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Selenium (Se) is an essential element for human health. However, our knowledge of the prevalence of Se deficiency is less than for other micronutrients of public health concern such as iodine, iron and zinc, especially in sub-Saharan Africa (SSA). Studies of food systems in SSA, in particular in Malawi, have revealed that human Se deficiency risks are widespread and influenced strongly by geography. Direct evidence of Se deficiency risks includes nationally representative data of Se concentrations in blood plasma and urine as population biomarkers of Se status. Long-range geospatial variation in Se deficiency risks has been linked to soil characteristics and their effects on the Se concentration of food crops. Selenium deficiency risks are also linked to socio-economic status including access to animal source foods. This review highlights the need for geospatially-resolved data on the movement of Se and other micronutrients in food systems which span agriculture–nutrition–health disciplinary domains (defined as a GeoNutrition approach). Given that similar drivers of deficiency risks for Se, and other micronutrients, are likely to occur in other countries in SSA and elsewhere, micronutrient surveillance programmes should be designed accordingly.

Purpose: This study aims to determine the effect of population, Human Development Index and Gross Regional Domestic Product on the number of poor people in Minahasa District   Theoretical Framework: The data used in this study uses secondary data sourced from the Central Bureau of Statistics for North Sulawesi Province in 2011-2021. This type of research is quantitative research.   Design/Methodology/Approach: The analysis technique used in this study is panel data regression analysis.   Findings: The results showed that population, Human Development Index, and Gross Regional Domestic Product have an effect on the number of poor people. The population number and Human Development Index variables have a positive and significant effect on the number of poor people. The Gross Regional Domestic Product variable has a negative and significant effect on the population   Research Practical and Social Implications: The Gross Regional Domestic Product variable has a negative and significant effect on the population.   Originality/Value: Partially the population has a positive and significant effect on the numberpoor people in Minahasa, North Minahasa, South Minahasa,Southeast Minahasa in 2012-2021.

Access to water and sanitation services (WSSs) in low- or middle-income countries is constrained by the poverty and vulnerability conditions of the population. In this context, it is urgent to establish public policies for WSSs that will increase the economic access to these services so that they will be more comprehensive and comprise the entire population, ensuring a balance between social and financial objectives. This paper contributes to a better understanding of the provision of WSSs in vulnerable areas and of the difficulty in achieving universal access using Brazil as a case study. The role of regulation in the provision of WSSs in vulnerable areas and the impact of the recent institutional reform that took place last year in that country is discussed. The different experiences analyzed provide interesting lessons that contribute to the improvement of the Brazilian status quo and that, at the same time, can be good practices that can be applied in other countries. One of the main conclusions of this research is related to the contributions that regulation should provide in the universalization of WSSs, mainly when the provision of these services is ruled by a contract. Furthermore, we observed that public authorities have resigned their role in this scope and that they must be more effective and, particularly, more proactive so that universalization can be achieved.

Background Factors associated with low birth weight at term (TLBW), a proxy for intrauterine growth restriction (IUGR), are not well-elucidated in socioeconomically vulnerable populations. This study aimed to identify the factors associated with TLBW in impoverished Brazilian women. Methods Records in the 100 Million Brazilian Cohort database were linked to those in the National System of Information on Live Births (SINASC) to obtain obstetric, maternal, birth and socioeconomic data between 2001 and 2015. Multivariate logistic regression was performed to investigate associations between variables of exposure and TLBW. Results Of 8,768,930 term live births analyzed, 3.7% presented TLBW. The highest odds of TLBW were associated with female newborns (OR: 1.49; 95% CI: 1.47–1.50), whose mothers were black (OR: 1.20; 95% CI: 1.18–1.22), had a low educational level (OR: 1.57; 95% CI: 1.53–1.62), were aged ≥35 years (OR: 1.44; 95% CI: 1.43–1.46), had a low number of prenatal care visits (OR: 2.48; 95% CI: 2.42–2.54) and were primiparous (OR: 1.62; 95% CI: 1.60–1.64). Lower odds of TLBW were found among infants whose mothers lived in the North, Northeast and Center-West regions of Brazil compared to those in the South. Conclusion Multiple aspects were associated with TLBW, highlighting the need to comprehensively examine the mechanisms underlying these factors, especially in more vulnerable Brazilian populations, in order to contribute to the elaboration of health policies and promote better conditions of life for poor and extremely poor mothers and children.

The implementation of this study aims to present information not only in the text/tabular form but also in the spatial/digital map form to support the poverty alleviation programs in East Kalimantan, especially in Samarinda and Balikpapan, hence information obtained can be more easily analyzed, identified in terms of the poor population distribution in the visualization form. It presented as a list an area, graph, detail data and geographical map integrated with Google Maps. This application was developed using PHP scripts, Apache Webserver, Codeigniter Framework, CCS Bootstrap, MySQL database, and google maps API. Poor data collected from the Central Bureau of Statistics and the results of the survey through questioner and interview respondents. The information presented in this system is the resident name, identification number, address, employment, income, education, residence status and photo of living conditions/person.

Marking the end of the five-year programme initiated by the Chinese Government to lift more than 70 million people out of poverty, the year 2020 is a milestone. Poverty alleviation has moved strongly forward in China and the major health indicators are now better than the average of all middle- and high-income countries. However, the dual burden of infectious and chronic diseases remains a challenge with respect to achieving the health target in the United Nations 2030 Agenda for sustainable development goals (SDGs). In 2015, about 44% of the poor population in China were impoverished by illness but already in 2018, multi-sectoral actions delivered by the Health-related Poverty Alleviation programme had reduced the number almost by half. In the past three years 15 million poor people (98% of the poor population) with infectious and chronic diseases had been treated and taken care of thanks to financial support through multiple health insurance schemes and other governmental subsidies. This article discusses the lessons learnt with regard to health-related poverty alleviation in China with special reference to those still remaining impoverished by illness. Consolidation of the achievements reached and provision of basic needs to those still disadvantaged and in poor health will require a major improvement of accessibility to, and affordability of, health services. The next step towards enhanced productivity and better living conditions will involve upgrading of the capacity of health professionals in the poor regions, promotion of coherent efforts in health-related poverty alleviation and rural revitalization measures. As an additional measure, data monitoring and research on health poverty alleviation should be strengthened as they are essential to generate the evidence and knowledge needed to support the move in the direction envisioned by the SDGs, and the new Healthy China 2030 programme.

We use herd history data collected among pastoralists in southern Ethiopia to study stochastic wealth dynamics among a poor population. Although covariate rainfall shocks plainly matter, household-specific factors, including own herd size, account for most observed variability in wealth dynamics. We find no support for the tragedy of the commons hypothesis. Past studies may have conflated costly self-insurance with stocking rate externalities. Biophysical shocks move households between multiple dynamic wealth equilibria - the lowest suggesting a poverty trap - according to nonconvex path dynamics. These findings have broad implications for development and relief strategies among a poor population vulnerable to climatic shocks.

The Doctor’s Office Adjacent to Pharmacies (DAP) model has grown exponentially in Mexico. Its proliferation is due to two factors. The first is the high cost of medical consultations in private hospitals, and the second is that public health services are insufficient. To gauge the importance this model has acquired, it is necessary to analyze the pattern of distribution and operation of this type of doctors’ offices and determine whether they are responding to the unmet demand of a population that is socio-territorially marginalized from health infrastructure. A database was created with updated, geo-referenced information on the precise location of DAP throughout Mexico City and its metropolitan area. Information was obtained on the location, condition and type of franchise, and the infrastructure of each establishment. The analysis found that the distribution pattern of DAP satisfies an unmet need in areas with the highest demand for health services. This situation occurs particularly in areas inhabited by the most marginalized population.

Bangladesh has achieved notable economic progress in recent decades while economic inequality increased. Special attention is warranted on the ultra-poor population of the country. An 18 month-long economic development program, designed based on an ultra-poor graduation approach, was implemented to alleviate poverty and improve child nutrition in rural Bangladesh. The study examined the impact of livelihood components of an economic development program on outcomes related to poultry/crop production, consumption, and income generation among the ultra-poor throughout quarterly follow-ups. This secondary data analysis used the monitoring records of 2960 poor or ultra-poor households receiving assets of (1) 9-26 ducks (n = 2125), (2) 11 chickens (n = 872), and/or (3) vegetable seeds (n = 2407). Data measuring the production of assets, income generation, and consumption of assets were collected quarterly throughout 2019. To examine a one-year-long trend in participation, production, income generation, and consumption of assets, a one-way analysis of variance was conducted across the follow-ups. Additional analyses of annual income and consumption comparing duck and chicken groups were performed using linear regression models. The number of poultry assets per household decreased between the April- June and July-Sep follow-ups, while consumption of poultry and vegetable assets increased during the monsoon season (p < 0.001 for all). The vegetable production reflected seasonal fluctuations, where the lowest production and income were reported during the monsoon and pre-monsoon seasons. We observed increasing voluntary adoption of poultry farming among the non-asset group for both duck and chicken over the follow-ups (p < 0.001 for all). The households provided with duck assets gained a greater mean annual income compared to the households provided with chicken assets. Our findings highlight opportunities for strengthening the ultra-poor graduation approach on livelihood promotion in future scale-up in rural Bangladesh.

Spatially-explicit, fine-scale mapping of poor population distribution at a village level is a necessary prerequisite for developing precise anti-poverty strategies in rural China. To address the data missing of poor population at a village scale, we proposed a modeling methodology from the perspective of spatial poverty, integrating BP and MGWR-SL (Mixed Geographically Weighted Regression model with Spatially Lagged dependent variable) that correspond to population estimation and poverty incidence estimation, respectively, to explore a more accurate and detailed village-level poor population distribution. Furthermore, we justified the accuracy, reliability, and scale effects of the model by using GIS spatial analysis and cross-validation. From the case test, we found that, the proposed model could characterize poor population distribution more accurately than other existing methods, resulting in that the errors of both population spatialization and poverty incidence for each village are less than 5% at a 500 * 500 m grid scale. It can also be inferred that the spatialization of socioeconomic data at a fine scale should take into full account of spatial heterogeneity and spatial autocorrelation for both dependent and independent variables, so as to improve the modeling accuracy. This study may provide a perspective for better understanding the detailed and accurate poverty status of data–scarce village in poverty-stricken rural areas, and serves as a scientific reference regarding decision-making in both promoting “entire-village advancement” anti-poverty harmonious development and constructing the new countryside of China.