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Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study
Gianattasio KZ, Wu Q, Glymour MM, Power MC.. Epidemiology (Cambridge, Mass.), 2019-03-01
Topic: Risk, prevention and care in population studies
Background Dementia ascertainment is time-consuming and costly. Several algorithms use existing data from the US-representative Health and Retirement Study (HRS) to algorithmically identify dementia. However, relative performance of these algorithms remains unknown. Methods We compared performance across five algorithms (Herzog-Wallace, Langa-Kabeto-Weir, Crimmins, Hurd, Wu) overall and within sociodemographic subgroups in participants in HRS and Wave A of the Aging, Demographics, and Memory Study (ADAMS, 2000-2002), an HRS substudy including in-person dementia ascertainment. We then compared algorithmic performance in an internal (time-split) validation dataset including participants of HRS and ADAMS Waves B, C, and/or D (2002-2009). Results In the unweighted training data, sensitivity ranged from 53% to 90%, specificity ranged from 79% to 97%, and overall accuracy ranged from 81% to 87%. Though sensitivity was lower in the unweighted validation data (range: 18%-62%), overall accuracy was similar (range: 79%-88%) due to higher specificities (range: 82%-98%). In analyses weighted to represent the age-eligible US population, accuracy ranged from 91% to 94% in the training data and 87% to 94% in the validation data. Using a 0.5 probability cutoff, Crimmins maximized sensitivity, Herzog-Wallace maximized specificity, and Wu and Hurd maximized accuracy. Accuracy was higher among younger, highly-educated, and non-Hispanic white participants versus their complements in both weighted and unweighted analyses. Conclusion Algorithmic diagnoses provide a cost-effective way to conduct dementia research. However, naïve use of existing algorithms in disparities or risk factor research may induce nonconservative bias. Algorithms with more comparable performance across relevant subgroups are needed.
Key findings
- Methods We compared performance across five algorithms (Herzog-Wallace, Langa-Kabeto-Weir, Crimmins, Hurd, Wu) overall and within sociodemographic subgroups in participants in HRS and Wave A of the Aging, Demographics, and Memory Study (ADAMS, 2000-2002), an HRS substudy including in-person dementia ascertainment.
- Results In the unweighted training data, sensitivity ranged from 53% to 90%, specificity ranged from 79% to 97%, and overall accuracy ranged from 81% to 87%.
- Though sensitivity was lower in the unweighted validation data (range: 18%-62%), overall accuracy was similar (range: 79%-88%) due to higher specificities (range: 82%-98%).
- Accuracy was higher among younger, highly-educated, and non-Hispanic white participants versus their complements in both weighted and unweighted analyses.
Why it matters
This study compared five methods for identifying dementia from existing Health and Retirement Study data rather than diagnosing it in person. The methods varied widely in sensitivity, so these algorithms are tools for estimating dementia rates in research, not for treating or preventing the disease.
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Study details
- Evidence strengthLow
How this study scored 48/100
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 | 2019 — 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 | 5 / 7 | Cited 129 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 14.3 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 6.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 20 other studies in this library |
| Still being cited | 3 / 4 | Cited 59 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 | 2 / 3 | University of California, San Francisco: institution h-index 1,915 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30461528) |
| Free to read | 2 / 2 | Free to read |
| Total | 48 / 100 | — |
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Read the original
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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., Wu, Q., Glymour, M. M., & Power, M. C. (2019). Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study. Epidemiology (Cambridge, Mass.). https://doi.org/10.1097/ede.0000000000000945
Vancouver
Gianattasio KZ, Wu Q, Glymour MM, Power MC. Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study. Epidemiology (Cambridge, Mass.). 2019. doi:10.1097/ede.0000000000000945. PMID: 30461528.
BibTeX
@article{gianattasio2019-30461528,
author = {Gianattasio, K. Z. and Wu, Q. and Glymour, M. M. and Power, M. C.},
title = {{Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study}},
journal = {Epidemiology (Cambridge, Mass.)},
year = {2019},
doi = {10.1097/ede.0000000000000945},
pmid = {30461528},
url = {https://doi.org/10.1097/ede.0000000000000945}
}
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