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Visualisation and optimisation of alcohol-related hospital admissions ICD-10 codes in Welsh e-cohort data

INTRODUCTION: The excessive consumption of alcohol is detrimental to long term health and increases the likelihood of hospital admission. However, definitions of alcohol-related hospital admission vary, giving rise to uncertainty in the effect of alcohol on alcohol-related health care utilization. O...

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Autores principales: Trefan, Laszl, Akbari, Ashley, Morgan, Jennifer Siân, Farewell, Daniel Mark, Fone, David, Lyons, Ronan A, Jones Hywel, Merfyn, Moore, Simon C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Swansea University 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103565/
https://www.ncbi.nlm.nih.gov/pubmed/34007894
http://dx.doi.org/10.23889/ijpds.v6i1.1373
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author Trefan, Laszl
Akbari, Ashley
Morgan, Jennifer Siân
Farewell, Daniel Mark
Fone, David
Lyons, Ronan A
Jones Hywel, Merfyn
Moore, Simon C
author_facet Trefan, Laszl
Akbari, Ashley
Morgan, Jennifer Siân
Farewell, Daniel Mark
Fone, David
Lyons, Ronan A
Jones Hywel, Merfyn
Moore, Simon C
author_sort Trefan, Laszl
collection PubMed
description INTRODUCTION: The excessive consumption of alcohol is detrimental to long term health and increases the likelihood of hospital admission. However, definitions of alcohol-related hospital admission vary, giving rise to uncertainty in the effect of alcohol on alcohol-related health care utilization. OBJECTIVES: To compare diagnostic codes on hospital admission and discharge and to determine the ideal combination of codes necessary for an accurate determination of alcohol-related hospital admission. METHODS: Routine population-linked e-cohort data were extracted from the Secure Anonymised Information Linkage (SAIL) Databank containing all alcohol-related hospital admissions (n,= 92,553) from 2006 to 2011 in Wales, United Kingdom. The distributions of the diagnostic codes recorded at admission and discharge were compared. By calculating a misclassification rate (sensitivity-like measure) the appropriate number of coding fields to examine for alcohol-codes was established. RESULTS: There was agreement between admission and discharge codes. When more than ten coding fields were used the misclassification rate was less than 1%. CONCLUSION: With the data at present and alcohol-related codes used, codes recorded at admission and discharge can be used equivalently to identify alcohol-related admissions. The appropriate number of coding fields to examine was established: fewer than ten is likely to lead to under-reporting of alcohol-related admissions. The methods developed here can be applied to other medical conditions that can be described using a certain set of diagnostic codes, each of which can be a known sole cause of the condition and recorded in multiple positions in e-cohort data.
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spelling pubmed-81035652021-05-17 Visualisation and optimisation of alcohol-related hospital admissions ICD-10 codes in Welsh e-cohort data Trefan, Laszl Akbari, Ashley Morgan, Jennifer Siân Farewell, Daniel Mark Fone, David Lyons, Ronan A Jones Hywel, Merfyn Moore, Simon C Int J Popul Data Sci Population Data Science INTRODUCTION: The excessive consumption of alcohol is detrimental to long term health and increases the likelihood of hospital admission. However, definitions of alcohol-related hospital admission vary, giving rise to uncertainty in the effect of alcohol on alcohol-related health care utilization. OBJECTIVES: To compare diagnostic codes on hospital admission and discharge and to determine the ideal combination of codes necessary for an accurate determination of alcohol-related hospital admission. METHODS: Routine population-linked e-cohort data were extracted from the Secure Anonymised Information Linkage (SAIL) Databank containing all alcohol-related hospital admissions (n,= 92,553) from 2006 to 2011 in Wales, United Kingdom. The distributions of the diagnostic codes recorded at admission and discharge were compared. By calculating a misclassification rate (sensitivity-like measure) the appropriate number of coding fields to examine for alcohol-codes was established. RESULTS: There was agreement between admission and discharge codes. When more than ten coding fields were used the misclassification rate was less than 1%. CONCLUSION: With the data at present and alcohol-related codes used, codes recorded at admission and discharge can be used equivalently to identify alcohol-related admissions. The appropriate number of coding fields to examine was established: fewer than ten is likely to lead to under-reporting of alcohol-related admissions. The methods developed here can be applied to other medical conditions that can be described using a certain set of diagnostic codes, each of which can be a known sole cause of the condition and recorded in multiple positions in e-cohort data. Swansea University 2021-03-24 /pmc/articles/PMC8103565/ /pubmed/34007894 http://dx.doi.org/10.23889/ijpds.v6i1.1373 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
Trefan, Laszl
Akbari, Ashley
Morgan, Jennifer Siân
Farewell, Daniel Mark
Fone, David
Lyons, Ronan A
Jones Hywel, Merfyn
Moore, Simon C
Visualisation and optimisation of alcohol-related hospital admissions ICD-10 codes in Welsh e-cohort data
title Visualisation and optimisation of alcohol-related hospital admissions ICD-10 codes in Welsh e-cohort data
title_full Visualisation and optimisation of alcohol-related hospital admissions ICD-10 codes in Welsh e-cohort data
title_fullStr Visualisation and optimisation of alcohol-related hospital admissions ICD-10 codes in Welsh e-cohort data
title_full_unstemmed Visualisation and optimisation of alcohol-related hospital admissions ICD-10 codes in Welsh e-cohort data
title_short Visualisation and optimisation of alcohol-related hospital admissions ICD-10 codes in Welsh e-cohort data
title_sort visualisation and optimisation of alcohol-related hospital admissions icd-10 codes in welsh e-cohort data
topic Population Data Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103565/
https://www.ncbi.nlm.nih.gov/pubmed/34007894
http://dx.doi.org/10.23889/ijpds.v6i1.1373
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