Dementia is one of the fastest emerging global public health concerns today, as the World Health Organisation has predicted that the number of cases will triple from 55 million in 2023 to 152 million by 2050. Current evidence indicates that approximately 45% of dementia cases can be prevented or delayed by acting on potentially modifiable risk factors. However, public knowledge regarding this remains unknown in numerous poorly resourced countries, including Nepal, where the prevalence of dementia continues to increase. The lack of availability of dementia knowledge or risk-measuring tools in the native language may be accountable for this identified gap. Thus, our study aimed to translate and culturally adopt two significant measuring tools, KoDeRR, which measures the Knowledge of Dementia Risk Reduction and the DRP, which generates a Dementia Risk Profile focusing on 9 modifiable risk factors identified by WHO. KoDeRR and DRP have been translated and adapted into Nepali following the TRAPD protocol. Cognitive interviews were then conducted with five bilingual individuals to pre-test KoDeRR and DRP for cultural appropriateness, face validity, and appropriateness of language usage. Certain terms, including dementia, do not exist in Nepali, and some English words do not have direct translation. Similarly, some English words must be translated into multiple Nepali words to suit audiences from different literacy levels and various regions of Nepal. It is essential to be mindful of the choice of words used in the tools since intergenerational language disparities exist in Nepali-speaking communities, and the cultural appropriateness of the language used can vary from one language to another. Translating and adapting dementia survey tools into non-English languages is challenging and time-consuming. Despite these challenges, translating and adapting measuring tools such as KoDeRR and DRP in non-English languages will facilitate researchers' understanding of risk reduction knowledge and the risk profile of diverse communities.
Read full abstract