Investigating ethnic differences in the effectiveness of type 2 diabetes medications and opportunities for individualised diabetes treatment
Lead Research Organisation:
London School of Hygiene and Tropical Medicine
Department Name: Epidemiology and Population Health
Abstract
The dramatic rise in prevalence of type 2 diabetes mellitus (T2DM) and diabetes-related complications in the UK urges for more effective diabetes care. With stark disparities in T2DM management across different population groups in the UK, more evidence is needed on how better tailor T2DM treatment to individuals.
Several classes of type 2 diabetes medication are currently recommended for second-line treatment, but there is little guidance on which medications should be prescribed to who. Therapeutic guidelines are predominantly based on clinical trial data, which often include populations primarily of white European descent, with a higher proportion of men and limited representation of individuals with comorbid conditions or older adults. Electronic health records (EHR) are potentially a good resource for studying treatment effects in underrepresented groups due to more diverse study populations and longer follow up.
This project explores the utility of UK EHR for studying the effectiveness of type 2 diabetes treatment in diverse populations, using novel statistical methods to study individual treatment response. The first studies will investigate where there are differences in the comparative effectiveness of medications by characteristics including ethnicity, sex and age. The final study in this project will use causal forest algorithms to identify predictors of treatment success and estimate individual treatment response.
Quantitative skills enhanced:
- Different ways of assessing the effectiveness of treatments and commonly used statistical techniques in pharmacoepidemiology.
- Triangulation between different quantitative data sources, including genomic and health utilization data.
- Computational skills required to managing and analyse big data.
- The application of complex quantitative techniques to control for confounding in observational datasets.
Keywords: diabetes, ethnicity, electronic health records, pharmacoepidemiology, high dimensional propensity scoring.
Several classes of type 2 diabetes medication are currently recommended for second-line treatment, but there is little guidance on which medications should be prescribed to who. Therapeutic guidelines are predominantly based on clinical trial data, which often include populations primarily of white European descent, with a higher proportion of men and limited representation of individuals with comorbid conditions or older adults. Electronic health records (EHR) are potentially a good resource for studying treatment effects in underrepresented groups due to more diverse study populations and longer follow up.
This project explores the utility of UK EHR for studying the effectiveness of type 2 diabetes treatment in diverse populations, using novel statistical methods to study individual treatment response. The first studies will investigate where there are differences in the comparative effectiveness of medications by characteristics including ethnicity, sex and age. The final study in this project will use causal forest algorithms to identify predictors of treatment success and estimate individual treatment response.
Quantitative skills enhanced:
- Different ways of assessing the effectiveness of treatments and commonly used statistical techniques in pharmacoepidemiology.
- Triangulation between different quantitative data sources, including genomic and health utilization data.
- Computational skills required to managing and analyse big data.
- The application of complex quantitative techniques to control for confounding in observational datasets.
Keywords: diabetes, ethnicity, electronic health records, pharmacoepidemiology, high dimensional propensity scoring.
People |
ORCID iD |
| Mia Harley (Student) |
 http://orcid.org/0000-0001-9065-3387
|
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/N013638/1 | 30/09/2016 | 30/03/2026 | |||
| 2580594 | Studentship | MR/N013638/1 | 30/09/2021 | 30/08/2025 | Mia Harley |