February 2005
Maternal-Neonatal Health (MNH) and
Poverty: Factors Beyond Care that Affect
MNH Outcomes1
Thomas W. Merrick
World Bank Institute
Washington, DC
1
Background paper for Maternal-Newborn Health and Poverty (MNHP) Working Area 4: Making health systems
respond to the health needs of mothers and their children, especially the poor and marginalized, during pregnancy,
childbirth and the postpartum period. Review 4.f: Other reproductive health matters and inter-sectoral concerns.
Table of Contents
Introduction ..................................................................................................................................... 3
The Pathways Framework........................................................................................................... 3
Data on MNH and Poverty ............................................................................................................. 6
Data and Measurement Issues ..................................................................................................... 6
Regional and Cross-National Differences .................................................................................. 6
Table 1: Comparison of 1995 and 2000 Regional and Global Totals..................................... 7
Table 2: 2001 Global and Regional Estimates of Neonatal Mortality .................................... 7
Poverty Linkages ........................................................................................................................ 8
Table 3: Attendance at Delivery by a Medically Trained Person by Wealth Quintile ........... 8
Table 4: Averages of Pathways Indicators for Countries Grouped by MMR Level .............. 9
Linkages with Other Factors ....................................................................................................... 9
Table 5: Pathways Indicators for Large Countries ............................................................... 10
Table 6: Pathways Indicators for Countries with Highest MMRs ........................................ 11
Cross-National Analyses. .......................................................................................................... 11
Table 7: Correlations between Pathways Variables and MMRs .......................................... 11
Table 8: Averages of Pathways Indicators for Countries Grouped by Neonatal Mortality
Rate ....................................................................................................................................... 12
Review of Evidence on Pathways Variables ................................................................................ 15
Other Reproductive Health Risk Factors .................................................................................. 15
Table 9: Total Fertility Rates by Wealth Quintile and Region ............................................. 15
Table 10: Contraceptive Prevalence by Wealth Quintile and Region .................................. 16
Table 11: Adolescent Fertility Rates by Wealth Quintile and Region.................................. 16
Table 12: Selected Findings on Other Reproductive Health Risks....................................... 21
Household and Community Factors.......................................................................................... 21
Health System Failures ............................................................................................................. 25
Table 13: Selected Findings on Health System Issues.......................................................... 28
Other Sectors ............................................................................................................................. 29
Table 14: Selected Findings on Transport Interventions ...................................................... 31
Table 15: Low Body-Mass in Reproductive-Age Women by Wealth Quintile and Region 33
Table 16: Child Malnutrition by Wealth Quintile and Region ............................................. 33
Public Policy and Governance: ................................................................................................. 33
Table 17: Governance Indicators for Countries Grouped by MMR Level ........................... 34
Addressing Obstacles and Information Gaps ................................................................................ 34
Policy and Program Actions ..................................................................................................... 34
Table 18: Actions to Address Obstacles ............................................................................... 35
Strengthening the Evidence Base.............................................................................................. 36
Table 19: Estimates for Burden of Disease for sub-Saharan Africa, 2002 ........................... 36
Table 20: Longitudinal Survey Programs ............................................................................. 38
Conclusion .................................................................................................................................... 39
Annex 1: Maternal Mortality in India ........................................................................................... 40
Annex Table 1: MMRs and Other Indicators for Indian States ............................................ 41
Annex 2: Country-Level Data Table............................................................................................. 42
References ..................................................................................................................................... 46
2
Introduction
Every year more than half a million maternal deaths and around four million perinatal deaths
occur in low and middle-income countries, mostly among the poorest groups within these
countries. There is an even larger toll of morbidities (more than 8 million each year according to
Koblinsky et al., 1993) resulting from non-fatal complications of delivery. Most of these deaths
and disabilities are preventable, and the interventions required to prevent them are known. The
sad reality is that in many instances these interventions are either not available to poor women or
so poor in quality that they are ineffective.
Other reports in this series address the “what” and “how to” of health care interventions to
prevent maternal and neonatal mortality and morbidity. This paper focuses on the obstacles that
prevent poor women from benefiting from the knowledge and technical expertise that is
available, and on the factors beyond care that shape maternal and neonatal health (MNH)
outcomes. It employs an adapted version of the “Pathways” framework from the World Bank’s
guidelines for Poverty Reduction Strategy Papers to link factors at various levels—from
individuals, households and communities to government policies in health and other sectors—
that directly or indirectly affect MNH outcomes.
The paper begins with an explanation of what the Pathways framework is and how it will be used
to guide this discussion. That is followed by a brief discussion of measurement issues and by a
review of cross-national evidence about linkages between factors identified in the Pathways
framework and MNH outcomes. More detailed country-level evidence on factors at each of the
levels (households and communities, health and other sectors, policy) will then be reviewed,
leading to recommendations for actions that could be taken to address obstacles at each of these
levels as well as research needed to strengthen the evidence base to assess the impact of these
actions.
In adapting the Pathways framework to MNH outcomes, the paper draws on other frameworks
that have focused specifically on contextual factors affecting maternal health outcomes, for
example the one developed by the Prevention of Maternal Mortality collaborators at Columbia
University and in West Africa (McCarthy and Maine, 1993; Thaddeus and Maine, 1994;
McCarthy, 1997) as well as broader frameworks that address a range of health outcomes (for
example, Hanson et al., 2003). These frameworks address the role of household and communitylevel variables on outcomes.2
The Pathways Framework
The chart below depicts a simple framework for assessing the impact of factors inside and
outside the health system that influence health, including MNH, as shown in the left-hand
column of the chart. These factors operate at three levels, shown in the other three columns of
the chart: (1) households and communities, (2) the health system (including health care, health
finance, health promotion) and sectors other than health such as education, infrastructure (water
and sanitation, transport and communications) that indirectly influence health outcomes, and (3)
2
The framework for addressing obstacles to access to priority health interventions developed by Hanson et al.
(2003) has five levels that parallel those in the Pathways framework used here: (1) communities and households; (2)
health service delivery; (3) health sector policy and strategic management; (4) cross-cutting public policy; and (5)
environmental and contextual characteristics.
3
public policies and actions that affect health systems and outcomes directly (health reforms, for
example) or indirectly (macro-economic policies).
Pathways to MNH Outcomes
Health
Outcomes
MNH
outcomes
Households/
Communities
Household
behaviors
& risk
factors
Household
assets
Community
factors
Health system & other
sectors
Government
policies &
actions
Health
care
Health
finance;
health
promotion
Supply in
related
sectors
Health
policies
Policies
in other
sectors
Households and Communities: Good health is dependent not only on the provision of good
medical services when required but also on healthy behavior. Healthy behavior means avoiding
or minimizing risks (e.g. practicing family planning and safe sex) and requires knowledge about
how to prevent disease and promote health and the ability to act on this information. Many
health-promoting behaviors (for example dietary habits, sanitary practices, fertility regulation,
childcare, and utilization of health services) take place within families. Such behaviors are often
related to ‘household assets’ such as income, education, access to health services, roads and
communication, membership in formal and informal support networks, as well as general
knowledge and information. Health too is itself a household asset. Economists view these
household-level determinants as demand-side factors, as opposed to the supply of health care
(Ensor and Cooper, 2004).
Economic research has tended to treat all members of a family, or household, as a single unit,
assuming that whatever benefits one member will benefit the entire household. This is clearly
not the case in reality, as it is widely acknowledged that intra-household differences in gender
and age may significantly affect how decisions are made and whether a decision is beneficial for
all members (Case and Deaton, 2002). Thus an understanding of household decision-making is
critical to an understanding of how policy decisions affect the welfare of families as a unit or of
individual members within them.
Gender disparities in access to education, credit and political influence have considerable impact
on how individual family members are valued and on the degree to which women as well as men
4
have a voice in household decision-making. Recognition of the importance of individuals and
households in producing good (or poor) reproductive health outcomes should lead policy makers
to focus on the constraints faced by vulnerable households and vulnerable members within those
households.
Household-level behaviors and risk factors are influenced and reinforced by conditions in the
community (Tinker, 2000). Community factors include both the values and norms that shape
household attitudes and behaviors and the physical and environmental conditions of the
community, for example terrain and weather conditions that affect households’ capacity to
produce better outcomes. Community factors that typically influence health outcomes are:




Gender norms, which are influenced by social and cultural values that shape the roles of
and relationships between men and women;
Existence of effective community groups, social cohesion (sometimes called social
capital) that support positive behaviors individuals and families or organize actions to
improve health outcomes directly (community health insurance or pooling of resources to
transport emergency cases) or indirectly (micro-credit programs);
Community access to public services (inside and outside the health sector); and
Environmental conditions (safe water, location—distance from a health facility, terrain,
weather conditions).
Health System and Other Sectors: The supply of health care and health information are key
determinants of maternal and neonatal health outcomes for the poor. Since these interventions
are covered in detail in other papers in the series, this paper will address two potential supplyside obstacles that may prevent poor women from benefiting from these services. These are: (1)
financial obstacles including fees and/or coverage of critical services in insurance benefit
packages; and (2) the organizational and institutional obstacles to scaling up effective
interventions so that poor women can access them.
Actions in other sectors also affect health outcomes, for example:
 Education, either formal education or training that enhance earnings capacity of
household members as well as their capacity for effective health-seeking behaviors;
 Transport and infrastructure, for example the availability of services and the quality of
roads that can affect travel time when a mother requires transport for management of an
obstetric emergency;
 Energy and communications, for example coverage in a cell-phone network so that help
can be sought it an emergency;
 Water and sanitation, important for avoiding infections; and
 Nutrition.
Government Policies and Actions can affect both the health system and related factors in other
sectors. Most countries have undertaken reform as a way to profoundly change the fundamentals
of the health sector including changes in organization and accountability, revenue generation
allocation and purchasing, as well as regulation. Government policies and actions in other
sectors also affect health because of their influence on attitudes and behavior and the supply of
related services such as education, transport, water and food security. Fees for schools and
health services are examples of public policies that may have an indirect effect on MNH
5
outcomes. Taxation may also be a strong influence on health, for example “sin taxes” on
tobacco or alcohol.
Data on MNH and Poverty
Data and Measurement Issues
The paper utilizes cross-national estimates of maternal mortality from the
WHO/UNICEF/UNFPA database prepared by AbouZahr and Wardlaw (2003). Estimates are
provided for 172 countries and are derived from a variety of sources, including vital registration
systems, direct and indirect estimating methods based on survey and census data, and statistical
modeling for the 62 countries for which no national data on maternal mortality were available.
This paper focuses on 142 countries in the World Bank's low and middle-income categories, and
most of the 62 countries with estimated maternal mortality ratios (MMRs) are in those
categories. The fact that statistical modeling was used to estimate MMRs creates some major
limitations for the analysis of cross-country differences, because many of potential explanatory
variables (fertility rates, GDP per capita, percentage of births assisted by a skilled attendant, and
regional dummy variables) have been used to estimate the proportion of deaths that are
considered “maternal” in country-level model life tables. The authors of the estimates emphasize
that they should not be used for trend analysis and urge caution in cross-national comparisons for
the reasons just stated.
Cross-national data on neonatal mortality rates (NMRs : deaths of liveborns during the first 28
days of life per 1000 live births) are from a compilation published in the State of the World’s
Newborns 2001 (Save the Children, 2002). Data for other cross-national indicators (per-capita
income, poverty, education, transport, governance, etc) are taken from the World Bank’s World
Development Indicators 2003, UNDP’s 2003 Human Development Report, and other sources
(see Annex 2).
In addition to cross-national comparisons and analysis, the paper will examine country-level
tabulations of key indicators from the Demographic and Health Surveys that have been tabulated
using a composite ‘household asset’ measure to show rich-poor differences in those indicators by
wealth quintiles (Gwatkin et al, 2004). Additional evidence from country and topic-related
studies will also be employed to fill out the picture of how factors beyond care may directly or
indirectly impact on MNH outcomes for the poor.
Regional and Cross-National Differences
Global, regional and country-level estimates of maternal mortality (Table 1) show a clear
connection between high maternal mortality ratios (MMRs) and poverty.
More than 99 percent of maternal deaths occur in developing regions, and more than 85 percent
occur in the poorest countries of Sub-Saharan Africa and South Central Asia. Country-level
estimates show that more than a quarter of those deaths occurred in India, and that several other
poor countries in these two regions (Bangladesh, Ethiopia, the Democratic Republic of Congo,
Nigeria, Pakistan, Tanzania) account for another quarter of them. The highest maternal mortality
ratios are found in poor countries in Sub-Saharan Africa. With the exception of Afghanistan, all
of the countries having maternal mortality ratios of 1000 or higher are found in Africa.
6
Table 1: Comparison of 1995 and 2000 Regional and Global Totals
Region
2000
Maternal
Maternal
Mortality Ratio
deaths
(000s)
400
529,000
20
2,500
28
2.2
440
527,000
830
251,000
130
4,600
920
247,000
330
253,000
55
11,000
520
207,000
210
25,000
190
9,800
190
22,000
240
530
WORLD TOTAL
DEVELOPED REGIONS*
Europe
DEVELOPING REGIONS
Africa
Northern Africa
Sub-Saharan Africa
Asia
Eastern Asia
South-central Asia
South-eastern Asia
Western Asia
Latin America & the Caribbean
Oceania
1995
Maternal
Maternal
Mortality Ratio
deaths
(000s)
400
515,000
21
2,800
36
3.2
440
512,000
1,000
273,000
200
7,200
1,100
265,000
280
217,000
60
13,000
410
158,000
300
35,000
230
11,000
190
22,000
260
560
Includes Canada, United States of America, Japan, Australia and New Zealand, which are excluded from the
regional averages.
Data on neonatal mortality appear to be even scarcer than those for maternal mortality. Regional
patterns for neonatal mortality (Table 2) are very similar to those for maternal mortality. Africa
and South Asia account for over 93 percent of global deaths.
Table 2: 2001 Global and Regional Estimates of Neonatal Mortality
Region
Number of live
births (1000s)
Neonatal deaths
(1000s)
Neonatal death
rate (per 1000
live births)
42
34
46
21
17
Africa
Asia*
South-Central Asia
Other Asia
Latin America and the
Caribbean
Pacific Islands*
Europe
North America
More Developed
Countries
Less Developed
Countries
Global
28,865
76,090
38,442
37,648
11,553
1,205
2,561
1,757
804
196
225
7,374
4,098
13,045
8
44
18
65
34
6
4
5
116,550
3,970
34
129,596
4.035
31
* Japan, Australia and New Zealand are included with the More Developed Countries but not in
the regional sub-estimates.
Source: Save the Children, 2002
Death rates are highest in the South Central Asia region, at 46 per thousand live births, followed
by Africa, with 42. The rate is lower for the Other Asia group because China is included there
and has a substantially lower neonatal death rate (23), compared to much higher rates for the
large countries in South Asia—Bangladesh (48), India (43), and Pakistan (49). While the Pacific
Islands account for a small proportion of neonatal deaths, their NNM rate is comparatively high.
7
Poverty Linkages
Measuring maternal and neonatal mortality for sub-groups of the population within countries is
even more challenging than country-level estimates. Graham and colleagues have developed a
technique for estimating rich-poor differentials in maternal mortality using Demographic and
Health Survey (DHS) data for 10 countries (Burkina Faso, Chad, Ethiopia, Indonesia, Kenya,
Mali, Nepal, Peru, Philippines and Tanzania) with large sample sizes using wealth-quintile
methodology developed at the World Bank (Graham et al, 2004; Gwatkin et al, 2004). In the
country with the largest sample size and also with two surveys, Indonesia, they found that the
poorest quintile accounted for one-third of all maternal deaths in both surveys, compared to
fewer than 13 percent of deaths in the richest quintile. They also found a high level of
association between the survival status of women and poverty status in all of the countries, and a
highly significant correlation between education and survival status.
Table 3: Attendance at Delivery by a Medically Trained Person by Wealth Quintile
Region
East Asia
Europe/Central Asia
L. America, Caribbean
Middle East, N. Africa
South Asia
Sub-Saharan Africa
All country average
No. of
countries
4
6
9
4
4
29
56
Regional
average
53.6
94.9
66.0
52.5
21.5
43.5
51.6
Poorest
quintile
26.6
88.4
43.2
33.6
7.0
24.2
32.7
Richest
quintile
90.4
99.2
93.3
80.3
56.7
77.1
81.7
Poor/rich
difference
63.8
10.8
50.1
46.7
49.7
53.4
49.1
Source: Gwatkin et al, 2004
It is also possible to get a sense of rich-poor differences for other countries (56 in all, including
the 10 for which MMRs by wealth quintiles have been calculated) by using the same DHS data
for countries on deliveries attended by medically trained persons. This indicator is known to be
highly correlated with both maternal and neonatal mortality (and has, in fact, been used to
estimate the proportion of maternal deaths in countries lacking other maternal mortality data).
Table 3 shows a 49 percentage point difference in the proportion of skilled attendance between
the richest and poorest quintiles for all 56 countries for which the tabulations have been made—
with the poorest quintile averaging 32.7 percent compared to 81.7 percent for the richest quintile.
Rich poor differences are greatest (64 percentage points) in East Asia, though only four countries
are included in the tabulations (Cambodia, Indonesia, the Philippines, and Vietnam) and least (11
percentage points) for Europe/Central Asia (six countries: Armenia, Kazakhstan, the Kyrgyz
Republic, Turkey, Turkmenistan, and Uzbekistan). That region also has the highest average
level of attendance, 88 percent. South Asia (four countries: Bangladesh, India , Nepal and
Pakistan) has the lowest overall level of skilled attendance, 21.5 percent and a 49.7 percentage
point poor-rich differential. Latin America has the second highest overall level of skilled
attendance (43 percent), but also a comparatively high (50 percentage point) differential between
the rich and the poor. Sub-Saharan Africa has a higher overall average for attended deliveries
(43.5 percent) than South Asia, which is puzzling given the MMR estimates for Africa. DHS
data are available for 29 countries in Africa, suggesting that the issue may be poor quality of
delivery care rather than under-representation of regional experience in the tabulations. Richpoor differences are in the middle range (53 percentage points) in Africa, though attendance for
the poorest quintile (25 percent) is second lowest in the tabulations after South Asia. The Middle
8
East/North Africa (MENA) group (four countries: Egypt, Morocco, Jordan, Yemen) is in the
middle of the range in terms of the regional average (52.5 percent) and rich-poor differential
(46.7 percentage points).
Another way of looking at the poverty link is to group countries by MMR level and then
compare them to average levels of per-capita GNP and other indicators in the Pathways
framework (female school enrollment, gender equality, paved roads, and governance).3 In Table
4, 142 low and middle-income countries are divided into five broad MMR groupings (0-30, 31100, 101-300, 301-1000, over 1000). GDP per capita for the group with the lowest MMRs
exceeds $10,000 (dollars in purchasing power parity terms—PPP), compared to just over $1,000
for the countries with the highest MMR levels, a ratio of greater than ten to one. Public health
expenditures per capita (also in PPP dollars) are more than ten times greater ($315) in the low
MMR group compared to the countries with the highest MMRs ($23).
Table 4: Averages of Pathways Indicators for Countries Grouped by MMR Level
MMR
Level
Number
of
countries
0-30
31-100
100-300
301-1000
Over 1000
17
39
32
41
13
GDP per
capita
(PPP$)
10,629
6,952
4,760
1,610
1,025
Public
health
spending
per capita
(PPP$)
315
168
118
30
23
Female
school
enrollment %
UNDP
gender
Index
72
74
70
43
31
.82
.79
.70
.48
.37
Paved
Roads
%
79
59
37
43
31
Effective
governance
0.41
-0.07
-0.35
-0.37
-1.08
Source: See Annex 2
Linkages with Other Factors
Large differences between the lowest and highest MMR groups are also seen for other indicators.
Female enrollment rates4 for the lowest group are more than twice as high as for the highest
group. The same is true for UNDP’s index of gender equality that combines a range of genderrelated indicators (where 1 is the top score). Countries with the lowest MMRs score more than
twice as high as those with high MMRs. A difference in the patterns for female school
enrollment and gender equality is that the first three MMR groups (those lower than 300) show
little variation in the three indicators, while countries with MMRs greater than 300 have
significantly lower levels.
There are also large differences between the low and high MMR groups for the percentage of
paved roads and effective governance scores. For the low MMR group, 79 percent of roads are
paved, compared to 31 percent for the high group. There is not the clustering among the first
three groups that was seen for school enrollment and gender equality. In the case of governance
scores (which can range from 2.5 as the best score to –2.5,the worst) the low MMR group has the
only positive average score (.41), while the high MMR group average –1.08.5 The table shows
simple averages. When population weighted averages were calculated, large countries like
3
Data for all 142 countries are found in Annex 2.
The indicator is the combined percentage of females in relevant age groups enrolled in primary, secondary and
tertiary levels of schooling. Data from UNDP 2003. Simple averages of the three enrollment rates from the World
Bank’s World Development Indicators were used for countries not included in the UNDP list.
5
Data and an explanation of the governance score can be found in Kaufmann, Kraay and Mastuzzi, 2003.
4
9
China and India significantly outweighed the other countries in their groups. Further, some
interesting patterns emerge if we examine the indicators for these two countries, along with
several other large countries. Table 5 shows indicators for the eight most populous countries.
Table 5: Pathways Indicators for Large Countries
Country
China
Russian
Federation
Indonesia
Brazil
Bangladesh
Pakistan
India
Nigeria
56
GDP
per
capita
PPP$
3,600
Public
health
exp. per
capita
(PPP$)
18
67
230
260
380
500
540
800
7,700
2,900
6,500
1,570
2,000
2,200
950
193
21
206
32
16
11
10
MMR
Female
school
enrollment
62
82
63
97
54
27
49
41
UNDP
gender
equity
index
0.72
0.77
0.68
0.77
0.50
0.47
0.57
0.45
22
Effective
governance
0.18
67
46
6
10
43
46
31
-0.40
-0.56
-0.22
-0.53
-0.50
-0.13
-1.12
Paved
Roads
Gini
Coef.
40.3
45.6
30.3
59.1
31.8
33.0
37.8
50.6
Source: See Annex 2
Countries are ranked by MMR level, with China the lowest and Nigeria the highest. China ranks
in the middle in terms of per capita GDP, health expenditures, and paved roads but at or near the
top on gender equity and governance, and also on income distribution, which is measured using
the Gini coefficient.6 India, which, per capita, spends on health nearly two-thirds the per capita
level of China, has an MMR rate almost ten times that of China and ranks next to last in MMR
level, while Brazil, which spends most on health, has an MMR higher than Indonesia, which
spends only a fraction of what Brazil does on health. Brazil has comparatively high levels of
female school enrollment and gender equality but has a low percentage of paved roads (an a very
large area to cover) and high-income inequality. India has a more equal income distribution but
worse gender indicators. Among the three South Asian countries (Bangladesh, India and
Pakistan), Bangladesh has the lowest MMR (though the decline, as recorded in the country’s
maternal health survey, is relatively recent). Nigeria, with the highest MMR, is near the bottom
on all indicators.
Averages for the large countries can mask considerable geographic variation within countries.
Several of India's states have larger populations than quite a number of countries in the global
comparisons, and state-level data reveal large differences in the MMRs between the poorer
northern and northeastern states and the more prosperous western and southern ones (see Annex
1 on Maternal Mortality in India).
When we look at the Pathways indicators of individual countries in the highest MMR grouping
(above 1000), a similar picture emerges. In Table 6, all eight countries have per capita GDP of
$1000 or less, and all have very low levels of per capita health expenditure. With the exception
of Malawi, female school enrollment is low. The gender equity indicator is available for only
five of the countries and is low for them, as are the percentages of paved roads and index of
Gini data from the World Bank’s World Development Indicators, 2003. The Gini coefficient ranges from 0 - 100,
with 100 indicating the worst inequality. Gini data were not available for many of the 142 countries in the larger
list, so averages were not calculated for this indicator in the previous table.
6
10
government effectiveness. For the last indicator, income inequality, Sierra Leone has the highest
level of income inequality and also the highest MMR, while the other countries on this list have
low to middle levels of inequality.
Table 6: Pathways Indicators for Countries with Highest MMRs
Country
MMR
Mali
Rwanda
Tanzania
Niger
Angola
Malawi
Afghanistan
Sierra Leone
1,200
1,400
1,500
1,600
1,700
1,800
1,900
2,000
Source: See Annex 2.
GDP
per
capita
(PPP$)
850
900
710
1,000
1,000
900
800
510
Public
health
exp. per
cap.
(PPP$)
15
18
22
13
28
29
11
3
Female
school
enrollment
26
51
31
14
26
70
n.d.
44
UNDP
gender
equity
index
.33
.42
.40
.48
n.d.
.38
n.d.
n.d.
Paved
Roads
12
8
4
8
10
19
13
8
Effective
governance
-0.84
-0.82
-0.51
-0.79
-1.16
-0.68
-1.39
-1.54
Gini
Coef.
50.5
28.9
38.2
50.5
n.d.
50.3
n.d
62.9
n.d. means no data available
While these comparisons of countries by MMR groups show a general pattern of poor indicators
for the high MMR group and good indicators for countries with low MMRs, they do not always
show a smooth progression of indicators from the low to high groups – as illustrated by the
roughly equal levels of female school enrollment in all three groups of countries with MMRs
below 300 and by individual country comparisons such as India, with a higher MMR than China
even though India has a higher ratio of paved roads.
Cross-National Analyses.
As noted earlier, statistical analysis of cross-national differences in MMRs is problematic
because many of the measures that one would like to use to explain cross-country differences
(per capita GDP, contraceptive prevalence, percent of deliveries with skilled attendant) have
been used to compile the estimates, so that correlations with these variables would be high and
reflect the estimating algorithm. However, correlations between MMRs and the Pathways
variables in Table 7 (which are not embedded in the MMR estimates, though correlated with
them) are also negative and highly significant. The strongest correlation is with public health
expenditures, though there are also significant relationships with education and gender equality,
government effectiveness, paved roads and safe water.
Table 7: Correlations between Pathways Variables and MMRs
Variable
Per capita public health
expenditures (log)
Female school
enrollment (percent of
school age pop)
Gender equality index
(1=maximum)
Number of
observations
142
Mean
4.12
Correlation with
MMR
-.733
140
60.4
-.661
116
.64
-.878
11
Percentage of paved
roads
Government
effectiveness
Safe water (percent of
households)
140
41.4
-.701
139
-.35
-.574
140
75.8
-.615
Source: See Annex 2; all correlations are significant at the .01 level.
A similar pattern appears when neonatal mortality data are grouped by level (Table 8) for the
indicators. These data are available for only 100 countries, with fewer richer countries as is clear
when looking at the group with the lowest NNM rates (0-10), for which the average GDP per
capita is $7,734 compared to an average of $10,629 for the lowest MMR group.
Table 8: Averages of Pathways Indicators for Countries Grouped by Neonatal Mortality
Rate
NNM level
Number of
countries
0-10
11-20
21-30
31-40
Over 40
22
19
22
16
21
GDP per
capita
(PPP$)
7,534
5,461
5,116
1,458
1,365
Public health
spending.
per cap.
(PPP$)
256
124
77
35
17
Female
school
enroll-ment
74
71
66
47
36
UNDP
gender
index
.88
.73
.59
.46
.43
Paved
Roads
62
43
48
25
18
Effective
governance
+.15
-.22
-.27
-.74
-.76
Source: See Annex 2
For the 21 countries with the highest NNM rates (over 40), average per capita GDP ($1,365) is
less than one-fifth that of the low NNM group. Public health expenditure per capita falls off
more steadily than GDP per capita. The education/NNM pattern is similar to the one for MMRs,
with the first three groups having higher rates than the two lower groups, while the remaining
three indicators decline steadily as the NNM levels rise. Overall, the relationships between the
NNM groups and the Pathways indicators are quite similar to those for the MMR groups.
Analytical work by World Bank researchers on what would be required to attain the Millennium
Development Goals in health has focused on these relationships using multivariate regression
techniques to study cross-national variation in the WHO’s 1995 maternal mortality estimates
(Wagstaff and Claeson, 2004). One of their main findings is that if interventions that are known
to be effective (skilled attendance at delivery, effective referral and management of obstetric
emergencies) could be increased from current levels to 99 percent, nearly four-fifths of the
529,000 maternal deaths that occur each year might be averted.
The authors and also address the question of how to ensure that these interventions are
implemented, particularly in ways that benefit poor women whom they are not now reaching. A
key question is whether additional government spending would help. One school of thought
holds that added government spending is likely to have little effect because of corruption and the
poor management of public services, but the authors of this study take the view that added
government spending could make a difference provided it is combined with improvements in
governance and is effectively targeted on the poor. They also call attention to the key role of
households, both as consumers of health services and as producers of health outcomes, in
ensuring that increases in delivery care will actually reduce maternal mortality and morbidity.
12
The study employed a simulation modeling process to project possible impacts of increased
government spending and improvements in government effectiveness as well as changes beyond
the health sector (added economic growth, increased education, improved water supply) on
achievement of the maternal mortality reduction target of the Millennium Development Goals.
The results are summarized in the chart below.
For each of the World Bank’s regions (the same regional breakdown as shown in the DHS
tabulations), the chart shows the rate of decline in MMRs from 1990 to 2000 (labeled “current”
with darker shading at top of bars) and the added decline that could occur (lighter shading at
bottom of each bar) if an additional 2.5 percentage points were added to the annual growth of
government health spending as a percentage of GDP, provided that countries achieve a level of
effectiveness in governance that is one standard deviation about the mean of cross-national
scores in the World Bank’s Country Policy and Institutional Assessment (CPIA) data.7 The chart
also shows (in the cross-hatched area in each bar) the potential contribution of “extra-sectoral
contributions” - added economic growth, better roads, quicker growth in female secondary
schooling, and improved access to drinking water.
Finally, the chart shows the rates of decline between 1990-2015 required in order to meet the
regional MDG goals for reduced maternal mortality (the black line across the graph–5.75 percent
annually) and rates that would be required between 2000 and 2015 for regions whose declines
were below the required level during 1990-2000 in order to catch up and reach the 2015 goal (the
small black squares in the bar).
Comparing the simulated results by region, the chart shows that only one region (the Middle East
and North Africa) experienced declines in the MMR during the 1990s at a rate that would enable
7
The model is based on cross-national regression analyses MMRs with the percentage of GDP expanded on health,
along with other variable including government effectiveness, female education and water supply. The methodology
and regression analyses are explained in Annex 2 of Wagstaff and Claeson, 2003.
13
them to reach the 2015 MDG goal. All of the others need higher rates of increase during 20002015 in order to catch up. In three regions (Europe/Central Asia, South and East Asia) a
combination of extra-sectoral contributions and additional government health expenditures
would bring the rate of decline up to the require level, but in two regions (Sub-Saharan Africa
and Latin America/Caribbean) the combination would not bring the rate up to the required level.
In the case of SSA, the projected impacts are just too weak to have an impact, and in LAC, the
levels of extra-sectoral contributions are currently high enough that added change does not
appear likely (though compared to other regions, MMR levels in LAC are already low).
This pessimism about the prospects for SSA has been countered by Jeffrey Sachs and his
colleagues at the Millennium Project, who recognize that governance issues have inhibited
efforts to reduce poverty in Africa but argue that the region suffers from a "poverty trap"
stemming from geopolitical and environmental problems. To escape from this trap, countries
need substantially larger investments in physical and human capital (Sachs et al, 2004). The
Millennium Project calls for massive increases in donor financial support to poor countries in
Africa. Critics of this recommendation caution that the effort is likely to run into substantial
bottlenecks in absorbing and effectively utilizing large increases in funding. The problems
arising from shortages of health personnel to scale up HIV/AIDS treatment is a case in point (see
Marchal et al, 2004).
In making the case for increased health expenditures, the World Bank authors emphasize that
they are talking not about across-the-board increases in expenditures but about targeted
expenditures to increase the quality and accessibility of the key interventions needed to reduce
the high maternal mortality ratios experienced by poor women. The report notes two promising
approaches to such targeting. One of these is Marginal Budgeting for Bottlenecks (MBB), which
targets added health expenditures on bottlenecks in health system performance. The second is
financing through Social Funds, which are targeted on improving the demand and capacities of
poor localities and households where these interventions need to occur.
MBB was developed by UNICEF, the World Bank, and WHO in conjunction with efforts to
ensure that funds created by debt relief actually benefit the poor (Soucat et al, 2004). It involves
the formulation of national or provincial-level medium expenditure plans that allocate newly
available resources to achieve an MDG like reduced MMR. MBB starts by using proxy
indicators from Demographic and Health Surveys and other data sources to identify potential
bottlenecks in five broad categories: (i) gaps in physical accessibility, (ii) human resource
bottlenecks, (iii) constraints related to supplies and logistics, (iv) demand and utilization
constraints, and (v) bottlenecks due to technical and organizational quality. In Mali, for
example, the proportion of deliveries attended by trained staff was used as a performance
measure and a costing and budgeting program was developed to reach an attended delivery
“performance frontier.” An epidemiological model was calculated to measure and then monitor
the impact of increased expenditures on reaching this frontier.
Social Funds (SFs) are “agencies that finance small projects to benefit a country’s poor and
vulnerable groups.” Projects are subject to specific eligibility criteria and are generated and
managed by communities (Wagstaff and Claeson, 10). Evaluations of SFs in several countries
have shown them to be effective mechanisms for channeling funding to poorer communities and
improving both the demand for and quality of health services. In Bolivia, for example, it was
reported that medicines and essential drugs were more available in SF facilities and, by going
14
beyond traditional approaches to investment in infrastructure, the SF raised the utilization of
services and contributed to a reduction in under-five mortality rates in SF communities (Newman
et. al, 2002). An evaluation of the impact of social funds on health in four countries (Bolivia,
Honduras, Nicaragua and Zambia) found that social fund health interventions had a positive
impact on infrastructure quality and on the availability of medical equipment, furniture, and
essential drugs. This, in turn, increased utilization of these facilities for critical services,
including maternal and child health (Rawlings et al, 2004).
Review of Evidence on Pathways Variables
Other Reproductive Health Risk Factors
Fertility and Contraceptive Use: High fertility rates and low levels of contraceptive use are
associated with poor maternal and neonatal health outcomes. Studies show that a woman’s
chances of dying as a result of pregnancy and delivery are affected by her age and parity (Maine
et al, 1994). Short birth intervals are associated with higher neonatal mortality (Population
Reports, 2002). While data on these associations are widely available, the causal relationships
underlying these linkages, both biological and social, have proved to be more difficult to
untangle.
Poor women generally have higher fertility and lower rates of contraceptive use than non-poor
women. Tabulations by wealth quintiles of Demographic and Health Surveys data carried out
during the late 1990s for 56 countries provide supporting evidence. Table 9 shows regional
averages in poor/rich differentials in the total fertility rate (rates for the poorest and richest
quintiles and the difference between the two) from these tabulations. Overall, women in the
poorest quintiles average nearly 3 more children than women in the richest quintile, with the
largest difference occurring in Latin America/the Caribbean and the lowest in the Europe/Central
Asian region.
Table 9: Total Fertility Rates by Wealth Quintile and Region
Region
East Asia
Europe/Central Asia
L. America, Caribbean
Middle East, N. Africa
South Asia
Sub-Saharan Africa
All country average
No. of
countries
4
6
9
4
4
29
56
Regional
average
3.2
2.7
3.7
4.6
3.8
5.4
4.6
Poorest
quintile
4.4
3.7
6.1
6.7
4.6
6.7
6.0
Richest
quintile
2.0
1.8
2.2
3.9
2.6
3.9
3.2
Poor/rich
difference
2.4
1.9
3.9
2.8
2.0
2.8
2.8
Source: Gwatkin et al, 2004
Poor/rich differences in fertility reflect similar patterns of differential use of modern
contraception. Table 10 shows average rates of contraceptive use for women in the poorest and
richest quintiles for the same regional groupings, along with the percentage point difference
between those quintiles. The average difference between the contraceptive use rates for rich and
15
poor women is 18.6 percentage points for the 56 countries, with the largest differentials
occurring in Latin America/Caribbean, South Asia, and the Middle East, while the smallest are in
Europe/Central Asia and East Asia. Africa has the lowest overall prevalence and lower rates for
most quintiles than the lowest quintile in other regions.
Table 10: Contraceptive Prevalence by Wealth Quintile and Region
Region
East Asia
Europe/Central Asia
L. America, Caribbean
Middle East, N. Africa
South Asia
Sub-Saharan Africa
All country average
No. of
countries
4
4
9
4
4
29
56
Regional
average
39.4
44.3
47.1
34.2
32.8
13.0
26.7
Poorest
quintile
31.3
38.2
33.1
22.4
22.9
6.5
18.3
Richest
quintile
41.8
48.1
56.8
45.0
45.8
25.1
36.9
Rich/poor
difference
10.5
9.9
23.7
22.7
22.9
18.6
18.6
Source: Gwatkin et al, 2004
The two most commonly recognized factors linking fertility/contraceptive use and
maternal/neonatal mortality rates are that risks are higher (1) when births occur at a very young
age, and (2) when birth intervals are short. A third line of argument is that older, higher-parity
women are more at risk.
Rani and Lule (2004) analyzed DHS data for 12 poor countries and found young women from
the poorest households were more likely than those from the richest ones to be married by age 18
and to have had at least one child by that age. These women were also more likely to have had a
mistimed birth and were less likely to practice family planning, use maternal health services and
have knowledge about prevention of sexually transmitted infections. Additional DHS
tabulations of age-specific fertility rate for women aged 15-19 (Table 11) show that the rate for
women in the poorest quintile is more than twice that of women in the richest group for 55
countries as a whole (Egypt was not included in this tabulation), and nearly five times greater for
poor women in Latin America and the Caribbean. The poor/rich differential is lowest in the
three MENA countries and in Europe/Central Asia and East Asia, which also have lower
regional average rates.
Table 11: Adolescent Fertility Rates by Wealth Quintile and Region
Region
East Asia
Europe/Central Asia
L. America, Caribbean
Middle East, N. Africa
South Asia
Sub-Saharan Africa
All country average
No. of
countries
4
4
9
3
4
29
55
Regional
average
46.0
52.7
94.7
62.7
108.8
131.9
106.5
Poorest
quintile
76.5
73.0
172.6
111.7
146.3
169.6
148.6
Richest
quintile
15.8
31.3
36.9
99.0
56.0
79.5
62.6
Poor/rich
difference
60.8
52.7
135.7
12.7
90.3
90.0
86.1
Source: Gwatkin et al, 2004
Research on the links between early childbearing and poor maternal and neonatal health
outcomes have concluded that most of the adverse health consequences (delivery complications
and maternal mortality, prematurity and higher perinatal death rates) of teen pregnancy are
associated more with socioeconomic factors than with the biological effects of age (Makinson,
16
1985; Miller, 1991). These studies are for populations in richer countries. For developing
countries, women aged 15-19 are twice as likely to die from childbearing as women in their 20s,
and women under age 17 face especially higher risk. Young women who become pregnant are at
risk of obstructed labor if they have not grown to their full height or pelvic size, and are also
more likely to suffer from eclampsia, which threatens them and their babies (Upadhyay and
Robey, 1999). In her review of research on the consequences of adolescent childbearing in
India, Jejeebhoy (1996) reports that infants of adolescent mothers are more likely to suffer higher
perinatal and neonatal mortality, that levels of anemia and complications of pregnancy are higher
for adolescent mothers, but that they are less likely to obtain antenatal care and trained
attendance at delivery than older mothers.
Births that are too closely spaced are also associated with higher perinatal mortality, and may be
a risk factor for maternal mortality (Miller, 1991). Infants whose births were spaced more than
two years have been less likely to be premature or suffer from low birth weights. Analysis of
DHS data for 18 countries (and encompassing more than 430,000 pregnancies) showed that
children born three to five years after a previous birth are more likely to survive (Venugopal and
Upadhyay, 2002). A cross-national study of 18 Latin American countries found that women
with interpregnancy intervals of less than six months had a higher risk of maternal death and
complications of delivery than those conceiving at 18 to 23 months, and also that intervals
greater than 59 months were associated with higher risks of eclampsia (Conde-Agudelo and
Belizan, 2000).
The association between short birth intervals and poor maternal and neonatal health has been
ascribed to the so-called “maternal depletion syndrome,” in which maternal nutrition plays a
critical role (Winkvist et al, 1992; King, 2003). As the biological competition for nutrients
increases during pregnancy, an inadequate supply contributes to poor fetal development and may
also be factor in higher maternal mortality. King found that “maternal depletion of energy and
protein resulting from short inter-pregnancy intervals or early pregnancies leads to a reduction in
maternal nutritional status at conception and altered pregnancy outcomes” (King, 2003: 1735s).
While there is a biological basis for the close associations between early or too closely spaced
pregnancies and poor maternal and neonatal health, causal explanations need to address the
possibility that other factors may influence both at the population level. In a study for rural
Bangladesh on the relationship between childbearing and maternal survival, Menken and
colleagues found no significant effects of early or closely spaced pregnancies on these outcomes
once other factors (education, height) were controlled. But while there were no significant
differences in the risk of dying during delivery between births that were early, closely spaced, or
numerous, their findings do suggest that lifetime childbearing does affect a woman’s survival
chances. “Each time a women has a child, she faces an increased risk of dying in the relatively
short period (two to three years) after that birth. Thus a woman who bears seven children has
seven chances of succumbing to this risk, whereas a woman who bears two children has only two
chances” (Menken et al, 2003). The authors refer to this as “extended maternal risk”. Their
findings complement earlier work by Ronsmans and Campbell (1998) who found no evidence
that short birth-to-conception intervals affected the risk of maternal mortality.
The interplay of biological and contextual factors is also emphasized by Zabin and Kiragu (1998)
in their review of the health consequences of adolescent sexual and fertility behavior in SubSaharan Africa, in which they document the “immense reservoir of suffering cause by childhood
17
marriage and immediate post pubertal childbearing among girls given in marriage at ages as
young as ten or 12.” They emphasize that analysis of the role of age in these adverse outcomes
should also take into account other proximate causes and cite two examples: radical forms of
circumcision and cephalopelvic disproportion, which is a factor in obstructed deliveries and
which, if not adequately managed, can cause obstetric fistulae. Circumcision often leaves so
much scar tissue that that the first child is lost - this health effect is related to age because
teenagers are more likely to be primiparous than older women but could still occur if
childbearing is delayed to a later age. Cephalopelvic disproportion occurs more frequently
among malnourished young girls whose bone development is incomplete, leading the authors to
conclude that “the root causes of this sort of tragic delivery are malnutrition and a lack of access
to or use of professional care”.
Unintended Pregnancies and Abortion: In addition to the risks associated with the number and
spacing of births, there are those associated with unintended pregnancies, including those that are
unwanted and those that are mistimed. While interpretation of data on pregnancy intentions
continues to be debated, they have proved to be valuable in addressing pregnancy-related health
risks. Women with unintended pregnancies are less likely to seek prenatal care, more likely to
use alcohol and tobacco during pregnancy, and more likely to suffer physical abuse and violence
(Santelli at al, 2003). Many unintended pregnancies end in abortion. Where abortion is safe,
abortion-related mortality and morbidity are lower than birth-related mortality and morbidity.
However, WHO estimates that 20 million unsafe abortions occur annually, mostly in developing
countries. This means that one out of ten pregnancies is terminated by an unsafe abortion, with a
ratio of one unsafe abortion per seven live births. South Central Asia accounts for the largest
share of unsafe abortions, followed by Africa and Latin America (Ahman et al, 2003).
Unsafe abortion is a significant cause of maternal mortality and morbidity. Nineteen million
unsafe abortions are estimated to have taken place during the year 2000, 98 percent of them in
developing countries. Over the period 1995-2000, an estimated 78,000 maternal deaths,
approximately 13 percent of all maternal deaths, were attributable to unsafe abortion. AbouZahr
and Ahman (1998) estimated that the abortion-related mortality risk was at least 15 times higher
in developing areas and that in some regions it may be 40-50 times higher than in more
developed regions. While unsafe abortion and abortion-related mortality risk are much greater in
poor countries, comprehensive data on rich-poor differences in risk are not available. However,
the fact that poor women have higher fertility and lower contraceptive use would imply that they
are at much greater risk. There is also evidence, still incomplete, that the incidence of unsafe
abortion and resulting mortality may be rising among unmarried adolescent women in urban
areas of developing countries.
Violence, Conflict and Refugee Settings: Mothers and children in conflict settings, including
those who are refugees, are subject to greater risk of poor reproductive and neonatal health
outcomes. UNFPA estimates that women and children account for 75-80 per cent of the world’s
37 million refugees and displaced persons at risk from war, famine, persecution and natural
disaster; that 25 per cent of this population at risk are women of reproductive age, and that one in
five is likely to be pregnant (UNFPA, 2001; Save the Children 2002). Maternal mortality for
women in conflict zones of the Darfur region of the Sudan is higher than the already high
national average of 1500 for the country as a whole (Collymore, 2004).
18
Women and children living in such circumstances are exposed to a range of risk factors,
including gender-related violence, rape, poor nutrition, psychological trauma, abuses in camp
settings, and a lack of access to basic health care. One of the “collateral” effects of conflict is the
destruction or breakdown of basic services. Conflict also creates obstacles for relief agencies,
further exacerbating health problems and making it difficult to measure outcomes. Reviews of
available information on the reproductive health in war-affected populations identified a range of
risk factors that depend on what was going on in specific settings at specific times (McGinn,
2003). Numerous case studies have documented deterioration in maternal and neonatal health
indicators for refugees in conflict settings, including Congo, Guatemala, Sierra Leone, and
Afghanistan (Ward, 2002). A 1999-2000 study of maternal mortality among Afghan refugees in
Pakistan, revealed a rate was 50 percent higher than the rate for Pakistan (291 vs. 200), though
lower than the estimate for Afghanistan itself (820). Most of the maternal deaths in camps were
preventable and resulted from lack of access to emergency services (Bartlett et al, 2002).
However, research on reproductive health in refugee camps in a number of countries (Sudanese
and Somalis in Ethiopia, Sudanese and Rwandans in Uganda, Burundians and Congolese in
Tanzania) found better outcome indicators (including maternal and neonatal mortality) than for
refugees’ home countries or their host countries, suggesting that once reproductive health
services are established in camps they can have a positive impact (Hynes et al, 2002).
Research on the links between gender violence and reproductive outcomes shows that women
who experience violence have poorer outcomes, including more unintended pregnancies, low
birth weight, fetal wastage and infant deaths (Nasir and Hyder, 2003; Heise et al, 1999). While
none of these reviews distinguished between poor and non-poor women in these conditions, it is
safe to assume that poor women represent a significant, if not substantial proportion of the total.
Violence in pregnancy is another risk factor and is a cause of poor delivery outcomes. Heise and
colleagues (1999) report that violence in pregnancy accounted for 16 percent of low birth weight
deliveries in Nicaragua, and that violence may be responsible for a sizeable but under-recognized
proportion of pregnancy-related deaths on the Indian subcontinent. The risk of pregnancyrelated deaths associated with violence was substantially higher for pregnant teenagers than for
other age groups.
Infections and Other Risk Factors: In addition to malnutrition, other risk factors may also trigger
or exacerbate obstetric complications (Koblinsky, 1995; Tinker, 2000). In addition to poor
nutrition and violence (discussed above) these include infections (sexually transmitted infections
and HIV/AIDS, malaria, tuberculosis, hepatitis), substance abuse, and harmful practices such as
female genital mutilation (FGM). A major gap in many programs aimed at prevention of the
transmission of HIV infection from mothers to children is treatment for mothers after they have
delivered, since most programs seek to save the life of the child but not the mother - ignoring
evidence that survival chances for children are lower for those whose mothers have died, not to
mention the basic ethical issues involved in such choices.
Some of the world’s highest MMRs are found in countries where the practice of FGM is
widespread. Among the long-term consequences of FGM are increased risk of obstructed labor,
delayed delivery, and increased risk of stillbirths (PATH, 1997). Another consequence is
increase vulnerability to sexually transmitted infections (STIs) and HIV/AIDS. STIs are known
to affect delivery and pregnancy outcomes, including premature birth and intrauterine growth
retardation (Haberland et al, 1999). The link between HIV/AIDS and maternal mortality has
been recognized in the latest estimates of MMRs, which include HIV prevalence in the
19
estimating equation for the proportion of deaths in reproductive ages that are considered
“maternal”. In South Africa, where the HIV prevalence rate is one of the highest in the world,
AIDS-related respiratory infections (pulmonary TB and pneumonia) are important factors in
pregnancy-related mortality (Kruger, 2003).
Malarial infection is another cofactor in pregnancy-related deaths of mothers and newborns.
Meremikwu (2003) notes that malaria is typically a more severe disease in pregnant women and
is a major contributing factor to maternal mortality in malarial areas of Africa. Malaria is
associated with maternal anemia, which is another cofactor in maternal death. Santosi (1997)
adds that malaria is associated with chronic health problems that frequently become acute during
pregnancy and are associated with higher rates of maternal mortality and morbidity in malarial
areas. Etard and colleagues (2003) report that malaria is a probably cofactor in seasonal swings
in maternal deaths in Senegal. One of the reasons why Malawi’s MMR remains so high despite
a comparatively high rating on education is its high prevalence of malaria (55-80 percent during
the rainy season) along with severe anemia in pregnant women.
20
Table 12: Selected Findings on Other Reproductive Health Risks
Country,
location
Latin America
Bangladesh
Type of study
Citation
Cross-national
analysis
Conde-Agudelo
and Belizan
(2002)
Matlab
surveillance
data
Menken et al.
(2003)
Global
Reviews
Africa
Review
Refugee
populations
Reviews
Africa
Reviews
Ronsmans and
Campbell (1998)
Santelli et al
(2003)
Ahman et al
(2003)
Zabin and Kiragu
(1998)
McGinn (2003),
Ward (2002),
Heise et al
(1999)
PATH (1997),
Kruger (2003),
Meremikwu
(2003)
Findings
Women with interpregnancy intervals of 5 months or
less had higher risks for maternal deaths and
complications; women with intervals greater than 59
months had higher risk of eclampsia.
No significant effects of early or closely spaced
pregnancies once other factors (education, height)
controlled; however, the overall number of deliveries
affects survival chances
No evidence that short birth-to-conception intervals
increase maternal mortality
Unwanted pregnancy and unsafe abortion contribute to
high MMRs, even in countries where abortion is legal.
Biological and contextual forces interact in determining
the adverse effects of adolescent sexual and fertility
behavior; malnutrition and lack of access to care
exacerbate these problems, which affect both married
and unmarried adolescents
Refugees and women who experience violence
experience worse MNH outcomes; RH services for
refugee populations can mitigate these risks.
Harmful practices (FGM) and infectious diseases (TB,
malaria and HIV/AIDS) increase the risks of obstetric
complications and higher MNM rates.
Household and Community Factors
As noted in the introduction to the Pathways framework, households and communities are
important but often-neglected actors in the effort to improve maternal and neonatal health
outcomes. Building on the Pathways framework, the World Bank analytical report on the
achievement of MDGs described in the previous section looked at households both as consumers
of health services and as producers of health outcomes. The report cited two main obstacles that
accounted for underutilization of key health interventions by poor households: (1) the prices that
households pay for those interventions; and (2) lack of knowledge about those interventions and
their importance to the health of the household members.
In addition, the Pathways framework recognizes that intra-household relationships, particularly
gender relations, affect utilization patterns.
Gender deserves special attention because of the inequity in social relationships that restricts the
rights of women to make decisions for themselves or to have fair access to household assets; and
the greater the inequity, the greater the obstacle to poor women’s access to life-saving
interventions. In Indonesia, women’s control over assets, which were found to be highly
correlated with education, positively affected their chances to get prenatal and delivery care
(Beegle et al, 2001). The authors note that while the government has made progress in reducing
price and distance barriers to obtaining care, the inequitable distribution of power in social
relationships remains a significant obstacle. Another Indonesian study based on interviews about
21
the circumstances surrounding more than 100 maternal deaths found “many families whose
personal poverty excludes them from even considering attempts to gain access to emergency
obstetrical care” and characterized these women as “too poor to live” because families failed to
take steps to seek care for complications because they feared the cost could be greater than they
could bear (Islander et al, 1996: p. 80).
The case study in Box 1 illustrates household and community level obstacles that prevent poor
women from obtaining life-saving interventions when they suffer an obstetric complication. The
case is taken from an oral autopsy that was conducted in Bangladesh during the early 1990s and
which played an important role in shaping the development of a maternal health strategy for
Bangladesh later in that decade (Blanchet, 1991). It describes the plight of a poor woman who
died as a result of complications during her tenth delivery. While the midwife diagnosed her
condition as “extreme anemia”, and the symptoms also suggest septicemia (once a leading cause
of maternal deaths, but now rare in richer countries), the case reveals much more about the
complex weave of causes of Safar Banu’s death.
What we observe is a complex interplay of forces that undermined Safar Banu’s survival
chances, as well as those of poor women around the world, in the face of life-threatening
obstetric emergencies. These include:
 Her subordinate status in the household, and her willingness to endure this;
 The fact that she was not using contraception, and that she had no voice in this despite
her concerns about having a tenth child;
 Her poor nutritional status (being the last and least fed in her household);
 The lack of prenatal care despite her experiencing swelling and dizziness;
 The fact that the household could not afford to pay for medicines;
 The husband’s reliance on the advice of the traditional healer, who failed to provide
adequate care, and his refusal to listen to the midwife;
 The misinformation about her condition among those around her, which delayed
treatment at the clinic; and
 Transport costs, for which the family had to sell land (creating a legacy of resentment)
and which caused further delay in her treatment.
No single factor was the “cause” of her death, but the combination of circumstances described in
the case capture many of the household/community-level factors mentions earlier: gender
relationships in the household, household behaviors relating to fertility regulation and nutrition,
lack of information, and household poverty and inability to pay for care. Further discussion of
the cost issue is found in the next section, which addresses health system failures.
22
Box 1: Why did Safar Ban Die?
Safar Banu, age 43, never attended antenatal clinic. She said she had already given birth to nine children
without the help of a clinic. Why should she need antenatal checkups now? She was too busy and had no
time for such visits. When several months pregnant, she worked in the fields shredding jute fibers. Such
work is usually performed only by the poorest women. Safar Banu did not use contraceptives. She wished to,
but her husband forbade her and threatened to divorce her if she did. According to her mother, when Safar
became pregnant for the tenth time, she was sorry and cried. The pregnancy itself did not present special
problems, but during the last month, Safar complained of swollen feet and dizziness. A week before giving
birth, she felt very weak and told her mother she feared she would not survive this birth. At the time, her
anemic condition, which later became obvious, was not recognized by her family, and especially by her
husband.
The husband held a great deal of faith in Gopal Daktar (the local healer). GD was called at the onset of labor
and gave two tablets to increase labor contractions. After the birth, he again gave medicine to reduce the pain.
All of these tablets were just aspirin, which GD admitted when later interviewed. In the delivery hut, Safar’s
mother was present with the dai (traditional birth attendant). The birth was difficult because Safar was so
weak. After the birth, she was unable to get up for ten hours. The dai and her mother knew that something
was seriously wrong. The husband did not realize that his wife’s condition had deteriorated. He became
alarmed only on the fifth day, when Safar’s entire body became swollen.
The women in her village explained Safar’s headaches by the blood that had risen from her womb to her head.
They see this blood as polluted and so as extremely harmful. Also, blood that circulates in the lower limbs
may kill if it goes to the head. It seems that Safar lost a considerable amount of blood after giving birth. This
did not cause as much concern because she was considered not to have bled long enough. Thus, she did not
completely eliminate the polluted and harmful blood that was believed to be the cause of her swelling.
On the fifth day, Safar had a sensation of burning in her limbs and asked for water to be poured over her head
for the entire day. She was sweating profusely, however she registered no fever. Two days before she died, a
midwife from the MCH clinic was called. She recommended that Safar be transferred immediately to the
district hospital. She diagnosed a case of extreme anemia and thought that a blood transfusion was necessary
and urgent. The husband did not listen to the midwife. He had more confidence in Gopal Daktar, who tried
various medications and injections but to no avail. GD later claimed that he gave no “good” (meaning
expensive) medicines because the family was too poor to pay. GD was annoyed with them for wasting his
time
On the seventh day, Safar Banu became unconscious. Her mother had her carried to the MCH clinic to get
treatment from the midwife. Safar’s condition had deteriorated considerably, and it was feared that she would
not survive the night. Her pulse was extremely low and she had difficulty breathing. The clinic offered to
pay for medicines, but transport to hospital was to be the family’s responsibility according to the official
policy. At this point, the elder son and a cousin took charge of the affair. They organized and paid for the
transport to the district hospital. Safar’s mother felt that it was probably too late and of little use. She
attempted to discourage her grandson. Half a “kani” of land had to be sold to pay for these costs, and the
husband is still bitter with the clinic for not having provided any financial help. The son gave his own blood
to save his mother but it was of no avail. The husband, a sick man himself, never went to the hospital. Safar
Banu died the following day.
Safar Banu’s mother confirmed how late the husband became aware of his wife’s critical state. Mention was
also made of how hard Safar worked and how little she ate after her husband and sons had been fed. She
drank water when there was no rice. When she was ill in the past, she had returned to her mother, a poor
woman herself, to get help and treatment. The husband only thought of his own illness and did not see his
wife’s problems. Safar Banu knew how to bear her sorrows in silence. Her husband praised her because she
was uncomplaining.
This story is based on interviews with the persons mentioned above, except Safar Banu.
23
Gender relationships played a key role in that the traditional birth attendant and Safar’s mother
knew that something was seriously wrong but would not go against the husband’s preference to
rely on the local quack. They delayed consulting a midwife until it was too late, and even then
the husband discounted the midwife’s advice.
The case highlights the importance of community factors (the gender system, misinformation,
lack of community support) and the potential of community mobilization to contribute to the
reduction of maternal and neonatal mortality. A number of community mobilization initiatives
are already proving themselves to be effective, including more involvement of community health
workers to create awareness about complications of delivery and demand for effective
management of emergencies (discussed in more detail in the section on the health workforce),
community support for transport (see the transport section), and support for mothers during
delivery. An example of the latter is the promotion of home-based life-saving skill training.
This approach involves both families and their communities to support birth preparedness and
involvement of key decision makers to take action when it is needed and reduce delays in
reaching referral centers (Silbey et al, 2001). Community-level safe motherhood committees and
local health volunteers can provide effective support for such initiatives, particularly when they
are linked effectively to the health system. Community groups were mobilized to conduct
maternal death audits in Malaysia, enabling them to identify and address the conditions that
contributed to the death of a mother in their community (Koblinsky et al, 1999).
A frequently cited strategy for reducing distance-related delays for women who live in remote
areas is to bring all of them to delivery centers or maternity waiting homes prior to delivery. In
her review of experience with this strategy, Figa-Talamanca (1996) cites the experience of Cuba,
which reduced its maternal mortality rate from 118 to 29 per 100,000 between 1962 and 1989.
To facilitate accessibility to facilities with capacity to manage obstetric emergences, Cuba
located waiting homes near these hospitals for women who lived in remote areas. Community
organizations were mobilized both to build and run the waiting homes and to encourage their
utilization, for example by providing childcare for pregnant mothers during the time of delivery.
Waiting homes were also a key factor in Honduras, where the MMR dropped 40 percent in seven
years (from 182 to 108) after the introduction of a multi-pronged strategy that included
upgrading of facilities and staff capacity as well as community organization and infrastructure
development (Danel, 2003). Figa-Talamanca's review also identified a number of factors that
need to be addressed in introducing waiting homes, including the acceptability of the practice in
societies where home delivery is the norm, the question of selecting women at greater risk of
complications (since any delivery has the potential for complications), and the range of services
that such centers provide and their costs to families.
Broader community efforts such as micro-enterprise and credit programs targeted at poor women
can reduce the gender disparity by ensuring that women have control over the money they earn
and ensuring that information and education networks reach beyond the traditional boundaries
and restrictions faced by poor women. In Africa, social action programs targeted to poor
households have simultaneously provided funding for family planning and reproductive health
services and created paying jobs for women. An evaluation of one such program in Malawi
found that it had directly improved women’s reproductive health outcomes and had indirectly
improved their status in the family, through its woman-focused educational, credit and
employment initiatives (Marc et al, 1995). In Bangladesh, women’s participation in rural credit
programs impacted positively on their demand for health care (Nanda, 1999). Women’s
24
empowerment, including higher control over resources, was addressed in the design of these
programs.
Health System Failures
It is widely recognized that improved maternal and neonatal health outcomes require a
continuum of care, from the household and community through the referral process to an
effectively functioning health system. Poor health system performance is one of the reasons why
poor women do not get life-saving interventions when they experience an obstetric complication.
In a cross-national review of health system failures that contribute to high maternal mortality,
Sundari (1992) identified several critical problems: shortage of trained personnel, lack of
equipment and facilities (including consumables such as blood products and antibiotics), and
poor patient management. These problems are also pinpointed in the World Bank assessment of
obstacles to the achievement of health-related MDGS. In addition to the cost issue mentioned
above, the report also mentions the quality of care as reflected in health workforce performance,
availability of medicines, as well as inadequate and ineffective public spending. On the last
point, a review of the benefit-incidence of public spending on health care in Africa by CastroLeal and colleagues found that subsidies are poorly targeted on the poor and typically favor the
better off, a phenomenon that is not limited to Africa.
Costs to consumers include payments for care and transport as well as opportunity cost, for
example lost wages or time that would have otherwise been spent in household production. Even
when publicly provided care is nominally free, under-the-table or side payments may be required
to obtain services or medicines. Evidence suggests that the poor already pay a lot out of pocket,
particularly for medicines, and could get better care for their money if health system
performance were improved (Nahar and Costello, 1998).
Health reformers have argued that cost recovery could improve the sustainability, quality and
equity of health services by bringing payments into the open, rationalizing the use of services
(for example, through incentives to use the level of care appropriate for the treatment required),
and by giving providers control over resources and the incentive to use resources to improve
quality. If combined with effective exemption schemes for the poor, cost recovery could also redirect public subsidies that typically benefit the rich more than the poor.
Evaluating the impact of cost recovery schemes on MNH outcomes for the poor is not an easy
task because of the many contextual and institutional influences that shape their implementation.
Gilson’s (1997) review of experiences with user fees in Africa, where many countries introduced
them during the 1980s, reports that:
 By themselves, fees tend to dissuade the poor more than the rich from using services;
 Fees, especially for community managed schemes like the Bamako Initiative that focus
on medicines, may be associated with quality improvements that offset some of that
negative impact on utilization;
 The equity impact depends a lot on the nature of the payment mechanism—direct
payments have a more negative effect;
 Fees did not appear to generate sufficient revenue to enable the hoped-for reallocation of
public subsidies to the poor;
 Exemption schemes did not really protect the poor, and often helped other groups (e.g.
public sector employees) more than the poor;
25


Differential geographic implementation of fee schedules may only exacerbate geographic
inequalities in access to care; and
There is very limited evidence on the impact of fees on the budgets of poor households
and their demand for health care.
Available research on the response of poor households to fees shows that there is a substantial
drop in utilization immediately after their introduction, followed by a partial return to pre-fee
levels, and that poor households are somewhat more sensitive to price changes. Kutzin (1995)
reports on research findings from Zimbabwe showing that intensified enforcement of user
charges contributed to a 30 percent decrease in maternal health services compared to the year
before enforcement, and that the numbers of babies born before reaching hospital increased by 4
percent as a possible result of mothers seeking to avoid per diem hospital charges. Mwabu and
colleagues (1995) found that attendance at public clinics in Kenya dropped by 50 percent during
the period when cost recovery was enforced (which lead to a suspension of fees), while
Newbrander and colleagues (2000) found that poor consumers delayed seeking care more than
rich ones in order to avoid costs. This response is particularly troubling in the case of maternal
and neonatal care since such delays contribute to higher mortality. That review also noted that
poor consumers tended to be unaware of exemption schemes or about how to take advantage of
them even when such schemes existed. Nanda (2002) reports that exemption schemes do not
include all reproductive health services and that their implementation is vulnerable to
subjectivity and distortion by providers.
Removal of financial obstacles to care has been a key policy change in countries that have
successfully reduced MMRs. Both Malaysia and Sri Lanka provided maternity care free to
clients who could not pay for services (Pathmanathan et al, 2003). Care was provided in public
facilities, whose quality was improved in terms of accessibility, health worker performance, and
availability of medicines. Countries that still have higher MMRs are attempting to reduce
financial barriers using alternative financing options, though their impact on maternal mortality
has yet to be measured. Bolivia's National Maternal and Child Health Program has increased
coverage of maternal and child health care, though in its initial phase the poorest quintile
appeared not to benefit because location and social exclusion proved to be as serious an obstacle
as cost, so that special outreach to them was added in a later phase (Seoane et al, 2003). A study
of the comparative impact of user fees versus community-based financing on utilization of
services in Niger found that the community schemes had a more positive effect (Diop et al,
2000), though attention to the coverage of maternity care in such schemes needs to be watched.
In Rwanda, the evaluation of an experimental community insurance program found that women
who are members of a prepayment scheme were three times more likely to deliver with
professional assistance than nonmember women, who were more likely to delivery at home and
alone (Schneider et al, 2001). Mexico's PROGRESA program targets cash payments to poor
households for education, nutrition and preventive health care and has had a positive impact on
the nutrition of pregnant women and reduced low birth weight in newborns (Gertler, 2000).
PROGRESA is also reported to have given poor women more say in household decision making
and control over household resources, as well as increased schooling for poor children (Skoufias
and McClaferty, 2001).
Poor health workforce performance is another critical obstacle to poor women’s access to lifesaving interventions, particularly in view of the key role that skilled attendance at delivery is
26
known to play in the reduction of maternal and neonatal mortality. The large rich-poor
differentials in skilled attendance documented in Table 3 suggest that even when countries have
skilled attendants in their health workforce, these attendants are not serving the poor. Sundari’s
(1992) review noted both the scarcity of such workers, particularly in rural areas, as well as lack
of training and incentives to motivate workers. Health workforce problems are further
exacerbated by losses of staff who die from AIDS or emigrate. An effort to upgrade delivery
care in rural areas of Bangladesh during the 1990s was hindered by the fact that attendants were
not available in facilities even when they were posted to them. Professional care was provided in
the case of scheduled normal deliveries, but not available for emergencies that could occur at any
time. In their study of contextual determinants on maternal mortality, Midhet and colleagues
(1998) found that lack of trained staff at peripheral health facilities and access to those facilities
accounted for most of the variation in maternal mortality in sixteen rural districts of rural
Pakistan. Kwast (1996) reviewed performance issues in several countries (Bolivia, Guatemala,
Indonesia and Nigeria), and also found that appropriate training of front-line staff combined
community outreach and empowerment of women to recognize the importance of their own
reproductive, maternal and neonatal health problems was critical to the reduction of physical and
financial obstacles to care.
In its discussion of links between health systems problems and efforts to improve maternal and
neonatal health outcomes, the interim report of the Millennium Project Task Force on Maternal
and Child Health reports that deficiencies in health workforce capacity are a major bottleneck. It
calls for development of health workforce strategies that expand the supply of critical skills,
including skilled birth attendants (Freedman et al, 2004). The recommended approach includes
more effective involvement of traditional healthcare workers (including traditional birth
attendants—TBAs), on whom many poor women still rely for reasons of cost, convenience,
services offered and trust. Up to now, TBA-based maternal care programs alone have failed to
reduce MNM, in large part because TBAs were not linked to a functioning health system.
Current thinking about building health workforce capacity calls for more effective involvement
of TBAs in skill-attendant strategies, particularly strengthening their role as advocates for skilled
care and linking them more effectively to a functioning referral system (WHO, 2004).
Experience in Malaysia and Brazil has demonstrated that effective involvement of TBAs in
community mobilization, awareness and demand creation, and referral of emergencies can be
effective, particularly as countries move from situations in which delivery by a skilled attendant
is rare toward fully functioning systems with deliveries by professional attendants in
comprehensive obstetric care facilities (Koblinsky et al, 1999).
Decentralization of health system management is another tool that health reformers have
employed in an effort to improve quality and accountability of front-line services. The rationale
for such reforms is that if local authorities, and the communities that they serve, have greater
control over human and financial resources, the system should be more responsive to local needs.
Evaluation of experience of the impact of decentralization on poor women’s access to maternal
health care is not easy because many forces are at work. In many cases, local managers are
responsible for managing care but do not have real control of people and money. Resource
allocation algorithms typically rely on formulas based on such measures as the number of
hospital beds in an area rather than health needs, and control over personnel decisions remains
centralized, so that local managers end up with the worst of both worlds. Further,
decentralization often weakens specialized technical units (in this case, maternal health units that
27
might have been providing critical technical leadership prior to decentralization) at the central
ministry, and there is not enough expertise at the local level to pick up the slack.
On the positive side, targeted strategies such as the Marginal Budgeting for Bottlenecks
described earlier have focused on training and incentives to improve workforce performance. A
few African countries have experimented with the use of funding freed up through debt relief to
create special incentive funds to support the redeployment of health workers to where they are
needed. For example, in Mauritania a MBB bottleneck-identification exercise found that a lack
of nurses and midwives in rural areas was a key obstacles to achievement of better maternal and
child health outcomes. In response funds that were freed up through debt relief were used to the
create incentive mechanisms to get staff to work in rural areas (Soucat et al, 2002). A similar
effort in Mali focused on geographic availability of care as well as staffing. Safe motherhood
strategies in a number of countries are focusing resources on the training and deployment of
skilled attendants in facilities that serve poor women.
The lack of consumables and equipment in facilities is another bottleneck. Even when an
emergency is recognized and the woman suffering it has been referred to a treatment facility, she
may still die if, in the case of a hemorrhage, blood products are not available, or antibiotics in the
case of infection. Facilities where poor women might go are chronically short of consumables in
many countries, though in some cases targeted cost sharing arrangements such as the Bamako
Initiative have been able to overcome this obstacle (Gilson, 1997). That model relies on
community management to ensure that revenues are used to address quality constraints and
ensure local accountability.
Performance problems seldom occur in isolation from each other. McPake and colleagues
(1999) have documented the behavioral responses of public health workers in Uganda to poor
incentives, scarcity of drugs and lack of management capacity. The situation encouraged drug
leakage, side payments to obtain drugs, and understaffing of public health centers. Quality of
service might have improved for consumers who could afford to pay, but probably at the expense
of poorer ones who had to rely on “free” publicly provided treatment. A summary of findings on
financial barriers, costs and cost recovery is presented in Table 13.
Table 13: Selected Findings on Health System Issues
Country,
location
Global
Type of
study
Review
Citation
Africa
Review
Newbrander et al
(2000)
Africa
Review
Gilson (1997)
Zimbabwe
Impact
Assessment
Reported in
Kutzin (1995)
Kutzin (1995)
Finding
Studies in many countries have shown that
poor people are more likely to be put off by
price increases; travel costs have a similar
deterrent effect.
There were significant inequalities in access to
health care under the user fee systems
studied. The poor delay and wait longer for
care, and often pay the same fees as the nonpoor.
Poor people reduce utilization more than the
rich when fees introduced, but quality
improvements can improve utilization;
administrative costs often high relative to fees,
and exemptions schemes are hard to manage.
User charges brought 30 percent reduction in
use of maternal health services and increased
28
Malaysia,
Sri Lanka
Case studies
Pathmanathan et
al (2003)
Global
Review
Global
Review
Global
Reviews
Ensor and Witter
(2000)
Freedman et al
(2004)
Koblinsky et al
(1999); WHO
(2004)
Africa
Case study
Soucat et al
(2002)
births outside of hospitals by 4 percent.
Removal of financial barriers a key policy
change in countries that have reduced
maternal mortality
Unofficial, under-the-table fees are charged
even when services are nominally free
Health workforce limitations are a critical
bottleneck in efforts to improve MNH.
TBAs can be effectively involved in skilledattendant strategies for community
mobilization, demand creation, and as referral
agents.
MBB is an effective mechanism for targeting
debt-relief funds to overcome health system
obstacles.
Other Sectors
Transportation: Delays in reaching a treatment facility pose key life-threatening obstacles for
women who experience an obstetric emergency. Such delays can be the result of physical
accessibility factors such as distance to a facility, the availability and cost of transport, and the
condition of roads, all of which affect the time required to get a mother to a facility once the
decision to seek care has been made (Thaddeus and Maine, 1994). A number of countries with
high MMRs (Afghanistan, Pakistan, Nepal) also have large segments of their population living in
remote areas that have poor road links to facilities that can provide life-saving interventions. In
Zimbabwe, unavailability of transport is reported to have been a factor in 28 percent of deaths in
a rural area that was studied (Fawcus et al, 1996). In the case of hemorrhage, 50 percent of
deaths were attributable to transport-related delays.
Measurement of road networks is complicated by differences in geographic settings and
population distribution. The index of road quality in Table 8 above and used in the World
Bank's analytical work on attainment of MDGs standardizes the proportion of the country's roads
that are paved by dividing that proportion by the area of the country. While the index provides
an approximation of variability in road access across countries, caution is needed in interpreting
it for countries with a large land area and whose populations (and roads) are concentrated along
coasts or in a smaller segment of the total land area (China, India, Brazil, and Nigeria, for
example).
Though there are few studies that focus specifically on these factors, and hardly any that link
poverty to adverse MNH outcomes for poor women, the available evidence suggests that
transport is a factor in the delays that threaten the lives of poor women. In their review of healthseeking behavioral responses to cost recovery, Newbrander and colleagues (2000) found that
poor people in Tanzania traveled an average of over 60 kilometers for care, whereas the nonpoor traveled only 15 kilometers. There were similar findings in Kenya. The authors mention
several possible reasons, including the likelihood that the non-poor have their own transport and
that the poor travel farther in order to attend a facility where fees would be waived. In their
review of obstacles to health care, Ensor and Cooper (2004) found studies that reported transport
accounting for 28 percent of all total patient costs in Burkina Faso, 25 percent in northeast
Brazil, and 27 percent in the United Kingdom. In Bangladesh, transport was reported to be the
second most expensive item for patients after medicines. To quote from one of the focus groups
in that study: “The hospital is far away and it costs a lot to travel there. We can easily buy
29
medicines from the village doctors with this money. We spend money to go to the hospital but
we don't even get medicines there, so why should we go to the hospital?” (CIET-Canada, 2001,
p. 38).
Country-level poverty analyses conducted by the World Bank have shown that the quality rural
road networks is a factor in the social and economic isolation of the rural poor. Research on the
impact of improved rural road networks has focused mainly on travel time. For example, a
poverty assessment for Guatemala found that road closures were a major constraint on access to
schools, work and markets, and that households in the poorest income quintiles were much more
affected (45 vs. 12 percent) than the richest (World Bank, 2003). Villagers identified “giving
birth” as a risk because mothers could not reach health centers due to inadequate road access,
particularly during the rainy season. Improved roads cut farm-to-market travel time from more
than 10 hours to 1-2 hours. While the impact on access to emergency obstetric care was not
assessed in the research, these reductions in travel time have clear implications for reducing
distance related obstacles to timely management of emergencies. An earlier study of the impact
of roads on access to services in Morocco found similar patterns. The road project contributed to
clear gains in women’s utilization of health services (World Bank, 1996). Similar results were
shown for girls’ school attendance, which increased much more (40 % vs. 10%) than for boys,
for whom lack of roads posed a lesser obstacle than for girls.
The quality and availability of roads is only one facet of the transport obstacles. The availability,
type and cost of transport is clearly another. Research conducted by African partners in the
Prevention of Maternal Mortality Network also found that poor roads, lack of vehicles and high
transport costs were major causes of delay in deciding to seek and in reaching emergency
obstetric care (Samai and Sengeh, 1997). That study reports the positive impact of efforts to
improve transport and communication, along with community support and education activities,
on the numbers of women getting treatment for obstetric emergencies, with consequent
reductions in maternal deaths in the project area. Similar results have been reported for Nigeria
(Essien et al, 1997), Uganda (Lalonde et al, 2003), and northwestern Tanzania (Ahluwalia et al,
2003). These studies emphasize the role of community organization in planning for emergencies
when they occur, including preparation of delivery plans, mobilizing resources through
community funds, reducing transport costs, and strengthening the referral chain.
Mention of the referral chain reminds us that improved roads and transport may in some cases be
necessary to reduce delays in management of obstetric emergencies but their overall impact on
outcomes depends on many other factors. This is illustrated by research in India over a ten-year
period when improved roads and transport led to increases in the number of women reaching
hospital but little reduction in case mortality rates: the improved roads made it possible for
women living farther away to get to the hospital but they were already in a moribund condition.
Poverty, social inequality and gender conditions (including very early marriage) in their villages
eroded the positive impact of improvements in infrastructure and treatment facilities (Pendse,
1999). A summary of findings on obstacles relating to distance and transportation is presented in
Table 14.
30
Table 14: Selected Findings on Transport Interventions
Country,
Location
Global
Type of
study
Review
Citation
Global
Review
Ensor and Cooper,
2004
Rural Guatemala
Poverty study
World Bank, 2003
Rural Morocco
Road impact
study
World Bank 1998
Thaddeus and
Maine, 1994
Finding
Distance and road conditions an obstacle to
emergency care; better roads can help, but
financial obstacles also need to be
addressed
Community health insurance schemes that
include transport costs in benefit package
have contributed to better access to care
Long travel time to health facilities when
roads poor, contributing factor to why “giving
birth” considered a health risk
Upgraded road network contributed to
increased use of health services by rural
women
Education: Education of women influences reproductive health through a variety of channels,
including childbearing attitudes, health-seeking behaviors, and earning opportunities. Early
gains in female literacy played an important role in MMR declines in Malaysia and Sri Lanka
(Pathmanathan, 2003). In her review of linkages between women’s education, autonomy and
reproductive behavior, Jejeebhoy (1995) notes that education enhances women’s knowledge
about the outside world and makes them more aware than uneducated women of the importance
of family health and hygiene as well as the treatment and prevention of illness. Another
consequence that she notes is greater decision-making autonomy within the home. At the same
time, she cautions that contextual factors influence the impact of education on women’s
participation in household decision making, so that this participation is likely to be weaker in a
society characterized by a high degree of gender stratification.
Focusing specifically on maternal mortality, McCarthy (1997) has noted several possible
channels though which women's education might impact maternal mortality:
 By reducing the number of pregnancies (and lifetime risk of complications) through later
marriage and increased use of contraceptives;
 By enabling women to be better informed about symptoms of complications and more
likely to make more timely decisions to seek treatment;
 By being healthier and less likely to suffer a complication;
 By having better physical access to treatment facilities (for example, because a higher
proportion of educated women live in urban centers); and
 By being better off and more able to pay for care, or be well treated by care providers
because of their status.
None of these potential linkages guarantees that education will have the hypothesized impacts.
As both McCarthy (1997) and Thaddeus and Maine (1994) note, the empirical evidence on
linkages between maternal education and utilization of health services is not at all clear-cut. We
are reminded that educated women are more likely to rely on self-care and self-medication and to
postpone visits to a facility until after such methods fail. They also note that if education is
associated with desire for fewer births and later marriage, there may be more unintended
pregnancies and higher abortion rates, which would pose greater risk when access to safe
abortion is limited.
31
Education is closely linked to gender status and the ways in which gender stratification affects
access to household resources and utilization of health care services (Kunst and Houweling,
2001). In their work on intra-household bargaining power in Indonesia, Beegle and colleagues
(2001) found that women who were more educated than their husbands were more likely to
obtain prenatal care and, generally, that education enables a woman to make decisions regarding
her reproductive health care. Education is also linked to several of the other factors that may
enhance or limit access to life-saving interventions. Research on the impact of cost recovery on
utilization of services has shown that educated women are more likely to understand and use
exemption schemes (Newbrander et al, 2000), and the transport literature also highlights the
links between education and access to and utilization of transport to get to health facilities.
Water and sanitation: Provision of safe water has been cited has a factor in the declines of
mortality in developed countries (Van Poppel and Van der Heijden, 1997), and lack of sanitation
and safe water, along with poor personal hygiene, are known to be major factors in the wide
prevalence of parasitic diseases in poor countries. Studies of the impact of safe water on infant
and childhood mortality typically do not focus separately on neonatal mortality, but recognize
that waterborne diseases can undermine the health of pregnant women because they cause
anemia, a risk factor for mothers as well as their newborns (Santiso, 1997). Paul (1993) cites
unsafe water supply as well as pollutants from fuels used in cooking as risk factors in the high
MMRs of the African countries he studied. When comparing countries by the level of MMR,
there is a sharp difference in the percent of households with safe water in the countries with
MMRs under 30 (92 percent) compared to those with MMRs over 1000 (51 percent). The link
between water supply and MMRs/NMRs involves both household and community factors. A
household’s consumption of water may be constrained by prices, income and other household
variables even if water is supplied at the community level. Jalan and Ravallion (2001) observed
that health gains largely bypassed poor children when piped water was available in their
community, particularly when the mother was poorly educated.
Nutrition: Poor nutrition is another key co-factor in maternal and neonatal mortality. The
section on birth spacing identified the close link between poor nutrition and closely spaced births
and maternal and neonatal mortality. The discussion in the previous section on malaria during
pregnancy also highlighted the role of anemia. Poor nutrition among pregnant women in a
number of the very high MMR countries is a factor contributing to those high rates. In India,
anemia is reported as an indirect factor in 64.4 percent of maternal deaths (Buckshee, 1997). As
the Safar Banu case demonstrated, gender stratification and attitudes also contribute through
household behaviors that deprive poor women of adequate nutrition, not only during pregnancy
but also during their childhood and adolescence, which leads to small stature and higher risk of
delivery complications.
The DHS tabulations cited earlier also document major rich-poor differentials in maternal and
child nutrition within countries, as shown in Tables 15 and 16. For the 36 countries that
collected data on body mass for reproductive-age women, those in the poorest wealth quintile
had an average of 14.5 percent of women with low body mass (LBM), compared to 7.7 percent
in the highest wealth quintile.
32
Table 15: Low Body-Mass in Reproductive-Age Women by Wealth Quintile and Region
Region
East Asia
Central Asia + Turkey
L. America, Caribbean
Middle East, N. Africa
South Asia
Sub-Saharan Africa
All country average
Source: Gwatkin et al, 2004
No. of
countries
0
4
8
3
2
19
36
Regional
average
*
5.1
5.5
10.2
40.2
12.9
11.7
Poorest
quintile
*
6.9
7.3
16.0
45.1
15.8
14.5
Richest
quintile
*
4.1
3.5
5.1
27.0
8.5
7.7
Poor/rich
difference
*
2.8
3.8
10.9
18.1
7.3
6.8
*not all countries collected these data
South Asia had both the highest overall average of LBM and the largest differential between rich
and poor quintiles. Latin America and the Caribbean along with Turkey and the two CentralAsian countries had the lowest overall average and the lowest rich-poor differentials. SubSaharan Africa and the Middle East/North Africa fell in between.
Table 16: Child Malnutrition by Wealth Quintile and Region
Region
East Asia
Central Asia + Turkey
L. America, Caribbean
Middle East, N. Africa
South Asia
Sub-Saharan Africa
All country average
Source: Gwatkin et al, 2004
No. of
countries
0
4
9
3
4
21
41
Regional
average
*
23.1
23.0
35.2
46.6
34.2
31.9
Poorest
quintile
*
35.1
36.0
45.1
56.6
40.7
41.0
Richest
quintile
*
13.1
6.5
21.1
29.8
22.6
18.7
Poor/rich
difference
*
22.0
29.5
24.0
26.8
18.1
22.7
*not all countries collected these data
Somewhat different patterns are found in the data for childhood malnutrition. Overall, the
poorest quintiles have more than twice the percentage of malnourished children than the richest
quintile. South Asia has the highest overall average, but Latin America and the Caribbean have
the largest rich-poor differential, echoing the high level of income inequality for that region (for
example, in Table 5 Brazil’s Gini coefficient was .59).
Public Policy and Governance:
Earlier in the paper, the table with countries grouped by the level of MMR showed that those
with lower MMRs had better governance than those with higher rates when they were compared
using a “governance effectiveness” indicator. That indicator was one of six compiled by World
Bank experts (Kaufmann et al, 2003) covering 199 countries using several hundred variables
drown from 25 separate data sources. The six indicators include government effectiveness, voice
and accountability, political stability, regulatory capacity, rule of law, and control of corruption.
The indicators have a mean of zero and a standard deviation of one, so that virtually all scores
fall between –2.5 and +2.5, with higher scores indicating better performance. In the countries in
the MMR sample, few countries (Bahamas, Singapore) had scores greater than one, and most
were below the mean of zero. In Table 17, tabulation of average scores by MMR group for each
of the indicators in the table below shows consistent falloff in performance as MMRs rise, and
that the group with MMRs greater than 1000 has an average score near or below one standard
33
deviation below the mean for all of the indicators. Political instability stands out as the indicator
with the lowest average for this group, and the highest average for the countries with low MMRs.
Table 17: Governance Indicators for Countries Grouped by MMR Level
MMR Level
0-30
31-100
100-300
301-1000
Over 1000
Number of Effective
countries governance
17
39
32
41
13
0.41
-0.07
-0.35
-0.37
-1.08
Voice &
accountability
Political
stability
Regulatory
quality
Rule of
Law
.29
-.24
-.23
-.35
-.86
.47
.04
-.20
-.37
-1.13
.51
-.07
-.27
-.34
-1.07
.35
-.12
-.36
-.38
-1.06
Control
of
corruption
.41
-.16
-.40
-.36
-.94
Source: Kaufmann et al, 2003
Addressing Obstacles and Information Gaps
Policy and Program Actions
The strong negative correlation between MMRs and per-capita health expenditures suggests that
more health spending is needed, but as the World Bank study on reaching the Millennium
Development Goals in health reminds us, spending alone is not enough. The added health
funding needs to be targeted on key obstacles that reduce poor women’s chances of accessing
life-saving interventions when they are needed. It also has to be combined with improvements in
government effectiveness and motivation of individuals and communities to make use of these
interventions. Success stories like Sri Lanka and Malaysia demonstrate that sustained efforts to
improve health system performance, increase women’s education, and improve infrastructure,
along with targeted investments and policy change to establish a cadre of trained midwives to
ensure safe delivery and effective management of emergencies do work (Pathmanathan et al,
2003). Other countries that have reduced maternal and neonatal mortality (Honduras, an
example cited earlier, and the Indian state of Kerala8) have proved that a combination of targeted
investments in life-saving interventions as well as social infrastructure can make a difference.
The Marginal Budgeting for Bottlenecks approach described earlier suggests that increased
public expenditures on health will have an even greater impact if they are targeted on key
obstacles that prevent poor women from accessing life-saving interventions. The first step in the
MBB approach is the identification of such obstacles. Using the Pathways framework outlined
in the introduction, this paper has shown that obstacles are both inside and outside the health
system, beginning with individuals, households and communities and including both health care
and health financing as well as other sectors—education, transportation, water/sanitation, and
nutrition. The relative importance of obstacles will vary by country and region, but a number of
them appear to affect most of the countries that currently have high maternal and neonatal
mortality. MBB also requires costing of interventions to address these obstacles and tracking of
expenditures and performance of the interventions that are funded to ensure that the targeted
spending is being used effectively and that the poor are benefiting.
Kerala’s MMR of 87 is less than a sixth of India’s national average and less than eighth of the rate in Orissa in the
country’s northern tier.
8
34
Table 18: Actions to Address Obstacles
Obstacle/Pathways Level
Other reproductive risks
Unintended pregnancy &
unsafe abortion
Violence and refugee status
Infectious diseases (TB,
malaria, HIV/AIDS)
Household level
Poor hygiene
Poor nutrition
Women's lack of autonomy in
decisions
Community level
Gender attitudes
Community support
Health system level
Inadequate staffing; unsafe
procedures, including unsafe
abortion
Lack of consumables and
medicines
Inadequate facilities
Other sectors
Education
Transport
Water & sanitation
Nutrition
Public policy
Public health expenditures
Good governance
Action
Indicator
Increase access to FP and
ensure abortion safety
Improve RH services in refugee
settings
Link RH and infectious disease
programs
Unmet need for family planning
Health education
Micronutrients during
pregnancy
Ensure that credit schemes &
social funds give decision
power to poor women
Hand washing
Maternal anemia
Leadership support of improved
women's status
Religious and community leaderships
groups engaged in gender issues
Community mobilization to
ensure safe delivery and
referral of emergencies
Percent of communities that
participate in life-saving skills training
and implementation
Targeted expenditures on
training and deployment of
midwives and key staff
Percent of outreach or first referral
points with trained midwives
Involve TBAs and others in
community in creating
awareness and referrals
Percent of TBAs who are engages as
community agents
Targeted expenditures to
reduce gaps
Targeted investments to
upgrade facilities
Percent of facilities with standard list
available
Percent of communities with delivery
homes and access to referral facilities
meeting service delivery norms
Increase female enrollment
Increase paved roads
Improve access to safe water
Nutrition supplementation
Enrollment rates
Percent of paved roads
Percent of households with safe water
Anemia rates
Targeted increases in health
spending
Percent of public expenditures on
health (identifying, if possible, those
targeted on MMR and NNM)
Implementation of effective budget
tracking procedures
Reforms in financial
management and resource
allocation
MMRs in refugee camps
HIV, TB and malaria treatment rates
for pregnant women
Women having control over money
Table 18 summarizes obstacles described in the paper and identifies possible program and policy
changes to address them. If the performance of targeted interventions is to going to be tracked
effectively, then better information is needed on rich-poor differentials in mortality, and on the
35
morbidities that follow on poorly managed obstetric emergencies. Expenditure tracking also
needs to be fine-tuned so that pro-poor expenditures on these investments can be monitored.
Achievement of lower maternal and neonatal mortality for poor women is possible if the
obstacles to their utilization of interventions that are known to be effective can be overcome. As
noted above, the countries that have reduced mortality rates have done this through a
combination of investments inside and outside the health system including improving the skills
and deployment of midwives and other key staff, reducing financial obstacles to care, improving
the quality of care and access to referral facilities, and mobilizing households and communities
to ensure safe delivery and effective management of emergencies when they occur. Parallel
investments in nutrition, malaria control, education, roads, and water/sanitation have also
contributed in situations where low performance in those areas has undermined the reproductive
health of poor women.
Strengthening the Evidence Base
While the tabulations of maternal and neonatal health-related indicators in Demographic and
Health Surveys (DHS) for wealth quintiles have helped to advance our understanding of linkages
between MNH and poverty, there are still significant gaps in the evidence base. Data on
maternal and neonatal mortality and morbidities are generally scarce, and particularly so for
studying rich-poor differences. Sample sizes in most DHS surveys are typically too small for
calculating rich-poor differences in maternal mortality, though Graham and colleagues (2004)
have developed methodology for calculating rich-poor differentials in maternal mortality from a
subset of larger DHS surveys. Special DHS supplements like the recent Bangladesh Maternal
Health Survey also offer an opportunity for gaining further insights into links between maternal
mortality and poverty.
If there are problems in addressing rich-poor differentials in maternal and neonatal mortality and
their consequences, there is an even greater evidence gap concerning the life-long morbidities
suffered by women who survive a poorly managed obstetric emergency. A comparison of
estimates of the burden of disease for maternal conditions with BOD for TB, malaria and
HIV/AIDS suggests that the impact of these morbidities is substantial. Table 19 shows estimates
of years of life lost (YLL) and years lived with disability (YLD) for the population aged 15 and
over for these four conditions for sub-Saharan Africa in 2002:
Table 19: Estimates for Burden of Disease for sub-Saharan Africa, 2002
Condition
TB
Malaria
HIV/AIDS
Maternal conditions
YLL (1000)
6,994
1,748
43,849
6,865
YLD (1000)
795
528
5,514
4,884
Ratio YLD/YLL
.11
.30
.13
.71
Source: WHO, GBD Estimates, www3.WHO.int/whosis, accessed 12/04/04
While the number of years of life lost due to maternal causes is substantially lower than for
HIV/AIDS (though about the same as for TB, a cause of death associated with HIV/AIDS, and
larger than for malaria, where deaths are concentrated among children), the years lived with
disability associated with maternal conditions is nearly as large as for HIV/AIDS. One of the
36
most poignant examples of this latter burden is that suffered by mostly young and poor African
women who survive obstructed labor but live on with an obstetric fistula, which has terrible
social and economic consequences. While fistulae and other obstetric morbidities can be treated,
resources are often not allocated to them, and women who suffer them continue to endure their
consequences as part of what may often be accepted as their normal lot. In an era in which
allocation of health resources is increasingly being driven by such evidence-based algorithms as
disability-adjusted life years, the combination of poorly measured and comparatively rare
maternal mortality (compared to other causes, including major communicable diseases and noncommunicable diseases) plus poorly measured morbidities highlights the importance of
improving the measurement of maternal morbidities, particularly for the poor in order to support
the call for allocation of adequate resources to address these problems.
Expenditure tracking is an important policy and advocacy tool in the health field and needs to be
employed more effectively by the champions of maternal and neonatal health. Countries that are
participating in debt-relief and/or poverty-reduction programs are required by the IMF and
World Bank to prepare Poverty Reduction Strategies. These strategies include analyses of the
causes and consequences of poverty as well as expenditure plans to address specific poverty
issues. The Medium Term Expenditure Framework (MTEF) is both a prospective mechanism
for allocating funding across key sectors that affect poverty and a retrospective tracking
mechanism to ensure that planned allocations are actually made. While the level of detail in the
typical MTEF is typically limited to the broad sectoral level (education, health, water and
sanitation, etc), expenditure tracking through the MTEF process offers an important opportunity
for champions of MNH to track key expenditures required to improve MNH (training and
deployment of trained birth attendants, sustained funding of facilities and referral networks for
the management of obstetric emergencies, etc.; see Reinikka and Svensson, 2002).
A stronger evidence base is also needed on linkages between poverty and MNH outcomes and on
the effectiveness of interventions inside and outside the health sector to address them. When
research funding is scarce, one way to achieve this is to add special inquiries of maternal and
neonatal health outcomes to on-going survey programs, particularly longitudinal surveys that
track groups of respondents over time. These permit analyses that show both the consequences
of poor MNH outcomes on the well-being, consumption and economic productivity of
households and different income/wealth levels and the impact of interventions inside (e.g.,
increase in skilled attendance) and outside (credit programs, community mobilization) the health
system on those outcomes. Survey initiatives in several countries (Indonesia, Kenya, Malawi,
Mexico, and the Philippines; see Table 20) might be engaged in this task.
37
Table 20: Longitudinal Survey Programs
Country
Data set
Population
Type of Survey
Mexico
PROGRESA panel
data
www.ifpri.org
PROGRESA program focuses on population in extreme
poverty in rural areas. Program addresses poverty
through monetary and in-kind benefits, and encouraging
investments in education, health and nutrition.
Longitudinal
Philippines
Cebu Longitudinal
Health and Nutrition
Survey
www.cpc.unc.edu/pr
ojects/cebu/
Longitudinal
Indonesia
Indonesian Family
Life Survey
www.rand.org/FLS/IF
LS
The Cebu Longitudinal Health and Nutrition Survey is
part of an ongoing study of a cohort of Filipino women
who gave birth between May 1, 1983 and April 30,
1984. The cohort of children born during that period,
their mothers, other caretakers, and selected siblings
have been followed through subsequent surveys
conducted in 1991-2, 1994, and 1999.
The Indonesian Family Life Survey (IFLS) is an ongoing longitudinal survey in Indonesia. The first wave of
the IFLS (IFLS1) was conducted in 1993/94. IFLS2 and
IFLS2+ were conducted in 1997 and 1998, respectively.
IFLS2+ covered a 25% sub-sample of the IFLS
households. IFLS3 was conducted in 2000 and covered
the full sample.
Kenya
www.ssc.upenn.edu/
Social_Networks/Lev
el%203/Kenya/level3
_kenya_data.htm
The first survey of the Kenya Diffusion and Ideational
Change Project (KDICP-1) was carried out in 1994-5,
and interviewed 925 ever-married women of
childbearing age and 859 men (of which 672 were
husbands of the currently married women ). In 1996-7
and in 2000, respectively, the second and third round of
the survey (KDICP-2 and KDICP-3) followed-up the
same respondents (if eligible), and also interviewed any
new spouse. All rounds of the KDICP were carried out
in four sites in Nyanza Province, in south-west Kenya.
Within each village, currently married women and their
husbands were eligible respondents
Cross-sectional,
individual-level
data
The MDICP is a sister project of the Kenya
project above also focuses on the role of
social networks in changing attitudes and
behavior regarding family size, family
planning, and HIV/AIDS in Malawi.
Cross-sectional,
individual-level
data for men
and women;
Malawi
http://www.ssc.upen
n.edu/Social_Networ
ks/Level%203/Malaw
i/level3_malawi_data
.htm
Longitudinal
Longitudinal,
individual-level
data
38
Conclusion
This paper has reviewed the evidence base on obstacles that poor women and newborns face
when they suffer life-threatening obstetric complications. It has drawn on available crossnational comparisons and analyses of data, broken down where possible to show rich-poor
differences in MNH outcomes, as well as on the extensive literature on factors within and
beyond the healthcare system that affect MNH outcomes. The Pathways framework was used to
organize the review of evidence. While it is not the only conceptual framework available, it has
the advantage of being the one recommended for the planning of health expenditures at countrylevel in Poverty Reduction Strategies currently being implemented in poor countries where
maternal and neonatal mortality rates are the highest. The evidence suggests that these obstacles
can seriously undermine poor women’s chances of getting life-saving care when they need it, but
that a combination of multi-sector efforts can overcome such obstacles. It has also shown that
there are important knowledge gaps to be addressed by building a stronger evidence base on
maternal and neonatal mortality and morbidities experienced by poor women, how well
expenditure is targeted to address obstacles, and the effectiveness of interventions.
39
Annex 1: Maternal Mortality in India
Of the estimated 529,000 maternal deaths estimated by WHO to have occurred globally during
the year 2000, 136,000 were estimated to have occurred in India. This reflects both the high
MMR for India (the MMR for year 2000 is estimated to have been 540) and India’s large
population (over 1 billion). Only China has a larger population, though with its MMR of 56 it
had fewer than one-tenth the number of maternal deaths (11,000 deaths).
Data on MMR differences within India are limited. In its National Human Development Report
2001, India’s Planning Commission (2002) published MMRs for 15 large states for 1997-1998
based on the Sample Registration System (SRS). Bhat (2001) has applied indirect estimation
techniques to those data to produce adjusted estimates. Bhat’s indirect method produced a
national level MMR estimate of 479 for the period 1987-1996, compared to a figure of 407 from
the SRS. Both estimates are lower than the WHO estimate for 2000, though Bhat’s figure is very
close to the 1995 estimate of 470 produced by WHO/UNICEF/UNFPA. Both direct and indirect
SRS estimates suggest substantial variation by state within India. The SRS data vary widely, and
give rates that are implausibly low for Gujarat and Tamil Nadu and high for Kerala, suggesting
that it would be better to use Bhat’s adjusted estimates. These are produced in column 2 of the
table on the next page, along with other indicators from the Planning Commission that relate to
the Pathways framework.
In the table, states are ranked from the highest to lowest MMRs, with estimates ranging from
over 700 in Assam, Uttar Pradesh and Madhya Pradesh to under 200 in Punjab, Tamil Nadu and
Kerala. SRS estimates are used for Punjab and Kerala because Bhat reported that the rates in
those two states were too low to estimate using indirect methods. Their SRS estimates may be
high. The populations in many Indian states are larger than those of many countries for which
WHO reports its MMR estimates. For example, Uttar Pradesh is as large as Brazil, the world’s
fifth largest country.
State-wise MMR differences are most closely correlated with the indicator for attended
deliveries (r = .89), with a general pattern of low rates of attended delivery (20-30 percent) in the
poorer northern tier states and higher rates (60-90 percent) in the more prosperous southern ones.
The Planning Commission’s gender disparity index (female attainment relative to male in
literacy, life expectancy and economic participation) ranges from 0.5 to 0.6 in the high MMR
states to around 0.8 in the low MMR states.
Data for other indicators used in cross-country comparisons (road density, public health
expenditures) do not reveal clear relationships with MMR differentials. The high MMR states
are generally the poorer ones, although Kerala has a much lower GDP per capita than Gujarat,
but Gujarat’s MMR is three times that of Kerala. Prevalence of anemia is generally higher in the
high MMR states than in the lower ones. The three low MMR states have the highest per capita
public health expenditures, but Assam with the highest MMR spends nearly as much as most of
the lower MMR states. The low level of attended deliveries in Assam reinforces the point that it
is not just the amount of expenditure, but how it is spent. Clearly Assam, UP and other high
MMR states are not spending enough to ensure that all deliveries have skilled attendants.
40
Annex Table 1: MMRs and Other Indicators for Indian States
Assam
Uttar Pradesh
Madhya
Pradesh
Orissa
Gujarat
Rajasthan
Bihar
Karnataka
Haryana
West Bengal
Maharashtra
Andra
Pradesh
Punjab
Tamil Nadu
Kerala
INDIA
MMR
Population
(Mil)
Att.
Del.
(%)
Gender
Index
984
737
26.6
174.5
21.5
23.0
.58
.52
Road
Density
(Km/
100KM2)
87.2
86.8
Female
Anemia
(%)
700
597
596
580
513
480
472
458
380
81.1
36.7
50.6
56.5
109.8
52.7
21.1
80.2
96.8
30.1
33.7
53.5
36.2
23.5
59.2
42.0
44.5
59.7
.66
.64
.71
.69
.47
.75
.71
.63
.79
45.1
168.7
46.7
37.9
50.8
75.1
63.7
85.0
117.6
54.3
63.0
46.3
48.5
63.4
42.4
47.0
62.7
48.5
2.0
2.3
3.0
3.1
1.7
3.0
2.8
2.7
3.0
1922
1666
3918
2226
1126
2866
4025
2977
5032
283
199*
195
195*
75.7
24.3
62.1
31.8
65.1
62.7
84.1
94.1
.80
.71
.81
.83
64.7
127.7
158.6
374.9
49.8
41.4
56.5
22.7
2.5
3.7
3.6
4.0
2550
4389
3141
2490
479
1027.2
42.3
.68
74.9
51.8
2.3
2840
69.7
48.7
Publ.
Health
Exp/P
in $
3.1
2.2
Net State
or
National
Prod/P
1675
1725
Sources: Bhat (2001) and GOI Planning Commission (2002); MMRs for Kerala and Punjab from SRS. Public health expenditures
per capita and net state domestic product per capita in constant 1980-81 rupees. Health expenditure data from Peters et al. (2002).
41
Annex 2: Country-Level Data Table
Country
MMR
Deaths/
100,000
births
(2000)
UNDP
status of
women
index
Govern
-ance
index
Combined
female
enrollment
data
Gini
Coefficient
Health
Expend.
Per
capita
Safe Water
Paved
(% of pop. roads as %
with
of total road
access)
KM
MMU <30
Slovak Republic
3
0.834
0.4
74
25.8
574
100
86.7
Kuwait
Croatia
5
8
0.813
0.802
0.16
0.19
57
69
29
605
410
100
76
80.6
84.6
Czech Republic
Yugoslavia, FR
(Serbia/Montenegro)
9
0.857
0.7
77
25.4
640
92
100
11
-0.73
53
127
83
62.3
Lithuania
Poland
13
13
0.823
0.839
0.61
0.61
88
91
36.3
31.6
273
392
75
90
91.3
68.3
Uzbekistan
Hungary
15
16
0.727
0.834
-1.1
0.78
74
36
26.8
24.4
109
372
85
99
87.3
43.4
Slovenia
Korea, Rep. Of
17
20
0.879
0.873
0.82
0.84
85
84
28.4
31.6
996
862
100
92
100
74.5
Macedonia, FYR
Saudi Arabia
23
23
70
57
28.2
0.743
-0.39
-0.05
141
332
71
95
63.8
30.1
Mauritius
Uruguay
24
27
0.77
0.83
0.53
0.51
68
89
44.8
288
849
100
98
97
90
Bahrain
Singapore
28
30
0.829
0.88
0.78
2.26
84
75
42.5
539
750
100
100
76.9
100
72
93
52.3
19.4
MMR 31-100
Bosnia and Herzegovina
Chile
31
31
-0.9
1.19
51
71
57.5
145
581
Turkmenistan
Bulgaria
31
32
-1.47
-0.06
81
79
40.8
31.9
90
193
61
100
81.2
94
Georgia
Cuba
32
33
-0.77
-0.26
70
77
38.9
94
109
79
91
93.5
49
Belarus
Ukraine
35
35
0.803
0.761
-1.03
-0.74
87
79
30.4
29
253
128
100
98
89
96.7
Moldova
Brunei
36
37
0.697
0.867
-0.63
0.96
63
84
36.2
133
857
92
87
34.7
Jordan
Malaysia
41
41
0.729
0.784
0.36
0.92
78
74
36.4
49.2
178
202
96
88
100
75.8
Latvia
Costa Rica
42
43
0.81
0.821
0.67
0.37
91
66
32.4
45.9
246
489
93
95
38.6
22
Thailand
Cyprus
44
47
0.766
0.886
0.28
1
69
75
43.2
327
731
84
100
97.5
57.4
Romania
United Arab Emirates
49
54
0.771
-0.33
0.83
70
74
30.3
136
816
58
97
49.5
100
Albania
55
0.732
-0.47
70
63
97
39
Armenia
55
0.727
-0.42
63
37.9
152
73
96.3
China
56
0.718
0.18
62
40.3
74
75
22.4
0.794
42
Country
MMR
Deaths/
100,000
births
(2000)
UNDP
status of
women
index
Govern
-ance
index
Combined
female
enrollment
data
Gini
Coefficient
Health
Expend.
Per
capita
Safe Water
Paved
(% of pop. roads as %
with
of total road
access)
KM
Bahamas, The
Estonia
60
63
0.811
0.831
1.4
0.78
77
93
37.6
1230
346
97
77
57.4
20.1
Dem Rep of Korea
Russian Federation
67
67
0.774
-1.78
-0.4
82
45.6
39
251
100
99
6.4
67.4
Turkey
Fiji
70
75
0.726
0.743
-0.2
0.06
54
75
40
46
231
214
82
47
34
49.2
Iran, Islamic Rep.
Argentina
76
82
0.702
0.839
-0.46
-0.49
63
94
43
200
823
92
71
56.3
29.5
Mexico
Egypt, Arab Rep.
83
84
0.79
0.634
0.15
-0.32
74
72
51.9
34.4
421
118
88
97
32.8
78.1
Jamaica
Oman
87
87
0.75
0.736
-0.07
0.69
71
56
37.9
212
334
92
39
70.1
30
Sri Lanka
Azerbaijan
92
94
0.726
0.03
-0.96
64
69
34.4
36.5
77
48
77
78
11.1
92.3
Barbados
Venezuela, RB
95
96
0.885
0.767
1.36
-1.14
94
70
46
49.1
814
298
100
83
98.6
33.6
Libya
Botswana
97
100
0.611
-0.87
0.87
91
81
63
221
219
72
95
57.2
55
Tajikistan
100
0.673
-1.23
65
34.7
94
60
71.5
Honduras
110
0.656
-0.73
61
59
156
88
20.4
Kyrgyz Republic
Mongolia
110
110
0.659
-0.81
-0.18
80
69
29
44
66
69
77
60
91.1
3.5
Suriname
Tunisia
110
120
0.727
-0.16
0.65
79
76
41.7
257
239
73
80
26
64.8
Colombia
Ecuador
130
130
0.774
0.716
-0.39
-0.96
72
71
57.1
43.7
507
186
91
85
14.3
18.9
Vietnam
Algeria
130
140
0.687
0.687
-0.27
-0.59
61
69
36.1
35.3
65
122
77
89
25.1
68.9
Belize
Qatar
140
140
0.756
-0.06
0.69
68
85
212
1105
92
100
17
53.1
Cape Verde
Dominican Republic
150
150
0.719
0.727
-0.2
-0.41
79
77
60
202
74
86
78
49.4
El Salvador
Lebanon
150
150
0.707
0.737
-0.53
-0.41
63
77
50.8
228
563
77
100
19.8
84.9
Panama
Syrian Arab Republic
160
160
0.781
0.668
-0.14
-0.57
78
61
48.5
449
109
90
80
34.6
23.1
Trinidad and Tobago
Guyana
160
170
0.796
0.73
0.47
-0.32
68
84
40.3
44.6
325
130
90
94
51.1
7.4
Paraguay
Philippines
170
200
0.739
0.748
-1.29
-0.06
64
81
57.7
46.1
246
100
78
86
9.5
21
Kazakhstan
Morocco
210
220
0.763
0.590
-0.8
0.07
78
46
31.2
39.5
127
159
91
80
94.7
56.4
MMR 101-300
47.4
43
Country
MMR
Deaths/
100,000
births
(2000)
UNDP
status of
women
index
Govern
-ance
index
Combined
female
enrollment
data
Gini
Coefficient
Health
Expend.
Per
capita
Safe Water
Paved
(% of pop. roads as %
with
of total road
access)
KM
Indonesia
Nicaragua
230
230
0.677
0.636
-0.56
-0.87
63
66
30.3
60.3
56
150
78
77
46.3
11
South Africa
Guatemala
230
240
0.678
0.638
0.52
-0.61
78
54
59.3
55.8
396
87
86
92
20.3
34.5
Iraq
Brazil
250
260
0.77
-1.64
-0.22
40
97
60.7
110
428
85
87
84.3
5.5
Namibia
Papua New Guinea
300
300
0.622
0.544
0.18
-0.78
75
39
70.7
50.9
312
77
77
42
13.6
3.5
Myanmar
Swaziland
360
370
-1.29
-0.44
47
75
60.9
78
118
72
0.536
12.2
55
Bangladesh
Peru
380
410
0.495
0.734
-0.53
-0.47
54
78
31.8
46.2
70
246
97
80
9.5
12.8
Bhutan
Bolivia
420
420
0.663
0.93
-0.53
80
44.7
82
153
62
83
60.7
6.5
Gabon
Cambodia
420
450
0.551
-0.45
-0.56
81
49
40.4
196
73
86
30
9.9
16.2
Comoros
Pakistan
480
500
0.521
0.469
-0.84
-0.5
36
27
33
47
71
96
90
76.5
43
Congo, Rep.
Gambia, The
510
540
0.496
0.457
-1.25
-0.81
53
43
47.8
101
52
51
62
9.7
35.4
Ghana
India
540
540
0.564
0.574
0.01
-0.13
42
49
39.6
37.8
45
84
73
84
29.6
45.7
Lesotho
Madagascar
550
550
0.497
0.467
-0.26
-0.38
65
43
56
46
100
18
78
47
18.3
11.6
Togo
Yemen, Rep.
570
570
0.483
0.424
-1.17
-0.87
53
34
33.4
34
33
54
69
31.6
11.5
Sudan
Eritrea
590
630
0.483
0.434
-1.11
-0.44
32
29
43
24
74
46
36.3
22.1
Lao PDR
Haiti
650
680
0.518
0.462
-0.8
-1.56
51
51
37
53
55
37
46
5
24.3
Cote d'Ivoire
Senegal
690
690
0.376
0.42
-0.18
31
34
36.7
41.3
57
71
81
78
9.7
29.3
Cameroon
Djibouti
730
730
0.488
-0.62
-0.88
43
19
47.7
38.6
86
48
58
100
12.5
12.6
Guinea
Nepal
740
740
-0.78
-0.51
26
57
40.3
36.7
52
41
48
88
16.5
30.8
Zambia
Liberia
750
760
0.376
-0.93
-1.51
43
43
52.6
64
33
64
46
16
6.2
Nigeria
Benin
800
850
0.45
0.395
-1.12
-0.62
41
38
50.6
35
39
62
63
30.9
20
Ethiopia
Equatorial Guinea
850
880
0.347
-0.89
-1.37
27
49
48.6
20
89
24
44
12
21.8
MMR 301-1000
0.479
44
Country
Uganda
Dem Rep of Congo
MMR
Deaths/
100,000
births
(2000)
UNDP
status of
women
index
Govern
-ance
index
Combined
female
enrollment
data
Gini
Coefficient
Health
Expend.
Per
capita
Safe Water
Paved
(% of pop. roads as %
with
of total road
access)
KM
880
990
0.483
0.353
-0.41
-1.6
66
24
37.4
44
22
52
45
6.7
0.5
Burkina Faso
Burundi
1000
1000
0.317
0.331
-0.69
-1.46
18
28
48.2
33.3
37
26
42
78
16
7.1
Kenya
Mauritania
1000
1000
0.488
0.445
-0.85
-0.16
52
40
44.5
37.3
58
73
57
37
12.1
11.3
Mozambique
1000
0.341
-0.41
32
39.6
50
57
18.7
Central African Republic 1100
0.352
-1.43
20
61.3
34
70
2.7
Chad
Guinea-Bissau
1100
1100
0.366
0.353
-0.75
-1.35
24
34
47
35
54
27
56
0.8
10.3
Somalia
Zimbabwe
1100
1100
0.489
-1.97
-0.8
7
58
56.8
11
130
26
83
11.8
47.4
Mali
Rwanda
1200
1400
0.327
0.416
-0.84
-0.82
26
51
50.5
28.9
34
35
65
41
12.1
8.3
Tanzania
Niger
1500
1600
0.396
0.279
-0.51
-0.79
31
14
38.2
50.5
36
27
68
59
4.2
7.9
Angola
Malawi
1700
1800
0.378
-1.16
-0.68
26
70
50.3
47
49
38
57
10.4
18.5
Afghanistan
Sierra Leone
1900
2000
62.9
28
31
13
57
13.3
7.9
MMR>1000
-1.39
-1.54
44
Sources (cells are empty when no data were available):
Maternal Mortality in 2000: Estimates Developed by WHO, UNICEF and UNFPA
Status of women: Human Development Indicators, Human Development Report 2003 Indicators
(http://www.undp.org/hdr2003/indicator/indic_197_1_1.html)
Governance index (government effectiveness): Governance Indicators 1996-2002, World Bank,
(http://info.worldbank.org/governance/kkz2002/tables.asp)
Female Enrollment: World Bank, http://devdata.worldbank.org/genderstats/home.asp and UNDP, Human
Development Report, 2003.
Gini coefficients: World Bank, World Development Indicators and UNDP, Human Development Report,
2003
Female Enrollment: World Bank, http://devdata.worldbank.org/genderstats/home.asp and UNDP, Human
Development Report, 2003.
Health Expenditures per capita: Human Development Report 2003 Indicators
(http://www.undp.org/hdr2003/indicator/indic_59_1_1.html)
Safe Water: UNDP, Human Development Report, 2003
Roads: http://millenniumindicators.un.org/unsd/mi/mi_goals.asp and other sources
45
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