Predictors of depression recovery in HIV-infected individuals managed through measurement-based care in infectious disease clinics

Abstract

Background Treatment of comorbid chronic disease, such as depression, in people living with HIV/AIDS (PLWHA) increasingly falls to HIV treatment providers. Guidance in who will best respond to depression treatment and which patient-centered symptoms are best to target is limited. Methods Bivariable analyses were used to calculate hazard ratios for associations between baseline demographic, mental health-related, and HIV-related factors on time to first depression remission among PLWHA enrolled in a randomized trial of measurement-based antidepressant management. Time-updated factors also were analyzed at time of antidepressant (AD) initiation/adjustment and 8 weeks post AD initiation/adjustment. Results Baseline comorbid depression and anxiety; comorbid depression, anxiety and substance abuse; and generalized anxiety disorder predicted a slower time to first remission. Being on ART but non-adherent, having panic disorder, having a history of a major depressive episode, or having been in HIV care for > 10 years prior to study initiation predicted a faster time to first remission. Sleep difficulty or fatigue at the time of AD initiation/adjustment predicted a slower time to remission. In non-remitters at 8 weeks post AD initiation/adjustment, sleep difficulty, anxiety, and fatigue each predicted a slower time to remission. Limitations Remission was determined by PHQ-9 scores, not diagnostic criteria. The results may apply only to depression recovery in this particular model of treatment. We conducted only exploratory analyses to determine magnitude of effects. Conclusions Baseline comorbid anxiety with or without substance abuse predicts slower time to depression remission among PLWHA treated in HIV clinics. Targeting anxiety or fatigue at the time of AD initiation/adjustment or sleep difficulty, anxiety, and fatigue at 8 weeks post AD initiation/adjustment could shorten time to depression remission in this model.

Depression Prevalence, Antidepressant Treatment Status, and Association with Sustained HIV Viral Suppression Among Adults Living with HIV in Care in the United States, 2009–2014

“…We constructed three models: (1) estimating the prevalence ratio of sustained viral suppression for those with any versus no depression, (2) estimating the prevalence ratio of sustained viral suppression for those with untreated depression or treated depression separately versus no depression, and (3) estimating the prevalence ratio of sustained viral suppression for those with treated versus untreated depression. Each model was adjusted for gender [30,31], race [31,32], homelessness [33,34], health insurance status [35,36], drug use [15,37], and time since HIV diagnosis [38,39]. In all multivariable analyses we included variables as potential confounders that were a priori and statistically determined to be significantly associated with any depression and sustained viral suppression (p ≤ 0.05).…”

The Depression Treatment Cascade: Disparities by Alcohol Use, Drug Use, and Panic Symptoms Among Patients in Routine HIV Care in the United States

“…31 In depressed PLWH, studies have shown that the presence of co-occurring psychiatric disorders is the norm rather than the exception, with more than half of those with depression also having at least one other psychiatric disorder. 1,5,6,28,[32][33][34] While evidence suggests that occurrence of drug use, panic disorder, or heavy alcohol use 27,30 is associated with disparities in HIV treatment outcomes, less is known about the impact of such co-occurrence on disparities in depression treatment. 32 Given the high burden of psychiatric comorbidities among PLWH, understanding how they affect disparities in depression treatment is crucial for improving mental health treatment and achieving improved health outcomes in this population.…”

Measurement of depression treatment among patients receiving HIV primary care: Whither the truth?

“…Increasingly, studies are using routinely captured electronic medical record data to address questions such as the effect of depression treatment on clinical outcomes (Bengtson et al, 2016a; Sowa et al, 2016). Thus, it is important to be able to account for misclassification errors stemming from the use of electronic medical records.…”