Characteristics of potential successful emerging commercial cotton farmers

This paper describes a typology of sample households in terms of their farming orientation. Data was collected in two cotton growing schemes in Mpumalanga, namely Moutse and Nkomazi. Cluster analysis was applied to generate various farmers\' groupings according to their success potential. This analysis yielded 4 clusters two of which could be regarded as very successful and the other two groups as less successful. The realistic description of the various socio-economic groupings can facilitate the design of the support system based on the needs and aspiration of such groups. South African Journal of Agricultural Extension Vol. 35 (1) 2006: pp. 1-1

CONTRIBUTION OF AGRICULTURE IN THE ETHIOPIAN ECONOMY: A TIME-VARYING PARAMETER APPROACH

The objective of this study was to perform causality tests between agriculture and the rest of the economy using a Granger (1969) causality test procedure. Years of bi-directional causality were found between agriculture and manufacturing and services sectors before 1975. During this period, markets were major actors of economic activity and various positive measures, which encouraged the participation of the private sector in economic activity, were implemented. The contribution of agriculture to growth in the manufacturing and services sectors was not significant between 1978 and 1998. This can be attributed to two factors. Firstly, various policies that discouraged private sector participation in economic activity were implemented during the socialist era (between 1978 and 1992). Secondly, although markets were major actors of economic activity between 1992 and 1998, the economy was in a process of transition. Thus little can be expected in such a short time. The contribution of agriculture to growth inthe manufacturing sector has been improving since 1989. It is concluded that the freer agriculture is from policy constraints, the higher its contribution becomes to growth in the manufacturing and services sectors

Plasmodium Vivax as a Causative Agent for Cerebral Malaria in a Group of Adults at Mizan Tepi Teaching Hospital: Case Series

Yosef Habtemariam,1 Molla Asnake,2 Misikr Alemu,3 Erkyehun Pawlos Shash,3 Tsegaw Worku Tessema,2 Zerubabel Girma Tesso,1 Michael Hawlet4 1School of Medicine, College of Medicine and Health Sciences, Mizan-Tepi University, Mizan Aman, Ethiopia; 2School of Medicine, Adult ICU Unit, College of Medicine and Health Sciences, Mizan-Tepi University, Mizan Aman, Ethiopia; 3School of Medicine, Department of Internal Medicine, College of Medicine and Health Sciences, Mizan-Tepi University, Mizan Aman, Ethiopia; 4Department of Pediatrics and Child Health, School of Medicine, College of Medicine and Health Sciences, Wolkite University, Wolkite, EthiopiaCorrespondence: Molla Asnake, School of Medicine, Adult ICU Unit, College of Medicine and Health Sciences, Mizan-Tepi University, Mizan Aman, Ethiopia, Email [email protected]: In 2022, there were 249 million cases of malaria globally, resulting in 608,000 deaths. The majority of cases and deaths occurred in the WHO (World Health Organization) African Region. A study in our region found that, out of 263,476 individuals, 148,734 had P. falciparum, 106,946 had P. vivax, and 7,796 had mixed infections. The prevalence of P. falciparum (Plasmodium falciparum) was 8.97% and P. vivax (Plasmodium Vivax) was 7.94%. Although there have been a few reported cases of cerebral malaria caused by P. vivax, there is currently no comprehensive analysis of such cases. All the cases that have been reported so far involved individuals living in malaria-endemic areas, who presented with symptoms characteristic of cerebral malaria. Cerebral malaria was diagnosed based on the clinical algorithm which WHO used except we used P. vivax instead of P. falciparum The diagnosis of these cases was confirmed through thin blood film examination and Rapid Diagnostic Tests (RDTs). Therefore, this report aims to provide additional data on the occurrence of P. vivax as a cause of cerebral malaria. It also recommends further studies to reassess the current clinical case definition of cerebral malaria mainly in endemic areas as it affects patient treatment outcome.Keywords: cerebral malaria, P. Vivax and Blood Fil

Age-sex differences in the global burden of lower respiratory infections and risk factors, 1990-2019: results from the Global Burden of Disease Study 2019

Background The global burden of lower respiratory infections (LRIs) and corresponding risk factors in children older than 5 years and adults has not been studied as comprehensively as it has been in children younger than 5 years. We assessed the burden and trends of LRIs and risk factors across a groups by sex, for 204 countries and territories.Methods In this analysis of data for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we used dinician-diagnosed pneumonia or bronchiolitis as our case definition for LRIs. We included International Classification of Diseases 9th edition codes 079.6, 466-469, 470.0, 480-482.8, 483.0-483.9, 484.1-484.2, 484.6-484.7, and 487-489 and International Classification of Diseases 10th edition codes A48.1, A70, B97.4 B97.6, 109-115.8, J16 J16.9, J20-121.9, J91.0, P23.0 P23.4, and U04 U04.9. We used the Cause of Death Ensemble modelling strategy to analyse 23109 site-years of vital r *stration data, 825 site-years of sample vital registration data, 1766 site-years of verbal autopsy data, and 681 site-years of mortality surveillance data. We used DisMod-MR 2.1, a Bayesian metaregression tool, to analyse age sex-specific incidence and prevalence data identified via systematic reviews of the literature, population-based survey data, and daims and inpatient data. Additio y, we estimated age sex-specific LRI mortality that is attributable to the independent effects of 14 risk factors.Findings Globally, in 2019, we estimated that there were 257 million (95% uncertainty interval [UI] 240-275) LRI incident episodes in males and 232 million (217-248) in females. In the same year, LRIs accounted for 1.30 million (95% UI 1.18-1.42) male deaths and 1.20 million (1.07-1.33) female deaths. Age-standardised incidence and mortality rates were 1.17 times (95% UI 1.16-1.18) and 1.31 times (95% UI 1.23-1.41) greater in males than in fe es in 2019. Between 1990 and 2019, LRI incidence and mortality rates declined at different rates across age groups and an increase in LRI episodes and deaths was estimated among all adult age groups, with males aged 70 years and older having the highest increase in LRI episodes (126.0% [95% UI 121.4-131.1]) and deaths (100.0% [83.4-115.9]). During the same period, LRI episodes and deaths in children younger than 15 years were estimated to have decreased, and the greatest dedine was observed for LRI deaths in males younger than 5 years (-70.7% [-77.2 to 61.8]). The leading risk factors for LRI mortality varied across age groups and sex. More than half of global LRI deaths in children younger than 5 years were attributable to child wasting (population attributable fraction [PAF] 53.0% [95% UI 37.7-61.8] in males and 56.4% [40.7-65.1] in females), and more than a quarter of LRI deaths among those aged 5-14 years were attributable to household air pollution (PAF 26.0% [95% UI 16.6-35.5] for males and PAF 25.8% [16.3-35.4] for females). PAFs of male LRI deaths attributed to smoking were 20.4% (95% UI 15.4-25.2) in those aged 15-49 years, 305% (24.1-36. 9) in those aged 50-69 years, and 21.9% (16. 8-27. 3) in those aged 70 years and older. PAFs of female LRI deaths attributed to household air pollution were 21.1% (95% UI 14.5-27.9) in those aged 15-49 years and 18 " 2% (12.5-24.5) in those aged 50-69 years. For females aged 70 years and older, the leading risk factor, ambient particulate matter, was responsible for 11-7% (95% UI 8.2-15.8) of LRI deaths.Interpretation The patterns and progress in reducing the burden of LRIs and key risk factors for mortality varied across age groups and sexes. The progress seen in children you - than 5 years was dearly a result of targeted interventions, such as vaccination and reduction of exposure to risk factors. Similar interventions for other age groups could contribute to the achievement of multiple Sustainable Development Goals targets, induding promoting wellbeing at all ages and reducing health inequalities. Interventions, including addressing risk factors such as child wasting, smoking, ambient particulate matter pollution, and household air pollution, would prevent deaths and reduce health disparities.Copyright 2022 The Author(s). Published by Elsevier Ltd