METHOD: We synthesised the evidence from our three previous systematic reviews (covering all literature from inception to 2023 from PubMed, Embase, and PsychInfo) on dementia risk prediction modelling. The aim was to identify models that have been specifically developed and tested specifically in LMICs. There were no language or time restrictions applied.
RESULT: To date, over 50 different dementia risk prediction models have been developed and tested with only 7 models reported from two LMICs including five studies from China and two studies from Mexico. The models incorporated variables typically linked to dementia including demographics (e.g., age, sex, education), health (e.g., diabetes, hypertension, heart disease) and lifestyle (e.g., smoking and alcohol) variables. The 7 models also have varying degrees of predictive accuracy (c-statistic range 0.65 [95%CI: 0.64-0.67] to 0.92 [95%CI: 0.88-0.95]) and none has undergone external validation. These models have been developed using traditional statistical approaches including Cox and Logistic Regression. Further, model development has not considered factors such as socioeconomic status, literacy, access to healthcare, diet, stress, pollution, and workplace hazards that may be crucial in predicting dementia risk in LMICs.
CONCLUSION: There is an urgent need to create context-specific dementia prediction models to inform the development of risk reduction and preventative interventions in LMICs where dementia case numbers are greatest. Dementia risk model development and testing need to be extended to LMICs across different regions (e.g., Asia, Middle East, Global South, Africa) and income levels (e.g., low, lower-middle, and upper-middle income).
RECOMMENDATIONS: Greater investment is needed into understanding dementia, and its risk factors in LMICs to inform the development of risk mitigation programs. Research should focus on developing accurate, resource-conscious models with affordable and obtainable variables for identifying those individuals likely to benefit the most from interventions targeting risk reduction.
METHODS: Blood donation data of 4120 donors, spanning from January to December 2020, were retrospectively reviewed. The blood were screened for TTI markers, including hepatitis B surface antigen (HBsAg), anti-hepatitis B core (anti-HBc), anti-hepatitis C virus (anti-HCV), anti-human immunodeficiency viruses 1 and 2 (anti-HIV1&2), anti-human T-lymphotropic virus types 1 and 2 (anti-HTLV-1&2), and syphilis antigen.
RESULTS: Positive TTI markers were detected in 10.9% of the donors. The most detected TTI marker was anti-HBc (8.9%), followed by HBsAg (0.7%). Other markers were individually detected in <1% of the donors. Anti-HBc-positive was significantly elevated among non-Saudi blood donors. There was an association between age groups and anti-HCV (p=0.002), anti-HTLV (p=0.004) and syphilis antigen (p=0.02) markers positivity. The AB positive blood group exhibited the most positivity for TTI markers, followed by O positive blood group. Similarly, association was found between ABO group and HBsAg (p=0.01), anti-HBc (p=0.001), and anti-HCV (p<0.001) markers positivity.
CONCLUSION: Emphasis on implementing robust screening measures for donated blood is underscored by this study. There is the need for future study to extensively evaluate TTI status to enhance our understanding of the trend in TTI.