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.

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.