smokeSALUD: exploring the effect of demographic change on the smoking prevalence at municipality level in Austria

Background: Reducing the smoking population is still high on the policy agenda, as smoking leads to many preventable diseases, such as lung cancer, heart disease, diabetes, and more. In Austria, data on smoking prevalence only exists at the federal state level. This provides an interesting overview about the current health situation, but for regional planning authorities these data are often insufficient as they can hide pockets of high and low smoking prevalence in certain municipalities. Methods: This paper presents a spatial-temporal change of estimated smokers for municipalities from 2001 and 2011. A synthetic dataset of smokers is built by combining individual large-scale survey data and small area census data using a deterministic spatial microsimulation approach. Statistical analysis, including chi-square test and binary logistic regression, are applied to find the best variables 24 for the simulation model and to validate its results. Results: As no easy-to-use spatial microsimulation software for non-programmers is available yet, a flexible web-based spatial microsimulation application for health decision support (called simSALUD) has been developed and used for these analyses. The results of the simulation show in general a decrease of smoking prevalence within municipalities between 2001 and 2011 and 29 differences within areas are identified. These results are especially valuable to policy decision makers for future planning strategies. Conclusions: This case study shows the application of smokeSALUD to model the spatial-temporal changes in the smoking population in Austria between 2001 and 2011. This is important as no data on smoking exists at this geographical scale (municipality). However, spatial microsimulation models are useful tools to estimate small area health data and to overcome these problems. The simulations and analysis should support health decision makers to identify hot spots of smokers and this should 36 help to show where to spend health resources best in order to reduce health inequalities

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

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