Determinants of cryptococcal antigen (CrAg) screening uptake in Kampala, Uganda: An assessment of health center characteristics.

Cryptococcal antigen (CrAg) screening and pre-emptive antifungal therapy for people with CD4 cell counts <100 cells/µl are recommended by the World Health Organization and several national HIV guidelines. We sought to evaluate CrAg screening program implementation across Uganda, in relation to health center level and distance from the capital. We conducted a cross-sectional study of 22 health centers across southern Uganda from April to June 2019. We reviewed laboratory records regarding number of CD4 cell count tests performed, proportion of outpatients with CD4 counts <200 cells/µl, and number of CrAg screening tests performed. We administered surveys to health center staff to understand barriers to advanced HIV care. We observed no significant difference in health center level and performance of CrAg screening; with each subsequent health center level, there was 1.17-fold (95% CI: 0.92-1.41) higher odds of CrAg screening performed per level. CrAg screening uptake was not associated with distance from the capital city (odds ratio = 0.96, 95% CI: 0.89-1.04). Qualitative data from surveys indicated that limitations to uptake of CrAg screening were secondary to dysfunctional CD4 machines, lack of provider awareness of CrAg screening guidelines, and inadequate/intermittent supply of CrAg tests. There were no significant associations between CrAg screening uptake and level of health center or distance of health center from the capital city. We identified systemic barriers to CrAg screening related to inadequate CD4 testing, insufficient knowledge regarding national screening guidelines, and irregular laboratory testing supplies. LAY SUMMARY: The objective of this study was to evaluate cryptococcal antigen (CrAg) screening program implementation in Uganda, by type of healthcare center and by distance from the capital city. CrAg screening uptake was not associated with distance from the capital city, or the type of healthcare center.

Barriers to HIV care in Uganda and implications for universal test-and-treat: a qualitative study.

Achieving universal HIV test-and-treat will require targeted interventions for those with worse outcomes, including advanced HIV. We conducted qualitative, semi-structured interviews with healthcare workers (HCWs) and people living with HIV (PLWH) at 5 HIV clinics in Kampala, Uganda, to understand barriers to care. PLWH enrolled started/restarted on HIV treatment ≤3 months prior. PLWH were grouped as 1) "ART-experienced" or those restarted therapy after ≥12 months off, 2) ART naïve CD4 count <100 cells/uL "late presenters" or 3) ART naïve CD4 count >350 cells/uL "early presenters". In-depth interviews were conducted in Luganda, translated, and transcribed verbatim. Between May and August 2017, 58 PLWH and 20 HCWs were interviewed. High stigma and low social support emerged as themes among all as barriers to care. Alcohol abuse was a barrier for men. Fear of domestic violence and abandonment were barriers for women, limiting disclosure of their HIV status to their male partners. Clinic factors such as rapport with staff, distance, efficiency, and privacy impacted care. Future interventions to decrease delayed ART initiation should target stigma and social support. Assisted disclosure, contact tracing, and alcohol abuse treatment should be implemented. Strengthening client support, reducing wait times, and increasing privacy assurances would improve care-seeking behaviors.

A Journey of Hope: giving research participants a voice to share their experiences and improve community engagement around advanced HIV disease in Uganda.

Over the last decade excellent progress has been made globally in HIV management thanks to antiretroviral therapy (ART) rollout and international guidelines now recommending immediate initiation of ART in all HIV-positive people. Despite this, advanced HIV disease (CD4 less than 200 cells/mL) and opportunistic infections remain a persistent challenge and contribute significantly to HIV-associated mortality, which equates to 23,000 deaths in Uganda in 2018 alone. Our Meningitis Research Team based in Uganda is committed to conducting clinical trials to answer important questions regarding diagnostics and management of HIV-associated opportunistic infections, including tuberculosis and cryptococcal meningitis. However, clinical research is impossible without research participants and results are meaningless unless they are translated into benefits for those affected by the disease. Therefore, we held a series of community engagement events with the aims of giving clinical research participants a voice in sharing their experiences of clinical research and messages of hope around advanced HIV disease with the community, dispelling myths and stigma around HIV, raising awareness about the complications of advanced HIV disease and local ongoing clinical research and recent scientific advances. The purpose of this Open Letter is to describe our community engagement experience in Uganda, which we hope will lay the foundation for further clinical research public engagement activities, giving research participants a greater voice to share their experiences.

Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder (NDD) with a high rate of comorbidity. The implementation of eye-tracking methodologies has informed behavioral and neurophysiological patterns of visual processing across ASD and comorbid NDDs. In this study, we propose a machine learning method to predict measures of two core ASD characteristics: impaired social interactions and communication, and restricted, repetitive, and stereotyped behaviors and interests. Our method extracts behavioral features from task performance and eye-tracking data collected during a facial emotion recognition paradigm. We achieved high regression accuracy using a Random Forest regressor trained to predict scores on the SRS-2 and RBS-R assessments; this approach may serve as a classifier for ASD diagnosis.

Classifying Individuals with ASD Through Facial Emotion Recognition and Eye-Tracking.

Individuals with Autism Spectrum Disorder (ASD) have been shown to have atypical scanning patterns during face and emotion perception. While previous studies characterized ASD using eye-tracking data, this study examined whether the use of eye movements combined with task performance in facial emotion recognition could be helpful to identify individuals with ASD. We tested 23 subjects with ASD and 35 controls using a Dynamic Affect Recognition Evaluation (DARE) task that requires an individual to recognize one of six emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise) while observing a slowly transitioning face video. We observed differences in response time and eye movements, but not in the recognition accuracy. Based on these observations, we proposed a machine learning method to distinguish between individuals with ASD and typically developing (TD) controls. The proposed method classifies eye fixations based on a comprehensive set of features that integrate task performance, gaze information, and face features extracted using a deep neural network. It achieved an 86% classification accuracy that is comparable with the standardized diagnostic scales, with advantages of efficiency and objectiveness. Feature visualization and interpretations were further carried out to reveal distinguishing features between the two subject groups and to understand the social and attentional deficits in ASD.