Application and applicability of non-invasive risk models for predicting undiagnosed prevalent diabetes in Africa: A systematic literature search. Issue 5 (October 2015) - Mbanya, Vivian; Hussain, Akhtar; Kengne, Andre Pascal
Highlights: Twenty three studies reporting non-invasive prevalent diabetes models were identified. Prevalent diabetes models were mostly developed in Europe, USA, Asia and Middle-East. Twenty models were validated. Existing prevalent diabetes prediction models have not been applied in African populations. Issues with measurements of key predictors makes their applicability likely inaccurate in African population. Abstract: Background and purpose: Prediction algorithms are increasingly advocated in diabetes screening strategies, particularly in developing countries. We conducted a systematic review to assess the application and applicability of existing non-invasive prevalent diabetes risk models to populations within Africa. Design: systematic review data sources A systematic search of English literatures in Medline via PubMed from 1999 until June, 2014. Study selection Included studies had to report on the development, validation or implementation of a model that was primarily constructed to predict prevalent undiagnosed diabetes using non-laboratory based predictors. Data extraction: Data were extracted on the type of statistical model, type and range of predictors in the model, performance measures in both internal and external validation, and whether the model was developed from, validated or implemented in an African population. Results: Twenty-three studies reporting on non-invasive prevalent diabetes models were identified. Ten from Europe (some with multiethnic populations), nine models were developed among Asian population, two from the USA and two from the Middle-East. The c-statistics for these models ranged from 0.65 to 0.88 in the development studies, and from 0.63 to 0.80 in the validation studies. Twenty models were validated, and none in Africa. Among predictors commonly included in models, parental/family history of diabetes and personal history of hypertension appear to be more prone to measurement errors in the African context. Conclusion: Existing prevalent diabetes prediction models have not been applied to African populations, and issues with the measurement of key predictors make their applicability likely inaccurate.