Statistical Methods for Health Equity
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.
Who can get involved?
This is an open space for individuals from a vast range of professionals from academic researchers and students to health data professionals and policy makers.
Bi-weekly reading group
A theoretical and methodological discussion forum, hosted by DSxHE (Data Science for Health Equity), aimed primarily at Early Career Researchers (ECRs). The sessions are designed to be low-commitment, encouraging active participation from all attendees.
Monthly Webinar on Statistical Methods for Health Equity
A collaborative effort between DSxHE (Data Science for Health Equity), UCL Statistical Science department, and the Alan Turing Institute's Health Equity Interest Group. The webinar aims to promote health equity by exploring statistical and machine learning methods that address inequalities and biases in healthcare and biomedical research. We encourage speakers to discuss the practical considerations associated with applying these methods in the real world.
A quarterly collaborating opportunity to collectively advance our comprehension of statistical methods and their application in promoting health equity.
How to get involved?
If you wish to learn more about these regular activities and/or wish to take active participation in them please reach out to us via email.
If you want to follow all of our Statistical Methods theme activities and discussions, please join our slack channel #theme-statistical-methods channel on our Slack workspace.