Dysinclusion: naming and defining the inequitable absence of marginalized populations in health research

Some groups-like people with disabilities, racialized communities, or those living in poverty-are often missing from health research. Even when they face some of the greatest health challenges, these groups are frequently left out of studies, underrepresented in data, or not even recognized as distinct populations. This absence has serious consequences: it limits what we know about their health, weakens the accuracy of research findings, and can make existing health disparities worse. This problem is common, but there is no widely used method or term in health research to describe or address it. Researchers typically think about who is missing from studies in terms of technical issues like bias or generalizability. These concepts do not fully capture the deeper problem of structural inequality, and make it seem as though ethical concerns, like health equity, are separate from the methods that lead to rigorous science. This paper introduces a new term: dysinclusion. Dysinclusion means the unfair or unjust absence of groups that should be part of health research. It's not just about who is missing-it's about why they are missing and what that says about the way research is designed. We outline three common ways dysinclusion happens: 1) When people are missing from the data we rely on. 2) When people are eligible to participate but cannot or would not. 3) When people are included in a study, but their identity is misclassified, ignored, or made invisible. Dysinclusion is a concept at the intersection of ethics and research methods. Naming and defining dysinclusion can help to guide research that treats equity as a core part of research quality. Just as we assess studies for bias or confounding, we should assess them for dysinclusion, and take steps to reduce it through better study design, more inclusive data collection, and clearer reporting. Addressing dysinclusion is not only about equity. It's also about better science.