Homework SourseA researcher is interested in whether a new program to stop
A researcher is interested in whether a new program to stop smoking is effective. She also is interested in whether its effectiveness is affected by how much they smoked to begin with. The two-way table is presented below. Use the Cramer’s V (V) to determine the association between smoking less or more than half a pack per day and whether they are smoking 6 months later. Interpret the association between the two variables using the measure of association, column percentages, and example interpretations from class
1/3rd of a pack per day
2/3rd of a pack per day
1 pack per day
Total
Not Smoking After 6 Months
25
17
10
52
Smoking After 6 Months
12
17
23
52
Total
37
34
33
104
.
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Solution
Ans-
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 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 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 help to show where to spend health resources best in order to reduce health inequalities.
Keywords
Health decision support Small area modelling Deterministic reweighting simSALUD Austria Spatial microsimulation Web-based application Smoking Demographic change Municipalities
Background
Smoking is directly responsible for many diseases, sometimes leading to death (worldwide this figure is estimated to be around 10 %). In addition, passive smokers are at high risk of also developing smoking-related diseases [1]. The Austrian Government is well known for offering a generous social support system, including one of the best health care systems in the world. However, the topic of health inequalities has attracted growing attention, both at the European Union (EU) level and in Austria itself. This issue is especially important in the field of health promotion and prevention. An effective resource distribution strategy is required for areas with high demand (e.g. high rates of smoking, obesity, drug addiction) and poor accessibility to health care providers. Health inequalities can be addressed through government actions and policies but need to be identified first. In particular, identifying regional inequalities is essential for the future distribution of government resources. But 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. However, spatial microsimulation models are useful tools for estimating small area health data and thus helping to overcome these problems. Many studies have used spatial microsimulation to estimate health care demand [2–4], 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.
This paper focuses on the topic of smoking because smoking is a major risk factor for poor health and premature mortality. As it is based on a poor lifestyle choice, it is in theory preventable. For effective preventive actions at the regional level, it is important to know where high numbers of smokers live and whether significant variations exist in such rates between municipalities. Recent Organisation for Economic Co-operation and Development (OECD) statistics show that 23.2 % of the adult population smoke regularly in Austria, which is 2.2 % above the average across all 41 OECD countries [1]. Austria also tends to follow the general pattern of gender differences across Europe, with higher smoking rates among men (27.3 %) in comparison to women (19.4 %) [7].
