Feminization of African agriculture and the meaning of decision-making for empowerment and sustainability

The purpose of this study was to assess women’s decision-making power in small-scale agriculture in six African countries in view of the feminization of agriculture and to discuss the meaning of decision-making in relation to women’s empowerment and sustainability. The data are drawn from a multisite and mixed-method agricultural research and development project in six sub-Saharan countries including two sites in each country. The five domains of empowerment outlined in the Women’s Empowerment in Agriculture Index are used to structure the analysis. The results indicate that in the selected sites in Malawi, Rwanda and South Africa, women farmers tend to dominate agricultural decision-making, while the result is more mixed in the Kenyan sites, and decision-making tends to be dominated by men in the sites in Tanzania and Ethiopia. Despite women participating in agricultural decision-making, the qualitative results show that women small-scale farmers were not perceived to be empowered in any of the country sites. It appears that the feminization of agriculture leads to women playing a more important role in decision-making but also to more responsibilities and heavier workloads without necessarily resulting in improvements in well-being outcomes that would enhance sustainability

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

From policy promises to result through innovation in African agriculture?

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InnovAfrica project endline survey data for Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania

A consortium of 16 institutions comprising five institutions from Europe and eleven institutions from Africa implemented a project entitled "Innovations in Technology, Institutional and Extension Approaches towards Sustainable Agriculture and enhanced Food and Nutritional Security in Africa (InnovAfrica)" in six countries of eastern and southern Africa namely Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania from June 2017 to November 2021. The InnovAfrica project collected endline data from 12 pilot sites (two sites per country) in the third years of the project. The data collected during the Endline survey is presented in this document.There is no restriction to use these data set.Funding provided by: H2020*Crossref Funder Registry ID: Award Number: 727201The endline data were collected from 12 pilot study sites comprising two sites each from Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania using structured questionnaire and focus group discussion

An Open Source Traffic Engineering Toolbox

We present the TOTEM open source Traffic Engineering (TE) toolbox and a set of TE methods that we have designed and/or integrated. These methods cover intra-domain and inter-domain TE, IP-based and MPLS-based TE. They are suitable for network optimisation, better routing of traffic for providing QoS, load balancing, protection and restoration in case of failure, etc. The toolbox is designed to be deployed as an on-line tool in an operational network, or used off-line as an optimisation tool or as a traffic engineering simulator. (c) 2005 Elsevier B.V. All rights reserved.EU E-Next No

InnovAfrica project baseline survey data for Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania

A data set was generated thorugh surveys to establish a baseline inforamtion for a project entitled "Innovations in Technology, Institutional and Extension Approaches towards Sustainable Agriculture and enhanced Food and Nutrition Security in Africa (Acronym - InnovAfrica)". The InnovAfrica is a consortium of 16 institutions comprising five institutions from Europe and eleven institutions from Africa and the project was implemented in six countries of eastern and southern Africa namely Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania from June 2017 to November 2021.There is no restriction to use these data set.Funding provided by: Horizon 2020Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100007601Award Number: 727201The baseline data was collected from 12 pilot sites (2 sites per country) with in the first 12 months of the project using structured questionnaire. Data was first collected using papper based printed questionnaire and later digitalized in KIPUS system (a smart data software)

Addressing hard‐to‐reach populations for achieving malaria elimination in the Asia Pacific Malaria Elimination Network countries

Abstract Member countries of the Asia Pacific Malaria Elimination Network are pursuing the regional goal of malaria elimination by 2030. The countries are in different phases of malaria elimination, but most have demonstrated success in shrinking the malaria map in the region. However, continued transmission in hard‐to‐reach populations, including border and forest malaria, remains an important challenge. In this article, we review strategies for improving intervention coverage in hard‐to‐reach populations. Currently available preventive measures, including long‐lasting insecticidal nets and long‐lasting insecticidal hammocks, and prompt diagnosis and treatment need to be expanded to hard‐to‐reach populations. This can be done through mobile malaria clinics, village volunteer malaria workers and screening posts. Malaria surveillance in the hard‐to‐reach areas can be enhanced through tools such as spatial decision support systems. Policy changes by the malaria programs will be required for implementing the strategies outlined in this article. However, strategies or tools may be suitable for some population groups but difficult to implement in other groups