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. 2021 Mar 24;6(1):1373.
doi: 10.23889/ijpds.v6i1.1373.

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

Laszl Trefan  1 , Ashley Akbari  2 , Jennifer Siân Morgan  3 , Daniel Mark Farewell  4 , David Fone  5 , Ronan A Lyons  6 , Merfyn Jones Hywel  7 , Simon C Moore  8
Affiliations

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

Laszl Trefan et al. Int J Popul Data Sci. .

Abstract

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.

Keywords: ICD-10 codes; alcohol; e-cohort data; hospital admission; optimisation.

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Conflict of interest statement

Conflict of interests: The authors declare that they do not have any conflict of interest.

Figures

Figure 1: Structure of the data used - extracted from Patient Episode Database for Wales (PEDW)
Figure 1: Structure of the data used - extracted from Patient Episode Database for Wales (PEDW)
Figure 2: Distribution of alcohol-related codes in different coding fields in admissions with single episode (n=68,484)
Figure 2: Distribution of alcohol-related codes in different coding fields in admissions with single episode (n=68,484)
Figure 3: Distribution of alcohol-related codes in different coding fields in admission and discharge (n=24,069)
Figure 3: Distribution of alcohol-related codes in different coding fields in admission and discharge (n=24,069)
Figure 4: Chord diagram of first alcohol codes in alcohol-related admission and discharge<sup>*</sup> (n=24,069)
Figure 4: Chord diagram of first alcohol codes in alcohol-related admission and discharge* (n=24,069)
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