Evidence-biased medicine? Applying a health equity lens to Sackett’s methodological framework

The inverse research lawThe 'inverse care law' highlights how those who need health care the most often receive the least.This includes socioeconomically disadvantaged individuals, racially minoritised groups, people with disabilities, and other marginalised communities.These inequalities are exacerbated by the intersectionality of the social determinants and economic factors that shape health outcomes, and the systemic barriers that continue to limit access to equitable care. 1 Expanding on this, the 'inverse research law' refers to the under-representation of these same underserved groups in medical research, even though they face a higher risk of ill health.Evidence-based medicine, the backbone of modern clinical practice, is only as good as the evidence it is derived from.If research representation does not truly reflect the diversity of the general population, medicines and other healthcare interventions may not work for everyone. 2Tackling the 'inverse research law' isn't only about health justice, it improves health care for all by creating more rigorous science.Sackett, the 'father of evidence-based medicine', described bias as: 'any process at any stage of inference which tends to produce results or conclusions that differ systematically from the truth'. 3Bias is defined by the Oxford Dictionary as: 'an inclination or prejudice for or against one person or group, especially in a way considered to be unfair' and 'a concentration on an interest in one particular area or subject'. 4When biases, whether political, institutional, or researcher driven, are embedded in medical research, they can become deeply rooted in clinical practice, perpetuate health disparities, and indirectly drive up healthcare costs through, for example, differential access to preventive measures, delayed diagnoses, and lack of treatment options. 5,6aps in understanding health conditions, and treatment effectiveness across 'real-world' diverse populations, pose a