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Development of Algorithmic Dementia Ascertainment for Racial/Ethnic Disparities Research in the US Health and Retirement Study
Gianattasio KZ, Ciarleglio A, Power MC.. Epidemiology (Cambridge, Mass.), 2020-01-01
Topic: Risk, prevention and care in population studies
Background Disparities research in dementia is limited by lack of large, diverse, and representative samples with systematic dementia ascertainment. Algorithmic diagnosis of dementia offers a cost-effective alternate approach. Prior work in the nationally representative Health and Retirement Study has demonstrated that existing algorithms are ill-suited for racial/ethnic disparities work given differences in sensitivity and specificity by race/ethnicity. Methods We implemented traditional and machine learning methods to identify an improved algorithm that: (1) had ≤5 percentage point difference in sensitivity and specificity across racial/ethnic groups; (2) achieved ≥80% overall accuracy across racial/ethnic groups; and (3) achieved ≥75% sensitivity and ≥90% specificity overall. Final recommendations were based on robustness, accuracy of estimated race/ethnicity-specific prevalence and prevalence ratios compared to those using in-person diagnoses, and ease of use. Results We identified six algorithms that met our prespecified criteria. Our three recommended algorithms achieved ≤3 percentage point difference in sensitivity and ≤5 percentage point difference in specificity across racial/ethnic groups, as well as 77%-83% sensitivity, 92%-94% specificity, and 90%-92% accuracy overall in analyses designed to emulate out-of-sample performance. Pairwise prevalence ratios between non-Hispanic whites, non-Hispanic blacks, and Hispanics estimated by application of these algorithms are within 1%-10% of prevalence ratios estimated based on in-person diagnoses. Conclusions We believe these algorithms will be of immense value to dementia researchers interested in racial/ethnic disparities. Our process can be replicated to allow minimally biasing algorithmic classification of dementia for other purposes.
Key findings
- Prior work in the nationally representative Health and Retirement Study has demonstrated that existing algorithms are ill-suited for racial/ethnic disparities work given differences in sensitivity and specificity by race/ethnicity.
- Methods We implemented traditional and machine learning methods to identify an improved algorithm that: (1) had ≤5 percentage point difference in sensitivity and specificity across racial/ethnic groups; (2) achieved ≥80% overall accuracy across racial/ethnic groups; and (3) achieved ≥75% sensitivity and ≥90% specificity overall.
- Final recommendations were based on robustness, accuracy of estimated race/ethnicity-specific prevalence and prevalence ratios compared to those using in-person diagnoses, and ease of use.
- Results We identified six algorithms that met our prespecified criteria.
- Our three recommended algorithms achieved ≤3 percentage point difference in sensitivity and ≤5 percentage point difference in specificity across racial/ethnic groups, as well as 77%-83% sensitivity, 92%-94% specificity, and 90%-92% accuracy overall in analyses designed to emulate out-of-sample performance.
Why it matters
Older ways of spotting dementia in the US Health and Retirement Study worked unevenly across racial and ethnic groups, which can distort comparisons between groups. This study built new algorithms whose recommended versions differed by no more than 3 points in sensitivity and 5 points in specificity across groups, were 90%–92% accurate overall, and gave group-to-group prevalence ratios within 1%–10% of those from in-person diagnoses. It is a tool for fairer dementia research, not a test for individuals or a finding about treatment or prevention.
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Study details
- Evidence strengthLow
How this study scored 42/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Epidemiology (Cambridge, Mass.)) |
| Journal's citation record | 2 / 4 | Epidemiology: journal h-index 249 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 69 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.6 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 4.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 9 other studies in this library |
| Still being cited | 2 / 4 | Cited 33 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Melinda C. Power: h-index 47 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | George Washington University: institution h-index 881 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31567393) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 42 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1097/ede.0000000000001101
- PubMed: 31567393
- Original source →
- Free full text: PubMed Central →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Gianattasio, K. Z., Ciarleglio, A., & Power, M. C. (2020). Development of Algorithmic Dementia Ascertainment for Racial/Ethnic Disparities Research in the US Health and Retirement Study. Epidemiology (Cambridge, Mass.). https://doi.org/10.1097/ede.0000000000001101
Vancouver
Gianattasio KZ, Ciarleglio A, Power MC. Development of Algorithmic Dementia Ascertainment for Racial/Ethnic Disparities Research in the US Health and Retirement Study. Epidemiology (Cambridge, Mass.). 2020. doi:10.1097/ede.0000000000001101. PMID: 31567393.
BibTeX
@article{gianattasio2020-31567393,
author = {Gianattasio, K. Z. and Ciarleglio, A. and Power, M. C.},
title = {{Development of Algorithmic Dementia Ascertainment for Racial/Ethnic Disparities Research in the US Health and Retirement Study}},
journal = {Epidemiology (Cambridge, Mass.)},
year = {2020},
doi = {10.1097/ede.0000000000001101},
pmid = {31567393},
url = {https://doi.org/10.1097/ede.0000000000001101}
}
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