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Factors associated with Alzheimer's Disease Dementia prevalence in the United States: A county-level spatial machine learning analysis
Abolfazl Mollalo, George Grekousis, Andreana Benitez, Hermes Florez, Brian Neelon, Leslie A Lenert, Alexander Alekseyenko. Not indexed, 2024-07-17
Topic: Amyloid, tau and blood-test diagnosis
The model outperformed conventional ones, with an out-of-bag R2 of 74.8%. Key findings showed the normalized difference vegetation index as the most influential environmental factor in 15.1% of US counties, lack of leisure time physical activity in 12.7%, binge drinking in 9.1%, and mobile homes as the main socioeconomic factor in 13.3% of US counties. Spatial machine learning analyses suggest that AD dementia prevalence could be impacted by county-specific targeted interventions that improve green space, reduce air pollution and support greater access to physical activity.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Mollalo, A., Grekousis, G., Benitez, A., Florez, H., Neelon, B., Lenert, L. A., et al. (2024). Factors associated with Alzheimer's Disease Dementia prevalence in the United States: A county-level spatial machine learning analysis. https://doi.org/10.1101/2024.07.16.24310529
Vancouver
Mollalo A, Grekousis G, Benitez A, Florez H, Neelon B, Lenert LA, et al. Factors associated with Alzheimer's Disease Dementia prevalence in the United States: A county-level spatial machine learning analysis. 2024. doi:10.1101/2024.07.16.24310529.
BibTeX
@article{mollalo2024-58e54ddd,
author = {Mollalo, Abolfazl and Grekousis, George and Benitez, Andreana and Florez, Hermes and Neelon, Brian and Lenert, Leslie A and Alekseyenko, Alexander},
title = {{Factors associated with Alzheimer's Disease Dementia prevalence in the United States: A county-level spatial machine learning analysis}},
year = {2024},
doi = {10.1101/2024.07.16.24310529},
url = {https://doi.org/10.1101/2024.07.16.24310529}
}
Built from the record alzhub holds for this paper, not looked up. This record has no journal, so the citation leaves it out. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- The model outperformed conventional ones, with an out-of-bag R2 of 74.8%.
- Key findings showed the normalized difference vegetation index as the most influential environmental factor in 15.1% of US counties, lack of leisure time physical activity in 12.7%, binge drinking in 9.1%, and mobile homes as the main socioeconomic factor in 13.3% of US counties.
- Spatial machine learning analyses suggest that AD dementia prevalence could be impacted by county-specific targeted interventions that improve green space, reduce air pollution and support greater access to physical activity.
Why it matters
This county-level analysis found that green space, lack of leisure-time physical activity, binge drinking and mobile homes were among the factors most linked to Alzheimer's disease dementia prevalence across U.S. counties. It does not test a treatment or cure, but it suggests that county-specific efforts to improve green space, reduce air pollution and support physical activity could be studied for their impact on dementia prevalence.
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Study details
- Effect size74.8%; 15.1%; 12.7%
- Evidence strengthLow
How this study scored 33/100
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 and impact for its field and year, so those were left out: it earned 30 of the 90 points we could judge it on, which is 33 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Not indexed) |
| 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 | 2024 — 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 | 1 / 7 | Cited 2 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 4 year(s) since 2024 |
| 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 | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Alexander V. Alekseyenko: h-index 38 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Sun Yat-sen University: institution h-index 851 (OpenAlex) |
| Multicenter collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 30 / 90 | Scored on 18 of 20 parts. We have no data for the rest, so they are left out: 30 of the 90 points we could judge it on is 33 out of 100. |
| Total | 33 / 100 | — |
What each of these 20 components measures, and what this score is not →
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