title: smokeSALUD: exploring the effect of demographic change on the smoking prevalence at municipality level in Austria.
abstract: 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.
  
  
  
  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 for the simulation model and to validate its 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 differences within areas are identified. These results are especially valuable to policy decision makers for future planning strategies.
  
  
  
  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 help to show where to spend health resources best in order to reduce health inequalities.
authors.affiliation: GeoHealth Laboratory, Department of Geography, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand. melanie.tomintz@canterbury.ac.nz.
authors.forename: Melanie
authors.initials: M
authors.lastname: Tomintz
authors.affiliation: Geoinformation and Environmental Technologies, Carinthia University of Applied Sciences, Europastrasse 4, 9524, Villach, Austria.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Kosar
authors.affiliation: School of Geography, University of Leeds, Leeds, LS2 9JT, UK.
authors.forename: Graham
authors.initials: G
authors.lastname: Clarke
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001317:Austria / Q000453:epidemiology; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D012907:Smoking / Q000453:epidemiology*; D012959:Socioeconomic Factors; D062211:Spatio-Temporal Analysis; D055815:Young Adult
keywords: Austria; Demographic change; Deterministic reweighting; Health decision support; Municipalities; Small area modelling; Smoking; Spatial microsimulation; Web-based application; simSALUD