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SUCCESS FACTORS FOR WOMEN’S AND CHILDREN’S HEALTH

BACKGROUND PAPER

A Quantitative Mapping of Trends in Reductions of Maternal and Child Mortality in the High Mortality-

burden Countdown to 2015 countries

REPORT DEVELOPED BY: TAGHREED ADAM AND JENNIFER FRANZ-VASDEKI Alliance for Health Policy and Systems Research and the Partnership for Maternal, Newborn & Child Health

December 17, 2012

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Executive summary The overall objective of the X-factor study is to understand the factors contributing to variable progress in achieving the Millennium Development Goals (MDGs) 4 (reduce the under-five mortality rate by two-thirds) and 5a (reduce maternal mortality ratio by 75%) by 2015 among the Countdown for 2015 countries—which include the 75 “high burden” countries that together account for more then 95% of maternal, newborn, and child deaths. This report builds on other analyses carried out as part of the X-factor study and was designed to expand our understanding on how different factors (health and ‘non-health’) are related to variations across countries in maternal and child mortality reduction. In order to do this, the following three hypotheses were formulated and tested:

1. Change in the absolute rate of mortality is a better indicator of progress, as annual rate of change (%) can mask significant reductions in numbers of maternal and child deaths;

2. Different causes of death are associated with different combinations of factors that may explain reductions of maternal and child mortality across countries;

3. ‘Non-health’ factors, including socio-economic, cultural, and environmental factors, affect progress differently in different contexts and over time.

Key findings

Hypothesis 1: Annual % rates of change can mask the significant progress made by countries starting with higher levels of mortality. Absolute change in rates, however, highlight progress made in both high and low mortality settings. For example, sub-Saharan Africa has reduced under-five deaths on average by 60/1000 live births since 1990—making it second only to South Asia (74/1000) in terms of progress made. If measuring instead by annual rate (%) of change, it appears the least progress has been achieved in this region (for example, Latin America/Caribbean reduced U5MR by 5%, South Asia 3.7%, while Sub-Saharan Africa by just 2%). Therefore, by looking at absolute changes in mortality over time, we get a better idea of absolute reductions in numbers of deaths.

Hypothesis 2: Despite significant progress in reducing under-five mortality, across all countries and irrespective of starting levels of mortality, addressing neonatal mortality remains a significant challenge with a virtually unchanging share of deaths between 2000 and 2010 occurring in the first 28 days of life. Children under-five are also dying from very different causes depending on age. For example, neonates (0-28 days) are dying primarily from preterm complications and intrapartum related events. Children aged 1-59 months, however, suffer primarily from, pneumonia, diarrhoea and nutrition-related causes. Between 2000 and 2010, the leading causes of death for both age groups showed no significant change, irrespective of starting levels of mortality in 1990. For mothers, factors influencing mortality vary by geographical region; for example, the leading cause of death in Africa is haemorrhage, while in Latin American and the Caribbean, hypertensive disorders dominate. The onset of HIV is an increasing cause of maternal deaths, particularly in Africa. The variation in cause of death will require a multivariate analysis to better understand context appropriate interventions.

Hypothesis 3: ‘Non-health’ factors, such as higher levels of GDP, health expenditure, schooling, fewer children per woman, and improved sanitation, all lead to lower mortality, for both children and mothers. In future analyses, exploring the variable contribution of these factors to mortality reduction among low and high mortality countries can offer important insight as to how and when these variables may influence or contribute to reducing mortality.

Conclusions and implications for further analysis Recognition of a country’s progress in reducing mortality should be based on lives saved, in order to equitably reflect progress over time, no matter if starting with low or high mortality. To better understand what children and mothers are dying from and when, there is a need to look at different ‘profiles’ of countries to allow for a more thorough analysis of health and ‘non-health’ factors that may be contributing to slower or faster reductions in mortality. Next steps will consider such contextual factors using a multi-factorial modeling approach to better understand how these factors may differ in their contribution to reducing mortality and in order to develop appropriate intervention strategies that take into account different country profiles and contexts.

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Introduction

The overall objective of the X-factor study is to understand the factors contributing to variable progress in achieving the Millennium Development Goals (MDGs) 4 (reduce the under-five mortality rate by two-thirds) and 5a (reduce maternal mortality ratio by 75%) by 2015 among the Countdown for 2015 countries—which include the 75 “high burden” countries that together account for more then 95% of maternal, newborn, and child deaths. This report builds on other analyses carried out as part of the X-factor study, including a review of the literature, in-depth, qualitative country case studies, as well as quantitative analyses that employed logistic regression techniques to explore variability in progress among countries. The primary objective of the work reported here was to build upon these analyses and expand our understanding of the role of varying contextual factors (health and ‘non-health’) and their importance in contributing to variation in mortality reduction. This report differs in its approach to answering the X-factor research question from earlier work in that absolute reduction in mortality is the indicator of focus for assessing progress, and for the analyses, countries are grouped based on starting levels of mortality in 1990. Previous quantitative analysis for the X-factor study focused on progress in line with the Countdown for 2015 definition of ‘on track’, which for U5MR is measured by a 4% reduction in under-five mortality between 1990 and 2010, or having achieved 40 or fewer deaths per 1000 live births; for MMR, it is a 5.5% reduction in maternal mortality or having already reached an absolute level of less than 100 deaths per 100,000 live births. In reviewing this work, however, it was felt the annual rate of change masks important progress made between countries—particularly those with high levels of mortality in 1990. For example, Sub-Saharan Africa is frequently cited for having the slowest progress in terms of annual % decline in maternal and child mortality, however, as a whole, the

region has made among the highest absolute declines in mortality. Figure 1-a and –b illustrate this point. In addition, by grouping countries into ‘low’, ‘medium’ and ‘high’ for the analysis, we are able to explore whether there is any merit in treating groups of countries with different levels of mortality in an explicit, disaggregated, manner in future analyses. In order to address our objective, the following three hypotheses were formulated and tested:

1. Change in the absolute rate of mortality is a better indicator of progress, as annual rate of change (%) can mask significant reductions in numbers of maternal and child deaths. Annual rates of change mask the significant range in progress made by countries (both those with high and low mortality in 1990) over the last 20 years. For example, for U5MR, rates of change vary between -1% to 6% between 1990 and 2011, while absolute values show a range of -20 to 189. A similar picture emerges for MMR, where rates of change between 1990 and 2010 vary between -2% and 8%, while absolute values run from -180 to 1130;

2. Different causes of death are associated with different combinations of factors in reductions of maternal and child mortality across countries; the main causes of death vary by age group as well as by region, requiring consideration of varying combinations of factors related to the main causes of death;

3. ‘Non-health’ factors, including socio-economic, cultural, and environmental factors, affect progress differently in different contexts and over time; exploring variability among these factors among low and high mortality countries offers important insight as to how these variables may differ in their contribution to reducing mortality.

We focus in this report, therefore, on exploring whether there is value in explicitly taking into account starting levels of mortality, different causes of death, and the associated contextual factors when analyzing varying rates of progress in reducing mortality. The findings are meant to inform the next phase of analysis, which will employ a comprehensive multi-factorial modeling exercise.

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Figure 1: Change in the under-five mortality rate (a) and maternal mortality ratio (b)-by region

(a) U5MR (b) MMR

Note: For Figures 1 and 2, the data are for the 74 Countdown to 2015 countries, grouped by region and based on the World Bank Regional classifications. Source: [3,5].

35

48

34

42

66

105

79

90

87

85

140

165

0 20 40 60 80 100 120 140 160 180

EastAsiaandPacific

Europe/CentralAsia

La nAmerica/Caribbean

MiddleEast/NorthAfrica

SouthAsia

Sub-SaharanAfrica

Deathsper1000livebirths

1990 2011

Rate:2%Abs:60/1000

Rate:3.7%Abs:74/1000

Rate:3.5%Abs:43/1000

Rate:5%

Abs:53/1000

Rate:3%

Abs:42/1000

Rate:3.7%

Abs:44/1000

174

55

139

107

266

481

472

73

274

307

792

762

0 200 400 600 800 1000

EastAsiaandPacific

Europe/CentralAsia

La nAmerica/Caribbean

MiddleEast/NorthAfrica

SouthAsia

Sub-SaharanAfrica

Deathsper100,000livebirths1990 2010

Rate4.6%Abs:200/100,000

Rate2.3%Abs:281/100,000

Rate5.3%Abs:526/100,000

Rate3.4%Abs:135/100,000

Rate1.6%Abs:18/100,000

Rate4.2%Abs:298/100,000

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Methods To begin testing our hypotheses, we started first by grouping the Countdown countries into ‘low’, ‘medium’ and ‘high’ groups

based on levels of mortality in 1990 (see Table 1). All the following analyses are disaggregated by these mortality groups to explore whether there is any merit in analyzing groups of countries with different levels of mortality in an explicit, disaggregated, manner in future analyses. Groups were determined by looking for natural breaks in the data and to a lesser extent also looking to keep groups roughly balanced in the first instance. For U5MR, data breaks were determined when a difference of more than 8-10 deaths per 1000 live births was observed. For maternal mortality, the cut off point was more arbitrary due to the larger variability in the MMR data. Further information on country groupings, data sources and definitions can be found in Annex 1. Table 1: Classification of countries for analysis based on mortality levels in 1990

Under-five mortality rate (U5MR)

Maternal mortality ratio (MMR)

Low mortality

<100 <300

Medium mortality

>100-170 >300-800

High mortality

>170 >800

To address our first hypothesis, we began with the assumption that countries with different starting levels of mortality need to be assessed differently when analyzing reduction in mortality and progress made. In order to do this, we first looked at how starting levels (based on 1990) of mortality correlate with absolute and % change in mortality between 1990 and 2010. We then sought to understand whether the rate of decline in mortality varied over time among countries with ‘low’, ‘medium’ or ‘high’ levels of mortality in 1990. We did this because it is possible that some countries that have shown a slow rate of decline in mortality, on average, have achieved much faster initial declines that tapered over time, or vice versa. If differences in the rate of decline are notable among different groups of countries, this will require a good understanding of the phenomena involved to inform our overall objectives of understanding the factors affecting reduction in mortality across countries. For our second hypothesis, we started with the assumption that cause of death varies by age group (especially for the neonates and 1-59 months of age) and context and then inquired as to whether this variation also depends on starting levels of mortality; we did this by looking at variation in cause of death by sub-groups (neonates and children age 1-59 months) and changes among these groups between 2000 and 2010, for ‘low’, ‘medium’, and ‘high’ levels of mortality. Due to limited data, variation in maternal mortality by cause of death was analysed at the regional level only. Building on our first two hypotheses, for our third hypothesis, we started with the assumption that ‘non-health’ factors affect progress differently in different contexts. The aim was not to test whether or which a selection of explaining variability in achieviening better health outcomes—the contribution of most of these factors is well established in the literature, including other work carried out for the X-factor study. Instead, this initial analysis seeks to complement and support our existing knowledge by asking whether explicitly taking absolute mortality and cause of death in ‘low’, ‘medium’ and ‘high’ mortality settings into account will also enhance the explanatory power of these ‘non-health’ factors, as well as the thinking about and development of possible interventions or policy options for improving progress.

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Results

Main findings from hypothesis #1:

There is no relationship between absolute decline and the rate of decline—i.e., countries from the three mortality groups observed rates of decline roughly spanning the full spectrum for both child and maternal mortality. See Annex 2, Figures 6 and 7.

Substantial reductions in absolute numbers are not always reflected in % reductions in mortality. Absolute decline, therefore, not rate of decline, should be the main outcome of focus in future analysis. Using rates of change (%) inherently benefits those countries that started with lower mortality1; it also masks the value we place on a life saved, no matter where it occurs. Figures 2-a and –b illustrate this point.

For U5MR, there was no strong indication of variability in rate of decline between 1990 and 2010 by mortality group (low, medium, high based on 1990 levels of mortality); see Annex 2, Figure 8.

MMR, however, showed more variability in average rates of decline over the entire period; therefore, an in-depth analysis of the reasons for variability in the rate of decline and the countries driving this variability would be useful in future analysis; see Annex 2, Figure 9.

Figure 2: Change in the under-five mortality rate (a) and maternal mortality ratio (b) -by mortality grouping

(a) U5MR (b) MMR

Note: As for Figures 2 and 3, the data for Figures 3 and 4 are for the 74 Countdown to 2015 countries; however, countries are grouped based on mortality levels in 1990 (see Table 1). Source: [3] [5].

1 Refer to St Gallen report for further discussion on absolute vs. annualized rates of decline for on and off track countries in reference to MDG4 and MDG 5a.

43

78

128

72

135

215

0 50 100 150 200 250

Low

Medium

High

Deathsper1000livebirths1990 2011

Rate2.6%Abs:87/1000

Rate2.8%Abs:57/1000

Rate3%Abs:29/1000

106

343

587

162

592

1071

0 200 400 600 800 1000 1200

Low

Medium

High

Deathsper100,000livebirths1990 2010

Rate3.3%Abs:484/100,000

Rate2.9%Abs:249/100,000

Rate2.5%Abs:56/100,000

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Main findings from hypothesis #2:

The distribution of cause of death during the neonatal period did not change over time and little progress has been made in reducing deaths in the first 28 days of life, irrespective of starting levels of mortality; Figure 3 illustrates this point. See also Annex 2, Figure 10.

Explicit or independent analysis of neonatal mortality will allow a much more informed and ‘efficient’ analysis of the factors contributing to lack of progress between 2000 and 2010 and what are the factors that are likely to improve progress for neonates; see Annex 2, Figure 11.

Some progress was made in addressing causes of death for children aged 1-59 months, particularly for measles-related deaths; malaria, however, remains a leading cause of death in countries that started with ‘high’ levels of mortality in 1990; Annex 2, Figure 12. Analysis of U5MR that takes differential cause-of-death and their distribution into account would be worth exploring.

An in-depth analysis considering differential burdens of MMR across regions will be important in understanding and identifying factors that may explain progress or (lack of) in reducing maternal deaths; Annex 2, Figure 13.

Figure 3: Child mortality by time of death (1990-2010)-by mortality grouping

(a) Low (b) Medium

Note: Figure 3 shows that for all three mortality groups, there was a slow decline in the NMR between 1990 and 2011 and that progress in reducing under-five deaths was more pronounced in (or focused on) the post-neonatal group (1-59 months), which made the contribution of neonatal deaths to overall U5 deaths grow over time (while absolute numbers are declining but very slowly). Source: [3].

(c) High

0-28 days

1-59 months

0

50

100

150

200

250

1990 1995 2000 2005 2010 2011

Dea

ths

per

100

0 liv

e b

irth

s

0-28 days

1-59 months

0

50

100

150

200

250

1990 1995 2000 2005 2010 2011

0-28 days

1-59 months

0

50

100

150

200

250

1990 1995 2000 2005 2010 2011

De

ath

s p

er

10

00

live

bir

ths

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Findings from hypothesis #3:

Countries in the ‘low’ mortality group have higher GDP per capita, while the opposite is true for countries in the ‘high’ mortality group. Figure 4 highlights this point.

There is a negative association found between health expenditure and mortality; see Annex 2, Figure 14.

Economic variables to be included in the multivariate analysis will depend on the type of model and correlation with other factors but GDP per capita seems to capture this group reasonably well; it also has the added advantage of less measurement error compared to health expenditure;

The Human Development Index (HDI) was not considered as GDP is one of the variables used in its calculation and because of its high multicollinearity with many of the other explanatory variables below (see Annex 4);

For the health infrastructure and workforce variables, there is a high correlation between hospital beds and availability of nurses and midwives which means that only one of them should be considered, if any (depending on other correlations), in a multivariate model; for both variables, the relationship is much stronger with maternal than with under-five mortality; see Annex 2, Figures 15 and 16;

For the environmental variables, we explored both access to improved sanitation and the Environmental Performance Index (EPI); knowing that these two variables are highly correlated and have a similar relationship to the mortality variables. Improved sanitation is a clear ‘actionable’ variable, which gives it an additional advantage; see Annex 2, Figure 17.

For the demographic and environmental variables, both variables are also highly correlated with GDP and it is unlikely that any of them would be considered for the final model; see Annex 2, Figures 18 and 19.

Figure 4: Relationship between GDP per capita (PPP) and (a) U5MR, (b) MMR-by mortality grouping

(a) U5MR (b) MMR

Note: Two countries have been circled in red, Eritrea (ERI) and Tanzania (TZA), in order to illustrate that some countries are not in the same mortality group for U5MR (a) and MMR (b). This

highlights that the factors affecting reduction in mortality (and choice of variables for the analysis) may vary among the two age groups. See Annex 1 for country groupings.

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Discussion The purpose of this analysis was to explore three hypotheses that may offer additional insight to future analysis to understand the key factors that are likely to contribute to reduction in under-five and maternal mortality over time. The key findings in this report highlight the importance of a life saved, no matter if occurring in a country starting with low or high mortality—a point that is overlooked with the focus on targets measuring % annual reductions. As a measure of progress, annual % change, by design, favors countries starting with low mortality. For example, over half of the countries (15/28) that are classified as ‘on track’ for MDG 4 using the Countdown for 2015 definition, are ‘low’ mortality countries based on starting levels in 1990. If we focus instead on absolute reduction in mortality, the top six countries that made the greatest reductions in mortality by 2011 for U5MR were all classified as ‘high’ based on mortality in 1990 and achieved on average a reduction of 140 or more lives saved per 1000 live births. For MMR, 4/9 of the ‘on track’ countries for MDG 5a as of 2010 were ‘high’ mortality in 1990, while 3/9 were ‘low’. The top seven countries that made the greatest reductions in MMR between 1990 and 2010 are all classified as ‘high’ mortality, and on average achieved reductions of 795 deaths per 100,000 births; (see Annex 7 for further discussion). When assessing patterns in mortality over time, It may be most appropriate in future analyses to consider shorter time periods, particularly with maternal mortality, as it is a statistically rare event and taking average values can mask variation due to acute events, such as war or famine. As with the need to disaggregate for time, we demonstrated a similar necessity to disaggregate mortality by cause of death for children, by age and context. For example, we found that while under-five deaths are declining globally, the share of under-five deaths occurring in the neonatal period are steady or rising around the world—irrespective of high or low levels of mortality in 1990. This highlights the need for future analyses to deal specifically with the causes of death that are relevant to each age group (i.e. neonates and 1-59 months) in order to recommend and effectuate the most appropriate interventions. Similarly for mothers, cause of death varies by geographical region and any focused analysis would need to develop context appropriate interventions to further reduce leading causes of maternal death. Just as contextual factors are determinate and variable for when and from what mothers and children are dying, ‘non-health’ factors, including socio-economic, cultural and environmental also vary in their contribution to reducing mortality among low and high mortality countries. Considerable care must be taken with generating conclusions from a bivariate analysis as it is limited by not being able to control for other factors. Exploration of actionable options can only be meaningfully considered, therefore, during the next phase, using a multivariate approach. Some recommendations for future steps on how to deal with this variation are offered in the next section.

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Conclusions and implications for further analysis While descriptive and exploratory in nature, this study offered valuable insights to shape the conceptual thinking and design of a much larger, multi-factorial analysis of the factors influencing successful reduction (or lack of) in mortality across countries. While not exhaustive of all important factors or possible hypotheses, it sets some principles and guiding frameworks that will be further explored and refined in the next phase of this study. In particular, future analysis will explore possible interventions that may have contributed to the progress made in some countries to be able to develop tailored options for countries wishing to accelerate progress in reducing mortality in the coming years. Examples of such interventions are coverage of skilled birth attendants, contraceptives, receiving four or more antenatal visits (ANC +4), vaccinations, etc. The selection of these variables will be guided by the prevailing causes of death for each mortality group, and known effective interventions that can address them—depending on data availability. Coverage of such interventions may offer some insights into tailored policy options. For example, contraceptive coverage is highly correlated with mortality reduction, especially among the under-five group, and countries with high levels of mortality may be able to save more lives if they improve coverage of family planning services. The picture is less clear for skilled birth attendants and antenatal coverage (ANC +4) (see Annex 5 for detailed analysis); however, where coverage spans a wide range and high coverage of these interventions was also observed in groups with high mortality (this may be a data quality issue). Figure 5: Relationship between contraceptive prevalence (% women age 15-49) (a) U5MR, (b) MMR-grouped by mortality level

(a) U5MR (b) MMR

The X-factor study in its many components has produced a remarkable amount of knowledge and the proposed next steps to extract key ‘actionable’ interventions is essential in turning this knowledge into usable information for policy makers, researchers and other interested partners and stakeholders, not only to help achieve progress towards the MDGs, but with only a few years to go to 2015, to help stakeholders plan and prepare for beyond the MDGs. It is hoped this work will help to guide the discussion on age and context-specific interventions. For maternal mortality, in particular, updated cause of death statistics are to be released by the WHO in 2012, allowing us to capitalize on this information by looking for changes over time for the development of ‘actionable’ interventions that are applicable, timely and of greatest urgency.

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Sources 1. Waage J, Banerji R, Campbell O, Chirwa E, Collender G, et al. (2010) The Millennium Development Goals: a cross-sectoral analysis

and principles for goal setting after 2015 Lancet and London International Development Centre Commission. Lancet 376: 991-1023.

2. UNICEF (2012) Progress Report 2012: A promise renewed. Geneva: UNICEF. 3. UN-IAGCME (2012) Levels & Trends in Child Mortality: Report 2012. Geneva: UNICEF. 4. Easterly W (2008) How the Millennium Development Goals are Unfair to Africa. World Development 37: 26-35. 5. WHO (2012) Trends in maternal mortality: 1990-2010. Geneva: World Health Organization 6. Pedersen J, Liu J (2012) Child mortality estimation: appropriate time periods for child mortality estimates from full birth histories.

PLoS Med 9: e1001289. 7. Khan KS, Wojdyla D, Say L, Gulmezoglu AM, Van Look PF (2006) WHO analysis of causes of maternal death: a systematic review.

Lancet 367: 1066-1074. 8. Hogan MC, Foreman KJ, Naghavi M, Ahn SY, Wang M, et al. (2010) Maternal mortality for 181 countries, 1980-2008: a systematic

analysis of progress towards Millennium Development Goal 5. Lancet 375: 1609-1623. 9. WHO (2011) World Health Statistics. Geneva, Switzerland: World Health Organization 10. UNICEF (2012) Monitoring the situation of children and women: Percentage of women aged 15–49 years attended at least four

times during pregnancy by any provider. In: UNICEF, editor. UNICEF.

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Annexes

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Annex 1: Data definitions, sources and country groupings

Definitions/Sources Variable Definition Year(s) Source

Under-five mortality rate (U5MR)

The under-five mortality rate (U5MR) is the indicator of analysis for deaths among children under age five. According to the World Health Organization (WHO), U5MR is not strictly a rate, but rather a probability of death derived from a life tables and expr essed as rate per 1,000 live births;

2011 [3]

Neonatal mortality rate (NMR)

The neonatal mortality rate (NMR) is defined as the number of deaths during the first 28 completed days of life per 1,000 live births in a given year or period. Data for under-five mortality, including neonatal mortality, presented in this report are from the recently released UN IAG, 2012 report: “Levels and Trends in Child Mortality from the UN Inter-Agency Group for Child Mortality Estimation”;

2011 [3]

Maternal mortality ratio (MMR)

The Maternal Mortality Ratio (MMR) measures the number of deaths per 100,000 live births; the MMR measures the number of deaths per 100,000 women of reproductive age. MMR data used throughout this report are based on the 1990-2010 UN Inter-Agency estimates produced by the Maternal Mortality Estimation Inter-agency Group (MMEIG). Maternal deaths refer to the ‘death of a woman while pregnant or within 42 days of termination of pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental or incidental causes’ (ICD-10) [5].

2010 [5]

GDP per capita (PPP)

Gross Domestic Product (PPP) per capita 2010 World Bank Development Indicators

Health expenditure per capita

Health expenditure per capita (PPP) 2010 NHA Indicators

Hospital Beds Hospital beds per 10,000 population Most current year available

[9]

Nurses and midwives

Nurses and Midwives per 10,000 population Most current year available

[9]

Improved sanitation facilities (% of population with access)

Improved sanitation facilities include private facilities only and indicate moving to a piped sewer system, septic tank, pit latrine, Ventilation improved (VIP) latrine, Pit latrine with slab, Composting toilet.

2010 World Bank Development Indicators

Total Fertility Rate Total fertility rate (children per women age 15-49)

2009 http://www.who.int/whosis/whostat/EN_WHS2011_Full.pdf

Mean years of schooling (% adults +25)

Mean years of schooling measures the number of years adults age 25 and over has sepnt in schools (MYS) and is one variable used in calculating the Human Development Index

Most current year available

UNDP http://hdrstats.undp.org/en/indicators/103006.html

Skilled birth attendants

Births Attended by Skilled Health Personnel (Percent of Births) 2000-2010 most current year available

[10]

Contraceptive prevalence

Contraceptive prevalence (proportion of women aged 15-49 currently married who (or partner) are using a contraceptive method (modern or traditional)

2000-2010 most current year available

http://www.who.int/whosis/whostat/EN_WHS2011_Full.pdf

ANC +4 Proportion of women attended at least four times during pregnancy by any provider (skilled or unskilled) for reasons related to the pregnancy (ANC 4+)

2000-2010 most current year available

http://www.childinfo.org/antenatal_care_four.php

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Country Groupings 1. 74 Countdown countries: grouped by ‘low’, ‘medium’ and ‘high’ based on U5MR in 1990

Code Country Region 1990 2000 2011 1990 2000 2011

SLB Solomon IslandsEAP 41.8 30.5 21.6 17.4 13.8 10.5

PRK Democratic People's Republic of KoreaEAP 45.0 57.7 33.2 21.8 25.9 17.5

IRQ Iraq MENA 46.0 42.8 37.9 22.9 21.7 19.9

MEX Mexico LAC 48.8 29.1 15.7 17.2 11.8 7.0

CHN China EAP 48.9 35.0 14.6 22.8 17.9 8.7

VNM Viet Nam EAP 49.9 33.9 21.7 22.4 16.9 11.9

BWA Botswana SSA 52.8 81.1 25.9 20.8 16.2 11.3

PHL Philippines EAP 57.0 38.8 25.4 22.1 16.9 12.2

BRA Brazil LAC 58.0 35.7 15.6 26.8 19.0 9.7

ZAF South Africa SSA 62.3 74.1 46.7 26.1 23.0 19.2

KGZ Kyrgyzstan ECA 70.3 47.4 30.6 28.3 21.8 15.8

PER Peru LAC 75.1 38.9 18.1 26.4 16.9 9.2

UZB Uzbekistan ECA 75.3 61.0 48.6 20.0 17.5 15.0

GTM Guatemala LAC 78.0 48.2 30.4 28.0 20.4 14.5

ZWE Zimbabwe SSA 79.2 105.8 67.1 32.2 30.0 29.9

MAR Morocco MENA 81.3 52.7 32.8 34.8 26.3 18.7

IDN Indonesia EAP 81.6 52.5 31.8 29.2 22.0 15.3

SWZ Swaziland SSA 83.3 114.2 103.6 32.2 32.5 34.6

EGY Egypt MENA 85.7 44.4 21.1 20.1 13.1 7.4 25th percentile

LSO Lesotho SSA 87.5 117.3 86.0 45.1 41.9 38.6

PNG Papua New GuineaEAP 88.0 71.7 57.8 29.4 25.9 22.6

TKM Turkmenistan ECA 94.3 71.4 52.5 31.3 26.5 21.6

GAB Gabon SSA 94.4 82.4 65.6 31.7 28.2 24.5

AZE Azerbaijan ECA 94.5 68.6 44.7 31.1 25.6 19.2

STP Sao Tome and PrincipeSSA 96.0 92.5 88.8 30.7 30.0 29.3

KEN Kenya SSA 97.8 113.1 72.8 32.5 33.1 26.7

MMR Myanmar EAP 107.4 83.5 62.4 41.6 35.9 29.9

IND India SA 114.2 87.7 61.3 47.1 40.5 32.3

TJK Tajikistan ECA 114.3 94.7 63.3 35.2 31.6 24.7

KHM Cambodia EAP 116.7 101.5 42.5 36.8 34.1 19.4

COG Congo SSA 118.8 108.8 98.8 35.2 33.5 31.7

BOL Bolivia (Plurinational State of)LAC 119.5 80.8 50.6 37.4 29.9 22.0

GHA Ghana SSA 120.9 98.7 77.6 38.1 34.0 29.5

DJI Djibouti SSA 121.6 105.7 89.5 39.1 36.2 32.9

COM Comoros SSA 121.7 99.6 79.3 40.5 36.2 31.6

PAK Pakistan SA 122.2 95.3 72.0 48.5 42.1 35.6

SDN Sudan SSA 122.8 103.7 86.0 38.1 34.7 31.1 50th percentile

MRT Mauritania SSA 124.7 117.9 112.1 42.8 41.5 40.4

YEM Yemen MENA 126.0 99.1 76.5 42.5 37.2 31.9

NPL Nepal SA 134.6 82.9 48.0 50.7 38.6 27.0

SEN Senegal SSA 135.9 130.4 64.8 39.8 39.0 25.8

ERI Eritrea SSA 137.7 98.2 67.8 32.4 26.9 21.5

BGD Bangladesh SA 138.8 84.4 46.0 51.9 39.3 26.4

HTI Haiti LAC 143.0 102.0 70.0 37.3 31.3 25.1

CMR Cameroon SSA 145.2 139.5 127.2 36.3 34.8 33.4

TGO Togo SSA 147.0 127.8 110.1 41.7 38.8 35.8

LAO Lao People's Democratic RepublicEAP 147.7 81.3 41.9 37.9 27.1 17.5

CIV CÙte d'Ivoire SSA 151.4 138.6 114.9 47.3 44.4 41.2

RWA Rwanda SSA 156.3 183.0 54.1 39.3 42.7 21.1

TZA United Republic of TanzaniaSSA 157.9 126.4 67.6 41.4 35.5 25.2

MDG Madagascar SSA 161.2 104.1 61.6 40.0 31.7 23.0

GMB Gambia SSA 164.6 130.3 100.6 44.3 39.3 34.1

CAF Central African RepublicSSA 169.1 172.0 163.5 46.3 46.3 46.1

BEN Benin SSA 177.3 139.7 106.0 40.3 35.8 30.8

UGA Uganda SSA 178.0 140.5 89.9 39.3 35.1 27.6

SOM Somalia SSA 180.0 180.0 180.0 50.1 50.1 50.1 75th percentile

COD Democratic Republic of the CongoSSA 181.4 181.4 167.7 48.9 48.9 47.1

BDI Burundi SSA 182.6 164.6 139.1 49.5 47.1 43.2

GNQ Equatorial GuineaSSA 189.6 152.2 118.1 47.4 42.6 37.3

AFG Afghanistan SA 192.0 136.2 101.1 50.5 42.6 36.2

ZMB Zambia SSA 192.8 153.8 82.9 42.7 36.4 27.4

ETH Ethiopia SSA 198.3 138.6 77.0 52.1 43.7 31.3

TCD Chad SSA 208.3 188.5 169.0 46.7 44.6 42.3

BFA Burkina Faso SSA 208.4 181.5 146.4 40.7 38.2 34.3

GNB Guinea-BissauSSA 210.4 185.8 160.6 49.7 47.0 43.8

NGA Nigeria SSA 213.6 187.9 124.1 51.4 48.5 39.4

MOZ Mozambique SSA 225.7 172.1 103.1 53.2 45.5 33.6

MWI Malawi SSA 227.0 164.1 82.6 47.5 38.9 26.8

GIN Guinea SSA 228.2 174.5 125.8 52.5 46.4 39.3

LBR Liberia SSA 241.2 163.8 78.3 49.3 41.2 27.4

AGO Angola SSA 243.2 199.3 157.6 52.9 48.5 43.3

MLI Mali SSA 257.3 214.4 175.6 58.4 54.0 49.2

SLE Sierra Leone SSA 266.7 240.6 185.3 58.0 55.6 49.4

NER Niger SSA 313.7 215.6 124.5 48.7 41.7 31.8

U5MR NNM

15

2. 74 Countdown countries: grouped by ‘low’, ‘medium’ and ‘high’ based on MMR in 1990

Code Region Country 1990 2000 2010

AZE ECA Azerbaijan 56 65 43

UZB ECA Uzbekistan 59 33 28

KGZ ECA Kyrgyzstan 73 82 71

TKM ECA Turkmenistan 82 91 67

IRQ MENA Iraq 89 78 63

MEX LAC Mexico 92 82 50

TJK ECA Tajikistan 94 120 65

PRK EAP Democratic People's Republic of Korea97 120 81

BRA LAC Brazil 120 81 56

CHN EAP China 120 61 37

BWA SSA Botswana 140 350 160

STP SSA Sao Tome and Principe 150 110 70

SLB EAP Solomon Islands 150 120 93

GTM LAC Guatemala 160 130 120

PHL EAP Philippines 170 120 99

PER LAC Peru 200 120 67

EGY MENA Egypt 230 100 66

VNM EAP Viet Nam 240 100 59

ZAF SSA South Africa 250 330 300

GAB SSA Gabon 270 270 230

DJI SSA Djibouti 290 290 200

MAR MENA Morocco 300 170 100

SWZ SSA Swaziland 300 360 320

PNG EAP Papua New Guinea 390 310 230

KEN SSA Kenya 400 490 360

COG SSA Congo 420 540 560

COM SSA Comoros 440 340 280

BOL LAC Bolivia (Plurinational State of)450 280 190

ZWE SSA Zimbabwe 450 640 570

ZMB SSA Zambia 470 540 440

PAK SA Pakistan 490 380 260

LSO SSA Lesotho 520 690 620

MMR EAP Myanmar 520 300 200

GHA SSA Ghana 580 550 350

IND SA India 600 390 200

IDN EAP Indonesia 600 340 220

UGA SSA Uganda 600 530 310

YEM MENA Yemen 610 380 200

HTI LAC Haiti 620 460 350

TGO SSA Togo 620 440 300

MDG SSA Madagascar 640 400 240

CMR SSA Cameroon 670 730 690

SEN SSA Senegal 670 500 370

BFA SSA Burkina Faso 700 450 300

GMB SSA Gambia 700 520 360

CIV SSA Côte d'Ivoire 710 590 400

MRT SSA Mauritania 760 630 510

BEN SSA Benin 770 530 350

NPL SA Nepal 770 360 170

BGD SA Bangladesh 800 400 240

KHM EAP Cambodia 830 510 250

TZA SSA United Republic of Tanzania870 730 460

ERI SSA Eritrea 880 390 240

SOM SSA Somalia 890 1000 1000

MOZ SSA Mozambique 910 710 490

RWA SSA Rwanda 910 840 340

TCD SSA Chad 920 1100 1100

CAF SSA Central African Republic 930 1000 890

COD SSA Democratic Republic of the Congo930 770 540

ETH SSA Ethiopia 950 700 350

SDN SSA Sudan 1000 870 730

BDI SSA Burundi 1100 1000 800

GNB SSA Guinea-Bissau 1100 970 790

MWI SSA Malawi 1100 840 460

MLI SSA Mali 1100 740 540

NGA SSA Nigeria 1100 970 630

AGO SSA Angola 1200 890 450

GNQ SSA Equatorial Guinea 1200 450 240

GIN SSA Guinea 1200 970 610

LBR SSA Liberia 1200 1300 770

NER SSA Niger 1200 870 590

AFG SA Afghanistan 1300 1000 460

SLE SSA Sierra Leone 1300 1300 890

LAO EAP Lao People's Democratic Republic1600 870 470

MMR

25th

percentile

50th

percentile

(median)

75th

percentile

16

Annex 2: Detailed Analysis from results

Detailed findings from hypothesis #1 There is no relationship between absolute decline and the rate of decline—i.e., countries from the three mortality groups observed rates of decline roughly spanning the full spectrum (from 0% to 6% for U5MR). This point is illustrated in Figure 6. A similar picture emerges for MMR (see Figure 7). These findings strengthen the argument for considering absolute rate rather than (or together with) annual rate of decline in mortality as the main outcome measure in future analysis.

Figure 6: Correlation between U5MR in 1990 (a) and 2011 (b) and annual rate of decline-by mortality group

(a) 1990 (b) 2011

Note: Figure 6 compares the level of U5MR in 1990 (-a) to that of 2010 (-b) and whether there is any association between level of mortality and annual rates of reduction for all Countdown countries by group (low, medium, high based on MMR in 1990). The Figure shows that while countries in the three groups exhibited a visible reduction in absolute levels of mortality over the analysis period, there is no relationship between absolute decline and the rate of decline—i.e., countries from the three mortality groups observed rates of decline roughly spanning all the spectrum (from -2% to 6%). Guinea (GIN) moved from 228 deaths per 1000 live births in 1990 to 126/1000 in 2011, an absolute reduction of 102 deaths per 1000 live births or 45% over the 21 years. Their annual rate of decline, however, was just 2.8% so they are not ‘on track’ despite very significant reductions. Kyrgyzstan (KGZ), meanwhile, categorized as ‘low’ based on a 1990 U5MR, moved from 70/1000 to 31/1000 (an absolute reduction of 39/1000 by 2011 with a 56% absolute decline over the 21-year period and are on track with an annual reduction of 4%. The absolute number of lives saved between the two countries, is quite significant and the massive progress made by Guinea can be lost if we only look at percentage decline. Source: [3].

17

Figure 7 Correlation between MMR in 1990 (a) and 2010 (b) and annual rate of decline-by mortality group

(a) 1990 (b) 2010

Note: Figure 7 compares the level of MMR in 1990 (-a) to that of 2010 (-b) and whether there is any association between level of mortality and annual rates of reduction for all Countdown countries by group (low, medium, high based on MMR in 1990). The Figure shows that while countries in the three groups exhibited a visible reduction in absolute levels of mortality over the analysis period, there is no relationship between absolute decline and the rate of decline—i.e., countries from the three mortality groups observed rates of decline roughly spanning all the spectrum (fro m -2% to 8%). As in the analysis for under-five mortality, these Figures suggest the value of considering absolute rate rather than (or together with) annual rate of decline for this and future analysis. For example, Malawi (see red circle around MWI) in 1990 had a MMR of 1100 deaths per 100,00 live births, which declined to 460/100,000 by 2010—an absolute decline of 640 deaths per 100’000. This translates to an annual rate of reduction of just 4.4%, however, far below the necessary 5.5% needed to be on track for MDG 5a. China, on the other hand, reduced their MMR from 120 in 1990 to 27 per 100’000 in 2010, an absolute decline of 83, but an annual rate of decline of 5.9%, which puts it well on track for MDG 5a. Source: [5].

18

There is no strong indication that rates of change in U5MR were different between 1990 and 2010, as shown in Figure 8; therefore, using the average annual rate of decline over the whole period may be appropriate for this and future analyses. There are some exceptions that will require closer analysis, including Rwanda in the ‘middle’ mortality group and some outliers in the ‘high’ group located primarily in sub-Saharan Africa. MMR declined steadily overtime in countries in the high mortality group, illustrated in Figure 9. However, the annual rates of decline of MMR over the entire period may mask some valuable information, particularly for ‘low’ and ‘middle’ mortality groups. On a country by country basis, looking at shorter time periods in mortality can help to reveal trends associated with isolated events, including drought, war, or acute epidemics [6]. For a cross-sectional analysis of this nature, it would be useful, at a minimum, to use the patterns identified here to control for countries (outliers) with isolated, time-dependent events when exploring common factors, or use a deeper understanding of the underlying factors for this change in pattern to inform the selection of additional variables that may enrich the analysis.

19

Figure 8: Change in U5MR 1990-2011- by mortality group

(a) Low (<100 U5MR in 1990)

(b) Medium (>100 <170 U5MR in 1990)

(c) High (>170 U5MR in 1990)

Note: To improve illustration, Y-axis differs for each panel. In Figure 8-a and -b we see for the majority of countries that mortality rates were declining between 1990 and 2011. For Figue 8–c, there was a much more uniform decline in mortality across countries in this group throughout entire period. For some outliers (labeled) located in sub-Saharan Africa, rates were rising between 1990 and 2000 and then declining after 2000 in all cases, most likely due to to HIV related causes of death. Overall, however, there is no strong argument for in-depth analysis of shorter time periods within the overall 20 year analysis period. Therefore, using the average values for mortality or other variablesover the whole period may be appropriate for future analyses, controling for outliers. Source: [3].

DPR Korea

Botswana South Africa

Zimbabwe Swaziland

Lesotho Kenya

0

20

40

60

80

100

120

1990 2000 2011

Rawanda

40

60

80

100

120

140

160

180

200

1990 2000 2011

60

100

140

180

220

260

300

1990 2000 2011

20

Figure 9: Change in MMR 1990-2011- by mortality group

(a) Low (<300 MMR in 1990)

(b) Medium (MMR >300<800 in 1990)

(c) High (MMR >800 in 1990)

Note: Y-axis scale varies by panel to improve visualization. For all three mortality groups, we see a number of countries with increasing MMR between 1990 and 2000, then decliningbetween 2000 and 2010; all are located in sub-Saharan Africa. Again, rising rates may be explained by HIV related causes of death. Overall, these graphs suggest that a more indepth analysis and undwerstanding of these patterns to be able to inform future analysis of changes in maternal mortality.; using an average values for mortality or other variables over the whole period may mask some valuable information, particularly for ‘low’ Figure 9-a) and ‘middle’ Figure 9-b) mortality groups. Source: [5].

Botswana

Djibouti

Gabon

South Africa

Swaziland

0

50

100

150

200

250

300

350

400

450

1990 1995 2000 2005 2010

MM

R (

de

ath

s p

er

100,

000

live

b

irth

s)

Cameroon

Congo

Kenya

Lesotho Zimbabwe

150

250

350

450

550

650

750

850

1990 1995 2000 2005 2010

MM

R (

dea

ths

per

100

,000

live

b

irth

s)

Chad

Liberia

Sierra Leone

200

400

600

800

1000

1200

1400

1600

1800

2000

1990 1995 2000 2005 2010

MM

R (

dea

ths

per

100

,000

liv

e b

irth

s)

21

Detailed findings from hypothesis #2:

Under-five mortality In 2012, UNICEF and others highlighted the tremendous improvement that has been made globally in reducing deaths in children between the ages of 0 and 5 [2]. Since 1990, more than approximately 4 million fewer children are dying every year and globally rates of decline have risen from 1.8% between 1990 and 2000 to 3.2% between 2000 and 2011 [2]. However, despite these significant reductions in under 5 deaths, the proportion of under 5 deaths occurring in the neonatal period is on the rise globally [3]. For all three mortality groups, there was a slow decline in the neonatal mortality rate (NMR) between 1990 and 2011 and that progress in reducing under-five deaths was more pronounced in (or focused on) the post-neonatal group (1-59 months), which made the contribution of neonatal deaths to overall under-five deaths grow over time (while absolute numbers are declining but very slowly) (

Figure 3). In addition, of the more than 2.7 million neonatal deaths worldwide in 2011 (compared with nearly 4 million in 1990), 65% occurred in just 8 countries (see Figure 10). This implies that analyses that consider differential burden of neonatal mortality (including cause of death) across countries will contribute to better understanding progress or (lack of) in reducing neonatal deaths. This would be particularly useful in the case of extreme outliers, such as India, where nearly one-third of all neonatal deaths occur, amounting to over 880,000 prenatal deaths per year, or 2400 per day. Figure 10: Burden of neonatal deaths (0-28 days)-Countdown countries

Note: ‘Other’ includes all 74 Countdown countries not specifically named in Figure. Source: [3].

We then looked at cause of death among neonates (babies between 0-27 days) by mortality group in 2000 (year MDGs were declared) and 2010. The most up to date cause of death data for children under-five are available annually for 2000-2010. 2 While the burden of deaths differs substantially by country and region, the cause of death among neonates shows less variation between groups, and over time. The harder to address causes of death (i.e. intrapartum and related events and preterm complications with highest proportional contribution to neonatal mortality (shown in Figure 11)—as well as maternal mortality (shown in Figure 13)—are practically unchanged. The same occurred for death due to sepsis and pneumonia. Considering cause of death distribution is, therefore, crucial in determining the key factors and strategies that may explain differential progress in reducing death due to these causes across countries.

2 Child Causes of Death and Annual Estimates 2000-2010 available from the Child Health Epidemiology Reference Group (CHERG) at http://cherg.org/datasets.html

Indonesia 4%

Bangladesh

5%

Ethiopia 3%

Congo, DR 2%

China 14%

Pakistan 5%

Nigeria 6%

India 32%

Other 29%

1990

Indonesia 2%

Bangladesh 3%

Ethiopia 3%

Congo, DR 5% China

5%

Pakistan 6%

Nigeria 9%

India 32%

Other 35%

2011

22

Figure 11: Cause of death for neonates (0-28 days)-by mortality grouping

2000 2010

Low

mo

rtal

ity

Med

ium

mo

rtal

ity

Hig

h m

ort

alit

y

23

Similarly, the cause of death for children aged 1-59 months did not show significant changes between 2000 and 2010; this is

illustrated in Figure 12. One notable exception to this is for deaths from measles in the high mortality group reduced from 13% in 2000 to 2% in 2010. For all three groups, there is relatively little progress in tackling diarrhea and pneumonia, and for countries in the ‘high’ mortality group malaria remains a leading cause of death. The share of deaths due to ‘other’ causes increased, which is likely in large part to nutrition-related deaths, which account for one-third of all deaths of children under-five years of age [3]. For two important reasons, the analysis of cause of death for neonatal and 1-59 months highlighted the merits of analyzing these two groups separately, or at least explicitly within one analysis: firstly, causes of death are very different, which means factors likely to affect them will be; secondly, explicit and independent analysis of neonatal mortality will allow a much more informed and ‘efficient’ analysis of the real causes of this lack of progress and the factors that are likely to improve progress for this particular age group.

Figure 12: Cause of death for children 1-59 months-by mortality grouping

2000 2010

Low

mo

rtal

ity

Med

ium

mo

rtal

ity

Hig

h m

ort

alit

y

24

Maternal mortality Globally, an estimated 287,000 maternal deaths occurred in 2010, a decline of 47% of maternal death from 1990. Sub-Saharan Africa (58%) and Southern Asia (30%) accounted for 88% of deaths among the Countdown countries in 2010; within these regions, India (20%) and Nigeria (14.5%) alone account for more than one-third or all maternal deaths. Like for U5MR, this implies that analyses considering differential burdens MMR across regions may contribute to better understanding of progress or (lack of) in reducing deaths. Like for U5MR, there also exists significant variation in what mothers are dying from at the regional level. Maternal mortality data by cause of death are not available on a country-by-country and/or annual basis, however, due to insufficient data. As shown in Figure 13, the leading cause of death in Africa is haemorrhage, while in Latin American and the Caribbean, hypertensive disorders are the leading cause of death [7]. Furthermore, with the onset of the HIV epidemic in 1990s, the rate of decline in MMR slowed [8] and the burden of HIV was an important contributor to maternal deaths in Africa. Figure 13: Maternal mortality by cause of death-by region

Note: Data from 1997-2002. MMR Figures by cause of death based on Khan et al, 2006 are currently being updated by the WHO and due to be available by the end of 2012; at this stage we’ll be able to look in further detail at changing patterns by cause of death between regions and over time. Source: Khan et al, 2006.

25

Detailed findings from hypothesis #3:

To explore our third hypothesis, we focused in this report on the association between major socioeconomic, environmental, demographic, and developmental factors and changes in mortality. The aim was not to test whether or which of these variables contribute to explaining variability in achieviening better health outcomes—the contribution of most of these factors is well established in the literature, including phase1 of this project. Instead, this initial analysis seeks to complement and support the analysis of the first and second hypotheses above by asking whether analysis that explicitly takes absolute mortality, its level and cause of death distribution into account will also enhance the explanatory power of these non-health factors, and in thinking about and developing tailored actionable options for improving progress. Furthemore, this analysis was not meant to be exhaustive of possible variables that have shown to have a relationship with health/mortality (which is better explored in a multivariate analysis) but rather indicative to serve the purpose of this analysis, i.e., whether explicity taking into level of mortality (and other) grouping matters. The range of variables included here was selected based on work completed around the H.O.P.E. hypothesis and the quantitative arm of Phase 1, which demonstrated several key factors relevant to this analysis, including female literacy, education, healthcare infrastructure and access, and environmental conditions. The correlation matrix below includes those variables included

in the main report. A correlation matrix and a description of all variables explored in the analysis can be found in Annex 3. Annex 4 includes a description of all variables explored but not included in the main analysis due to high multicollinearity (i.e. HDI), insufficient observations (n <65) (i.e. the Gender Inequality Index), and/or low correlation coefficients with mortality (out of pocket expenditure, political stability). While there is a high correlation between U5MR and MMR3, we see from the analysis below three important points: firstly, that while most variables are similarly correlated with both U5MR and MMR, this is not the case for all; secondly, while most countries belong to the same mortality group for both U5MR and MMR, for some countries this is not the case. This point is highlighted in Figure 4. This reinforces the need to select relevant variables for the subsequent analysis separately for child and maternal mortality; finally, graphical presentation of these variables facilitated identification of important outliers that should be taken into account in subsequent analysis. Table 2: Correlation matrix of variables explored in main analysis for hypothesis 3

3 See also St Galen report for a discussion on high correlation between MDG4 and MDG5A goals

(obs=63) U5MR MMR

GDPPER

CAPITA

HEALTH

EXPENDITURE

PERCAP BEDS

NURSES/

MIDWIVES

IMPROVED

SANITATION

FERTILITY

RATE SCHOOLING

U5MR 1.000

MMR 0.799 1.000

GDPPERCAPITA -0.513 -0.488 1.000

HEALTHEXPENDITUREPERCAP -0.486 -0.418 0.895 1.000

BEDS -0.402 -0.502 0.325 0.309 1.000

NURSES/MIDWIVES -0.427 -0.523 0.567 0.512 0.661 1.000

IMPROVEDSANITATION -0.638 -0.642 0.507 0.468 0.568 0.658 1.000

FERTILITYRATE 0.716 0.628 -0.573 -0.499 -0.482 -0.513 -0.635 1.000

SCHOOLING -0.638 -0.601 0.634 0.552 0.573 0.737 0.652 -0.616 1.000

26

Economic variables In exploring the economic variables as part of this hypothesis, we focused on: income level (gross domestic product [GDP] per capita in purchasing-power-parity (PPPs)) and the relative priority placed on health spending (total health expenditure per capita in PPPs). Correlation between mortality and THE as a percent of GDP or out of pocket expenditure as % of THE was very low while HDI was highly correlated with GDP and many of the other explanatory variables (see correlation matrix in Annex 4), and thus not considered for this analysis. GDP per capita (PPP) and Health Expenditure per capita are presented in log scale rather than normal scale to better illustrate the relationships. Figures in normal scales are reproduced in Annex 6. As expected, countries in the lower right quandrant (where GDP per capita is highest and mortality is lowest) are predominantly those with low mortality in 1990, while those in the upper left quandrant (where GDP per capita is lowest and mortality is highest) are those with high mortality levels in 1990. This is illustrated in Figure 4-a, -b. For both under-five and maternal mortality the higher the health expenditure per capita the lower the mortality, despite the notable outlier of Eq. Guinea (GNQ), as shown in Figure 14-a and -b. Both variables GDP per capita and health expenditure per capita may be useful in explaining some of the reasons for variability in mortality reduction. Given the similarity in the relationship between these two variables and mortality, GDP per capita may be favorable given that it is readily available and may have less measurement error compared to THE per capita. Figure 14: Relationship between health expenditure per capita (PPP) and (a) U5MR, (b) MMR-by mortality grouping

(a) U5MR

(b) MMR

27

Healthcare workforce and infrastructure In exploring the healthcare workforce and infrastructure variables as part of this hypothesis, we focused on: hospital beds and nurses and midwives per capita. As seen in

Figure 15 and

28

Figure 16, log transformations improved exposition; these Figures in normal scales are in Annex 6. Both variables are more highly correlated with MMR than U5MR. As shown

in Table 2, both variables are highly correlated so only one variable, if any, should be considered in the final model. Nurses and midwives show a stronger negative correlation with the mortality variables, with less within group variability—particularly for MMR. Important outliers can also be seen, such as Guinea (GIN), as well as Nigeria (NGA), which had a relatively high number of nurses and midwives per capita, but still high levels of MMR in 2010 (which may be a data quality issue). Figure 15: Relationship between hospital beds and (a) U5MR, (b) MMR-by mortality grouping

(a) U5MR

(b) MMR

29

Figure 16: Relationship between availability of nurses and midwives and (a) U5MR, (b) MMR-by mortality group

(a) U5MR, log scale (b) MMR, log scale

30

Sanitation and Environmental Performance

In exploring sanitation and environmental performance variables as part of this hypothesis, we focused on: access to improved sanitation and the Environmental Performance Index (EPI). Due to high multicolinearity between these variables (see Annex 3), only improved sanitation was considered here; Figures for the EPI can be seen in Annex 6. As expected, both variables have a similar relationship to the mortality variables, i.e. higher access to sanitation or higher EPI score leads to lower mortality. We also see less

variability in the ‘low’ group for MMR (Figure 17-b) than for U5MR, with the exception of some outliers, i.e. Sao Tome and Principe (STP) and Gabon (GAB). Figure 17: Relationship between access to improved sanitation and (a) U5MR (b) MMR-by mortality group

(a) U5MR

(b) MMR

31

Demographic and Education

In exploring demographic and education variables as part of this hypothesis, we focused on the fertility rate (Figure 18) and mean years of schooling of adults (Figure 19). These

variables are highly correlated and thus grouped in the same category. As expected, both variables are highly correlated with mortality and GDP per capita (see Table 2). Between group correlations show a much stronger relationship between fertility and GDP for countries with “low” levels mortality at onset (r = -.44) than for the other two groups, while for schooling, the coefficients are similar between the three groups (not shown). Figure 18: Relationship between Fertility rate and (a) U5MR, (b) MMR, by mortality group

(a) U5MR (b) MMR

32

Figure 19: Relationship between schooling and (a) U5MR, (b) MMR, grouped by levels of mortality in 1990

(a) U5MR (b) MMR

33

Annex 3: Correlation matrix of all variables considered/explored in analysis

Annex 4: Description of variables not included in main analysis due to limited observations, multicolinearity, or low correlation coefficients with mortality variables

Human Development Index (HDI)

The Human Development Index (HDI) is a summary composite index that measures a country's average achievements in three basic aspects of human development: health, knowledge, and income.

2011 See http://hdr.undp.org/en/media/FAQs_2011_HDI.pdf for further information.

THE as percent of GDP Total Health Expenditure (THE) as a % of GDP 2010 NHA Indicators

Out of pocket expenditure

Out of pocket expenditure on health as % of total health expenditure (THE); 2010 NHA Indicators

Environmental Performance Index (EPI)

The Environmental Performance Index ranks countries on performance indicators tracked across policy categories that cover environmental and public health and ecosystem vitality; gauging at the national level how close countries are to established environmental policy goals. The scale runs from 0 [low] to 100 [highest].

2012 http://epi.yale.edu/epi2012/rankings

Multidimensional Poverty Index (MPI)

The Multidimensional Poverty Index is estimated based on 10 indicators, measuring health, education and living standards

2011 http://hdr.undp.org/en/reports/global/hdr2011/download/

Political Stability Worldwide Governance Indicators (WGI): Political Stability, No violence

2010 www.govindicators.org

Freedom of Press The Press Freedom Index is an annual ranking of countries compiled and published by Reporters Without Borders based upon the organization's assessment of the countries' press freedom records in the previous year. A smaller score in the index corresponds to greater freedom of the press.

2010 http://www.freedomhouse.org/sites/default/files/inline_images/FOTP%20Scores%20and%20Status%201980-2011.xls

Gender Inequality Index (GII)

A composite measure reflecting inequality in achievements between women and men in three dimensions: reproductive health, empowerment and the labor market; the higher the score, the higher the inequality (from 0-1)

2011

http://hdr.undp.org/en/reports/global/hdr2011/download/

U5MR11 MMR2010

GDPPER

CAPITA

HEALTH

EXP. BEDS

NURSES/

MIDWIVES

IMPROVED

SANITATION EPI

FERTILITY

RATE SCHOOLING CONTRACEPTION

BIRTH

ATTENDANTS ANC+4 HDI OOP

THEAS%

GDP MPI

POLITICAL

STABILITY

FREEDOM

OFPRESS

U5MR11 1.000

MMR2010 0.799 1.000

GDPPERCAPITA -0.393 -0.363 1.000

HEALTHEXP. -0.346 -0.311 0.867 1.000

BEDS -0.363 -0.444 0.363 0.451 1.000

NURSES/MIDWIVES -0.363 -0.455 0.724 0.671 0.614 1.000

IMPROVEDSANITATION -0.670 -0.627 0.414 0.501 0.552 0.608 1.000

EPI -0.587 -0.596 0.378 0.310 0.465 0.448 0.474 1.000

FERTILITYRATE 0.683 0.638 -0.533 -0.471 -0.427 -0.486 -0.604 -0.503 1.000

SCHOOLING -0.567 -0.497 0.565 0.537 0.513 0.746 0.632 0.414 -0.550 1.000

CONTRACEPTION -0.791 -0.649 0.413 0.422 0.420 0.401 0.685 0.636 -0.732 0.565 1.000

BIRTHATTENDANTS -0.259 -0.363 0.576 0.560 0.364 0.578 0.439 0.262 -0.415 0.576 0.397 1.000

ANC+4 -0.159 -0.069 0.385 0.386 0.074 0.479 0.156 0.199 -0.256 0.612 0.302 0.570 1.000

HDI -0.781 -0.685 0.728 0.603 0.489 0.711 0.687 0.530 -0.793 0.837 0.721 0.557 0.415 1.000OOP 0.143 0.038 0.007 -0.102 0.119 0.054 0.083 -0.037 -0.216 -0.044 -0.097 -0.169 -0.343 0.110 1.000

THEAS%GDP 0.271 0.201 -0.179 0.168 0.084 -0.046 0.032 -0.219 0.233 -0.089 -0.122 0.053 0.047 -0.306 -0.126 1.000MPI 0.671 0.562 -0.608 -0.556 -0.476 -0.653 -0.702 -0.564 0.791 -0.831 -0.714 -0.575 -0.452 -0.906 -0.158 0.190 1.000

POLITICALSTABILITY -0.075 0.061 0.231 0.285 0.082 0.076 -0.062 -0.070 0.064 0.155 0.092 0.402 0.424 0.110 -0.446 0.257 -0.023 1.000

FREEDOMOFPRESS -0.096 -0.097 0.099 0.036 0.333 0.189 0.356 0.191 -0.119 0.092 0.142 0.149 -0.247 0.080 0.277 0.171 -0.233 -0.232 1.000GENDERDEVELOPMENT

INDEX 0.711 0.683 -0.452 -0.470 -0.650 -0.630 -0.747 -0.498 0.674 -0.653 -0.651 -0.580 -0.214 -0.740 0.042 -0.067 0.671 -0.261 -0.233

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Annex 5: Additional findings based on ‘Next Steps’ analysis

Figure 20: Relationship between % live births attended by skilled birth attendants (a) U5MR, (b) MMR-by mortality group

(a) U5MR (b) MMR

Figure 21: Relationship between women attended 4 times or more during pregnancy by any provider (a) U5MR, (b) MMR-by mortality group

(a) U5MR (b) MMR

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Annex 6: Additional Figures based on hypothesis 3 analysis for U5MR and MMR

Relationship between GDP per capita and (a) U5MR, (b) MMR (normal scales)-by mortality group

(a) U5MR (b) MMR

This type of graph is informative as outliers give us an idea of countries or groups that may require particular attention in the multivariate analysis, i.e. see GNQ in figure above.

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Relationship between Health Expenditure per capita and (a) U5MR, (b) MMR (normal scales), by mortality group

(a) U5MR (b) MMR

Relationship between hospital beds per 10,000 and (a) U5MR, (b) MMR (normal scales)-by mortality groups

(a) U5MR (b) MMR

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Relationship between nurses and midwives and (a) U5MR, (b) MMR (normal scales)- by mortality group

(a) U5MR (b) MMR

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Relationship between Human Development Index and (a) U5MR, (b) MMR-by mortality group

(a) U5MR (b) MMR

Relationship between Environmental Performance Index and (a) U5MR, (b) MMR-by mortality group

(a) U5MR (b) MMR

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Annex 7: Additional analysis in support of hypothesis 1

In additional support of our first hypothesis, the following two questions were explored in further detail:

1. How many countries that are ‘on track’ for MDG 4 and MDG 5a are similarly those countries that had ‘low’ mortality based on our ranking in 1990; 2. How are countries classified in 2010/2011 compared to 1990, i.e. did countries move up, down or stay the same.

For 2010 (MMR) and 2011 (U5MR), the countries were grouped using similar criteria to the 1990 groupings, i.e. looking for some natural breaks and roughly trying to keep the groups even. The groupings are shown in Table 3. Table 3: Classification of countries for analysis based on mortality levels in 1990 and 2010/2011

Low mortality Medium mortality High mortality

1990 2011 1990 2010 1990 2010 Under-five mortality rate (U5MR)

<100 <50 >100-170 >50-90 >170 >90

Maternal mortality ratio (MMR)

<300 <200 >300-800 >200-400 >800 >400

1) The measuring of progress based on % annual decline inherently favors those countries that started with lower levels of mortality. Based on updated interagency under-five mortality data, approximately 28 Countdown countries were ‘on track’ for MDG 4 in 2012 (reduce by 4% between 1990-2010 or achieved rate <40). More than 50% (15/28) of these countries started with ‘low’ mortality in 1990, and 8/28 ‘medium’. For MDG 5a, approximately 9 Countdown countries are on track to reduce MMR by 5.5% between 1990 and 2010 or <100 as of 2010. Of these 9 countries, 45% (4/9) were classified as ‘high’ mortality based on groupings in 1990 and 33% (3/9) classified as ‘low’. 2) Looking at relative movement among countries from ‘low’, ‘medium’ and ‘high’ mortality between 1990 and 2010/2011 gives us an idea of within group (relative) movement over the 20 year period. For U5MR, the majority of countries (50/74) showed no change between groups from 1990 to 2011. This finding shows overall improvement across all groups and that to ‘out perform’ countries within a similar range of mortality was not that common (i.e. only 9/74 countries managed to do this) and was harder than ‘under performing’, as 15/74 countries slipped to a ‘higher’ mortality group in 2011 than they were in 1990. Furthermore, of those countries that are ‘on track’ in 2012, approximately 21 did not move mortality groups at all between 1990 and 2011, while just 7 went from a higher to a lower group. For MMR, again the majority of countries 55/74 showed no change between groups between 1990 and 2010, however, unlike for U5MR, more countries moved from a high to a lower mortality group (10/74), than from lower to higher (9/10).