The Clustering of World Countries Regarding Causes of Death and Health Risk Factors.

Selen Yilmaz Işikhan, Dilek Güleç

Abstract read

In one paragraph

Article in Iranian journal of public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Clustering of countries for COVID-19 cases based on disease prevalence, health systems and environmental indicators.Chaos, solitons, and fractals · 2021
Article

4 · The record

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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

2 authors.

Selen Yilmaz IşikhanVocational School of Social Sciences, Hacettepe University, Ankara, Turkey.

Dilek GüleçPolatlı Vocational School of Health Services, Hacettepe University, Ankara, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe aimed to determine how many clusters, WHO member countries would be grouped based on the causal rates of disease-specific deaths and preventable risk factors, and evaluated the cluster memberships using some sociodemographic and socioeconomic factors.

methodsWe constructed a dataset relating to 146 WHO countries using reports and some official websites. An explanatory factor analysis was implemented to reveal the underlying patterns of the dataset. The Ward Hierarchical clustering method and gap statistical analyses were used to group countries that have similar causes of death. Clusters were then compared using subgroup analysis based on some socioeconomic and sociodemographic indicators.

resultsWe divided 146 countries into six meaningful clusters. In a comparative analysis, the differences between clusters were found to be statistically significant according to disease-specific causes of death, risk factors, socioeconomic, and sociodemographic indicators (

conclusionIncome levels, expenditure rates on health, educational levels, and causes of death in a country are directly proportional to one another. Furthermore, it was surprising that the country clusters regarding causes of death and health risk factors showed regional distributions.

Indexed as

Cluster analysisDeathRisk factorsSocioeconomic and sociodemographics indicators

Identifiers

PMID30524982

PMCPMC6277727

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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.