the development process involves substantial health , economic , and social changes , which affect directly the lives of billions of people around the world . the millennium development goals ( mdg ) set up by the united nations around year 2000 were targets associated with a selection of these changes and were based on relevant indicators measuring progress in development . some of these targets were economic ( poverty , hunger ) or social ( primary education , gender equality , women empowerment ) , and several were related to health ( mortality , morbidity ) . among the latter category , the fourth goal ( mdg-4 ) targets the mortality of young children . as stated in the original document , mdg-4 proposed to reduce by two thirds , over a 25-year period ( 1990 - 2015 ) , the under-5 mortality rate ( u5mr ) , expressed as the number of deaths of children under the age of 5 years per 1000 live births . the u5mr is one of the leading indicators of the health transition and focuses on the age group in which mortality is the highest in high mortality populations , outside of very old ages . this indicator varies from some 500 per 1000 and above in very high mortality situations to 5 per 1000 and below in low mortality situations . this very wide range of variations , a scale from 1 to 100 , makes it highly sensitive to measure progress in the health sector . mortality decline is primarily the result of modern public health ( hygiene , preventive medicine , curative medicine , nutrition ) and is highly responsive to health services and programs . so levels and trends in mortality are choice indicators for monitoring progress in the health sector . however , mortality levels and trends also depend on external circumstances , such as emerging infectious diseases ( eg , hiv / aids , resistant malaria ) , health and environmental hazards ( eg , industrial pollution , pesticides ) , behavior ( eg , smoking , alcoholism ) , natural disasters ( eg , drought , tsunami ) , occupational hazards ( eg , mines , industry ) , man - made disasters ( eg , wars , civil unrest , famine ) , as well as social circumstances ( eg , poverty , discrimination ) . so mortality levels and trends may reflect not only progresses in public health but also the local circumstances , whether favorable or unfavorable . the aim of this article is to review evidence of changes in under-5 mortality in sub - saharan africa and their implications for evaluating mdg-4 . several essays of reconstructing mortality levels and trends were considered : those made by international organizations and those made by the author . the main period investigated was from 1985 to 2010 , and comparison with the previous 25 years ( 1960 - 1985 ) is also provided . several papers have already addressed this issue of evaluating mdg-4 , but without discussing in detail data quality and local circumstances . most sub - saharan african countries have no reliable registration of births and deaths , as developed countries , and therefore have no time series that could be used for investigating changes in under-5 mortality . there are a few exceptions with comprehensive vital registration , in particular islands ( eg , mauritius , reunion , cape verde ) . in continental africa , only south africa now has high level of coverage of registration of births and deaths , but this is a recent situation ( since the mid-2000s ) , and registration is still incomplete for deaths of infants and children . so for sub - saharan africa one has to rely on other sources for investigating mortality trends . two major sources of data have been used in the past : population censuses and sample demographic surveys , which have their own specific features and weaknesses . censuses provide a full coverage of mortality for the country as a whole , but data tend to be of lower quality , simply because population censuses are huge operations conducted in a short period of time during which data quality is hard to maintain . in contrast , demographic surveys provide mortality estimates for only a sample of households . data quality tends to be higher because more time is devoted to data collection and quality control , but mortality estimates are hampered by statistical variations due to the sample size ( usually a few thousands of households ) and due to the sampling scheme ( usually stratified cluster sampling ) . several large programs of censuses and surveys have provided mortality data in african countries : the decennial census program ( since the 1960s ) , the insee survey program ( 1960s and 1970s ) , the world fertility survey program ( 1974 - 1983 ) , the measure - dhs program ( in place since 1985 ) , the mics survey program ( since the mid-1990s ) , and a few others . these surveys vary in data collection methods , in data quality , and in geographical and time coverage . censuses often rely on deaths in the 12 months preceding the census , which may provide reliable estimates when great care is devoted to the precise timing of the death and to the age at death , but might be seriously distorted otherwise . censuses and some surveys ( such as mics surveys ) often rely on indirect methods , based on children ever - born and children surviving to women in their reproductive ages . this method has serious shortcomings , in particular because it can not provide a point estimate , but only a weighted average of many point estimates . furthermore , in order to convert the proportion of children who died into standard under-5 death rates , several hypotheses are required , in particular regular mortality change , no fertility change , and use of reliable model life tables . violation of theses hypotheses , and possible underdeclaration of children who died in early infancy , often lead to estimates that are far from true values . the most reliable method of data collection is the recording of full births histories , with precise dates of birth and dates of death , as done in dhs surveys . these data provide robust estimates of levels and trends in under-5 mortality in the years preceding the survey . in certain cases , they may be somewhat biased , because they are based on births to women aged 15 to 49 at time of survey , so births that occurred during the same time period to older women are missed . this tends to overestimate mortality levels in earlier periods , because children born to younger women have higher mortality , and therefore overestimate declining trends . but the most critical issue of these mortality estimates derived from dhs surveys is the sample size and the sampling scheme . the dhs surveys are the most useful source for the purpose of monitoring levels and trends , and are presented in detail elsewhere . an average dhs survey in sub - saharan africa is based on a sample of 7750 households , 24 400 births , 4200 deaths , of which some 1120 are deaths of under-5 children in the past 5-years , the basis of under-5 mortality point estimates . as a result , an average level of under-5 mortality of 128 per 1000 is given with a standard error of 5.85 per 1000 ( of which about 63% is due to sample size and the rest to design effect ) , that is , a precision of about 10% ( range 5% to 35% ) . these sampling variations imply that 2 surveys conducted 5 years apart do not permit to assess a 10% mortality change , which is the order of magnitude of expected mortality decline over 5 years . however , beyond point estimates , the same dhs surveys allow to measure mortality trends with much higher precision . details on the method to investigate trends are given elsewhere . in brief , trends are estimated directly from each yearly data point of u5mr and fitted with a regression line . point estimates are notoriously imprecise , because of data collection , method of analysis , and sample size . mortality trends are usually better documented , but here again with variations depending on the source . some institutions use a mix of sources and methods ( census and surveys , direct and indirect methods ) , which is often confusing because the differences between sources may be larger than real differences in mortality levels . this analysis focuses on a reconstruction of under-5 mortality trends based solely on dhs survey data and direct calculations . furthermore , despite similar methods of data collection and data analysis , one sometimes finds inconsistencies between surveys in the same country , usually because of different sampling schemes , and sometimes because of varying data quality . as will be seen in the results section , the reconstruction using a robust method may lead to conclusions different from that obtained with strategies mixing data sources and calculation methods , with major implications for evaluating mdg-4 . this section presents variations in levels and trends in under-5 mortality and consequences for evaluating mdg-4 from 4 sources : reconstruction of trends from dhs data available at ferdi , estimates made by the united nations population division ( unpd ) , estimates made by unicef and the world health organization ( who ) under the inter - agency group for mortality estimation ( igme ) , and estimates made by the institute for health metrics and evaluation ( ihme ) . this study is restricted to 35 countries with available dhs data , for the 1960 to 2010 period , and divided into two 25-year periods : 1960 to 1985 and 1985 to 2010 . the second period is used for testing the capacity of available data to evaluate mortality changes . the mdg-4 targeted originally the 1990 to 2015 period , but data for this period will be available only around year 2020 , and the point of this article is simply to show the difficulties in pursuing such an evaluation . the first 2 data sets are complete for the whole 1960 to 2010 period , but the igme set is incomplete for many countries before 1980 , and the ihme set starts only in 1970 . overall , for the 35 countries considered together , mortality decline has been impressive over the 1960 to 2010 period ( figure 1 ) . estimates vary somewhat between the first 3 sources , but the picture is the same : a steady decline at average speed ( about 1.7% per year ) from 1960 to 1985 , a slower decline from 1985 to 1997 ( about 1.2% per year ) , and a faster decline from 1997 to 2010 ( about 3.7% a year ) . overall , under-5 mortality was reduced by 65% in 50 years , from 275 per 1000 in 1960 to 96 per 1000 in 2010 . only the ihme estimates differed , with a lower slope , due to lower levels in 1970 ( 210 per 1000 ) and higher levels in 2010 ( 139 per 1000 ) , the main discrepancies being in the last 10 years . the overall mortality decline in africa can be compared with that in sweden a century before ( 1860 - 1910 ) , where the decline was similar in levels and trends , from 240 per 1000 in 1860 to 117 per 1000 in 1910 , a 51% reduction in 50 years . in africa , mortality decline in the last 25 years ( 1985 - 2010 ) , although impressive , failed to meet the ambitious mdg-4 ( 47% mortality reduction instead of 67% targeted ) . trends in under-5 mortality in 35 sub - saharan african countries , 1960 - 2010 ( by source ) , and comparison with expectations from mdg-4 , and with sweden ( 1860 - 1910 ) . if the overall pattern emerged clearly , variations between sources were large and were due to estimation methods and data interpretation ( table 1 ) . not only slopes differed in absolute value between sources , but even the sign of the slope ( positive or negative ) was different in a number of cases . in the 2 congos , the slope was positive ( mortality increase ) in the ihme estimates but negative in the other sources ; in lesotho , in swaziland , and in zimbabwe , 2 sources found a mortality increase , but the other 2 found a mortality decline ; in nigeria the ihme estimates indicated a mortality increase , contrary to the other 3 sources . in all these cases , the dhs yearly data indicated no change in mortality trends in the recent period : steady mortality increase in lesotho , swaziland , and zimbabwe and steady mortality decline in the 2 congos and in nigeria , whereas indirect estimates from various sources were confusing . in the 3 southern african countries , mortality increase was obviously due to hiv / aids , before prevention and care could inverse the increasing mortality trends , as already happening in south africa and namibia after 2005 . in nigeria , the 3 recent dhs surveys ( 2008 , 2010 , 2013 ) all indicated a steady mortality decline . in congo - brazza , the last 2012 dhs survey also indicated a steady mortality decline , as was the case in congo - kinshasa with the 2013 dhs survey . estimates of change in under-5 mortality from 1985 to 2010 according to sources . abbreviations : igme , inter - agency group for mortality estimation ; who , world health organization ; ihme , institute for health metrics and evaluation . beyond the direction of the trends , serious discrepancies could also be noted in the magnitude of the slope between sources , with consequences for evaluating mdg-4 . this happened for instance in south africa , because of the complex changes in mortality , with major increase ( due to increasing hiv / aids prevalence ) followed by major decrease in mortality ( due to prevention and treatment of hiv ) : if 3 sources ( but ihme ) agreed on the pattern , they differed somewhat in the timing and the magnitude of the changes , with consequences for the average change over the 1985 to 2010 period . , the first 2 sources agreed on the speed of the mortality decline , whereas the third source indicated a stronger mortality reduction ( 30% vs 20% ) . as a consequence , the number of countries meeting goals such as 30% , 40% , or 50% mortality reduction differed markedly by source . for instance , out of the 35 countries investigated , 15 countries were meeting the 50% mortality reduction from 1985 to 2010 according to the first source , 8 countries with the second ( unpd ) , 13 countries with the third ( igme ) , and only 3 countries with the last source ( ihme ) . these differences reveal the difficulty in evaluating mdg-4 from imperfect data ( table 1 ) . a goal such as mdg-4 assumes implicitly a steady mortality decline and targets its speed as an indicator of regular progress in public health services and programs . but steady mortality decline was not a feature of most african countries . among the 35 countries investigated , examples of such countries are shown on figure 2 . even in these smooth cases , there were at times variations in the speed of mortality decline , reflecting a variety of difficulties in each case , which complicate the trend analysis between 1985 and 2010 . selected countries with steady under-5 mortality decline , sub - saharan africa ( from dhs data ) . the other countries endured periods of mortality increase because of political crises , economic crises , or social crises ( 19 cases ) or because emerging or resurging diseases ( 11 cases ) , namely , hiv / aids and malaria . erratic trends complicate the interpretation of mortality changes over a fixed period of time , such as 1985 to 2010 . figure 3 displays selected countries with large fluctuations in mortality trends . in rwanda , the civil war period ( 1991 - 1999 ) was associated with major disruption in already erratic trends . in zambia , the copper crisis period ( 1975 - 1992 ) and hiv hiv / aids induced drastic increases in mortality . in congo - brazza , as in congo - kinshasa , major political crises induced large fluctuations in under-5 mortality . many other countries ( not shown ) also underwent serious disruption in mortality trends for similar reasons . these chaotic situations hamper any simple trend analysis and make the evaluation of mdg-4 difficult without taking the local context into account . selected countries with erratic under-5 mortality decline , sub - saharan africa ( from dhs data ) . furthermore , trend analysis between 2 points such as 1985 and 2010 depend not only on the regularity of the decline but also on the situation in 1985 ( normal or outstanding ) and the consistency of data points . for instance , the country with the fastest mortality decline from 1985 to 2010 was madagascar . this was due in part to the fact that the 1985 baseline year had abnormally high mortality due to a 12-year economic and political crisis peaking in 1985 , which was followed by a quick recovery . some of best performers were countries with high mortality levels at baseline , and steady mortality decline , such as benin and mali . however , in benin , mortality decline seemed exaggerated by the fact that the last 2012 dhs survey showed lower mortality levels than previous surveys . on the other side of the spectrum , some countries endured major mortality increase because of hiv / aids . this was the case in all countries located in southern africa , in several east african countries , and in some west african countries . had hiv not been prevalent in these countries , mortality decline would probably have been steady over the 1985 to 2010 period , because public health services and programs did in fact improve dramatically . so mortality trends follow first the prevalence of hiv ( say from 1985 to 2000 ) , and second the prevention of mother to child transmission ( since the early 2000s ) and more important antiretroviral therapies ( since the mid-2000s ) . last , resistant malaria caused a mortality increase in the 1990s in selected countries ( as in senegal ) , and the new programs of prevention ( bednets ) and care ( new antimalarial drugs ) caused major mortality declines in many countries with high malaria mortality . therefore , these erratic trends are in part due to external causes ( emerging diseases or medical innovation ) and not to the simple functioning of the health system . overall , for the 35 countries considered together , mortality decline has been impressive over the 1960 to 2010 period ( figure 1 ) . estimates vary somewhat between the first 3 sources , but the picture is the same : a steady decline at average speed ( about 1.7% per year ) from 1960 to 1985 , a slower decline from 1985 to 1997 ( about 1.2% per year ) , and a faster decline from 1997 to 2010 ( about 3.7% a year ) . overall , under-5 mortality was reduced by 65% in 50 years , from 275 per 1000 in 1960 to 96 per 1000 in 2010 . only the ihme estimates differed , with a lower slope , due to lower levels in 1970 ( 210 per 1000 ) and higher levels in 2010 ( 139 per 1000 ) , the main discrepancies being in the last 10 years . the overall mortality decline in africa can be compared with that in sweden a century before ( 1860 - 1910 ) , where the decline was similar in levels and trends , from 240 per 1000 in 1860 to 117 per 1000 in 1910 , a 51% reduction in 50 years . in africa , mortality decline in the last 25 years ( 1985 - 2010 ) , although impressive , failed to meet the ambitious mdg-4 ( 47% mortality reduction instead of 67% targeted ) . trends in under-5 mortality in 35 sub - saharan african countries , 1960 - 2010 ( by source ) , and comparison with expectations from mdg-4 , and with sweden ( 1860 - 1910 ) . if the overall pattern emerged clearly , variations between sources were large and were due to estimation methods and data interpretation ( table 1 ) . not only slopes differed in absolute value between sources , but even the sign of the slope ( positive or negative ) was different in a number of cases . in the 2 congos , the slope was positive ( mortality increase ) in the ihme estimates but negative in the other sources ; in lesotho , in swaziland , and in zimbabwe , 2 sources found a mortality increase , but the other 2 found a mortality decline ; in nigeria the ihme estimates indicated a mortality increase , contrary to the other 3 sources . in all these cases , the dhs yearly data indicated no change in mortality trends in the recent period : steady mortality increase in lesotho , swaziland , and zimbabwe and steady mortality decline in the 2 congos and in nigeria , whereas indirect estimates from various sources were confusing . in the 3 southern african countries , mortality increase was obviously due to hiv / aids , before prevention and care could inverse the increasing mortality trends , as already happening in south africa and namibia after 2005 . in nigeria , the 3 recent dhs surveys ( 2008 , 2010 , 2013 ) all indicated a steady mortality decline . in congo - brazza , the last 2012 dhs survey also indicated a steady mortality decline , as was the case in congo - kinshasa with the 2013 dhs survey . abbreviations : igme , inter - agency group for mortality estimation ; who , world health organization ; ihme , institute for health metrics and evaluation . beyond the direction of the trends , serious discrepancies could also be noted in the magnitude of the slope between sources , with consequences for evaluating mdg-4 . this happened for instance in south africa , because of the complex changes in mortality , with major increase ( due to increasing hiv / aids prevalence ) followed by major decrease in mortality ( due to prevention and treatment of hiv ) : if 3 sources ( but ihme ) agreed on the pattern , they differed somewhat in the timing and the magnitude of the changes , with consequences for the average change over the 1985 to 2010 period . , the first 2 sources agreed on the speed of the mortality decline , whereas the third source indicated a stronger mortality reduction ( 30% vs 20% ) . other differences were of smaller magnitude . as a consequence , the number of countries meeting goals such as 30% , 40% , or 50% mortality reduction differed markedly by source . for instance , out of the 35 countries investigated , 15 countries were meeting the 50% mortality reduction from 1985 to 2010 according to the first source , 8 countries with the second ( unpd ) , 13 countries with the third ( igme ) , and only 3 countries with the last source ( ihme ) . these differences reveal the difficulty in evaluating mdg-4 from imperfect data ( table 1 ) . a goal such as mdg-4 assumes implicitly a steady mortality decline and targets its speed as an indicator of regular progress in public health services and programs . but steady mortality decline was not a feature of most african countries . among the 35 countries investigated , only 9 had a steady mortality decline . examples of such countries are shown on figure 2 . even in these smooth cases , there were at times variations in the speed of mortality decline , reflecting a variety of difficulties in each case , which complicate the trend analysis between 1985 and 2010 . selected countries with steady under-5 mortality decline , sub - saharan africa ( from dhs data ) . the other countries endured periods of mortality increase because of political crises , economic crises , or social crises ( 19 cases ) or because emerging or resurging diseases ( 11 cases ) , namely , hiv / aids and malaria . erratic trends complicate the interpretation of mortality changes over a fixed period of time , such as 1985 to 2010 . figure 3 displays selected countries with large fluctuations in mortality trends . in rwanda , the civil war period ( 1991 - 1999 ) was associated with major disruption in already erratic trends . in zambia , the copper crisis period ( 1975 - 1992 ) and hiv hiv / aids induced drastic increases in mortality . in congo - brazza , as in congo - kinshasa , major political crises induced large fluctuations in under-5 mortality . many other countries ( not shown ) also underwent serious disruption in mortality trends for similar reasons . these chaotic situations hamper any simple trend analysis and make the evaluation of mdg-4 difficult without taking the local context into account . selected countries with erratic under-5 mortality decline , sub - saharan africa ( from dhs data ) furthermore , trend analysis between 2 points such as 1985 and 2010 depend not only on the regularity of the decline but also on the situation in 1985 ( normal or outstanding ) and the consistency of data points . for instance , the country with the fastest mortality decline from 1985 to 2010 was madagascar . this was due in part to the fact that the 1985 baseline year had abnormally high mortality due to a 12-year economic and political crisis peaking in 1985 , which was followed by a quick recovery . some of best performers were countries with high mortality levels at baseline , and steady mortality decline , such as benin and mali . however , in benin , mortality decline seemed exaggerated by the fact that the last 2012 dhs survey showed lower mortality levels than previous surveys . on the other side of the spectrum , some countries endured major mortality increase because of hiv / aids . this was the case in all countries located in southern africa , in several east african countries , and in some west african countries . had hiv not been prevalent in these countries , mortality decline would probably have been steady over the 1985 to 2010 period , because public health services and programs did in fact improve dramatically . so mortality trends follow first the prevalence of hiv ( say from 1985 to 2000 ) , and second the prevention of mother to child transmission ( since the early 2000s ) and more important antiretroviral therapies ( since the mid-2000s ) . last , resistant malaria caused a mortality increase in the 1990s in selected countries ( as in senegal ) , and the new programs of prevention ( bednets ) and care ( new antimalarial drugs ) caused major mortality declines in many countries with high malaria mortality . therefore , these erratic trends are in part due to external causes ( emerging diseases or medical innovation ) and not to the simple functioning of the health system . overall and despite serious difficulties , african countries underwent a major mortality decline from 1960 to 2010 , and especially in the last 10 years . at this baseline level of mortality , the decline in africa compares favorably with than in sweden a century before . this is impressive in itself , and can be considered as a very positive achievement , especially given the level of income and of social organization of these countries , and the strong perturbations provoked by the hiv / aids epidemics and by malaria . note that impressive mortality declines were achieved in countries with low levels of income and education , as in some of the sahelian countries . formally , mdg-4 was not met , except by a few countries , and even these few cases could be discussed at length . mdg-4 appears therefore as overambitious , especially when compared with past experience in africa ( 1960 - 1985 ) , and with past experience in europe . why african countries should undergo a faster mortality decline than european countries at the same level of mortality furthermore , had the hiv / aids epidemic not occurred , mortality decline would have been faster , showing that these countries made in fact major efforts in public health . some of the estimates appeared far from those shown in dhs data , usually considered as the most consistent and reliable . in a few cases , trends were in fact in the opposite direction , which is surprising . more work is needed to better interpret alternative data sets , in particular estimates derived from indirect methods , which can be confusing when hypotheses on which the methods are based are too strongly violated . even in this case , one could underestimate or overestimate levels and trends because of the large fluctuations due to sample size and sampling scheme . ultimately , one wishes to have reliable birth and death registration in order to obtain more robust mortality estimates , and a better grip on achievements in mdgs . last , a formal analysis of achievements in public health outcomes must take the local context into account . only a detailed analysis of the local situation will allow one to understand whether the country did well or not , and if not , why it did not . one should remember that mortality decline was not always smooth in europe , russia , or asia in the 19th and early 20th century at similar levels of mortality . increases in mortality could be due to wars ( world wars or civil wars ) , diseases ( tuberculosis , spanish flu ) , severe economic or political crises , increasing inequalities and poverty , and other factors .
background . the fourth millennium development goal ( mdg-4 ) proposed to reduce under-5 mortality rates ( u5mr ) by two thirds within 25 years . 
 the article discusses changes in u5mr for 35 sub - saharan african countries , for which dhs surveys are available . 
 methods . 
 analysis of dhs data , reconstruction of time series of u5mr , and comparison with other series . 
 findings . 
 few countries were able to achieve mdg-4 from 1985 to 2010 , and the few who did seem to have achieved the goal apparently either because of abnormally high baseline or a surprisingly low endpoint . if all countries experienced significant mortality decline , only a minority had a steady decline , and many had periods of rising and falling mortality , for a variety of reasons . 
 interpretation . 
 discussion focuses on data quality , on methods for estimating levels and trends in under-5 mortality , and on the circumstances explaining rises and falls in mortality . 
 mdg-4 appeared overambitious for africa , given high mortality levels , political instability , economic crises , and above all emerging diseases , in particular hiv / aids . 
 the last 10 years from 2000 to 2010 appeared as the most favorable period since 1960 in african countries , with the exception of countries with widespread hiv / aids .