Evidence map›Paper›PMID 36081593›Full record

ArticleInternational statistical review = Revue internationale de statistique2020

Small Area Estimation for Disease Prevalence Mapping.

Jon Wakefield, Taylor Okonek, Jon Pedersen

Abstract read
In one paragraph

Article in International statistical review = Revue internationale de statistique, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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  9. Smoothed model-assisted small area estimation of proportions.The Canadian journal of statistics = Revue canadienne de statistique · 2024
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jon WakefieldDepartment of Biostatistics, University of Washington, Seattle, USA.
Taylor OkonekDepartment of Biostatistics, University of Washington, Seattle, USA.
Jon PedersenFafo AVF, Oslo, Norway.

Funding

STATISTICAL ISSUES IN AIDS RESEARCHR37AI029168 · NIAID · UNIVERSITY OF WASHINGTON · PI JAMES P HUGHES · 1999 to 2026
$9.8M
STATISTICAL ISSUES IN AIDS RESEARCHR01AI029168 · NIAID · UNIVERSITY OF WASHINGTON · PI HUGHES, JAMES P · 1989 to 2018
$5.3M
NIAID NIH HHS R01 AI029168NIAID NIH HHS R37 AI029168
6 · The paper itself

Abstract

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area-level and at the unit-level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015-2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to COVID-19 is discussed.

Indexed as

Area-level modelsBayesian methodscomplex surveysdesign-based inferencedirect estimationindirect estimationmodel-based inferencespatial smoothingunit-level modelsweighting

Identifiers

PMID36081593
PMCPMC9451141

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.