The use of culturally adapted and translated depression screening questionnaires with South Asian haemodialysis patients in England

BACKGROUND: Depression is common amongst patients receiving haemodialysis (HD). Assessment and intervention when faced with language and cultural barriers is challenging. To support clinician decisions, we conducted a cross-sectional study to assess the use of culturally adapted and translated versions of commonly-used depression screening questionnaires with South Asian patients receiving HD in England. METHODS: Patients completed adapted versions of the Patient Health Questionnaire (PHQ-9), the Centre for Epidemiological Studies Depression Scale Revised (CESD-R), and the Beck Depression Inventory II (BDI-II). All questionnaires were available in Gujarati, Punjabi, Urdu, and Bengali. A comparative sample of white-Europeans completed the questionnaires in English. The research was based across 9 National Health Service (NHS) Trusts in England. Structural validity of translated questionnaires was assessed using confirmatory factor analysis. Diagnostic accuracy was explored in a subgroup of South Asians against ICD-10 categories using the Clinical Interview Schedule Revised (CIS-R) with receiver operating curve (ROC) analysis. RESULTS: 229 South Asian and 120 white-European HD patients participated. A single latent depression factor largely accounted for the correlations between items of the PHQ-9, CESD-R and BDI-II. Issues with measurement equivalence implied that scores on the translations may not be comparable with the English language versions. Against CIS-R based ICD-10 diagnosis of depression, sensitivity was modest across scales (50-66.7%). Specificity was higher (81.3-93.8%). Alternative screening cut-offs did not improve positive predictive values. CONCLUSIONS: Culturally adapted translations of depression screening questionnaires are useful to explore symptom endorsement amongst South Asian patients. However, data indicate that standard cut-off scores may not be appropriate to classify symptom severity. Use of the CIS-R algorithms for optimal case identification requires further exploration in this ...