Evidence map›Paper›PMID 36817868›Full record

ArticleThe Lancet regional health. Western Pacific2023

The spatial-temporal risk profiling of

Hai-Yan Xiao, Jong-Yil Chai, Yue-Yi Fang, Ying-Si Lai

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

4 authors.

Hai-Yan XiaoDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, PR China.
Jong-Yil ChaiDepartment of Parasitology and Tropical Medicine, Seoul National University College of Medicine, and Institute of Endemic Diseases, Seoul National University Medical Research Center, Seoul, 110-799, South Korea.
Yue-Yi FangInstitute of Parasitic Diseases, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, PR China.
Ying-Si LaiDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Over the past 50 years, two national control programs on Methods: Advanced Bayesian geostatistical joint models with spatial-temporal random effects were developed to analyze disease data collecting by a systematic review with potential influencing factors, and to handle issues of preferential sampling and data heterogeneities. Changes of the infection risk were analyzed. Findings: We presented the first spatial-temporal risk maps of Interpretation: The first nationwide campaign and the CEP showed effectiveness on control of Funding: The National Natural Science Foundation of China (project no. 82073665) and the Natural Science Foundation of Guangdong Province (project no. 2022A1515010042).

Indexed as

Bayesian spatial-temporal joint modelClonorchis sinensisControl programHigh-resolution risk mapPreferential sampling

Identifiers

PMID36817868
PMCPMC9932111

What OpenQuestion holds

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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.