Predicting the onset of major depression in primary care: international validation of a risk prediction algorithm from Spain

Abstract

Context: The different incidence rates of major depression and its associated risk factors suggest the need for specific national rather than supranational risk algorithms. Objectives: Develop and validate a predictD-Spain-risk-algorithm for the onset of major depression and compare the performance of the predictD-Spain-risk-algorithm with the predictDEurope-risk-algorithm in Spanish primary health care. Setting: Health Centers in Europe and South-America. Participants: In Spain (4574), Chile (2133) and other 5 European countries (5184), 11891 non depressed adult primary care attendees formed our at risk population. Main Outcome Measures: DSM-IV major depression (Composite International Diagnostic Interview). Results: The predictD-Spain-risk-algorithm was developed in 2787 primary care attendees in Spain and its use validated in Chile (1844) and five other European countries (4075). Six variables were patient characteristics or past events (sex, age, sex*age interaction, education, physical child abuse, and lifetime depression) and six were current status (SF-12-physical-score, SF-12-mental-score, dissatisfaction with unpaid work, number of serious problems in very close persons, dissatisfaction with living together at home, and taking medication for stress, anxiety or depression). Province was the thirteenth factor. The C-index of the predictD-Spain-risk-algorithm was 0.82 (95%CI=0.79-0.84) and in other countries it ranged between 0.70-0.83. Both the test for C-index differences (difference=0.0316; 95%CI=0.0121-0.0530; p<0.0022) and calibration plots showed that the predictD-Spain-risk-algorithm functioned better than the predictD-Europe-risk-algorithm in Spain. However, this did not hold true when 69 applied to other countries in Europe or Chile.