Evaluation and Estimation of the Provincial Infant Mortality Rate in China's Sixth Census

Objective To assess the data quality and estimate the provincial infant mortality rate（1q0） from China＇s sixth census. Methods A log-quadratic model is applied to under-fifteen data. We analyze and compare the average relative errors（AREs） for 1q0 between the estimated and reported values using the leave-one-out cross-validation method. Results For the sixth census, the AREs are more than 100% for almost all provinces. The estimated average 1q0 level for 31 provinces is 12.3‰ for males and 10.7‰ for females. Conclusion The data for the provincial 1q0 from China＇s sixth census have a serious data quality problem. The actual levels of 1q0 for each province are significantly higher than the reported values.

SutteARIMA:A Novel Method for Forecasting the Infant Mortality Rate in Indonesia

This study focuses on the novel forecasting method(SutteARIMA)and its application in predicting Infant Mortality Rate data in Indonesia.It undertakes a comparison of the most popular andwidely used four forecasting methods:ARIMA,Neural Networks Time Series(NNAR),Holt-Winters,and SutteARIMA.The data used were obtained from the website of the World Bank.The data consisted of the annual infant mortality rate(per 1000 live births)from 1991 to 2019.To determine a suitable and best method for predicting InfantMortality rate,the forecasting results of these four methods were compared based on the mean absolute percentage error(MAPE)and mean squared error(MSE).The results of the study showed that the accuracy level of SutteARIMA method(MAPE:0.83%andMSE:0.046)in predicting InfantMortality rate in Indonesia was smaller than the other three forecasting methods,specifically the ARIMA(0.2.2)with a MAPE of 1.21%and a MSE of 0.146;the NNAR with a MAPE of 7.95%and a MSE of 3.90;and the Holt-Winters with aMAPE of 1.03%and aMSE:of 0.083.

Linear Mixed Model Analysis of Worldwide Longitudinal Infant Mortality Rate Data and Association with Human Development Index

A linear mixed model is used to determine the explaining infant mortality rate data of United Nations countries. The HDI （human development index） has a significant negative linear relationship with infant mortality rate. United Nations data shows that the infant mortality rate has a descending trend over the period 1990-2010. This study aims to assess the value of the HDI as a predictor of infant mortality rate. Findings in the paper suggest that significant percentage reductions in infant mortality might be possible for countries for controlling the HDI.

How Infant Mortality Was Reduced in the Early Twentieth Century in Osaka

In 1983, the Vice Secretary-General of United Nations Children＇s Fund （UNICEF）, Karl Knutsson, visited Japan and remarked that the method of reducing the Japanese infant mortality rate （IMR） was a model for every country. In the early twentieth century, Osaka and at the time of UNICEF＇s plan in the 1980s, diarrhea was the cause of most babies＇ deaths, so we consider infant nutrition to be the central issue. The average IMR was 155.4 in rural areas in Japan, and IMR in Osaka city was 231.6 during 1906 to 1910. IMR in Osaka city might have been influenced by somewhat negative urban factors, which we can call the ＂urban penalty＂. Dr. Hiroshi Maruyama discovered the a-index in 1938. The a-index represents infant mortality number divided by neonatal mortality number. After all, Maruyama set one month after birth as a boundary to divide endogenous and exogenous. The a-index shows a qualitative measure of infant mortality. Post neonatal mortality was increased due to acquired diseases such as diarrhea, pneumonia, and beriberi. This shows that the effect of the urban penalty was raising the a-index. The a-index of the industrial zones shows that bad maternal conditions affected endogenous factors. Most mothers suffered from a deficiency of breast-feeding capability.

An Assessment of Association between Neighborhood Socioeconomic Status and Infant Mortality in High Focus States in India

Neighbourhood characteristics influence infant mortality above and beyond individual/household factors. In India, there are very few studies discussing the effects of neighbourhood characteristics on infant mortality. This study examined the effect of neighbourhood socioeconomic characteristics on infant mortality using data from the India’s Third District Level Household Survey conducted in 2007-2008. Multilevel analyses applied on the representative sample of 168,625 nested within 14,193 communities using MCMC procedure. Results established that place of residence, neighbourhood socio-economic factors as important determinants of infant mortality. Overall, being born in affluent (OR: 0.79, p < 0.01), more educated (OR: 0.86, p < 0.01) and socially disadvantaged caste (OR: 0.83, p < 0.01) neighbourhood was associated with the significant reduction in hazards of infant death. The finding of this study suggests that effort should be made to reduce infant mortality in these high focus states by including policies which aim at improving infant survival in the neighbourhood that is economically and socially deprived.