Nguyen, Vincent;
(2024)
Triangulating epidemiological and data science approaches to evaluate the impact of public health interventions on the prevention of type 2 diabetes.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Background: The NHS has deployed the NHS Health Check (NHSHC) and the Diabetes Prevention Programme (NHSDPP) to reduce rates of preventable diseases such as type 2 diabetes (T2DM) without randomised controlled trial evidence. Previous observational investigations were limited by missing data, confounding, selection bias and more. I aimed to reduce these limitations when using observational data to evaluate the effectiveness of the programmes on the incidence of T2DM and to complement different analytical techniques to address these issues. Methods: Using linked electronic health records, I triangulated a range of study designs to investigate the impact of the NHSHC and NHSDPP on preventing T2DM. Study designs included: systematic reviews, interrupted time series, target trial emulation, and data science approaches. Results: Systematic reviews suggest that the NHSHC detects the same or higher rates of T2DM and is attributable to detection of undiagnosed cases in attendees. Interrupted time series failed to detect population level impacts of the NHSHC, likely attributable to limited uptake. Trial emulation found that the NHSDPP reduces individual incidence of recorded T2DM in high-risk adult with a 59% [54%-62%] risk reduction at 3 years and that the NHSHC reduced the incidence of recorded T2DM by 54% [95%CI 53% - 55%] at 10-years. Data Science approaches produced potential targetable subgroups to improve effectiveness based upon ethnicity, blood glucose measures and body mass index. Triangulation suggests that whilst the NHSHC/NHSDPP can prevent individual cases of recorded T2DM, (non-targeted) uptake of the NHSHC could lead to failures of detecting population level impacts. Conclusion: Using multiple approaches to understand the impact of public health interventions on the incidence of recorded T2DM allowed me to triangulate, complement and synergise methods to provide a balanced assessment of the likely impact of the NHSHC/DPP on the incidence of T2DM and reduced uncertainties in any isolated study design.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Triangulating epidemiological and data science approaches to evaluate the impact of public health interventions on the prevention of type 2 diabetes |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10187041 |
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