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ChildMortality Estimation

Harmonization Prospects

Edilberto LoaizaBangkok, January 15, 2009

ESCAP workshop on MDG monitoring

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What is the main issue?What is the main issue?

Often data produced and used at the country level is Often data produced and used at the country level is different from the one produced and used at the global different from the one produced and used at the global level. This has been observed more recently during the level. This has been observed more recently during the UNICEF yearly reporting on the State of the World’s UNICEF yearly reporting on the State of the World’s Children and in particular the reporting of the under five Children and in particular the reporting of the under five mortality indicatormortality indicator

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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!!

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Harmonization CycleHarmonization Cycle

• Data collection Data collection • Data compilationData compilation• Data analysis and methodological workData analysis and methodological work• Data use and disseminationData use and dissemination• Statistical capacity buildingStatistical capacity building

• Data used for evidence-based programmingData used for evidence-based programming

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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)

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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?

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INDONESIA Under five mortality data in the 1970s

0

50

100

150

200

250

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

U5

MR

(5

per

10

00 li

ve b

irth

s)

cen71q5i wfs76q5d wfs76q5i

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INDONESIA Under five mortality data in the 1980s

0

50

100

150

200

250

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

U5

MR

(5

per

10

00 li

ve b

irth

s)

cen71q5i wfs76q5d wfs76q5i cen80q5i dhs87q5d dhs87q5i

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INDONESIA Under five mortality data in the 1990s

0

50

100

150

200

250

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

U5

MR

(5

per

10

00 li

ve b

irth

s)

cen71q5i wfs76q5d wfs76q5i cen80q5i dhs87q5d dhs87q5i dhs91q5d

dhs91q5i cen90q5i dhs94q5d dhs94q5i dhs97q5d dhs97q5i

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INDONESIA Under five mortality data 1960-2002

0

50

100

150

200

250

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

U5M

R (

5 pe

r 1

000

live

birt

hs)

cen71q5i wfs76q5d wfs76q5i cen80q5i dhs87q5d dhs87q5i dhs91q5d dhs91q5i

cen90q5i dhs94q5d dhs94q5i dhs97q5d dhs97q5i cen00q5i dhs02q5d dhs02q5i

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INDONESIA Under five mortality Estimates 1960-2007

0

50

100

150

200

250

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

U5M

R (

5 pe

r 1

000

live

birt

hs)

cen71q5i wfs76q5d wfs76q5i cen80q5i dhs87q5d dhs87q5i

dhs91q5d dhs91q5i cen90q5i dhs94q5d dhs94q5i dhs97q5d

dhs97q5i cen00q5i dhs02q5d dhs02q5i 2007IGME

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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

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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

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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

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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

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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)

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Approaches

• SPLINE

• LOESS

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• The model used is:

• 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

SPLINE: Weighted Least Square with Variable Slope

ln ( ) ( ) ( ) ( ) . . .5 0 0 1 2 31 2q b b da te b postk b postk ei i i i i

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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])

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Standard Weights

Type of Data Initial WeightCivil 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 0.0

20-24 0.2

25-29 1.2

30-34 1.2

35-39 1.2

40-44 0.8

45-49 0.4

When both direct and indirect estimates are available, weights are halved

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Methods: Weighted Least Square with Variable Slope

• Fit a model of log(q5) to time

Fixed slope Splines with variable slopes (5 knots)

Prob

abili

ty o

f Dyi

ng b

y Age

5 (q

5)

Date

Birth histories - direct Birth histories - indirect Birth histories - direct Birth histories - indirect

1960 1970 1980 1990 2000

0

100

200

300

Prob

abili

ty o

f Dyi

ng b

y Age

5 (q

5)

Date

Birth histories - direct Birth histories - indirect Birth histories - direct Birth histories - indirect

1960 1970 1980 1990 2000

0

100

200

300

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Underweighting a data set

The weights are modified if one data set appears to be an outlier

Standard weights Weighting 1979 WFS to zero

WFS 79WFS 79

Prob

abili

ty o

f Dyi

ng b

y Age

5 (q

5)

Date

Censuses Birth histories - direct Birth histories - indirect Birth histories - direct

1960 1970 1980 1990 2000

0

100

200

300

Prob

abili

ty o

f Dyi

ng b

y A

ge 5

(q5)

Date

Censuses Birth histories - direct Birth histories - indirect Birth histories - direct

1960 1970 1980 1990 2000

0

100

200

300

WFS ‘79WFS ‘79

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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

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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

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Loess: What Does α Do?

20

50

100

200

Und

er-5

Mor

tality

Rat

e (lo

g ss

scal

e)

1960 1970 1980 1990 2000Year

20

50

100

200

Und

er-5

Mor

talit

y R

ate

(log

sssc

ale)

1960 1970 1980 1990 2000Year

Small α Big α

Small α fits many small local regressions, averages results

Large α fits few wider regressions, averages results

Note log scale on y axis

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Smoothing U5MR

0

50

100

150

200

250

Und

er-

5 M

ort

alit

y R

ate

1960 1970 1980 1990 2000Year

Observed Fitted

Simple regression of log(U5MR) on Date of Observation:

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Smoothing U5MR Loess regression of log(U5MR) on Date of Observation, α = 0.1:

0

50

100

150

200

Und

er-

5 M

ort

alit

y R

ate

1960 1970 1980 1990 2000Year

Observed Loess Bandwidth 0.1

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Smoothing U5MR

Loess regression of log(U5MR) on Date of Observation, α = 0.4:

0

50

100

150

200

Und

er-

5 M

ort

alit

y R

ate

1960 1970 1980 1990 2000Year

Observed Loess Bandwidth 0.4

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Smoothing U5MR Loess regression of log(U5MR) on Date of Observation, α = 1.0:

0

50

100

150

200

Und

er-

5 M

ort

alit

y R

ate

1960 1970 1980 1990 2000Year

Observed Loess Bandwidth 1.0

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Smoothing U5MR Are there any data sets we should exclude?

0

50

100

150

200

Und

er-

5 M

ort

alit

y R

ate

1960 1970 1980 1990 2000Year

NLT Census70 PCS74 WFS75d

WFS75i Census80 CPS81 CPS84PCS85 DHS87d DHS87i PCS89Census90 Census00 MICS05

1990 Census

1980 Census

DHS 1987 (direct)

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Smoothing U5MR Loess regression of log(U5MR) on Date of Observation, α = 0.4,

Dropping observations from 1980, 1990 censuses and 1987 DHS Direct

0

50

100

150

200

Und

er-

5 M

ort

alit

y R

ate

1960 1970 1980 1990 2000Year

Observed Loess Bandwidth 0.4

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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

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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

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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

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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

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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

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Focal point for CM estimation in New York

Edilberto Loaiza

eloaiza@unicef.org.

Tel. 212-326 7243

QUESTIONS?