Member agencies of the Inter-agency Group for Child Mortality Estimation are:

World Health Organization, WHO The World Bank

United Nations Population Division, UNPD

www.childmortality.org www.childinfo.org

Levels and Trends of Child Mortality in 2006

[Working Paper]

Estimates developed by the Inter-agency Group for Child Mortality Estimation

United Nations Children’s Fund, UNICEF

This is a working document. It has been prepared to facilitate the exchange of knowledge and to stimulate discussion. The text has not been edited to official publication standards and the member agencies of the Inter-agency Group for Child Mortality Estimation–UNICEF, WHO, The World Bank and UN Population Division–accept no responsibility for errors. The designations in this publication do not imply an opinion on legal status of any country or territory, or of its authorities, or the delimitation of frontiers. For any corrigenda please visit the following webpage: http://www.childinfo.org/areas/childmortality/infant_child_mortality_2006.pdf Recommended citation: UNICEF, WHO, The World Bank and UN Population Division, ‘Levels and Trends of Child Mortality in 2006: Estimates developed by the Inter-agency Group for Child Mortality Estimation’, New York, 2007.

Levels and Trends of Child Mortality in 2006

Estimates developed by the Inter-agency Group for Child Mortality Estimation

WORKING PAPER

United Nations Children’s Fund, UNICEF World Health Organization, WHO

The World Bank United Nations Population Division, UNPD

New York, December 2007

CONTENTS ACRONYMS AND ABBREVIATIONS ………………..…………..……………….....04 EXECUTIVE SUMMARY……………………………………………………………...05 1. INTRODUCTION………………………………………………………………….....08 2. MEASURING INFANT AND CHILD MORTALITY………………………………09 2.1 Definitions and measures 2.2 Sources of data 2.3 Approaches to calculating infant and under-five mortality rates 3. THE DEVELOPMENT OF THE 2006 ESTIMATES OF INFANT AND UNDER-FIVE MORTALITY…………………………………………………………...19 3.1 Collecting the data 3.2 Estimation methodology used to calculate rates and trends for each country 3.3 Applying the methodology 3.4 Calculating the absolute number of deaths 3.5 Regional estimates 4. ANALYSIS AND INTERPRETATION OF THE 2006 MORTALITY ESTIMATES…………………………………………………………….…………........29

4.1 Under-five mortality levels and trends 4.2 Infant mortality levels and trends 5. IS THE MDG4 ACHIEVABLE?..................................................................................32 6. NEXT STEPS……………………………………………………………………........34 6.1 Using the 2006 infant and under-five mortality estimates 6.2 Improving the estimates ANNEXES

Annex 1. Estimated under-five mortality rates (U5MR) from 1960 to 2006, by country..............................................................................................................36

Annex 2. Estimated infant mortality rates (IMR) from 1960 to 2006, by country………………………………………………………………………..41

Annex 3. Average annual rate of reduction in under-five mortality and progress towards the MDG target, by country……………………………………..…..46

TABLES

Table 1. Estimated under-five mortality rates (U5MR) from 1960 to 2006, by UNICEF regions………………………………………………………………30

Table 2. Estimated infant mortality rates (IMR) from 1960 to 2006, by UNICEF regions………………………………………………………………32

Table 3. Average annual rate of reduction in under-five mortality and progress towards the MDG target, by UNICEF regions..………………………………33

FIGURES

Figure 1. Initial estimate using standard weights: Data on under-five mortality in Egypt and estimate of trend, 2006……………………………………….........25

Figure 2. Final estimate using adjusted weights: Data on under-five mortality in Egypt and estimate of trend, 2006……………………………………………26

Figure 3. Survey data on mortality in Egypt plotted against Coale-Demeny model life tables………………………………………………………………28

Figure 4. Estimated number of deaths (in millions) occurring before the age of five in 2006………………………………………………………........30

BOXES

Box 1. Definitions of mortality in young children…………………………………….09

Box 2. Statistical measures of infant and under-five mortality………………………..10

Box 3. International survey programmes that collect information on infant and child mortality…………………………………………………………….15

Box 4. Direct methods for estimating infant and under-five mortality rates…………....17

Box 5. Indirect methods for estimating infant and under-five mortality rates………….18

Box 6. Standard weights assigned to data sources on infant and under-five mortality…………………………………………………………...22

Box 7. Statistical model for estimating trends (Hill et al., 1999)...…………………...24

Box 8. Computing the number of deaths in a country…………………………….…..29 REFERENCES………………………………………………………………………..51 COVER PHOTO CREDITS……………………………………………………….…53

ACRONYMS AND ABBREVIATIONS AARR Average annual rate of reduction AIDS Acquired immunodeficiency syndrome CRING Country Reports on Indicators for the Goals CEE/CIS Central and Eastern Europe and the Commonwealth of Independent States DHS Demographic and Health Survey ECLAC Economic Commission for Latin America and the Caribbean IMR Infant mortality rate LSMS Living Standards Measurement Study MDG Millennium Development Goal MICS Multiple Indicator Cluster Survey U5MR Under-five mortality rate UNICEF United Nations Children’s Fund UNPD United Nations Population Division USAID United States Agency for International Development WHO World Health Organization

04

EXECUTIVE SUMMARY The Millennium Development Goals (MDGs) call for a two-thirds reduction in the mortality rate among children under age five between 1990 and 2015. Accurate and timely estimates of infant and under-five mortality are needed to help countries set priorities, design programmes to reduce mortality, and monitor progress towards the MDG4. Developing these estimates poses a considerable challenge because of the limited data available for many developing countries and lack of agreement on the best way to calculate infant and child mortality levels and trends. In response, experts at the United Nations Children’s Fund (UNICEF), The World Bank, the World Health Organization (WHO), the United Nations Population Division (UNPD) and members of the academic community joined together in 2004 to form the Inter-agency Group for Child Mortality Estimation. The Inter-agency Group has worked to seek out and share new sources of data on child mortality, to improve and harmonize estimation methods, and to produce consistent estimates on the levels and trends in child mortality worldwide. This report describes the methods used by the Inter-agency Group to calculate infant and under-five mortality rates and presents the 2006 estimates. Estimation methods While the amount of data on infant and child mortality in developing countries has grown, many countries still lack accurate, reliable and timely data. Vital registration systems are the preferred source of data on infant and under-five mortality because they collect information prospectively and cover the entire population. However, many developing countries lack fully functioning vital registration systems that accurately record all births and deaths. Most information on infant and child mortality is collected retrospectively from mothers during census and survey interviews. National censuses have the advantage of covering the entire population, but they are usually only conducted at ten-year intervals and collect limited data. Thus, household surveys, such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), have become the primary source of data on infant and child mortality in developing countries. Surveys cover nationally representative samples and are generally conducted every three to five years. They can collect detailed birth histories as well as information on socio-economic, educational and other variables that can help target programmes to reduce child mortality. There are two approaches to calculating infant and under-five mortality rates. Direct methods require each child’s date of birth, survival status, and date or age at death. This information can come from vital registration systems or household surveys that collect complete birth histories. Indirect methods require less detailed information that is available in censuses and general surveys, including the total number of children a

05

woman has ever borne, the number who survive and the woman’s age (or the number of years since she first gave birth). However, indirect methods require model life tables to adjust the data for the age pattern of mortality in the general population. Finding an appropriate model life table can be challenging, since the Coale and Demeny model life tables are derived largely from European experience. Different data sources and calculation methods often yield widely differing estimates of infant and child mortality for a given time and place. In order to reconcile these differences, members of the Inter-agency Group have developed a method to fit a smoothed trend to a set of observations and to extrapolate that trend to the present time. The first step in the process is to proactively seek out all possible sources of data, including vital registration systems, national censuses, household surveys conducted by global programmes, and multi-purpose surveys conducted without international sponsorship. After plotting all available values for infant and under-five mortality, analysts use weighted least squares to fit a multi-spine regression line to the data points and extrapolate the trend to the present. The use of weights allows analysts to make a judgement about the relative quality of each data set and how representative it is likely to be of the population. The last step is to decide which set of estimates (for infant mortality or under-five mortality) is more consistent and to use a model life table to derive the other set of estimates from it. The use of a regression model to reconcile different data sources and extrapolate trends means that the mortality estimates produced by the Inter-agency Group do not match the values generated by any specific census, survey or vital registration system—or the official estimates disseminated by national governments. It also means that the 2006 estimates should not be interpreted as precise measures of infant and under-five mortality: there is a range of uncertainty associated with the mortality estimates that varies between countries, depending on the amount, quality and type of data available. Levels and trends in infant and child mortality Worldwide the number of children dying before age five has reached a record low, falling below 10 million for the first time in 2006. This is a 25 per cent drop from the nearly 13 million child deaths in 1990. Among UNICEF regions, by far the highest rates of both infant and under-five mortality are found in sub-Saharan Africa, where underdevelopment, armed conflict and the spread of HIV/AIDS have seriously undermined the efforts to improve child survival. The estimated under-five mortality rate exceeds 200 deaths per 1,000 live births in ten countries in this region. Infant and child mortality also remains relatively high in South Asia. 06

By 2006, however, three regions—East Asia and the Pacific, Latin America and the Caribbean, and Central and Eastern Europe and the Commonwealth of Independent States (CEE/CIS)—had achieved under-five mortality rates below 30 deaths per 1,000 live births. This compares with 6 deaths per 1,000 live births in developed regions. Infant and child mortality have declined in every UNICEF region since 1990, which is the baseline for the Millennium Development Goal (MDG) targets. The drop has been greater in East Asia and the Pacific, Latin America and the Caribbean, and CEE/CIS, where estimated under-five mortality in 2006 was about half that in 1990. Over the same time period, under-five mortality has fallen only 14 per cent in sub-Saharan Africa. Achieving the MDG4 requires that the under-five mortality rate declines, on average, by 4.4 per cent annually between 1990 and 2015. Three regions—East Asia and the Pacific, Latin America and the Caribbean, and CEE/CIS—achieved this benchmark through 2006 or came close to it, putting them on track to achieve the MDG4. In contrast, the average annual rate of reduction in under-five mortality since 1990 has been just 1 per cent in sub-Saharan Africa. In recent years, under-five mortality has actually increased in a dozen sub-Saharan countries. The AIDS epidemic, armed conflict and social instability, among others, have contributed to the worsening situation for children in parts of sub-Saharan Africa. Meeting the MDG target for child mortality in these countries will require dramatic measures. The situation in South Asia and the Middle East and North Africa, lies in between the two extremes described above. Some progress has been made, but the current rate of improvement will not be sufficient to meet the target for 2015. Much remains to be done to achieve the MDG4. It will require an extraordinary effort by the international community, governments, NGOs, civil society and others. However, effective and affordable interventions are available to prevent or treat each major cause of under-five mortality. Scaling up these proven child survival interventions has the potential to reduce infant and child mortality and help countries meet the MDG4. 07

1. INTRODUCTION

Reducing mortality and improving the health of young children has long been a concern of the international community. One of the eight Millennium Development Goals (MDGs) adopted after the Millennium Summit in 2000 is to reduce child mortality (MDG4). Donors and development agencies, the United Nations and national governments around the world committed themselves to the goal of reducing the under-five mortality rate by two-thirds between 1990 and 2015 (UN Millennium Declaration). Two of the key indicators for monitoring progress towards this goal are the under-five mortality rate (U5MR) and the infant mortality rate (IMR) (UN Development Group, 2003). Country estimates of the level and trends in infant and under-five mortality are needed to help set priorities, shape policies, design programmes and monitor progress towards the MDG at the national level. These estimates are also needed at the international level to inform funding decisions for activities directed towards reducing child mortality. To be useful for the latter purpose, the country estimates must be internationally comparable. Yet developing accurate and timely estimates of infant and under-five mortality poses a considerable challenge. There are limited data in many developing countries and a lack of agreement on how best to generate estimates from what data are available. Until recently, UNICEF, The World Bank, and the World Health Organization (WHO) produced and published separate estimates of infant and under-five mortality rates around the world. The three agencies did not always use the same data sources, they assigned different weights to those data sources, and they used different methodologies to extrapolate trends. The resulting discrepancies between their mortality estimates occasionally ranged as high as 50 per cent (Child Mortality Coordination Group, 2006). In 2004, the Inter-agency Group for Child Mortality Estimation was formed to share data between the agencies and to ensure consistency among their estimates of infant and under-five mortality. Since its inception, the Inter-agency Group has been driven by a common desire to improve its estimates, refine its working methods and expand its data sources. After conducting a critical review of the procedures used to compile data and produce estimates, the group has harmonized and coordinated its estimation and projection methodology. This now forms the basis for yearly estimates of under-five and infant mortality rates, which are published in annual reports by UNICEF on The State of the World’s Children (UNICEF, 2007b) and by The World Bank on World Development Indicators (World Bank, 2007). They are also included in the World Health Organization’s digest of World Health Statistics (WHO, 2007). Membership has expanded to include the United Nations Population Division (UNPD), Harvard University, the United States Census Bureau, the Economic Commission for Latin America and the Caribbean (ECLAC), MEASURE DHS, and other universities and research institutes. The Inter-agency Group is also actively working to increase the transparency of its estimation process and the dissemination of data to interested parties 08

through the development of a child mortality database (www.childmortality.org). Although this database presents consensus estimates, individual reports of members of the Inter-agency Group may differ with the figures included in the database. These differences are mainly due to the timing of the respective publications, which allow for the inclusion of new data and evidence. Other agencies adapt the child and adult mortality estimates to produce life-span estimates of fertility, mortality and migration. This report presents the global, regional and country estimates of infant and under-five mortality in 2006 as well as the trends observed over the period from 1960 to 2006. It describes the challenges involved in measuring infant and under-five mortality, the limitations on available data sources, and the methods used by the Inter-agency Group to arrive at the 2006 estimates. It interprets the results and their implications for achieving the MDG on reducing child mortality. The final section discusses the use and limitations of the current estimates. The annexes include data tables of the mortality estimates for individual countries. 2. MEASURING INFANT AND CHILD MORTALITY

2.1 Definitions and measures In 2006, deaths among children under age five accounted for about four-fifths of global mortality among children under age 18. This makes it an excellent indicator of child health and survival. It can also be viewed as an indicator of overall development, since it reflects the social, economic and environmental conditions in which children live, including their health care (UN Development Group, 2003). Mortality among young children can be subdivided and categorized by their exact age at death (see Box 1). Deaths in certain age groups may have practical programme and policy implications. Neonatal mortality, for example, is considered to be a useful indicator of maternal and newborn health and care. Box 1. Definitions of mortality in young children

Includes deaths that occur:

Neonatal mortality During the first 28 days of life

Post-neonatal mortality At ages 1 to 11 months

Infant mortality Between birth and exact age 1

Child mortality At ages 1 to 4 years

Under-five mortality Between birth and exact age five

09

Infant mortality, which includes deaths during the first year of life, is a potentially important indicator. This is because mortality tends to decline more slowly among infants than among children age 1 to 4. As the rate of under-five mortality decreases, infant deaths—especially neonatal deaths—make up an increasing proportion of all under-five deaths. Reducing mortality during the first year of life is essential to achieving the MDG4, and thus tracking infant mortality becomes extremely important (Child Mortality Coordination Group, 2006). Both the infant mortality rate (IMR) and the under-five mortality rate (U5MR) are expressed as a rate per 1,000 live births (see Box 2). The Inter-agency Group uses WHO’s definition of a live birth, which states:

A live birth is the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of the pregnancy, which, after such separation, breathes or shows any other evidence of life—such as beating of the heart, pulsation of the umbilical cord, or definite movement of voluntary muscles—whether or not the umbilical cord has been cut or the placenta is attached. Each product of such a birth is considered a live birth.

Until recently, some countries in Central and Eastern Europe and the Commonwealth of Independent States used the old Soviet definition of a live birth, which produced lower estimates of infant and child mortality (Wuhib et al., 2003). Strictly speaking, the IMR and U5MR are not rates, since they are not calculated by dividing the number of deaths by the population at risk. Rather, both of these measures represent the probability of dying by a certain age derived from a life table (see Box 2). Box 2. Statistical measures of infant and under-five mortality

Infant mortality rate (IMR)

The probability that a child born in a specific year will die before reaching the age of one, if subject to current age-specific mortality rates. Expressed as a rate per 1,000 live births.

Under-five mortality rate (U5MR)

The probability that a child born in a specific year or time period will die before reaching the age of five, if subject to current age-specific mortality rates. Expressed as a rate per 1,000 live births.

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2.2 Sources of data Data on infant and under-five mortality come from a variety of sources, including vital registration systems, sample registration systems, national population censuses and household surveys. The first two are prospective, that is, they collect data as deaths occur. The last two are retrospective, that is, they interview people about events in the past, including the births and deaths of their children. Vital registration The preferred source of information on infant and under-five mortality is a vital registration system that routinely and accurately records all births and deaths that occur in a country. Individual events are directly reported shortly after they occur, which promotes accuracy (Hill et al., 1999). Another advantage is that the system produces data—and estimates of mortality—annually. Thus, vital registration is a good source for recent information about mortality levels and trends, and it can monitor short-term as well as long-term demographic changes (Setel et al., 2007). While vital registration systems are the source of published figures on fertility and mortality in industrialized countries, many developing countries lack vital registration systems, which are complex and costly to operate. In some nations, the vital registration system only covers part of the country, such as urban areas. Even where the vital registration system covers the entire country, it may fail to record all births and deaths (Mathers et al., 2005). Household surveys have found that the proportion of children under age five whose births have been registered ranges from less than 10 per cent in Bangladesh, Tanzania and Uganda to more than 99 per cent in Cuba and Uzbekistan (UNICEF, 2007a). Poor and rural families are less likely to register births, and unregistered children have less access to health care and higher mortality rates than registered children (UNICEF, 2005b). This could create a downward bias in child mortality rates based on vital registration data. Deaths among young children may be even less likely to be registered than births. Even when deaths are registered, the age at death may be misreported (Hill et al., 2007). Developing countries, especially in Africa and South Asia, account for almost all unregistered births and deaths worldwide (Mahapatra et al., 2007; Setel et al., 2007; UNICEF, 2007a). Fully functioning vital registration systems produce all the information needed to directly measure infant mortality each year, including the number of deaths among children under one year old and the number of births recorded in the same year (UN Development Group, 2003). In countries where the vital registration system has not achieved 100 per cent coverage, however, the registration of deaths is often less complete than the registration of births. As a result, estimates of infant mortality based on vital registration may underestimate the true value (Hill et al., 1999). 11

In countries where a large majority of births and under-five deaths are registered, there are methods available to correct for the incompleteness of vital registration data and improve mortality estimates. Estimates of under-five mortality usually require census data as well as vital registration data. Vital registration systems are not a reliable source of information on the total number of children in older age groups, due to migration and imperfections in the registration system. Thus information from the vital registration system on the number of deaths among young children by age is typically combined with census data on the number of children in each age group to calculate under-five mortality (Hill et al., 1999). In consequence, IMR estimates are considered more robust than U5MR estimates from vital registration (UN Development Group, 2003). WHO routinely collects data on births and deaths from the vital registration systems of member states. The organization uses demographic techniques to estimate the coverage and completeness of the data, which together determine the proportion of all vital events that are registered in a country. Analysts at WHO have also developed techniques to adjust for completeness and coverage when calculating infant and under-five mortality estimates based on vital registration data. Infant and under-five mortality estimates for several dozen countries in 2006 were based solely on vital registration data. These include Argentina, Cuba, and Spain. Sample registration systems and demographic surveillance sites Some countries have a sample registration system or demographic surveillance site that follows a small portion of the population over time and records all vital events as they occur. A major advantage of both these systems is that they collect data prospectively: births and deaths are recorded soon after they occur. The data collection methodology—routine household visits at frequent intervals—also helps ensure the accuracy and completeness of information (Hill et al., 1999). Sample registration systems are designed to collect information from a representative sample of the population in lieu of a nationwide vital registration system. This allows the data to be generalized to the country as a whole. While complex and expensive to operate—and therefore rare—sample registration systems provide information on cause of death and other socio-economic factors that are valuable for planning programmes to reduce child mortality. The government of India established a Sample Registration System in 1964 that now includes about 7,000 urban and rural sampling units and covers nearly one per cent of the nation’s population. Part-time registrars in each locality record births and deaths as they occur, and a survey team verifies the information during household interviews that are conducted twice a year. Independent evaluation suggests that the system captures about 85 per cent of deaths (Hill et al., 2007). 12

The system produces child mortality estimates that are close to household survey estimates, which confirms the strength of the system. In contrast, the sample registration system initially deployed in China was hampered by budget constraints, and it badly underestimated child mortality. A new system introduced in 2004 has been designed to collect more reliable data and to act as a precursor to a full vital registration system (Hill et al., 2007). Together the sample registration systems in India and China cover one-third of the world’s births and one-quarter of the world’s under-five deaths. While demographic surveillance sites may share the same data collection systems as sample registration systems, their coverage is profoundly different. Demographic surveillance sites are limited to a small, geographically defined population, typically 50,000 to 200,000 people. While this makes them easier to operate, it means that they may not be representative of the country as a whole. Often they are the sites of intervention trials, making it even more difficult to generalize their results (Hill et al., 2007). As a result, demographic surveillance sites do not contribute to national mortality estimates even though they may produce other insights into the causes and patterns of infant and child mortality. National population censuses Most countries conduct regular censuses of their population. A census is a unique source of basic demographic data—such as the number of people by age and gender—because interviewers visit every household and enumerate the entire population, including all men, women, and children. Census data are frequently used as the denominator for mortality, fertility and other rates. Perhaps the greatest limitation on the usefulness of census data is its infrequency. Because of their large scale and high cost, national censuses are typically conducted at ten-year intervals. In addition, census questionnaires must be relatively short, so only a small number of health questions are included (Boerma and Stansfield, 2007). However, many censuses in developing countries do ask all women age 15-49 how many live-born children they have ever had and how many are still alive. This information is sufficient to calculate infant and child mortality estimates using indirect methods (described in section 2.3). Since censuses collect information on the survival history of children of women as young as 15 and as old as 49, they gather information about deaths—and about mortality levels—over a period of many years. Thus census data are generally used to produce a time series of estimates going back 10 to 15 years before the survey. Census data also permit analysis of mortality differentials by region and population subgroup (Hill et al., 2007). While census data are not subject to sampling errors, there may be substantial errors of closure; that is, poorly conducted censuses may not enumerate all of the population (Ahmad et al., 2000). Recall errors also arise since censuses collect data retrospectively. 13

Census respondents in developing countries may not be able to report women’s exact age, which is a key piece of information for indirect estimates of infant and child mortality. Unless censuses are specifically designed to gather data for mortality estimates, they may also be apt to omit dead children (Rutstein and Rojas, 2006). When asked about children ever born, respondents are especially likely to leave out children who died years before or who do not live with them. Alternatively, respondents may include stillbirths along with live births, which leads to overestimates of infant and child mortality. Because censuses gather information about infant and child mortality from adult respondents, infant and child mortality estimates based on census data are also subject to survivor selection bias. That is, they may exclude children born to women who have died. This may bias child mortality estimates downwards, since orphans may have elevated mortality risks. The effect may be especially great in countries with a high prevalence of HIV/AIDS. Estimates of infant and child mortality obtained from census data may also suffer from age truncation, since they only use information collected for women age 15-49 years. Thus they include children born recently to mothers of all ages. As the census reaches back in time, however, the average age of the women drops and mortality data refer to children born to progressively younger women. This may distort time trends, and it also biases the survey towards capturing mortality in recent years. Household surveys Household surveys have become the primary source of data on infant and child mortality in developing countries given the lack of vital registration systems and the infrequency of national censuses. While challenging to mount, well-designed and well-implemented household surveys produce high-quality data on mortality levels and trends. Such surveys gather data on infant and child mortality either by collecting detailed birth histories from women of reproductive age (which are used to make direct estimates of infant and child mortality) or by asking women about the number of children even born and the number surviving (which are used to make indirect estimates of infant and child mortality). Household surveys also collect data on socio-economic status, health and education. This information—which is not available from vital registration data—is critical to targeting programmes to reduce child mortality. Like censuses, household surveys collect information on the survival history of children of women as young as 15 and as old as 49, so they gather information about deaths that have occurred over a period of many years. Consequently a single survey produces multiple mortality estimates for different points in time, usually expressed as the number of years prior to the survey (Child Mortality Coordination Group, 2006). International survey programmes, beginning with the World Fertility Survey in the 1970s, have systematically collected information on infant and child mortality in 14

developing countries. Today, the Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) programmes are the largest sources for survey-based child mortality estimates (see Box 3). DHS surveys are especially notable because they collect detailed, reliable and complete birth histories; women are asked a series of questions about the dates and survival status of all births, pregnancies that did not end in a live birth and current pregnancy status. This permits direct estimates of infant and child mortality. Many other surveys—including The World Bank’s Living Standards Measurement Study (LSMS) surveys and surveys designed and implemented by national governments—also collect enough information to make indirect estimates of child mortality (Carraro et al., 2004). The International Household Survey Network (IHSN) is making it easier to identify relevant surveys in developing countries by maintaining a database of ongoing and planned surveys and by creating a central survey catalog with links to datasets (IHSN, 2007). Box 3. International survey programmes that collect information on infant and child mortality

Demographic and Health Surveys (DHS)

More than 200 DHS surveys have been undertaken in 75 countries since 1984 (MEASURE DHS, 2007). Sponsored by the United States Agency for International Development (USAID), each survey usually samples 5,000 to 30,000 households although the sample size is sometimes larger. (For example, the survey in India sampled over 100,000 households.) Three standard core questionnaires (for households, women and men) collect a wide range of data on population and health issues that can be compared across countries. Women of reproductive age (15-49) who live in the households sampled are asked for detailed birth histories.

Multiple Indicator Cluster Surveys (MICS)

Originally developed by UNICEF to help assess progress towards the goals established by the 1990 World Summit for Children, MICS surveys now serve as a monitoring tool for the MDGs and other international commitments (UNICEF, 2005a). Three rounds of MICS surveys have been conducted, in 1994-95, 2000-01, and 2005-06. Over 50 countries participated in each round. MICS surveys typically sample 4,000 to 5,000 households, although samples can range up to 15,000 households. The women’s questionnaire includes a module on child mortality which asks how many children a woman has ever borne, when she first gave birth and how many of her children have died or survived.

Household surveys are generally conducted at three- to five-year intervals. This is frequent enough to capture changes in child mortality, which does not vary much from one year to the next except under exceptional circumstances. In fact, differences between more frequent surveys would probably reflect random fluctuations from the errors 15

inherent in the data rather than any real change in mortality levels. Household surveys typically include nationally representative samples of 3,000 to 30,000 households. The sample size needs to be sufficiently large to produce statistically reliable estimates of infant and under-five deaths, which are relatively uncommon events. However, the expense and technical demands of household surveys—which require intensive training and close supervision of interviewers to assure data quality—places a limit on the sample size (Boerma and Stansfield, 2007; Hill et al., 1999). Thus, household surveys are subject to sampling errors, and the mortality estimates they produce may carry wide confidence intervals (Child Mortality Coordination Group, 2006). This can make it difficult to compare survey estimates over time or across countries. To reduce sampling errors, infant and under-five mortality estimates based on household survey data are generally presented as period rates. For example, DHS surveys typically calculate mortality rates for five-year periods preceding the survey or, for smaller subgroups, a ten-year period (Rutstein and Rojas, 2006). Others have calculated rates for two-year periods in order to capture more information about recent trends (Murray et al., 2007). Surveys are also subject to non-sampling errors, in part because they rely on women’s recall of events, some of which took place many years in the past (Ahmad et al., 2000; Hill et al., 1999, Rutstein and Rojas, 2006; UN Development Group, 2003). Like census data, survey data on infant and child mortality may omit births and deaths, include stillbirths along with live births, and suffer from survivor selection bias and age truncation. Direct estimates of infant and child mortality based on survey data may also suffer from mothers misreporting their children’s birth dates, current age or age at death—perhaps more so if the child has died. The heaping of deaths at age 12 months is especially common. Age heaping may transfer deaths across the one-year boundary and lead to underestimates of infant mortality rates. However, it has little effect on under-five mortality rates. As a result, household survey data are considered to produce more robust estimates of the under-five mortality rate than the infant mortality rate (UN Development Group, 2003). 2.3 Approaches to calculating infant and under-five mortality rates Direct methods Direct methods of calculating infant and under-five mortality rates require data on:

• Each child’s date of birth, • His or her survival status, and • The date or age at death of every child who has died (Rutstein and Rojas, 2006).

This information is typically found in vital registration systems and in household surveys that collect complete birth histories from women of childbearing age. Birth histories include a series of detailed questions on each child a woman has given birth to during her lifetime, including the date the child was born, whether or not the child is still alive, and if the child has died, the age at death. 16

There are three direct methods of calculating infant and under-five mortality rates (see Box 4). The vital statistics approach produces a true rate of mortality, which may vary with the number of births. In contrast, both life table approaches calculate the probability of death. A major drawback of the true cohort life table approach is that all of the children in that cohort must be fully exposed to the risk of death. In other words, all of the children must have been born at least 12 months before the survey to assess infant mortality, or five years before the survey to assess under-five mortality. Hence this approach excludes the most recent experience with child mortality. The synthetic cohort life table approach is preferred by many because it allows full use of the most recent events (Rutstein and Rojas, 2006). Box 4. Direct methods for estimating infant and under-five mortality rates

Vital statistics approach The number of deaths to children under age 12 months during a particular time period is divided by the number of births in that same period.

True cohort life table approach The number of deaths to children under age 12 months (or age five years) among a specific cohort of births is divided by the number of births in that cohort.

Synthetic cohort life table approach Mortality probabilities for small age segments based on real cohort mortality experience are combined into common age segments (e.g., infant and under-five mortality).

Indirect methods Indirect methods of estimating infant and child mortality require relatively little information. Only a few short, simple questions are required to collect it. Thus, the needed information may be found in censuses and general surveys that do not collect detailed birth histories (Rutstein and Rojas, 2006). Indirect methods are often called “Brass methods” after their original developer, William Brass. Brass’s original method assumes that the age of the mother can serve as a proxy for the age of her children and thus for how long they have been exposed to the risk of dying. It converts the proportion of children who have died among women in a certain age group into the probability of dying by an exact childhood age (Brass, 1964; Brass, 1975). The only information required is:

• A woman’s age, • The total number of children she has ever borne, and • The number of those children who have died (or, alternatively, the number who

are still alive).

The method assumes that the mortality rates reflect the time period when the children were born, not the age of their mothers. This is not always the case, however. Information gathered from women ages 15-24 almost always yields higher mortality estimates than information gathered from older women. 17

In part, this is because the risk of dying is truly higher for children born to young mothers. However, there is also a selection effect at work: women from lower socio-economic classes tend to start childbearing early and their children face above average mortality risks (Hill and Figueroa, 1999). Random errors also are larger for estimates based on the reports of young women, since they have fewer children ever born. Given all these problems, analysts frequently discount information from women in the two youngest age groups when they make indirect estimates of infant and child mortality—which means the estimates exclude the most recent mortality experience.

Two alternative approaches have been proposed to overcome these problems (see Box 5). Instead of using mother’s age as a proxy for children’s exposure to the risk of mortality, one approach uses the duration of marriage. This eliminates the socio-economic selection bias, since couples of all social classes tend to have children rapidly during the first five years of marriage. The approach is unworkable, however, in countries where many births take place outside formal unions. A more promising approach uses time since a woman first gave birth—rather than a woman’s age—as a proxy for children’s exposure to the risk of dying. It is less affected by socio-economic selection biases than mother’s age, and it works even when many children are born outside marriage (Hill and Figueroa, 1999). This approach does require some additional information, namely the month and year when a woman first gave birth. While the date of a woman’s first birth is already available in surveys that collect detailed birth histories (such as DHS surveys), other surveys have not generally collected this information. However, a question on the date of a woman’s first birth was added to the questionnaire used in the second and third rounds of MICS surveys. Members of the Inter-agency Group have begun testing this approach, and the initial results are promising. They suggest that time since first birth produces better results than duration of marriage, but may have higher sampling errors than mother’s age (Inter-agency Group, 2006). Box 5. Indirect methods for estimating infant and under-five mortality rates

Age of mother The proportion of children who have died among women in a five-year age group (e.g., 15-19, 20-24, 25-29, etc.) is converted into the probability of dying by an exact childhood age.

Duration of marriage The proportion of children who have died among women who have been married for a certain number of years is converted into the probability of dying by an exact childhood age.

Time since first birth The proportion of children who have died among women whose first birth was a certain number of years ago is converted into the probability of dying by an exact childhood age.

18

All of the indirect methods use model life tables to adjust the data for the age pattern of mortality in the general population. Choosing a life table that is appropriate to a specific population is essential to generating accurate estimates (Ahmad et al., 2000). However, the Coale and Demeny model life tables used for this purpose are derived largely from European experience. Many countries, especially those in sub-Saharan Africa, may not fit any of the model life tables (Child Mortality Coordination Group, 2006).

Indirect methods have another weakness. It is a challenge to locate their mortality estimates in time. Indirect methods estimate the probability of a child dying based on women’s experience that can extend back as many as twenty years. Methods used to assign mortality estimates to a particular number of years before the survey assume that there has been little or no change in fertility levels and age patterns, and either no change or a linear decline in mortality—which may or may not be true (Rutstein and Rojas, 2006).

3. THE DEVELOPMENT OF THE 2006 ESTIMATES OF INFANT AND UNDER-FIVE MORTALITY

3.1 Collecting the data Number, variety, quality and timeliness of data sources While the amount of data on infant and child mortality in developing countries has grown, many countries still lack accurate, reliable and timely data. Indeed, the higher the estimated rates of infant and child mortality in a country, the fewer recent data points exist (Child Mortality Coordination Group, 2006). The amount, variety, quality and timeliness of information on infant and child mortality varies widely among countries. In Congo, for example, there are just two sources of data on levels and trends in infant and child mortality since 1960: a census conducted in 1974 and a DHS survey conducted in 2005. In contrast, infant and child mortality estimates for Brazil and Egypt can draw on data from a vital registration system, several censuses, and multiple household surveys. Most data sources for Brazil, however, date to the 1970s and 1980s, and there are questions about the coverage and completeness of the only recent sources: the 2000 census and the vital registration system. Thus, recent reliable data are lacking. In contrast, Egypt’s data sources are more evenly spread over the past three decades, and they include a series of DHS surveys conducted at regular intervals from 1992 to 2005. These surveys provide a source of recent, high-quality data on child mortality. Current estimates of IMR and U5MR are generally based on empirical data from several or even many years before. There are three reasons for this. First, only vital registration systems collect data annually. Mortality estimates more often rely on household surveys, which are typically conducted every three to five years or at even longer intervals, and on decennial censuses. Second, even when there is a recent survey or census available, the 19

data collected is retrospective. That is, the data describe births and deaths that occurred some years before the interview. Third, there is a lag time between the collection and publication of data—generally two years for vital registration, one year for surveys, and one to three years for censuses (Child Mortality Coordination Group, 2006). Current mortality estimates are generated by extrapolating forward from these older data. (The process is described in section 3.2.) The validity of the extrapolations depends, in part, on the quality, quantity and timeliness of the input data. Identifying data sources The difficulty of locating all possible data sources is a problem for monitoring many, if not all, of the MDGs. National censuses, vital registration systems and household surveys conducted by global survey programmes, such as DHS and MICS, are easy to locate. It is more challenging to identify multi-purpose surveys conducted without international sponsorship, yet these additional data sources may have a profound impact on mortality estimates (Carraro et al., 2004). To seek out national data sources that might be overlooked, UNICEF conducts an annual exercise called the Country Reports on Indicators for the Goals (CRING). CRING gathers recent information for all indicators regularly reported on by UNICEF, including the infant and under-five mortality rates. Each year, UNICEF’s executive director sends out a request for information to all of the organization’s regional directors and country representatives, who are stationed in over 150 countries around the world. They are asked to update the data already available at headquarters by submitting new values for each indicator along with copies of the original source documents, such as survey reports. Adequate documentation, preferably including a microdata file for further analysis, is essential to generating good estimates, because it enables analysts to rigorously assess the quality of the data. As mentioned above, household surveys conducted without international sponsorship are among the easiest data sources to miss. Therefore, the 2007 CRING exercise also asked respondents to list all nationally representative household surveys conducted in the past three years and all surveys planned for the next three years. Child mortality database All information on infant and child mortality collected by UNICEF and its partners is entered into the existing child mortality database. This database has been in development since the early 1990s and is currently in the process of being adapted to the DevInfo technology. The database contains information for countries represented at the United Nations and covers five indicators: neonatal mortality, post-neonatal mortality, infant 20

mortality, child mortality (ages 1-4), and under-five mortality. Both data and documentation are housed in the database. Users can access and download basic data from surveys, vital registration systems, and other data sources, which they can use to conduct their own analyses. Data are organized by:

• Estimation method, • Data source, and • Background variables, including sex, urban/rural place of residence, mother’s

education, and household wealth. The database also contains the survey reports and other documents that supply the context for each data set. These include information about the data collection process and methodology that may shed light on the quality of the data. Finally, the database includes the estimates of child mortality levels and trends produced by the Inter-agency Group, accompanied by a detailed explanation of how each estimate was arrived at. To increase the transparency of the Inter-agency Group’s estimation process and to help interested parties understand the basis for specific country estimates, the database is currently available and accessible to the public at www.childmortality.org. The database includes data at the country level according to sources, methods of estimation, uncertainty levels, metadata and current agreed estimates. The database also contains estimation areas in which users could produce their own estimation. In the long run, the objective is for countries not only to consult the database, but also to provide data inputs as new results become available from vital registration systems, censuses, and household surveys. 3.2 Estimation methodology used to calculate rates and trends for each country Different data sources and calculation methods frequently yield widely differing estimates of infant and child mortality for a given time and place. The more varied the sources and methods used, the less consistent these point estimates are likely to be. These disparities make it difficult to determine the actual mortality level for any given year, to analyze trends over time, and to make comparisons between countries (Ahmad et al., 2000; Carraro et al., 2004). In order to reconcile the differences between multiple data sources, UNICEF has developed, in coordination with WHO, The World Bank, and UNPD, an estimation methodology that minimizes the errors embodied in each estimate and harmonizes trends over time. The goal is to provide an explicit, consistent, and replicable method to:

• fit a smoothed trend to a set of observations, and • extrapolate the trend to cover the period from 1960 to the present.

The methodology is designed to improve the quality of the estimates, and it makes the estimation process more transparent. 21

Applying a consistent methodology also allows comparisons between countries, despite the varied number and types of data sources. Regression analysis using weights The first step is to plot all available values for under-five (or infant) mortality over the past fifty years. Analysts then use weighted least squares to fit a multiple-spline regression line to the data points and to extrapolate the trend to cover the period from 1960 to the present. The use of weights allows analysts to make a judgement about the relative quality of each data set and how representative that data set is likely to be of the population. Box 6 presents the standard weights assigned to each data source by the Inter-agency Group. Box 6. Standard weights assigned to data sources on infant and under-five mortality

Data source Standard weights Rationale

Vital registration system

0.25 for each annual estimate Total combined weight for 20 years of data: 5.0

- Involves large number of events - No substantial lag between the event and the report - Contributes so many data points that it may outweigh other data sources

Sample registration system or surveillance site

0.25 for each annual estimate Total combined weight for 20 years of data: 5.0

- Data collection methodology promotes accuracy - No substantial lag between the event and the report - Contributes so many data points that it may outweigh other data sources

Household survey or national population census: One or two data points only

Weights by number of data points For one data point: 2.5 For two data points: 1.25 each

Total combined weight: 2.5

- Contributes so few data points that it should not have too large an influence on regression estimates

Household survey: Direct estimates with five data points

Weights by time period of estimate (number of years before survey was conducted) 0-4 years: 1.2 5-9 years: 1.2 10-14 years: 1.2 15-19 years: 0.8 20-24 years: 0.6 Total combined weight: 5.0

- Recent information is more likely to be accurate than information for periods further in the past

22

Household survey: Direct estimates with three data points

Weights by time period of estimate (number of years before survey was conducted) 0-4 years: 1.8 5-9 years: 1.8 10-14 years: 1.4 Total combined weight: 5.0

- Recent information is more likely to be accurate than information for periods further in the past

Household survey: Direct estimates with six or more data points

Weight assigned to each data point is 5.0 divided by total number of data points Total combined weight: 5.0

- Sum of weights for single survey should not exceed a total of 5.0

Household survey or national population census: Indirect estimates using mother’s age

Weights by age group 15-19 years: 0 20-24 years: 0.2 25-29 years: 1.2 30-34 years: 1.2 35-39 years: 1.2 40-44 years: 0.8 45-49 years: 0.4

Total combined weight: 5.0

- Selection problems affect estimates based on young women: early childbearing is higher among the poor, who also suffer the highest child mortality rates - Information about events long ago is more prone to error

In some respects, the standard weights are objective because they remain the same across data sets and across countries. However, they are often adjusted to counteract deficiencies in the data (for example, in some recent DHS surveys births and deaths from the most recent five-year period are shifted to the earlier period, a bias that tends to overestimate reductions in child mortality) and in response to evidence from other data sources. Thus, the final weights are based on data quality and consistency, along with experience and judgement. The results of a robust regression exercise involving data from 13 countries do offer some support for the standard weights. Robust weights generated by the exercise confirm that direct and indirect estimates are about equally satisfactory and that indirect estimates based on the reports of young women are unreliable. However, they also suggest that the passage of time may have less of an impact than presumed on the quality of women’s reports regarding their children’s births and deaths (Hill et al. 1999). Some household surveys, such as DHS surveys, may contribute both direct estimates based on birth histories and indirect estimates based on information about the number of children ever born and children surviving. To make sure that a single data source does not have too much influence, the weights assigned to each of these estimates are cut in half. Thus, all estimates derived from a single DHS survey carry a total weight of 5, half of which comes from the direct estimates and half from the indirect estimates. Varying the rate of change When fitting a trend to a set of data points, a simple model assumes that the rate of change remains the same throughout the period under study. 23

In actuality, however, mortality may decline more steeply during some years than others, or it may plateau or even rise. Therefore, the model used by the Inter-agency Group allows the rate at which infant or under-five mortality changes over time to vary. The model assumes that one set of indirect or direct estimates from a household survey is sufficient to define a trend. Based on this principle, the slope of the line (which represents the rate of change in mortality) is allowed to shift whenever there are a sufficient number of data points. The more data points there are for a particular country, the more often the slope of the line can shift, and the more flexible the trend in infant or under-five mortality will be. The moments in time when the rate of change in infant or under-five mortality is allowed to shift are known as “knot” dates. Knots are defined working backwards in time from the most recent observation following the equation in Box 7. The weights for successive observations are summed, and a knot is defined every time that the sum of weights reaches a multiple of five. Since the total combined weight for one set of indirect or direct survey estimates is five, this means that each DHS, MICS or other survey defines a particular slope. Box 7. Statistical model for estimating trends (Hill et al., 1999) ln(nq0)i = b0 + b1(date) + b2(postk1) + b3(postk2) + b4(postk3) +...+ ei The variable date is simply calendar year; postk1 is date minus the date of the earliest defined knot if positive, or zero otherwise, and picks up any change in trend after the first knot (note that the knots are defined from the present backwards into the past, but the earliest knot is defined to ensure at least five observations between it and the start of the series); postk2 is date minus the date of the second defined knot if positive, or zero otherwise, and picks up any change in trend after the second knot; and so on. Thus, the number of slope-changing time variables varies with the number and weight of the observations over time. The coefficients on postk1, postk2, etc. can be interpreted as changes in the rate of change of infant or under-five mortality with time in that particular period. Thus the rate of change in period 1 is b1; in period 2, (b1 + b2); in period 3, (b1 + b2 + b3); and so on. It should be noted that the error term ei is assumed to be normally distributed around the logarithm of the mortality indicator. As a result, estimates of mortality obtained by exponentiating an estimated value of the logarithm of the mortality indicator will be biased upwards by an amount that will depend on the goodness of fit of the model. This is a relatively benign bias in the sense that the infant or under-five mortality estimates obtained will be conservative, and the poorer the fit of the model the more conservative the estimates will be. 3.3 Applying the methodology Step 1: Fitting the equation using standard weights The first step in the smoothing and extrapolation process is to fit the equation in Box 7 using appropriate date variables and the standard weights listed in Box 6. The infant mortality rate and the under-five mortality rate are fitted independently. 24

The data points and the regression line fitted to them are displayed in graphs (one for infant mortality and another for under-five mortality) like the one shown in Figure 1, which shows U5MR data from Egypt. Figure 1: Initial estimate using standard weights: Data on under-five mortality in Egypt and estimate of trend, 2006

Step 2: Adjusting the weights The second step is to critically examine the data and the graphs and identify data sets that are problematic, such as a vital registration sequence with incomplete data that is consistently lower than all other estimates of infant mortality. In Figure 1, for example, the estimates from the 1976 and 1986 censuses in Egypt are out of line with the rest of the data. They pull down the trend line (shown in green) and give an almost certainly erroneous impression that under-five mortality rose in Egypt in the 1960s. When analysts identify an aberrant data set, they reduce the weights assigned to the entire data set and calculate a new trend line using the revised weights. 25

When weights are reduced to zero, as is sometimes the case, estimates from that data set are essentially excluded from the analysis. Figure 2 shows how the green trend line for Egypt changed after estimates from the 1976 and 1986 censuses were given zero weight. Figure 2: Final estimate using adjusted weights: Data on under-five mortality in Egypt and estimate of trend, 2006

The decision to underweight some data sets relative to others can have a large effect on the resulting estimates. To reduce the subjectivity inherent in the decision, analysts are expected to follow the following guidelines (Jones, 2007):

• The decision is made based on an inspection of the graph and a review of the data sources.

• If the estimates from one source are clearly higher or lower than the bulk of other estimates or if their time trend is clearly different, the weights assigned to the entire data set may be reduced by a constant factor (usually one-half or one-quarter), or they may be set to zero.

26

The larger the difference between the data sources, the more the weights are reduced. When two data sets suggest very different levels of mortality, the set with the higher rate is assumed to be more likely to be right. The presumption is that errors are more likely to result in underestimates of infant or under-five mortality than in overestimates.

To increase transparency, the Inter-agency Group now presents an initial estimate of infant and child mortality in each country using standard weights as well as a final estimate using adjusted weights. These estimates are accompanied by an explanation of how and why the weights were adjusted. Step 3: Selecting the most consistent series The last step is to decide which of the two sets of estimates (the infant mortality rate or the under-five mortality rate) has more consistent data. Then the other set of mortality estimates is derived from it, using the appropriate Coale-Demeny model life table (Coale and Demeny, 1966, Coale et al., 1983). Thus only one set of empirical data, for either IMR or U5MR, becomes the basis for the estimates of both indicators. The decision as to which series is better is based partly on the graphs, but it also considers the nature of the information. For example, vital registration data are stronger for infant than under-five mortality, so preference generally goes to the IMR series when the vital registration system is the key data source. In the absence of good-quality vital registration or sample registration data, U5MR is usually taken as the base. In Egypt, U5MR is considered the better set of estimates, given weaknesses in the vital registration data which result in unrealistically low infant mortality estimates. Thus, IMR estimates are derived from the U5MR series, in this case using the Coale-Demeny West model life table. This life table was chosen based on the direct mortality data available for Egypt, as shown in Figure 3. 27

Figure 3: Survey data on mortality in Egypt plotted against Coale-Demeny model life tables

As already mentioned, the use of model life tables raises some questions. Many developing countries do not fit the model life tables (Child Mortality Coordination Group, 2006). The problem is especially acute in some African countries, such as Burkina Faso and Niger, where the observed age mortality pattern differs considerably from the patterns embedded in existing life table models. Therefore, members of the Inter-agency Group are testing a regression approach to estimate IMR from U5MR in these countries, instead of using a model life table (Jones, 2007). 3.4 Calculating the absolute number of deaths The absolute number of deaths among infants and children in a given year and country can be calculated using the formulas in Box 8. For greater accuracy, the number of deaths is calculated separately for males and females and for infants and children age 1-4. Deaths in each of these four sub-groups are summed to yield the total number of under-five deaths in a country. Country estimates are added together to produce a global estimate. 28

Box 8. Computing the number of deaths in a country

The number of deaths among children under age five is derived from the central death rates of age groups 0 and 1-4 years, computed back from the probability of dying 5q0 and 1q0, to which population is applied. First, 1q4 is derived from 1q0 and 5q0 as follows:

4q1= (5q0-1q0)/(1-1q0) Then, for each age group (0 and 1-4) the central death rate nMx is computed as follows: where x is the beginning of the age group, n is the width of the age group, and ax is the fraction of the interval of life for those who die in the age group started by x. Finally, country population estimates from the UN Population Division are applied to the death rates to obtain the number of deaths.

4. ANALYSIS AND INTERPRETATION OF THE 2006 MORTALITY ESTIMATES 4.1 Under-five mortality levels and trends The 2006 infant and under-five mortality estimates produced by the Inter-agency Group show us how much progress has been made towards the MDG4 and illuminate the challenges that remain. Worldwide the number of children dying before age five has reached a record low, falling below 10 million for the first time in 2006. This is a 25 per cent drop from the nearly 13 million child deaths in 1990. Of the estimated 9.7 million children who perished in 2006, about half (4.8 million) were from sub-Saharan Africa and almost one-third (3.1 million) were from South Asia (see Figure 4). Table 1 shows the under-five mortality rates from 1960 to 2006 for each UNICEF region. By far the highest rates of under-five mortality are found in sub-Saharan Africa (186 deaths per 1,000 live births in West and Central Africa and 131 per 1,000 in Eastern and Southern Africa), where conflict and the spread of HIV/AIDS have undermined hard-won gains in child survival. All but one of the eleven countries with an estimated U5MR of at least 200 deaths per 1,000 live births are located in sub-Saharan Africa (see Annex 1). In descending order, these countries are: Sierra Leone (270 per 1,000), Angola (260 per 1,000), Niger (253 per 1,000), Afghanistan (257 per 1,000), Liberia (235 per 1,000), Mali (217 per 1,000), Burkina Faso (204 per 1,000), Chad (209 per 1,000), Equatorial Guinea (206 per 1,000), Democratic Republic of the Congo (205 per 1,000), and Guinea-Bissau (200 per 1,000). 29

])1(1[ xnxn

xnxn qan

qnM−−

=

Figure 4: Estimated number of deaths (in millions) occurring before the age of five in 2006

Table 1. Estimated under-five mortality rates (U5MR) from 1960 to 2006, by UNICEF regions

Under-five mortality rate (U5MR) Region 1960 1970 1980 1990 1995 2000 2005 2006 Sub-Saharan Africa 277 243 200 187 183 170 162 160 Eastern and Southern Africa 252 220 179 165 158 145 133 131 West and Central Africa 300 264 220 208 205 193 187 186 Middle East and North Africa 248 195 133 79 65 55 47 46 South Asia 238 199 163 123 109 96 85 83 East Asia and Pacific - 121 74 55 49 40 30 29 Latin America and Caribbean 154 123 84 55 44 35 28 27 Central and Eastern Europe and the Commonwealth of Independent States (CEE/CIS) - 91 70 53 49 39 29 27 Industrialized countries 39 27 15 10 8 7 6 6 Developing countries 219 164 128 103 96 88 81 79 Least developed countries 276 244 207 180 168 154 144 142 World 184 145 115 93 88 80 73 72

30

More moderate levels of U5MR are seen in South Asia, at 83 deaths per 1,000 live births, and in the Middle East and North Africa, at 79 deaths per 1,000. By 2006, three regions had achieved U5MRs below 30 deaths per 1,000 live births: East Asia, Latin America and the Caribbean, and Central and Eastern Europe and the Commonwealth of Independent States (CEE/CIS). By contrast, the 2006 U5MR was 6 deaths per 1,000 live births in industrialized countries. Data collected by DHS, MICS and similar household surveys conducted from 1995 to 2006 show that under-five mortality is considerably higher in rural than urban areas. It is also higher for children living in the poorest households (Gwatkin et al., 2007; National Statistical Office of Mongolia, 2007; Statistics Sierra Leone, 2006; Wang, 2003). Every region of the world shows some progress since 1990, which is the baseline year for the MDG targets. For every 1,000 live births in developing countries in 2006, there were 24 fewer deaths among children under five than there were in 1990. However, the extent to which child mortality has declined varies widely between regions. The 2006 U5MR estimates in the Middle East and North Africa, East Asia and the Pacific, Latin America and the Caribbean, and CEE/CIS are about half the 1990 estimates. In contrast, the 2006 U5MR is just 14 per cent lower than the 1990 figure in sub-Saharan Africa. Because of the slow rate of progress in sub-Saharan Africa, this region accounts for an increasing proportion of deaths among children under age five. In 2006, almost half of the world’s under-five deaths took place in sub-Saharan Africa, compared with about one-third in 1990. The number of under-five deaths in sub-Saharan Africa increased from 4.1 million in 1990 to 4.8 million in 2006, while falling everywhere else. 4.2 Infant mortality levels and trends Regional patterns in infant mortality parallel those in under-five mortality (see Table 2). Among UNICEF regions, the highest rates of infant mortality are found in sub-Saharan Africa, especially in West and Central Africa where 107 children died in infancy for every 1,000 live births in 2006. This is only 10 per cent lower than the estimated infant mortality rate of 119 per 1,000 in 1990. In Eastern and Southern Africa, infant mortality has fallen 19 per cent over the same period, from 102 deaths per 1,000 in 1990 to 83 per 1,000 in 2006. The 2006 infant mortality rates are considerably lower in South Asia, at 62 deaths per 1,000 live births, and in the Middle East and North Africa, at 36 deaths per 1,000. These are 29 per cent and 38 per cent lower, respectively, than the 1990 IMRs. Once again the greatest progress is seen in East Asia and the Pacific, Latin America and the Caribbean, and CEE/CIS. In 2006 the IMR in these three regions was 22 to 24 deaths per 1,000—a little more than half the rates prevailing in 1990 (41 to 43 deaths per 1,000). 31

By comparison, there were 5 infant deaths per 1,000 live births in industrialized countries in 2006.

Table 2. Estimated infant mortality rates (IMR) from 1960 to 2006, by UNICEF regions

Infant mortality rate (IMR) Region 1960 1970 1980 1990 1995 2000 2005 2006 Sub-Saharan Africa 161 141 117 111 108 101 96 95 Eastern and Southern Africa 150 132 109 102 98 91 84 83 West and Central Africa 171 149 125 119 117 111 107 107 Middle East and North Africa 157 128 91 58 49 42 37 36 South Asia 157 132 111 87 79 70 63 62 East Asia and Pacific - 83 53 41 38 32 24 23 Latin America and Caribbean 103 86 63 43 36 29 23 22 Central and Eastern Europe and the Commonwealth of Independent States (CEE/CIS) - 70 56 43 41 33 25 24 Industrialized countries 32 21 13 9 7 6 5 5 Developing countries 140 108 86 70 66 60 55 54 Least developed countries 168 149 128 113 106 98 91 90 World 120 96 77 64 60 55 50 49

5. IS THE MDG4 ACHIEVABLE?

The target of the fourth MDG is to reduce under-five mortality by two-thirds between 1990 and 2015. Achieving this goal requires that U5MR decline by 4.4 per cent every year during that 25-year period. Between 1990 and 2006, however, the actual average annual rate of reduction (AARR) in under-five mortality was only 1.6 per cent globally. To reach the 2015 target set by the MDG4, the pace of change must accelerate: globally child mortality would have to decrease by an average of 9.4 per cent per year from 2007 to 2015. However, the global numbers mask wide variations between and even within UNICEF regions, which are shown in Table 3 and Annex 3. Three regions—East Asia and the Pacific, Latin America and the Caribbean, and CEE/CIS—have either achieved the benchmark of a 4.4 per cent AARR through 2006 or come close. All three of these regions are considered to be on track to achieve the MDG on child mortality. At the other extreme, the U5MR in sub-Saharan Africa has declined by just 1 per cent annually, on average, since 1990. Sub-Saharan Africa accounts for 24 of the 27 countries that are rated as making no progress towards the fourth MDG. 32

In recent years under-five mortality has actually increased in a dozen of these countries, so that children are less likely to survive to their fifth birthday today than they were in 1990. The deterioration in child mortality has been most extreme in Botswana, where the U5MR has increased an average of 4.7 per cent each year since 1990, followed by Swaziland and Zimbabwe, which have experienced average annual increases of 2.5 per cent and 2.0 per cent, respectively.

Table 3. Average annual rate of reduction in under-five mortality and progress towards the MDG target, by UNICEF regions

Under-five mortality rate (U5MR)

Average annual rate of reduction (%)

Region 1990 2006

MDG target for

2015 Observed: 1990-2006

Required to meet MDG

target: 2006-2015

Progress towards the MDG

target***

Sub-Saharan Africa 187 160 62 1.0 10.5 insufficient

Eastern and Southern Africa 165 131 55 1.4 9.6 insufficient

West and Central Africa 208 186 69 0.7 11.0 no progress

Middle East and North Africa 79 46 26 3.4 6.2 insufficient

South Asia 123 83 41 2.5 7.8 insufficient

East Asia and Pacific 55 29 18 4.0 5.1 on track

Latin America and Caribbean 55 27 18 4.4 4.3 on track Central and Eastern Europe and the Commonwealth of Independent States (CEE/CIS) 53 27 18 4.2 4.7 on track

Industrialized countries 10 6 3 3.2 6.6 on track

Developing countries 103 79 34 1.7 9.3 insufficient

Least developed countries 180 142 60 1.5 9.6 insufficient

World 93 72 31 1.6 9.4 insufficient *On track is defined as either:

(1) U5MR < 40 deaths per 1,000 or (2) U5MR ≥ 40 per 1,000 and AARR ≥ 4%

Insufficient progress is defined as: U5MR ≥ 40 per 1,000 and 1% ≤AARR < 4%

No progress is defined as U5MR ≥ 40 deaths per 1,000 and AARR < 1% In these and many other countries in sub-Saharan Africa, infant and child mortality rates fell substantially from 1960 to around 1990, when the downward trend slowed or, in some cases, even started to reverse itself. In some countries, HIV/AIDS is a major cause of recent increases in child mortality. 33

However, armed conflict and social instability have also contributed to the worsening situation for children in countries, including Afghanistan as well as parts of sub-Saharan Africa (Mukelabai, 2004). Meeting the MDG target for child mortality in these countries will require dramatic measures. However, armed conflict and social instability have also contributed to the worsening situation for children in countries, including Afghanistan as well as parts of sub-Saharan Africa (Mukelabai, 2004). Meeting the MDG target for child mortality in these countries will require dramatic measures. The situation in the Middle East and North Africa and in South Asia lies somewhere in between the two extremes described above. Some progress has been made, but the current rate of improvement will not be sufficient to meet the target for 2015. South Asia, for example, will require a jump in the AARR from 2.5 per cent to 7.8 per cent if it is to reach the goal. The Middle East and North Africa does not have as far to go, since its AARR is 3.4 per cent to date. Much remains to be done to reach the MDG4. It will require an extraordinary effort by the international community, governments, NGOs, civil society, and others. However, research and programme experience have made it clear what actions need to be taken. Most child deaths are preventable. Over half are due to communicable diseases, including pneumonia, diarrhoea, malaria, measles, and HIV/AIDS. Many other deaths are due to neonatal disorders, such as preterm delivery, asphyxia at birth, and neonatal pneumonia and sepsis. Undernutrition is also a contributing factor in most cases (Black et al. 2003; Bryce et al., 2005). There is at least one effective and affordable intervention available to prevent or treat each major cause of under-five mortality. These include skilled attendants at childbirth, early and exclusive breastfeeding, immunization, vitamin A supplementation, the use of insecticide-treated bed nets to prevent malaria and oral rehydration therapy. Scaling up coverage of these proven child survival interventions has the potential to prevent most child deaths (Jones et al., 2003). For example, measles mortality fell by 60 per cent worldwide between 1999 and 2005, and by 75 per cent in sub-Saharan Africa, largely due to increased vaccination coverage (Wolfson et al., 2007). 6. NEXT STEPS 6.1 Using the 2006 infant and under-five mortality estimates The mortality estimates produced by the Inter-agency Group on Child Mortality Estimation are drawn from observed empirical data. Often, however, these estimates do not match the values reported by government agencies at the country level, which are obtained and reported directly from a specific census, survey or vital registration system. Rather the estimates use a regression model to reconcile the differences between multiple data sources and to extrapolate the trends to the current year. 34

In contrast, national governments may base their official estimates of infant and under-five mortality on a single recent data source, such as a census or DHS survey. Alternatively, they may use a different combination of data sources and estimation methods than the Inter-agency Group to produce their estimates. As a result, the mortality rate estimates produced by the Inter-agency Group frequently differ from official government estimates produced at the country level. The Inter-agency Group’s estimation methodology also means that the 2006 estimates presented here cannot be directly compared with estimates from past years. The Inter-agency Group updates its country estimates of infant and under-five mortality annually, incorporating new data as they become available. New data can shift the entire regression curve, past trends and the extrapolated values for the present day. Thus differences in mortality estimates from one year to the next may reflect increased knowledge of the situation rather than an actual change in mortality rates, which tend to change little from one year to the next. The 2006 estimates should not be interpreted as precise measures of infant and under-five mortality. Given the nature of the data, there is a range of uncertainty associated with each of the figures. Thus one must be wary of interpreting small differences between countries or over time as representing real differences in infant and child mortality. Child mortality estimates at the country level are associated with at least a 10 per cent level of uncertainty. The amount of uncertainty depends on the extent to which IMR and U5MR values rely on:

• actual observations from a vital registration system, • measurements corrected for known biases (such as survey or census data

corrected for age heaping), and • extrapolations forward in time (Murray et al., 2007).

The child mortality database includes detailed information on the data sources and methods used to generate the estimates for specific countries, which can shed some light on the uncertainty associated with the estimates. 6.2 Improving the estimates The Inter-agency Group is actively working to improve and refine its infant and child mortality estimates. For example, group members are proactively seeking out additional data sources together with adequate documentation, assessing data quality, testing alternative approaches to indirect estimation, and reconsidering the model life tables used to produce estimates. They are also investigating ways to more accurately estimate infant and child mortality where HIV/AIDS is prevalent and to measure the uncertainty associated with the mortality estimates for each country. At the same time, the Inter-agency Group is promoting greater understanding of how to interpret and use the mortality estimates by developing a publicly accessible database on child mortality and by conducting regional workshops on mortality estimation. estimation methods, metadata 35

The database (available at www.childmortality.org) includes data at the country level for different data sources and for data sources and estimation methods, and levels of uncertainty around the yearly estimates produced and agreed on by the group. Annex 1. Estimated under-five mortality rates (U5MR) from 1960 to 2006, by country

Under-five mortality rate (U5MR)

Country 1960 1970 1980 1990 1995 2000 2005 2006 Afghanistan 360 320 280 260 257 257 257 257 Albania 151 109 72 45 34 25 18 17 Algeria 261 220 134 69 53 44 39 38 Andorra - - - 6 - 4 3 3 Angola 345 300 265 260 260 260 260 260 Antigua and Barbuda - - - - 21 15 12 11 Argentina 73 71 41 29 23 19 16 16 Armenia - - 76 56 48 36 26 24 Australia 24 20 13 10 7 6 6 6 Austria 43 33 17 10 7 6 5 5 Azerbaijan - - 123 105 98 93 89 88 Bahamas 68 49 35 29 24 19 15 14 Bahrain 150 82 30 19 16 12 11 10 Bangladesh 248 239 205 149 120 92 73 69 Barbados 90 54 29 17 15 13 12 12 Belarus - - 26 24 22 17 14 13 Belgium 35 29 15 10 7 6 5 4 Belize - - 71 43 30 23 17 16 Benin 296 252 214 185 170 160 150 148 Bhutan 300 267 227 166 133 100 75 70 Bolivia 255 243 175 125 105 84 65 61 Bosnia and Herzegovina 160 82 39 22 19 17 15 15 Botswana 173 142 84 58 66 101 120 124 Brazil 176 136 91 57 41 30 21 20 Brunei Darussalam 87 78 22 11 9 9 9 9 Bulgaria 70 32 24 18 18 16 15 14 Burkina Faso 308 287 241 206 194 194 203 204 Burundi 238 244 191 190 183 181 181 181 Cambodia - - 153 116 123 104 85 82 Cameroon 255 215 173 139 151 151 149 149 Canada 33 23 13 8 7 6 6 6 Cape Verde - - 80 60 50 42 35 34 Central African Republic 349 232 189 173 192 186 177 175 Chad - - 228 201 202 205 208 209 Chile 155 98 45 21 14 11 10 9 China - 118 60 45 44 37 25 24 Colombia 122 105 51 35 31 26 21 21

36

Comoros 265 215 165 120 101 84 71 68 Congo 198 142 102 103 110 117 125 126 Congo, Democratic Republic of the 302 245 210 205 205 205 205 205 Cook Islands - - 37 32 29 24 20 19 Costa Rica 123 83 31 18 16 14 12 12 Côte d’Ivoire - 237 169 153 144 136 129 127 Croatia 98 42 23 12 10 8 7 6 Cuba 54 43 22 13 12 9 7 7 Cyprus 36 33 20 12 9 6 5 4 Czech Republic 25 24 19 13 9 5 4 4 Denmark 25 19 10 9 6 6 5 5 Djibouti - - 205 175 161 147 133 130 Dominica - - - 17 18 17 15 15 Dominican Republic 149 127 92 65 53 40 31 29 Ecuador 178 140 98 57 43 32 25 24 Egypt 278 235 176 91 68 51 37 35 El Salvador 191 162 118 60 46 35 27 25 Equatorial Guinea - - - 170 187 200 205 206 Eritrea - 237 192 147 122 97 78 74 Estonia 52 26 24 16 19 11 7 7 Ethiopia 273 241 212 204 179 151 127 123 Fiji - 65 41 22 19 18 18 18 Finland 28 16 9 7 5 4 4 4 France 34 24 13 9 6 5 5 4 Gabon - - 115 92 91 91 91 91 Gambia 360 311 214 153 149 132 116 113 Georgia - - 57 46 41 37 33 32 Germany 40 26 16 9 6 5 5 4 Ghana 212 183 150 120 111 113 119 120 Greece 64 54 23 11 9 7 5 4 Grenada - - - 37 33 26 21 20 Guatemala 202 168 139 82 64 53 43 41 Guinea - 338 282 235 207 184 165 161 Guinea-Bissau - - - 240 233 218 203 200 Guyana - - 106 88 79 70 63 62 Haiti 247 222 200 152 142 109 84 80 Holy See - - - - - - - - Honduras 204 170 102 58 49 40 29 27 Hungary 57 39 26 17 12 11 8 7 Iceland 22 14 8 7 6 3 3 3 India 236 192 156 115 102 89 78 76 Indonesia 216 172 125 91 66 48 36 34 Iran (Islamic Republic of) 281 191 130 72 55 44 36 34 Iraq 158 125 80 53 48 48 47 46 Ireland 36 27 14 10 8 7 5 5 Israel 39 27 19 12 8 7 5 5 Italy 50 33 17 9 7 5 4 4

37

Jamaica 75 62 46 33 33 32 31 31 Japan 40 21 11 6 6 5 4 4 Jordan 139 107 65 40 35 30 26 25 Kazakhstan - - 73 60 60 43 31 29 Kenya 205 156 115 97 111 117 120 121 Kiribati - - - 88 77 70 65 64 Korea, Democratic People's Republic of 120 70 43 55 55 55 55 55 Korea, Republic of 127 54 18 9 6 5 5 5 Kuwait 128 59 35 16 14 11 12 11 Kyrgyzstan - - 110 75 62 51 43 41 Lao People's Democratic Republic 235 218 200 163 131 101 79 75 Latvia 44 26 26 18 21 13 10 9 Lebanon 85 54 44 37 34 32 30 30 Lesotho 203 186 130 101 91 108 132 132 Liberia 288 263 235 235 235 235 235 235 Libyan Arab Jamahiriya 270 160 70 41 28 22 19 18 Liechtenstein - - - 10 8 6 4 3 Lithuania 70 28 22 13 15 11 9 8 Luxembourg 41 26 16 10 5 5 4 4 Madagascar 186 180 175 168 156 137 119 115 Malawi 362 341 266 221 193 155 125 120 Malaysia 113 70 42 22 17 14 12 12 Maldives - 264 168 111 85 54 33 30 Mali 500 400 300 250 233 224 218 217 Malta 42 32 17 11 11 7 6 6 Marshall Islands - - - 92 81 68 58 56 Mauritania 310 250 170 133 127 125 125 125 Mauritius 92 86 42 23 21 18 15 14 Mexico 133 110 77 53 45 39 36 35 Micronesia (Federated States of) - - 65 58 52 47 42 41 Moldova, Republic of - 65 51 37 30 24 20 19 Monaco - - - 9 6 6 5 4 Mongolia - - 128 109 83 62 45 43 Montenegro - - - 16 14 13 11 10 Morocco 211 184 144 89 69 54 40 37 Mozambique 313 278 230 235 212 178 145 138 Myanmar 252 179 134 130 117 110 105 104 Namibia 168 135 108 86 77 69 62 61 Nauru - - - - 30 30 30 30 Nepal 292 238 193 142 118 86 63 59 Netherlands 22 15 11 9 7 6 5 5 New Zealand 26 20 16 11 9 8 6 6 Nicaragua 193 165 113 68 53 43 37 36 Niger 354 330 320 320 295 270 256 253 Nigeria 290 265 228 230 230 207 194 191 Niue - - - - - - - - Norway 23 15 11 9 5 5 4 4

38

Occupied Palestinian Territory - - 65 40 33 27 23 22 Oman 280 200 95 32 19 15 12 12 Pakistan 227 181 153 130 118 108 99 97 Palau - - 32 21 17 14 11 11 Panama 88 68 46 34 30 26 24 23 Papua New Guinea 212 158 118 94 87 80 74 73 Paraguay 94 78 61 41 33 27 23 22 Peru 239 174 121 78 63 41 27 25 Philippines 110 90 81 62 49 40 33 32 Poland 70 36 24 18 15 9 8 7 Portugal 112 62 31 14 10 8 5 5 Qatar 140 65 32 26 24 23 21 21 Romania 82 57 36 31 26 22 19 18 Russian Federation - 40 33 27 27 24 17 16 Rwanda 206 209 213 176 205 183 164 160 Saint Kitts and Nevis - - - 36 30 25 20 19 Saint Lucia - - - 21 20 16 14 14 Saint Vincent and Grenadines - - - 25 22 23 20 20 Samoa 131 101 74 50 41 34 29 28 San Marino - - - 14 8 6 3 3 Sao Tome and Principe 109 106 103 100 99 97 96 96 Saudi Arabia 250 185 85 44 34 29 26 25 Senegal 311 276 213 149 148 133 119 116 Serbia - - - - - 13 9 8 Seychelles 83 59 32 19 16 15 13 13 Sierra Leone 390 368 319 290 282 277 271 270 Singapore 40 27 13 9 5 4 3 3 Slovakia 40 29 23 14 12 10 9 8 Slovenia 45 29 18 10 7 6 4 4 Solomon Islands - - - 121 103 88 75 73 Somalia - - 250 203 183 165 149 145 South Africa - - 91 60 59 63 68 69 Spain 57 34 16 9 7 6 5 4 Sri Lanka 133 100 48 32 25 19 14 13 Sudan 208 172 142 120 106 97 90 89 Suriname - - 56 48 44 41 39 39 Swaziland 225 196 143 110 110 142 160 164 Sweden 20 15 9 7 5 4 4 3 Switzerland 27 18 11 9 6 6 5 5 Syrian Arab Republic 200 128 74 38 27 20 15 14 Tajikistan - 140 127 115 114 93 71 68 Tanzania, United Republic of 241 218 175 161 159 141 122 118 Thailand 148 102 59 31 20 13 8 8 The former Yugoslav Republic of Macedonia 177 119 70 38 26 16 17 17 Timor-Leste - - - 177 154 107 61 55 Togo 264 219 177 149 139 124 111 108

39

Tonga 65 50 39 32 29 26 24 24 Trinidad and Tobago 71 54 41 34 33 34 37 38 Tunisia 254 201 100 52 40 31 24 23 Turkey 219 201 133 82 63 44 29 26 Turkmenistan - - 126 99 88 71 54 51 Tuvalu - - 67 54 48 43 38 38 Uganda 224 170 185 160 156 145 136 134 Ukraine - 36 30 25 23 23 24 24 United Arab Emirates 222 84 33 15 12 10 9 8 United Kingdom 27 23 14 10 7 6 6 6 United States 30 26 15 12 9 9 8 8 Uruguay 61 56 42 23 22 16 13 12 Uzbekistan - - 108 74 68 62 46 43 Vanuatu 209 155 107 62 50 48 38 36 Venezuela (Bolivarian Rep. of) 79 62 46 33 28 25 21 21 Viet Nam 112 87 66 53 44 30 19 17 Yemen 340 303 205 139 122 110 102 100 Zambia 213 181 155 180 182 182 182 182 Zimbabwe 158 135 108 76 99 105 105 105

40

Annex 2. Estimated infant mortality rates (IMR) from 1960 to 2006, by country

Infant mortality rate

Country

1960 1970 1980 1990 1995 2000 2005 2006

Afghanistan 245 215 185 168 165 165 165 165Albania 105 78 55 37 29 22 16 15Algeria 166 143 94 54 43 37 34 33Andorra - - - 5 - 3 3 3Angola 208 180 158 154 154 154 154 154Antigua and Barbuda - - - - 18 13 11 10Argentina 61 59 36 25 20 17 14 14Armenia - - 62 47 41 32 23 21Australia 20 17 11 8 6 5 5 5Austria 37 26 14 8 5 5 4 4Azerbaijan - - 95 84 80 77 74 73Bahamas 51 38 28 22 18 15 13 13Bahrain 94 55 23 15 13 10 9 9Bangladesh 149 145 129 100 83 66 54 52Barbados 74 40 22 15 13 12 11 11Belarus - - 22 20 18 15 12 12Belgium 31 21 12 8 6 5 4 4Belize - - 54 35 26 20 15 14Benin 176 149 127 111 102 95 89 88Bhutan 175 156 135 107 93 77 65 63Bolivia 152 147 115 89 76 63 52 50Bosnia and Herzegovina 105 60 31 18 16 14 13 13Botswana 118 99 62 45 50 74 87 90Brazil 115 95 70 48 36 27 20 19Brunei Darussalam 63 58 19 10 8 8 8 8Bulgaria 49 28 20 14 14 14 12 12Burkina Faso 183 170 143 123 116 116 121 122Burundi 141 144 114 114 110 109 109 109Cambodia - - 104 85 89 78 67 65Cameroon 151 127 105 85 89 88 87 87Canada 28 19 11 7 6 5 5 5Cape Verde - - 61 45 37 31 26 25Central African Republic 198 141 121 114 123 120 115 115Chad - - 135 120 121 122 124 124Chile 118 78 35 18 13 10 8 8China - 84 47 36 35 30 21 20Colombia 77 68 37 26 24 20 17 17Comoros 200 159 120 88 74 62 53 51Congo 118 88 66 67 70 74 79 79Congo, Democratic Republic of the 174 148 133 129 129 129 129 129Cook Islands - - 28 26 24 20 17 16Costa Rica 87 62 26 16 14 13 11 11Côte d'Ivoire - 158 115 105 100 95 91 90 41

Croatia 70 34 20 10 8 7 6 5Cuba 37 34 22 11 9 7 5 5Cyprus 30 29 18 11 8 5 4 3Czech Republic 22 21 17 11 7 4 3 3Denmark 22 14 9 7 5 5 4 4Djibouti - - 134 116 106 97 88 86Dominica - - - 15 15 15 13 13Dominican Republic 102 91 71 50 42 33 26 25Ecuador 107 87 64 43 34 27 22 21Egypt 185 157 119 67 52 40 31 29El Salvador 129 111 84 47 37 29 23 22Equatorial Guinea - - - 103 112 120 123 124Eritrea - 143 116 88 74 61 50 48Estonia 40 21 20 12 15 9 6 5Ethiopia 162 142 126 122 107 92 80 77Fiji - 50 33 19 17 16 16 16Finland 22 13 8 6 4 4 3 3France 29 18 10 7 5 4 4 4Gabon - - 73 60 60 60 60 60Gambia 204 180 133 103 102 94 86 84Georgia - - 48 39 36 32 29 28Germany 34 22 13 7 5 4 4 4Ghana 126 110 92 76 71 72 75 76Greece 53 38 20 9 8 6 4 4Grenada - - - 30 26 21 17 16Guatemala 136 115 97 60 49 39 32 31Guinea - 202 167 139 123 111 100 98Guinea-Bissau - - - 142 138 129 121 119Guyana - - 77 64 58 52 47 46Haiti 165 149 134 105 99 79 63 60Holy See - - - - - - - -Honduras 137 116 74 45 39 32 24 23Hungary 51 36 24 15 11 9 6 6Iceland 17 13 8 5 4 3 2 2India 158 130 107 82 73 66 59 57Indonesia 128 104 79 60 48 36 28 26Iran (Islamic Republic of) 164 122 92 54 43 36 31 30Iraq 109 89 60 42 38 38 37 37Ireland 31 20 12 8 6 6 4 4Israel 32 24 16 10 7 6 4 4Italy 44 30 15 8 6 5 4 4Jamaica 56 48 36 28 27 26 26 26Japan 31 14 8 5 4 3 3 3Jordan 97 77 52 33 29 25 22 21Kazakhstan - - 60 51 50 37 27 26Kenya 122 96 73 64 72 77 79 79Kiribati - - - 65 57 52 48 47Korea, Democratic People's Republic of 85 52 32 42 42 42 42 42Korea, Republic of 90 43 16 8 6 5 5 5Kuwait 89 49 29 14 11 9 10 9 42

Kyrgyzstan - - 90 63 53 44 37 36Lao People's Democratic Republic 155 145 135 120 99 77 62 59Latvia 35 21 21 14 18 11 8 8Lebanon 65 45 38 32 30 28 27 26Lesotho 151 140 101 81 73 86 102 102Liberia 190 180 157 157 157 157 157 157Libyan Arab Jamahiriya 159 105 55 35 25 20 18 17Liechtenstein - - - 9 7 5 3 3Lithuania 52 23 19 10 12 8 7 7Luxembourg 33 19 12 8 5 4 4 4Madagascar 112 109 106 103 95 84 74 72Malawi 218 204 158 131 115 95 79 76Malaysia 72 46 31 16 13 11 10 10Maldives - 161 110 78 62 43 28 26Mali 285 225 176 140 131 124 120 119Malta 37 25 14 10 9 6 5 5Marshall Islands - - - 63 59 55 51 50Mauritania 182 151 108 85 81 79 78 78Mauritius 67 64 33 21 19 16 13 13Mexico 93 79 58 42 36 32 30 29Micronesia (Federated States of) - - 50 45 41 37 34 33Moldova, Republic of - 50 41 30 25 21 17 16Monaco - - - 7 6 5 4 3Mongolia - - 90 79 62 48 36 34Montenegro - - - 15 12 11 9 9Morocco 132 119 99 69 56 45 36 34Mozambique 183 168 149 158 145 122 100 96Myanmar 169 122 94 91 83 78 75 74Namibia 102 85 71 60 55 50 46 45Nauru - - - - 25 25 25 25Nepal 195 159 130 99 84 64 49 46Netherlands 18 13 9 7 5 5 4 4New Zealand 22 17 13 9 7 6 5 5Nicaragua 130 113 82 52 41 34 30 29Niger 211 197 191 191 176 159 150 148Nigeria 165 140 117 120 120 107 100 99Niue - - - - - - - -Norway 19 13 9 7 4 4 3 3Occupied Palestinian Territory - - 55 34 28 24 21 20Oman 164 126 73 25 15 12 10 10Pakistan 139 120 110 100 93 85 79 78Palau - - 27 18 15 13 10 10Panama 58 46 34 27 23 20 19 18Papua New Guinea 142 110 84 69 64 60 55 54Paraguay 68 58 46 33 28 23 20 19Peru 160 119 86 58 48 33 23 21Philippines 69 56 50 41 35 30 25 24Poland 62 32 21 16 13 8 6 6Portugal 81 53 25 11 7 6 4 3Qatar 94 45 25 21 20 19 18 18 43

Romania 69 46 29 23 21 19 16 16Russian Federation - 33 27 23 23 20 15 14Rwanda 122 125 126 106 122 110 100 98Saint Kitts and Nevis - - - 30 25 21 18 17Saint Lucia - - - 17 16 14 13 12Saint Vincent and Grenadines - - - 20 18 19 17 17Samoa 92 73 56 40 33 28 24 23San Marino - - - 13 8 6 3 3Sao Tome and Principe 69 68 66 65 64 64 63 63Saudi Arabia 150 118 65 35 27 23 21 21Senegal 124 114 94 72 72 66 61 60Serbia - - - - - 11 8 7Seychelles 62 46 27 17 14 13 12 12Sierra Leone 221 208 183 169 165 162 160 159Singapore 31 22 11 7 4 3 2 2Slovakia 33 25 20 12 11 8 7 7Slovenia 37 25 16 8 6 5 3 3Solomon Islands - - - 86 75 65 56 55Somalia - - 148 121 110 100 91 90South Africa - - 64 45 45 50 55 56Spain 46 27 13 7 5 4 4 4Sri Lanka 83 65 36 26 21 16 12 11Sudan 123 104 86 74 69 65 62 61Suriname - - 40 35 33 31 30 29Swaziland 150 132 99 78 78 98 110 112Sweden 16 11 7 6 4 3 3 3Switzerland 22 15 9 7 5 5 4 4Syrian Arab Republic 134 90 56 31 23 17 13 12Tajikistan - 108 99 91 90 75 59 56Tanzania, United Republic of 142 129 106 102 100 88 76 74Thailand 103 74 46 26 17 11 8 7The former Yugoslav Republic of Macedonia

120 85 52 33 23 14 15 15

Timor-Leste - - - 133 118 85 52 47Togo 156 123 100 88 86 78 71 69Tonga 50 40 32 26 24 22 20 20Trinidad and Tobago 59 46 36 30 29 30 32 33Tunisia 170 135 72 41 32 25 20 19Turkey 163 150 103 67 52 38 26 24Turkmenistan - - 105 81 71 59 47 45Tuvalu - - 51 42 38 35 31 31Uganda 133 100 107 93 92 85 79 78Ukraine - 30 25 22 20 19 20 20United Arab Emirates 149 63 27 13 11 9 8 8United Kingdom 23 18 12 8 6 6 5 5United States 26 20 13 10 8 7 7 6Uruguay 51 48 37 20 19 14 12 11Uzbekistan - - 86 61 57 52 40 38Vanuatu 141 107 77 48 40 38 31 30Venezuela (Bolivarian Rep. of) 59 48 37 27 24 21 18 18 44

Viet Nam 70 55 44 38 32 23 16 15Yemen 225 202 135 98 89 81 76 75Zambia 126 109 90 101 102 102 102 102Zimbabwe 96 84 70 52 64 68 68 68 45

Annex 3. Average annual rate of reduction in under-five mortality and progress towards the MDG target, by country

Under-five mortality rate (U5MR)

Average annual rate of reduction (%)

Country 1990 2006

MDG target

for 2015

Observed: 1990-2006

Required to meet MDG

target: 2006-2015

Progress towards the MDG target*

Afghanistan 260 257 87 0.1 12.1 no progress Albania 45 17 15 6.1 1.4 on track Algeria 69 38 23 3.7 5.6 on track Andorra 6 3 2 4.3 4.5 on track Angola 260 260 87 0.0 12.2 no progress Antigua and Barbuda - 11 - - - - Argentina 29 16 10 3.7 5.6 on track Armenia 56 24 19 5.3 2.8 on track Australia 10 6 3 3.2 6.6 on track Austria 10 5 3 4.3 4.6 on track Azerbaijan 105 88 35 1.1 10.2 insufficient Bahamas 29 14 10 4.6 4.1 on track Bahrain 19 10 6 4.0 5.1 on track Bangladesh 149 69 50 4.8 3.6 on track Barbados 17 12 6 2.2 8.3 on track Belarus 24 13 8 3.8 5.4 on track Belgium 10 4 3 5.7 2.1 on track Belize 43 16 14 6.2 1.2 on track Benin 185 148 62 1.4 9.7 insufficient Bhutan 166 70 55 5.4 2.6 on track Bolivia 125 61 42 4.5 4.2 on track Bosnia and Herzegovina 22 15 7 2.4 8.0 on track Botswana 58 124 19 -4.7 20.7 no progress Brazil 57 20 19 6.5 0.6 on track Brunei Darussalam 11 9 4 1.3 9.9 on track Bulgaria 18 14 6 1.6 9.4 on track Burkina Faso 206 204 69 0.1 12.1 no progress Burundi 190 181 63 0.3 11.7 no progress Cambodia 116 82 39 2.2 8.3 insufficient Cameroon 139 149 46 -0.4 13.0 no progress Canada 8 6 3 1.8 8.9 on track Cape Verde 60 34 20 3.5 5.9 on track Central African Republic 173 175 58 -0.1 12.3 no progress Chad 201 209 67 -0.2 12.6 no progress Chile 21 9 7 5.3 2.8 on track China 45 24 15 3.9 5.2 on track Colombia 35 21 12 3.2 6.5 on track

46

Comoros 120 68 40 3.5 5.9 insufficient Congo 103 126 34 -1.3 14.5 no progress Congo, Democratic Republic of the 205 205 68 0.0 12.2 no progress Cook Islands 32 19 11 3.3 6.4 on track Costa Rica 18 12 6 2.5 7.7 on track Côte d'Ivoire 153 127 51 1.2 10.1 insufficient Croatia 12 6 4 4.3 4.5 on track Cuba 13 7 4 3.9 5.4 on track Cyprus 12 4 4 6.9 0.0 on track Czech Republic 13 4 4 7.4 -0.8 on track Denmark 9 5 3 3.7 5.7 on track Djibouti 175 130 58 1.9 8.9 insufficient Dominica 17 15 6 0.8 10.8 on track Dominican Republic 65 29 22 5.0 3.2 on track Ecuador 57 24 19 5.4 2.6 on track Egypt 91 35 30 6.0 1.6 on track El Salvador 60 25 20 5.5 2.5 on track Equatorial Guinea 170 206 57 -1.2 14.3 no progress Eritrea 147 74 49 4.3 4.6 on track Estonia 16 7 5 5.2 3.1 on track Ethiopia 204 123 68 3.2 6.6 insufficient Fiji 22 18 7 1.3 10.0 on track Finland 7 4 2 3.5 6.1 on track France 9 4 3 5.1 3.2 on track Gabon 92 91 31 0.1 12.1 no progress Gambia 153 113 51 1.9 8.8 insufficient Georgia 46 32 15 2.3 8.2 on track Germany 9 4 3 5.1 3.2 on track Ghana 120 120 40 0.0 12.2 no progress Greece 11 4 4 6.3 0.9 on track Grenada 37 20 12 3.8 5.4 on track Guatemala 82 41 27 4.3 4.5 on track Guinea 235 161 78 2.4 8.0 insufficient Guinea-Bissau 240 200 80 1.1 10.2 insufficient Guyana 88 62 29 2.2 8.3 insufficient Haiti 152 80 51 4.0 5.1 on track Holy See - - - - - - Honduras 58 27 19 4.8 3.7 on track Hungary 17 7 6 5.5 2.3 on track Iceland 7 3 2 5.3 3.0 on track India 115 76 38 2.6 7.6 insufficient Indonesia 91 34 30 6.2 1.3 on track Iran (Islamic Republic of) 72 34 24 4.7 3.9 on track Iraq 53 46 18 0.9 10.6 no progress Ireland 10 5 3 4.3 4.6 on track Israel 12 5 4 5.5 2.5 on track Italy 9 4 3 5.1 3.2 on track

47

Jamaica 33 31 11 0.4 11.5 on track Japan 6 4 2 2.5 7.7 on track Jordan 40 25 13 2.9 7.0 on track Kazakhstan 60 29 20 4.5 4.1 on track Kenya 97 121 32 -1.4 14.7 no progress Kiribati 88 64 29 2.0 8.7 insufficient Korea, Democratic People's Republic of 55 55 18 0.0 12.2 no progress Korea, Republic of 9 5 3 3.7 5.7 on track Kuwait 16 11 5 2.3 8.1 on track Kyrgyzstan 75 41 25 3.8 5.5 insufficient Lao People's Democratic Republic 163 75 54 4.9 3.6 on track Latvia 18 9 6 4.3 4.5 on track Lebanon 37 30 12 1.3 9.9 on track Lesotho 101 132 34 -1.7 15.2 no progress Liberia 235 235 78 0.0 12.2 no progress Libyan Arab Jamahiriya 41 18 14 5.1 3.0 on track Liechtenstein 10 3 3 7.5 -1.1 on track Lithuania 13 8 4 3.0 6.9 on track Luxembourg 10 4 3 5.7 2.1 on track Madagascar 168 115 56 2.4 8.0 insufficient Malawi 221 120 74 3.8 5.4 insufficient Malaysia 22 12 7 3.8 5.5 on track Maldives 111 30 37 8.2 -2.3 on track Mali 250 217 83 0.9 10.6 no progress Malta 11 6 4 3.8 5.4 on track Marshall Islands 92 56 31 3.1 6.7 insufficient Mauritania 133 125 44 0.4 11.5 no progress Mauritius 23 14 8 3.1 6.6 on track Mexico 53 35 18 2.6 7.6 on track Micronesia (Federated States of) 58 41 19 2.2 8.4 insufficient Moldova, Republic of 37 19 12 4.2 4.8 on track Monaco 9 4 3 5.1 3.2 on track Mongolia 109 43 36 5.8 1.9 on track Montenegro 16 10 5 2.9 7.1 on track Morocco 89 37 30 5.5 2.4 on track Mozambique 235 138 78 3.3 6.3 insufficient Myanmar 130 104 43 1.4 9.7 insufficient Namibia 86 61 29 2.1 8.4 insufficient Nauru - 30 - - - - Nepal 142 59 47 5.5 2.5 on track Netherlands 9 5 3 3.7 5.7 on track New Zealand 11 6 4 3.8 5.4 on track Nicaragua 68 36 23 4.0 5.1 on track Niger 320 253 107 1.5 9.6 insufficient Nigeria 230 191 77 1.2 10.1 insufficient Niue - - - - - - Norway 9 4 3 5.1 3.2 on track

48

Occupied Palestinian Territory 40 22 13 3.7 5.6 on track Oman 32 12 11 6.1 1.3 on track Pakistan 130 97 43 1.8 9.0 insufficient Palau 21 11 7 4.0 5.0 on track Panama 34 23 11 2.4 7.9 on track Papua New Guinea 94 73 31 1.6 9.4 insufficient Paraguay 41 22 14 3.9 5.3 on track Peru 78 25 26 7.1 -0.4 on track Philippines 62 32 21 4.1 4.8 on track Poland 18 7 6 5.9 1.7 on track Portugal 14 5 5 6.4 0.7 on track Qatar 26 21 9 1.3 9.8 on track Romania 31 18 10 3.4 6.2 on track Russian Federation 27 16 9 3.3 6.4 on track Rwanda 176 160 59 0.6 11.1 no progress Saint Kitts and Nevis 36 19 12 4.0 5.1 on track Saint Lucia 21 14 7 2.5 7.7 on track Saint Vincent and Grenadines 25 20 8 1.4 9.8 on track Samoa 50 28 17 3.6 5.7 on track San Marino 14 3 5 9.6 -5.0 on track Sao Tome and Principe 100 96 33 0.3 11.8 no progress Saudi Arabia 44 25 15 3.5 5.9 on track Senegal 149 116 50 1.6 9.4 insufficient Serbia - 8 - - - - Seychelles 19 13 6 2.4 8.0 on track Sierra Leone 290 270 97 0.4 11.4 no progress Singapore 9 3 3 6.9 0.0 on track Slovakia 14 8 5 3.5 5.9 on track Slovenia 10 4 3 5.7 2.1 on track Solomon Islands 121 73 40 3.2 6.6 insufficient Somalia 203 145 68 2.1 8.5 insufficient South Africa 60 69 20 -0.9 13.8 no progress Spain 9 4 3 5.1 3.2 on track Sri Lanka 32 13 11 5.6 2.2 on track Sudan 120 89 40 1.9 8.9 insufficient Suriname 48 39 16 1.3 9.9 on track Swaziland 110 164 37 -2.5 16.6 no progress Sweden 7 3 2 5.3 3.0 on track Switzerland 9 5 3 3.7 5.7 on track Syrian Arab Republic 38 14 13 6.2 1.1 on track Tajikistan 115 68 38 3.3 6.4 insufficient Tanzania, United Republic of 161 118 54 1.9 8.7 insufficient Thailand 31 8 10 8.5 -2.8 on track The former Yugoslav Republic of Macedonia 38 17 13 5.0 3.2 on track Timor-Leste 177 55 59 7.3 -0.8 on track Togo 149 108 50 2.0 8.6 insufficient

49

Tonga 32 24 11 1.8 9.0 on track Trinidad and Tobago 34 38 11 -0.7 13.5 on track Tunisia 52 23 17 5.1 3.2 on track Turkey 82 26 27 7.2 -0.5 on track Turkmenistan 99 51 33 4.1 4.8 on track Tuvalu 54 38 18 2.2 8.3 on track Uganda 160 134 53 1.1 10.2 insufficient Ukraine 25 24 8 0.3 11.8 on track United Arab Emirates 15 8 5 3.9 5.2 on track United Kingdom 10 6 3 3.2 6.6 on track United States 12 8 4 2.5 7.7 on track Uruguay 23 12 8 4.1 4.9 on track Uzbekistan 74 43 25 3.4 6.2 insufficient Vanuatu 62 36 21 3.4 6.1 on track Venezuela (Bolivarian Rep. of) 33 21 11 2.8 7.2 on track Viet Nam 53 17 18 7.1 -0.4 on track Yemen 139 100 46 2.1 8.6 insufficient Zambia 180 182 60 -0.1 12.3 no progress Zimbabwe 76 105 25 -2.0 15.8 no progress

*On track is defined as either:

(1) U5MR < 40 deaths per 1,000 or (2) U5MR ≥ 40 per 1,000 and AARR ≥ 4%

Insufficient progress is defined as: U5MR ≥ 40 per 1,000 and 1% ≤AARR < 4%

No progress is defined as U5MR ≥ 40 deaths per 1,000 and AARR < 1% 50

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Hill K, Pande R, Mahy M, Jones, G. Trends in Child Mortality in the Developing World: 1960 to 1996. New York: UNICEF; 1999. Jones G. Estimation of child mortality: 1960 to present. New York: Inter-agency Group for Child Mortality Estimation; 2007. Jones G, Steketee RW, Black RE, Butta ZA, Morris SS, and the Bellagio Child Survival Study Group. How many child deaths can we prevent this year? Lancet 2003;362:65-71. Inter-agency Group for Child Mortality Estimation. Coordinating meeting on mortality estimation, New York, July 5-7, 2006. New York: UNICEF; 2006. International Household Survey Network (IHSN). Overview of activities. Washington, DC: IHSN; 2007. Available at http://www.surveynetwork.org. Mahapatra P, Shibuya K, Lopez AD, Coullare F, Notzon FC, Rao C, Szreter S, on behalf of the Monitoring Vital Events (MoVE) writing group. Civil registration systems and vital statistics: successes and missed opportunities. Lancet 2007;370:1653-1663. Mathers CD, Fat DM, Inoue M, Rao, C, Lopez AD. Counting the dead and what they died from: an assessment of the global status of cause of death data. Bull World Health Organ 2005; 83(3):171-77. MEASURE DHS. DHS surveys. Calverton, MD: Macro International Inc.; 2007. Available at http://www.measuredhs.com/aboutsurveys/dhs/start.cfm Mukelabai K. Achieving the Millennium Development Goal to reduce under-five child mortality: a UNICEF perspective. Proceedings of the Seminar on the Relevance of Population Aspects for the Achievement of the Millennium Development Goals, New York, 17-19 November, 2004. New York: United Nations; 2005. Available at http://www.un.org/esa/population/publications/PopAspectsMDG/16_UNICEF1.pdf Murray CJL. Towards good practice for health statistics: lessons from the Millennium Development Goal health indicators. Lancet 2007;369:862-873. Murray CJK, Loakso T, Shibuya K, Hill K, Lopez AD. Can we achieve Millennium Development Goal 4? New analysis of country trends and forecasts of under-5 mortality to 2015. Lancet 2007;370:1040-1054. National Statistical Office of Mongolia. Mongolia Multiple Indicator Cluster Survey 2005-2006, Key Findings. Ulaanbaatar, Mongolia: National Statistical Office of Mongolia; 2007. Rutstein SO, Rojas G. Guide to DHS Statistics. Calverton, MD: Demographic and Health Surveys, ORC Macro; 2006. Setel PW, Macfarlane SB, Szreter S, Mikkelsen L, Jha P, Stout S, AbouZahr C, on behalf of the Monitoring of Vital Events (MoVE) writing group. A scandal of invisibility: making everyone count by counting everyone. Lancet 2007;370:1569-77. 52

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Member agencies of the Inter-agency Group for Child Mortality Estimation are:

World Health Organization, WHO The World Bank

United Nations Population Division, UNPD

www.childmortality.org www.childinfo.org

Levels and Trends of Child Mortality in 2006

[Working Paper]

Estimates developed by the Inter-agency Group for Child Mortality Estimation

United Nations Children’s Fund, UNICEF