Temporal and spatial analysis of Neural tube defects and detection of geographical factors in Shanxi Province, China

Background: Neural tube defects (NTDs) are congenital birth defects that occur in the central nervous system, and they have the highest incidence among all birth defects. Shanxi Province in China has the world's highest rate of NTDs. Since the 1990s, China's government has worked on many birth defect prevention programs to reduce the occurrence of NTDs, such as pregnancy planning, health education, genetic counseling, antenatal ultrasonography and serological screening. However, the rate of NTDs in Shanxi Province is still higher than the world's average morbidity rate after intervention. In addition, Shanxi Province has abundant coal reserves, and is the largest coal production province in China. The objectives of this study are to determine the temporal and spatial variation of the NTD rate in rural areas of Shanxi Province, China, and identify geographical environmental factors that were associated with NTDs in the risk area. Methods: In this study, Heshun County and Yuanping County in Shanxi Province, which have high incidence of NTDs, were selected as the study areas. Two paired sample T test was used to analyze the changes in the risk of NTDs from the time dimension. Ripley's k function and spatial filtering were combined with geographic information system (GIS) software to study the changes in the risk of NTDs from the spatial dimension. In addition, geographical detectors were used to identify the risk geographical environmental factors of NTDs in the study areas, especially the areas close to the coal sites and main roads. Results: In both Heshun County and Yuanping County, the incidence of NTDs was significantly (P<0.05) reduced after intervention. The results from spatial analysis showed that significant spatial heterogeneity existed in both counties. NTD clusters were still identified in areas close to coal sites and main roads after interventions. This study also revealed that the elevation, fault and soil types always had a larger influence on the incidence of NTDs in our study areas. In addition, distance to the river was a risk factor of NTDs in areas close to the coal sites and main roads. Conclusion: The existing interventions may have played an important role to reduce the incidence of NTDs. However, there is still spatial heterogeneity in both counties after using the traditional intervention methods. The government needs to take more measures to strengthen the environmental restoration to prevent the occurrence of NTDs, especially those areas close to coal sites and main roads. The outcome of this research provides an important theoretical basis and technical support for the government to prevent the occurrence of NTDs

Complex Systems Analysis using Space-Time Information Systems and Model Transition Sensitivity Analysis

Real-world systems are dynamic, complex and geographic, yet many mathematical modeling tools do not evaluate sensitivity of results to underlying assumptions, and GIS do not adequately represent time. This presentation describes two new approaches: Space-Time Information Systems (STIS), and Model Transition Sensitivity Analysis (MTSA). Current GIS are based on spatial data models that inadequately characterize the temporal dimension needed for effective representation of complex systems. They do not deal readily with spacetime georeferencing nor space-time queries, and are best suited to “snapshots ” of static systems. These deficiencies prompted many geographers to call for a “higher-dimensional GIS ” (a STIS) to better represent space-time dynamics. When formulating models of complex systems, critical choices are made regarding model type and complexity. Model type is the mathematical approach employed, for example, a deterministic model versus a stochastic model. Model complexity is determined by the amount of abstraction and simplification employed during model construction. A growing body of work demonstrates that choice of model type and complexity has substantial impacts on simulation results and on model-based decisions. This paper briefly describes STIS and MTSA approaches that allow researchers to more effectively represent complex systems and to evaluate the sensitivity of their results to underlying assumptions. 1