infant and child mortality multiple indicator cluster surveys- mics3 analysis and report writing...
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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)
0
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
0
10
20
30
40
50
60
70
80
90
100
110
120
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
0
10
20
30
40
50
60
70
80
90
100
110
120
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 DHSd96 DHSd02
UZBEKISTAN - UNDER-FIVE MORTALITY
0
10
20
30
40
50
60
70
80
90
100
110
120
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 DHSd96 MCSi00 DHSd02
UZBEKISTAN - UNDER-FIVE MORTALITY
0
10
20
30
40
50
60
70
80
90
100
110
120
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Year
Un
de
r-five
mo
rta
lity r
ate
(p
er
10
00
bir
ths)
WHO VR DHSd96 DHSi96 MCSi00 DHSd02 DHSi02
UZBEKISTAN - UNDER-FIVE MORTALITY
0
10
20
30
40
50
60
70
80
90
100
110
120
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Year
Un
de
r-five
mo
rta
lity r
ate
(p
er
10
00
bir
ths)
WHO VR DHSd96 DHSi96
MCSi00 DHSd02 DHSi02
UZBEKISTAN - UNDER-FIVE MORTALITY
0
10
20
30
40
50
60
70
80
90
100
110
120
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Year
Under-
five m
ort
alit
y r
ate
(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
0
20
40
60
80
100
120
140
160
180
200
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