The determinants of infant and child mortality in Tanzania

Abstract

This paper investigates the determinants of infant and child mortality in Tanzania using the 1991/92 Tanzania Demographic and Health Survey. A hazards model is used to assess the relative effect of the variables hypothesized to influence under-five mortality. Short birth intervals, teenage pregnancies and previous child deaths are associated with increased risk of death. The Government of the United Republic of Tanzania should therefore maintain its commitment to encouraging women to space their births at least two years apart and delay childbearing beyond the teenage years. Further, this study shows that there is a remarkable lack of infant and child mortality differentials by socioeconomic subgroups of the population, which may reflect post-independence health policy and development strategies. Whilst lack of socioeconomic differentials can be considered an achievement of government policies, mortality remains high so there is still a long way to go before Tanzania achieves its stated goal of 'Health for All'.

Frequency and Timing of Antenatal Care Visits and Its Impact on Neonatal Mortality in EAG States of India

“…The first 28 days of extensive care for a new-born is of paramount importance, most of the neonatal deaths occurred in this period of which over 70% of neonatal deaths occur in the first six days of birth [7,8]. A study carried out by Singh et al [9] also showed lack of substantial evidence on the effectiveness of the content, frequency and timing of visits in standard ANC programs in maternal and child health.…”

Statistical Modeling of the Number of Deaths of Children in Bangladesh

“…The GPR model allows flexibility in dealing with over-dispersion or under-dispersion [3]. More specifically, an NBR model is suggestive for dealing with over-dispersion [15].…”

“…However, in the presence of over-dispersion, Poisson regression model does not perform well in best fitting the data and for prediction. In this study, we test over-dispersion, [15,16] where the null hypothesis is that there is no overdispersion in the data. Hence, the Poisson regression model is the null model against any other alternative model with overdispersion.…”

“…Hence, the Poisson regression model is the null model against any other alternative model with overdispersion. Following the notations of Deans and Lawless [15,16], we let Y i be the response from their subject with covariates X i. . and the null hypothesis for testing over-dispersion becomes, H 0: τ = 0.…”

“…Failure to reject the null hypothesis leads to the Poisson regression model. In this study we perform, Dean's P B [15] test for over-dispersion using a R packaged DCluster [18] that generates the test statistic P B = 13.5292 with p-value <0.000. Rejection of H 0 leads to the application of Negative Binomial type models.…”