Country progress towards the Millennium Development Goals: adjusting for socioeconomic factors reveals greater progress and new challenges

The Millennium Development Goals (MDGs) originate from the 2000 United Nation (UN) Millennium Declaration adopted by 189 UN member states during the Millennium Summit [1]. Health MDGs 4 and 5a had respective targets to reduce the under-5 mortality rate (U5MR, the number of deaths of children under 5 per 1,000 live births) by two thirds and the maternal mortality ratio (MMR, the number of maternal deaths per 100,000 live births) by three quarters by 2015 and to achieve universal access to reproductive health [1]. MDG 4 was partially based on observations of reductions in child mortality in three countries that achieved high rates of progress between 1960 and 1990 (China, Sri Lanka and Vietnam) [2]. This targeted improvement was then applied as a global goal to achieve similar progress worldwide from 1990–2015. The Millennium Declaration uses the collective pronoun, “We” and refers to duties to the “world’s people”, leading many to conclude that the goals and indicators were intended as motivational aspirations for the aggregate world population. Whatever the General Assembly’s original intentions, numerous reports and studies have reinterpreted the MDGs as goals applying to each individual country [3]-[5]. Many of these reports have noted that the vast majority of low- and middle-income countries, especially in Africa, are “off track” for meeting the MDGs despite rapid progress in some places against considerable concurrent challenges. This has led some to suggest that applying a global standard to every country may be inappropriate [6]. Country-specific targets could allow people of a given country to better assess whether they are truly underperforming given their resources and potential.

The High Level Panel on the Post-2015 development agenda and the Open Working Group on Sustainable Development reports indicate that assessment of country progress towards maternal and child health goals will continue to be part of the post-2015 landscape [7],[8]. Because assessment of country progress will continue to be important after 2015, assessment tools designed specifically to compare a country’s progress to its own specific potential should be considered. Despite the great merit of the MDGs as global aspirations, global targets do not adjust for local factors that might inhibit or enhance progress. Very few sub-Saharan countries have a realistic chance to meet proposed 2030 targets for ending preventable newborn and child mortality or achieving a U5MR of 25 and a MMR of less than 70 [9],[10]. This paper explores methods to supplement a single global target to demonstrate what can be learned when accounting for country factors.

Target 1—the minimum performance target—was based on a model of how each country’s time trend and GDP growth affected mortality from 1990 to 2000. The model was used to project the respective mortality rates for each country to 2010, based on their expected rates of economic growth and yearly improvements. The statistical method fit a log-linear regression of the health indicator against a time trend and GDP per capita then applied the estimated coefficients to each country’s GDP growth to adjust each country’s expected degree of health improvement. The statistical method also adjusted for country specific fixed effects. To meet the target, a country had to simply achieve mortality improvements per dollar of their own GDP growth at the average rate of mortality improvement per dollar of growth observed in the sample of countries. Countries that did not reach the minimum performance target for U5MR and MMR by 2010 must have been achieving less health improvement per dollar of their GDP growth than the average performance for countries at similar GDP levels. In theory, if the model fit the data perfectly, half of all countries should be found to be below the average of all the countries in the sample and half would be above this average. In practice, country fixed effects and omitted factors could cause more countries to perform better than average. Countries were considered on track in 2010 for each target if their reported U5MR and MMR were lower than the respective targets, or below 40/1,000 live births for U5MR. Tests of robustness and sensitivity and other technical details are presented in the Additional file 1: Appendix.

Target 2—the fast-track target—was based on a model of how a set of key socioeconomic and policy factors that have been established in the scientific literature as contributing to improvements in maternal and child health, as well as how past time trends and GDP affected mortality rates. The factors used to adjust the model are shown in Table 1 and their derivation is discussed in the Additional file 1: Appendix and in a prior paper [8]. The list of independent variables is highly correlated because most are correlated with overall development. Hence an analysis of robustness found that shifting this list of variables by excluding one or adding another had a negligible impact on the country specific target and the determination of which countries were meeting their fast-track targets (Additional file 1: Appendix). The model projected mortality to 2010, but assumed that each country had maximal rates of improvement on the key socioeconomic determinants of mortality from 2000 to 2010. Maximal rates were established by the best rates of improvement in socioeconomic factors observed anywhere in the country’s UN sub-region from 2000 to 2010. To meet the fast-track target, a country’s 2000–2010 actual health improvement had to achieve the health improvement that would have occurred had it accomplished the maximum improvement rate in each of the policy variables that was observed by high-performing countries in its sub-region between 2000 and 2010.

Multivariate regression adjusting for country-specific trends and fixed effects was used to make country-specific projections of the minimum and fast-track performance targets. The dependent variables were log transformations of U5MR and MMR corresponding to MDGs 4 and 5. For the minimum target the independent variables were GDP per capita and a time trend. For the fast-track target, independent variables were based on the United Nations Development Programme proposal that plans designed to meet the MDGs should include investments in seven “clusters” of public policy [17]. We use these clusters as a guide to group variables into priority areas that could represent a complete set of resources, together with health and socioeconomic public practices that impact child and maternal health.

Our final model included the following variables, all of which have statistical as well as theoretical and empirical support: percent of the population with access to clean water, [11],[18]-[20] percent of children under 2 who received the measles vaccine, [11],[21]-[23] a control of corruption index reported by the World Bank, [11],[24] power consumption per capita, [20] urbanization, [18],[23]-[26] percent of girls enrolled in primary school, [26]-[28] total fertility rate, [11],[18],[29],[30] physicians per capita, [11],[20],[26],[27],[31]-[33] percent of births attended by a skilled birth attendant, [11],[21],[34] health spending per capita lagged by five years (since health spending in a given year may be endogenous to our model) [15],[26],[32],[35],[36], and Gini coefficient [21],[34],[37].

We excluded all countries classified as high-income by their 2000 GDP per capita (defined as $9,266 [38] in 2000 US dollars) from both analyses. We additionally excluded all European countries from the MMR analysis, since MMR in those countries is comparable to high-income countries. These criteria left 144 countries eligible for the U5MR analysis and 116 countries in the MMR analysis.

Data for outcome and independent variables of interest was extracted and compared between the following sources: WHO Global Health Observatory, [39] WHO National Health Accounts (NHA), [40] UNdata, [41] UNDP, [42] UNICEF’s Childinfo, [43] the World Bank DataBank, [44] and Demographic and Health Surveys (DHS). [45] Completeness of the dataset varied by indicator, see Table 1. For MMR data, our analysis uses the national estimates from UNICEF’s Child Info [43]. For U5MR, estimates are developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA, UNDP). For those independent variables lacking complete data, simple regression imputation was used. The inclusion of imputed data had little effect on the targets for countries that had complete data. Since complete vital registration is uncommon in low income countries, the official measures of mortality are derived by supplementing household survey data about deaths in a household with demographic models. As noted by Deaton, the health experiences of low income countries are so different in composition and in the way mortality data are acquired that the analysis should not combine low and high income countries [46]. The data on mortality that we used are the exact same official measures that are used to track MDG progress by the UN. We replicated our results with Institute of Health Metrics measures and found results that were very similar. Until vital registration systems improve, there will be no other recourse but to use the mortality indicators that are on hand.

Table 2 shows that poverty and a higher starting initial mortality make it harder to reach the 66% mortality reduction target. Absolute point reductions in U5MR averaged 74 points in the lowest income countries and 16 points in the highest income countries across four GDP per capita quartiles (Table 2). However, expressed as percent reductions, the countries with the lowest income reduced U5MR by 42%, while LMIC counties with the highest incomes lowered U5MR by 51%. The population weighted percent reduction in U5MR for these 144 LMIC countries was 55% between 1990 and 2010.

The population weighted percent reduction in MMR for the world is 51%, during the same period. Likewise, MMR progress across the four GDP per capita quartiles shows that absolute point reductions averaged 386 points in the lowest income countries and 50 points in the highest income countries. Expressed as percent reductions, the lowest income countries lowered MMR by 43%, while the highest income countries lowered MMR by 27%.

Figure 1 shows that the number of countries on track to achieve MDGs 4 and 5 is much lower in the lowest-growth LMICs compared to the highest-growth group.

Table 3 shows the reported U5MR of each country in our dataset in 1990 and 2010. These can be compared to an MDG target showing what a country’s U5MR would be in 2010 if it were on a linear path to achieve a 67% reduction in the 1990 U5MR by 2015. Of 144 countries available for analysis, we find that 99 (69%) are on track by the MDG 4 target including 12 (27%) in sub-Saharan Africa. In contrast, under our U5MR minimum performance target 107 countries of 144 (74%) would be considered on track and 18 of 45 (40%) of sub-Saharan African countries would be considered on track (Figure 2A-B).

According to our fast-track U5MR target, only 29 countries out of 144 (20%) are on the fast track. Of these top performers, 4 are in Africa (Botswana, Liberia, Niger and Rwanda). On average across countries the minimum performance target requires achieving a U5MR in 2010 that is 25% higher than the MDG 4 target for 2010. The fast-track target on average requires a U5MR that is 5% lower in 2010 than the MDG target, although in SSA the average fast-track target is actually 5% higher (i.e. less ambitious) than the average MDG target.

Likewise, Table 4 shows the original starting MMR of each country in our dataset in 1990, the latest estimate for 2010, and what a country’s MMR would have been in 2010 if it were on a linear path to achieve a 75% reduction in the 1990 MMR by 2015. Of 116 countries available for analysis, 25 (22%) are on track by the MDG 5 target. Of the 42 sub Saharan African countries studied, 5 (12%) are on track by the MDG 5 target. In contrast, under the MMR minimum performance target, 69 countries of 116 (59%) would be considered on track and 25 of 42 (60%) sub Saharan African countries would be considered on track. On the other hand, for the MMR fast-track target, eight countries of 116 (7%) would be considered on track and 0 of 42 (0%) of sub-Saharan African countries would be considered on track (Figure 2C-D). On average, the minimum performance target requires an MMR for 2010 that is 67% higher than the MDG target would require. The fast-track target requires a 2010 MMR that is 30% lower than the MDG target for 2010.

If one interprets the intent of the Millennium Declaration as an aspiration for the entire human population, the world as a whole is on track to achieve MDG 4 by 2015. As of 2010, the population weighted average reduction in U5MR since 1990 was 55 percent (Table 2). Extrapolating past performance, where U5MR drops 2.75 (=55/20) percentage points per year for five more years towards 2015 yields the projection that the world will have attained a 68.75% (=55 + (2.75 × 5)) reduction in U5MR by 2015—meeting the 2015 goal. However, if weighted by number of births, rather than population, progress would be less. The 144 LMIC countries in our sample as a whole have only reduced a birth-cohort-weighted U5MR by 39.5%, because large countries that maintained high levels of fertility rate, i.e. D.R. Congo, Nigeria, and India have made less overall progress than the large countries that reduced their birth cohort significantly: Bangladesh, Indonesia, Brazil, and China. Reasons to prefer population weighted vs. birth cohort weighted estimates are discussed in the Additional file 1: Appendix.

These findings demonstrate the value of global measurements as a simple and intelligible method to evaluate global progress and strengthen aspirations for further improvements. However, if one pursues a separate objective of assessing individual country performance, then applying a single benchmark to all countries can be inappropriately alarmist. Our analysis showed that country-specific targets based on each country’s specific trajectory of GDP growth enables more countries to receive the credit they deserve for their progress. In particular the minimum performance targets (Table 3) revealed that in Africa, Eritrea, Mozambique, Namibia, Nigeria, Senegal, and Uganda have been making an acceptable degree of progress given their resource constraints. Conventional MDG-based standards show these six countries to be not on track, which is overly pessimistic. Poorer than average GDP growth and unfortunate starting trajectories in these countries were critical contributors to their failure to obtain a 66% reduction in child mortality.

The HIV epidemic in Africa made achieving the global MDGs there significantly more difficult. The fact that only 27% of sub-Saharan African countries are on track for MDG4 and 12% are on track for MDG5a is in no doubt partly due to HIV, in addition to the other challenges faced by these countries. Indeed, the MDG4 target for under-5 mortality was even more ambitious than the fast-track target in this analysis, underscoring how ambitious—and unrealistic—such a goal was for most countries in the region. That the HIV epidemic did not factor at all in setting the MDG mortality targets in Africa demonstrates the limitations of applying global goals to each country in a diverse world. Both our targets target allows a more nuanced evaluation of African countries’ progress.

In addition to disclosing that progress has been higher than anticipated, the minimum target makes the detection of stalled progress more salient. Using a GDP-adjusted performance target to detect countries that are off track on maternal and child health implies that their stalled progress cannot be simply due to low national income. Failing to do as well as comparable countries have, controlling for GDP, implies a need for more accountability and better stewardship of a country’s resources. For suggestions about how to achieve exceptional results one can turn to the fast-track countries.

The fast-track targets were based on what would have happened to U5MR and MMR had a country experienced the regionally best possible improvements in ten known determinants of child survival. These more stringent targets highlight the success in lowering U5MR of Botswana, Liberia, Niger, and Rwanda in sub-Saharan Africa. Other top performers are Brazil, China, Egypt, El Salvador, Lebanon, Maldives, Mexico, Mongolia, Oman, Peru, Turkey, Vanuatu, and most LMIC European countries. Case studies of these high performers are proving their value in showing how these countries are achieving results beyond what would be expected [47]-[51].

Our study revealed that many African countries are actually meeting the minimum performance target and do not deserve to be unfairly labeled as “off track”. Many so called “off track” countries have actually achieved what was reasonably achievable given their available resources. Yet still, more than half of sub-Saharan Africa has not met a minimum standard, even adjusting for their economic circumstances. Failing to meet the minimum performance target suggests that the chief problem is not low GDP growth, but below-average capacity to transform economic growth into better health. The remedy for countries that are not achieving GDP adjusted targets will not lie in economic growth, but rather in better use of existing resources to improve health. Examining success factors for the superior performers on the fast-track standard for the region can indicate how countries have achieved better value for money. Botswana, Liberia, Niger, and Rwanda are distinguished in this category. Botswana has a relatively high GDP, but some of its success can be attributed to having the best control of corruption in all of sub-Saharan Africa and the second best access to clean water (after Mauritius). Botswana also has nearly complete vaccine coverage. Rwanda’s superb performance in maternal and child health has been well documented, especially lower financial barriers for health services [47] and dramatic improvements in coverage for rural attended deliveries [48]. Rwanda also had the fourth best control of corruption score in sub-Saharan Africa in 2010. Niger started out with the worst U5MR in the world in 1990, and has now improved to about average in its sub-region, by improving to average its control of corruption, measles immunization rates, and female primary schooling, despite still performing poorly on the other indicators in the model [51]. Liberia’s recent successes and challenges were detailed in a case study [52].

Four countries—China, Egypt, Maldives, and Turkey—all MICs, meet the fast-track target for both MDG4 and 5. China’s success is well known, [53] and we predict that case studies of Egypt, Turkey and Maldives public health policies will be particularly instructive.

DB, TA and SK conceived the idea. RC, YNA, and DB conducted the study, acquired data, and performed literature review, and data analysis. All authors were involved in data interpretation and drafting of the paper. All authors read and approved the final manuscript.