BackgroundIt is widely recognized that there are multiple risk factors for early-life mortality. In practice most interventions to curb early-life mortality target births based on a single risk factor, such as poverty. However, most premature deaths are not from the targeted group. Thus interventions target many births that are at not at high risk and miss many births at high risk.MethodsUsing data from the second wave of Demographic and Health Surveys from India and a hierarchical Bayesian model, we estimate infant mortality risk for 73.320 infants in India as a function of 4 risk factors. We show how this information can be used to improve program targeting. We compare our novel approach against common programs that target groups based on a single risk factor.ResultsA conventional approach that targets mothers in the lowest quintile of income correctly identifies only 30% of infant deaths. By contrast, using four risk factors simultaneously we identify a group of births of the same size that includes 57% of all deaths. Using the 2012 census to translate these percentages into numbers, there were 25.642.200 births in 2012 and 4.4% died before the age of one. Our approach correctly identifies 643.106 of 1.128.257 infant deaths while poverty only identifies 338.477 infant deaths.ConclusionOur approach considerably improves program targeting by identifying more infant deaths than the usual approach that targets births based on a single risk factor. This leads to more efficient program targeting. This is particularly useful in developing countries, where resources are lacking and needs are high.Electronic supplementary materialThe online version of this article (10.1186/s12963-018-0172-6) contains supplementary material, which is available to authorized users.

Background Goal 3.2 from the Sustainable Development Goals (SDG) calls for reductions in national averages of Under-5 Mortality. However, it is well known that within countries these reductions can coexist with left behind populations that have mortality rates higher than national averages. To measure inequality in under-5 mortality and to identify left behind populations, mortality rates are often disaggregated by socioeconomic status within countries. While socioeconomic disparities are important, this approach does not quantify within group variability since births from the same socioeconomic group may have different mortality risks. This is the case because mortality risk depends on several risk factors and their interactions and births from the same socioeconomic group may have different risk factor combinations. Therefore mortality risk can be highly variable within socioeconomic groups. We develop a comprehensive approach using information from multiple risk factors simultaneously to measure inequality in mortality and to identify left behind populations. Methods We use Demographic and Health Surveys (DHS) data on 1,691,039 births from 182 different surveys from 67 low and middle income countries, 51 of which had at least two surveys. We estimate mortality risk for each child in the data using a Bayesian hierarchical logistic regression model. We include commonly used risk factors for monitoring inequality in early life mortality for the SDG as well as their interactions. We quantify variability in mortality risk within and between socioeconomic groups and describe the highest risk sub-populations.

The effects of democracy on living conditions among the poor are disputed. Previous studies have addressed this question by estimating the average effect of democracy on early-life mortality across all countries. We revisit this debate using a research design that distinguishes between the aggregated effects of democracy across all countries and their individual effects within countries. Using Interrupted Time Series methodology and estimating model parameters in a Bayesian framework, we find the average effect of democracy on early-life mortality to be close to zero, but with considerable variation at the country-level. Democratization was followed by fewer child deaths in 21 countries, an increase in deaths in eight, and no measurable changes in the remaining 32 cases. Transitions were usually beneficial in Europe, neutral or beneficial in Africa and Asia, and neutral or harmful in Latin America. The distribution of country-level effects is not consistent with common arguments about the conditional effects of democratic transitions. Our results open a new line of research into the sources of theses heterogeneous effects.

Most studies on inequality in infant and child mortality compare average mortality rates between large groups of births, for example, comparing births from different countries, income groups, ethnicities, or different times. These studies do not measure within-group disparities. The few studies that have measured within-group variability in infant and child mortality have used tools from the income inequality literature, such as Gini indices. We show that the latter are inappropriate for infant and child mortality. We develop novel tools that are appropriate for analyzing infant and child mortality inequality, including inequality measures, covariate adjustments, and ANOVA methods. We illustrate how to handle uncertainty about complex inference targets, including ensembles of probabilities and kernel density estimates. We illustrate our methodology using a large data set from India, where we estimate infant and child mortality risk for over 400,000 births using a Bayesian hierarchical model. We show that most of the variance in mortality risk exists within groups of births, not between them, and thus that within-group mortality needs to be taken into account when assessing inequality in infant and child mortality. Our approach has broad applicability to many health indicators.

Democracy and development is one of the most important books on the relationship between political regimes and material well-being, the winner of the 2001 Woodrow Wilson Foundation Award for the best book published in 2000 in the United States in government, politics, or international affairs. Indeed, the book represents an astonishing achievement and deserves attention. Its ambitious goals, its quantity of data, and its sophisticated statistical analysis are noteworthy. Its purpose is no less than to study the impact of political regimes on material well-being, broadly defined as economic growth rates, investment, factor productivity, population growth, birth and death rates and per capita income. The book uses a database of 141 countries from 1950 to 1990, covering 1645 years of democracy and 2482 years of dictatorship, with 39 transitions from democracy to dictatorship and 49 transitions the other way around. It is also a remarkable achievement because of its main finding: political regimes have no impact on development. Accordingly, although political institutions probably matter for development, it seems that thinking in terms of political regimes will not help us to increase our knowledge about the mechanics of development. At the beginning of the book, the authors point out how tricky it can be to ask the wide-ranging question about the relationship between material well-being and political regimes: are dictatorships more effective in leading underdeveloped nations toward nova Economia_Belo Horizonte_16 (2)_345-361_maio-agosto de 2006

Background: Various studies suggest that corruption affects public health systems across the world. However, the extant literature lacks causal evidence about whether anti-corruption interventions can improve health outcomes. We examine the impact of randomized anti-corruption audits on early-life mortality in Brazil. Methods: The Brazilian government conducted audits in 1,949 randomly selected municipalities between 2003 and 2015. To identify the causal effect of anti-corruption audits on early-life mortality, we analyse data on health outcomes from individual- level vital statistics (DATASUS) collected by Brazil government before and after the random audits. Data on the audit intervention are from the Controladoria-Geral da Uniao, the government agency responsible for the anti-corruption audits. Outcomes are neonatal mortality, infant mortality, child mortality, preterm births, and prenatal visits. Analyses examine aggregate effects for each outcome, as well as effects by race, cause of death, and years since the intervention. Results: Anti-corruption audits significantly decreased early-life mortality in Brazil. Expressed in relative terms, audits reduced neonatal mortality by 6.7% (95% CI -8.3%, -5.0%), reduced infant mortality by 7.3% (-8.6%, -5.9%), and reduced child mortality by 7.3% (-8.5%, -6.0%). This reduction in early mortality was higher for nonwhite Brazilians, who face significant health disparities. Effects are greater when we look at deaths from preventable causes, and show temporal persistence with large effects even a decade after audits. In addition, analyses show that the intervention led to a 12.1% (-13.4%, -10.6%) reduction in women receiving no prenatal care, as well as a 7.4% (-9.4%, -5.5%) reduction in preterm births; these effects are likewise higher for nonwhites and are persistent over time. All effects are robust to various alternative specifications. Interpretation: Governments have the potential to improve health outcomes through anti-corruption interventions. Such interventions can reduce early-life mortality and mitigate health disparities. The impact of anti-corruption audits should be investigated in other countries, and further research should further explore the mechanisms by which combating corruption affects the health sector.

This paper offers the first large scale analysis of the effects of democratization on the rich-poor gap in child mortality across the developing world. Theories predict that democratic institutions should help those at the bottom of the income distribution more than those at the top. Yet, previous cross-national studies on democracy and child mortality have not focused on the rich-poor gap in health outcomes. Using an unique data set with more than 5 million birth records from 50 middle and low income countries, this study is the first one to test whether those at the bottom of the income distribution benefit more from the democratic transitions than those at the top. Although the rich and poor gap in child mortality is reducing over time, this change does not seem to be driven by regime type.Yet, there is remarkeble heterogeneity on the effects of democratization on health that deserves further investigation.ii The thesis of Antonio Pedro Ramos is approved.

Background: Goal 3.2 from the Sustainable Development Goals (SDG) calls for reductions in national averages of Under-5 Mortality. However, it is well known that within countries these reductions can coexist with left behind populations that have mortality rates higher than national averages. To measure inequality in under-5 mortality and to identify left behind populations, mortality rates are often disaggregated by socioeconomic status within countries. While socioeconomic disparities are important, this approach does not quantify within group variability since births from the same socioeconomic group may have different mortality risks. This is the case because mortality risk depends on several risk factors and their interactions and births from the same socioeconomic group may have different risk factor combinations. Therefore mortality risk can be highly variable within socioeconomic groups. We develop a comprehensive approach using information from multiple risk factors simultaneously to measure inequality in mortality and to identify left behind populations. Methods: We use Demographic and Health Surveys (DHS) data on 1,691,039 births from 182 different surveys from 67 low and middle income countries, 51 of which had at least two surveys. We estimate mortality risk for each child in the data using a Bayesian hierarchical logistic regression model. We include commonly used risk factors for monitoring inequality in early life mortality for the SDG as well as their interactions. We quantify variability in mortality risk within and between socioeconomic groups and describe the highest risk sub-populations. Findings: For all countries there is more variability in mortality within socioe- conomic groups than between them. Within countries, socioeconomic membership usually explains less than 20% of the total variation in mortality risk. In contrast, country of birth explains 19% of the total variance in mortality risk. Targeting the 20% highest risk children based on our model better identifies under-5 deaths than targeting the 20% poorest. For all surveys, we report efficiency gains from 26% in Mali to 578% in Guyana. High risk births tend to be births from mothers who are in the lowest socioeconomic group, live in rural areas and/or have already experienced a prior death of a child. Interpretation: While important, differences in under-5 mortality across socioeconomic groups do not explain most of overall inequality in mortality risk because births from the same socioeconomic groups have different mortality risks. Similarly, policy makers can reach the highest risk children by targeting births based on several risk factors (socioeconomic status, residing in rural areas, having a previous death of a child and more) instead of using a single risk factor such as socioeconomic status. We suggest that researchers and policy makers monitor inequality in under-5 mortality us- ing multiple risk factors simultaneously, quantifying inequality as a function of several risk factors to identify left behind populations in need of policy interventions and to help monitor progress toward the SDG.