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

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@article{337ee34120fe4f9eadaf0f09209e66de,
title = "Derivation of a prediction model for a diagnosis of depression in young adults: a matched case-control study using electronic primary care records.",
abstract = "BackgroundApproximately 80,000 children and young people in the UK suffer from severe depression but many are untreated due to poor identification of early warning signs and risk factors. AimsDerive and investigate discrimination characteristics of a prediction model for a first diagnosis of depression in young people aged 15-24 years.MethodA matched case control study, using electronic primary care records. Stepwise conditional logistic regression modelling investigated 42 potential predictors including symptoms, co-morbidities, social factors, drug and alcohol misuse. ResultsOf the socioeconomic 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 males aged 15 to 18 years, work stress in females aged 19 to 24 years.ConclusionOur model is a first step in the development of a predictive model identifying early warning signs of depression in young people in primary care.",
author = "Linda Nichols and Ronan Ryan and Charlotte Connor and Maximillian Birchwood and Tom Marshall",
year = "2016",
month = mar,
day = "30",
doi = "10.1111/eip.12332",
language = "English",
journal = "Early Intervention in Psychiatry",
issn = "1751-7885",
publisher = "Wiley",

}

RIS

TY - JOUR

T1 - Derivation of a prediction model for a diagnosis of depression in young adults: a matched case-control study using electronic primary care records.

AU - Nichols, Linda

AU - Ryan, Ronan

AU - Connor, Charlotte

AU - Birchwood, Maximillian

AU - Marshall, Tom

PY - 2016/3/30

Y1 - 2016/3/30

N2 - BackgroundApproximately 80,000 children and young people in the UK suffer from severe depression but many are untreated due to poor identification of early warning signs and risk factors. AimsDerive and investigate discrimination characteristics of a prediction model for a first diagnosis of depression in young people aged 15-24 years.MethodA matched case control study, using electronic primary care records. Stepwise conditional logistic regression modelling investigated 42 potential predictors including symptoms, co-morbidities, social factors, drug and alcohol misuse. ResultsOf the socioeconomic 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 males aged 15 to 18 years, work stress in females aged 19 to 24 years.ConclusionOur model is a first step in the development of a predictive model identifying early warning signs of depression in young people in primary care.

AB - BackgroundApproximately 80,000 children and young people in the UK suffer from severe depression but many are untreated due to poor identification of early warning signs and risk factors. AimsDerive and investigate discrimination characteristics of a prediction model for a first diagnosis of depression in young people aged 15-24 years.MethodA matched case control study, using electronic primary care records. Stepwise conditional logistic regression modelling investigated 42 potential predictors including symptoms, co-morbidities, social factors, drug and alcohol misuse. ResultsOf the socioeconomic 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 males aged 15 to 18 years, work stress in females aged 19 to 24 years.ConclusionOur model is a first step in the development of a predictive model identifying early warning signs of depression in young people in primary care.

U2 - 10.1111/eip.12332

DO - 10.1111/eip.12332

M3 - Article

JO - Early Intervention in Psychiatry

JF - Early Intervention in Psychiatry

SN - 1751-7885

ER -