Evidence map›Paper›PMID 33010800›Full record

ArticleInternational journal of health geographics2020

Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease maps.

Matthew Tuson, Matthew Yap, Mei Ruu Kok, Bryan Boruff, Kevin Murray, Alistair Vickery, Berwin A Turlach, David Whyatt

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of health geographics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 8% of its field
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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

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  15. Improving the Efficiency of Geographic Target Regions for Healthcare Interventions.International journal of environmental research and public health · 2022
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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

8 authors at 1 institution in 1 country.

Matthew TusonMedical School, University of Western Australia, Perth, Australia.
Matthew YapMedical School, University of Western Australia, Perth, Australia.
Mei Ruu KokMedical School, University of Western Australia, Perth, Australia.
Bryan BoruffUWA School of Agriculture and Environment, University of Western Australia, Perth, Australia.
Kevin MurraySchool of Population and Global Health, University of Western Australia, Perth, Australia.
Alistair VickeryMedical School, University of Western Australia, Perth, Australia.
Berwin A TurlachDepartment of Mathematics and Statistics, University of Western Australia, Perth, Australia.
David WhyattMedical School, University of Western Australia, Perth, Australia. david.whyatt@uwa.edu.au.ORCID 0000-0002-6905-1979
The University of Western Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn disease mapping, fine-resolution spatial health data are routinely aggregated for various reasons, for example to protect privacy. Usually, such aggregation occurs only once, resulting in 'single-aggregation disease maps' whose representation of the underlying data depends on the chosen set of aggregation units. This dependence is described by the modifiable areal unit problem (MAUP). Despite an extensive literature, in practice, the MAUP is rarely acknowledged, including in disease mapping. Further, despite single-aggregation disease maps being widely relied upon to guide distribution of healthcare resources, potential inefficiencies arising due to the impact of the MAUP on such maps have not previously been investigated.

resultsWe introduce the overlay aggregation method (OAM) for disease mapping. This method avoids dependence on any single set of aggregate-level mapping units through incorporating information from many different sets. We characterise OAM as a novel smoothing technique and show how its use results in potentially dramatic improvements in resource allocation efficiency over single-aggregation maps. We demonstrate these findings in a simulation context and through applying OAM to a real-world dataset: ischaemic stroke hospital admissions in Perth, Western Australia, in 2016.

conclusionsThe ongoing, widespread lack of acknowledgement of the MAUP in disease mapping suggests that unawareness of its impact is extensive or that impact is underestimated. Routine implementation of OAM can help avoid resource allocation inefficiencies associated with this phenomenon. Our findings have immediate worldwide implications wherever single-aggregation disease maps are used to guide health policy planning and service delivery.

Indexed as

Brain IschemiaStrokeComputer SimulationHumansResearch DesignWestern AustraliaDisease mappingModifiable areal unit problemResource allocation efficiencySingle-aggregation disease mapsZonation-dependence

Identifiers

PMID33010800
PMCPMC7532343
OpenAlexW3090615643

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.