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

Published online by Cambridge University Press:  05 April 2011

J. Á. Bellón*
Affiliation:
Centro de Salud El Palo, Unidad de Investigación del Distrito de Atención Primaria de Málaga (redIAPP, grupo SAMSERAP); Departamento de Medicina Preventiva, Universidad de Málaga, Spain;
J. de Dios Luna
Affiliation:
Departamento de Bioestadística (redIAPP, grupo SAMSERAP), Universidad de Granada, Spain;
M. King
Affiliation:
Department of Mental Health Sciences, University College London, UK;
B. Moreno-Küstner
Affiliation:
Fundación IMABIS; Unidad de Investigación del Distrito de Atención Primaria de Málaga (redIAPP, grupo SAMSERAP); Departamento de Personalidad, Evaluación y Tratamiento Psicológico, Universidad de Málaga, Spain;
I. Nazareth
Affiliation:
Department of Primary care and Population Health, University College London and Medical Research Council General Practice Research Framework, UK;
C. Montón-Franco
Affiliation:
Centro de Salud Casablanca. (redIAPP, grupo Aragón); Departamento de Medicina y Psiquiatría, Universidad de Zaragoza, Spain;
M. J. GildeGómez-Barragán
Affiliation:
Unidad Docente de Medicina Familiar y Comunitaria de La Rioja, Servicio Riojano de la Salud, Logroño, La Rioja, Spain;
M. Sánchez-Celaya
Affiliation:
Unidad Docente de Medicina Familiar y Comunitaria, Área I de Atención Primaria, Madrid, Coordinadora de Investigación de la Sociedad Española de Medicina Familiar y Comunitaria, Spain;
M. Á. Díaz-Barreiros
Affiliation:
Centro de Salud Vecindario, Gerencia de Atención Primaria de Gran Canaria, Servicio Canario de Salud, Las Palmas, Spain;
C. Vicens
Affiliation:
Centro de Salud son Serra-La Vileta, Unidad Docente de Medicina Familiar y Comunitaria de Mallorca, Instituto Balear de la Salud (redIAPP, grupo Baleares), Palma de Mallorca, Illes Balears, Spain;
J. A. Cervilla
Affiliation:
CIBERSAM, Departamento de Psiquiatría y Medicina legal, Universidad de Granada, Spain;
I. Švab
Affiliation:
Department of Family Medicine, University of Ljubljana, Slovenia;
H.-I. Maaroos
Affiliation:
Faculty of Medicine, University of Tartu, Estonia;
M. Xavier
Affiliation:
Faculdade Ciências Médicas, University of Lisbon, Portugal;
M. I. Geerlings
Affiliation:
University Medical Centre, Utrecht, The Netherlands;
S. Saldivia
Affiliation:
Departamento de Psiquiatría y Salud Mental, Universidad de Concepción, Chile;
B. Gutiérrez
Affiliation:
CIBERSAM, Departamento de Psiquiatría y Medicina legal, Universidad de Granada, Spain;
E. Motrico
Affiliation:
Fundación IMABIS, Unidad de Investigación del Distrito de Atención Primaria de Málaga (redIAPP, grupo SAMSERAP); Departamento de Psicología Social, Universidad de Málaga, Spain;
M. T. Martínez-Cañavate
Affiliation:
Fundación IAVANTE, Granada, Spain;
B. Oliván-Blázquez
Affiliation:
Unidad de Investigación de Atención Primaria (redIAPP, grupo Aragón); Instituto Aragonés de Ciencias de la Salud, Zaragoza, Spain;
M. S. Sánchez-Artiaga
Affiliation:
Centro de Salud Condes de Barcelona-Boadilla, Área 6 de Atención Primaria, Madrid, Spain;
S. March
Affiliation:
Unidad de Investigación de Atención Primaria de Baleares (redIAPP, grupo Baleares), Mallorca, Spain;
M. del Mar Muñoz-García
Affiliation:
Departamento de Psiquiatría y Medicina legal, Universidad de Granada, Spain;
A. Vázquez-Medrano
Affiliation:
Unidad Docente de Medicina Familiar y Comunitaria de La Rioja, Servicio Riojano de la Salud, Logroño, La Rioja, Spain;
P. Moreno-Peral
Affiliation:
Fundación IMABIS, Unidad de Investigación del Distrito de Atención Primaria de Málaga (redIAPP, grupo SAMSERAP), Málaga, Spain
F. Torres-González
Affiliation:
CIBERSAM, Departamento de Psiquiatría y Medicina legal, Universidad de Granada, Spain;
*
*Address for correspondence: J. Á. Bellón, M.D., Ph.D., Departamento de Medicina Preventiva, Facultad de Medicina, Universidad de Málaga, Campus de Teatinos, 29071 Málaga, Spain. (Email: JABELLON@terra.es)

Abstract

Background

The different incidence rates of, and risk factors for, depression in different countries argue for the need to have a specific risk algorithm for each country or a supranational risk algorithm. We aimed to develop and validate a predictD-Spain risk algorithm (PSRA) for the onset of major depression and to compare the performance of the PSRA with the predictD-Europe risk algorithm (PERA) in Spanish primary care.

Method

A prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multi-level logistic regression and inverse probability weighting to build the PSRA. In Spain (4574), Chile (2133) and another five European countries (5184), 11 891 non-depressed adult primary care attendees formed our at-risk population. The main outcome was DSM-IV major depression (CIDI).

Results

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 [Short Form 12 (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]. The C-index of the PSRA was 0.82 [95% confidence interval (CI) 0.79–0.84]. The Integrated Discrimination Improvement (IDI) was 0.0558 [standard error (s.e.)=0.0071, Zexp=7.88, p<0.0001] mainly due to the increase in sensitivity. Both the IDI and calibration plots showed that the PSRA functioned better than the PERA in Spain.

Conclusions

The PSRA included new variables and afforded an improved performance over the PERA for predicting the onset of major depression in Spain. However, the PERA is still the best option in other European countries.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2011

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