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

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Multiple Indicator Cluster Surveys- MICS3

Analysis and Report Writing Workshop

Panama City, July 12-20, 2006

Infant and Child Mortality


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Indicators’ Definition

  • Under-five mortality rateProbability of dying by exact age 5 years

  • Infant mortality rateProbability of dying by exact age 1 year


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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


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Why to measure child mortality

  • Reasons:

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

  • to evaluate impact of interventions based on trends


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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


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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?


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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


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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)


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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


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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)


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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


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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


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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


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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


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Model age patterns of child mortality

  • Similar across human populations

  • Model life-tables. Single parameter (level) for different age patterns

    • Coale-Demeny patterns by region:

    • East, North, South, and West

    • United Nations patterns by region:

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


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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.


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The Age Pattern of Mortality in Childhood in...

  • 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


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The Age Pattern of Mortality in Childhood in...

  • How to select a mortality pattern?

    • Independent information

    • Successive data sets

    • Geographical proximity

    • Graphic interpolation


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Mortality pattern in the LAC countries in...

  • CountryLife table model

  • Belize West (East?)

  • Dominican Republic West

  • Guyana West (South?)

  • Jamaica West

  • Suriname West

  • Trinidad and Tobago East (West?)

  • Mongolia West


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Methodology for calculation in...

  • 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


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SPSS Program in...

  • 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



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Disaggregation of estimates by background variables in...

  • 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


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Issues for Discussion in...

  • 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


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Are we measuring the same? in...

  • Existing research indicates that:

  • There are evidences of mis-reporting and/or omission of deaths

  • Measurement errors


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The inter-agency mortality estimation group in...

  • 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


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MORTPAK in...

  • 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)


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Thank You! in...


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