Prevalence estimates of major neurocognitive disorders in a rural Nigerian community
Bibliographic record
Abstract
OBJECTIVE: There is paucity of information on major neurocognitive disorders in sub-Saharan Africa where the number of individuals with neurocognitive disorders is expected to increase due to demographic transition. This study aims to report on the prevalence estimates of dementia and MCI (mild cognitive impairment) in a rural community in southwest Nigeria. MATERIALS AND METHODS: This was a two-stage cross-sectional study of persons aged 65 years and above resident in Lalupon community, Oyo State. The Identification and IDEA (Intervention for Dementia in Elderly Africans) Study Questionnaire was used for initial screening by trained community health care workers, utilized followed by cognitive assessment using the validated IDEA cognitive screen. Functional and cognitive assessment of selected individuals was carried out during the second stage. Information obtained was used for consensus diagnosis and participants were categorized into normal, MCI and dementia using standard criteria. RESULTS: Six hundred and thirteen participants completed the study with 111 (18.1%) diagnosed as MCI while 17 (2.8%) had dementia. The age-adjusted prevalence estimates were 18.4% (95% CI: 14.9-21.9%) and 2.9% (95% CI 1.6-4.4%) for MCI and dementia, respectively. Probable Alzheimer's disease and amnestic MCI predominated. Individuals with dementia were older than both MCI and normal cases while those with MCI had significantly fewer years of schooling than the other diagnostic categories. CONCLUSION: Almost one out of five older persons in Lalupon community had major neurocognitive impairment with MCI being six-times more common than dementia. Alzheimer's disease was the most common dementia sub-type.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".