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The Leading Neighborhood-Level Predictors of Drug Overdose: A Mixed Machine Learning and Spatial Approach
Amir Karami. Not indexed, 2025
Topic: Risk, prevention and care in population studies
Our results showed that XGBoost was the top-performing modeling approach for each domain. The variables that contributed the most to the predictive model included the proportion of households with food stamps, households with income below $35,000, opioid prescription rate, smoking accessories expenditures, and spatial accessibility to opioid treatment programs and hospitals. Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross-validation. The ensemble model using ML also outperformed the GWR method. This study identified strong neighborhood-level predictors that place a community at risk of experiencing drug overdoses, as well as protective factors. Our findings may shed light on several specific avenues for targeted intervention in neighborhoods at risk for high drug overdose burdens.
Key findings
- Our results showed that XGBoost was the top-performing modeling approach for each domain.
- The variables that contributed the most to the predictive model included the proportion of households with food stamps, households with income below $35,000, opioid prescription rate, smoking accessories expenditures, and spatial accessibility to opioid treatment programs and hospitals.
- Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross-validation.
- The ensemble model using ML also outperformed the GWR method.
- This study identified strong neighborhood-level predictors that place a community at risk of experiencing drug overdoses, as well as protective factors.
Why it matters
This study used machine learning to identify neighborhood-level predictors of drug overdose, such as food stamp use, low household income and opioid prescription rates. It provides no information on treating, curing or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 15/100
Preprint — not yet peer-reviewed. It scored 15 on the same rubric, which is below the 16 cap we apply to non-peer-reviewed sources. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 14 of the 94 points we could judge it on, which is 15 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Not indexed) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| 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 | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| 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 | 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 | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 3 year(s) since 2025 |
| 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 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Amir Karami: h-index 24 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Kennesaw State University: institution h-index 218 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 14 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 14 of the 94 points we could judge it on is 15 out of 100. |
| Total | 15 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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. This record has no journal, so the citation leaves it out. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Karami, A. (2025). The Leading Neighborhood-Level Predictors of Drug Overdose: A Mixed Machine Learning and Spatial Approach. https://doi.org/10.2139/ssrn.5843002
Vancouver
Karami A. The Leading Neighborhood-Level Predictors of Drug Overdose: A Mixed Machine Learning and Spatial Approach. 2025. doi:10.2139/ssrn.5843002.
BibTeX
@article{karami2025-a692ea92,
author = {Karami, Amir},
title = {{The Leading Neighborhood-Level Predictors of Drug Overdose: A Mixed Machine Learning and Spatial Approach}},
year = {2025},
doi = {10.2139/ssrn.5843002},
url = {https://doi.org/10.2139/ssrn.5843002}
}
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