The incidence of all‐cause dementia and Alzheimer’s disease from around the world: data from the COSMIC collaboration
Bibliographic record
Abstract
Abstract Background High‐income countries (HICs) are over‐represented in current global dementia incidence rates, skewing estimates. Variance in diagnostic methods between HICs and low‐ and middle‐income countries (LMICs) is speculated to contribute to the regional differences in rates. Cohort Studies of Memory in an International Consortium (COSMIC) offers a unique opportunity to address these research inequalities by harmonising data from international studies, including representation from LMICs. This study aimed to identify dementia incidence rates by age and sex in various regions worldwide, where data for dementia diagnosis were available. Method Data were obtained from 36 members of COSMIC, representing 28 countries across 6 continents (HICs: Australia, Canada, Faroe Islands, France, Germany, Greece, Italy, Japan, Netherlands, South Korea, Spain, Sweden, & USA; LMICs: Brazil, China, Cuba, Dominican Republic, Ecuador, Indonesia, Malaysia, Mexico, Nigeria, Peru, Philippines, Republic of Congo, & Tanzania). For each member study, we calculated incidence rates for all‐cause dementia. Findings from 14 studies, with a consensus diagnosis are presented in the results. Using an Item Response Theory approach, we are currently calculating a comparable incidence rate for those studies without a consensus diagnosis. Result Consistent with previous trends, incidence rates (per 100 person‐years) increased with age, from 65‐70 years‐old to 85‐90 years‐old, for both males (i.e., Republic of Congo, 4.41 to 19.57; France, 0.46 to 3.89; USA, 0.17 to 3.22; Spain, 0.31 to 4.22; 65‐70 & 85‐90 cohorts respectively) and females (i.e., Republic of Congo, 3.57 to 15.31; France, 0.45 to 3.72; USA, 0.22 to 4.25; Spain, 0.36 to 4.96; 65‐70 & 85‐90 cohorts respectively). There were no sex differences in incidence rates in younger age groups (60‐65). Among older age groups, however, women tended to have higher incidence rates than men, in some countries (Faroe Islands, Germany, Sweden, and USA). Conclusion Geographical differences in dementia incidence rates likely represent inherent variation among countries, beyond methodological considerations. We are working to expand the range of studies and regions for which we calculate dementia incidence rates. This involves the development of approaches to classify and harmonise incident dementia in studies lacking consensus diagnoses. Doing so will bolster LMIC representation.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".