ArticleInternational journal of environmental research and public health2019
Spatially Filtered Multilevel Analysis on Spatial Determinants for Malaria Occurrence in Korea.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions.Journal of the Royal Society, Interface · 2021Pooled it
- Local Characteristics Related to SARS-CoV-2 Transmissions in the Seoul Metropolitan Area, South Korea.International journal of environmental research and public health · 2021Article
- Spatial pattern of tuberculosis (TB) and related socio-environmental factors in South Korea, 2008-2016.PloS one · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since its re-emergence in 1993, the spatial patterns of malaria outbreaks in South Korea have drastically changed. It is well known that complicated interactions between humans, nature, and socio-economic factors lead to a spatial dependency of vivax malaria occurrences. This study investigates the spatial factors determining malaria occurrences in order to understand and control malaria risks in Korea. A multilevel model is applied to simultaneously analyze the variables in different spatial scales, and eigenvector spatial filtering is used to explain the spatial autocorrelation in the malaria occurrence data. The results show that housing costs, average age, rice paddy field ratio, and distance from the demilitarized zone (DMZ) are significant on the level-1 spatial scale; health budget per capita and military base area ratio are significant on the level-2 spatial scale. The results show that the spatially filtered multilevel model provides better analysis results in handling spatial issues.
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Registered trials
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