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1. Household Water and Food Insecurity Are Positively Associated with Poor Mental and Physical Health among Adults Living with HIV in Western Kenya

   https://doi.org/10.1093/jn/nxab030  

   Miller, Joshua D ; Frongillo, Edward A ; Weke, Elly ; Burger, Rachel ; Wekesa, Pauline ; Sheira, Lila A ; Mocello, A Rain ; Bukusi, Elizabeth A ; Otieno, Phelgona ; Cohen, Craig R ; et al ( March 2021 , The Journal of Nutrition)

   ABSTRACT Background Household food insecurity (FI) and water insecurity (WI) are prevalent public health issues that can co-occur. Few studies have concurrently assessed their associations with health outcomes, particularly among people living with HIV. Objectives We aimed to investigate the associations between FI and WI and how they relate to physical and mental health. Methods Food-insecure adult smallholder farmers living with HIV in western Kenya were recruited to participate in a cluster-randomized controlled trial of a multisectoral agricultural and asset loan intervention. We used baseline data on experiences of FI (using the Household Food Insecurity Access Scale, range: 0–27) andmore »WI (using a modified scale developed for this region, range: 0–51) in the prior month (n = 716). Outcomes included probable depression (using the Hopkins Symptom Checklist), fatigue and diarrhea in the prior month, and overall mental and physical health (using the Medical Outcomes Study HIV Health Survey, range: 0–100). We first assessed Pearson correlations between FI, WI, and sociodemographic characteristics. We then developed 3 regressions for each health outcome (control variables and FI; control variables and WI; control variables, FI, and WI) and compared model fit indexes. Results Correlations between household FI, WI, and wealth were low, meaning they measure distinct constructs. FI and WI were associated with numerous physical and mental health outcomes; accounting for both resource insecurities typically provided the best model fit. For instance, when controlling for FI, each 10-point higher WI score was associated with a 6.42-point lower physical health score (P < 0.001) and 2.92 times greater odds of probable depression (P < 0.001). Conclusions Assessing both FI and WI is important for correctly estimating their relation with health outcomes. Interventions that address food- and water-related issues among persons living with HIV concurrently will likely be more effective at improving health than those addressing a single resource insecurity. This trial was registered at clinicaltrials.gov as NCT02815579.« less
2. Not Another Medication Adherence App: Critical Reflections on Addressing Public HIV-related Stigma Through Design

   https://doi.org/10.1145/3434171  

   Maestre, Juan F. ; Zdziarska, Patrycja ; Min, Aehong ; Baglione, Anna N. ; Chung, Chia-Fang ; Shih, Patrick C. ( January 2021 , Proceedings of the ACM on Human-Computer Interaction)

   People living with HIV experience a high level of stigma in our society. Public HIV-related stigma often leads to anxiety and depression and hinders access to social support and proper medical care. Technologies for HIV, however, have been mainly designed for treatment management and medication adherence rather than for helping people cope with public HIV-related stigma specifically. Drawing on empirical data obtained from semi-structured interviews and design activities with eight social workers and 29 people living with HIV, we unpack the ways in which needs for privacy and trust, intimacy, and social support create tensions around key coping strategies. Reflectingmore »on these tensions, we present design implications and opportunities to empower people living with HIV to cope with public HIV-related stigma at the individual level.« less
3. Learning Robust Multilabel Sample Specific Distances for Identifying HIV-1 Drug Resistance

   https://doi.org/10.1089/cmb.2019.0329  

   Brand, Lodewijk ; Yang, Xue ; Liu, Kai ; Elbeleidy, Saad ; Wang, Hua ; Zhang, Hao ; Nie, Feiping ( November 2019 , Journal of Computational Biology)

   AIDS is a syndrome caused by the HIV. During the progression of AIDS, a patient's immune system is weakened, which increases the patient's susceptibility to infections and diseases. Although antiretroviral drugs can effectively suppress HIV, the virus mutates very quickly and can become resistant to treatment. In addition, the virus can also become resistant to other treatments not currently being used through mutations, which is known in the clinical research community as cross-resistance. Since a single HIV strain can be resistant to multiple drugs, this problem is naturally represented as a multilabel classification problem. Given this multilabel relationship, traditional single-labelmore »classification methods often fail to effectively identify the drug resistances that may develop after a particular virus mutation. In this work, we propose a novel multilabel Robust Sample Specific Distance (RSSD) method to identify multiclass HIV drug resistance. Our method is novel in that it can illustrate the relative strength of the drug resistance of a reverse transcriptase (RT) sequence against a given drug nucleoside analog and learn the distance metrics for all the drug resistances. To learn the proposed RSSDs, we formulate a learning objective that maximizes the ratio of the summations of a number of ℓ1-norm distances, which is difficult to solve in general. To solve this optimization problem, we derive an efficient, nongreedy iterative algorithm with rigorously proved convergence. Our new method has been verified on a public HIV type 1 drug resistance data set with over 600 RT sequences and five nucleoside analogs. We compared our method against several state-of-the-art multilabel classification methods, and the experimental results have demonstrated the effectiveness of our proposed method.« less
4. Learning Robust Multi-label Sample Specific Distances for Identifying HIV-1 Drug Resistance

   https://doi.org/10.1007/978-3-030-17083-7_4  

   Brand, Lodewijk ; Yang, Xue ; Liu, Kai ; Elbeleidy, Saad ; Wang, Hua ; Zhang, Hao ( April 2019 , The Proceedings of the 23rd Annual International Conference on Research in Computational Molecular Biology (RECOMB 2019))

   Acquired immunodeficiency syndrome (AIDS) is a syndrome caused by the human immunodeficiency virus (HIV). During the progression of AIDS, a patient’s the immune system is weakened, which increases the patient’s susceptibility to infections and diseases. Although antiretroviral drugs can effectively suppress HIV, the virus mutates very quickly and can become resistant to treatment. In addition, the virus can also become resistant to other treatments not currently being used through mutations, which is known in the clinical research community as cross-resistance. Since a single HIV strain can be resistant to multiple drugs, this problem is naturally represented as a multi-label classificationmore »problem. Given this multi-class relationship, traditional single-label classification methods usually fail to effectively identify the drug resistances that may develop after a particular virus mutation. In this paper, we propose a novel multi-label Robust Sample Specific Distance (RSSD) method to identify multi-class HIV drug resistance. Our method is novel in that it can illustrate the relative strength of the drug resistance of a reverse transcriptase sequence against a given drug nucleoside analogue and learn the distance metrics for all the drug resistances. To learn the proposed RSSDs, we formulate a learning objective that maximizes the ratio of the summations of a number of ℓ1-norm distances, which is difficult to solve in general. To solve this optimization problem, we derive an efficient, non-greedy, iterative algorithm with rigorously proved convergence. Our new method has been verified on a public HIV-1 drug resistance data set with over 600 RT sequences and five nucleoside analogues. We compared our method against other state-of-the-art multi-label classification methods and the experimental results have demonstrated the effectiveness of our proposed method.« less
5. COVID and Coraje: Negotiating Latinx Immigrant Experiences of the Pandemic

   Duncan, Whitney ; Horton, Sarah B. ; Yarris, Kristin ( April 2021 , Society for Psychological Anthropology Biennial Meetings (Virtual))

   In this paper, we compare observations from engaged ethnography and participant observation with Latinx immigrants in Colorado and Oregon during the COVID-19 pandemic. In particular, we focus on lived experiences of structural vulnerability, as well as the ways in which COVID-related disparities have become internalized as stigma and have amplified immigrants’ experiences of stress, anxiety, and “aislamiento,” or isolation. Indeed, Latinx immigrants in the US—especially those without legal status and those in mixed-status families—face a range of exclusions, discourses of blame and (un)deservingness, and forms of precarity that have contributed to disproportionate risk, suffering, and fear as the pandemic hasmore »unfolded. At the same time, by laying bare blatant injustices and racist exclusions, the pandemic has prompted some Latinx immigrants in our research and advocacy sites to enact new forms of resistance and contestation. We detail the range of ways which, in efforts to stay healthy and to challenge discriminatory portrayals of themselves as either disease carriers unlikely to heed public health warnings or as “public charges,” they insist upon their own rights, worth, belonging, and dignity. Finally, we conclude by discussing some of the ways in which these two U.S. states—and the health and social service organizations working with Latinx communities within them—have attempted to address coronavirus disparities among Latinx communities, showing how particular approaches can assuage short-term suffering and improve access to healthcare and other social supports, while others may create a new set of barriers to access for already marginalized communities.« less