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Enhancing Bayesian Small Area Level Methods with Applications in Health
James Hogg. Not indexed
Topic: Risk, prevention and care in population studies
This thesis developed new Bayesian models to enhance the spatial and temporal resolution of health estimates at the small area level in Australia. It introduced a novel approach for modelling sparse survey data, which was applied nationally to derive detailed cancer risk factor estimates. These estimates were then summarised into Australia’s first area-level cancer risk factor indices. Additionally, the thesis explored the application of Bayesian models to burden of disease studies, a relatively unexplored area. The statistical innovations and applications provided in this thesis enhance our understanding of health disparities in Australia.
Key findings
- This thesis developed new Bayesian models to enhance the spatial and temporal resolution of health estimates at the small area level in Australia.
- It introduced a novel approach for modelling sparse survey data, which was applied nationally to derive detailed cancer risk factor estimates.
- These estimates were then summarised into Australia’s first area-level cancer risk factor indices.
- Additionally, the thesis explored the application of Bayesian models to burden of disease studies, a relatively unexplored area.
- The statistical innovations and applications provided in this thesis enhance our understanding of health disparities in Australia.
Why it matters
This thesis is about Bayesian small area models for health estimates in Australia, including cancer risk factors, not Alzheimer's disease, so it does not provide information on treating, curing or preventing Alzheimer's. It developed models to improve spatial and temporal resolution of health estimates.
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Study details
- Evidence strengthLow
How this study scored 30/100
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. We have no data on how recent, journal's citation record, citations per year, impact for its field and year and institution's track record, so those were left out: it earned 23 of the 77 points we could judge it on, which is 30 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 | — / 5 | No year recorded — left out of this score |
| 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 | — / 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 | 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 / 4 | Last-listed author James C. Hogg: h-index 106 (OpenAlex, whole career) |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 0 institution(s) in 0 countries — 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 | 23 / 77 | Scored on 15 of 20 parts. We have no data for the rest, so they are left out: 23 of the 77 points we could judge it on is 30 out of 100. |
| Total | 30 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.5204/thesis.eprints.253583
- Original source →
- Free full text: Queensland University of Technology →
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 year, journal, so the citation leaves them out. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Hogg, J. (n.d.). Enhancing Bayesian Small Area Level Methods with Applications in Health. https://doi.org/10.5204/thesis.eprints.253583
Vancouver
Hogg J. Enhancing Bayesian Small Area Level Methods with Applications in Health. doi:10.5204/thesis.eprints.253583.
BibTeX
@article{hogg-91088ac3,
author = {Hogg, James},
title = {{Enhancing Bayesian Small Area Level Methods with Applications in Health}},
doi = {10.5204/thesis.eprints.253583},
url = {https://doi.org/10.5204/thesis.eprints.253583}
}
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