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Mapping three versions of the international classification of diseases to categories of chronic conditions

INTRODUCTION: Administrative health data capture diagnoses using the International Classification of Diseases (ICD), which has multiple versions over time. To facilitate longitudinal investigations using these data, we aimed to map diagnoses identified in three ICD versions – ICD-8 with adaptations...

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Autores principales: Hamad, Amani F., Vasylkiv, Viktoriya, Yan, Lin, Sanusi, Ridwan, Ayilara, Olawale, Delaney, Joseph A., Wall-Wieler, Elizabeth, Jozani, Mohammad Jafari, Hu, Pingzhao, Banerji, Shantanu, Lix, Lisa M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Swansea University 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104065/
https://www.ncbi.nlm.nih.gov/pubmed/34007901
http://dx.doi.org/10.23889/ijpds.v6i1.1406
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author Hamad, Amani F.
Vasylkiv, Viktoriya
Yan, Lin
Sanusi, Ridwan
Ayilara, Olawale
Delaney, Joseph A.
Wall-Wieler, Elizabeth
Jozani, Mohammad Jafari
Hu, Pingzhao
Banerji, Shantanu
Lix, Lisa M.
author_facet Hamad, Amani F.
Vasylkiv, Viktoriya
Yan, Lin
Sanusi, Ridwan
Ayilara, Olawale
Delaney, Joseph A.
Wall-Wieler, Elizabeth
Jozani, Mohammad Jafari
Hu, Pingzhao
Banerji, Shantanu
Lix, Lisa M.
author_sort Hamad, Amani F.
collection PubMed
description INTRODUCTION: Administrative health data capture diagnoses using the International Classification of Diseases (ICD), which has multiple versions over time. To facilitate longitudinal investigations using these data, we aimed to map diagnoses identified in three ICD versions – ICD-8 with adaptations (ICDA-8), ICD-9 with clinical modifications (ICD-9-CM), and ICD-10 with Canadian adaptations (ICD-10-CA) – to mutually exclusive chronic health condition categories adapted from the open source Clinical Classifications Software (CCS). METHODS: We adapted the CCS crosswalk to 3-digit ICD-9-CM codes for chronic conditions and resolved the one-to-many mappings in ICD-9-CM codes. Using this adapted CCS crosswalk as the reference and referring to existing crosswalks between ICD versions, we extended the mapping to ICDA-8 and ICD-10-CA. Each mapping step was conducted independently by two reviewers and discrepancies were resolved by consensus through deliberation and reference to prior research. We report the frequencies, agreement percentages and 95% confidence intervals (CI) from each step. RESULTS: We identified 354 3-digit ICD-9-CM codes for chronic conditions. Of those, 77 (22%) codes had one-to-many mappings; 36 (10%) codes were mapped to a single CCS category and 41 (12%) codes were mapped to combined CCS categories. In total, the codes were mapped to 130 adapted CCS categories with an agreement percentage of 92% (95% CI: 86%–98%). Then, 321 3-digit ICDA-8 codes were mapped to CCS categories with an agreement percentage of 92% (95% CI: 89%–95%). Finally, 3583 ICD-10-CA codes were mapped to CCS categories; 111 (3%) had a fair or poor mapping quality; these were reviewed to keep or move to another category (agreement percentage = 77% [95% CI: 69%–85%]). CONCLUSIONS: We developed crosswalks for three ICD versions (ICDA-8, ICD-9-CM, and ICD-10-CA) to 130 clinically meaningful categories of chronic health conditions by adapting the CCS classification. These crosswalks will benefit chronic disease studies spanning multiple decades of administrative health data.
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spelling pubmed-81040652021-05-17 Mapping three versions of the international classification of diseases to categories of chronic conditions Hamad, Amani F. Vasylkiv, Viktoriya Yan, Lin Sanusi, Ridwan Ayilara, Olawale Delaney, Joseph A. Wall-Wieler, Elizabeth Jozani, Mohammad Jafari Hu, Pingzhao Banerji, Shantanu Lix, Lisa M. Int J Popul Data Sci Population Data Science INTRODUCTION: Administrative health data capture diagnoses using the International Classification of Diseases (ICD), which has multiple versions over time. To facilitate longitudinal investigations using these data, we aimed to map diagnoses identified in three ICD versions – ICD-8 with adaptations (ICDA-8), ICD-9 with clinical modifications (ICD-9-CM), and ICD-10 with Canadian adaptations (ICD-10-CA) – to mutually exclusive chronic health condition categories adapted from the open source Clinical Classifications Software (CCS). METHODS: We adapted the CCS crosswalk to 3-digit ICD-9-CM codes for chronic conditions and resolved the one-to-many mappings in ICD-9-CM codes. Using this adapted CCS crosswalk as the reference and referring to existing crosswalks between ICD versions, we extended the mapping to ICDA-8 and ICD-10-CA. Each mapping step was conducted independently by two reviewers and discrepancies were resolved by consensus through deliberation and reference to prior research. We report the frequencies, agreement percentages and 95% confidence intervals (CI) from each step. RESULTS: We identified 354 3-digit ICD-9-CM codes for chronic conditions. Of those, 77 (22%) codes had one-to-many mappings; 36 (10%) codes were mapped to a single CCS category and 41 (12%) codes were mapped to combined CCS categories. In total, the codes were mapped to 130 adapted CCS categories with an agreement percentage of 92% (95% CI: 86%–98%). Then, 321 3-digit ICDA-8 codes were mapped to CCS categories with an agreement percentage of 92% (95% CI: 89%–95%). Finally, 3583 ICD-10-CA codes were mapped to CCS categories; 111 (3%) had a fair or poor mapping quality; these were reviewed to keep or move to another category (agreement percentage = 77% [95% CI: 69%–85%]). CONCLUSIONS: We developed crosswalks for three ICD versions (ICDA-8, ICD-9-CM, and ICD-10-CA) to 130 clinically meaningful categories of chronic health conditions by adapting the CCS classification. These crosswalks will benefit chronic disease studies spanning multiple decades of administrative health data. Swansea University 2021-04-15 /pmc/articles/PMC8104065/ /pubmed/34007901 http://dx.doi.org/10.23889/ijpds.v6i1.1406 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Population Data Science
Hamad, Amani F.
Vasylkiv, Viktoriya
Yan, Lin
Sanusi, Ridwan
Ayilara, Olawale
Delaney, Joseph A.
Wall-Wieler, Elizabeth
Jozani, Mohammad Jafari
Hu, Pingzhao
Banerji, Shantanu
Lix, Lisa M.
Mapping three versions of the international classification of diseases to categories of chronic conditions
title Mapping three versions of the international classification of diseases to categories of chronic conditions
title_full Mapping three versions of the international classification of diseases to categories of chronic conditions
title_fullStr Mapping three versions of the international classification of diseases to categories of chronic conditions
title_full_unstemmed Mapping three versions of the international classification of diseases to categories of chronic conditions
title_short Mapping three versions of the international classification of diseases to categories of chronic conditions
title_sort mapping three versions of the international classification of diseases to categories of chronic conditions
topic Population Data Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104065/
https://www.ncbi.nlm.nih.gov/pubmed/34007901
http://dx.doi.org/10.23889/ijpds.v6i1.1406
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