Predicting the risk of depression among adolescents in Nepal using a model developed in Brazil: the IDEA Project

Abstract

The burden of adolescent depression is high in low- and middle-income countries (LMICs), yet research into prevention is lacking. Development and validation of models to predict individualized risk of depression among adolescents in LMICs is rare but crucial to ensure appropriate targeting of preventive interventions. We assessed the ability of a model developed in Brazil, a middle-income country, to predict depression in an existing culturally different adolescent cohort from Nepal, a low-income country with a large youth population with high rates of depression. Data were utilized from the longitudinal study of 258 former child soldiers matched with 258 war-affected civilian adolescents in Nepal. Prediction modelling techniques were employed to predict individualized risk of depression at age 18 or older in the Nepali cohort using a penalized logistic regression model. Following a priori exclusions for prior depression and age, 55 child soldiers and 71 war-affected civilians were included in the final analysis. The model was well calibrated, had good overall performance, and achieved good discrimination between depressed and non-depressed individuals with an area under the curve (AUC) of 0.73 (bootstrap-corrected 95% confidence interval 0.62-0.83). The Brazilian model comprising seven matching sociodemographic predictors, was able to stratify individualized risk of depression in a Nepali adolescent cohort. Further testing of the model's performance in larger socio-culturally diverse samples in other geographical regions should be attempted to test the model's wider generalizability.

Keywords: Adolescence; External validation; LMIC; Mental health; Prediction model; Risk calculator.

Fig. 1

ROC curve for a the Pelotas model externally validated in Nepal data [area under the curve (AUC) = 0.73; bootstrap-corrected 95% confidence interval 0.62–0.83], and b the Pelotas model refitted in the Nepal data (AUC = 0.83; bootstrap-corrected 95% confidence interval 0.74–0.91). The y-axis shows the true positive rate: the proportion of adolescents correctly identified with depression. The x-axis shows the false positive rate: the proportion of adolescents who were wrongly identified as having depression. The grey diagonal line represents a model that discriminates the same as chance

Fig. 2

Calibration plot for a the Pelotas model externally validated in the Nepal dataset, and b when the intercept of the Pelotas model was adjusted. Graphical display of model predictions (as depicted by the black flexible calibration line with 95% confidence limits around the model predictions as dashed lines) on the x-axis and observed proportion of depression in the Nepali cohort on the y-axis. The calibration plots show how well the model predictions align with the observed rate of depression in Nepal. Perfect agreement between the predictions and the observed rate are indicated by perfect alignment on the ideal line