Abstract
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.
Highlights
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
Many studies have used spatial microsimulation to estimate health care demand [2,3,4], but in Austria little research exists to date with the exception of the research project SpAtiaL SimUlation for Decision support (SALUD) (SpatiAL microsimUlation for Decision support) which focuses on building a spatial microsimulation model for Austria
Nationwide, there is no data on smoking prevalence for municipalities available, but alternatives could include carrying out a survey for some municipalities and potentially adjusting the model based on the results
Summary
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. One of the problems with the official surveys conducted by Statistics Austria is that health related data mainly exists at the federal state level only This data provides an interesting overview of the health of the nation, but for regional planning purposes these data are often insufficient and provide no reliable estimates below state level. Many studies have used spatial microsimulation to estimate health care demand [2,3,4], but in Austria little research exists to date with the exception of the research project SALUD (SpatiAL microsimUlation for Decision support) which focuses on building a spatial microsimulation model for Austria Within this project a web-based spatial microsimulation application (simSALUD) was developed to estimate, validate and visualize smoking prevalence at the municipality level using deterministic reweighting approaches. Some microsimulation applications exist on the Web [5, 6] but an intensive literature search through current spatial microsimulation frameworks shows that at the moment no easy-to-use web-based spatial microsimulation applications, which includes spatial visualization methods for non-programmers, are available as yet
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