Major Determinants of Infant Mortality

2018 ◽  
Vol 20 (3) ◽  
pp. 345-362
Author(s):  
Subhanil Banerjee
Keyword(s):  
Panel Data ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Nutritional Status ◽  
Birth Spacing ◽  
Infant Mortality Rate ◽  
Development Indicators ◽  
Behavioural Factors ◽  
Health Programmes ◽  
Better Than

Infant mortality rate (IMR) is one of the most important development indicators. In India, there is a severe interstate disparity regarding IMR. Kerala registers a very low IMR; whereas in Odisha it is pretty high. It is somewhat paradoxical as Odisha fares substantially better than many other states with lower IMR regarding total fertility rate, antenatal care and in many other aspects. The present article attempts to investigate the applicability of usually perceived major determinants of IMR as evidenced in the existing literature for Odisha. The panel data multiple regression carried out with data of 30 districts of Odisha over three years indicates that physiological and behavioural factors together with maternal and demographic factors are perhaps more important than the health programmes for reduction of IMR in Odisha. Moreover, many of the usually perceived major determinants of IMR including economic betterment are statistically insignificant for Odisha. The policymakers should take into account these facts and instead of a series of health programmes, they might resort to awareness building regarding breastfeeding and birth spacing. Mother’s nutritional status should also be strengthened so that they can sustain exhaustive breastfeeding for first six months after the birth of the child.

Determinants of Infant Mortality Rate in Malaysia: Evidence from Dynamic Panel Data Study

2019 ◽  
Vol 21 (4) ◽  
pp. 443-450
Author(s):  
Chor Foon Tang
Keyword(s):  
Panel Data ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Health Spending ◽  
Mortality Rates ◽  
Dynamic Panel Data ◽  
Infant Mortality Rate ◽  
Female Education ◽  
Dynamic Panel ◽  
Governance Quality

Understanding the factors associated with the infant mortality rate is essential as it may guide policymaking in efforts to alleviate the high incidence of infant mortality. The aim of this study is to explore the major determinants of the infant mortality rate with specific focus accorded to research and development (R&D) and governance quality. Our analysis utilizes unbalanced panel data from 122 countries from 2001 to 2013. Using the dynamic panel data generalized method of moments (GMM) estimator, we find that income, health spending, female education, technological progress and governance quality have significant negative impact on infant mortality rates. It can thus be surmised that policies to reduce infant mortality rates should focus upon improving the level of income, female education, health spending and governance quality, besides encouraging R&D activities.

scholarly journals FACTORS AFFECTING INFANT MORTALITY RATE IN KARANGASEM, BALI

2019 ◽  
Vol 4 (1) ◽  
pp. 12-15
Author(s):  
Ni Luh Putu Suciptawati ◽  
Ni Luh Putu Suciptawati ◽  
Made Asih ◽  
Kartika Sari ◽  
I G A M Srinadi
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Linear Model ◽  
Birth Spacing ◽  
Linear Analysis ◽  
Infant Mortality Rate ◽  
Categorical Variables ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Log Linear

The purpose of this study was to determine the factors that influence the infant mortality rate in Karangasem, Bali. The method used in this research is the Log Linier model. In the Log linear model analyze relationship pattern among group of categorical variables which include an association of two or more variables, either simultaneously or partially. A Patterned relationship between variables can be seen from the interaction between variables. Log linear analysis does not distinguish between explanatory variables and response variables. The population in this study was all babies born in Karangasem in 2015 that is as many as 7,895 babies with live birth status and as many as 7,835 babies and 60 infants died. As a sample, 100 babies were taken, of which 60 were live and 40 died. The results show that infant mortality is affected by infant weight, how old the mother during childbirth, and interaction between birth spacing and infant weight  

scholarly journals PANEL DATA REGRESSION ANALYSIS FOR FACTORS AFFECTING INFANT MORTALITY RATE IN EAST JAVA 2013-2017

2021 ◽  
Vol 16 (3) ◽  
pp. 437
Author(s):  
Faishal Azhar Wardhana ◽  
Rachmah Indawati
Keyword(s):  
Panel Data ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Data Availability ◽  
Infant Mortality Rate ◽  
Complete Coverage ◽  
Cross Sectional ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Government

ABSTRACTThe escalating infant mortality rate (IMR) in Indonesia has not been able to fulfill the target of Sustainable Development Goals (SDGs) that restrict the limit of IMR to just 12 of 1,000 live births. According to such fact, this research was designed as the application of panel data regression in an IMR case study of East Java from 2013–2017. Regression panel data enable research in describing cross-sectional and time series information. The variety of data availability in this method were capable of producing a high degree of freedom, allowing it to meet the prerequisites and statistical properties. This method was considered the most suitable one for analyzing the rising IMR. This research was classified as non-reactive research. All regencies/cities in East Java served as this study’s population. Data collection included K4 coverage, childbirth assistance, and KN complete coverage. The result of panel data regression showed a significant connection between K4 coverage (0.0230), childbirth assistance (p = 0.0105), and KN complete coverage (0.0205). Adjusted R-Square value was obtained with an amount of 80%, which means that all independent variables were able to explain the dependent one of that value, while the remaining were explained by other factors. This study can provide some suggestions to support IMR in East Java, including handling from the government or related pregnant families to support IMR on an ongoing basis. Keywords: panel data regression, IMR, K4, childbirth assistance, KN complete

Relationship between the nutritional status of pregnant women and the incident of Low Birth Weight infant

2020 ◽  
Vol 1 (3) ◽  
pp. 58-62
Author(s):  
Mardiaturrahmah Mardiaturrahmah ◽  
Anjarwati Anjarwati
Keyword(s):  
Birth Weight ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Low Birth Weight ◽  
Nutritional Status ◽  
Pregnant Women ◽  
Infant Mortality Rate ◽  
P Value ◽  
Case Group ◽  
Control Approach

The maternal mortality rate is 19,500 to 20,000 people every year or occurs every 26-27 minutes. The caus of maternal death is bleeding 30.5%, infection 22,5%, gestosis 17,5 and anesthesia 2%.  The infant mortality rate is around 10,000 to 280,000 per 18-20 minutes. The cause of infant mortality is due to Low Birth Weight (LBW) of 15/1000%.  The infant mortality rate in Indonesia is still the highest problem in other ASEAN countries. The infant mortality rate in Indonesia from 2008 was around 248 per 100,000 live births. Basic Health Research (RISKESDA) 2013 shows there are still 10,2% of babies with LBW, which is less than 2,500 grams. Neonatal death because LBW is basically affected by the nutritional status of pregnant women. This study aims to determine the relationship between the nutritional status of pregnant women and the  incidence  of  LBW. This  quantitative  research  uses  a  case  control  approach  using  a  retrospective approach. The population in this study were mothers who had given birth to babies during the last two years (2016-2017). The sampling technique uses total sampling for control cases by using a ratio of 1: 1 for the case group of 40: 40 samples. Analysis using Chi Square with p value 0,000 (OR=3,500, CI 95%=2,313-5,296). There is a relationship between nutritional status of pregnant women and the incidence of LBW. Health Technology Assessment (HTA) which can seek 1000 first day of life can be a breakthrough in assessing and providing interventions of nutrition in families, especially in pregnant women.

Reflections on Microfinance

2019 ◽  
pp. 188-207
Author(s):  
Sovik Mukherjee ◽  
Ramesh Chandra Das
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Poverty Alleviation ◽  
Poverty Reduction ◽  
Poverty Line ◽  
Infant Mortality Rate ◽  
Self Sufficiency ◽  
Socio Economic Development

Microfinance has become the latest buzzword in the credit markets where it shoulders the responsibility of alleviating poverty coupled with socio-economic development. Dealing with microfinance coupled with the issue of poverty reduction, the first concern is to handle the twin objectives of poverty alleviation and achievement of financial self-sufficiency, which are often at loggerheads. To begin with, the present chapter constructs a five-dimensional human poverty index (HPI) in terms of the rate of unemployment, state wise illiteracy rate, state-wise infant mortality rate, state-wise percentage of population below the poverty line, and the percentage of population not having an access to electricity for the states across India. Thus, this will serve as an index for the extent of poverty. Consequently, a fall in the value of the index actually implies poverty alleviation. This empirical model does not justify the hypothesis that “microfinance reduces poverty” at the macro level using cross-state panel data for India.

scholarly journals Socioeconomic Determinants of Infant Mortality Rate in Asean: A Panel Data Analysis

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Vita Kartika Sari ◽  
Dwi Prasetyani
Keyword(s):  
Health Status ◽  
Panel Data ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Health Expenditure ◽  
Strong Influence ◽  
Mortality Rates ◽  
Infant Mortality Rate ◽  
Gdp Per Capita ◽  
Per Capita

The infant mortality rate indicates the health status of a country. Previous studies have proven that socioeconomic factors have a significant influence on infant mortality rates in both developed and developing countries. Further studies on infant mortality rates are useful for public service strategic policy in the health sector. The main purpose of this study was to analyze the socioeconomic factors influencing infant mortality rates in ASEAN based on panel data estimates for 2000-2017. The dependent variable for this study was infant mortality rate, while the independent variables were health expenditure, female labor force, maternal fertility rate, and GDP per capita. The authors concluded that the main cause of infant mortality in ASEAN is care during delivery. Other influencing factors include family health status, maternal education level, and socio-economic inequality. This study found that the size of the female workforce has a strong influence on increasing the infant mortality rate in ASEAN. The fertility rate also had a strong influence on increasing infant mortality rate in ASEAN, while GDP per capita had a negative influence on infant mortality rate.  Health expenditure is proven to have no effect on the increase of infant mortality rates in ASEAN.

Nutritional Status and Infant Mortality Rate in Saiha District, Mizoram, India

2016 ◽  
Vol 110 (12) ◽  
pp. 2280 ◽  
Author(s):  
Vishwambhar Prasad Sati ◽  
Lalrinpuia Vangchhia
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Nutritional Status ◽  
Infant Mortality Rate

BIOSOCIAL COVARIATES OF ADULT MALE BODY MASS INDEX IN CENTRAL INDIA

2007 ◽  
Vol 39 (6) ◽  
pp. 875-893 ◽  
Author(s):  
RAJESH K. GAUTAM
Keyword(s):  
Body Mass Index ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Life Expectancy ◽  
Nutritional Status ◽  
Central India ◽  
Gender Development ◽  
Infant Mortality Rate ◽  
Development Index ◽  
Gender Development Index

SummaryBody mass index (BMI) is the ‘measuring rod’ of nutritional status. This study investigates the type and extent of correlation between adult male BMI and socioeconomic, cultural and bio-demographical variables using data from 11,496 individuals from 38 districts of Central India. For each individual, stature, body weight and sitting height data were collected, their Cormic index and BMI computed, and averages for each district calculated. Mean BMI was found to be lowest for the population of Tikamgarh (17·90±1·91 kg m−2) and highest for that of Durg district (19·33±2·16 kg m−2), whereas the mean BMI for the total population of Central India was 18·67±2·18 kg m−2, which is lower than that of well-to-do individuals in India as a whole. The F ratio indicates that there is inter-district variation in anthropometric characteristics of populations. District-wise biosocial indicators were obtained, namely population density per square kilometre, percentage urban population, percentage of population that is of scheduled caste/tribe, sex ratio, average rural population per PHC/CHC (primary or community health centre), literacy rate, life expectancy, total fertility rate, infant mortality rate, gender development index and human development index. Most of these variables were found to be significantly correlated with each other, but BMI was only significantly correlated with three of them, viz. gender development index (R2=0·211), life expectancy (R2=0·130) and infant mortality rate (R2=0·128). Gender development index and life expectancy were positively correlated with BMI, whereas infant mortality rate was negatively correlated. It is concluded that if BMI increases then life expectancy will also increase. Thus better nutritional status may be a helpful tool for reducing infant mortality rate, which is an indicator of socioeconomic status, health condition, health care and ultimately overall development of a region or population.

Some Determinants of Infant Mortality Rate in SAARC Countries: an Empirical Assessment through Panel Data Analysis

2020 ◽  
Vol 13 (6) ◽  
pp. 2093-2116
Author(s):  
Ujjal Protim Dutta ◽  
Hemant Gupta ◽  
Asok Kumar Sarkar ◽  
Partha Pratim Sengupta
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Panel Data Analysis ◽  
Infant Mortality Rate ◽  
Empirical Assessment

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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