Predicting type 2 diabetes prevalence for people with severe mental illness in a multi-ethnic East London population. (April 2023)

• Record Type: Journal Article
• Authors: Shamsutdinova, Diana; Das-Munshi, Jayati; Ashworth, Mark; Roberts, Angus; Stahl, Daniel
• Is Part Of: International journal of medical informatics. Volume 172(2023)
• Publication Date: 2023-04
• Subjects: Type 2 diabetes -- Severe mental illness -- Schizophrenia -- Prediction modelling -- Electronic health records -- Physical and mental health; SMI severe mental illness -- T2DM type 2 diabetes mellitus -- LR logistic regression -- ROC-AUC area under the receiver-operator curve -- BMI body mass index -- CRIS Clinical Record Interactive Search system -- CVD cardiovascular diseases -- EHR electronic health records; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2023.105019
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Type 2 diabetes mellitus (T2DM) is common in people with severe mental illness. Few T2DM prediction models include severe mental illness (SMI); none seem to target SMI. We derived T2DM prevalence model for people with SMI living in East London. UK primary care data can be used to develop well-performing models for SMI. Ensemble of a linear and a machine-learning model can help quantify data non-linearity. Abstract: Background and aims: Prevalence of type two diabetes mellitus (T2DM) in people with severe mental illness (SMI) is 2–3 times higher than in general population. Predictive modelling has advanced greatly in the past decade, and it is important to apply cutting-edge methods to vulnerable groups. However, few T2DM prediction models account for the presence of mental illness, and none seemed to have been developed specifically for people with SMI. Therefore, we aimed to develop and internally validate a T2DM prevalence model for people with SMI. Methods: We utilised a large cross-sectional sample representative of a multi-ethnic population from London (674, 000 adults); 10, 159 people with SMI formed our analytical sample (1, 513 T2DM cases). We fitted a linear logistic regression and XGBoost as stand-alone models and as a stacked ensemble. Age, sex, body mass index, ethnicity, area-based deprivation, past hypertension, cardiovascular diseases, prescribed antipsychotics, and SMI illness were the predictors. Results: Logistic regression performed well while detecting T2DM presence for people with SMI: area under the receiver operator curve (ROC-AUC) was 0.83 (95 % CI 0.79–0.87). XGBoost and LR-XGBoost ensemble performed equally well, ROC-AUC 0.83 (95 % CI 0.79–0.87), indicating a negligible contribution of non-linear terms to predictive power. Ethnicity was the most important predictor after age. We demonstrated how the derived models can be utilised and estimated a 2.14 % (95 %CI 2.03 %-2.24 %) increase in T2DM prevalence in East London SMI population in 20 years' time, driven by the projected demographic changes. Conclusions: Primary care data, the setting where prediction models could be most fruitfully used, provide enough information for well-performing T2DM prevalence models for people with SMI. We demonstrated how thorough internal cross-validation of an ensemble of a linear and machine-learning model can quantify the predictive value of non-linearity in the data.