TY - JOUR
T1 - Dementia risk prediction modelling in low- and middle-income countries
T2 - current state of evidence
AU - Alshahrani, Maha
AU - Sabatini, Serena
AU - Mohan, Devi
AU - Brain, Jacob
AU - Pakpahan, Eduwin
AU - Tang, Eugene Y.H.
AU - Robinson, Louise
AU - Siervo, Mario
AU - Naheed, Aliya
AU - Stephan, Blossom Christa Maree
PY - 2024/9/18
Y1 - 2024/9/18
N2 - 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.
AB - 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.
KW - ageing
KW - dementia
KW - low-and middle-income countries
KW - risk prediction
KW - risk prediction algorithm
UR - http://www.scopus.com/inward/record.url?scp=85204379203&partnerID=8YFLogxK
U2 - 10.3389/fepid.2024.1397754
DO - 10.3389/fepid.2024.1397754
M3 - Short survey
AN - SCOPUS:85204379203
SN - 2674-1199
VL - 4
SP - 1
EP - 7
JO - Frontiers in Epidemiology
JF - Frontiers in Epidemiology
M1 - 1397754
ER -