Approved Research

Disease Pattern Clustering: Social Determinants in developing diseases

Lay summary

This project aims to explore the current trends of diseases in the UK,, and how these diseases are tied to things like income, education, and living conditions. We'll be using data from the UK BioBank and machine learning tools to see if certain patterns within these social factors can lead to certain diseases.

The research design is justified from two perspectives. Firstly, social determinants have a significant impact on health outcomes, as supported by theoretical and empirical evidence. For example, The theory of fundamental cause suggests that social conditions serve as fundamental causes of diseases, influencing health outcomes through various mechanisms such as resource access and health-related behaviors. Social epidemiology, a branch of epidemiology, focuses on the influence of social structures, institutions, and relationships on health determinants. By incorporating biological mechanisms, social epidemiologytries to shed light on how these external factors can directly affect our bodies and cause diseases.

Secondly, the clustering method enables a bottom-up, holistic examination of the relationship of interest. It means we're looking at the big picture first, then breaking it down. Many studies exploring the impact of social determinants of health adopt a top-down approach, starting with a core idea or specific mechanism and then collecting evidence to support the proposed hypothesis. However, this approach often lacks a comprehensive perspective for assessing complex relationships between risk factors and diseases. Additionally, omitting related diseases in research designs introduces bias when the pathways connecting risk factors and the focused disease are unclear. Single risk factor research designs also overlook the multifactorial nature of many diseases, the fact that many diseases are caused by a mix of factors. It will hinder the discovery of genuine relationships. This is especially important when omitted risks are interrelated with selected risk factors.

The results of this study will contribute to a deeper understanding of the relationship between diseases and social determinants of health, potentially leading to the development of new theoretical frameworks. Furthermore, it will provide valuable insights for health policymakers in designing more effective strategies to address health inequalities driven by social disparities. This aligns with the goal of "closing the gap in a generation: health equity through action on the social determinants of health," as advocated by the World Health Organization's Commission on Social Determinants of Health.