HIV Care Initiation Delay among Rural Residents in the Southeastern United States, 1996 to 2012

Background: Delaying HIV care initiation may lead to greater morbidity, mortality, and further HIV transmission. Rural residence may be associated with delayed diagnosis and linkage to care, with negative clinical outcomes. Objective: To examine the association between rural patient residence and CD4 cell count at HIV care initiation in a large HIV clinical cohort in the Southeastern United States. Methods: We included HIV-infected patients who initiated care between 1996 and 2012 with a geocodable address and no previous history of HIV clinical care. Patient residence was categorized as urban or rural using United States Department of Agriculture Rural Urban Commuting Area codes. Multivariable linear regression models were fit to estimate the association between patient residence and CD4 cell count at HIV care initiation. Results: Among 1396 patients who met study inclusion criteria, 988 had a geocodable address. Overall, 35% of patients resided in rural areas and presented to HIV care with a mean CD4 cell count of 351 cells/mm 3 (SD, 290). Care initiation mean CD4 cell counts increased from 329 cells/mm 3 (SD, 283) in 1996-2003 to 391 cells/mm 3 (SD, 292) in 2008-2012 (P = 0.006). Rural in comparison with urban patients presented with lower CD4 cell counts with an unadjusted and adjusted mean difference of -48 cells/mm 3 [95% confidence interval, -86 to -10) and -37 cells/mm 3 (95% confidence interval: -73 to -2), respectively, consistently observed across calendar years. Conclusions: HIV care initiation at low CD4 cell counts was common in this Southeastern US cohort and more common among rural area residents

Beyond binary retention in HIV care: Predictors of the dynamic processes of patient engagement, disengagement, and re-entry into care in a US clinical cohort

Objectives: Studies examining engagement in HIV care often capture cross-sectional patient status to estimate retention and identify predictors of attrition, which ignore longitudinal patient care-seeking behaviors. We describe the cyclical nature of (dis)engagement and re-entry into HIV care using the state transition framework. Design: We represent the dynamic patterns of patient care-retention using five states: engaged in care, missed one, two, three, or more expected visits, and deceased. Then we describe various care-seeking behaviors in terms of transitioning from one state to another (e.g. from disengaged to engaged). This analysis includes 31 009 patients enrolled in the Center for AIDS Research Network of Integrated Systems (CNICS) in the United States from 1996 to 2014. Method: Multistate models for longitudinal data were used to identify barriers to retention and subgroups at higher risk of falling out of care. Results: The initial 2 years following primary engagement in care were a crucial time for improving retention. Patients who had not initiated antiretroviral therapy, with lower CD4+ cell counts, higher viral load, or not having an AIDS-defining illness were less likely to be retained in care. Conclusion: Beyond the individual patient characteristics typically used to characterize retention in HIV care, we identified specific periods of time and points in the care continuum associated with elevated risk of transitioning out of care. Our findings can contribute to evidence-based recommendations to enhance long-term retention in CNICS. This approach can also be applied to other cohort data to identify retention strategies tailored to each population

Virologic suppression and CD4 + cell count recovery after initiation of raltegravir or efavirenz-containing HIV treatment regimens

Objective: To explore the effectiveness of raltegravir-based antiretroviral therapy (ART) on treatment response among ART-naive patients seeking routine clinical care. Design: Cohort study of adults enrolled in HIV care in the United States. Methods: We compared virologic suppression and CD4 + cell count recovery over a 2.5 year period after initiation of an ART regimen containing raltegravir or efavirenz using observational data from a US clinical cohort, generalized to the US population of people with diagnosed HIV. We accounted for nonrandom treatment assignment, informative censoring, and nonrandom selection from the US target population using inverse probability weights. Results: Of the 2843 patients included in the study, 2476 initiated the efavirenz-containing regimen and 367 initiated the raltegravir-containing regimen. In the weighted intent-To-Treat analysis, patients spent an average of 74 (95% confidence interval: 41, 106) additional days alive with a suppressed viral load on the raltegravir regimen than on the efavirenz regimen over the 2.5-year study period. CD4 + cell count recovery was also superior under the raltegravir regimen. Conclusion: Patients receiving raltegravir spent more time alive and suppressed than patients receiving efavirenz, but the probability of viral suppression by 2.5 years after treatment was similar between groups. Optimizing the amount of time spent in a state of viral suppression is important to improve survival among people living with HIV and to reduce onward transmission

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information

Background Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study’s population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants’ protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects. Methods This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. Results Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients’ coded census tract locations. Conclusions This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives

Compound retention in care and all-cause mortality among persons living with human immunodeficiency virus

Background: To obtain optimal health outcomes, persons living with human immunodeficiency virus (HIV) must be retained in clinical care. We examined the relationships between 4 possible combinations of 2 separate retention measures (missed visits and the Institute of Medicine [IOM] indicator) and all-cause mortality. Methods: The sample included 4162 antiretroviral therapy (ART)–naive patients who started ART between January 2000 and July 2010 at any of 5 US sites of the Center for AIDS Research Network of Integrated Clinical Systems. The independent variable of interest was retention, captured over the 12-month period after the initiation of ART. The study outcome, all-cause mortality 1 year after ART initiation, was determined by querying the Social Security Death Index or the National Death Index. We evaluated the associations of the 4 categories of retention with all-cause mortality, using the Cox proportional hazards models. Results: Ten percent of patients did not meet retention standards for either measure (hazard ratio [HR], 2.26; 95% confidence interval [CI], 1.59–3.21). Patients retained by the IOM but not the missed-visits measure (42%) had a higher HR for mortality (1.72; 95% CI, 1.33–2.21) than patients retained by both measures (41%). Patients retained by the missed-visits but not the IOM measure (6%) had the same mortality hazards as patients retained by both measures (HR, 1.01; 95% CI, .54–1.87). Conclusions: Missed visits within the first 12 months of ART initiation are a major risk factor for subsequent death. Incorporating missed visits in clinical and public health retention and viral suppression programming is advised

Who Will Show? Predicting Missed Visits Among Patients in Routine HIV Primary Care in the United States

Missed HIV medical visits predict poor clinical outcomes. We sought to identify patients at high risk of missing visits. We analyzed 2002–2014 data from six large US HIV clinics. At each visit, we predicted the likelihood of missing the next scheduled visit using demographic, clinical, and patient-reported psychosocial variables. Overall, 10,374 participants contributed 105,628 HIV visits. For 17% of visits, the next scheduled appointment was missed. The strongest predictor of a future missed visit was past-year missed visits. A model with only this predictor had area under the receiver operator curve = 0.65; defining “high risk” as those with any past-year missed visits had 73% sensitivity and 51% specificity in correctly identifying a future missed visit. Inclusion of other clinical and psychosocial predictors only slightly improved performance. Past visit attendance can identify those at increased risk for future missed visits, allowing for proactive allocation of resources to those at greatest risk

Resilience and HIV: a review of the definition and study of resilience

We use a socioecological model of health to define resilience, review the definition and study of resilience among persons living with human immunodeficiency virus (PLWH) in the existing peer-reviewed literature, and discuss the strengths and limitations of how resilience is defined and studied in HIV research. We conducted a review of resilience research for HIV-related behaviors/outcomes of antiretroviral therapy (ART) adherence, clinic attendance, CD4 cell count, viral load, viral suppression, and/or immune functioning among PLWH. We performed searches using PubMed, PsycINFO and Google Scholar databases. The initial search generated 14,296 articles across the three databases, but based on our screening of these articles and inclusion criteria, n = 54 articles were included for review. The majority of HIV resilience research defines resilience only at the individual (i.e., psychological) level or studies individual and limited interpersonal resilience (e.g., social support). Furthermore, the preponderance of HIV resilience research uses general measures of resilience; these measures have not been developed with or tailored to the needs of PLWH. Our review suggests that a socioecological model of health approach can more fully represent the construct of resilience. Furthermore, measures specific to PLWH that capture individual, interpersonal, and neighborhood resilience are needed

Estimating multiple time-fixed treatment effects using a semi-Bayes semiparametric marginal structural Cox proportional hazards regression model

Marginal structural models for time-fixed treatments fit using inverse-probability weighted estimating equations are increasingly popular. Nonetheless, the resulting effect estimates are subject to finite-sample bias when data are sparse, as is typical for large-sample procedures. Here we propose a semi-Bayes estimation approach which penalizes or shrinks the estimated model parameters to improve finite-sample performance. This approach uses simple symmetric data-augmentation priors. Limited simulation experiments indicate that the proposed approach reduces finite-sample bias and improves confidence-interval coverage when the true values lie within the central “hill” of the prior distribution. We illustrate the approach with data from a nonexperimental study of HIV treatments

Patterns of efavirenz use as first-line antiretroviral therapy in the United States: 1999-2015

Background: Efavirenz has been a mainstay of antiretroviral therapy (ART) for over 15 years in the US. Its association with neuropsychiatric side effects may influence clinical prescribing and management. Methods: We included HIV-infected adults enrolled in care at seven sites across the US, who initiated combination ART between 1999 and 2015. We examined the proportion initiating and continuing on efavirenz, overall and by mental health status. Log binomial and Cox models were used to estimate associations between mental health, clinical and sociodemographic characteristics and initiating or switching from efavirenz as first-line ART. Results: Of the 8,230 participants included, 3,710 (45%) initiated efavirenz. In multivariable analyses, prior mono- or dual-ART, ART initiation after 2006, being female, intravenous drug use, antidepressant prescription, previous mental health diagnosis and baseline CD4+ T-cell count >350 cells/mm3 were inversely associated with initiating efavirenz. Participants initiating efavirenz had a faster time to a regimen switch, compared with those initiating an efavirenz-free regimen (P-value <0.01). Among efavirenz initiators, starting efavirenz in more recent time periods and a previous mental health diagnosis were associated with faster time to switching from efavirenz. Despite this, 40-50% of participants with a previous mental health diagnosis initiated and continued on efavirenz for much of the follow-up period. Conclusions: Multiple clinical factors, including mental health diagnoses, appeared to influence efavirenz use. While mental health diagnosis status and more recent treatment starts were associated with shorter duration of efavirenz therapy, a previous mental health diagnosis did not preclude efavirenz initiation or continuation in many participants

Depressive Symptoms and Engagement in Human Immunodeficiency Virus Care Following Antiretroviral Therapy Initiation

The effect of depressive symptoms on progression through the human immunodeficiency virus (HIV) treatment cascade is poorly characterized. Methods. We included participants from the Centers for AIDS Research Network of Integrated Clinic Systems cohort who were antiretroviral therapy (ART) naive, had at least 1 viral load and HIV appointment measure after ART initiation, and a depressive symptom measure within 6 months of ART initiation. Recent depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9) and categorized using a validated cut point (PHQ-9 =10). We followed participants from ART initiation through the first of the following events: loss to follow-up (12 months with no HIV appointment), death, administrative censoring (2011-2014), or 5 years of follow-up. We used log binomial models with generalized estimating equations to estimate associations between recent depressive symptoms and having a detectable viral load (=75 copies/mL) or missing an HIV visit over time. Results. We included 1057 HIV-infected adults who contributed 2424 person-years. At ART initiation, 30% of participants reported depressive symptoms. In multivariable analysis, recent depressive symptoms increased the risk of having a detectable viral load (risk ratio [RR], 1.28; 95% confidence interval [CI], 1.07, 1.53) over time. The association between depressive symptoms and missing an HIV visit (RR, 1.20; 95% CI, 1.05, 1.36) moved to the null after adjustment for preexisting mental health conditions (RR, 1.00; 95% CI, 0.85, 1.18). Conclusions. Recent depressive symptoms are a risk factor for unsuppressed viral load, while preexisting mental health conditions may influence HIV appointment adherence