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Bias from small-count suppression in county-level cancer disparity estimates: a calibrated simulation study
Kamalakanta Gahan. Not indexed, 2026-06-08
Topic: Risk, prevention and care in population studies
Methods In a cross-sectional ecological study of 3,143 U.S. counties (analytic sample 3,018 with computable exposure) using one frozen public release of NCI State Cancer Profiles incidence and mortality data and ACS 2018–2022 5-year data, we estimated the most-versus least-deprived ICE(race+income) quintile rate ratio (RR) and rate difference for female breast, stomach, and cervix cancers under four suppression-handling methods: complete-case, available-case, bounding, and model-based small-area estimation. Results The suppressed fraction rose with rarity: 7.4% of counties for breast, 61.3% for stomach, and 75.7% for cervix incidence.
Key findings
- Methods In a cross-sectional ecological study of 3,143 U.S. counties (analytic sample 3,018 with computable exposure) using one frozen public release of NCI State Cancer Profiles incidence and mortality data and ACS 2018–2022 5-year data, we estimated the most-versus least-deprived ICE(race+income)
- Results The suppressed fraction rose with rarity: 7.4% of counties for breast, 61.3% for stomach, and 75.7% for cervix incidence.
Why it matters
This study is about cancer disparity estimates and suppression of small counts in U.S. counties, not Alzheimer's disease, so it does not provide information on treating, curing or preventing Alzheimer's. It found that the suppressed fraction of counties rose with cancer rarity.
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Study details
- Study designcross-sectional
- Effect size7.4%; 61.3%; 75.7%
- Evidence strengthLow
How this study scored 37/100
Published in a peer-reviewed journal, and it is a snapshot of people at one point in time. Scored zero for sample size, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, impact for its field and year and senior author's track record, so those were left out: it earned 32 of the 86 points we could judge it on, which is 37 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 | 4 / 12 | A snapshot of people at one point in time |
| 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 | 5 / 5 | 2026 — within 1 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) |
| 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 2 year(s) since 2026 |
| 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 | — / 4 | OpenAlex's profile for last-listed author Kamalakanta Gahan holds only 4 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 2 / 3 | Duke University: institution h-index 1,541 (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 | 32 / 86 | Scored on 17 of 20 parts. We have no data for the rest, so they are left out: 32 of the 86 points we could judge it on is 37 out of 100. |
| Total | 37 / 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
Gahan, K. (2026). Bias from small-count suppression in county-level cancer disparity estimates: a calibrated simulation study. https://doi.org/10.64898/2026.06.05.26355021
Vancouver
Gahan K. Bias from small-count suppression in county-level cancer disparity estimates: a calibrated simulation study. 2026. doi:10.64898/2026.06.05.26355021.
BibTeX
@article{gahan2026-10f9837b,
author = {Gahan, Kamalakanta},
title = {{Bias from small-count suppression in county-level cancer disparity estimates: a calibrated simulation study}},
year = {2026},
doi = {10.64898/2026.06.05.26355021},
url = {https://doi.org/10.64898/2026.06.05.26355021}
}
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