Classifying risk status of non-clinical adolescents using psychometric indicators for psychosis spectrum disorders

  1. Fonseca-Pedrero, E. 126
  2. Gooding, D.C. 3
  3. Ortuño-Sierra, J. 4
  4. Pflum, M. 3
  5. Paino, M. 56
  6. Muñiz, J. 15
  1. 1 Centro de Investigacion Biomedica en Red de Salud Mental
    info

    Centro de Investigacion Biomedica en Red de Salud Mental

    Madrid, España

    ROR https://ror.org/009byq155

  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  3. 3 University of Wisconsin–Madison
    info

    University of Wisconsin–Madison

    Madison, Estados Unidos

    ROR https://ror.org/01y2jtd41

  4. 4 Universidad Loyola Andalucía
    info

    Universidad Loyola Andalucía

    Sevilla, España

    ROR https://ror.org/0075gfd51

  5. 5 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  6. 6 Prevention Program for Psychosis (P3), Oviedo, Spain
Revista:
Psychiatry Research

ISSN: 0165-1781

Año de publicación: 2016

Volumen: 243

Páginas: 246-254

Tipo: Artículo

DOI: 10.1016/J.PSYCHRES.2016.06.049 SCOPUS: 2-s2.0-84978301550 WoS: WOS:000383812800040 GOOGLE SCHOLAR

Otras publicaciones en: Psychiatry Research

Resumen

This study is an attempt to evaluate extant psychometric indicators using latent profile analysis for classifying community-derived individuals based on a set of clinical, behavioural, and personality traits considered risk markers for psychosis spectrum disorders. The present investigation included four hundred and forty-nine high-school students between the ages of 12 and 19. We used the following to assess risk: the Prodromal Questionnaire-Brief (PQ-B), Oviedo Schizotypy Assessment Questionnaire (ESQUIZO-Q), Anticipatory and Consummatory Interpersonal Pleasure Scale-Adolescent version (ACIPS-A), and General Health Questionnaire 12 (GHQ-12). Using Latent profile analysis six latent classes (LC) were identified: participants in class 1 (LC1) displayed little or no symptoms and accounted for 38.53% of the sample; class 2 (LC2), who accounted for 28.06%, also produced low mean scores across most measures though they expressed somewhat higher levels of subjective distress; LC3, a positive schizotypy group (10.24%); LC4 (13.36%), a psychosis high-risk group; LC5, a high positive and negative schizotypy group (4.45%); and LC6, a very high distress, severe clinical high-risk group, comprised 5.34% of the sample. The current research indicates that different latent classes of early individuals at risk can be empirically defined in adolescent community samples using psychometric indicators for psychosis spectrum disorders. These findings may have implications for early detection and prevention strategies in psychosis spectrum disorders. © 2016 Elsevier Ireland Ltd