Bibliographic record
Abstract
An article in AIDS by Brumme et al.[1] addressed the accelerated progression of HIV infection in a Canadian province whose very high number of cases is predominantly in Indigenous persons. This article was sharply criticized in a letter published in this issue of AIDS. Brumme et al.[1] reported that HIV adaptation to human leukocyte antigen (HLA) alleles commonly expressed in Indigenous populations is a ‘plausible explanation’ for accelerated progression to AIDS. Chelico et al.[2] allege in their letter that a quotation in the media by the authors ‘further stigmatized HIV-1 in a province already dealing with the ongoing effects of colonialism and racism and chronic under-resourcing’ for HIV-1 infection. Further allegations are that the study interfered with the ‘right of Indigenous peoples to control their own data’ and disregarded their ‘community or collective interests in not being stigmatized and marginalized.’ This commentary will discuss the ethical issues raised by research with existing specimens and data and how they might be better addressed. The study by Brumme et al.[1] should be viewed in the context of deep anger in Indigenous people (and in the United States, Blacks and Latinx persons) over long-standing disparities in health outcomes, inequalities in educational and employment opportunities, and discrimination. Because these groups suffer disproportionately from HIV, other infectious diseases such as coronavirus disease 2019, and many chronic medical diseases, they could greatly benefit from well designed research on these conditions. However, their willingness to participate in research may be undermined by mistrust related to highly publicized cases of research misconduct, such as the Tuskegee syphilis study and the Henrietta Lacks case in the United States [3,4]. In Canada, Nuu-chah-nulth Indigenous communities provided samples for research on rheumatic diseases, but the samples were subsequently used to study human migration and retrovirus infections [5,6]. Use of existing specimens and data increases the efficiency of important research because no new specimens need to be collected. Moreover, there is no additional physical risk or inconvenience to research participants. The main risk to individual participants in secondary research studies is breaches of confidentiality, which might lead to stigmatization or loss of employment. Research participants who agree to research on one medical condition, such as heart disease, are generally willing to agree to research on other medical conditions, such as dementia or cancer [7]. However, some research projects using existing specimens and data touch on racial and ethnic identity and foundational cultural beliefs. The studies may be considered objectionable by some participants. In a highly publicized case, the Havasupai Native American tribe in the United States, who had supported a study of diabetes in their nation, objected strongly when the samples were later used to study the genetics of schizophrenia, inbreeding, and migration [3]. According to the tribe, these additional studies were stigmatizing, violated their beliefs about their tribal origins, and were outside the scope of the original consent to donate samples. To settle a lawsuit, the research university apologized and agreed to return the samples to the tribe. This Havasupai Native American case highlights difficulties with consent for secondary research with existing specimens. Not all future uses of a specimen can be anticipated, and, when giving consent, research participants may not have in mind sensitive studies that they would object to if they knew about them [7]. As this case illustrates, anonymization of specimens does not address the ethical concern. Blanket consent to all future research and presumed consent (i.e., an individual is regarded as consenting unless they object) are now regarded as ethically problematic. An ethically preferred solution is for a checklist in which the study participants agree to certain types of research or topics but not others. In addition, there should be a governance mechanism in which the biobank or institution that holds the samples screens requests for secondary use and reviews requests that may be considered sensitive or objectionable by a substantial minority of persons who provided the original materials [7]. The study by Brumme et al.[1] differs from the Havasupai case in important ways. The Brumme samples were not collected under the auspices of an Indigenous sovereign nation but as part of routine clinical care for persons with HIV infection. The topic of the study was the condition for which patient received care. The study did not carry out new analyses on the specimens. Institutional review boards approved the study and waived informed consent because the accessed existing data were anonymized. Of note, sharing of genomics sequencing data with other researchers, with appropriate protections against reidentification of participants, is good research practice and is required in National Institutes of Health-funded studies. Simply following the legal requirements for research, however, may fall short of high ethical standards and aspirations. The study by Brumme et al.[1] can be expected to raise concerns about respect, discrimination, and group stigma because it addressed genetic susceptibility to a stigmatized condition disproportionately prevalent in Indigenous persons, who lack access to good healthcare. How can researchers carry out important research on sensitive topics with members of Indigenous communities and other populations who have suffered and continue to suffer severe injustice? For future studies involving participants from vulnerable communities, researchers should engage these communities as the study is being planned. Community engagement can be tailored to the nature of the study. In Canada, the 2018 Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans, must be followed in research funded by the Canadian Institutes of Health Research [8]. The equivalent in the United States is the National Institutes of Health. The Tri-Council Policy Statement contains requirements for research involving Indigenous peoples. It is not clear that such engagement was required in the project by Brumme et al.[1], a secondary analysis of HLA subtypes that inferred conclusions about Indigenous peoples. Nonetheless, researchers in similar studies would be well advised to ‘seek culturally informed advice before the use of such data to determine if harms may result and if other considerations, such as sharing of the research results, should be explored with the original source community [8].’ In contrast, research projects that involve collection of new specimens and data generally require an ongoing, long-term partnership with the community to assure that their research priorities and concerns are addressed and that risks are acceptable. In the United States, there is no advice from government agencies similar to the Canadian Tri-Council Policy Statement. Seeking culturally informed advice regarding the perspectives of Indigenous persons is always a good starting point. Of note, in projects involving secondary use of existing data, the issue is not obtaining approval from a tribal ruling body to carry out the project. Rather, the goal of engagement in this context is to strengthen the project by seeking ‘culturally informed advice before the use of such data to determine if harms may result and if other considerations, such as sharing of the research results, should be explored with the original source community [8].’ When planning such a secondary analysis project, investigators can be advised to initiate discussions about the study with representatives of leaders of community organizations providing services to Indigenous people [9]. During the data analysis and writing, these leaders might suggest changing some wording to be more respectful, giving appropriate attention to poor access to healthcare and social determinants of health disparities as alternative explanations for findings. Then, results can be presented effectively to Indigenous communities and to the press. Although researchers cannot prevent misrepresentations of their work, they should anticipate misunderstandings and take steps to prevent them. Such engagement can help both researchers and critics achieve their common ultimate goal – to improve outcomes for Indigenous and other disadvantaged persons living with HIV infection. Acknowledgements Conflicts of interest There are no conflicts of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 teacher head, 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".