Screening for Alcohol Use Disorder Among Hospitalised Patients: Learning from a Retrospective Cohort Study in Secondary Care

<b>Background:</b> Excessive alcohol consumption is among the leading causes of hospitalisation in high-income countries and contributes to over 200 medical conditions. We aimed to determine the prevalence and characteristics of alcohol use disorder (AUD), describe the distribution of AUD in ICD-10 discharge diagnosis groups and ascertain any relationship between them in secondary care. <b>Methods:</b> The study group was a retrospective cohort of adult patients admitted to Nottingham University Hospital (NUH) between 4 April 2009 and 31 March 2020. Uni- and multivariable analysis was performed to determine the relationship between AUD and covariable high-risk characteristics and describe the distribution of AUD in ICD-10 discharge diagnosis groups defined by an alcohol-attributable fraction. <b>Results:</b> A total of 44,804 patients (66,440 admissions) were included, with a mean age of 63.1 years (SD ± 19.9); of these, 48.0% <i>(n</i> = 20,863) were male and 71.2% were (<i>n</i> = 30,994) white. AUDIT-C was completed in 97.1% (<i>n</i> = 43,514) of patients, and identified 16.5% (<i>n</i> = 7164) as having AUD, while 2.1% (<i>n</i> = 900) were found to be alcohol-dependent. In patients with AUD, 4.0% (<i>n</i> = 283) had an ICD-10 diagnosis that was alcohol-specific and 17.5% (<i>n</i> = 1255) were diagnosed with alcohol-related disorders; the remainder were not diagnosed with either disorder. Two-thirds (64.7%) of the patients with AUD had associated mental and behavioural disorders. Multivariable logistic regression analysis revealed that patients aged 60-69 had the highest risk of AUD (OR 4.19, 95% CI 3.53-4.99). Being single (OR 1.18, 95% CI 1.11-1.26) and a history of emergency admission (OR 1.21, 95% CI 1.14-1.29) were associated with increased odds of AUD. Conversely, females compared to males (OR 0.34, 95% CI 0.35-0.39), individuals from minority ethnic backgrounds compared to white Caucasians (OR 0.39, 95% CI 0.35-0.45), and those from more deprived areas (IMD quintile 1: OR 0.79, 95% CI 0.74-0.86) had lower odds of AUD. <b>Conclusions:</b> One in six admitted patients had AUD, with a higher risk in males, ages 60-69, and emergency admissions. Mental disorders are highly prevalent among hospitalised patients with AUD. The performance of the AUDIT-C score varied among hospitalised patients based on their ICD-10 diagnosis, which should be considered when implementing universal alcohol screening in these settings.

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Publication Details

Journal
Journal of Clinical Medicine
Published
2024-12-13
DOI
https://doi.org/10.3390/jcm13247617
Citations
6
Primary Topic
Substance Abuse Treatment and Outcomes
Type
article
Field-Weighted Citation Impact
2.32
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article

Screening for Alcohol Use Disorder Among Hospitalised Patients: Learning from a Retrospective Cohort Study in Secondary Care

Stephen Ryder, Joanne R Morling, Mohsan Subhani, Guruprasad Padur Aithal et al.
6 citations
Journal of Clinical Medicine
Substance Abuse Treatment and Outcomes
2.32
article

Screening for Alcohol Use Disorder Among Hospitalised Patients: Learning from a Retrospective Cohort Study in Secondary Care

Stephen Ryder, Joanne R Morling, Mohsan Subhani, Guruprasad Padur Aithal, Usman Talat, Amardeep Khanna, Ahmed Ali, Dipaka Rani Nath, Aqsa Imtiaz
article en
6 citations

Abstract

<b>Background:</b> Excessive alcohol consumption is among the leading causes of hospitalisation in high-income countries and contributes to over 200 medical conditions. We aimed to determine the prevalence and characteristics of alcohol use disorder (AUD), describe the distribution of AUD in ICD-10 discharge diagnosis groups and ascertain any relationship between them in secondary care. <b>Methods:</b> The study group was a retrospective cohort of adult patients admitted to Nottingham University Hospital (NUH) between 4 April 2009 and 31 March 2020. Uni- and multivariable analysis was performed to determine the relationship between AUD and covariable high-risk characteristics and describe the distribution of AUD in ICD-10 discharge diagnosis groups defined by an alcohol-attributable fraction. <b>Results:</b> A total of 44,804 patients (66,440 admissions) were included, with a mean age of 63.1 years (SD ± 19.9); of these, 48.0% <i>(n</i> = 20,863) were male and 71.2% were (<i>n</i> = 30,994) white. AUDIT-C was completed in 97.1% (<i>n</i> = 43,514) of patients, and identified 16.5% (<i>n</i> = 7164) as having AUD, while 2.1% (<i>n</i> = 900) were found to be alcohol-dependent. In patients with AUD, 4.0% (<i>n</i> = 283) had an ICD-10 diagnosis that was alcohol-specific and 17.5% (<i>n</i> = 1255) were diagnosed with alcohol-related disorders; the remainder were not diagnosed with either disorder. Two-thirds (64.7%) of the patients with AUD had associated mental and behavioural disorders. Multivariable logistic regression analysis revealed that patients aged 60-69 had the highest risk of AUD (OR 4.19, 95% CI 3.53-4.99). Being single (OR 1.18, 95% CI 1.11-1.26) and a history of emergency admission (OR 1.21, 95% CI 1.14-1.29) were associated with increased odds of AUD. Conversely, females compared to males (OR 0.34, 95% CI 0.35-0.39), individuals from minority ethnic backgrounds compared to white Caucasians (OR 0.39, 95% CI 0.35-0.45), and those from more deprived areas (IMD quintile 1: OR 0.79, 95% CI 0.74-0.86) had lower odds of AUD. <b>Conclusions:</b> One in six admitted patients had AUD, with a higher risk in males, ages 60-69, and emergency admissions. Mental disorders are highly prevalent among hospitalised patients with AUD. The performance of the AUDIT-C score varied among hospitalised patients based on their ICD-10 diagnosis, which should be considered when implementing universal alcohol screening in these settings.

Journal of Clinical MedicineVol. 13(24)
Nottingham University Hospitals NHS Trust (GB), University of Nottingham (GB), Fatima Jinnah Medical University (PK), Dyson (United Kingdom) (GB), NIHR Nottingham Digestive Diseases Biomedical Research Unit (GB), Nottingham Biomedical Research Centre (GB), Imperial College London (GB)
No poverty
Openalex Percentile: Top 10%
Substance Abuse Treatment and Outcomes
2.32
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