Infant and Child Mortality Multiple Indicator Cluster Surveys- MICS3 Analysis and Report Writing Workshop Panama City, July 12-20, 2006.

Infant and Child Mortality

Multiple Indicator Cluster Surveys- MICS3Analysis and Report Writing Workshop

Panama City, July 12-20, 2006

Indicators’ Definition

Under-five mortality rate Probability of dying by exact age 5 years

Infant mortality rate Probability of dying by exact age 1 year

Goals World Submit for Children (WSC)Between 1990 and the year 2000, reduction of infant and under-five child mortality rate by one third or to 50 and 70 per 1000 live births respectively, whichever is less

Millennium Development Goals (MDGs)Reduce by two-thirds, between 1990 and 2015, under-five mortality

Indicator 13 – Under-5 Mortality Rate

Indicator 14 – Infant Mortality Rate

Why to measure child mortality

Reasons:

• 5q0 is a broad indicator of social development/health status

• to evaluate impact of interventions based on trends

Data sources/methods

• Vital registration• Population census• Longitudinal or prospective sample surveys• Household surveys

– Data from birth histories as from DHS– Data to use “Brass methods” as from MICS3

Which countries included this module in MICS3?

• 6 out of 7• Belize, Dominican Republic, Guyana, Jamaica, Suriname and Trinidad and Tobago• Cuba did not• Mongolia?

U5MR estimates for Caribbean countries conducting MICS3. UNICEF 2004 (2006 SOWC)

0

10

20

30

40

50

60

70

Cuba Jamaica Trinidadand Tobago

DominicanRepublic

Belize Suriname Guyana

U5MR

Which is the approach in MICS3?

• Indirect estimation using the Brass method to derive values for U5MR and IMR• Use other existing estimates and compare along time to produce trends along time• Report within the existing context and limitations

The “Brass” approach

• Data needed – Number of women by age (5 years)– Number of children ever born– Number of children dead/alive (surviving)

• Selection bias– Uses data for surviving mothers only– May be greater in countries affected by HIV/AIDS

(prevalence of 5% or more)

Characteristics of the “Brass” method

• Questions are short and simple• Provide acceptable mortality estimates over a

period of 10 years and more• Does not provide estimates for:

– the age patterns of child mortality– causes of death

The “Brass” equation

• Brass was the first to develop a procedure for converting proportion dead of children ever born (D(i)) reported by women in age groups 15-19, 20-24, etc. into estimates of probability of dying before attaining certain exact childhood ages, q(x):

q(x) = K(i)*D(i) where the multiplier K(i) is meant to adjust for non

mortality factors determining the value of D(i)

What does the “Brass” method do?

• Brass found that the relation between the proportion of children dead D(i), and a life-table mortality measure, q(x), is primarily influenced by the age pattern of fertility, because it is this pattern that determines the distribution of the children of a group of women by length of exposure to the risk of dying

• Brass developed a set of multipliers to convert observed values of D(i) into estimates of q(x), the multipliers being selected according to the value of P(1)/P(2), where P(i) is the average parity or number of children ever born reported by women in the age group i

• Brass used a third-degree polynomial of fixed shape

What does the “Brass” method do?

• Brass estimated the k(i) multipliers by using– a third-degree polynomial of fixed shape but variable age

location to represent fertility,– The logit system generated by the general standard to

provide the mortality element, and– A growth rate of 2% per annum to generate a stable age

distributions for females

Modifications of the “Brass” method

• Sullivan computed another set of multipliers using LSR to fit the equation to data generated from observed fertility schedules and the Coale-Demeny life tables

• Trussel estimates a third set of multipliers by the same means but using data generated from the model fertility schedules developed by Coale and Trusell.

• Feeney developed an estimation procedure to establish the set of years to which infant mortality estimated from data on children ever born and children surviving refer

Assumptions of the “Brass” method

• A constant patterns and level of mortality have prevailed in the recent past

• Fertility has been roughly constant in the recent past

• Child mortality has been changing in a linear way in the recent past

Model age patterns of child mortality

• Similar across human populations• Model life-tables. Single parameter (level) for different

age patterns– Coale-Demeny patterns by regionCoale-Demeny patterns by region:

East, North, South, and West

– United Nations patterns by regionUnited Nations patterns by region:

Latin America, Chilean, South Asian, Far Eastern, and General

Coale-Demeny Models(Trussel equations)

Total

.027 .004 1 .005 .7 .005 .7 .005 .7 .005 .7

.678 .056 2 .063 1.6 .065 1.6 .063 1.6 .064 1.6

2.135 .081 3 .083 3.1 .087 3.1 .085 3.2 .086 3.2

3.421 .086 5 .089 5.0 .090 5.1 .089 5.3 .089 5.2

4.227 .112 10 .122 7.2 .119 7.5 .118 7.7 .118 7.6

5.096 .114 15 .122 9.8 .119 10.3 .118 10.5 .119 10.2

5.605 .133 20 .139 12.8 .137 13.5 .136 13.8 .137 13.3

15-19

20-24

25-29

30-34

35-39

40-44

45-49

Agegroup

Mean childrenever born

Proportionchildren

dead Age i Q(i) North t(i) North Q(i) South t(i) South Q(i) East t(i) East Q(i) West t(i) West

Choice of inappropriate age pattern of mortality results in...

• A misestimation of trends

• However, the 5q0 estimate obtained from women 30-34 and referred to about 6 years before the survey is little affected.

The Age Pattern of Mortality in Childhood

How to select a mortality pattern?– Independent information– Successive data sets– Geographical proximity

The WEST model appears to be the more common age pattern of mortality in the region

The Age Pattern of Mortality in Childhood

How to select a mortality pattern?– Independent information– Successive data sets– Geographical proximity– Graphic interpolation

Coale and Demeny family patterns (1q0 vs 5q0)

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50

100

150

200

250

300

0 20 40 60 80 100 120 140 160 180 200

1qo * 1000

5q0

* 10

00

North South East West

Mortality pattern in the LAC countries

Country Life table model

Belize West (East?)

Dominican Republic West

Guyana West (South?)

Jamaica West

Suriname West

Trinidad and Tobago East (West?)

Mongolia West

Methodology for calculation

• SPSS program to produce tables for preliminary and final MICS3 reports

• MORTPAK program to produce estimates when data set is not available but basic data can be used

SPSS Program

• Generates basic tables (CM.1A)• Generates IMR and U5MR total and by

background variables (CM.2)• The program assumes:

– Definition of a pattern from the Coale and Demeny families (i.e. East, West, North, or South)

– Definition of age groups used to produce the mortality estimates included in table 8 (20-24, 25-29, 30-34)

• These choices have to be done before running the SPSS program

Table CM.1a: Mean number of children ever born (CEB) andproportion dead by mother's age, Country, Year

.013 .000 2445

.360 .057 1981

1.102 .103 1428

1.849 .097 1270

2.259 .115 1192

2.631 .124 1137

2.907 .147 790

1.235 .115 10243

15-19

20-24

25-29

30-34

35-39

40-44

45-49

Age

Total

Mean numberof CEB

Proportiondead

Numberof women

Under-five Mortality RateTotal

2005.0 .000 2005.0 .000 2005.0 .000 2005.0 .000

2004.1 .079 2004.1 .072 2004.1 .069 2004.1 .074

2002.6 .096 2002.6 .092 2002.5 .090 2002.5 .093

2000.7 .089 2000.6 .090 2000.4 .089 2000.5 .089

1998.5 .105 1998.2 .113 1998.0 .110 1998.1 .108

1995.9 .098 1995.4 .109 1995.2 .106 1995.5 .102

1993.0 .102 1992.2 .119 1991.9 .114 1992.4 .109

15-19

20-24

25-29

30-34

35-39

40-44

45-49

Agegroup

Referencedate North

Under-fiveMortali ty

Rate NorthReferencedate South

Under-fiveMortali ty

Rate SouthReferencedate East

Under-fiveMortali tyRate East

Referencedate West

Under-fiveMortali tyRate West

Infant and under-five mortality rates bybackground and demographic characteristics

[BASED ON NORTH], Country, Year

.066 .100

.048 .073

.051 .076

.060 .091

.057 .087

Male

Female

Sex

Urban

Rural

Area

Total

Infant Mortal i tyRate

Under-fiveMortali ty Rate

Fertility and Mortality values

Country TFR U5MR

Belize 3.1 39

Cuba 1.6 7

Dominican Republic 2.7 32

Guyana 2.2 64

Jamaica 2.4 20

Suriname 2.6 39

Trinidad and Tobago 1.6 20

Mongolia 2.4 52

Disaggregation of estimates by background variables

• Use dichotomous variables as much as possible: boys/girls, urban/rural, mothers with education/without education, poorest 60%/richest 40%, etc.)

• No more than 4 groups for region and ethnic group• Beware of sampling errors when reporting current

differences or trends

Issues for Discussion• Disaggregation of estimates by background variables

– Use dichotomous variables (poorest 60%/richest 40%, etc.)– Beware of sampling errors

• Differences between household survey estimates and those from administrative records and vital registration

• Current estimates produced by the inter-agency mortality estimation group

IMR estimates for Caribbean countries conducting MICS3. UNICEF 2004 (2006 SOWC) and ECLAC 2003

0

10

20

30

40

50

Cuba Jamaica Trinidadand

Tobago

DominicanRepublic

Belize Suriname Guyana

IMR

2004 UNICEF 2003 ECLAC

Are we measuring the same?

Existing research indicates that:• There are evidences of mis-reporting and/or

omission of deaths • Measurement errors

The inter-agency mortality estimation group

• Sponsored at the global level by UNICEF, WHO, the WB, the UNPD

• Produces country estimates of U5MR and IMR and trends from all available values

• Estimates are obtained by a regression model fitted to all available values

• Estimates are yearly presented as part of the agencies’ yearly publication and as part of the MDG report

UZBEKISTAN - UNDER-FIVE MORTALITY

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1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Under-

five m

ort

alit

y r

ate

(per

10

00 b

irth

s)

WHO VR

UZBEKISTAN - UNDER-FIVE MORTALITY

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1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

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Under-

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(per

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WHO VR DHSd96 DHSd02

UZBEKISTAN - UNDER-FIVE MORTALITY

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1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

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Under-

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(per

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WHO VR DHSd96 MCSi00 DHSd02

UZBEKISTAN - UNDER-FIVE MORTALITY

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1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

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r-five

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bir

ths)

WHO VR DHSd96 DHSi96 MCSi00 DHSd02 DHSi02

UZBEKISTAN - UNDER-FIVE MORTALITY

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1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

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r-five

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WHO VR DHSd96 DHSi96

MCSi00 DHSd02 DHSi02

UZBEKISTAN - UNDER-FIVE MORTALITY

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1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Under-

five m

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alit

y r

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(per

1000 b

irth

s)

WHO VR DHSd96 DHSi96 MCSi00

DHSd02 DHSi02 EST_E

MORTPAK

• Package developed by the UN Statistics/Population(?) Division

• Includes many modules• Mortality estimation via the Brass approach is one of the

modules• Requires inputs and decisions from user:

– Values for year and month of survey, and sex

– Selection of region fro C & D patterns

– Analysis of results and decision on age groups to be used (20-24, 25-29, 30-34 or averages)

Thank You!

MONGOLIA - UNDER-FIVE MORTALITY

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140

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220

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Und

er-f

ive

mor

talit

y ra

te (

per

1000

birt

hs)

DSi94 RHSd98 RHSi98 MICSi00

RHSd03 RHSi03 EST