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Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study

BACKGROUND: Diagnosis codes in administrative health data are routinely used to monitor trends in disease prevalence and incidence. The International Classification of Diseases (ICD), which is used to record these diagnoses, have been updated multiple times to reflect advances in health and medical...

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Autores principales: Sanusi, Ridwan A., Yan, Lin, Hamad, Amani F., Ayilara, Olawale F., Vasylkiv, Viktoriya, Jozani, Mohammad Jafari, Banerji, Shantanu, Delaney, Joseph, Hu, Pingzhao, Wall-Wieler, Elizabeth, Lix, Lisa M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994899/
https://www.ncbi.nlm.nih.gov/pubmed/35397596
http://dx.doi.org/10.1186/s12889-022-13118-8
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author Sanusi, Ridwan A.
Yan, Lin
Hamad, Amani F.
Ayilara, Olawale F.
Vasylkiv, Viktoriya
Jozani, Mohammad Jafari
Banerji, Shantanu
Delaney, Joseph
Hu, Pingzhao
Wall-Wieler, Elizabeth
Lix, Lisa M.
author_facet Sanusi, Ridwan A.
Yan, Lin
Hamad, Amani F.
Ayilara, Olawale F.
Vasylkiv, Viktoriya
Jozani, Mohammad Jafari
Banerji, Shantanu
Delaney, Joseph
Hu, Pingzhao
Wall-Wieler, Elizabeth
Lix, Lisa M.
author_sort Sanusi, Ridwan A.
collection PubMed
description BACKGROUND: Diagnosis codes in administrative health data are routinely used to monitor trends in disease prevalence and incidence. The International Classification of Diseases (ICD), which is used to record these diagnoses, have been updated multiple times to reflect advances in health and medical research. Our objective was to examine the impact of transitions between ICD versions on the prevalence of chronic health conditions estimated from administrative health data. METHODS: Study data (i.e., physician billing claims, hospital records) were from the province of Manitoba, Canada, which has a universal healthcare system. ICDA-8 (with adaptations), ICD-9-CM (clinical modification), and ICD-10-CA (Canadian adaptation; hospital records only) codes are captured in the data. Annual study cohorts included all individuals 18 + years of age for 45 years from 1974 to 2018. Negative binomial regression was used to estimate annual age- and sex-adjusted prevalence and model parameters (i.e., slopes and intercepts) for 16 chronic health conditions. Statistical control charts were used to assess the impact of changes in ICD version on model parameter estimates. Hotelling’s T(2) statistic was used to combine the parameter estimates and provide an out-of-control signal when its value was above a pre-specified control limit. RESULTS: The annual cohort sizes ranged from 360,341 to 824,816. Hypertension and skin cancer were among the most and least diagnosed health conditions, respectively; their prevalence per 1,000 population increased from 40.5 to 223.6 and from 0.3 to 2.1, respectively, within the study period. The average annual rate of change in prevalence ranged from -1.6% (95% confidence interval [CI]: -1.8, -1.4) for acute myocardial infarction to 14.6% (95% CI: 13.9, 15.2) for hypertension. The control chart indicated out-of-control observations when transitioning from ICDA-8 to ICD-9-CM for 75% of the investigated chronic health conditions but no out-of-control observations when transitioning from ICD-9-CM to ICD-10-CA. CONCLUSIONS: The prevalence of most of the investigated chronic health conditions changed significantly in the transition from ICDA-8 to ICD-9-CM. These results point to the importance of considering changes in ICD coding as a factor that may influence the interpretation of trend estimates for chronic health conditions derived from administrative health data. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13118-8.
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spelling pubmed-89948992022-04-11 Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study Sanusi, Ridwan A. Yan, Lin Hamad, Amani F. Ayilara, Olawale F. Vasylkiv, Viktoriya Jozani, Mohammad Jafari Banerji, Shantanu Delaney, Joseph Hu, Pingzhao Wall-Wieler, Elizabeth Lix, Lisa M. BMC Public Health Research BACKGROUND: Diagnosis codes in administrative health data are routinely used to monitor trends in disease prevalence and incidence. The International Classification of Diseases (ICD), which is used to record these diagnoses, have been updated multiple times to reflect advances in health and medical research. Our objective was to examine the impact of transitions between ICD versions on the prevalence of chronic health conditions estimated from administrative health data. METHODS: Study data (i.e., physician billing claims, hospital records) were from the province of Manitoba, Canada, which has a universal healthcare system. ICDA-8 (with adaptations), ICD-9-CM (clinical modification), and ICD-10-CA (Canadian adaptation; hospital records only) codes are captured in the data. Annual study cohorts included all individuals 18 + years of age for 45 years from 1974 to 2018. Negative binomial regression was used to estimate annual age- and sex-adjusted prevalence and model parameters (i.e., slopes and intercepts) for 16 chronic health conditions. Statistical control charts were used to assess the impact of changes in ICD version on model parameter estimates. Hotelling’s T(2) statistic was used to combine the parameter estimates and provide an out-of-control signal when its value was above a pre-specified control limit. RESULTS: The annual cohort sizes ranged from 360,341 to 824,816. Hypertension and skin cancer were among the most and least diagnosed health conditions, respectively; their prevalence per 1,000 population increased from 40.5 to 223.6 and from 0.3 to 2.1, respectively, within the study period. The average annual rate of change in prevalence ranged from -1.6% (95% confidence interval [CI]: -1.8, -1.4) for acute myocardial infarction to 14.6% (95% CI: 13.9, 15.2) for hypertension. The control chart indicated out-of-control observations when transitioning from ICDA-8 to ICD-9-CM for 75% of the investigated chronic health conditions but no out-of-control observations when transitioning from ICD-9-CM to ICD-10-CA. CONCLUSIONS: The prevalence of most of the investigated chronic health conditions changed significantly in the transition from ICDA-8 to ICD-9-CM. These results point to the importance of considering changes in ICD coding as a factor that may influence the interpretation of trend estimates for chronic health conditions derived from administrative health data. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13118-8. BioMed Central 2022-04-09 /pmc/articles/PMC8994899/ /pubmed/35397596 http://dx.doi.org/10.1186/s12889-022-13118-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sanusi, Ridwan A.
Yan, Lin
Hamad, Amani F.
Ayilara, Olawale F.
Vasylkiv, Viktoriya
Jozani, Mohammad Jafari
Banerji, Shantanu
Delaney, Joseph
Hu, Pingzhao
Wall-Wieler, Elizabeth
Lix, Lisa M.
Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study
title Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study
title_full Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study
title_fullStr Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study
title_full_unstemmed Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study
title_short Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study
title_sort transitions between versions of the international classification of diseases and chronic disease prevalence estimates from administrative health data: a population-based study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994899/
https://www.ncbi.nlm.nih.gov/pubmed/35397596
http://dx.doi.org/10.1186/s12889-022-13118-8
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