Myanmar is one of south East Asia country having 14 States and Regions with the estimated population in 2007-2008 was 57.504 millions. About 70 percent of population resides in rural areas, whereas the rest are urban dwellers. Among them 70 percent of population is residing in malaria risk areas, including Bago Region. It is located within the low risk to moderate risk area of Malaria in Myanmar. This research is intended to depict the spatial patterns of malaria situations of the Region by using geospatial technologies. The distributional patterns are depicted and analyzed by using spatial statistics facilities of GIS. Some statistical procedures are adopted to make better conclusion. The climatic conditions of Bago Region enhance the breeding of mosquitoes, particularly in Kyaukkyi, Shwekyin, Htantabin and Nyaunglebin townships covering the eastern part of the township which is high in Normalized Different Vegetation Index (NDVI). The malaria morbidity and mortality rates are relatively high in these townships.
Generally the morbidity rate is high in townships with moderate population density. It is observed that the areas with high malaria morbidity rate are nearly the same with the areas that enhance the high concentration of mosquitoes, high value of NDVI and moderate population density. Among the environmental factors related to malaria, six are chosen as dependent variables to build a model which can be used for the prediction of morbidity rate. Besides, types of mosquitoes that can cause malaria, life cycle of mosquito, incubation period, types and distribution of health care centres and treatment conditions are thoroughly studied and presented. The above-mentioned factors would be helpful in predicting the morbidity rates in Bago Region and in locating the areas for preventive measures. Further more, using Multiple Linear Regressions (Stepwise Method), malaria morbidity index is equal to 4.191 + 0.433NDVI - 0.535 Number of Health care centres is worked out, that can be used for prediction of malaria morbidity in any area within Bago Region. For the effective reduction of the morbidity and mortality rates, such socio-economic factors as education level, the use of drinking water, health education and accessibility and status of health centres in the remote areas are examined and the necessary measures are recommended. If the discussions and suggestions presented in this paper could be solved by the responsible persons, the malaria morbidity and mortality rates in Bago Region would be reduced notably.
The study area, Bago region lies between north latitudes 16˙ 50’ and 19˙ 30’ and east longitudes 94˙ 45’ and 97˙ 15’. It is bordered on the north by Magway and Mandalay region, on the east by Kayin and Mon states, on the south by the Gulf of Mottama and Yangon Region, on the west by Ayeyarwady Region.
To find out the major influencing factors upon the distribution of malaria morbidity and mortality which are the problems of Bago Region.
- Relationship between malaria incidences and climatic conditions
- Relationship between malaria risk areas and vegetation
- Relationship between malaria incidences and population density
- Relationship between malaria incidences and number of health care centres
The aim of this study is to identify the influencing factors that cause malaria morbidity and mortality in Bago Region and to investigate the relationship between the prevalence of certain diseases and specific geographic features so as to be able to focus the treated area and provide necessary preventive measures, which will eventually lead to the reduction of malaria incidence to the Millennium Development Goals (MDG) level in Bago Region.
The objectives of this research work are (1) to identify the malaria risk areas, (2) to evaluate the spatial variation of malaria incidence area, (3) to investigate the epidemiological factors of malaria and characteristics of Anopheles, and (4) to assess the relationship between malaria incidences and environmental conditions.
Sources of Data and Methodology
The spatial technologies-Geographic Information Systems and Remote Sensing are applied throughout the research work. The advantage of using geospatial technology is that GIS database of Bago Region will become available for future research works and monitoring of spatial patterns of malaria Risk Areas.
Many analytical models are set-up for particular spatial analysis procedures. Some mathematical models are constructed by using interpolation methods (IDW) and zonal statistical procedures are employed to figure out township level data concerning climatic conditions.
The Normalized Difference Vegetation Index (NDVI) values for the Region and township level are worked out by using fine resolution satellite imagery Landsat 7 ETM+ 2007 (Spatial Resolution 30m). Because, searching the NDVI is to study the relationship between Vegetation Index and Morbidity Rates.
Some statistical procedures serve the explanation of patterns and well structured estimate model by using Multiple Linear Regression. Hierarchical Clustering method, which begins by finding the closest pair of objects (cases or variables) according to a distance measure and combines them to form cluster. Thus, Cluster Analysis is used to be able to differentiate the village tracts that have the same monthly changing pattern of malaria morbidity within the study area.
In this research, it is impossible to study all the villages in Bago Region due to the difficulties in transport; inaccessibility to remote areas, due to security reasons and shortage of man power. Thus, in collecting data, case study area is chosen among the groups resulted from using Residual Method. And then, target population is acquired from total population by means of Stratified Sampling Method and then Village Tracts are chosen for case study. The sample size (number of questionnaires) represents 30 percent of the target population. In acquiring primary data, questionnaires are distributed according to purposive sampling design. Since relevant methods are employed in data collection, the results of analysis on these data are believed to be taken for granted.
Figure 1.1 States and Regions of Myanmar
Source: Survey Department, Yangon
This model can be used to compute predicted values for a new sample. For example, if the township named Daik-u have a NDVI (Normalized Difference Vegetation Index) value of 45.18 then simply multiply 45.18 by the estimate of the slope (0.433), the number of Health Centre of 33 then simply multiply 33 by the estimate of the slope (0.535) and add the intercept (4.191) to find the predicted value of malaria morbidity rate (6.33).
In this model, NDVI and Health Centre are the basic predictors and thus are more suitable to use only in the different places within Bago Region. The result would be better if the resolution level is less than 30 metres in using satellite image, as the smaller the resolution level, the more accurate is the result. For regions other than Bago Region, the model should be appropriately modified by using more relevant variables existing in the area concerned.