Measuring differential maternal mortality using census data
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Transcript of Measuring differential maternal mortality using census data
Measuring differential maternal mortality using
census data
Tiziana LeoneLSE Health
Outline
Definitions Background Objectives and rationale Lesotho, Nicaragua and Zimbabwe Mortality/fertility adjustments Differential analysis Discussion
Definition
A maternal death is the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental causes.
A pregnancy related death the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the cause of death.
Measures of Maternal Mortality
000,100#
#X
livebirths
athsmaternaldeMMRatio
000,14915#
#X
women
athsmaternaldeMMRate
Background
Pressure to get the indicators right to measure progress of MDG 5
Vital registration coverage not sufficient to record maternal deaths
Maternal mortality ‘rare’ event: sample surveys need big sample in order to collect enough information Differential analysis even more challenging
Census has been recommended in countries that lack complete vital registration The data are unused
Objectives
Apply methodology to three different settings : Nicaragua, Lesotho and Zimbabwe
Apply smoothing functions to differential mortality
Few numbers
Population
TFR MMR GNI per capita
Net migratio
n
HIV
Lesotho 1.8m 3.1(4.2)
960(530)
$1,000 -0.78 ‰(-1)
40 (57)
23%(~9%)
Nicaragua
5.7m 3.2 83-170
$980 -1.13‰ 71 0.2%
Zimbabwe
11m 3.9 880 $340 -22‰ 44 15.6%
0e
Data refer to latest available year. Number in brackets for Lesotho refer to 1995
Data
Nicaragua 1995-2005 census Lesotho 1986-1996 census Zimbabwe 1992-2002 census
Methods for the PRMRSeries of evaluations methods based on demographic ‘indirect techniques’ with adjustments when needed. Hill et al 2001.
Check degree of death coverage in the population
General Growth BalanceSynthetic extinct generation
Check quality of fertility dataP/F Ratio 20-24
Check quality of information on pregnancy related deaths
No formal methods.
Mortality Adjustment
General Growth Balance - Zimbabwe, female, 1992-2002
0.0000
0.0100
0.0200
0.0300
0.0400
0.0500
0.0600
0.0000 0.0100 0.0200 0.0300 0.0400 0.0500
Death Rate x+
En
try -
Gro
wth
Rate
x+
Observed values
Fitted values
General Growth Balance - Lesotho, female, 1986-1996
-0.0100
0.0000
0.0100
0.0200
0.0300
0.0400
0.0500
0.0600
0.0000 0.0050 0.0100 0.0150 0.0200 0.0250 0.0300
Death Rate x+
En
try -
Gro
wth
Rate
x+
Observed values
Fitted values
General Growth Balance - Nicaragua, female, 1995-2005
0.0000
0.0100
0.0200
0.0300
0.0400
0.0000 0.0100 0.0200 0.0300 0.0400 0.0500
Death Rate x+
En
try -
Gro
wth
Rate
x+
Observed values
Fitted values
Regression line fitted for (5+)-(65+)
Synthetic Extinct Generations - Nicaragua, female, 1995-2005
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
5- 9 10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
Age Group
Com
ple
teness
of
Death
Reco
rdin
g
Adjustment factorsLesotho Nicaragua Zimbabwe
GGB coverage 30-65+ 71% 130% 75%
SEG coverage 15-65+ 56% 135% 79%
Intercept of fitted line 0.0034 0.0068 0.0008
Coverage of census 1 to census 2
1.034 1.0709 1.09
P/F ratio 20-24 1.292 1.122 1.016
Plausibility checks
Proportions of Births and Pregnancy-Related Deaths, Zimbabwe 2002
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
15-19 20-24 25-29 30-34 35-39 40-44 45-49
Age Group
Pro
po
rtio
n
Births
Preg-Related Deaths
Proportions of Births and Pregnancy-Related Deaths, Nicaragua 2005
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0 2 4 6 8
Age Group
Pro
po
rtio
n
Births
Preg-Related Deaths
Proportions of Births and Pregnancy-Related Deaths, Lesotho 1996
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
15-19 20-24 25-29 30-34 35-39 40-44 45-49
Age Group
Pro
po
rtio
n
Births
Pregnancy relateddeaths
MMR
Census Census unadjusted
UNICEF/WHO*
estimate
Reported
(2000-07)
Lesotho 568
(1996)
552 529 (1995)
760
Nicaragua 133
(2005)
129 170 (2005)
87
Zimbabwe 771
(2002)
1000 880
(2005)
560
*
Age specific PRMRAge Specific PRMR, Nicaragua 2005
0
500
1000
1500
2000
2500
15-19 20-24 25-29 30-34 35-39 40-44 45-49
Age
PR
MR
Age Specific PRMR Zimbabwe 2002
0
200
400
600
800
1000
1200
1400
1600
15-19 20-24 25-29 30-34 35-39 40-44 45-49
Age
PRM
R
Age specific PRMR, Lesotho 1996
0
500
1000
1500
2000
2500
3000
15-19 20-24 25-29 30-34 35+ 40-44 45-49
Age
PR
MR
Limitations PRMR
• Combines limitation of two adjustment measures• Balance between migration and HIV issues (5-65+ vs
30-65+)• Adjustment is intercensal while PRD refer to year
before the census– Same for fertility
• In a period of rapid fertility decline and increasing mortality (e.g. Lesotho and Zimbabwe) it might not be wise to use intercensal estimates.
• All causes of MM included– Only approximation of real MM
Differential analysis(Lesotho, Nicaragua)
• Residence
• Education level– Head of Household
• Wealth calculated using asset index Filmer and Pritchett
• Assumed adjustment factors constant
Differential PMMRResidence Education level Head of
HouseholdWealth
Urban Rural No ed 1-3 years
4-7 years
8+ Poor Middle Rich
Lesotho 314 565 892 903 492 388 822 624 516
Nicaragua 102 101 139 112 57 116 98 56
Smoothing modelling
LOESS function in R (Cleveland and Devlin, 1988)
logit (ma)=s(a) + ea
• Where m=PRMR• a=age• e=random error term
By differentials (e.g.: education, wealth, residence)
Scatterplot smoothing algorithm that behaves like a generalised linear model but without having to specify the form of independence
15 20 25 30 35 40 45
05
00
10
00
15
00
RuralUrban
PRMR by Residence, Lesotho 1996
Age
PR
MR
Some work and some don’t…
Discussion on differential analysis
• Differential analysis can spot differential inconsistencies
• Oversensitive on the tales due to low numbers– Loess curve a feasible option
• Best function to adapt data– Loess curves perform better than splines and polynomials
as based on local estimation hence less influences by values at the extremes
• Interpretation should focus on trend rather than single points
• Need for sensitivity analysis
Discussion on MM in census data
Census data give reasonable estimatesAlthough it’s only pregnancy related
Quick fix not feasible with high levels of migration-e.g. Zimbabwe
Constant adjustment by age might not work for maternal mortality
Need to cross-validate with DSS data.
More synergies needed between adult mortality and MM
Need for more advocacy and training
poormidrich
15 20 25 30 35 40
40
06
00
80
01
00
0
Age
PR
MR
PRMR by wealth quintile, Lesotho 1996
15 20 25 30 35 400
10
02
00
30
04
00
50
06
00
70
0
PoorMiddleRich
Age
PR
MR
PRMR by Wealth, Nicaragua 2005