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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]

The sustainable availability and resilient supply of safe drinking water remains a challenge that is far from fulfilled. As of 2024, around 2 billion people worldwide do not have access to safe drinking water. The regions of Sub-Saharan Africa and South Asia are the most vulnerable regions in this regard. This is despite the fact that these regions are home to some of the world's fastest-growing economies. However, economic growth and rapid urbanization may also be connected to the increasing water stress in the suburban and rural areas. We propose addressing the two main challenges in this regard, which are the selection of the suitable treatment and distribution systems for different contexts using a decision support system following a Whole-of-Resource Approach (WORA). This approach considers the different water sources and sinks as contributing to a positive or negative change in the single resource, irrespective of whether the resource, in this case water, is used for domestic, industrial, or agricultural purposes. In this regard, this paper presents an illustrative proof of concept for such a decision support tool based on a multiple-criteria decision analysis. The decision support tool is aimed at facilitating water management within a WORA by providing recommendations regarding rainwater harvesting and savings in agricultural water consumption, in addition to suggesting water treatment and distribution systems, enabling systemic water resource management adapted to local context-specific conditions and needs. [ABSTRACT FROM AUTHOR]

Hepatitis E virus (HEV) is one of the emerging zoonotic diseases in Sub-Saharan Africa. Domestic pigs are considered to be the main reservoir for this infectious disease. A third of the world's population is thought to have been exposed to the virus. The zoonotic transmission of the HEV raises serious zoonotic and food safety concerns for the general public. This is a major public health issue in both developed and developing countries. The World Health Organization (WHO) estimated that 44,000 people died in 2015 as a result of HEV infection. East and South Asia have the highest prevalence of this disease overall. In this study, we proposed, developed, and analyzed the transmission routes of the infection using a fractional-order derivative approach. The existence, stability, and uniqueness of solutions were established using the approach and concept in Banach space. Local and global stability was determined using the Hyers–Ulam (HU) stability approach. Numerical simulation was conducted using existing parameter values, and it was established that, as the susceptible human population declines, the number of infected human populations rises with a change in fractional order θ ^. When the susceptible pig population increases, the number of infected pig populations rises with a change in θ ^. It was observed that a few variations in the fractional derivative order did not alter the function's overall behavior with the results of numerical simulations. Moreover, as the number of recovered human populations increases, there is a corresponding increase in the population of recovered pigs with a change in θ ^. The exponential increase in the infected pig population can be controlled by treatment of the infected pigs and prevention of the susceptible pigs. The authors recommend policymakers, and stakeholders prioritize the fight against the virus by enforcing the prevention of humans and treatment of infected pigs. The model can be extended to optimal control and cost-effectiveness analysis to determine the most effective control strategy that comes with less cost in the combat of the disease. [ABSTRACT FROM AUTHOR]

Background: Hyperglycemia during pregnancy leads to adverse maternal and fetal outcomes. Thus, strict monitoring of blood glucose levels is warranted. This study aims to determine the association of early to mid-pregnancy HbA1c levels with the development of pregnancy complications in women from three countries in South Asia and Sub-Saharan Africa. Methods: We performed a secondary analysis of the AMANHI (Alliance for Maternal and Newborn Health Improvement) cohort, which enrolled 10,001 pregnant women between May 2014 and June 2018 across Sylhet-Bangladesh, Karachi-Pakistan, and Pemba Island-Tanzania. HbA1c assays were performed at enrollment (8 to < 20 gestational weeks), and epidemiological data were collected during 2–3 monthly household visits. The women were followed-up till the postpartum period to determine the pregnancy outcomes. Multivariable logistic regression models assessed the association between elevated HbA1c levels and adverse events while controlling for potential confounders. Results: A total of 9,510 pregnant women were included in the analysis. The mean HbA1c level at enrollment was found to be the highest in Bangladesh (5.31 ± 0.37), followed by Tanzania (5.22 ± 0.49) and then Pakistan (5.07 ± 0.58). We report 339 stillbirths and 9,039 live births. Among the live births were 892 preterm births, 892 deliveries via cesarean section, and 532 LGA babies. In the multivariate pooled analysis, maternal HbA1c levels of ≥ 6.5 were associated with increased risks of stillbirths (aRR = 6.3, 95% CI = 3.4,11.6); preterm births (aRR = 3.5, 95% CI = 1.8–6.7); and Large for Gestational Age (aRR = 5.5, 95% CI = 2.9–10.6). Conclusion: Maternal HbA1c level is an independent risk factor for predicting adverse pregnancy outcomes such as stillbirth, preterm birth, and LGA among women in South Asia and Sub-Saharan Africa. These groups may benefit from early interventional strategies. [ABSTRACT FROM AUTHOR]