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Articles by Stang Abdul Rahman
Total Records ( 2 ) for Stang Abdul Rahman
  Stang Abdul Rahman , Amran Rahim and Anwar Mallongi
  Background: Standard statistical models generally assume that any incident of dengue disease in one location are independent with the incidence of dengue disease in other locations. However, the independent assumption does not apply in the spread of dengue disease. The spread of dengue disease tends to occur almost simultaneously in a same area or in the adjacent area due to similar environmental factors in the area. Similar environmental factors results in their risk spatial correlation of disease spread. If spatial correlation aspects is not considered in modeling then the conclusion of the significant factors that influence on the risk of spreading disease becomes inaccurate. Objective: The purpose of this study was to make mapping the risk of dengue fever incidence in Bone Regency South Sulawesi province by region (districts) with non-stationary spatial geostatistics model. Materials and Methods: The analysis variables included are larvae density, temperature, population density, rainfall, altitude from sea level and the incidence rate of dengue fever. Results: The results showed that risk rate model of dengue in Bone Regency with stationary spatial geostatistics models are as follows: log(pi) = -0.08+0.006X1+0.02X2-0.02X3-0.04X4-0.01X5. X1 = The density of larvae, X2 = Air temperature, X3 = Population density, X4 = Rainfall and X5 = The height of the sea level. Risk rate model of dengue in Northern Bone Regency (cluster 1) with non-stationary spatial geostatistics models are as follow: log(pi) = -0.02-0.0009X1-0.17X2+0.003X3+0.22X4-0.006X5. Risk rate model of dengue in Southern Bone Regency (cluster 2) with non-stationary spatial geostatistics models are as follow: log(pi) = -0.02-0.02X1-0.01X2-0.02X3-0.02X4-0.03X5. Risk rate model of dengue in Western Bone Regency (cluster 3) with non-stationary spatial geostatistics models are as follow: log(pi) = -0.08-0.007X1-0.02X2-0.03X3-0.009X4-0.06X5. Conclusion: The prediction error values on the non-stationary model between (0.27-3.6) lower than stationary model with variation between (0.68-6.37).
  Jamaluddin M. Sakung , Saifuddin Sirajuddin , Andi Zulkifli , Stang Abdul Rahman , A. Arsunan Arsin , Masni , Nurhaedar Jafar and Bohari
  Background and Objective: Current efforts to overcome hypertension have been predominantly based on medical treatment. Nutritional education and local food use are still limited in efforts to lower blood pressure. This study was conducted to analyze the effect of nutritional education intervention and the provision of chayote on changes in blood pressure of pre-diabetic high school teachers. Methodology: This research study is an experiment consisting of a sample of high school teachers (SMA) with pre-diabetes in Palu city. The number of subjects per group was 25 people and they were categorized as the following: Group I = Intervention of instant chayote, Group II = Nutrition education intervention and Group III = Nutrition education intervention and provision of chayote. Systolic and diastolic blood pressure was measured before and after intervention. Data analysis were performed using pairwise t-test and Wallis crucial test and significance was considered at p<0.05. Results: Change in the mean and standard deviation of systolic blood pressure after intervention with instant infusion of chayote was 15.24±10.54 mm Hg (p = 0.00), after nutrition education intervention was 3.12±12.34 mm Hg (p = 0.190) and intervention in combination of nutrition education and instant chayote were 14.48±12.00 (p = 0.00). The change in diastolic blood pressure after intervention with the administration of siam gourd was 11.24±8.29 mm Hg (p = 0.00), nutritional education intervention was 1.88±10.33 mm Hg (p = 0.253) and intervention in combination of nutritional education and chayote was 6.84±11.76 mm Hg (p = 0.005). Conclusion: Instant chayote interventions combined with nutritional education may significantly decrease systolic and diastolic blood pressures in pre-diabetic high school teachers.
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