The Nairobi Declaration—Reducing the burden of dementia in low‐ and middle‐income countries (LMICs): Declaration of the 2022 Symposium on Dementia and Brain Aging in LMICs
Bibliographic record
Abstract
Delegates of the 2022 Symposium on Dementia and Brain Aging in Low- and Middle-Income Countries, representing over 40 countries, met in Nairobi, Kenya, December 5–9 to highlight advances in dementia prevention, diagnosis, care, and research, as well as explore the future needs of the global community. Dementia poses a major threat to optimal brain health and remains a priority for the demographically ever-changing worldwide population. It incurs substantial individual, societal, and global costs. By 2030, the majority of the 78 million people with dementia will be living in low- and middle-income countries (LMICs). Upon consideration of these grave statistics and new diagnostic paradigms with available prevention and treatment strategies, we, the undersigned delegates of the symposium, including the Organizing Committee and speakers, and the African Dementia Consortium (AfDC), with frontline and lived experience, call upon the global community, including governments, policymakers, international economic forums, health and social care providers, together with private and public research funding agencies, research-focused organizations such as universities, nongovernmental organizations, and technology and pharmaceutical companies, to act as follows: Rethink a global approach to dementia, being more focused on the diversity of underserved and underrepresented populations. Shift the balance of investment further toward LMICs, which bear a high burden, to tackle the challenges and seize opportunities and to mitigate the burden of various forms of dementia, including Alzheimer's disease, vascular dementia, and others, globally. Engage and influence policymakers and advocacy organizations to encourage implementation and evaluation of population-level dementia risk reduction interventions at a more diverse global level. In addition to promoting education, controlling cardiovascular risk, and preventing stroke, seriously consider nutritional factors as well as psychosocial activities for brain health and longevity. Ensure that the health and social care systems are equipped to meet the needs of aging populations in the LMICs as well as low-resource settings in high-income countries (HICs). Support research into more affordable, pragmatic, and effective solutions to improve the quality of life of people living with dementia and reduce the expenses of hospitalization, long-term care, and loss of income and indirect costs resulting from dementia. Equip higher education institutions in HICs and LMICs with the capacity to develop a pipeline of local highly motivated early career researchers (ECRs) to ensure future research will be responsive to local population needs and to leverage opportunities offered by different countries. Ensure a research framework with international collaboration that will unwind the rigid structures in LMICs and encourage young, enthusiastic people to give the best of their potential in their countries, thereby preventing brain drain. We believe that timely intervention to address these goals will bring about significant and sustainable improvements in the prevalence, outcomes, and personal and societal impacts of dementia, resulting in a higher quality of life, better care, and global benefits. Nairobi, Kenya, December 9, 2022 Organizing Committee and Speakers of the 2022 Symposium on Dementia and Brain Aging in Low- and Middle-Income Countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".