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Original Article Distribution and Spatial Pattern Analysis on Malnutrition Cases: a Case Study in Pontianak City

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Original Article Distribution and Spatial Pattern Analysis on Malnutrition Cases: a Case Study in Pontianak City Malaysian Journal of Public Health Medicine 2020, Vol. 20 (2): 56-64 ORIGINAL ARTICLE DISTRIBUTION AND SPATIAL PATTERN ANALYSIS ON MALNUTRITION CASES: A CASE STUDY IN PONTIANAK CITY Agus Fitriangga1, Gerry Albilardo2 and Muhammad Pramulya3 1Department of Community Medicine, Faculty of Medicine, Univesitas Tanjungpura, Indonesia, PO Box 78124 2Third-year Medical Student, Faculty of Medicine, Universitas Tanjungpura, Indonesia, PO Box 78124 3Department of Regional Planning, Faculty of Agriculture, Universitas Tanjungpura, Indonesia, PO Box 78124 Corresponding author: Agus Fitriangga Email Address : [email protected] ABSTRACT Based on the Basic Health Research (Riskesdas) in 2018, malnutrition cases in West Kalimantan reached 23.8 percent. In 2015, Pontianak City documented 27 cases of malnutrition. Then, the cases increased in 2016 and 2017 as many as 29 and 41 cases. The utilization of Geographic Information System (GIS) is required as a method for public health surveillance and monitoring. This study aims to analyze the distribution of malnutrition cases based on several clinical and non-clinical factors using GIS between 2016 to 2017. The dependent variable was malnutrition cases and the independent variables included household income level, parent’s educational level, comorbidities factors, and distance to the primary health care service. A total of 65 cases of malnutrition in Pontianak City were collected from six sub- districts in Pontianak City. This research was a cross-sectional study. The results showed that of 65 cases of malnutrition occurred on under 5-year-old children in Pontianak in 2016-2017, malnutrition cases taking place in East Pontianak sub-district were 29 cases (44.6%). In addition, malnutrition with clinical symptoms was reported 63 cases (96.9%), while the distance from home to primary health care less than 1 km was 32 cases (49.23%). The study also revealed that malnutrition with comorbidities were 78,5%. Finally, household income levels with malnutrition were below Pontianak regional minimum wage (Rp 2,515,000/month or $176,88). The mapping of malnutrition cases using Geographic Information Systems can facilitate the nutrition programmer in Pontianak City Health Office and Public Health Centre in intervening the social determinant of health to overcome malnutrition. Keywords: Mapping, Malnutrition Cases, spatial pattern, under 5-year-old children, Pontianak City INTRODUCTION clinical signs of malnutrition cases. In 2017, there were 392 malnutrition cases4. Meanwhile in Malnutrition cases are still a problem in several Pontianak City, in 2016, there were 29 cases of countries. Asian countries report 70% of malnutrition and it increased in 2017 as 41 cases5. malnutrition cases, followed by Africa with 26% cases and 4% in Latin America. It was recorded Spatial analysis using Geographic Information that one of three children in the world dies every Systems (GIS) is one of the important methods for year due to poor nutritional quality1. The Ministry public health surveillance and monitoring. This is of Health of the Republic of Indonesia showed that because the function of GIS in the health sector at least 3.5 million children die every year due to can produce a spatial picture of health events, malnutrition and poor food quality, supported by analyze relationships between locations, malnutrition while still in the womb. This environments, and disease events6. condition can damage a child’s health that can not be restored when the child is growing up1. Previous studies have indicated the importance of GIS in mapping malnutrition cases. Murni Su et al, According to Health Basic Research (Riskesdas) for instance, showed that the utilization of data in Indonesia, in 2013, there were 19.6% cases remote sensing data such as land use, NVDI, soil of children under 5 years old suffering from moisture DEM, and flood-prone areas have malnutrition. This figure increases compared to significantly proved to be useful in identifying Riskesdas data in 2010 which was 17.9% and environmental factors contributing to Riskesdas 2007 was 18.4%2. West Kalimantan has malnutrition7. Ali Almasi showed that the decreased the prevalence of malnutrition of prevalence of stunting, wasting, and overweight children aged 0-59 which is 6.67% in 2016 to 6.50% in children under 5-year-old was not accidental in 20173 Based on the results of the nutrition and has emerged in the cluster form based on a program report of the West Kalimantan Provincial regular occurrence in countries around the Health Office in 2016, from all districts/cities world8. Meanwhile, E Mohammed’s research there were 401 cases of malnutrition. This figure showed thematic maps that present the variation was obtained from the case report based on of the distribution of Severe Acute Malnutrition Malaysian Journal of Public Health Medicine 2020, Vol. 20 (2): 56-64 children in Khartoum State, influenced by several the average nearest neighbor method. Mapping factors such as socioeconomic and environmental of the malnutrition epidemiology distribution realities of Khartoum State9. trend identified a relationship with specific physical characteristics. Further, distribution Therefore, this study attempts to analyze the and pattern analysis were conducted to distribution of malnutrition cases by using GIS. understand whether there were any dominant The distribution map of malnutrition cases is also distributions and patterns for the malnutritiron11, possible to show the information of location in 12. Pattern distribution details were minimized each administrative region in Pontianak City using pattern analysis. The clustered pattern between 2016 to 2017 by patient’s names and showed there was a factor that causes addresses. malnutrition epidemiology in that area. METHODS Spatial Mean Center The spatial mean center is the average x and y This cross-sectional study was conducted in coordinates of all features in the study area. It is Pontianak City, West Kalimantan Province from useful to detect changes in distribution or to January 2019 to October 2019. The population in compare features of distribution. This analysis this study were all under 5-year-old children who showed leaning-centered phenomena, especially, suffered malnutrition. They were registered in to visualize the means of malnutrition cases the Pontianak City Health Office between 2016 to reported in Pontianak City. The spatial mean 2017. Further, 65 malnutrition children who have center aims to study changes in distribution received treatments by using total sampling detection to compare the type and feature of techniques were selected to participate as a distributions. The spatial mean center is able to sample in this study. The instruments used in this create a new feature point classification where study were location maps, Global Positioning every feature represents the mean center. The System (GPS), photographic tools, and ArcGIS. mean of x and y showed the value of mean center which means that the dimensional field was Data Collection included as the product of features. Demographic data of malnutrition cases reported at Pontianak Health Office in the years of 2016 The mean center is calculated as follows: ∑n x and 2017 contains (1) Location, (2) number of X = i=1 i cases, (3) household characteristics, and (4) n clinical data recorded. During the years (1) ∑n y surveyed, a total of 65 cases were reported Y = i=1 i n throughout Pontianak City selected for this (2) study. Latitude and longitude coordinates of where xi and yi are coordinates for i, and n is the patients’ locality were extracted from the data total number of the features. collection to perform distribution and spatial The weighted mean is calculated as analysis. follows: n ∑i=1 Wixi Xw = n Statistical Analysis ∑i=1 Wi Statistical analysis is used to identify and confirm (3) n spatial patterns, such as the center of a group of ∑i=1 Wiyi Yw = n features, the directional trend, and whether ∑i=1 Wi features form clusters. It also classifies and (4) symbolizes the data which can obscure or where wi is the weigh on characteristic i. overemphasize patterns. Statistical functions analyze the underlying data and measures that The method to calculate center for three can be used to confirm the existence and dimensions is the z attribute for every feature: 10 ∑n z strength of the pattern . Thus, the statistical Z = i=1 i analyses in this study will help to provide n (5) answers for linking the distribution pattern of n ∑i=1 Wizi malnutrition cases with household Zw = n ∑i=1 Wi characteristics. (6) Meanwhile, geographical distribution is Standard Distant performed to measure a set of features for Standard distant measures distribution density, calculation of value which represents a which provides a single value, represents the characteristic of the distribution, such as the distribution around the mean center. The scatter 10 center, orientation, or compactness . This study of points around the center needs measurement. focused on two methods: Spatial distribution and The value is the distance represented by spatial distribution pattern analysis. The spatial deviations. Therefore, measures of spatial distribution included spatial mean center, dispersion give information about malnutrition standard distance, and directional distribution cases around a center. The expression used is the analysis. Spatial distribution pattern analysis is Malaysian Journal of Public Health Medicine 2020, Vol. 20 (2): 56-64 standard distance, which is equivalent to the where xi and yi are the coordinates for feature i, statistician’s standard deviation. The standard {X, Y} shows the mean center, and n is the total distance is a tool used to create a round polygon number of features. of points dispersed around a center. In this study, a round of polygon represented dispersed The angle of rotation calculated as Equation (2) malnutrition cases reported in each year from the below: 10 A+B mean center . tan θ = C Standard distant shows a new feature class which (11) n 2 n 2 includes a round, centered polygon on the mean A = ∑i=1 xi − ∑i=1 yi center (a single mean and a circle for every case).
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