Dementia risk prediction modelling in low- and middle-income countries: current state of evidence

Autor: Maha Alshahrani, Serena Sabatini, Devi Mohan, Jacob Brain, Eduwin Pakpahan, Eugene Y. H. Tang, Louise Robinson, Mario Siervo, Aliya Naheed, Blossom Christa Maree Stephan
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Frontiers in Epidemiology, Vol 4 (2024)
Druh dokumentu: article
ISSN: 2674-1199
DOI: 10.3389/fepid.2024.1397754
Popis: Dementia is a leading cause of death and disability with over 60% of cases residing in low- and middle-income countries (LMICs). Therefore, new strategies to mitigate risk are urgently needed. However, despite the high burden of disease associated with dementia in LMICs, research into dementia risk profiling and risk prediction modelling is limited. Further, dementia risk prediction models developed in high income countries generally do not transport well to LMICs suggesting that context-specific models are instead needed. New prediction models have been developed, in China and Mexico only, with varying predictive accuracy. However, none has been externally validated or incorporated variables that may be important for predicting dementia risk in LMIC settings such as socio-economic status, literacy, healthcare access, nutrition, stress, pollutants, and occupational hazards. Since there is not yet any curative treatment for dementia, developing a context-specific dementia prediction model is urgently needed for planning early interventions for vulnerable groups, particularly for resource constrained LMIC settings.
Databáze: Directory of Open Access Journals