Iodine Deficiency Disorder in Taxila, Pakistan: A GIS Perspective

Abstract

Mapping spatial distribution of disease is useful to detect and monitor potential public health hazards. This thesis research highlights the statistical and geographical weighted regression model development based on the analysis of iodine content in drinking water. The study introduces a systematic geospatial methodology to identify Iodine Deficiency Disorder (IDD) risk areas and to minimize the disease risk by indentifying the risk factor for the Taxila Tehsil of Rawalpindi District, Punjab, Pakistan. Iodine deficiency goiter ceases reported from the study area are spatially analyzed with respect to iodine content in the drinking water and population density. Patients registered with goiter due to iodine deficiency were acquired from different hospitals. The patients from different locations of the study area were standardized in reference to population and neighboring regions using standardized mortality/morbidity ratio. Drinking water samples were collected from selected locations using standard sampling techniques. Spatial location of all water samples was recorded using Garmin handheld OPS device. Chemical analyses were performed on the drinking water samples for the iodine content. The mean iodine concentration of the area is 5 parts per billion. A cluster analysis was performed using standardized mortality/morbidity ratio (SMR). Standardized mortality/morbidity ratio represents spatial pattern of the disease distribution. It re-expresses the data as the ratio between observed number of cases and the expected number of cases. A geographical weighted regression analysis was used in the study to identify high, moderate and low risk areas. Geographical weighted regression (GWR) models the relationship between IDD and environmental factors which include iodine content in water and population