Hospital Anxiety and Depression Scale Anxiety subscale (HADS-A) for detecting anxiety disorders in adults

Abstract

Background: Despite being highly prevalent mental health conditions, anxiety disorders frequently go undiagnosed, prompting the use of questionnaires for anxiety screening as a potential solution. This review summarises the test accuracy of the Hospital Anxiety and Depression Scale Anxiety subscale (HADS-A) for screening purposes.

Objectives: To assess the test accuracy of the HADS-A in screening for any anxiety disorder (AAD), generalised anxiety disorder (GAD) and panic disorder in adults, and to investigate how the test accuracy varies by sources of heterogeneity and across all cutoffs.

Search methods: We searched Embase, MEDLINE, PubMed-not-MEDLINE subset and PsycINFO from 1990 to 10 July 2024. We checked the reference lists of included studies and review articles.

Selection criteria: We included studies in adults in which the HADS-A was administered cross-sectionally alongside structured or semi-structured clinical interviews, allowing the creation of 2x2 tables. We excluded case-control studies, studies with a time gap exceeding four weeks between administering the HADS-A and the reference standard, and studies with diagnostic criteria based on the Diagnostic and Statistical Manual of Mental Disorders Third Edition or earlier versions. We also excluded studies involving people who were recruited based on mental health symptoms.

Data collection and analysis: At least two review authors independently decided on the eligibility of the articles, extracted data, and assessed the methodological quality of the included studies using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). For each target condition, we present the sensitivity and specificity of each study along with 95% confidence intervals (CIs). For the primary analyses, we used bivariate models to obtain summary estimates for the recommended HADS-A cutoff score of 8 or higher (≥ 8); if the bivariate models did not converge, we used multiple thresholds models. For the secondary analyses, we obtained summary estimates for all cutoffs using bivariate and multiple thresholds models. From the multiple thresholds model, we derived the summary estimates of all available cutoffs from the summary receiver operating characteristic (SROC) curve and the area under the curve (AUC) as a measure of overall accuracy. We explored sources of heterogeneity using meta-regression models.

Main results: We identified 67 studies, encompassing data from 18,467 participants that were available for the analyses. Fifty-four studies contributed to the analyses of HADS-A for detecting AAD, 35 for GAD, and 10 for panic disorder. The median prevalence of AAD, GAD and panic disorder was 17%, 7% and 6%, respectively. The included studies showed a wide spectrum of clinical and methodological differences. We considered the overall risk of bias to be low in 19 studies. The most frequent limitations were related to non-consecutive patient selection and to post-hoc cutoff determination. The applicability was of low concern across three domains in nine studies. The main limitations of applicability were the presence of prediagnosed anxiety (prior to undergoing HADS-A) or the fact that this information was not collected or reported. The estimates of both sensitivity and specificity varied strongly between studies. With regard to the recommended cutoff ≥ 8, the HADS-A subscale demonstrated a summary sensitivity of 0.74 (95% CI 0.70 to 0.78) and a summary specificity of 0.76 (95% CI 0.73 to 0.79) for detecting AAD; a summary sensitivity of 0.82 (95% CI 0.76 to 0.87) and a summary specificity of 0.74 (95% CI 0.70 to 0.77) for detecting GAD; and a summary sensitivity of 0.80 (95% CI 0.69 to 0.88) and a summary specificity of 0.66 (95% CI 0.55 to 0.76) for detecting panic disorder. Results from the multiple thresholds model showed an AUC of 0.81 (95% CI 0.79 to 0.82) for detecting AAD, 0.82 (95% CI 0.80 to 0.84) for GAD and 0.81 (95% CI 0.77 to 0.85) for panic disorder. The observed heterogeneity remained largely unexplained, except for the investigations of heterogeneity with regard to GAD, which showed that the setting had a significant impact on specificity; and prevalence and the reference standard had a significant impact on sensitivity. With respect to panic disorder, a formal heterogeneity assessment was not feasible.

Authors' conclusions: The use of the HADS-A for screening purposes with a cutoff ≥ 8 in an exemplary cohort of 1000 individuals with an AAD prevalence of 17% would result in 675 individuals testing negative, of whom 44 would be false negatives, while 325 would test positive. Of these, 199 would be false positives, potentially straining the available healthcare resources. However, caution is warranted in interpreting the review findings, as the strength of evidence was limited by the risk of bias, concerns regarding applicability and substantial, unexplained heterogeneity. The use of estimates derived from clinical populations in which HADS-A is applied would be a reasonable approach. However, subgrouping by clinical population is currently unfeasible due to the limited number of studies per population category. This represents an area of further exploration in future research. The unexplained heterogeneity makes it challenging to reliably predict the results of future studies. Given these limitations, the universal use of the HADS-A with a cutoff ≥ 8 for screening in different settings and populations is currently questionable.