Abstract
Background and Objectives
Rapid increase in life expectancy has resulted in an increase in the global burden of dementia that is expected to become a leading cause of morbidity in the future. Low- and middle-income countries are expected to bear an increasing majority of the burden, but lack data for accurate burden estimates that are key for informing policy and planning. Bayesian methods have recently gained recognition over traditional frequentist approaches for modelling disease burden for their superiority in dealing with severely limited data. This study provides updated estimates of dementia prevalence in Latin America and the Caribbean (LAC) for the years 2015, 2020 and 2030. Given the paucity of data, estimates were developed using a Bayesian methodology and confirmed by the traditional frequentist approach, with the aim of providing methodological insights for future disease burden estimates.
Methods
A comprehensive systematic literature search was conducted to identify all relevant primary studies published between the years 2010–2018. The quality of the included studies was critically assessed. A random-effects model (REM) and a Bayesian normal-normal hierarchical model (NNHM) were used to obtain the pooled prevalence estimate of dementia for people aged 60 and above. The latter model was also developed to estimate age-specific dementia prevalence. Using UN population estimates, total and age-specific projections of the burden of dementia were calculated.
Results
The prevalence of dementia in LAC was found to be 14% (10–21%) in those above age 60 based on REM, and 8% (5–11.5%) based on NNHM. The prevalence increased from 2% (1–4%) in people aged 60–69 to 29% (20–37%) in people above the age of 80. The number of people living with dementia in LAC in 2015 was estimated at 5.68 million, with future projections of 6.86 million in 2020 and 9.94 million in 2030.
Conclusions
The findings of this review found that burden of dementia in LAC is substantial and continues to rapidly grow. The projected rise in dementia cases in the future should prompt urgent governmental response to address this growing public health issue. We were also able to demonstrate that given the overall paucity of data, a Bayesian approach was superior for estimating disease prevalence and burden.
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