Evidence map›Paper›PMID 30182200›Full record

ArticleInternational journal of biometeorology2018

Modeling the spatially varying risk factors of dengue fever in Jhapa district, Nepal, using the semi-parametric geographically weighted regression model.

Bipin Kumar Acharya, ChunXiang Cao, Tobia Lakes, Wei Chen, Shahid Naeem, Shreejana Pandit

Abstract read
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In one paragraph

Article in International journal of biometeorology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 5 pooled it
–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

19 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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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.

Bipin Kumar AcharyaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing, 100094, China.ORCID http://orcid.org/0000-0002-3824-5973
ChunXiang CaoInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing, 100094, China. caocx@radi.ac.cn.
Tobia LakesDepartment of Geography, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany.
Wei ChenInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing, 100094, China.
Shahid NaeemInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing, 100094, China.
Shreejana PanditKanti Children's Hospital Maharajgunj, Kathmandu 3, Nepal.

Funding

National Key Research and Development Program 2016YFB0501505
6 · The paper itself

Abstract

Dengue fever is expanding rapidly in many tropical and subtropical countries since the last few decades. However, due to limited research, little is known about the spatial patterns and associated risk factors on a local scale particularly in the newly emerged areas. In this study, we explored spatial patterns and evaluated associated potential environmental and socioeconomic risk factors in the distribution of dengue fever incidence in Jhapa district, Nepal. Global and local Moran's I were used to assess global and local clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semi-parametric geographically weighted regression (s-GWR) models were compared to describe spatial relationship of potential environmental and socioeconomic risk factors with dengue incidence. Our result revealed heterogeneous and highly clustered distribution of dengue incidence in Jhapa district during the study period. The s-GWR model best explained the spatial association of potential risk factors with dengue incidence and was used to produce the predictive map. The statistical relationship between dengue incidence and proportion of urban area, proximity to road, and population density varied significantly among the wards while the associations of land surface temperature (LST) and normalized difference vegetation index (NDVI) remained constant spatially showing importance of mixed geographical modeling approach (s-GWR) in the spatial distribution of dengue fever. This finding could be used in the formulation and execution of evidence-based dengue control and management program to allocate scare resources locally.

Indexed as

Population DensityDengueHumansIncidenceNepalRisk FactorsSpatial RegressionUrban PopulationDengue feverGeographically weighted regressionNepalRisk factorsSpatial heterogeneity

Identifiers

PMID30182200

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.