ArticleBMC public health2010
Assessing local determinants of neural tube defects in the Heshun Region, Shanxi Province, China.
Article in BMC public health, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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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
9 citing papers in PubMed.
- Integration of Moran's I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran Provinces, Iran.Modeling earth systems and environment · 2023Article
- Spatial variations and social determinants of life expectancy in China, 2005-2020: A population-based spatial panel modelling study.The Lancet regional health. Western Pacific · 2022Article
- Genetic Polymorphisms in DNA Repair Gene APE1/Ref-1 and the Risk of Neural Tube Defects in a High-Risk Area of China.Reproductive sciences (Thousand Oaks, Calif.) · 2021Article
- Neural Tube Defects and Associated Factors among Neonates Admitted to the Neonatal Intensive Care Units in Hiwot Fana Specialized University Hospital, Harar, Ethiopia.Global pediatric health · 2020Article
- Polymorphism rs1052536 in Base Excision Repair Gene Is a Risk Factor in a High-Risk Area of Neural Tube Defects in China.Medical science monitor : international medical journal of experimental and clinical research · 2018Article
- Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China.Scientific reports · 2017Article
- Article
- A spatial model to predict the incidence of neural tube defects.BMC public health · 2012Article
- Determinants of the incidence of hand, foot and mouth disease in China using geographically weighted regression models.PloS one · 2012Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNeural tube defect (NTD) prevalence in northern China is among the highest worldwide. Dealing with the NTD situation is ranked as the number one task in China's scientific development plan in population and health field for the next decade. Physical and social environments account for much of the disease's occurrence. The environmental determinants and their effects on NTD vary across geographical regions, whereas factors that play a significant role in NTD occurrence may be buried by global statistics analysis to a pooled dataset over the entire study area. This study aims at identification of the local determinants of NTD across the study area and exploration of the epidemiological implications of the findings.
methodsNTD prevalence rate is represented in terms of the random field theory, and Rushton's circle method is used to stabilize NTD rate estimation across the geographical area of interest; NTD determinants are represented by their measurable proxy variables and the geographical weighted regression (GWR) technique is used to represent the spatial heterogeneity of the NTD determinants.
resultsInformative maps of the NTD rates and the statistically significant proxy variables are generated and rigorously assessed in quantitative terms.
conclusionsThe NTD determinants in the study area are investigated and interpreted on the basis of the maps of the proxy variables and the relationships between the proxy variables and the NTD determinants. No single determinant was found to dominate the NTD occurrence in the study area. Villages where NTD rates are significantly linked to environmental determinants are identified (some places are more closely linked to certain environmental factors than others). The results improve current understanding of NTD spread in China and provide valuable information for adequate disease intervention planning.
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