Assessing Spatial Patterns Using GIS

Assessing Spatial Patterns of Both Disease and Environmental Pollution Using Geographical Information System (GIS)
Name
Course
Instructor
Date
Introduction
Spatial representations of cervical cancer help to visualize the effect of social, economic and environmental determinants of the disease disparity. In many cases, the socioeconomic status of a population in a geographical area is often associated with the extent to which there is accessibility of health services, and economic wealth. Since there is a need to take into account local spatial variation as this avoids bias, when establishing the link between a disease and the explanatory variables (Chang, Atkinson & Shahani, 2011). Disease analysis that relies on Geographical Information System (GIS) helps to identify the local spatial variations, which then makes it easier to identify risk factors. As such, GIS is beneficial in disease analysis since the spatially referenced data represented in mapping focuses on visualizing spatial patterns. This paper will summarize the method used to present data on cervical cancer and socioeconomic status in Lesvos it will also give a description of the interaction as well as provide relevant figure to establish whether there is a relation between socioeconomic indicators and cervical cancer (Cancer Research UK.org, 2015). Summary of methods
The incidences of cervical cancer were tracked down, with geocoding helping to provide a general overview of differences in cervical cancer for the various municipalities of Lesvos As such, the there were significant spatial clusters of different incident rates ascertained using the ArcMap (ArcGIS) . The incidence rates are then vital to understand how the population estimates were related to the demographic profile of women who were more at risk of cervical cancer. As such, the number of women with cervical cancer per 1000 people was represented on the map with different colours to facilitate mapping. ArcMap (ArcGIS) also helped to map spatial patterns on socioeconomic status for the divergent municipalities to help evaluate the link between cervical cancer incidences rates the economic status. Including the explanatory variables that represented the socioeconomic status is assumed to be normally distributed to allow analysis. At the same time, data was aggregating for the various regions because of the confidentiality restrictions.
Data synthesis, analysis, interpretation and discussion
The number of women with cervical cancer per 1,000 differed showed spatial representation of cancer incidences rates. It is noteworthy that municipalities in the east reported higher incidence rates compared to other regions in the island of Lesvos. At the same time, the most western municipality and one southwestern municipality had the third highest rates between 0.042855 - 0.089626. Most other regions areas in the central region, north and south reported the second least and least incidence rates. On the other hand, the highest rates on the socioeconomic status were in the east regions and the southern western municipalities. These areas also correlated with the higher rates of cervical rates in Lesvos.
In a study conducted by (Hernandez 2011, pp1022-1023), the rates of colorectal cancer were reported to be associated with neighborhood-level poverty. This highlights the likely health impact of having low access to medical information and services in the low economic areas. On the other hand, the possible effect of pollution in areas with high cervical cancer rates would show that there is a possibility that the areas where people living near high pollution areas might report higher incidences of cancer. People are likely to have better access to medical services since they are more likely to undergo screening tests. According to Melnick (2002, p.149), GIS software helps to establish some determinants since they tend to cluster in populations, and this highlights the benefits of the GIS to health officials as they target preventive programs.
The z- score was 17.37, meaning that there is less than 1 % likelihood that the clustered pattern could be the result of random chance. On the other hand the p- value was 0.0 while the Moran’s index was 1.800. The Moran’s index indicates that there was high positive spatial autocorrelation, meaning that similar values occur near each other since the Moran’s I is more powerful compared to the Geary’s C measure, although it is sensitive to the extreme values. The p- value is small and this supports the observation the association/ correlation did not occur because of random sampling, since small p- values.
Chang, Atkinson & Shahani (2011, p. 9), showed that there was a correlation between cervical cancer and economic status in England, and typically local variations occur. As such, there is a need to understand how the population structure is related to the economic status (Chang, Atkinson & Shahani 2011, p.10). In Greece, the people who have prior knowledge about screening tests for cervical cancer are likely to seek more attention, especially those living in the more affluent areas. This is a common trend in Europe with the relatively poor Eastern Europe recording more mortality rates from cancer compar...