Introduction
One of the most important components of disease prevention has always been having access to information on distribution of patients, their gender and age. With this information, we find the areas in which further prevention or care programs need to be implemented. In addition, this information helps in determining the effects of factors contributing to the spread of a disease in different areas. Thus, using new technologies such as GIS in such a field can be quite advantageous. Methods of spatial-statistical analysis can help us in mapping disease diffusion, its predicted future trend, and factors affecting that disease. Therefore, public health organizations have recently used such technologies to develop more appropriate health, prevention, and treatment plans.
As a major health problem, gastric cancer is reported to be the most common type of cancer and the second leading cause of cancer-related deaths in the world. Following cardiovascular disease, cancer is the second leading cause of death in Iran. GIS capabilities have made it possible to use a variety of spatial-statistical models for gastric cancer. The present study seeks to analyze the spatial distribution of gastric cancer in Hamedan province, identify the incidence and prevalence of this disease based on gender and age of patients, and map the geographical factors affecting this disease. In this regard, methods of interpolation, classification, and clustering of highly affected points are used. Spatial correlation is also calculated using regression methods.
 
Materials and Methods
In order to prepare spatial distribution maps of gastric cancer in Hamadan province, related data on all gastric cancer patients between 2011 and 2015 were collected from the Population-Based Cancer Registry of Hamadan Medical Sciences University. Collected data included age, gender, and address of patients. Data were first classified based on the address registered for each patient in each year. Then, geocoding process was used to convert addresses into positions using Google Maps and create related layers in GIS software. Descriptions were then assigned to the layer of cities and 1157 points were produced for these 5 years and added to the maps. According to the obtained points, spatial distribution maps were prepared based on age and gender of patients. Age distribution of gastric cancer patients was also calculated using interpolation analysis and the IDW method. In the next step, maps of provinces in critical situation were prepared according to the Hotspot method using the Getis index. In cooperation with Water and Sewage Authority, data were also collected on water pollution (including nitrate, and lead in water and water hardness) to determine the relationship between the severity of the disease and environmental variables (water pollution). Ecological analysis was then performed based on regression analysis using ordinary least squares (OLS) method.
 
Result and Discussion
Results of the study and age distribution maps indicated lack of any significant clustering in the studied cities. Moreover, the age groups were sparsely distributed in each year. However, statistical analysis of patients’ age and gender showed a higher incidence of gastric cancer in men and in over 70-year age group during the reference period. Cluster analysis of areas with higher incidence of gastric cancer based on Hotspot method identified Qahavand as one of the cities having a critical situation regarding gastric cancer during the reference period with 99% confidence interval. Hamadan City was ranked second with 95% confidence interval. Laljin was ranked third with 90% confidence during the reference period. Regression analysis performed to determine the relationship between disease severity and environmental variables (water pollution) indicated the presence of a positive relationship between the level of lead and nitrate in drinking water and cancer incidences. However, an ideal fit was not reached for the regression model due to unavailability of recently collected data, small sample size, and inadequate data distribution.
 
Conclusion
Since a different life style is considered to be the most crucial basis of combating a disease, education, empowerment, policymaking and enactment of laws and regulations can create an appropriate environment to promote healthy lifestyle and behaviors. In fact, a useful intervention in society can eliminate or reduce the impact of many risk factors. Cooperation between the fields of geography and medical sciences can result in designing and implementing an acceptable system in the society. GIS is one of the technologies used in this regard to provide health warnings for people at risk based on proper analysis. The present study showed the efficiency of techniques such as classifications, interpolation, hotspot, and regression analysis in assessing disease severity, and factors affecting its incidence.