Mapping the medical outcomes study HIV health survey (MOS-HIV) to the EuroQoL 5 Dimension (EQ-5D-3L) utility index

10.1186/s12955-019-1135-8Health and Quality of Life Outcomes1718

Machine learning approaches for grain seed quality assessment: a comparative study of maize seed samples in Malawi

Abstract The study assessed machine and deep learning algorithms’ ability to predict and classify the quality of maize grain seed for increased agricultural output. It relied on a dataset of 2460 maize seed samples examined by a KEPHIS ISTA-accredited seed testing facility. The K-NN and Logistic Regression algorithms performed the best in predicting and classifying seed samples, with 100% accuracy, precision, recall, and fi-score. The algorithms found that 46.2% of the grain maize seed was correctly classified as poor-quality seed due to improper handling, and poses a danger to productivity and food security for smallholder farmers. The Deep Learning Convolutional Neural Network presented a 92% accuracy with slight fluctuations, mainly due to the simple and structured nature of the data, which was not in a grid-like or time series format. The study therefore recommends using K-Nearest Neighbor and/or Logistic Regression for grain seed classification when presented with well-structured agricultural data. Still, it also suggests expanding the methodology to other agricultural commodities and implementing seed management measures to prevent low-quality seed distribution. This includes training traders on how to maintain ISTA-required levels of germination, purity, and moisture content in their stores. The study highlights the significance of high-quality seeds for smallholder farmers to improve production and food security

Agricultural subsidies in a political economy: Can collective action make smallholder agriculture contribute to development?

Malawi’s economy is heavily dependent on agriculture, of which a majority are smallholder farmers. With smallholder farmers constituting more than 80% of the population, the government’s policies have mainly focused on achieving redistribution goals of the society, minding less about the inefficiencies in smallholder farming. The current study assessed and compared the efficiency levels of large scale and small-scale farmers amidst huge government expenditures in agricultural subsidies on smallholder farmers. Through a SWOT analysis and literature review approach that dwelt much on the qualitative case study approach, the study found that it is only through attaining economies of scale that small farms can attain the efficiency levels of large-scale farms. Following this finding that large farmers are more efficient; the study proposes models that would mimic the behaviour of large farmers. In this study, we evaluated the strengths, weaknesses, opportunities and threats of various models proposed to improve efficiency of small farms. Three models were evaluated namely, contract farming, cooperative development and land consolidation models. These models were selected for review because the theory of collective action ensures that they mimic the farm behaviour of a large farmer. The paper recommends a hybrid of land consolidation model with selected elements of contract and cooperative development models

Does shifting from subsistence to commercial farming improve household nutrition and poverty? evidence from Malawi, Tanzania and Nigeria

The current study sought to assess the effect of smallholder crop commercialization on household nutrition security and poverty status. Recent government efforts have gone beyond investing in agricultural production, thereby establishing markets for smallholder farmers in order to commercialize the agricultural sector. As such, developing countries like Malawi, Tanzania, and Nigeria are slowly transitioning to a market economy in order to improve the livelihoods of their people. To that effect, the study used country-wide representative data from Malawi, Tanzania, and Nigeria collected under the World Bank Living Standards Measurement Surveys (LSMS) and employed an instrumented-censored (probit and tobit) model to solve for endogeneity bias. The results show that poverty and nutrition insecurity were higher among subsistence farmers, emphasizing the need for a shift towards commercialization of the country's agricultural sector. Furthermore, farmer social and institutional context significantly influenced market participation. The study hence recommends a tailor-made extension delivery system, cutting across gender divides and other social barriers among smallholder farm households, in order to improve crop production among subsistence farmers, ensure household food security, and increase income from the sale of surplus crop output

Nucleoside reverse-transcriptase inhibitor cross-resistance and outcomes from second-line antiretroviral therapy in the public health approach: an observational analysis within the randomised, open-label, EARNEST trial

Background Cross-resistance after first-line antiretroviral therapy (ART) failure is expected to impair activity of nucleoside reverse-transcriptase inhibitors (NRTIs) in second-line therapy for patients with HIV, but evidence for the effect of cross-resistance on virological outcomes is limited. We aimed to assess the association between the activity, predicted by resistance testing, of the NRTIs used in second-line therapy and treatment outcomes for patients infected with HIV. Methods We did an observational analysis of additional data from a published open-label, randomised trial of second-line ART (EARNEST) in sub-Saharan Africa. 1277 adults or adolescents infected with HIV in whom first-line ART had failed (assessed by WHO criteria with virological confirmation) were randomly assigned to a boosted protease inhibitor (standardised to ritonavir-boosted lopinavir) with two to three NRTIs (clinician-selected, without resistance testing); or with raltegravir; or alone as protease inhibitor monotherapy (discontinued after week 96). We tested genotypic resistance on stored baseline samples in patients in the protease inhibitor and NRTI group and calculated the predicted activity of prescribed second-line NRTIs. We measured viral load in stored samples for all patients obtained every 12–16 weeks. This trial is registered with Controlled-Trials.com (number ISRCTN 37737787) and ClinicalTrials.gov (number NCT00988039). Findings Baseline genotypes were available in 391 (92%) of 426 patients in the protease inhibitor and NRTI group. 176 (89%) of 198 patients prescribed a protease inhibitor with no predicted-active NRTIs had viral suppression (viral load <400 copies per mL) at week 144, compared with 312 (81%) of 383 patients in the protease inhibitor and raltegravir group at week 144 (p=0·02) and 233 (61%) of 280 patients in the protease inhibitor monotherapy group at week 96 (p<0·0001). Compared with results with no active NRTIs, 95 (85%) of 112 patients with one predicted-active NRTI had viral suppression (p=0·3) and 20 (77%) of 26 patients with two or three active NRTIs had viral suppression (p=0·08). Over all follow-up, greater predicted NRTI activity was associated with worse viral load suppression (global p=0·0004). Interpretation Genotypic resistance testing might not accurately predict NRTI activity in protease inhibitor-based second-line ART. Our results do not support the introduction of routine resistance testing in ART programmes in low-income settings for the purpose of selecting second-line NRTIs. Funding European and Developing Countries Clinical Trials Partnership, UK Medical Research Council, Institito de Salud Carlos III, Irish Aid, Swedish International Development Cooperation Agency, Instituto Superiore di Sanita, WHO, Merck