scholarly journals Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method

2018 ◽  
Vol 1025 ◽  
pp. 012106
Author(s):  
Alan Prahutama ◽  
Sudarno
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Poisson Regression ◽  
Regression Method ◽  
Infant Mortality Rate ◽  
Generalized Poisson ◽  
Central Java ◽  
Generalized Poisson Regression

scholarly journals Ecuadorian infant mortality linked to socioeconomic factors during the last 30 years

2019 ◽  
Vol 19 (2) ◽  
pp. 295-301
Author(s):  
Natalia Romero-Sandoval ◽  
Diego Del Alcázar ◽  
Jacob Pastor ◽  
Miguel Martín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Poisson Regression ◽  
Sociodemographic Factors ◽  
Complex Interaction ◽  
Infant Mortality Rate ◽  
The Difference ◽  
The Impact

Abstract Objectives: to analyze the difference among geographical units and the evolution of infant mortality rate (IMR) based on Ecuadorian censuses (1990-2001-2010). Methods: artificial Neural Network analyzed the impact of sociodemographic factors over the variability of IMR. Poisson regression analyzed the variation of the standardized IMR (sIMR). Results: the decrease in the national IMR was 63.8%; however, 42.8% provinces showed an increase in 2001-2010. The variability was explained mainly by illiteracy decrease. The adjusted RR between provincial sIMR with illiteracy and poverty revealed a trend towards the unit. Conclusions: the variation of IMR reflects a complex interaction of the sociodemographic factors.

scholarly journals Modelling Infant and Maternal Mortality Rate based on slum household uses Bivariate Spline Regression Method

2018 ◽  
Vol 73 ◽  
pp. 12002
Author(s):  
Alan Prahutama ◽  
Budi Warsito ◽  
MochAbdul Mukid
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Maternal Mortality ◽  
Spline Function ◽  
Predictor Variable ◽  
Regression Method ◽  
Infant Mortality Rate ◽  
Spline Regression ◽  
Maternal Mortality Rate ◽  
High Flexibility

Maternal mortality and infant mortality rate is an interrelated issue. Therefore, maternal and infant mortality modeling can be done bivariate. One method used to model the relationship between response variables and predictor variables is regression. The regression approach that does not use the assumption is spline regression. Spline regression is a regression method based on spline function. Spline function is a polynomial piece that has high flexibility. In this study the response variable used is bivariate, the maternal mortality rate and infant mortality rate, while the predictor variable used is the percentage of slum households. The weighting used is based on the value of the covariance variant. Determination of point knots using Mean Square Error (MSE). The results obtained modeling maternal and infant mortality rates based on the percentage of slum households resulted inMAPE 55.55%. Number of knots obtained as much as 5 point knots with linear order.

scholarly journals CORRELATIONS BETWEEN MOTHER’S KNOWLEDGE LEVEL OF EXCLUSIVE BREASTFEEDING AND THE PATTERN IN THE 6-MONTH CHILD IN POSYANDU OF JATIMULYO VILLAGE, PETANAHAN SUB-DISTRICT, KEBUMEN REGENCY, CENTRAL JAVA, INDONESIA

2020 ◽  
Vol 2 (2) ◽  
pp. 53
Author(s):  
Hastin Ika Indriyastuti ◽  
Wuri Utami ◽  
Juad Juad
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Exclusive Breastfeeding ◽  
Quantitative Methods ◽  
Univariate Analysis ◽  
Infant Mortality Rate ◽  
Bivariate Analysis ◽  
P Value ◽  
Central Java ◽  
Level Of Knowledge

Background: Globally, the infant mortality rate (IMR) is still extremely high. One of the efforts to improve children's health is exclusive breastfeeding for six months. Exclusive breastfeeding can reduce infant mortality rate caused by various infectious diseases. Thus, the community, especially mothers need to have proper knowledge about exclusive breastfeeding, and then they are expected to practice it. This study aims to determine the relationship between mothers’ knowledge of exclusive breastfeeding and the breastfeeding patterns of 6-month children in Jatimulyo Village, Petanahan Sub-district, Kab, Indonesia. Kebumen Regency, Central Java Province, Indonesia.Methods: The study was conducted in Jatimulyo Village using quantitative methods and correlation design with a cross-sectional approach. This study sample consisted of 56 mothers who had children aged 6-24 months selected based on the total sampling approach. Data were analyzed using univariate analysis and bivariate analysis using chi-square statistical tests.Results: This study found that most of the respondents are 26-30 years old and have a high school education level with multigravida parity and have a moderate level of knowledge. The study showed a relationship between the level of exclusive breastfeeding knowledge and breastfeeding patterns with a p-value of 0.002 (<0.05) and a correlation value of 0.499.Conclusions: Therefore, it can be concluded that there is a relationship between the level of knowledge about exclusive breastfeeding and breastfeeding patterns with a p-value of 0.002 (<0.05) and a correlation value of 0.499. 

scholarly journals Analisis Angka Harapan Hidup di Jawa Timur Tahun 2015

2018 ◽  
Vol 6 (1) ◽  
pp. 17
Author(s):  
Lina Septi Danasari ◽  
Arief Wibowo
Keyword(s):  
Public Health ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Life Expectancy ◽  
Secondary Data ◽  
Infant Mortality Rate ◽  
North Coast ◽  
Health Improvement ◽  
Central Java ◽  
The Government

Life expectancy is one of the indicators to calculate the Human Development Index (HDI) which determined by infants’ health, toddlers’ health, frequency of liveborn children and death rate in the community. East Java Province has four dominant cultural areas such as Mataraman including the western part of the border of Central Java to Kediri, Madura including Bangkalan to Pamekasan, Arek including north coast of Surabaya to Malang and Tapal Kuda including Pasuruan, Probolinggo, Situbondo, Bondowoso, Lumajang and Jember. Those four cultural areas have different characteristic that can affect public health status especially life expectancy in East Java Province. The analysis aimed to know the correlation between infant mortality rate and life expectancy and to know the differences of life expectancy among four cultural areas in East Java year 2015. This analysis used secondary data obtained from Central Bureau of Statistic of East Java on May, 2017. The data were life expectancy as dependent variable, infant mortality rate as independent variable and cultural areas in East Java as grouping variables. The result showed that there was correlation between infant mortality rate with life expectancy (p=0.000) and there was different in life expectancy among four cultural areas in East Java year 2015 (p=0.000) such as cultural areas Mataraman-Madura, Mataraman-Tapal Kuda and Arek-Tapal Kuda. It suggested the government to continue improving the socio-economic welfare of the community and public health improvement in the Tapal Kuda area which had high infant mortality rate and low life expectancy.

scholarly journals Identifikasi Faktor-faktor yang Mempengaruhi Angka Kematian Bayi di Provinsi Jawa Barat melalui Model Berbasis Regional

2016 ◽  
Vol 32 (1) ◽  
pp. 194
Author(s):  
Nusar Hajarisman ◽  
Yayat Karyana
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Regression Model ◽  
Poisson Regression ◽  
Spatial Diversity ◽  
Infant Mortality Rate ◽  
Poisson Regression Model ◽  
West Java ◽  
Infant Deaths ◽  
Geographic Modeling

In geographic modeling, global models such as ordinary linear regression (OLR) model theoretically it provides quite reliable local information if there is not any spatial diversity by region. In other words, OLR model cannot describe the relations between variables in heterogeneous difference of each region. This study will consider a model that will be used to estimate or predict the infant mortality rate in the several regencies / cities in West Java Province. Because the response variable observed in this study is count data which is assumed Poisson distributed, geographically weighted Poisson regression model (GWPR) is used. A better model is used to analyze the data of infant deaths in each regency / city in West Java based on the AIC value, GWPR model has the smallest value (compared to Poisson regression model), in which there is an interesting and important difference from each regency/city about the factors that significantly influence the Infant Mortality rate in each region.

scholarly journals PENERAPAN REGRESI GENERALIZED POISSON UNTUK MENGATASI FENOMENA OVERDISPERSI PADA KASUS REGRESI POISSON

2013 ◽  
Vol 2 (2) ◽  
pp. 49
Author(s):  
I PUTU YUDANTA EKA PUTRA ◽  
I PUTU EKA NILA KENCANA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Poisson Regression ◽  
Discrete Data ◽  
Mean Value ◽  
Regression Method ◽  
Response Variable ◽  
Generalized Poisson ◽  
The Mean ◽  
Generalized Poisson Regression ◽  
Better Than

The Poisson regression is generally used to analyze the response variable that is a discrete data. Poisson regression has assumption which must be met, that is condition equidispersion. But in fact this assumption is often violated, that is the value of the variance is greater or less than the mean value. The condition when value of the variance is greater than the mean value is called overdispersion. One method that can be used for overdispersion data is Generalized Poisson regression. In this research, it was found that the Generalized Poisson regression method was better than Poisson regression method.

scholarly journals PEMODELAN GEOGRAPHICALLY WEIGHTED GENERALIZED POISSON REGRESSION (GWGPR) PADA KASUS KEMATIAN IBU NIFAS DI JAWA TENGAH

2021 ◽  
Vol 10 (2) ◽  
pp. 259-268
Author(s):  
Wahyu Sabtika ◽  
Alan Prahutama ◽  
Hasbi Yasin
Keyword(s):  
Maternal Mortality ◽  
Poisson Regression ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Mean Value ◽  
Significant Variable ◽  
Spatial Factors ◽  
Generalized Poisson ◽  
Central Java ◽  
Generalized Poisson Regression

Maternal mortality is one indicator to describing prosperity in a country and indicator of women's health. Most of the maternal mortality caused by postpartum maternal mortality. The number of postpastum maternal mortality is events that the probability of the incident is small, where the incident depending on a certain time or in a certain regions with the results of the observation are variable diskrit and between variable independent each other that follows the Poisson distribution, so that the proper statistical method is Poisson regression. However, in Poisson regression model analysis sometimes assumptions can occur violations, where the value of variance is greater than the mean value called overdispersion. Generalized Poisson Regression (GPR) is one model that can be used to handle overdispersion problems. This modeling produces global parameters for all locations (regions), so to overcome this we need a method of statistical modeling with due regard to spatial factors. The analytical method used to determine the factors that influence the number of postpartum maternal mortality in Central Java that have overdispersion and there are spatial factors, is Geographically Weighted Generalized Poisson Regression (GWGPR) using the Maximum Likelihood Estimation method and Adaptive Bisquare weighting. Poisson regression and GPR modeling produces a variable percentage of pregnant women doing K1 which has a significant effect on the number of postpartum maternal mortality, while for GWGPR modeling is divided into four cluster in all regency/city in Central Java based on the same significant variable. From the comparison of AIC values, it was found that the GWGPR model is better for analyzing postpartum maternal mortality in Central Java because it has the smallest AIC value.Keywords: The Number of Postpartum Maternal Mortality, Overdispersion, Generalized Poisson Regression, Spatial, Geograpically Weighted Generalized Poisson Regression, AIC

scholarly journals PEMODELAN REGRESI POISSON BIVARIAT PADA JUMLAH KEMATIAN IBU HAMIL DAN NIFAS DI JAWA TENGAH TAHUN 2017

2019 ◽  
Vol 8 (3) ◽  
pp. 317-329
Author(s):  
Arbella Maharani Putri ◽  
Alan Prahutama ◽  
Budi Warsito
Keyword(s):  
Mortality Rate ◽  
Health Service ◽  
Maternal Mortality ◽  
Pregnant Women ◽  
Poisson Distribution ◽  
Poisson Regression ◽  
Regression Method ◽  
Healthy Behavior ◽  
Maternal Mortality Rate ◽  
Central Java

The maternal mortality rate is one of the indicators that determine the prosperity level of society in a country. Most of the maternal mortality caused by pregnancy maternal mortality and postpartum maternal mortality. Central Java is one of the provinces with the biggest number of pregnancy maternal mortality and postpartum maternal mortality in Indonesia. The number of pregnancy maternal mortality and postpartum maternal mortality follow Poisson Distribution and it has a significant correlation. Therefore, the writer analyzed factor that influences the number of pregnancy maternal mortality and postpartum maternal mortality using Univariate and Bivariate Poisson Regression method. Results from this study obtained that in the Univariate Poisson Regression variables that significantly influence pregnancy maternal mortality and postpartum maternal mortality are the percentage of pregnant women implementing K1 (X1), percentage of childbirth women that has puerperal health service (X6) and percentage of household with clean and healthy behavior (X7). In the Bivariate Poisson, the best model is the second model which assuming that covariance is an equation.Keywords: Pregnancy of Maternal Mortality, Postpartum Maternal Mortality, Bivariate Poisson Regression.

Perbandingan Linieritas Dalam Pendeteksian Faktor-Faktor Yang Mempengaruhi Angka Kematian Bayi Di Provinsi Aceh

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Desfira Ahya ◽  
Inas Salsabila ◽  
Miftahuddin
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Linear Model ◽  
Health Workers ◽  
Additive Model ◽  
Infant Mortality Rate ◽  
Mortality Data ◽  
Important Indicator ◽  
Penalized Spline ◽  
Aceh Province

Angka Kematian Bayi/ Infant Mortality Rate (IMR) merupakan indikator penting dalam mengukur keberhasilan pengembangan kesehatan. Nilai IMR juga dapat digunakan untuk mengetahui tingkat kesehatan ibu, kondisi kesehatan lingkungan dan secara umum, tingkat pengembangan sosio-ekonomi masyarakat. Penelitian ini bertujuan untuk memperoleh model IMR terbaik menggunakan tiga pendekatan: Model Linear, Model Linear Tergeneralisir dan Model Aditif Tergeneralisir dengan basis P-spline. Sebagai tambahan, berdasarkan model tersebut akan terlihat variabel yang mempengaruhi tingkat kematian bayi di provinsi Aceh. Penelitian ini menggunakan data jumlah kematian bayi di tahun 2013-2015. Data dalam penelitian ini diperoleh dari Profil Kesehatan Aceh. Hasil menunjukkan bahwa model terbaik dalam menjelaskan angka kematian bayi di provinsi Aceh tahun 2013-2015 ialah Model Linear Tergeneralisir dengan basis P-spline menggunakan parameter penghalusan 100 dan titik knots 8. Faktor yang sangat mempengaruhi angka kematian ialah jumlah pekerja yang sehat.   Infant mortality rate (IMR) is an important indicator in measuring the success of health development. IMR also can be used to knowing the level of maternal health, environmental health conditions and generally the level of socio-economic development in community. This research aims to get the best model of infant mortality data using three approaches: Linear Model, Generalized Linear Model and Generalized Additive Model with Penalized Spline (P-spline) base. In addition, based on the model can be seen the variables that affect to infant mortality in Aceh Province. This research uses data number of infant mortality in Aceh Province period 2013-2015. The data in this research were obtained from Aceh’s Health Profile. The results show that the best model can be explain infant mortality rate in Aceh Province period 2013-2015 is GAM model with P-spline base using smoothing parameter 100 and knots 8. Factor that high effect to infant mortality is number of health workers.

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