He used the proximate determinants framework developed by Mosely and Chen (1984) as the analytical model for analysis. The data was collected for the period of ten years (1982-1991) and econometric model was the in-sample method using data from Pakistan Integrated Household Survey (1991). The socioeconomic determinants were died into three categories i.e. at The Community Level, The Household Level and The Individual Level. The statistical methods used were Hazard Logistic Regression, Generalized Estimation Equation and Model Building Strategy considering the outcome of the interest in the study as infant death. He concluded that infant mortality levels had stagnated in Pakistan in early 80’s and that the major determinants were the lack of access to health and educational units for the lower socioeconomic group and the low status and health of the mother.
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The purpose of the study was to verify and quantify the importance of various socioeconomic factors affecting child mortality at different child ages. They examined the role of household, demographic and environment factors as the determinants and used Sequential Probit Model based on sequence of binary choice model for their study to get a higher response category. The data from Pakistan Integrated Household Survey (2001) was used to estimate the sequential model and different variables were taken under consideration as the factors that significantly affect the mortality of child. They discussed that the level of socioeconomic development of the nation determines the variable effects of socioeconomic factors on infant mortality and concluded that mother’s feeding and mother’s education is strongly related to the neonatal mortality, infant mortality as well as child mortality through improved child care practices as well as other proximate determinants as parental care, income and environmental contamination.
The main idea behind his research was to study the impact of demographic changes, socioeconomic inequality and availability of health resources on infant mortality rate (IMR) in Singapore. The data collected was from year 1969 to 2008 (40 years); a retrospective study design having IMR as the dependent variable and three main determinants were demographics, socioeconomic status and health resources. The statistical analysis was done by using the Structural Equation Model (SEM) which included the independent variable and the three main determinants. He concluded that providing more health facilities reduces the risk of infant mortality and that the socioeconomically disadvantaged are indirectly at higher risk for high infant mortality via health resources especially during recession when unemployment occurs and the access to health units becomes difficult for them.
Their study purpose was to investigate whether socioeconomic factors such as parent’s education, occupation and income play as any risk factors in perinatal mortality. They conducted a case control study covering all perinatal deaths in Kuwait for 1 year i.e. October 1997 to September 1998. The case as perinatal death and control as live birth were considered. Information from 463 matched pairs was conducted in which 274 were Kuwaitis and 189 were non-Kuwaitis. The statistical methods used were Bivariate and Multivariate analysis methods. The bivariate analysis showed that factors like lower education and lower income increased the risk of perinatal mortality but due to government policies and programs aimed at reducing social inequality in Kuwait, the IMR is low and reducing.
The study aimed at the impacts of the rural development programs by the Pakistan Government on the infant mortality trend which actually started declining amidst the government regulations for mother’s education development programs. The data was taken by interviewing 1600 married women and Bogue pregnancy history technique was utilized for the study.
They observed 150 infants; 51 in neonatal period and 99 in post-neonatal period. The study was cross-sectional and concluded that males were more at risk for infant morbidity and that exposure to diseases like diarrhea, birth asphyxia and others contribute majorly to mortality and breast feeding decreases both infant morbidity and mortality.
The study is well organized and also based on historical data and articles. It was an analytical cross-sectional study through the data analysis of 2003 Kenyan Demographic and Health Survey and the variables include socioeconomic, demographic and health outcome predictors and infant mortality as dependent variable. The statistical models of analysis were Univariate, Bivariate and Multivariate. They concluded that in urban areas the child survival depends on mother’s education and maternal awareness while in rural it depends whether they are in occupation and giving time to child or not, also the fertility factor and most importantly breast feeding is found as major determinant of child health leading to mortality along with family planning programs in Kenya.
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The data in the study had been retrieved from national health registers on infant death rates from both the countries with a time span from 1980-1988. The analysis was made on Odd Ratios comparing Demark with Sweden. The factors considered were birth weight, age, parity and socioeconomic level. It was concluded that even after stratification for these factors a difference between the countries remain and the IMR in Denmark was relatively higher than that of Sweden.
The main purpose of study was the ecological analysis of the relationship between infant mortality and economic status in metropolitan Ohio. The data was taken for time period of 1960-2000. An ecological framework was utilized with primary analytical unit being the consensus tract of mother’s usual residence. The dependent variable, being infant mortality and independent were taken as low income families. The study concluded that thought infant mortality had decreased rapidly in this time span, yet a strong inverse relation between income status and infant mortality and that there are strong socioeconomic determinants relating to infant mortality.
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