Application of geographic information systems and simulation modelling to dental public health: Where next?

Public health research in dentistry has used geographic information systems since the 1960s. Since then, the methods used in the field have matured, moving beyond simple spatial associations to the use of complex spatial statistics and, on occasions, simulation modelling. Many analyses are often descriptive in nature; however, and the use of more advanced spatial simulation methods within dental public health remains rare, despite the potential they offer the field. This review introduces a new approach to geographical analysis of oral health outcomes in neighbourhoods and small area geographies through two novel simulation methods-spatial microsimulation and agent-based modelling. Spatial microsimulation is a population synthesis technique, used to combine survey data with Census population totals to create representative individual-level population datasets, allowing for the use of individual-level data previously unavailable at small spatial scales. Agent-based models are computer simulations capable of capturing interactions and feedback mechanisms, both of which are key to understanding health outcomes. Due to these dynamic and interactive processes, the method has an advantage over traditional statistical techniques such as regression analysis, which often isolate elements from each other when testing for statistical significance. This article discusses the current state of spatial analysis within the dental public health field, before reviewing each of the methods, their applications, as well as their advantages and limitations. Directions and topics for future research are also discussed, before addressing the potential to combine the two methods in order to further utilize their advantages. Overall, this review highlights the promise these methods offer, not just for making methodological advances, but also for adding to our ability to test and better understand theoretical concepts and pathways

Ion mobility mass spectrometry enhances low-abundance species detection in untargeted lipidomics

We describe a simple method for the detection of low intensity lipid signals in complex tissue samples, based on a combination of liquid chromatography/mass spectrometry and ion mobility mass spectrometry. The method relies on visual and software-assisted analysis of overlapped mobilograms (diagrams of mass-to-charge ratio, m/z, vs drift time, DT) and was successfully applied in untargeted lipidomics analyses of mouse brain tissue to detect relatively small variations in a scarce class of phospholipids (N-acyl phosphatidylethanolamines) generated during neural tissue damage, against a background of hundreds of lipid species. Standard analytical tools, including Principal Component Analysis, failed to detect such changes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-016-0971-3) contains supplementary material, which is available to authorized users

Spatial Microsimulation and Agent-Based Modelling

This chapter critically reviews the state-of-the-art in spatial microsimulation and agent-based modelling approaches with an emphasis on efforts to combine them in order to address applied geography problems. Spatial microsimulation typically involves the merging of census and social survey data to simulate a population of individuals within households (for different geographical units) whose characteristics are as close to the real population as it is possible to estimate (and for small areas for which this information is not available from published sources). Microsimulation is closely linked conceptually to another type of individual-level modelling: agent-based models (ABM). ABM are normally associated with the behaviour of multiple agents in a social or economic system. This chapter offers an overview of the state-of-the-art of both modelling approaches as well as a discussion of attempts to combine them, with an articulation of a relevant research agenda