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Human Immunodeficiency Virus (HIV) Quality Indicators Are Similar Across HIV Care Delivery Models

All other individual quality indicators in screening, immunization, and HIV management were not significantly different between HIV care models. Table 3 reports the unadjusted and adjusted results of the composite score for each domain with overall comparison scores. Process and screening domain measure analyses were similar in the 3 groups with unadjusted and adjusted analyses.

Pairwise testing showed that ID plus generalists had nonsignificantly higher composite scores than ID in unadjusted models Results of the adjusted models are depicted in Figure 2. Sensitivity analyses that did not adjust for visits had similar results data not shown. Composite human immunodeficiency virus HIV care model quality metrics.

Supplemental Content

The adjusted proportion of composite quality metrics by domain is shown comparing HIV care models. ID, infectious disease provider only.


There were differences between groups in adjusted analyses with the ID plus generalists group achieving significantly higher quality measures than the ID group in HIV management and higher quality measures than generalists in immunization. Regardless of who delivered HIV and primary care, care delivery was robust and quality indicator screening and HIV management were effective, because all groups performed at or above previously reported HIV quality indicator rates [ 19—24 ].

Our study inclusion criteria 1 patient visit in may have eliminated nonengaged patients and resulted in inflated quality indicator rates.

Human Immunodeficiency Virus (HIV) Quality Indicators Are Similar Across HIV Care Delivery Models

It is also possible that case management or outreach models contributed to these high numbers. We conclude that the high-quality indicator rates suggests that many approaches are feasible, especially because HIV care is increasingly streamlined and because many settings are addressing HIV treatment scale up and integration of HIV and chronic disease management [ 1 , 2 , 5 ]. Data from the s showed that generalists provided HIV care equivalent to that provided by specialists and that level of HIV experience affected both mortality and ability to incorporate new data into practice [ 25—31 ].

Studies from the mids found no differences in retention, ART prescription, or viral suppression in hospital versus community-based clinics [ 32 ] or in viral suppression by provider level factors [ 33 ]. However, further studies did show that integrated specialist and mutidisciplinary teams in the veterans affairs system and providers with larger HIV panel size in the Kaiser system for ART-naive patients achieved better viral suppression rates [ 33 , 34 ].

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Recent work using data showed provider experience affects viral suppression and that family providers are less likely to prescribe ART than specialists or providers in combined family and specialist models [ 35—37 ]. These contradictory data may suggest that multidisciplinary approaches or large panel size impact HIV quality metrics such as viral suppression more than the make-up of the physician HIV care team. The family practitioner data from Canada is more difficult to generalize to US HIV care models because internists are considered specialists in Canada, but they are considered generalists in this study.

In the current study, the small but significant differences between groups could be the effect of multiple providers, because the ID plus generalist group had the highest completion rates for immunizations and HIV management in adjusted analyses. The differences could also be explained by inherent differences in the patient populations, with higher comorbidity rates and visit number in the ID plus generalist group indicating a population more medically complex and engaged in care.

In a previous study, we showed no difference in noncommunicable disease NCD screening between HIV care models [ 38 ], and in this study we show small absolute differences in HIV screening and HIV management quality metrics. To date, there is a lack of data for chronic disease management by ID providers, including common disorders such as hypertension and diabetes.

Prior survey data suggest ID providers are less comfortable managing these disorders [ 39 ], which likely explains why patients with more comorbidities are more likely to be in the ID plus generalist models.

We plan to examine differences in NCD management between groups to help elucidate whether specific patient populations would benefit from certain HIV care models. There are limitations generalizing our results to other populations given the variation in HIV care models geographically and by population density as well as in underresourced locations given that our study was conducted in an urban academic medical system. Our assessment of the generalist group performance was limited by a relatively small sample size.

We attempted to minimize misclassification and measurement bias through validation of exposure and outcomes. Another limitation was use of guidelines for our outcomes: Finally, although we adjusted for factors previously linked to differences in quality metrics, we did not adjust for other factors impacting quality indicator performance such as mental health or drug use [ 10 , 19 , 22 , 41 ], patient or physician preference, or provider panel size because the provider panel size data was limited by a single year analysis instead of a 3-year panel size as recommended by the HIV Medicine Association [ 42 ].

Strengths of the study include 1 a large healthcare system-based cohort with rich data and 2 rigorous HIV care model classification that examines a question that existing national databases cannot currently address. In conclusion, we found minimal differences in HIV quality metrics between HIV care models, with performance across groups surpassing previous results reported in the literature. Small but significant differences favoring care delivered by ID and generalist providers combined over either individual specialty were observed in immunizations and HIV management, suggesting that this model may offer an incremental benefit.

Whether these differences translate into improved morbidity or mortality remains unknown. Our results suggest many models of HIV care are effective for HIV-related screening and management and that healthcare system, feasibility, and patient and physician preference may guide HIV care model selection. Supplementary material is available at Open Forum Infectious Diseases online. Potential conflicts of interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed. National Center for Biotechnology Information , U.

Open Forum Infect Dis. Published online Jan Singer , 2 , 4 and Virginia A.

This article has been cited by other articles in PMC. HIV, primary care models, quality indicators. Covariates Covariates included patient demographics, primary language English vs non-English , CD4 and HIV viral load most recent laboratory values , weighted Charlson comorbidity index score [ 11 , 12 ], and socioeconomic status, estimated by household income using zip code-level medians from the American Community Survey [ 13 ] and transformed median household income to percentage of the state-wide median household income [ 14 ] with prespecified cut points [ 15 ].