Enabling cross-cultural data pooling in trials: linguistic validation of head and neck cancer measures for Indian patients

Abstract

Head and neck cancers (HNC) and their treatments cause dysfunction and distress. Ongoing psychological assessment using disease-specific patient-reported measures may optimize clinical decision-making, facilitate interventions to reduce psychosocial burden. As most such measures are developed in English, non-English speaking patients are disadvantaged. This study translated HNC-specific measures (Body Image Scale, Patient Concerns Inventory, Zung’s Self-Rating Anxiety and Depression Scales and Patient Health Questionnaire-9) into three Indian languages (Hindi, Tamil and Telugu) and linguistically validated them. Translation followed established guidelines on translation and linguistic validation of measures. Process involved two independent forward translations, reconciliation, two independent backward translations by bilingual experts, and cognitive debriefing interviews with nine healthcare professionals (HCPs) and 29 HNC patients. Translated versions were compared with the original versions for semantic, cultural and conceptual equivalence. Overall, 17 Hindi items, 19 Tamil items and 13 Telugu items were identified to have semantic, cultural and/or conceptual issues. These were resolved to achieve equivalence with the original measures. Interviews with HCPs indicated that equivalent terms for words such as anxiety, panicky, sexuality, and self-conscious might be difficult to understand. Interviews with patients indicated all items were understandable, easy, sensitive, unambiguous and relevant. Hence, no further revisions were made. The translated Hindi, Tamil and Telugu versions of the Body image scale, Patient concerns inventory, Zung’s self-rating anxiety and depression scales and Patient health questionnaire-9 measures are conceptually and linguistically validated and equivalent with the original English versions. Psychometric validation of these measures with relevant patient populations is needed.