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International Journal of Health Geographics

, 15:36

First Online: 07 October 2016Received: 09 May 2016Accepted: 29 September 2016DOI: 10.1186-s12942-016-0066-4

Cite this article as: Tomintz, M., Kosar, B. & Clarke, G. Int J Health Geogr 2016 15: 36. doi:10.1186-s12942-016-0066-4


BackgroundReducing 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.

MethodsThis 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.

ResultsAs 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.

ConclusionsThis 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.

KeywordsHealth decision support Small area modelling Deterministic reweighting simSALUD Austria Spatial microsimulation Web-based application Smoking Demographic change Municipalities AbbreviationsAHISAustrian Health Interview Survey

APIApplication Programming Interface

COCombinatorial Optimization

ESRIEnvironmental Systems Research Institute

EUEuropean Union

GISGeographic Information Software

IPFIterative Proportional Fitting

MAUPModified Area Unit Problem

OECDOrganisation for Economic Co-operation and Development

PSAEPercentage Standardized Absolute Error

SAEStandardized Absolute Error

SALUDSpAtiaL SimUlation for Decision support

TAETotal Absolute Error

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Author: Melanie Tomintz - Bernhard Kosar - Graham Clarke


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