Our work could help develop future interventions specifically designed to reduce infections in people with diabetes. Better knowledge of infection risks and their links with diabetes and ethnicity could inform the response to future waves of COVID-19 and other pandemics e.g. influenza.
Infections are common in people with diabetes and can substantially affect quality of life. Previous studies included mostly older people with type 2 diabetes (T2DM). We intend to estimate i) infection rates in specific patient groups (T2DM, T1DM and those with pre-diabetes or “intermediate hyperglycaemia”; IH), and how these risks vary by key characteristics; ii) how long-term glycaemic control and fluctuations in control (HbA1c) affect these risks.
We will obtain cohorts of people with diagnosed diabetes (T1DM, T2DM) or evidence of IH, age-sex and practice matched to people without DM or IH (control cohort). An alternative set of controls will be matched by ethnicity. We will include patients actively registered as of 1/1/2015 (1/1/2020 for COVID-19), followed to the end of 2019 (before the COVID-19 pandemic) and up to 2021 for COVID-19 infections. We will use Read codes to find people with diabetes, and also measurements of HbA1c or fasting blood glucose to select those with IH. Our main outcomes will be infections identified by Read codes accompanied by a relevant prescription in primary care. We will also investigate hospitalisation for infection, and infection-related mortality. We will estimate event rates for specific infections and infections overall during 2015-2019 (2020-2021 for COVID-19) using Poisson regression to adjust for confounders. Where possible we will assess effect modification (e.g. by age, sex, or ethnicity) by adding cross-product terms to models. To investigate associations with glycaemic control, we will estimate both average (mean) and variability (covariance) in HbA1c primarily focused on recordings made during the 4 years prior to study onset, including these estimates in multivariable models. We will stratify results by age, sex, and, where appropriate, ethnicity.
Our study will provide better estimates of infection risk and help identify people at greatest risk, enabling subsequent intervention development.
The primary outcome of interests are infections recorded in health care records. Specific infections will be chosen from those routinely treated in primary care and / or already shown to be associated with diabetes (see Appendix 1). Infections will be defined clinically based on Read codes plus an appropriate prescription (anti-bacterial, anti-fungal or anti-viral). We will link to HES and ONS Mortality data to assess risk of infection-related hospitalisation and mortality, respectively.
We will assess:
i) rates of specific infections (see Appendix 1 for details of infection outcomes);
ii) overall infection rates;
iii) infection related hospitalisation;
iv) risk of infection related mortality.
Tess Harris - Chief Investigator - St George's, University of London
Julia Critchley - Corresponding Applicant - St George's, University of London
Derek Cook - Collaborator - St George's, University of London
Elizabeth Limb - Collaborator - St George's, University of London
Iain Carey - Collaborator - St George's, University of London
Stephen DeWilde - Collaborator - St George's, University of London
Umar Chaudhry - Collaborator - St George's, University of London