Transcript Measuring differential maternal mortality using census data in developing countries.

Measuring differential
maternal mortality using
census data
Tiziana Leone
LSE 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
# maternaldeaths
MMRatio 
X 100,000
# livebirths
# maternaldeaths
MMRate 
X 1,000
# women15  49
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
migration
e0
Lesotho
1.8m
3.1
(4.2)
960
(530)
$1,000
-0.78 ‰
(-1)
40
(57)
23%
(~9%)
Nicaragua
5.7m
3.2
83170
$980
-1.13‰
71
0.2%
Zimbabwe
11m
3.9
880
$340
-22‰
44
15.6%
Data refer to latest available year. Number in brackets for Lesotho refer to 1995
HIV
Data
 Nicaragua
1995-2005 census
 Lesotho 1986-1996 census
 Zimbabwe 1992-2002 census
Methods for the PRMR
Series 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 Balance
Synthetic extinct generation
Check quality of fertility data
P/F Ratio 20-24
Check quality of information on pregnancy related
deaths
No formal methods.
Mortality Adjustment
Synthetic Extinct Generations - Nicaragua, female, 1995-2005
General Growth Balance - Lesotho, female, 1986-1996
General Growth Balance - Nicaragua, female, 1995-2005
1.20
0.0600
Entry - Growth
x+ Recording
Completeness
of Rate
Death
0.0400
0.0500
0.0300
0.0200
Observed values
0.0100
0.0000
0.0000
Fitted values
0.0050
0.0100
0.0150
0.0200
0.0250
1.00
0.0300
0.90
0.0200
0.80
0.70
0.50
0.0000 5-9 100.0000 14
0.0300
Observed values
Fitted values
0.0100
0.60
15- 20- 25- 30- 35- 40- 45- 500.0100
19
24
29 0.0200
34 39 44 0.0300
49 54
Death Age
RateGroup
x+
-0.0100
Death Rate x+
General Growth Balance - Zimbabwe, female, 1992-2002
0.0600
0.0500
Entry - Growth Rate x+
Entry - Growth Rate x+
0.0400
1.10
0.0400
0.0300
0.0200
Observed values
Fitted values
0.0100
0.0000
0.0000
0.0100
0.0200
0.0300
0.0400
Death Rate x+
Regression line fitted for (5+)-(65+)
0.0500
55- 60- 65- 70- 75- 800.0500
590.0400
64 69 74
79 84
Adjustment factors
Lesotho
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,
Lesotho 1996
Proportions of Births and Pregnancy-Related Deaths,
Nicaragua 2005
0.40
Births
0.35
0.30
Pregnancy related
deaths
0.30
0.25
Proportion
0.35
0.20
0.15
Preg-Related Deaths
0.20
0.15
0.10
0.05
0.05
0.00
Births
0.25
0.10
0.00
15-19
20-24
25-29
30-34
35-39
40-44
45-49
0
2
Age Group
4
Age Group
Proportions of Births and Pregnancy-Related
Deaths, Zimbabwe 2002
0.40
0.35
Births
0.30
Proportion
Proportion
0.40
Preg-Related Deaths
0.25
0.20
0.15
0.10
0.05
0.00
15-19
20-24
25-29
30-34
Age Group
35-39
40-44
45-49
6
8
MMR
Census
Census
unadjusted
Lesotho
568
(1996)
552
Nicaragua
133
(2005)
Zimbabwe
771
(2002)
*
UNICEF/
WHO*
estimate
529
(1995)
Reported
(2000-07)
129
170
(2005)
87
1000
880
(2005)
560
760
Age specific PRMR
Age specific PRMR, Lesotho 1996
Age Specific PRMR, Nicaragua 2005
3000
2500
2500
2000
PRMR
1500
1500
1000
1000
500
500
0
0
15-19
20-24
25-29
30-34
35+
40-44
15-19
45-49
20-24
25-29
30-34
Age
Age
Age Specific PRMR Zimbabwe 2002
1600
1400
1200
1000
PRMR
PRMR
2000
800
600
400
200
0
15-19
20-24
25-29
30-34
Age
35-39
40-44
45-49
35-39
40-44
45-49
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 PMMR
Residence
Education level Head of
Household
Wealth
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
57
116
98
56
112
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
PRMR
1000
1500
PRMR by Residence, Lesotho 1996
0
500
Rural
Urban
15
20
25
30
Age
35
40
45
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 estimates
Although it’s only pregnancy related
Quick fix not feasible with high levels of migratione.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
PRMR by Wealth, Nicaragua 2005
Poor
Middle
Rich
0
100
400
poor
mid
rich
200
PRMR
600
PRMR
300
400
500
800
600
700
1000
PRMR by wealth quintile, Lesotho 1996
15
20
25
30
Age
35
40
15
20
30
25
Age
35
40