Diabetes causes a large and unevenly distributed health and economic burden within the population living with diabetes. Improved health behaviours have the potential to avert a large share of morbidity and mortality attributable to diabetes. However, adherence to recommended self-management remains challenging for many patients. This may (at least partly) explain the large overall disease burden in people with diabetes, as well as how that burden is distributed among patients. A better understanding of the patient and community level characteristics that affect behaviour change can inform more personalised, more effective health interventions that stimulate positive health behaviour changes, in turn reducing the overall burden associated with diabetes.
CASCARA aims to provide novel and much needed evidence on characteristics predictive of (1) health behaviour change subsequent to a diabetes diagnosis and (2) of the resulting changes in diabetes complication risk factors. To achieve this, I will use causal econometric and epidemiologic methods as well as machine learning (ML) and causal mediation analysis. The commonly recommended behaviour changes I focus on comprise: improving diet, increasing physical activity, reducing smoking and alcohol consumption. In particular, CASCARA will address the following research objectives using longitudinal observational data from continental Europe, the UK and the US:
1. Investigate the effect of a diabetes diagnosis on health behaviours and potential heterogeneities across gender and socioeconomic status
2. Use of ML to identify potentially unanticipated socioeconomic, demographic and clinical characteristics affecting health behaviour change, for a more detailed understanding of its potential drivers
3. Use causal mediation analysis to identify the impact of different health behaviour changes on risk factors for diabetes complications (body mass index, hypertension status and blood glucose levels) post-diabetes diagnosis.
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