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Estimating county-level overweight prevalence in Kenya using small area methodology
Noah Cheruiyot Mutai. South African Statistical Journal, 2022
Topic: Risk, prevention and care in population studies
Public health surveillance of overweight prevalence is essential to assess the extent of the problem, identify regions and groups most affected and inform policy-making. However, the needed reliable data at disaggregated levels is lacking in Kenya. The Kenya STEPwise Survey for Non-communicable Diseases and RiskFactors (KSSNDRF) was nationally representative. It was used to obtain various indicators of non-communicable diseases and risk factors including overweight. However, due to small sample sizes at lower levels like at the county, overweight prevalence estimates are statistically imprecise (i.e., high variance). Therefore, to increase the effective sample size we combine data from the KSSNDRF and the Kenya Population and Housing Census by model-based small area methods. In particular, we fit an arcsine square-root transformed Fay–Herriot model. To transform back to the original scale, we use a bias-corrected back transformation. For this model, we smooth the design variance using Generalised Variance Functions. We compute the mean squared error estimates using a bootstrap procedure. We found that counties within urban areas — including the major towns like Nairobi, Nakuru, Nyeri and Mombasa — have a higher prevalence of overweight compared to rural counties. Although the paper focuses on overweight prevalence in Kenya, the presented method can also be applied to other indicators in developing countries with similar data sources.
Key findings
- We found that counties within urban areas — including the major towns like Nairobi, Nakuru, Nyeri and Mombasa — have a higher prevalence of overweight compared to rural counties.
Why it matters
This study used statistical modeling to estimate overweight prevalence at the county level in Kenya, finding higher rates in urban counties. It provides no information on treating, curing or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 24/100
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 (South African Statistical Journal) |
| Journal's citation record | 0 / 4 | South African Statistical Journal: journal h-index 16 (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 | 2022 — 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 | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 0 / 6 | Cited 0.0× 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 | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Noah Cheruiyot Mutai: h-index 8 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Freie Universität Berlin: institution h-index 744 (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 |
| Total | 24 / 100 | — |
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Read the original
- DOI: 10.37920/sasj.2022.56.1.1
- Original source →
- Free full text: South African Statistical Journal →
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
Mutai, N. C. (2022). Estimating county-level overweight prevalence in Kenya using small area methodology. South African Statistical Journal. https://doi.org/10.37920/sasj.2022.56.1.1
Vancouver
Mutai NC. Estimating county-level overweight prevalence in Kenya using small area methodology. South African Statistical Journal. 2022. doi:10.37920/sasj.2022.56.1.1.
BibTeX
@article{mutai2022-8a69b167,
author = {Mutai, Noah Cheruiyot},
title = {{Estimating county-level overweight prevalence in Kenya using small area methodology}},
journal = {South African Statistical Journal},
year = {2022},
doi = {10.37920/sasj.2022.56.1.1},
url = {https://doi.org/10.37920/sasj.2022.56.1.1}
}