#### Welcome to Childhood Mortality Analysis Platform!

Introduction

• Early childhood mortality is attributed by Neonatal Mortality Rate (NNMR), Post-Neonatal Mortality Rate (PNMR), Infant Mortality Rate (IMR), Child Mortality Rate (CMR) (Ages 1-4), and Under-five Mortality Rate (U5MR).
• For the purpose of tracking progresses made towards reducing early childhood mortality, indicators need to be estimated precisely and accurately.
• With that regard, the objective of this section is to present results of shorter time periods’ estimation models that enable more accurate estimation and allow the effective capturing of the effects of man-made and natural disaster events.

Method

Sources of Data:
• Ethiopian Demographic Health Survey (EDHS) conducted during 2000, 2005, 2011, and 2016 G.C.
Calculations:

A synthetic cohort life table is one of the variants of direct estimation method. This method uses data on date of birth of children, their survival status, and the dates of deaths or ages at death of deceased children. Procedurally, first comes, the tabulation of component death probabilities for all birth cohorts of children.

[a1, a2) are age groups or segments described as [0,1),[1,2),[3,5),[6,11),[12,23),[24,35),[36,47) ranges.
[t1, t2) are lower and upper limits of the time period for the date of mothers’ interview.
In order to find 1y or one year gap mortality estimation, the two time points must be similar [t1=t, t2 = t). This is useful to find a reference date of estimation.

t1 = Date of interview - j = t2 where j = {0, 1, 2, ……., n},
0: is for year of the survey
1: is one year before the survey
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.
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n: is n year before the survey
The component death probabilities are calculated as follow:

##### $Pi= { 0.5Di1+Di2+Di3\over Ei2+0.5*(Ei1+Ei3)}$
For, time period ends with date of the survey.
Pi = Is component death probability for the ith age group
Di1,Di2,Di3 = Number of deaths for the ith age group from cohort 1,2 and 3 respectively
Ei1,Ei2,Ei3 = Number of alive children for the ith age group from cohort 1,2 and 3 respectively
Finally, component death probabilities estimations was used to calculate the mortality rates.

Work-flow of the model:

Key Findings

• From the year 1990 up to 2016, under-five mortality decreased from 207 to 66 deaths per one thousand live births.
• In 2016 neonatal contributed almost 33% of under-five mortality. Which is almost 10% more contribution compared to that of 1990.
• The neonatal mortality rate in Ethiopia is not decreasing at the expected level. During the year2019, for instance, there was 1 more death per thousand live births as compared to that of 2016.
• Comparing under five and neonatal mortality between genders, there are 21 and 17 more death rates respectively in male children compared to that of females.
• The level of literacy among mothers is found to be a factor contributing to childhood mortality. Hence, mothers with a limited or no literacy level are seen to increase the risk of losing their children to death.
• On the other hand, the age of the mother plays a pivotal role in determining the wellbeing of the child. With that regard, mothers below the age of 20 and above the age of 40 do have the probability of losing their children to death.
• At all sub-national levels under-five mortality is decreasing.