Research on the Development of Severe Acute Respiratory Syndrome Coronavirus 2 Variants of Concern in People With Advanced Human Immunodeficiency Virus Disease Should Highlight Structural Conditions and Avoid Harmful Stereotypes
Bibliographic record
Abstract
To theEditor—A growing body of research about the genomic epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) focuses on the potential development of variants of concern in people with advanced human immunodeficiency virus (HIV) disease or AIDS [1–4]. Two recent articles in Clinical Infectious Diseases by Maponga et al and Riddell et al provide early evidence supporting the hypothesis that prolonged SARS-CoV-2 infections in people with advanced HIV disease can lead to the development of concerning SARS-CoV-2 variants [1, 2]. Those authors and others focus on the potential for immunocompromised people to experience lengthy SARS-CoV-2 infections [5, 6]. This can, in some cases, lead to the development of SARS-CoV-2 variants with characteristics conferring greater transmissibility, capacities for immune escape, worsened pathogenicity, and other characteristics that could exacerbate the COVID-19 pandemic [7]. New studies about HIV/SARS-CoV-2 coinfection are welcome. This includes projects that might highlight how people with advanced HIV disease or other immunocompromising conditions could drive troubling forms of SARS-CoV-2 evolution. Indeed, I collaborate with one research group studying these issues in a country with high HIV endemicity. With that group and in other contexts, I work in the role of a social scientist and public health ethicist on issues related to the ethics, practice, and politics of genomic epidemiology [8, 9]. From this vantage, the emerging wave of studies about people with advanced HIV disease potentially harboring more troubling SARS-CoV-2 variants has raised several red flags that merit discussion within the infectious disease community. Specifically, the emergent literature connecting advanced HIV disease to problematic SARS-CoV-2 viral evolution could potentially reinforce longstanding stereotypes about people with HIV as dangerous “carriers” of illnesses and related harmful tropes. This concern is amplified by the fact that papers about this topic have garnered mainstream media attention, including stories highlighting the “danger” angle [10]. However, concerns in this area are not only about language; they also extend to the presentation of the research findings. For example, neither the Maponga et al nor the Riddell et al articles adequately highlight economic and other social and structural determinants of health—such as weak health systems, poverty, and stigma—that cause some people with HIV to not be retained in care and thus to develop AIDS [1, 2]. Studies about the development of SARS-CoV-2 variants in people with AIDS should not only focus on biomedical aspects of specific cases. They should also highlight the inadequate structures of care, economic inequalities, and social systems that lead some people with HIV to develop AIDS because they are not able to access antiretroviral medications and consistent medical care. How can SARS-CoV-2 genomic epidemiology researchers investigating the development of problematic variants in people with HIV ensure that their research does not reinforce harmful stereotypes? How can novel findings about SARS-CoV-2/HIV coinfection be mobilized to address the underlying material conditions that lead some people with HIV—a treatable condition—to unnecessarily develop AIDS? These are some challenges that researchers in this area should address as they build new approaches and as their methods are translated into routine public health practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".