Literature DB >> 27027490

Derivation of a prediction model for a diagnosis of depression in young adults: a matched case-control study using electronic primary care records.

Linda Nichols1, Ronan Ryan1, Charlotte Connor2, Max Birchwood3, Tom Marshall1.   

Abstract

BACKGROUND: Approximately 80 000 children and young people in the UK suffer from depression, but many are untreated because of poor identification of early warning signs and risk factors. AIMS: This study aimed to derive and to investigate discrimination characteristics of a prediction model for a first recorded diagnosis of depression in young people aged 15-24 years.
METHOD: This study used a matched case-control method using electronic primary care records. Stepwise conditional logistic regression modelling investigated 42 potential predictors including symptoms, co-morbidities, social factors and drug and alcohol misuse.
RESULTS: Of the socio-economic and symptomatic predictors identified, the strongest associations were with depression symptoms and other psychological conditions. School problems and social services involvement were prominent predictors in men aged 15-18 years, work stress in women aged 19-24 years.
CONCLUSION: Our model is a first step in the development of a predictive model identifying early warning signs of depression in young people in primary care.
© 2016 John Wiley & Sons Australia, Ltd.

Entities:  

Keywords:  depression; primary care; young adults

Mesh:

Year:  2016        PMID: 27027490     DOI: 10.1111/eip.12332

Source DB:  PubMed          Journal:  Early Interv Psychiatry        ISSN: 1751-7885            Impact factor:   2.732


  3 in total

1.  Identification of children at risk for mental health problems in primary care-Development of a prediction model with routine health care data.

Authors:  Nynke R Koning; Frederike L Büchner; Robert R J M Vermeiren; Mathilde R Crone; Mattijs E Numans
Journal:  EClinicalMedicine       Date:  2019-10-17

2.  Identification of child mental health problems by combining electronic health record information from different primary healthcare professionals: a population-based cohort study.

Authors:  Nynke R Koning; Frederike L Büchner; Nathalie A Leeuwenburgh; Irma Jm Paijmans; Dj Annemarie van Dijk-van Dijk; Robert Rjm Vermeiren; Mattijs E Numans; Mathilde Crone
Journal:  BMJ Open       Date:  2022-01-12       Impact factor: 2.692

3.  Machine learning models to detect anxiety and depression through social media: A scoping review.

Authors:  Arfan Ahmed; Sarah Aziz; Carla T Toro; Mahmood Alzubaidi; Sara Irshaidat; Hashem Abu Serhan; Alaa A Abd-Alrazaq; Mowafa Househ
Journal:  Comput Methods Programs Biomed Update       Date:  2022-09-09
  3 in total

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