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A Spatial Variance‐Smoothing Area Level Model for Small Area Estimation of Demographic Rates
Peter A. Gao, Jonathan Wakefield. International Statistical Review, 2023-10-17
Topic: Risk, prevention and care in population studies
Summary Accurate estimates of subnational health and demographic indicators are critical for informing policy. Many countries collect relevant data using complex household surveys, but when data are limited, direct weighted estimates of small area proportions may be unreliable. Area level models treating these direct estimates as response data can improve precision but often require known sampling variances of the direct estimators for all areas. In practice, the sampling variances are estimated, so standard approaches do not account for a key source of uncertainty. To account for variability in the estimated sampling variances, we propose a hierarchical Bayesian spatial area level model for small area proportions that smooths both the estimated proportions and sampling variances to produce point and interval estimates of rates of interest. We demonstrate the performance of our approach via simulation and application to vaccination coverage and HIV prevalence data from the Demographic and Health Surveys.
Key findings
- Many countries collect relevant data using complex household surveys, but when data are limited, direct weighted estimates of small area proportions may be unreliable.
- Area level models treating these direct estimates as response data can improve precision but often require known sampling variances of the direct estimators for all areas.
- In practice, the sampling variances are estimated, so standard approaches do not account for a key source of uncertainty.
- To account for variability in the estimated sampling variances, we propose a hierarchical Bayesian spatial area level model for small area proportions that smooths both the estimated proportions and sampling variances to produce point and interval estimates of rates of interest.
- We demonstrate the performance of our approach via simulation and application to vaccination coverage and HIV prevalence data from the Demographic and Health Surveys.
Why it matters
This paper proposes a statistical model to improve small-area estimates of health and demographic rates such as vaccination coverage and HIV prevalence. It provides no information on treating, curing or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 35/100
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 (International Statistical Review) |
| Journal's citation record | 1 / 4 | International Statistical Review: journal h-index 146 (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 | 3 / 5 | 2023 — within 6 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 | 2 / 7 | Cited 6 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.2 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 2 / 6 | Cited 1.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 6 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Jonathan C. Wakefield: h-index 21 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | University of Washington: institution h-index 3,540 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 35 / 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. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Gao, P. A., & Wakefield, J. (2023). A Spatial Variance‐Smoothing Area Level Model for Small Area Estimation of Demographic Rates. International Statistical Review. https://doi.org/10.1111/insr.12556
Vancouver
Gao PA, Wakefield J. A Spatial Variance‐Smoothing Area Level Model for Small Area Estimation of Demographic Rates. International Statistical Review. 2023. doi:10.1111/insr.12556.
BibTeX
@article{gao2023-9282a496,
author = {Gao, Peter A. and Wakefield, Jonathan},
title = {{A Spatial Variance‐Smoothing Area Level Model for Small Area Estimation of Demographic Rates}},
journal = {International Statistical Review},
year = {2023},
doi = {10.1111/insr.12556},
url = {https://doi.org/10.1111/insr.12556}
}
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