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Background. Heterogeneity in HIV microepidemics across US cities necessitates locally oriented, combination implementation strategies to prioritize resources. We calibrated and validated a dynamic, compartmental HIV transmission model to establish a status quo treatment scenario, holding constant current levels of care for 6 US cities. Methods. Built off a comprehensive evidence synthesis, we adapted and extended a previously published model to replicate the transmission, progression, and clinical care for each microepidemic. We identified a common set of 17 calibration targets between 2012 and 2015 and used the Morris method to select the most influential parameters for calibration. We then applied the Nelder-Mead algorithm to iteratively calibrate the model to generate 2000 best-fitting parameter sets. Finally, model projections were internally validated with a series of robustness checks and externally validated against published estimates of HIV incidence, while the face validity of 25-year projections was assessed by a Scientific Advisory Committee (SAC). Results. We documented our process for model development, calibration, and validation to maximize its transparency and reproducibility. The projected outcomes demonstrated a good fit to calibration targets, with a mean goodness-of-fit ranging from 0.0174 (New York City [NYC]) to 0.0861 (Atlanta). Most of the incidence predictions were within the uncertainty range for 5 of the 6 cities (ranging from 21% [Miami] to 100% [NYC]), demonstrating good external validity. The face validity of the long-term projections was confirmed by our SAC, showing that the incidence would decrease or remain stable in Atlanta, Los Angeles, NYC, and Seattle while increasing in Baltimore and Miami. Discussion. This exercise provides a basis for assessing the incremental value of further investments in HIV combination implementation strategies tailored to urban HIV microepidemics.

Quantile regression provides a detailed and robust picture of the distribution of a response variable, conditional on a set of observed covariates. Recently, it has be been extended to the analysis of longitudinal continuous outcomes using either time-constant or time-varying random parameters. However, in real-life data, we frequently observe both temporal shocks in the overall trend and individual-specific heterogeneity in model parameters. A benchmark dataset on HIV progression gives a clear example. Here, the evolution of the CD4 log counts exhibits both sudden temporal changes in the overall trend and heterogeneity in the effect of the time since seroconversion on the response dynamics. To accommodate such situations, we propose a quantile regression model, where time-varying and time-constant random coefficients are jointly considered. Since observed data may be incomplete due to early drop-out, we also extend the proposed model in a pattern mixture perspective. We assess the performance of the proposals via a large-scale simulation study and the analysis of the CD4 count data.

Recently, the joint analysis of longitudinal and survival data has been an active research area. Most joint models focus on survival data with only one type of failure. The research on joint modeling of longitudinal and competing risks survival data is sparse. Even so, many joint models for this type of data assume parametric function forms for both longitudinal and survival sub-models, thus limits their use. Further, the common data features that are usually observed in practice, such as asymmetric distribution and missingness in response, measurement errors in covariate, need to be taken into account for reliable parameter estimation. The statistical inference is complicated when all these factors are considered simultaneously. In the article, driven by a motivating example, we assume nonparametric function forms for the varying coefficients in both longitudinal and competing risks survival sub-models. We propose a Bayesian nonparametric mixed-effects joint model for the analysis of longitudinal-competing risks data with asymmetry, missingness, and measurement errors. Simulation studies are conducted to assess the performance of the proposed method. We apply the proposed method to an AIDS dataset and compare a few candidate models under various settings. Some interesting results are reported.

Despite numerous potential health outcomes of dog guardianship, their value has not been examined in the HIV-positive population. The study objective was to examine the relationship between dog guardianship and HIV clinical outcomes (antiretroviral adherence [>95% versus <95%], HIV viral load [> or =48 versus <48 copies/mL], and CD4 count) among HIV-positive individuals. The authors conducted a secondary analysis of baseline data of 370 HIV-positive men on antiretroviral regimen enrolled in the Duo Project. Generalized estimating equations were used for inferential regression analyses, while controlling for the focal dog guar- dianship variable and nonfocal covariates. Current dog guardianship was reported in 28.7% of participants. Dog guardianship may be associated with higher CD4 (coefficient = 60.6, P = .052) and adherence >95% (odds ratio [OR] = 1.80, P = .048); however, having a detectable viral load was not related to dog guardianship (OR = 0.94, P = .85). Further clinical research with detailed dog guardianship data is needed to further examine the association between dog guardianship and HIV clinical outcomes.

Background: CD4 counts vary in different populations. The present study was conducted to determine CD4 counts in different World Health Organization (WHO) clinical stages in antiretroviral therapy naive individuals and to find out optimum CD4 cutoffs., Method: Data of adult HIV seropositive patients who underwent CD4 count and total lymphocyte count (TLC) testing were included for analysis. The severity of immunosuppression was graded based on WHO criteria. To establish optimum CD4 cutoff values, receiver-operator characteristics (ROC) curves were generated., Results: Of 754 patients, 52.2% had CD4 counts <200 cells/mm3, but only 2.3% belonged to WHO stage IV. Newer CD4 cutoffs generated were 280, 120-280, <120 cells/mm3. Spearman rank correlation between CD4 counts and TLC was found to be weak (r = .32)., Conclusion: The cutoff values of CD4 counts for HIV disease may need to be revised for India. Regular CD4 count estimation is a must for monitoring disease progression in people living with HIV/AIDS.

Background: Studies have identified several programmatic and nonprogrammatic indicators that affect adherence to highly active antiretroviral therapy (HAART). Depression has been shown to impact adherence to HAART. This cross-sectional analysis of data collected from Nigeria, Uganda, Zambia, and Tanzania in 2008 examined the relationship between levels of depressive symptoms, clinical progression, and adherence to HAART., Methods: A multinational, multicenter, observational, retrospective cross-sectional evaluation of a population of focus comprised randomly selected patients on HAART. The dependent variable was adherence to HAART. The primary variable of interest to be assessed was patients' level of depressive symptom score. A multivariable logistic regression model was used to examine the relationship between explanatory variables and adherence to HAART., Results: A total of 2344 patients were recruited for adherence survey. About 70% of the study sample reported having some level of depression. Logistic regression results show that patients who reported, respectively, low, moderate, and high levels of depressive symptoms are 35% (P < .001), 56% (P < .001), and 64% (P < .001) less likely to adhere to HAART than those who reported having no depressive symptoms. At multivariate analysis, adherence to HAART was independently associated with the levels of depressive symptoms, older age, CD4 count >200 cells/mm3, Truvada (tenofovir [TDF]/emtricitabine [FTC])-based regimens, good knowledge about HAART, and longer period on therapy., Conclusions: These results indicate that mental health and clinical parameters are significant factors in determining patients' adherence to their HAART, which need to be more aggressively addressed as a critical component of care and treatment support.

Aims: To test a stress-disease hypothesis by investigating the influence of social network and social support factors on the course over time of the CD4 lymphocytes in an HIV-positive population of gay men., Methods: The study is a prospective cohort study of a representative population of HIV-positive gay men, undertaken at the Department of Infectious Diseases, Malmö University Hospital. This is the only clinic providing care for HIV-positive individuals in the city of Malmö in southern Sweden (population 248,000). A total of 115 HIV-positive homosexual men, who had not received an AIDS diagnosis, were invited to take part in the study. Seventy-five men (65%) accepted to be interviewed and 64 men (56%) fulfilled the inclusion criteria for the statistical analysis., Results: In a multivariate analysis adjustments were made for age, level of the first CD4 count and time since first contact with the clinic. Men with high family contact frequency had a longer half-life (20.3 years) of the CD4 lymphocyte count than men with a low family contact frequency (7.4 years) (p = 0.03). Men with high social participation also had a longer half-life of CD4 lymphocyte count (14.7 years), compared with men with low social participation (6.3 years, p = 0.10)., Conclusions: The findings of this study imply the importance of social support and social network factors as modifiers of the rate of decline of the CD4 lymphocyte level, which is an important prognostic marker of survival in HIV-positive homosexual men.