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Multiple estimates exist of global monetary and multidimensional poverty, but populations at risk of social exclusion still lack a worldwide estimate. This paper fills this gap by providing the first estimates of the share and number of populations at risk of social exclusion worldwide. The paper contributes to the literature in three important respects. First, it develops a conceptual framework of social exclusion that emphasizes the relative, multidimensional, and dynamic features of exclusion. Second, it proposes a macro-counting methodology that allows measuring populations at risk of exclusion based on identity, circumstances, and socioeconomic conditions, while advancing a protocol to avoid double counting of individuals at risk of social exclusion. Third, the empirical strategy provides to the best of our knowledge, the first estimates of populations at risk of social exclusion by dimensions of exclusion on a global and regional scale. Overall, we estimate that between 2.33 and 2.43 billion people—roughly 32 per cent of the global population—are at risk of social exclusion. The South Asia and East Asia and Pacific regions contain 1.3 billion such people, with India and China alone home to 840 million of them. Meanwhile, 52 per cent of sub-Saharan Africa's population is vulnerable to exclusion, the greatest share of any region. Our findings have important policy implications. While antipoverty policies can support household consumption and smooth its volatility among the poor, they are unlikely to address social exclusion stemming from ethnic, racial, or gender discrimination. Therefore, addressing exclusion necessitates a suite of multiple interventions tailored to distinct groups and sustained over time. [ABSTRACT FROM AUTHOR]

A large literature has demonstrated the link between poverty and mental ill-health. Yet, the potential causal effects of poverty alleviation measures on mental disorders are not well-understood. In this systematic review, we summarize the evidence of the effects of a particular kind of poverty alleviation mechanism on mental health: the provision of cash transfers in low- and middle-income countries. We searched eleven databases and websites and assessed over 4,000 studies for eligibility. Randomized controlled trials evaluating the effects of cash transfers on depression, anxiety, and stress were included. All programs targeted adults or adolescents living in poverty. Overall, 17 studies, comprising 26,794 participants in Sub-Saharan Africa, Latin America, and South Asia, met the inclusion criteria of this review. Studies were critically appraised using Cochrane's Risk of Bias tool and publication bias was tested using funnel plots, egger's regression, and sensitivity analyses. The review was registered in PROSPERO (CRD42020186955). Meta-analysis showed that cash transfers significantly reduced depression and anxiety of recipients (dpooled = -0.10; 95%-CI: -0.15, -0.05; p<0.01). However, improvements may not be sustained 2–9 years after program cessation (dpooled = -0.05; 95%-CI: -0.14, 0.04; ns). Meta-regression indicates that impacts were larger for unconditional transfers (dpooled = -0.14; 95%-CI: -0.17, -0.10; p<0.01) than for conditional programs (dpooled = 0.10; 95%-CI: 0.07, 0.13; p<0.01). Effects on stress were insignificant and confidence intervals include both the possibility of meaningful reductions and small increases in stress (dpooled = -0.10; 95%-CI: -0.32, 0.12; ns). Overall, our findings suggest that cash transfers can play a role in alleviating depression and anxiety disorders. Yet, continued financial support may be necessary to enable longer-term improvements. Impacts are comparable in size to the effects of cash transfers on, e.g., children's test scores and child labor. Our findings further raise caution about potential adverse effects of conditionality on mental health, although more evidence is needed to draw robust conclusions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has changed the course of human development. In this manuscript we analyze the long-term effect of COVID-19 on poverty at the country-level across various income thresholds to 2050. We do this by introducing eight quantitative scenarios that model the future of Sustainable Development Goal 1 (SDG1) achievement using alternative assumptions about COVID-19 effects on both economic growth and inequality in the International Futures model. Relative to a scenario without the pandemic (the No COVID scenario), the COVID Base scenario increases global extreme poverty by 73.9 million in 2020 (the range across all scenarios: 43.5 to 155.0 million), 63.6 million in 2030 (range: 9.8 to 167.2 million) and 57.1 million in 2050 (range: 3.1 to 163.0 million). The COVID Base results in seven more countries not meeting the SDG1 target by 2030 that would have achieved the target in a No COVID scenario. The most pessimistic scenario results in 17 more countries not achieving SDG1 compared with a No COVID scenario. The greatest pandemic driven increases in poverty occur in South Asia and sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Background: Neonatal mortality remains a public health problem in the developing world. Globally, around 2.5 million neonatal deaths are reported annually with the highest mortality concentrated in sub-Saharan Africa and South Asia. In comparison with countries demonstrating the lowest neonatal mortality, the risk of mortality is over 30 times higher in sub-Saharan Africa. Ethiopia is among the countries with a high neonatal mortality rate, and the burden of this mortality remains unreported in many pastoralist areas such as Somali Regional State, Eastern Ethiopia. We aimed to investigate factors associated with neonatal mortality in public Hospitals of the Somali Regional State in Eastern Ethiopia. Methods: A facility-based cross-sectional study was conducted from May 1st to 30th, 2020 in three public Hospitals of Somali Regional State in Eastern Ethiopia. A total of 510 neonates admitted to neonatal intensive care units from January 2018 to December 2019 were enrolled in the study. The charts of neonates were randomly selected and retrieved. Data were collected using a pretested and validated structured questionnaire. The collected were entered into Epidata version 3.1 and exported to SPSS version 22 (IBM SPSS Statistics, 2013) for further analysis. Descriptive statistics were carried out using frequency tables, proportions, and summary measures. Predictors were assessed using a multivariable logistic regression analysis model and reported using adjusted odds ratio (AOR) with 95% Confidence Interval (CI). Statistical significance was considered at a p-value <0.05. Results: Overall, the neonatal mortality was 18.6% [95%CI (15.31, 22.30)], equating to a rate of 186 per 1000 live births. The most common causes of mortality were prematurity (44.6%), low birth weight (33.5%), and birth asphyxia (27.6%). In the final model of multivariable analysis, predictors such as: lack of antenatal care follow-up[AOR = 3.71, 95%CI (2.13, 6.44)], neonatal sepsis [AOR = 1.84, 95%CI (1.07, 3.19], preterm birth [AOR = 2.20, 95%CI (1.02, 4.29], and birth asphyxia [AOR = 2.40, 95%CI(1.26,4.43)], and birth weight of less than 2500gms[AOR = 3.40, 95%CI(1.92, 6.01)] were statistically associated with neonatal mortality. Conclusion: In this study, the neonatal mortality rate was high compared to national and global targets because one in five neonates dies due to preventable causes. Modifiable and non-modifiable risk factors were identified as predictors. This result calls for all stakeholders to provide due attention to low birth weight and premature babies. Early identification and management of birth asphyxia and neonatal sepsis are also very crucial to reduce the risks of neonatal deaths. [ABSTRACT FROM AUTHOR]

Background: The infant mortality rate (IMR) is a critical indicator of population health, but its measurement is subject to response bias in countries without complete vital registration systems who rely instead on birth histories collected via sample surveys. One of the most salient bias is the fact that child deaths in these birth histories tend to be reported with a large amount of heaping at age 12 months. Because of this issue, analysts and international agencies do not directly use IMR estimates based on surveys such as Demographic and Health Surveys (DHS); they rely instead on mortality models such as model life tables. The use of model life tables in this context, however, is arbitrary, and the extent to which this approach appropriately addresses bias in DHS-based IMR estimates remains unclear. This hinders our ability to monitor IMR levels and trends in low-and middle-income countries. The objective of this study is to evaluate age heaping bias in DHS-based IMR estimates and propose an improved method for adjusting this bias. Methods and findings: Our method relies on a recently-developed log-quadratic model that can predict age-specific mortality by detailed age between 0 and 5. The model's coefficients were derived from a newly constituted database, the Under-5 Mortality Database (U5MD), that represents the mortality experience of countries with high-quality vital registration data. We applied this model to 204 DHS surveys, and compared unadjusted IMR values to IMR values adjusted with the log-quadratic model as well as with the classic model life table approach. Results show that contrary to existing knowledge, age heaping at age 12 months rarely generates a large amount of bias in IMR estimates. In most cases, the unadjusted IMR values were not deviating by more than +/- 5% from the adjusted values. The model life table approach, by contrast, introduced an unwarranted, downward bias in adjusted IMR values. We also found that two regions, Sub-Saharan Africa and South Asia, present age patterns of under-5 mortality that strongly depart from the experience represented in the U5MD. For these countries, neither the existing model life tables nor the log-quadratic model can produce empirically-supported IMR adjustments. Conclusions: Age heaping at age 12 months produces a smaller amount of bias in DHS-based IMR estimates than previously thought. If a large amount of age heaping is present in a survey, the log-quadratic model allows users to evaluate, and whenever necessary, adjust IMR estimates in a way that is more informed by the local mortality pattern than existing approaches. Future research should be devoted to understanding why Sub-Saharan African and South Asian countries have such distinct age patterns of under-five mortality. [ABSTRACT FROM AUTHOR]

Introduction: Diarrhea is responsible for the death of more than 90% of under-five children in low and lower-middle income countries. Regionally, South Asia and sub-Saharan Africa accounted for 88% of deaths with the same age group. Therefore, the aim of this study was to determine the prevalence and associated factors of diarrhea among children under-five years in sub-Saharan Africa. Methods: The appended, most recent demographic and health survey datasets of 34 sub-Saharan African countries were used to determine the prevalence and associated factors of diarrhea among under-five children in the region. A total weighted sample of 330,866 under-five children were included in the study. Both bivariable and multivariable multilevel logistic regression were done to determine the associated factors of diarrhea among under five children in sub-Saharan Africa. The Odds Ratio (OR) with a 95% Confidence Interval (CI) was calculated for those potential factors included in the final model. Result: The overall prevalence of diarrhea in this study was 15.3% (95% CI: 15.1–15.4). Those children of mothers aged 15–24 (AOR = 1.26; 95% CI: 1.23, 1.30) and 25–34 years (AOR = 1.15; 95%CI: 1.12, 1.18), those children of mothers with no education (AOR = 1.69; 95%CI: 1.57–1.82), primary education (AOR = 1.73; 95%CI: 1.61–1.86) and secondary education (AOR = 1.49; 95%CI: 1.38–1.59) had higher odds of having diarrhea. Those children from poorest (AOR = 1.14; 95%CI: 1.10, 1.19), poorer (AOR = 1.12; 95%CI: 1.08–1.17), middle (AOR = 1.06; 95%CI: 1.02, 1.10), and richer (AOR = 1.14; 95%CI: 1.04–1.12) households had higher chance of having diarrhea compared to their counterparts. Conclusion: This study found that the prevalence of childhood diarrhea morbidity in sub-Saharan Africa was high. Maternal age, wealth index, maternal education, maternal occupation, age of child, time of initiation of breast feeding and time to get water source were significantly associated with diarrhea. Therefore, intervention through health education and health promotion for mothers/caretakers who are poor, less educated, and young should be designed to prevent diarrhea in the region. [ABSTRACT FROM AUTHOR]

In 2017–2018, a group of international development funding agencies launched the Crops to End Hunger initiative to modernize public plant breeding in lower-income countries. To inform that initiative, USAID asked the International Food Policy Research Institute and the United States Department of Agriculture's Economic Research Service to estimate the impacts of faster productivity growth for 20 food crops on income and other indicators in 106 countries in developing regions in 2030. We first estimated the value of production in 2015 for each crop using data from FAO. We then used the IMPACT and GLOBE economic models to estimate changes in the value of production and changes in economy-wide income under scenarios of faster crop productivity growth, assuming that increased investment will raise annual rates of yield growth by 25% above baseline growth rates over the period 2015–2030. We found that faster productivity growth in rice, wheat and maize increased economy-wide income in the selected countries in 2030 by 59 billion USD, 27 billion USD and 21 billion USD respectively, followed by banana and yams with increases of 9 billion USD each. While these amounts represent small shares of total GDP, they are 2–15 times current public R&D spending on food crops in developing countries. Income increased most in South Asia and Sub-Saharan Africa. Faster productivity growth in rice and wheat reduced the population at risk of hunger by 11 million people and 6 million people respectively, followed by plantain and cassava with reductions of about 2 million people each. Changes in adequacy ratios were relatively large for carbohydrates (already in surplus) and relatively small for micronutrients. In general, we found that impacts of faster productivity growth vary widely across crops, regions and outcome indicators, highlighting the importance of identifying the potentially diverse objectives of different decision makers and recognizing possible tradeoffs between objectives. [ABSTRACT FROM AUTHOR]