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Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study
Wang T, Pate V, Kim DH, Power MC, Garden G, Palta P, Knopman D, Jonsson-Funk M et al.. American journal of epidemiology, 2025-12-01
Topic: Risk, prevention and care in population studies
There is an urgent need to improve dementia ascertainment robustness in real-world studies assessing drug effects on dementia risk. We developed algorithms to dementia identification algorithms using Medicare claims (inpatient/outpatient/prescription) from 3318 Visit 5 (2011-2013) and 1828 Visit 6 (2016-2017) participants of the Atherosclerosis Risk in Communities (ARIC) Study, validated against ARIC's rigorous syndromic dementia classification. Algorithm performance was compared to existing algorithms (Jain, Bynum, Lee). We further evaluated algorithms effectiveness in a 20% random Medicare sample aged ≥70 years who initiating liraglutide or dipeptidyl peptidase 4 inhibitors (DPP4i) to assess 3-year adjusted risk difference (aRD) for dementia. Our incident dementia algorithm required two dementia diagnostic codes within 1-year, or one dementia code plus a new dementia prescription within 90 days. It achieved a positive predictive value (PPV) of 69.2%, specificity of 99.0%, and sensitivity of 34.6% (population prevalence: 8.8%), comparable to extant algorithms (PPV, 58.7-68.6%; sensitivity 25.5-40.4%). Prevalent dementia algorithm (without requiring incident diagnoses/prescriptions) demonstrated similar performance. In the Medicare sample, dementia risk ranged from 3.0% to 12.5%, aRD comparing liraglutide to DPP4i varied -1.2% to -3.6%, with our algorithm closely matching the Bynum algorithm. Algorithm selection significantly impacts treatment effect estimates, highlighting its importance in in pharmacoepidemiologic research.
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APA 7th
Wang, T., Pate, V., Kim, D. H., Power, M. C., Garden, G., Palta, P., et al. (2025). Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study. American journal of epidemiology. https://doi.org/10.1093/aje/kwaf166
Vancouver
Wang T, Pate V, Kim DH, Power MC, Garden G, Palta P, et al. Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study. American journal of epidemiology. 2025. doi:10.1093/aje/kwaf166. PMID: 40754790.
BibTeX
@article{wang2025-40754790,
author = {Wang, T. and Pate, V. and Kim, D. H. and Power, M. C. and Garden, G. and Palta, P. and Knopman, D. and Jonsson-Funk, M. and others},
title = {{Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study}},
journal = {American journal of epidemiology},
year = {2025},
doi = {10.1093/aje/kwaf166},
pmid = {40754790},
url = {https://doi.org/10.1093/aje/kwaf166}
}
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Key findings
- Algorithm performance was compared to existing algorithms (Jain, Bynum, Lee).
- Prevalent dementia algorithm (without requiring incident diagnoses/prescriptions) demonstrated similar performance.
- Algorithm selection significantly impacts treatment effect estimates, highlighting its importance in in pharmacoepidemiologic research.
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Study details
- Effect size69.2%; 99.0%; 34.6%
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomized / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multicenter collaboration, so those were left out: it earned 26 of the 58 points we could judge it on, which is 45 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (American journal of epidemiology) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 4 / 5 | 2025 — within 3 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40754790) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 26 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 26 of the 58 points we could judge it on is 45 out of 100. |
| Total | 45 / 100 | — |
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