Prescription of ART measures assessed the proportion of patients with an HIV viral load <200 copies/mL or with ≥1 prescription for any ART during the measurement period, respectively. The measurement periods for all measures were each of the full calendar years within the study (i.e., 2014, 2015, and 2016), except for the #2079 HIV medical visit frequency measure, where a 24-month measurement period was required. Patient characteristics Unless otherwise specified, all patient demographics and clinical characteristics were captured as of January 1 of each measurement year. AIDS-defining events (ADE) were defined according to the 1993 Centers for Disease Control and Prevention AIDS case definition [11]. The Veterans Aging Cohort Study (VACS) mortality index scores were determined by summing pre-assigned points for age, CD4 count, HIV-1 RNA, hemoglobin, platelets, aspartate and alanine transaminase, creatinine, and viral hepatitis C infection [12], and were only calculated for patients with test results for all score components in the 12 months prior to each measurement period. Comorbidities were based on documented diagnoses (either code based or extracted as per text strings with logic applied to Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 exclude rule out diagnoses). The only exception was renal impairment and chronic kidney disease which were based on estimated glomerular filtration rates. Statistical analysis The proportion of patients meeting the criteria for each measure was calculated for each measurement period (2014, 2015, and 2016). For NQF measure #2079 (HIV medical visit frequency), where a 24-month evaluation period was required, the displayed calendar year was the period in which the last 12 months were measured. Measure achievement ratios were not standardized to account for changes in OPERA demographics. Patient characteristics were summarized using medians and interquartile ranges for continuous data and percentages for categorical data. Patient characteristics were presented in aggregate at the population level for each measure and contrasted at measure level by criteria achievement (met vs. not met) using Pearson’s chi-square or Fisher exact tests for categorical variables and Wilcoxon rank-sum test for continuous variables. The Sidak correction was applied to account for multiple comparisons between groups. Generalized estimating equations were used to fit four repeated measures logistic regression models to assess trends in measure achievement over time while controlling for changes in demographics and HIV-specific clinical characteristics (adjusted analyses). OPERA complies with all Health Insurance Portability and Accountability Act and Health Information Technology for Economic and Clinical Health requirements and receives annual institutional review board approval by Advarra, including a waiver of informed consent and authorization for use of protected health information. Results Description of the OPERA population Through 2016, there were 75,579 patients with HIV in the OPERA database, representing approximately 8% of patients with HIV diagnosed and linked to care in the US. The number of patients for each measurement period ranged from 23,059 patients for the #2079 HIV medical visit Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 frequency measure in 2014 to 42,285 patients for the #2082 HIV viral load suppression and #2083 Prescription of ART measures in 2016 (Table 2). The median age across 2014−2016 was 45 years; 15%−18% of patients were <30 years of age, 46%−51% were 30−49 years of age, and 35%−37% were ≥50 years of age. Although median age was constant across the measuring period, the proportions of patients <30 and ≥50 years of age significantly increased over time (p<0.0001). Overall, 83% of patients were male and 45%−49% were classified as men who have sex with men (MSM). Over 2014−2016, 35%−40% of patients were African-American, and 24%−25% were Hispanic or Latino. There was a moderate increase in the proportion of African-American patients over time (Table 2). Over half of patients (54%−56%) resided in the Southern US (Table 2). The proportion of patients residing in the Northeast, South, and Midwest increased over time, and there was a corresponding decrease in those residing in the West (p<0.0001). Viral load and VACS score remained consistent across the measurement periods, whereas CD4 cell count increased over 2014−2016 (p<0.0001). Across the measurement periods, the proportion of patients who had a history of syphilis, diabetes mellitus, mild renal impairment, and moderate/severe chronic kidney disease increased (p<0.0001) whereas ADE, hyperlipidemia, and chronic hepatitis C significantly decreased (p<0.0001). Patients meeting NQF-endorsed measures Figure 2 depicts the percentage of patients who met the criteria for each quality measure. Patient engagement diminished over 2014−2016, as evidenced by decreases in the percentage of patients with visits at least every 6 months over both a 12-month (#2080 No gaps in HIV visits) and a 24- month (#2079 HIV medical visit frequency) period. In contrast, there were modest increases in the proportion of patients meeting the #2082 HIV viral load suppression measure or the #2083 Prescription of ART measure. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 The unadjusted trends (Figure 2) varied by age group, with younger patients significantly less likely to meet each of the NQF measures. While levels of engagement in patients <30 years of age was largely unchanged across performance years (~50% measure #2079, ~76% #2080), the proportion of patients prescribed ART increased from 85.1% to 91.0% and the proportion achieving viral suppression increased from 57.8% to 64.5%. Levels of engagement/retention were highest among patients ≥50 years of age (~73% measure #2079, ~87% #2080), although still below the 90% NHAS target. Moreover, levels of engagement/retention in this population appeared to decrease over time in the unadjusted analysis. Despite decreasing levels of engagement across performance years, the proportion of patients ≥50 years of age prescribed ART increased from 94.4% to 95.1% and the proportion achieving viral suppression increased from 78.4% to 79.4%. Measure achievement also varied by gender, ethnicity, census region, and risk of infection. There were significant differences in the distribution of key covariates in the underlying OPERA population across performance years (Table 2). Since rates of measure achievement varied notably by age, gender, ethnicity, census region, risk of infection and several other clinical characteristics, no conclusion could be drawn regarding trends in measure achievement in the unadjusted analysis. Figure 3 presents adjusted odds ratios for measure achievement by select demographic and clinical characteristics. Similar to the unadjusted analyses, patients <30 years of age were significantly less likely to achieve each of the four quality measures compared with patients 30−49 years of age, whereas patients ≥50 years of age were significantly more likely to meet all measure criteria versus patients 30−49 years of age (both p<0.0001). In addition, female (vs. male), MSM (vs. not MSM) and Northeastern US (vs. Southern US) patients were significantly more likely to meet all measures (all p<0.0001). Hispanic/Latino patients were significantly more likely to meet all measure criteria compared with non-Hispanic/Latino patients (p<0.0001), except for the #2083 Prescription of ART measure, which was not significant (p=0.93). Patients with a history of ADE had lower odds of meeting the #2080 No gaps in HIV visits measure; however, they were significantly more likely to Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 achieve the #2079 HIV medical visit frequency, #2082 HIV viral load suppression, and #2083 Prescription of ART measures (p≤0.0001). Compared with non-African-American patients, African- American patients had greater odds of meeting the #2079 HIV medical visit frequency, #2080 No gaps in HIV visits, and #2083 Prescription of ART measures (all p≤0.02); however, they were significantly less likely to achieve the #2082 HIV viral load suppression measure (p<0.0001). Table 3 presents both the unadjusted and adjusted odds of measure achievement associated with each measurement year. The adjusted odds of meeting #2079 HIV medical visit frequency and #2080 No gaps in HIV visits measures decreased by 25% and 17% per calendar year, respectively (both p<0.0001). Conversely, the adjusted odds of meeting measures #2082 HIV viral load suppression and #2083 Prescription of ART were increased by 3% and 19%, per calendar year, respectively (both p<0.0001). These data suggest that even after adjusting for changes in the underlying OPERA population, there was an increase in the odds of meeting the ART prescription measure despite decreases in the engagement/retention measures. Moreover, comparatively large increases in the odds of meeting the ART prescription measure (19%) did not translate to the same magnitude of increase in achievement of viral suppression (3%). Discussion This study provides important real-world data on rates of measure achievement for four NQF- endorsed HIV-specific quality measures over 2014−2016 in a large, geographically diverse cohort of PLWH in the US. These data not only establish trends in measure achievement for engagement and retention in care, ART prescription, and viral suppression over time, but also demonstrate the variability in measure achievement across a wide array of demographic and clinical characteristics. This type of benchmarking is critical for helping payers, providers, and state governments identify areas to improve patient care, facilitate achievement of NHAS and UNAIDS targets, and develop future impactful measures. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 In this analysis, only the #2083 Prescription of ART measure reached the 90% NHAS and UNAIDS threshold across all measurement periods, with the adjusted odds of meeting this measure increasing by 19% over 2014−2016. Despite improvement in ART prescription measure achievement, the proportion of virally suppressed patients continues to fall short of the NHAS 80% (UNAIDS 90%) target (73% in 2016), with the odds of achieving this measure increasing by only 3% between 2014 and 2016. It is important to note that the ART prescription measure only assesses whether a patient was prescribed ART during a measurement period [8] but does not evaluate complete regimens or consider multiple prescriptions over time; therefore, it does not measure treatment adherence. In addition to early linkage and retention in care, adherence to ART is essential for both achieving and maintaining virologic suppression. [13-15]. Poor or non-adherence to ART in PLWH has been widely associated with higher morbidity and mortality, increased risk of HIV transmission, and ART drug resistance [16]. Despite the negative outcomes associated with suboptimal adherence, adherence rates continue to be low, ranging from 27% to 80% across different populations. [17, 18] still generally below the threshold targeted (80-95%) to achieve viral suppression [17-20]. Study finding are similar to those reported by Bradley et al. in a 2016 study which used data from the Medical Monitoring Project (MMP) to assess trends in ART prescription and viral suppression. [21] The authors conclude that rates of ART prescription rose significantly (p<.01) from 89% in 2009 to 94% in 2013, slightly higher than the 92-94% (2014-2016) we report in the current study. Rates of viral suppression in the MMP based study rose from 72% in 2009 to 80% in 2013, higher than the 71%-73% viral suppression rate observed in the current study. Differences in the rate of viral suppression between the studies may be an artifact of population selection as patients with any encounter during a performance year are included in the NQF performance measure even if that encounter occurs towards the end of the year. In contrast, the Bradley study restricted eligibility to those with an encounter in the first four months of the year evaluated, allowing time for newly diagnosed patients prescribed ART to achieve suppression. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Rates of retention observed in the current study are similar to those reported by Rebeiro and colleagues in a 2016 study. The authors used data from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) to evaluate geographic variation in retention among patients who had been successfully linked to, and established, in-care.[22] Retention rates, which rose significantly (p<.01) during the course of the study (2000-2010), were highest in the Midwest (87%) and lowest in the West (72%). Retention rates in the current study, using a comparable retention definition (NQF #2080), ranged from 80-85%. In contrast to the NA-ACCORD study, the odds of meeting this retention metric in the current study decreased significantly (p<.0001). The decreasing levels of care engagement observed in our study may be an artifact of increasing ART prescription, as better drugs translate into guidelines that recommend starting treatment early with less frequent monitoring. Given the potency of newer, first line, therapies, many providers are seeing patients more frequently. Accordingly, the decrease observed in patient engagement between 2014 and 2016 in the current study may therefore be the product of changes in practice patterns and NQF measure requirement that patients be seen once every six months. While the definition of retention remains in flux, early engagement in care following diagnosis and subsequent retention in care remain critical elements of the HIV care continuum and have been associated with increased viral suppression and reduced viral load burden in PLWH [23-26]. This study highlights a clear opportunity for improvement in achieving various HIV quality care measures, particularly for viral suppression. While the odds of achieving viral suppression increased significantly between 2014 and 2016, only 73% of patients met the measure, despite ART prescription rates above both NHAS and UNAIDS targets. In this analysis, measure achievement varied by key patient characteristics including age, gender, race, and ethnicity. The most notable variation was observed in different age groups, whereby older patients (≥50 years of age) had the greatest levels of achievement across all measures and Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 measurement periods evaluated. Younger patients (<30 years of age) had the lowest levels of measure achievement and were significantly less likely to meet each of the four measures compared with patients 30−49 years of age. This finding is in line with previous studies, where the proportions of patients meeting HIV quality care measures of ART adherence and care retention were also lower in younger versus older patients [10, 27, 28]. Keeping younger people engaged in care is of concern, especially among patients <30 years of age; this study showed that only half of younger patients met the visit frequency measure, and a quarter had a gap in their visit schedule. In addition, while ART prescription in younger patients rose above the 90% threshold for the first time in 2016, levels of viral suppression remained low at 65%. This may be indicative of an overall lack of engagement with care for younger PLWH. Younger PLWH have been reported to have lower performance than older adults in all steps of the cascade of care, resulting in less than 6% maintaining viral suppression [29]. Moreover, younger people in the US continue to represent an active population for HIV, with people 13–24 years of age representing 21% of new HIV diagnoses in 2016 [30]. Therefore, effective interventions tailored for younger PLWH are needed to successfully engage them in the cascade of HIV care programs [29]. Our study is not without limitations and results should therefore be interpreted with caution. Like other observational analyses, this study may be subject to potential information- and confounding- bias due to missing data and unknown confounders that were not included in the EHR data. Additionally, although the OPERA database includes complete patient health records managed in EHR systems, several issues confronting population-level assessments need to be highlighted. These include the effects of differential medical care by practice size and specialty, the academic and research orientation of the healthcare practitioner, gender- and ethnic-based attitudes, and geographical regional healthcare practices. As data are collected at the point-of-care, they are also subject to the record-keeping practices of each healthcare provider and the standards of each clinic or organization. Patients may also consult multiple physician practices for various conditions, which might result in incomplete case ascertainment. To mitigate information bias, OPERA employs a host Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 of quality assurances processes, most notably the use of proprietary algorithms to sort, classify and aggregate patient diagnoses and procedures from all major coding systems in addition to text based natural language processing. This process includes automated classification of clinical terms which are reviewed by trained medical staff. Finally, data are collected for the medical management of patients and are not directly intended for research purposes. Key strengths of the study include the large sample size and a geographically diverse cohort of PLWH, which expands the current knowledge gained from smaller, geographically-centered studies of HIV quality care measures. Although this study identified various areas for improvement across the evaluated measures, further research is required to investigate the underlying reasons for these identified gaps in HIV care. Conclusions This real-world evaluation of four NQF-endorsed measures of HIV quality care demonstrates that improvement is needed in the care of PLWH, with care engagement and viral suppression levels continuing to fall short of both NHAS and UNAIDS targets. Strategies for quality improvement may be more effective if tailored by age group. Funding This work was supported by ViiV Healthcare (HO-17-18909). The funders of the study had a role in study design, data analysis, data interpretation, and writing of the report and are also funding the article processing charges and open access fee. Authorship All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this manuscript, take responsibility for the integrity of the work, contributed to the writing and reviewing of the manuscript, and have given final approval of the version to be published. RH, RM, KLS, and GPF had full access to all the data in this study and take complete responsibility for the integrity of the data and accuracy of the data analysis. RH, RM, KLS, JLP, and Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 GPF were involved in the conception and design of the study and data interpretation. RH, RM, and GPF were involved in the acquisition of data. KLS and RM were involved in the data analysis. Conflicts of interest RH is an employee of AIDS Healthcare Foundation. RM, KLS and GPF are the employees of Epividian Inc. JLP is an employee of ViiV Healthcare and holds stocks and shares in GSK as part of her employment. Acknowledgments Editorial support (in the form of writing assistance during development of the initial draft, assembling tables and figures, collating authors’ comments, grammatical editing, and referencing) was provided by Meghan Betts, PhD, and Eshvendar Reddy, PhD, of Fishawack Indicia Ltd, UK, and was funded by ViiV Healthcare. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 References: 1. Backus LI, Boothroyd DB, Phillips BR, et al. National quality forum performance measures for HIV/AIDS care: the Department of Veterans Affairs' experience. Arch Intern Med 2010; 170(14): 1239-46. 2. Horberg MA, Aberg JA, Cheever LW, Renner P, O'Brien Kaleba E, Asch SM. Development of national and multiagency HIV care quality measures. Clin Infect Dis 2010; 51(6): 732-8. 3. Valdiserri RO, Forsyth AD, Yakovchenko V, Koh HK. Measuring what matters: development of standard HIV core indicators across the U.S. Department of Health and Human Services. Public Health Rep 2013; 128(5): 354-9. 4. HIV Medicine Association. Tools for Monitoring HIV Care: HIV Clinical Quality Measures. Available at: http://www.thebodypro.com/content/75884/tools-for-monitoring-hiv-care-hiv- clinical-quality.html. Accessed on 17 October, 2018 5. National HIV/AIDS strategy for the United States: Updated to 2020. https://files.hiv.gov/s3fs- public/nhas-update.pdf, 2015. 6. UNAIDS 90‐90‐90. 90-90-90 An ambitious treatment target to help end the AIDS epidemic. Available at: http://www.unaids.org/sites/default/files/media_asset/90‐90‐90_en.pdf. Accessed on 21 October, 2018. 7. Johnston S, Kendall C, Hogel M, McLaren M, Liddy C. Measures of Quality of Care for People with HIV: A Scoping Review of Performance Indicators for Primary Care. PLoS One 2015; 10(9): e0136757. 8. Department of Health and Human Services. NQF-Endorsed Measures for Infectious Disease, 2016-2017, Technical Report. Available from: http://www.qualityforum.org/Publications/2017/08/Infectious_Disease_Final_Report.aspx. 9. Backus L, Czarnogorski M, Yip G, et al. HIV Care Continuum Applied to the US Department of Veterans Affairs: HIV Virologic Outcomes in an Integrated Health Care System. J Acquir Immune Defic Syndr 2015; 69(4): 474-80. 10. Horberg M, Hurley L, Towner W, et al. HIV quality performance measures in a large integrated health care system. 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Baseline clinical characteristics, antiretroviral therapy use, and viral load suppression among HIV- positive young men of color who have sex with men. AIDS Patient Care STDS 2011; 25 Suppl 1: S9–14. 16. Stricker SM, Fox KA, Baggaley R, et al. Retention in care and adherence to ART are critical elements of HIV care interventions. AIDS Behav 2014; 18 Suppl 5: S465–75. 17. Bezabhe WM, Chalmers L, Bereznicki LR, Peterson GM. Adherence to Antiretroviral Therapy and Virologic Failure: A Meta-Analysis. Medicine (Baltimore) 2016; 95(15): e3361. 18. Iacob SA, Iacob DG, Jugulete G. Improving the Adherence to Antiretroviral Therapy, a Difficult but Essential Task for a Successful HIV Treatment-Clinical Points of View and Practical Considerations. Front Pharmacol 2017; 8: 831. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 19. Altice F, Evuarherhe O, Shina S, Carter G, Beaubrun AC. Adherence to HIV treatment regimens: systematic literature review and meta-analysis. Patient Prefer Adherence 2019; 13: 475-90. 20. Viswanathan S, Detels R, Mehta SH, Macatangay BJ, Kirk GD, Jacobson LP. Level of adherence and HIV RNA suppression in the current era of highly active antiretroviral therapy (HAART). AIDS Behav 2015; 19(4): 601-11. 21. Bradley H, Mattson CL, Beer L, Huang P, Shouse RL, Medical Monitoring P. Increased antiretroviral therapy prescription and HIV viral suppression among persons receiving clinical care for HIV infection. AIDS 2016; 30(13): 2117-24. 22. Rebeiro PF, Gange SJ, Horberg MA, et al. Geographic Variations in Retention in Care among HIV-Infected Adults in the United States. PLoS One 2016; 11(1): e0146119. 23. Giordano TP, Gifford AL, White AC, Jr., et al. Retention in care: a challenge to survival with HIV infection. Clin Infect Dis 2007; 44(11): 1493–9. 24. Yehia BR, Stewart L, Momplaisir F, et al. Barriers and facilitators to patient retention in HIV care. BMC Infect Dis 2015; 15: 246. 25. Gardner EM, McLees MP, Steiner JF, Del Rio C, Burman WJ. The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clin Infect Dis 2011; 52(6): 793-800. 26. Reveles KR, Juday TR, Labreche MJ, et al. Comparative value of four measures of retention in expert care in predicting clinical outcomes and health care utilization in HIV patients. PLoS One 2015; 10(3): e0120953. 27. Yehia BR, Fleishman JA, Metlay JP, et al. Comparing different measures of retention in outpatient HIV care. AIDS 2012; 26(9): 1131–9. 28. Silverberg MJ, Leyden W, Horberg MA, DeLorenze GN, Klein D, Quesenberry CP, Jr. Older age and the response to and tolerability of antiretroviral therapy. Arch Intern Med 2007; 167(7): 684–91. 29. Zanoni BC, Mayer KH. The adolescent and young adult HIV cascade of care in the United States: exaggerated health disparities. AIDS Patient Care STDS 2014; 28(3): 128–35. 30. Center for Disease Control and Prevention. HIV Among Youth. Available at: https://www.cdc.gov/hiv/group/age/youth/index.html. Accessed 03 December 2018. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Figure 1: HIV-infected population and OPERA clinic locations in the US HIV-infection based on CDC 2008–2010 data. CDC, Center for Disease Control and Prevention; HIV, human immunodeficiency virus; OPERA, Observational Pharmaco‐ Epidemiology Research and Analysis; US, United States. Figure 2: Unadjusted proportion of patients meeting NQF-endorsed quality measures by measure and measurement period. ART, antiretroviral therapy; HIV, human immunodeficiency virus; NQF, National Quality Forum. Figure 3: Adjusted odds ratios for NQF-endorsed quality measures by select demographics over 2014–2016 (GEE model) *The history of syphilis covariate was removed from the #2079 HIV medical visit frequency quality measure model as it neither improved model fit nor was significant. NQF-endorsed measure definitions: #2079 HIV medical visit frequency; #2080 No gaps in HIV visits; #2082 HIV viral load suppression; #2083 Prescription of ART. ADE, AIDS-defining event; ART, antiretroviral therapy; CI, confidence interval; GEE, generalized estimating equations; HIV, human immunodeficiency virus; MSM, men who have sex with men; NQF, National Quality forum; OR, odds ratio. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Tables and figures Table 1: NQF-endorsed quality measures NQF-endorsed quality measure Measure description #2079 Percentage of patients with HIV with ≥1 medical visit in each 6-month period of the 24-month measurement period HIV medical visit frequency (minimum of 60 days between visits), used to gauge engagement or retention of care. Percentage of patients with HIV who had a medical visit in both the first and last 6 months of the 12-month #2080 measurement period, also used to gauge engagement or retention of care. This measure was inverted from the No gaps in HIV visits original NQF-endorsed Gap in HIV medical visits measure. #2082 Percentage of patients with a HIV viral load <200 copies/mL at their last HIV viral load test during the measurement HIV viral load suppression period. #2083 Percentage of patients with HIV with ≥1 prescription for ART at any point during the measurement period. Prescription of ART ART, antiretroviral therapy; HIV, human immunodeficiency virus; NQF, National Quality Forum. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Table 2: Demographics and clinical characteristics of the OPERA study population Measurement period Demographics 2014 2015 2016 p-value for trend* (N=34,657) (N=37,187) (N=42,285) Age, years, n (%) <0.0001 <30 5051 (14.6) 6020 (16.2) 7421 (17.5) 30–49 17,581 (50.7) 17,852 (48.0) 19,433 (46.0) ≥50 12,025 (34.7) 13,315 (35.8) 15,431 (36.5) Median (IQR) 45.1 (35.1, 52.1) 45.1 (34.1, 53.1) 45.1 (33.1, 53.1) 0.0003 Gender 0.9537 Male 28,847 (83.2) 30,914 (83.1) 35,187 (83.2) Female 5800 (16.7) 6254 (16.8) 7069 (16.7) Race <0.0001 African-American 12,183 (35.2) 13,726 (36.9) 16,735 (39.6) Not African-American 20,741 (59.8) 21,850 (58.8) 23,757 (56.2) Unknown 1733 (5.0) 1611 (4.3) 1793 (4.2) Ethnicity <0.0001 Hispanic/Latino 8539 (24.6) 9287 (25.0) 10,302 (24.4) Not Hispanic/Latino 24,743 (71.4) 26,693 (71.8) 30,586 (72.3) Unknown 1375 (4.0) 1207 (3.2) 1397 (3.3) Region <0.0001 South 18,875 (54.5) 20,239 (54.4) 23,862 (56.4) West 12,515 (36.1) 13,090 (35.2) 13,579 (32.1) Northeast 2982 (8.6) 3373 (9.1) 4136 (9.8) Midwest 285 (0.8) 484 (1.3) 708 (1.7) Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Payer Commercial 7,768 (22.4) 9410 (25.3) 11,712 (27.7) <0.0001 Medicaid 7044 (20.3) 7667 (20.6) 7730 (18.3) <0.0001 Medicare 3531 (10.2) 3772 (10.1) 4068 (9.6) 0.0071 Ryan White 7304 (21.1) 7640 (20.5) 7616 (18.0) <0.0001 Other payer 98 (0.3) 139 (0.4) 113 (0.3) 0.5830 No Payer Information 11,733 (33.9) 11,743 (31.6) 13,918 (32.9) 0.0154 Risk of infection <0.0001 MSM 16,855 (48.6) 17,267 (46.4) 19,164 (45.3) Not MSM 17,802 (51.4) 19,920 (53.6) 23,121 (54.7) History of syphilis 7918 (22.8) 8999 (24.2) 10,343 (24.5) <0.0001 History of ADE 6240 (18.0) 6289 (16.9) 6472 (15.3) <0.0001 Viral load, copies/mL <0.0001 Median (IQR) 19.0 (19.0, 88.0) 19.0 (19.0, 40.0) 19.0 (19.0, 35.0) CD4 count, cells/μL <0.0001 Median (IQR) 564.0 (386.0, 578.0 (390.0, 592.0 (403.0, 770.0) 786.0) 810.0) Common comorbidities Hyperlipidemia 11,039 (31.9) 11,753 (31.6) 12,706 (30.0) <0.0001 Hypertension 8905 (25.7) 9,915 (26.7) 10,996 (26.0) 0.4188 Mild renal impairment 6159 (17.8) 7296 (19.6) 8753 (20.7) <0.0001 Anxiety disorders 5924 (17.1) 6611 (17.8) 7330 (17.3) 0.4551 Depression 3630 (10.5) 3896 (10.5) 4251 (10.1) 0.0482 Diabetes mellitus 2660 (7.7) 3088 (8.3) 3569 (8.4) 0.0002 Chronic Hepatitis C 2365 (6.8) 2391 (6.4) 2348 (5.6) <0.0001 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Moderate/Severe CKD 799 (2.3) 1047 (2.8) 1394 (3.3) <0.0001 VACS score Median (IQR) 13.0 (6.0, 24.0) 13.0 (6.0, 25.0) 13.0 (6.0, 24.0) 0.1816 *Sidak Correction was applied (adjusted α<0.001) to adjust for multiple comparisons and statistically significant results are bolded. Data are presented as n (%) unless otherwise stated. Renal status is based on the last two consecutive CKD-EPI calculated eGFR results prior to the start of the calendar year. ADE, AIDS-defining event; AIDS, acquired immunodeficiency syndrome; CD4, cluster of differentiation 4; CKD-EPI, chronic kidney disease epidemiology collaboration; eGFR, estimate glomerular filtrate rate; IQR, interquartile range; MSM, men who have sex with men; OPERA, Observational Pharmaco-Epidemiology Research and Analysis; VACS, veterans aging cohort study. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Table 3: Odds ratios for NFQ-endorsed measures over 2014–2016 Unadjusted Adjusted* NFQ-endorsed quality measure Per Calendar Year Per Calendar Year Odds ratio (95% Cl) Odds ratio (95% Cl) #2079 0.77 (0.76, 0.78) 0.75 (0.74, 0.77)§ HIV medical visit frequency #2080 0.84 (0.82, 0.86) 0.83 (0.81, 0.85)§ No gaps in HIV visits #2082 1.03 (1.02, 1.05) 1.03 (1.02, 1.05)§ HIV viral load suppression #2083 1.18 (1.15, 1.20) 1.19 (1.16, 1.22)§ Prescription of ART *Adjusted for changes in demographics and HIV-specific clinical characteristics. § p<0.0001 ART, antiretroviral therapy; CI, confidence interval; HIV, human immunodeficiency virus; NQF, National Quality Forum. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Figure 1 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Figure 2 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz418/5578463 by Ed 'DeepDyve' Gillespie user on 01 October 2019 Figure 3 Accepted Manuscript

Journal

Open Forum Infectious DiseasesOxford University Press

Published: Oct 1, 2019

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