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Assessing mental health from registry data: what is the best proxy?
BACKGROUND: Medical registries frequently underestimate the prevalence of health problems compared with surveys. This study aimed to determine the registry variables that can serve as a proxy for variables studied in a mental health survey. METHODS: Prevalences of depressive symptoms and antidepress...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10595249/ http://dx.doi.org/10.1093/eurpub/ckad160.630 |
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author | Beerten, S G De Pauw, R Van Pottelbergh, G Casas, L Vaes, B |
author_facet | Beerten, S G De Pauw, R Van Pottelbergh, G Casas, L Vaes, B |
author_sort | Beerten, S G |
collection | PubMed |
description | BACKGROUND: Medical registries frequently underestimate the prevalence of health problems compared with surveys. This study aimed to determine the registry variables that can serve as a proxy for variables studied in a mental health survey. METHODS: Prevalences of depressive symptoms and antidepressant use from the 2018 Belgian Health Interview Survey (HIS), stratified by sex and age, were compared with same-year prevalences from INTEGO, a Belgian primary care registry. Participants aged 15 and above were included. We assessed correlation and agreement using Spearman's rho (SR), the intraclass correlation coefficient (ICC) and the limits of agreement (LOAs). RESULTS: HIS questions about depressive symptoms were compared with the following variables from INTEGO: symptom codes (SR 0.763, ICC 0.026), diagnosis codes (SR 0.653, ICC 0.195), free text (SR 0.653, ICC 0.322), antidepressant prescriptions (SR 0.793, ICC 0.391) and the combinations symptom + diagnosis codes (SR 0.640, ICC 0.270) and symptom + diagnosis codes + free text (SR 0.653, ICC 0.386). LOAs varied from [-0.120, 0.021] for antidepressants to [-0.015, 0.061] for the full combination. HIS questions about antidepressant use were compared with prescription frequencies in INTEGO of at least once (SR 0.899, ICC 0.586) and at least three times yearly (SR 0.895, ICC 0.835). Corresponding LOAs were [-0.093, -0.008] and [-0.053, 0.007]. CONCLUSIONS: Correlation between the HIS and INTEGO was high, agreement fair to poor. Agreement increased by combining certain variables, by including free text, or by increasing the prescription frequency to resemble chronic use. Prevalences from INTEGO were mostly underestimates. A considerate choice of variables and prescription chronicity is needed to accurately use a registry as a surveillance tool for mental health. KEY MESSAGES: • The external validity of medical registries can be poor, especially compared with survey data. • Researchers should be aware of the choice of variables when using registry data as a proxy for population measures. |
format | Online Article Text |
id | pubmed-10595249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-105952492023-10-25 Assessing mental health from registry data: what is the best proxy? Beerten, S G De Pauw, R Van Pottelbergh, G Casas, L Vaes, B Eur J Public Health Parallel Programme BACKGROUND: Medical registries frequently underestimate the prevalence of health problems compared with surveys. This study aimed to determine the registry variables that can serve as a proxy for variables studied in a mental health survey. METHODS: Prevalences of depressive symptoms and antidepressant use from the 2018 Belgian Health Interview Survey (HIS), stratified by sex and age, were compared with same-year prevalences from INTEGO, a Belgian primary care registry. Participants aged 15 and above were included. We assessed correlation and agreement using Spearman's rho (SR), the intraclass correlation coefficient (ICC) and the limits of agreement (LOAs). RESULTS: HIS questions about depressive symptoms were compared with the following variables from INTEGO: symptom codes (SR 0.763, ICC 0.026), diagnosis codes (SR 0.653, ICC 0.195), free text (SR 0.653, ICC 0.322), antidepressant prescriptions (SR 0.793, ICC 0.391) and the combinations symptom + diagnosis codes (SR 0.640, ICC 0.270) and symptom + diagnosis codes + free text (SR 0.653, ICC 0.386). LOAs varied from [-0.120, 0.021] for antidepressants to [-0.015, 0.061] for the full combination. HIS questions about antidepressant use were compared with prescription frequencies in INTEGO of at least once (SR 0.899, ICC 0.586) and at least three times yearly (SR 0.895, ICC 0.835). Corresponding LOAs were [-0.093, -0.008] and [-0.053, 0.007]. CONCLUSIONS: Correlation between the HIS and INTEGO was high, agreement fair to poor. Agreement increased by combining certain variables, by including free text, or by increasing the prescription frequency to resemble chronic use. Prevalences from INTEGO were mostly underestimates. A considerate choice of variables and prescription chronicity is needed to accurately use a registry as a surveillance tool for mental health. KEY MESSAGES: • The external validity of medical registries can be poor, especially compared with survey data. • Researchers should be aware of the choice of variables when using registry data as a proxy for population measures. Oxford University Press 2023-10-24 /pmc/articles/PMC10595249/ http://dx.doi.org/10.1093/eurpub/ckad160.630 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the European Public Health Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Parallel Programme Beerten, S G De Pauw, R Van Pottelbergh, G Casas, L Vaes, B Assessing mental health from registry data: what is the best proxy? |
title | Assessing mental health from registry data: what is the best proxy? |
title_full | Assessing mental health from registry data: what is the best proxy? |
title_fullStr | Assessing mental health from registry data: what is the best proxy? |
title_full_unstemmed | Assessing mental health from registry data: what is the best proxy? |
title_short | Assessing mental health from registry data: what is the best proxy? |
title_sort | assessing mental health from registry data: what is the best proxy? |
topic | Parallel Programme |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10595249/ http://dx.doi.org/10.1093/eurpub/ckad160.630 |
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