Incidence, prevalence, and potential risk factors for diabetic foot ulceration: A retrospective review at a multidisciplinary centre in Barbados

OBJECTIVE: Diabetes and lower extremity amputation rates in Barbados are among some of the highest globally, with peripheral neuropathy and peripheral vascular disease found to be independent risk factors for this population. Despite this, there is currently a lack of research evidence on rates of diabetic foot ulceration, which has amputation as its sequela. We aimed to evaluate the incidence and prevalence rates of active ulceration in a population of people with diabetes in Barbados. Secondly, we explored the risk factors for new/recurrent ulceration. RESEARCH DESIGN AND METHODS: Data were extracted from the electronic medical records for the period January 1, 2019 to December 31, 2020 for a retrospective cross‐sectional study for patients of a publicly‐funded diabetes management programme. Eligible records included people aged 18 years and above with a diagnosis of type 1 or 2 diabetes. Potential risk factors were explored using univariable logistic regression models. RESULTS: A total of 225 patients were included in the study (96% type 2 diabetes, 70.7% female, 98.7% Black Caribbean). The 1‐year period prevalence of diabetic foot ulceration was 14.7% (confidence interval [CI]: 10.5, 20.1). Incidence of ulceration in the same period was 4.4% (CI: 4.4, 4.5). Risk factors associated with diabetic foot ulceration included: retinopathy (OR 3.85, CI: 1.24, 11.93), chronic kidney disease (OR 9.86, CI: 1.31, 74.22), aspirin use (OR 3.326, CI: 1.02, 10.85), and clopidogrel use (OR 3.13, CI: 1.47, 6.68). CONCLUSION: This study provided some insight into potential risk factors for foot ulceration in this population, which previous studies have shown to have higher rates of lower extremity amputations. Further research in this understudied group through a larger prospective cohort would allow more meaningful associations with risk factors and would be useful for the creation of risk prediction models.