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

Maha Alshahrani, Serena Sabatini, Devi Mohan, Jacob Brain, Eduwin Pakpahan, Eugene Y.H. Tang, Louise Robinson, Mario Siervo, Aliya Naheed, Blossom Christa Maree Stephan*

*Corresponding author for this work

Research output: Contribution to journalShort surveypeer-review

Abstract

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.

Original languageEnglish
Article number1397754
Pages (from-to)1-7
Number of pages7
JournalFrontiers in Epidemiology
Volume4
DOIs
Publication statusPublished - 18 Sept 2024

Keywords

  • ageing
  • dementia
  • low-and middle-income countries
  • risk prediction
  • risk prediction algorithm

Cite this