This theme aims to improve our understanding of health inequalities and reduce disparities through the development of better statistical methods.
To advance health equity, we need to understand the factors underlying health inequalities. These are generally highly complex, involving numerous social, cultural, economic, environmental and genetic factors. Through careful statistics, we can use data to begin to disentangle some of these factors to both gain a better understanding of disease and health, and also to inform better decision and policy making. Here are three examples of major challenges that statistical methods could help address. First of all, health research data has a significant bias towards Western and wealthy individuals. This lack of diversity in health data is an example of ascertainment bias, meaning that our sample is not representative of the population of interest. Second, identifying the determinants of health inequalities requires disentangling multiple complex factors, requiring careful statistical modelling integrating large amounts of data from multiple sources. Thirdly, if we want to reduce inequalities through interventions or data-based solutions, we need to be able to reliably evaluate their impact in terms of both their benefits and their costs. The focus of this theme is on the investigation and development of statistical methods that have the potential to help us use data to better understand health inequalities and to promote health equity.