Transcript Levels and Trends in Child Mortality

Data and Methodology to Estimate
Child Mortality
Danzhen You
UNICEF
Dec 8, 2009
Prepared for the ESCWA Workshop in Beirut, Lebanon
MDG 4
MDG4 – reduce under-five mortality rate
(U5MR) by two thirds from 1990 to 2015
Child Mortality Indicators: Definition
Mortality among young children can be subdivided by age group
Category
Includes deaths that occur:
Neonatal mortality
During the first 28 days of life
Post-neonatal mortality
At ages 1 to 11 months
Infant mortality
Between birth and exact age 1
Child mortality
At ages 1 to 4 years
Under-five mortality
Between birth and exact age 5
Child Mortality Indicators: Definition (cont’d)
Mortality rates such as U5MR and IMR are not
strictly rates but are the probability of dying within a
specific period
Indicator
Definition
Under-five mortality
rate
Probability of dying between birth and exact
age 5 years expressed as per 1000 live
births.
Infant mortality rate
Probability of dying between birth and exact
age 1 year expressed as per 1000 live births.
Sources of Data
There are a variety of sources used to estimate child mortality
 Vital registration
 Sample registration
 Demographic surveillance sites
 Population censuses
 Household surveys
The first three are prospective – collect data as deaths occur.
The last two are generally retrospective – interview people about
events in the past
Vital Registration

The preferred source of data for child mortality if system is good.
 Records all births and deaths that occur in a country. Individual
events are reported shortly after they occur.

Produces estimates annually and for sub-national areas.

Deaths are often less registered than births

Poor and rural families are less likely to register births and deaths
 Good systems are not common in developing countries as they
require an extensive infrastructure that is consistent and accurate.
Currently around 50 countries have vital registration data that are
considered good enough to be the sole source of data for child mortality.
Sample Registration
 Sample registration systems are designed to collect information
from a representative sample of the population. This allows both
national and sub-national estimates of births and deaths to be
produced provided the sample is large enough.
 They generally provide data on causes of deaths, which are
valuable for planning and evaluating programmes to reduce child
mortality.
 However, such systems are complex and expensive. While they
are rare, they function in the two largest countries in the world.
Demographic Surveillance
 Demographic surveillance sites have similarities with sample
registration systems, but their coverage and purpose are quite
different. Such sites are limited to small geographically defined
populations, typically less than 200,000 people.
 Their primary aim is not to represent the national population,
but rather to provide a base for specific studies and intervention
trials.
While easier to operate than sample registration systems, they
still require substantial ongoing resources and continuity.
Population Census
 Population censuses are carried out in most countries. They
provide a unique source of demographic data since they aim to
collect data on every member of the national population. This
provides not only national data on basic household and person
characteristics, but also provides such data for the smallest
administrative units.
 However, because of their very large scale and high cost,
national censuses are typically conducted at ten-year intervals.
Such resource and logistical constraints also limit the content of
census questionnaires. Nevertheless they usually collect data on
children ever born and those still living, thus enabling indirect
estimates of child mortality to be calculated. Although sampling
errors are absent, non-sampling errors are present.
Household Survey
 Given the lack of good vital registration systems and the infrequency
of population censuses, household surveys have become the primary
source of data on child mortality.
 While challenging to successfully carry out, well-designed and wellimplemented household surveys can produce high-quality data on child
mortality levels and trends.
 Such surveys often collect a range of other data on health, education
and other socio-economic indicators, which provide essential information
for guiding and assessing programmes to reduce child mortality.
 These surveys are constrained by their sample size, and increasingly
by the amount of data collected. Survey sample sizes are increasing, as
is content, and while this improves the range of disaggregation that can be
reported, they also pose logistical and quality challenges.
Data Problems
 Sampling errors (Surveys only)
 Omission of Deaths
 Misreporting of child’s age at death or date of birth (direct only)
 Selection bias
 Violation of assumptions (indirect only)
Examples: U5MR, Syria
Examples: U5MR, Egypt
Examples: IMR, Tunisia
Examples: U5MR, Nigeria
Work of IGME
 Inter-agency Group for Child Mortality Estimation (IGME),
formed in 2004, including UNICEF, WHO, UN Population
Division, World Bank
 Technical Advisory Group of the IGME
 IGME aims to develop methods to better estimate child
mortality, share data, harmonize estimates between partners,
and increase the transparency of the estimates
 Other activities: Child mortality workshops, Country Support
 CME Info database
Data Collection
 Collect all available data – census, household surveys
(DHS, MICS, etc.), vital registration, and so on
 UNICEF’s main data collection process – Country
Report on Indicators for the Goals (CRING)
 WHO routine data collection process - vital
registration data
Data Evaluation
 Response rate
 Age misreporting, age heaping
 Birth transference (DHS only)
 Omission of deaths
 Others
Estimation Methods
 Estimates
• Fitting a regression curve to the data points which are
believed having good quality
Spline: Weighted Least Square with Various Slope
 LOESS: Locally weighted Lease square
• Different methods for countries with high HIV/AIDS
prevalence
Estimation Methods: Spline
 The model used is:
ln( 5 q0 ) i  b0  b1 (date) i  b2 ( postk1) i  b3 ( postk 2) i  ... ei
 Date is calendar year
 Postkj

= (date - dateknotj) if (date-dateknotj) is positive
=0
if (date-dateknotj) is negative
 The knots are defined backward into the past and each time the sum
of the weights reaches a multiple of 5
 Thus number and location of knots is data-driven
Estimation Methods: LOESS
Function estimated is
log(y) = β0 + β1(x) + β2(z) + ε
Where y is U5MR, x is date and z is a dummy variable indicating
whether the observation is from civil registration
Selection of α:
• Range from 0.05 (or smallest value that captures at least 3
points) to 2.0 (or largest value that allows some variability)
Uncertainty: 1,000 draws per value of α
Examples: U5MR, Syria
Examples: U5MR, Egypt
Examples: IMR, Tunisia
Examples: U5MR, Nigeria
CME Info (www.childmortality.org)
Structure of the CME Info System
Series Window
Data points Window
Estimates Window
U5MR in West Asia
Under five mortality rate
Country
Bahrain
Egypt
Iraq
Jordan
Lebanon
Morocco
OPT
Oman
Sudan
Syria
Tunisia
UAE
Yemen
1990
16
90
53
38
40
88
38
31
124
37
50
17
127
MDG target in
2015
2008
12
23
44
20
13
36
27
12
109
16
21
8
69
5
30
18
13
13
29
13
10
41
12
17
6
42
Average annual rate of reduction
Observed
1990-2008
Required
2009-2015
Progress
towards the
MDG target
1.6
7.6
1.0
3.6
6.2
5.0
1.9
5.3
0.7
4.7
4.8
4.2
3.4
11.7
-3.8
13.0
6.5
-0.3
2.9
10.8
2.2
13.9
3.8
3.3
4.8
7.0
on track
on track
insufficient
on track
on track
on track
on track
on track
no change
on track
on track
on track
insufficient
Progress towards MDG4 in the World, 2008
On track: under-five mortality rate (U5MR) is less
No Progress: U5MR is 40
than 40, or U5MR is 40 or more and the average
annual rate of reduction (AARR) in the U5MR
observed for 1990-2008 is 4.0 percent or more
or more and AARR is less
than 1.0 per cent
Insufficient Progress: U5MR is 40 or more and
Data not available
AARR is less than 4.0 percent but equal to or greater
than 1.0 percent