Evidence map›Paper›PMID 31706302›Full record

ArticleInternational journal of health geographics2019

A new combination rule for Spatial Decision Support Systems for epidemiology.

Luciana Moura Mendes de Lima, Laísa Ribeiro de Sá, Ana Flávia Uzeda Dos Santos Macambira, Jordana de Almeida Nogueira, Rodrigo Pinheiro de Toledo Vianna, Ronei Marcos de Moraes

Abstract read
In one paragraph

Article in International journal of health geographics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. 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

6 authors.

Luciana Moura Mendes de LimaGraduate Program in Decision Models and Health, Department of Statistics, Federal University of Paraíba, João Pessoa, Paraíba, Brazil. lumouramendes@gmail.com.ORCID 0000-0002-0370-2361
Laísa Ribeiro de SáGraduate Program in Decision Models and Health, Department of Statistics, Federal University of Paraíba, João Pessoa, Paraíba, Brazil.ORCID 0000-0003-2806-6178
Ana Flávia Uzeda Dos Santos MacambiraDepartment of Statistics, Federal University of Paraíba, João Pessoa, Paraíba, Brazil.ORCID 0000-0002-1590-8113
Jordana de Almeida NogueiraDepartment of Nursing, Federal University of Paraíba, João Pessoa, Paraíba, Brazil.ORCID 0000-0002-2673-0285
Rodrigo Pinheiro de Toledo ViannaDepartment of Nutrition, Federal University of Paraíba, João Pessoa, Paraíba, Brazil.ORCID 0000-0002-5358-1967
Ronei Marcos de MoraesDepartment of Statistics, Federal University of Paraíba, João Pessoa, Paraíba, Brazil.ORCID 0000-0001-8436-8950

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDecision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS).

methodsMakes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map.

resultsAn example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: "non-priority", "non-priority tendency", "priority tendency" and "priority", for the fight against diseases.

conclusionThe new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities.

Indexed as

Decision MakingBrazilCitiesGeographic Information SystemsHumansTuberculosisBrazilEpidemiologyMultiple Criteria Decision MakingSpace–time analysisSpatial analysisSpatial Decision Support Systems

Identifiers

PMID31706302
PMCPMC6842522

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Registered trials

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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.