APA
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Bazo-Alvarez, J. C., Avgerinou, C., Nimmons, D., Hayes, J., Osborn, D. P. J., Cooper, C., … Petersen, I. (2024). Defining Mental Health Conditions Within Primary Care Data: A Validation Study With a Mixed Qualitative and Quantitative Approach. Journal of Evaluation In Clinical Practice.
Chicago/Turabian
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Bazo-Alvarez, Juan Carlos, C. Avgerinou, Danielle Nimmons, Joseph Hayes, David P J Osborn, Claudia Cooper, Kate Walters, and Irene Petersen. “Defining Mental Health Conditions Within Primary Care Data: A Validation Study With a Mixed Qualitative and Quantitative Approach.” Journal of Evaluation In Clinical Practice (2024).
MLA
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Bazo-Alvarez, Juan Carlos, et al. “Defining Mental Health Conditions Within Primary Care Data: A Validation Study With a Mixed Qualitative and Quantitative Approach.” Journal of Evaluation In Clinical Practice, 2024.
BibTeX Click to copy
@article{juan2024a,
title = {Defining Mental Health Conditions Within Primary Care Data: A Validation Study With a Mixed Qualitative and Quantitative Approach},
year = {2024},
journal = {Journal of Evaluation In Clinical Practice},
author = {Bazo-Alvarez, Juan Carlos and Avgerinou, C. and Nimmons, Danielle and Hayes, Joseph and Osborn, David P J and Cooper, Claudia and Walters, Kate and Petersen, Irene}
}
ABSTRACT Objectives To validate codelists for defining a range of mental health (MH) conditions with primary care data, using a mixed qualitative and quantitative approach and without requiring external data. Methods We validated Read codelists, selecting and classifying them in three steps. The qualitative step included an in‐depth revision of the codes by six doctors. Simultaneously, the quantitative step performed on UK primary care data included an exploratory factor analysis to cluster Read codes in MH conditions to obtain an independent classification. The statistical results informed the qualitative conclusions, generating a final selection and classification. Results From a preselected list of 2007 Read codes, a total of 1638 were selected by all doctors. Later, they agreed on classifying these codes into 12 categories of MH disorders. From the same preselected list, a total of 1364 were quantitatively selected. Using data from 497,649 persons who used these Read codes at least once, we performed the exploratory factor analysis, retaining five factors (five categories). Both classifications showed good correspondence, while discrepancies informed decisions on reclassification. Conclusions We produced a comprehensive set of medical codes lists for 12 MH conditions validated by a combination of clinical consensus panel and quantitative cluster analysis with cross‐validation.