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

Low rates of prescribing alcohol relapse prevention medicines in Australian Aboriginal Community Controlled Health Services

Introduction: Alcohol dependence is a chronic condition impacting millions of individuals worldwide. Safe and effective medicines to reduce relapse can be prescribed by general practitioners but are underutilised in the general Australian population. Prescription rates of these medicines to Aboriginal and Torres Strait Islander (First Nations) Australians in primary care are unknown. We assess these medicines in Aboriginal Community Controlled Health Services and identify factors associated with prescription. Methods: Baseline data (spanning 12 months) were used from a cluster randomised trial involving 22 Aboriginal Community Controlled Health Services. We describe the proportion of First Nations patients aged 15+ who were prescribed a relapse prevention medicine: naltrexone, acamprosate or disulfiram. We explore associations between receiving a prescription, a patient AUDIT-C score and demographics (gender, age, service remoteness) using logistic regression. Results: During the 12-month period, 52,678 patients attended the 22 services. Prescriptions were issued for 118 (0.2%) patients (acamprosate n = 62; naltrexone n = 58; disulfiram n = 2; combinations n = 4). Of the total patients, 1.6% were ‘likely dependent’ (AUDIT-C ≥ 9), of whom only 3.4% received prescriptions for these medicines. In contrast, 60.2% of those who received a prescription had no AUDIT-C score. In multivariate analysis, receiving a script (OR = 3.29, 95% CI 2.25–4.77) was predicted by AUDIT-C screening, male gender (OR = 2.24, 95% CI 1.55–3.29), middle age (35–54 years; OR = 14.41, 95% CI 5.99–47.31) and urban service (OR = 2.87, 95% CI 1.61–5.60). Discussion and Conclusions: Work is needed to increase the prescription of relapse prevention medicines when dependence is detected. Potential barriers to prescription and appropriate ways to overcome these need to be identified

Unintended consequences : Alcohol screening at urban Aboriginal Community Controlled Health Services was suppressed during COVID-19 lockdowns

Introduction: Regular screening for risky drinking is important to improve the health of Aboriginal and Torres Strait Islander Australians. We explored whether the rate of screening for risky drinking using the Alcohol Use Disorders Identification Test—Consumption (AUDIT-C) questions was disrupted at Aboriginal Community Controlled Health Services (ACCHS) during state-wide and territory-wide COVID-19 lockdowns in 2020. Methods: Retrospective analysis of screening data from 22 ACCHSs located in New South Wales, the Northern Territory, Queensland, South Australia, Victoria and Western Australia. These services provide holistic and culturally appropriate primary care. A multi-level Poisson regression, including AR(1) autocorrelation, was used to predict counts of AUDIT-C screening at ACCHSs. Results: AUDIT-C screening was suppressed during state-wide and territory-wide lockdowns in 2020 (incident rate ratio [IRR] 0.42 [0.29, 0.61]). The effect of lockdowns differed by service remoteness. While there was a substantial reduction in AUDIT-C screening for urban and inner regional services (IRR 0.25 [95% confidence interval (CI) 0.15, 0.42]), there was not a statistically significant change in screening at outer regional and remote (IRR 0.60 [95% CI 0.33, 1.09]) or very remote services (IRR 0.67 [95% CI 0.40, 1.11]). Discussion and Conclusions: The COVID-19 lockdowns in Australia likely suppressed rates of screening for risky drinking in urban and inner regional regions. As harm from alcohol consumption may have increased during lockdowns, policymakers should consider implementing measures to enable screening for risky drinking to continue during future lockdowns

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\u27 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

No improvement in AUDIT-C screening and brief intervention rates among wait-list controls following support of Aboriginal Community Controlled Health Services: Evidence from a cluster randomised trial

Background While Aboriginal and Torres Strait Islander Australians are less likely to drink any alcohol than other Australians, those who drink are more likely to experience adverse alcohol-related health consequences. In a previous study, providing Aboriginal Community Controlled Health Services (ACCHSs) with training and support increased the odds of clients receiving AUDIT-C alcohol screening. A follow-up study found that these results were maintained for at least two years, but there was large variability in the effectiveness of the intervention between services. In this study, we use services that previously received support as a comparison group to test whether training and support can improve alcohol screening and brief intervention rates among wait-list control ACCHSs. Methods Design: Cluster randomised trial using routinely collected health data. Setting: Australia. Cases: Twenty-two ACCHSs that see at least 1000 clients a year and use Communicare as their practice management software. Intervention and comparator: After initiating support, we compare changes in screening and brief intervention between wait-list control services and services that had previously received support. Measurement: Records of AUDIT-C screening and brief intervention activity in routinely collected data. Results During the reference period we observed 357,257 instances where one of 74,568 clients attended services at least once during a two-monthly data extraction period. Following the start of support, the odds of screening (OR = 0.94 [95% CI 0.67, 1.32], p = 0.74, BF10 role= presentation style= margin: 0px; box-sizing: inherit; display: inline-block; line-height: normal; word-spacing: normal; overflow-wrap: normal; text-wrap: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; position: relative; \u3e10≈ role= presentation style= margin: 0px; box-sizing: inherit; display: inline-block; line-height: normal; word-spacing: normal; overflow-wrap: normal; text-wrap: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; position: relative; \u3e≈ 0.002) and brief intervention (OR = 1.43 [95% CI 0.69, 2.95], p = 0.34, BF10 role= presentation style= margin: 0px; box-sizing: inherit; display: inline-block; line-height: normal; word-spacing: normal; overflow-wrap: normal; text-wrap: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; position: relative; \u3e10≈ role= presentation style= margin: 0px; box-sizing: inherit; display: inline-block; line-height: normal; word-spacing: normal; overflow-wrap: normal; text-wrap: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; position: relative; \u3e≈ 0.002) did not improve for the wait-list control group, relative to comparison services. Conclusions We did not replicate the finding that support and training improves AUDIT-C screening rates with wait-list control data. The benefits of support are likely context dependent. Coincidental policy changes may have sensitised services to the effects of support in the earlier phase of the study. Then the COVID-19 pandemic may have made services less open to change in this latest phase. Future efforts could include practice software prompts to alcohol screening and brief intervention, which are less reliant on individual staff time or resources. Trial registration Retrospectively registered on 2018-11-21: ACTRN12618001892202

No improvement in AUDIT-C screening and brief intervention rates among wait-list controls following support of Aboriginal Community Controlled Health Services: evidence from a cluster randomised trial

Background: While Aboriginal and Torres Strait Islander Australians are less likely to drink any alcohol than other Australians, those who drink are more likely to experience adverse alcohol-related health consequences. In a previous study, providing Aboriginal Community Controlled Health Services (ACCHSs) with training and support increased the odds of clients receiving AUDIT-C alcohol screening. A follow-up study found that these results were maintained for at least two years, but there was large variability in the effectiveness of the intervention between services. In this study, we use services that previously received support as a comparison group to test whether training and support can improve alcohol screening and brief intervention rates among wait-list control ACCHSs. Methods: Design: Cluster randomised trial using routinely collected health data. Setting: Australia. Cases: Twenty-two ACCHSs that see at least 1000 clients a year and use Communicare as their practice management software. Intervention and comparator: After initiating support, we compare changes in screening and brief intervention between wait-list control services and services that had previously received support. Measurement: Records of AUDIT-C screening and brief intervention activity in routinely collected data. Results: During the reference period we observed 357,257 instances where one of 74,568 clients attended services at least once during a two-monthly data extraction period. Following the start of support, the odds of screening (OR = 0.94 [95% CI 0.67, 1.32], p = 0.74, 0.002) and brief intervention (OR = 1.43 [95% CI 0.69, 2.95], p = 0.34, 0.002) did not improve for the wait-list control group, relative to comparison services. Conclusions: We did not replicate the finding that support and training improves AUDIT-C screening rates with wait-list control data. The benefits of support are likely context dependent. Coincidental policy changes may have sensitised services to the effects of support in the earlier phase of the study. Then the COVID-19 pandemic may have made services less open to change in this latest phase. Future efforts could include practice software prompts to alcohol screening and brief intervention, which are less reliant on individual staff time or resources. Trial registration: Retrospectively registered on 2018-11-21: ACTRN12618001892202