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