Indigenous Australian drinking risk: Comparing risk categorisations based on recall of recent drinking occasions to AUDIT-C screening in a representative sample

Alcohol screening in 22 Australian Aboriginal Community Controlled Health Organisations: Clinical context and who is screened

INTRODUCTION

Alcohol screening among Indigenous Australians is important to identify individuals needing support to reduce their drinking. Understanding clinical contexts in which clients are screened, and which clients are more or less likely to be screened, could help identify areas of services and communities that might benefit from increased screening.

METHODS

We analysed routinely collected data from 22 Aboriginal Community Controlled Health Organisations Australia-wide. Data collected between February 2016 and February 2021 were analysed using R, and aggregated to describe screening activity per client, within 2-monthly extraction periods. Descriptive analyses were performed to identify contexts in which clients received an Alcohol Use Disorders Identification Test consumption (AUDIT-C) screen. Multi-level logistic regression determined demographic factors associated with receiving an AUDIT-C screen. Three models are presented to examine if screening was predicted by: (i) age; (ii) age and gender; (iii) age, gender and service remoteness.

RESULTS

We observed 83,931 occasions where AUDIT-C was performed at least once during a 2-monthly extraction period. Most common contexts were adult health check (55.0%), followed by pre-consult examination (18.4%) and standalone item (9.9%). For every 10 years' increase in client age, odds of being screened with AUDIT-C slightly decreased (odds ratio 0.98; 95% confidence interval [CI] 0.98, 0.99). Women were less likely to be screened with AUDIT-C (odds ratio 0.95; 95% CI 0.93, 0.96) than men.

DISCUSSION AND CONCLUSIONS

This study identified areas where alcohol screening can be increased (e.g., among women). Increasing AUDIT-C screening across entire communities could help reduce or prevent alcohol-related harms. Future Indigenous-led research could help identify strategies to increase screening rates.

More than three times as many Indigenous Australian clients at risk from drinking could be supported if clinicians used AUDIT-C instead of unstructured assessments

BACKGROUND

Aboriginal and Torres Strait Islander ('Indigenous') Australians experience a greater burden of disease from alcohol consumption than non-Indigenous peoples. Brief interventions can help people reduce their consumption, but people drinking at risky levels must first be detected. Valid screening tools (e.g., AUDIT-C) can help clinicians identify at-risk individuals, but clinicians also make unstructured assessments. We aimed to determine how frequently clinicians make unstructured risk assessments and use AUDIT-C with Indigenous Australian clients. We also aimed to determine the accuracy of unstructured drinking risk assessments relative to AUDIT-C screening. Finally, we aimed to explore whether client demographics influence unstructured drinking risk assessments.

METHODS

We performed cross-sectional analysis of a large clinical dataset provided by 22 Aboriginal Community Controlled Health Services in Australia. We examined instances where clients were screened with unstructured assessments and with AUDIT-C within the same two-monthly period. This aggregated data included 9884 observations. We compared the accuracy of unstructured risk assessments against AUDIT-C using multi-level sensitivity and specificity analysis. We used multi-level logistic regression to identify demographic factors that predict risk status in unstructured assessments while controlling for AUDIT-C score.

RESULTS

The primary variables were AUDIT-C score and unstructured drinking risk assessment; demographic covariates were client age and gender, and service remoteness. Clinicians made unstructured drinking risk assessments more frequently than they used AUDIT-C (17.11% and 10.85% of clinical sessions respectively). Where both measures were recorded within the same two-month period, AUDIT-C classified more clients as at risk from alcohol consumption than unstructured assessments. When using unstructured assessments, clinicians only identified approximately one third of clients drinking at risky levels based on their AUDIT-C score (sensitivity = 33.59% [95% CI 22.03, 47.52], specificity = 99.35% [95% CI 98.74, 99.67]). Controlling for AUDIT-C results and demographics (gender and service remoteness), clinicians using unstructured drinking risk assessments were more likely to classify older clients as being at risk from alcohol consumption than younger clients.

CONCLUSIONS

Evidence-based screening tools like AUDIT-C can help clinicians ensure that Indigenous Australian clients (and their families and communities) who are at risk from alcohol consumption are better detected and supported.