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Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England

OBJECTIVES: We aimed to examine agreement between common mental disorders (CMDs) from primary care records and repeated CMD questionnaire data from ALSPAC (the Avon Longitudinal Study of Parents and Children) over adolescence and young adulthood, explore factors affecting CMD identification in prima...

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Autores principales: Smith, Daniel, Willan, Kathryn, Prady, Stephanie L, Dickerson, Josie, Santorelli, Gillian, Tilling, Kate, Cornish, Rosie Peggy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8524296/
https://www.ncbi.nlm.nih.gov/pubmed/34663669
http://dx.doi.org/10.1136/bmjopen-2021-053624
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author Smith, Daniel
Willan, Kathryn
Prady, Stephanie L
Dickerson, Josie
Santorelli, Gillian
Tilling, Kate
Cornish, Rosie Peggy
author_facet Smith, Daniel
Willan, Kathryn
Prady, Stephanie L
Dickerson, Josie
Santorelli, Gillian
Tilling, Kate
Cornish, Rosie Peggy
author_sort Smith, Daniel
collection PubMed
description OBJECTIVES: We aimed to examine agreement between common mental disorders (CMDs) from primary care records and repeated CMD questionnaire data from ALSPAC (the Avon Longitudinal Study of Parents and Children) over adolescence and young adulthood, explore factors affecting CMD identification in primary care records, and construct models predicting ALSPAC-derived CMDs using only primary care data. DESIGN AND SETTING: Prospective cohort study (ALSPAC) in Southwest England with linkage to electronic primary care records. PARTICIPANTS: Primary care records were extracted for 11 807 participants (80% of 14 731 eligible). Between 31% (3633; age 15/16) and 11% (1298; age 21/22) of participants had both primary care and ALSPAC CMD data. OUTCOME MEASURES: ALSPAC outcome measures were diagnoses of suspected depression and/or CMDs. Primary care outcome measure were Read codes for diagnosis, symptoms and treatment of depression/CMDs. For each time point, sensitivities and specificities for primary care CMD diagnoses were calculated for predicting ALSPAC-derived measures of CMDs, and the factors associated with identification of primary care-based CMDs in those with suspected ALSPAC-derived CMDs explored. Lasso (least absolute selection and shrinkage operator) models were used at each time point to predict ALSPAC-derived CMDs using only primary care data, with internal validation by randomly splitting data into 60% training and 40% validation samples. RESULTS: Sensitivities for primary care diagnoses were low for CMDs (range: 3.5%–19.1%) and depression (range: 1.6%–34.0%), while specificities were high (nearly all >95%). The strongest predictors of identification in the primary care data for those with ALSPAC-derived CMDs were symptom severity indices. The lasso models had relatively low prediction rates, especially in the validation sample (deviance ratio range: −1.3 to 12.6%), but improved with age. CONCLUSIONS: Primary care data underestimate CMDs compared to population-based studies. Improving general practitioner identification, and using free-text or secondary care data, is needed to improve the accuracy of models using clinical data.
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spelling pubmed-85242962021-11-02 Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England Smith, Daniel Willan, Kathryn Prady, Stephanie L Dickerson, Josie Santorelli, Gillian Tilling, Kate Cornish, Rosie Peggy BMJ Open Epidemiology OBJECTIVES: We aimed to examine agreement between common mental disorders (CMDs) from primary care records and repeated CMD questionnaire data from ALSPAC (the Avon Longitudinal Study of Parents and Children) over adolescence and young adulthood, explore factors affecting CMD identification in primary care records, and construct models predicting ALSPAC-derived CMDs using only primary care data. DESIGN AND SETTING: Prospective cohort study (ALSPAC) in Southwest England with linkage to electronic primary care records. PARTICIPANTS: Primary care records were extracted for 11 807 participants (80% of 14 731 eligible). Between 31% (3633; age 15/16) and 11% (1298; age 21/22) of participants had both primary care and ALSPAC CMD data. OUTCOME MEASURES: ALSPAC outcome measures were diagnoses of suspected depression and/or CMDs. Primary care outcome measure were Read codes for diagnosis, symptoms and treatment of depression/CMDs. For each time point, sensitivities and specificities for primary care CMD diagnoses were calculated for predicting ALSPAC-derived measures of CMDs, and the factors associated with identification of primary care-based CMDs in those with suspected ALSPAC-derived CMDs explored. Lasso (least absolute selection and shrinkage operator) models were used at each time point to predict ALSPAC-derived CMDs using only primary care data, with internal validation by randomly splitting data into 60% training and 40% validation samples. RESULTS: Sensitivities for primary care diagnoses were low for CMDs (range: 3.5%–19.1%) and depression (range: 1.6%–34.0%), while specificities were high (nearly all >95%). The strongest predictors of identification in the primary care data for those with ALSPAC-derived CMDs were symptom severity indices. The lasso models had relatively low prediction rates, especially in the validation sample (deviance ratio range: −1.3 to 12.6%), but improved with age. CONCLUSIONS: Primary care data underestimate CMDs compared to population-based studies. Improving general practitioner identification, and using free-text or secondary care data, is needed to improve the accuracy of models using clinical data. BMJ Publishing Group 2021-10-18 /pmc/articles/PMC8524296/ /pubmed/34663669 http://dx.doi.org/10.1136/bmjopen-2021-053624 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Epidemiology
Smith, Daniel
Willan, Kathryn
Prady, Stephanie L
Dickerson, Josie
Santorelli, Gillian
Tilling, Kate
Cornish, Rosie Peggy
Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England
title Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England
title_full Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England
title_fullStr Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England
title_full_unstemmed Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England
title_short Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England
title_sort assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (alspac) in southwest england
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8524296/
https://www.ncbi.nlm.nih.gov/pubmed/34663669
http://dx.doi.org/10.1136/bmjopen-2021-053624
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