New resolutions for equity
Authors: None The Lancet Digital Health
Journal: The Lancet Digital Health (2021)

As we head into a new year, we reflect on The Lancet Digital Health's efforts, and the efforts of health-care communities around the world for racial equity in medicine and global health. There is overwhelming evidence that race-based diagnostic algorithms are leading to mistreatment of minorities and exacerbating disparities for a broad range of conditions. In this issue of The Lancet Digital Health, Ramachandran Vasan and Edwin van den Heuvel show that use of race data in a cardiovascular disease prediction tool creates substantial and “biologically implausible” differences between predicted cardiovascular risk in Black and White patients. Although collection of race data could strengthen measurement, analysis, and reporting on racial minority populations, Vasan and van den Heuvel highlight that use of race data in routine diagnostic algorithms supports the unproven notion that true biological difference exists between races. In Dorothy E Roberts’ recent Perspective, she advocated for abolishing race-based diagnostics so that doctors can assess medical risk more accurately and treat patients as equals. These calls for action have led to development of new risk models—for example, two studies have described new tools that do not incorporate race for estimating kidney function with a high level of accuracy. Darshali Vyas and colleagues highlight that the cardiovascular risk prediction tool evaluated by Vasan and van den Heuvel was developed from cohorts containing nearly five times as many White individuals as Black individuals. The dominance of biased datasets in medicine is a major barrier to equity in health care and this is shown on a global scale by David Wen and colleagues’ systematic review of 21 skin cancer databases, in which they found just 11 of 2436 images with information on ethnicity were from people recorded as having brown, dark brown, or black skin. To date, AI tools have not been built on representative datasets and remain unproven on diverse populations. For instance, another study previously published in the journal showed that algorithms for detecting eye diseases are mostly trained on patients from the USA, Europe, and China, potentially making the tools ineffective for other countries and populations. To raise awareness of the prevalence of biased datasets The Lancet Digital Health editors are requesting demographics of population cohorts, including ethnicity and race, to be reported in research papers. Even if these data are not available, the implications of the absence of representation must be discussed, including the future work needed to ensure that benefits of the reported research reach the widest range of people and do not exacerbate existing inequalities. Although biases in health data will take time to rectify, calls for action have been heeded. For example, the US Agency for Healthcare Research and Quality has established a review of the use of race-based clinical algorithms in standard medical practice and the UK health secretary has announced a review into systemic racism and gender bias in medical devices and AI systems. Funding for research into health disparities and minority ethnic groups has been lacking, but progress is being made on this front too. In a Comment by Marie A Bernard, the US National Institutes of Health (NIH) Chief Officer for Scientific Workforce Diversity stated the NIH's commitment to increase the participation of under-represented groups in science and medicine and ensure transparency about workforce and reviewer demographics at funders, institutions, and publishers. As highlighted by Bernard, publishers like The Lancet group, have a responsibility to advance racial equity in health. Since April, 2020, The Lancet group have published 393 pieces examining race and ethnicity. Of these, 109 research papers have been published with implications for clinical practice and policy. We are proud of this progress but more research is needed, so The Lancet have published a call for papers, supported by an independent advisory board, for a dedicated issue on advancing racial and ethnic equity in science, medicine, and health. The Lancet Digital Health encourages submission of research that shows that commonly used risk prediction tools using race can be surpassed with more accurate alternatives without compromising equal care for patients. The Lancet Digital Health has also expanded its international advisory board and broadened the global spread of peer reviewers and commissioned authors to ensure our network of digital health experts is diverse. The next step is to measure and monitor improvements in inclusion and diversity in journal content and practices, and we invite feedback on the change our readers want to see. Revising the atherosclerotic cardiovascular disease calculator without raceMomentum has increased to eliminate race correction from clinical algorithms, but efforts have focused primarily on examples in which adjusting by race might worsen an existing racial inequity, such as in the revised vaginal birth after cesarean section calculator and equations for estimated glomerular filtration rate.1 Other examples, such as the atherosclerotic cardiovascular disease (ASCVD) Risk Estimator, raise the question of whether race correction is acceptable if used in algorithms that could be considered somewhat protective towards racial or ethnic groups that have been historically marginalised. Full-Text PDF Open AccessCharacteristics of publicly available skin cancer image datasets: a systematic reviewPublicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and their respective content is currently unclear. This systematic review aimed to identify and evaluate all publicly available skin image datasets used for skin cancer diagnosis by exploring their characteristics, data access requirements, and associated image metadata. A combined MEDLINE, Google, and Google Dataset search identified 21 open access datasets containing 106 950 skin lesion images, 17 open access atlases, eight regulated access datasets, and three regulated access atlases. Full-Text PDF Open AccessDifferences in estimates for 10-year risk of cardiovascular disease in Black versus White individuals with identical risk factor profiles using pooled cohort equations: an in silico cohort studyThe PCE might generate substantially divergent cardiovascular disease risk estimates for Black versus White individuals with identical risk profiles, which could introduce race-related variations in clinical recommendations for cardiovascular disease prevention. Full-Text PDF Open Access