Transcript UNICEF: Child Mortality Estimation

Child
Mortality Estimation
Harmonization Prospects
Edilberto Loaiza
Bangkok, January 15, 2009
ESCAP workshop on MDG monitoring
1
What is the main issue?
Often data produced and used at the country level is
different from the one produced and used at the global
level. This has been observed more recently during the
UNICEF yearly reporting on the State of the World’s
Children and in particular the reporting of the under five
mortality indicator
2
What are exactly the main issues?
• Discrepancies occur at different levels and
moments in the harmonization cycle
– Between UNICEF offices (HQ-RO-CO)
– Between International Organizations (IO)
– Between IO and Governments
• How to deal with these issues?
– Inter Agency Work and Coordination!!
3
Harmonization Cycle
•
•
•
•
•
•
Data collection
Data compilation
Data analysis and methodological work
Data use and dissemination
Statistical capacity building
Data used for evidence-based programming
4
Reasons for the differences
• Different data sources: vital registration, population census,
household surveys, sample registration systems or a
combination of them
• Different methods of calculation for IMR and U5MR
• Different methods of estimation when combining data
sources and methods
• Reference year to which the estimates correspond and
when the estimates are produced (usually in June for
UNICEF but published in November)
5
The reality of child mortality estimates
• The majority of child mortality occurs in countries
without adequate vital registration systems hence use has to be made of alternative sources,
primarily household surveys and censuses.
• How do these data sources look like graphically,
and what might be the derived mortality
estimates?
6
INDONESIA
Under five mortality data in the 1970s
250
U5MR (5 per 1000 live births)
200
150
100
50
0
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
Year
cen71q5i
wfs76q5d
wfs76q5i
7
INDONESIA
Under five mortality data in the 1980s
250
U5MR (5 per 1000 live births)
200
150
100
50
0
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
Year
cen71q5i
wfs76q5d
wfs76q5i
cen80q5i
dhs87q5d
dhs87q5i
8
INDONESIA
Under five mortality data in the 1990s
250
U5MR (5 per 1000 live births)
200
150
100
50
0
1955
cen71q5i
dhs91q5i
1960
1965
wfs76q5d
cen90q5i
1970
1975
wfs76q5i
dhs94q5d
1980
1985
Year
cen80q5i
dhs94q5i
1990
dhs87q5d
dhs97q5d
1995
2000
dhs87q5i
dhs97q5i
2005
2010
dhs91q5d
9
INDONESIA
Under five mortality data 1960-2002
250
U5MR (5 per 1000 live births)
200
150
100
50
0
1955
cen71q5i
cen90q5i
1960
wfs76q5d
dhs94q5d
1965
1970
wfs76q5i
dhs94q5i
1975
1980
1985
1990
Year
cen80q5i
dhs87q5d
dhs97q5d
dhs97q5i
1995
dhs87q5i
cen00q5i
2000
2005
dhs91q5d
dhs02q5d
2010
dhs91q5i
dhs02q5i
10
INDONESIA
Under five mortality Estimates 1960-2007
250
U5MR (5 per 1000 live births)
200
150
100
50
0
1955
1960
cen71q5i
dhs91q5d
dhs97q5i
1965
1970
wfs76q5d
dhs91q5i
cen00q5i
1975
1980
1985
Year
wfs76q5i
cen90q5i
dhs02q5d
1990
cen80q5i
dhs94q5d
dhs02q5i
1995
2000
dhs87q5d
dhs94q5i
2007IGME
2005
2010
dhs87q5i
dhs97q5d
11
The Inter-agency Group for Child Mortality
Estimation (IGME)
• Initiated by UNICEF, WHO, The World Bank and the
United Nations Population Division
• To work on data sources and methodologies used for child
mortality estimation
• To produce agreed estimates of infant and under five
mortality estimates at the country level
• Harmonize and disseminate the work and results
– Child mortality data base (CMEInfo, a DevInfo
application)
– Regional workshops
12
For estimation purposes the work aims to..
• Compile all nationally representative data sets on
child mortality
• Fit simple (objective, transparent) model of an
indicator of child mortality (typically U5MR) on
time
• Extrapolate as necessary to required target date
• Derive time series of multiple child mortality
indicators (IMR) using life-table models
13
Using….
• Compiled data and estimates from vital registration, national
censuses and surveys from 1960 onwards
• A number of data sets that varies by country
• Data of different quality within and between country and
survey
• Not always standard time series: observations are unevenly
spaced, gaps, overlap
14
Data Types
• Vital registration if available provides (annual) series of
Infant Mortality Rates
• Birth histories (WFS and DHS surveys) provide “direct”
estimates of Infant Mortality Rate (IMR) and Under-Five
Mortality Rate (U5MR), typically for periods 0 to 4, 5 to 9
and 10 to 14 years before survey
• Summary birth histories (WFS and DHS surveys, other
household surveys such as MICS, NHS and population
censuses) provide “indirect” estimates of U5MR for six
time points covering roughly the period 2 to 12 years
before the survey
15
Data Problems
• Sampling errors (surveys only)
• Omission of deaths
• Misreporting of child’s age at death or date of birth (direct
only)
• Selection biases
• Violation of assumptions (indirect only)
16
17
18
19
20
21
22
Approaches
• SPLINE
• LOESS
23
SPLINE: Weighted Least Square with Variable Slope
• 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
24
Smoothing
• Basic idea is to use weighted least squares
regression of ln(U5MR) on time, with weights that
reflect priors about data quality
• Data errors (and thus weights) may be
characteristic of..
– A data set (e.g. a bad survey affecting all points)
– A type of observation (e.g. an indirect estimate based
on reports of women aged 15 to 19 [selection bias] or a
direct estimate based on reported births 10-14 years
before the survey [recall lapse])
25
Standard Weights
Type of Data
Initial Weight
Civil Registration/Prospective Survey
1.0 per year
Full birth history
0-4 2.0
5-9 1.8
10-14 1.2
Summary birth history
15-19
20-24
25-29
30-34
35-39
40-44
45-49
0.0
0.2
1.2
1.2
1.2
0.8
0.4
When both direct and indirect estimates are available, weights are halved
26
Methods: Weighted Least Square with
Variable Slope
• Fit a model of log(q5) to time
Birth histories - direct
Birth histories - direct
Birth histories - direct
Birth histories - direct
Birth histories - indirect
Birth histories - indirect
Birth histories - indirect
Birth histories - indirect
Probability of Dying by Age 5 (q5)
Probability of Dying by Age 5 (q5)
300
300
200
100
0
200
100
0
1960
1970
1980
Date
Fixed slope
1990
2000
1960
1970
1980
1990
2000
Date
Splines with variable slopes (5 knots)
27
Underweighting a data set
The weights are modified if one data set appears to be an outlier
Censuses
Birth histories - indirect
Censuses
Birth histories - indirect
Birth histories - direct
Birth histories - direct
300
Probability of Dying by Age 5 (q5)
300
Probability of Dying by Age 5 (q5)
Birth histories - direct
Birth histories - direct
200
100
WFS ‘79
WFS 79
200
100
WFS ‘79
WFS 79
0
0
1960
1970
1980
Date
Standard weights
1990
2000
1960
1970
1980
1990
2000
Date
Weighting 1979 WFS to zero
28
LOESS Smoothing
• LOESS stands for Locally Weighted Least Squares
• Value of fitted line at a given point is determined by a
regression line, weighting observations by function of
distance from point
• Key parameter is α, the bandwidth or range of observations
included
• Exclusion of “outliers”, as with Spline
29
LOESS Smoothing (continued)
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
30
Loess: What Does α Do?
Small α fits many small local regressions, averages results
Large α fits few wider regressions, averages results
200
Under-5 Mortality Rate (log ssscale)
200
100
50
20
100
50
20
1960
1960
1970
1980
Year
1990
2000
Small α
1970
1980
Year
1990
2000
Big α
Note log scale on y axis
31
Smoothing U5MR
Simple regression of log(U5MR) on Date of Observation:
250
200
150
100
50
0
1960
1970
1980
Year
Observed
1990
2000
Fitted
32
Smoothing U5MR
Loess regression of log(U5MR) on Date of Observation, α = 0.1:
200
150
100
50
0
1960
1970
Observed
1980
Year
1990
2000
Loess Bandwidth 0.1
33
Smoothing U5MR
Loess regression of log(U5MR) on Date of Observation, α = 0.4:
200
150
100
50
0
1960
1970
Observed
1980
Year
1990
2000
Loess Bandwidth 0.4
34
Smoothing U5MR
Loess regression of log(U5MR) on Date of Observation, α = 1.0:
200
150
100
50
0
1960
1970
Observed
1980
Year
1990
2000
Loess Bandwidth 1.0
35
Smoothing U5MR
Are there any data sets we should exclude?
200
DHS 1987 (direct)
150
1990 Census
100
50
1980 Census
0
1960
1970
NLT
WFS75i
PCS85
Census90
1980
Year
Census70
Census80
DHS87d
Census00
1990
PCS74
CPS81
DHS87i
MICS05
2000
WFS75d
CPS84
PCS89
36
Smoothing U5MR
Loess regression of log(U5MR) on Date of Observation, α = 0.4,
Dropping observations from 1980, 1990 censuses and 1987 DHS Direct
200
150
100
50
0
1960
1970
Observed
1980
Year
1990
2000
Loess Bandwidth 0.4
37
Major Differences: Spline vs. Loess
• As implemented, Loess smooths series more strongly than
Spline (high values of α predominate)
• Loess provides more stable forecasts
• Within range of observations, differences tend to be small
38
Comparison of Spline- and Loess-based
approaches for the estimation of child mortality
Richard Silverwood and Simon Cousens
London School of Hygiene and Tropical Medicine
16th April 2008
39
Levels and Trends of
Child Mortality in 2006
[Working Paper]
Estimates developed by the Inter-agency Group for Child Mortality Estimation
http://www.childinfo.org/areas/childmortality/methodology.php
40
The child mortality data base
• A DevInfo application
• www.childmortality.org
• The idea is for countries to become users for data entry,
assessment, estimation and dissemination
• Training to be implemented via regional workshops
41
Regional workshops for coordination and capacity
building
• Started in September 2008 in Bangkok
– One week training for Maternal Mortality and Child Mortality
• Workshop for LAC (March 2009)
•
Representatives of MOH and NSOs
• Review of data sources, methodologies, and estimation
procedures
• Hands-on training
42
Focal point for CM estimation in
New York
Edilberto Loaiza
[email protected].
Tel. 212-326 7243
QUESTIONS?
43