Spatial pattern and predictors of malaria in Ethiopia: Application of auto logistics regression.

IntroductionMalaria is a severe health threat in the World, mainly in Africa. It is the major cause of health problems in which the risk of morbidity and mortality associated with malaria cases are characterized by spatial variations across the county. This study aimed to investigate the spatial patterns and predictors of malaria distribution in Ethiopia.MethodsA weighted sample of 15,239 individuals with rapid diagnosis test obtained from the Central Statistical Agency and Ethiopia malaria indicator survey of 2015. Global Moran's I and Moran scatter plots were used in determining the distribution of malaria cases, whereas the local Moran's I statistic was used in identifying exposed areas. The auto logistics spatial binary regression model was used to investigate the predictors of malaria.ResultsThe final auto logistics regression model was reported that male clients had a positive significant effect on malaria cases as compared to female clients [AOR = 2.401, 95% CI: (2.125-2.713) ]. The distribution of malaria across the regions was different. The highest incidence of malaria was found in Gambela [AOR = 52.55, 95%CI: (40.54-68.12)] followed by Beneshangul [AOR = 34.95, 95%CI: (27.159-44.963)]. Similarly, individuals in Amhara [AOR = 0.243, 95% CI:(0.195-0.303], Oromiya [AOR = 0.197, 955 CI: (0.158-0.244)], Dire Dawa [AOR = 0.064, 95%CI(0.049-0.082)], Addis Ababa[AOR = 0.057,95%CI:(0.044-0.075)], Somali[AOR = 0.077,95%CI:(0.059-0.097)], SNNPR[OR = 0.329, 95%CI: (0.261-0.413)] and Harari [AOR = 0.256, 95%CI:(0.201-0.325)] were less likely to had low incidence of malaria as compared with Tigray. Furthermore, for one meter increase in altitude, the odds of positive rapid diagnostic test (RDT) decreases by 1.6% [AOR = 0.984, 95% CI: (0.984-0.984)]. The use of a shared toilet facility was found as a protective factor for malaria in Ethiopia [AOR = 1.671, 95% CI: (1.504-1.854)]. The spatial autocorrelation variable changes the constant from AOR = 0.471 for logistic regression to AOR = 0.164 for auto logistics regression.ConclusionsThis study found that the incidence of malaria in Ethiopia had a spatial pattern which is associated with socio-economic, demographic, and geographic risk factors. Spatial clustering of malaria cases had occurred in all regions, and the risk of clustering was different across the regions. The risk of malaria was found to be higher for those who live in soil floor-type houses as compared to those who lived in cement or ceramics floor type. Similarly, households with thatched, metal and thin, and other roof-type houses have a higher risk of malaria than ceramics tiles roof houses. Moreover, using a protected anti-mosquito net was reducing the risk of malaria incidence

Application of Support Vector Machine Regression and Partial Least-Square Regression Models for Predicting Sugarcane Leaf Nutrients Content Using Near Infra-Red Spectroscopy

Near infra-red spectroscopy (NIRS) has been suggested as a rapid, cost-effective, and accurate diagnostic tool for leaf nutrient analysis that could replace more traditional laboratory diagnostics. To ease operational workflows, there would advantage in estimating nutrients using a single method, namely NIRS. This study evaluated the potential of NIRS as a diagnostic method for the measurement of key macro and micronutrients in sugarcane leaf samples. Three hundred and fifty-one sugarcane leaf samples used in quality control reference analysis in Fertiliser Advisory Service (FAS) were used for model calibration. About 35% of the samples were from growers within South Africa, while the remainder were from estates across southern and eastern Africa. Dried and milled leaf material was scanned on a Bruker MPA-NIRS instrument, and spectral pre-processing was performed. Support vector machine regression (SVMR) and partial least squares regression (PLSR) were used for calibrating the estimation models with the test validation(Tval) procedure. The results showed both the PLSR and SVMR model resulted in the best calibration for (K, Ca, Mg)(R2 >87% and the ratio of performance to inter-quartile distance(RPIQ) > 2.1). With a test validation dataset, the SVMR yielded relatively high accuracy(R2 >85% and the RPIQ > 2.5, while PLSR yielded poor accuracy. The SVMR also outperforms Zn(R2 = 0.86; and RPIQ = 2.5) for model calibration. NIR Spectrometry combined with the SVMR technique resulted in a practical option to accurately measure leaf nutrient concentrations and evaluate sugarcane mineral contents accurately. These results indicate that NIRS can replace the current analytical methods used in FAS.</p

Data_Sheet_1_The spatial effects of the household's food insecurity levels in Ethiopia: by ordinal geo-additive model.docx

BackgroundFood insecurity and vulnerability in Ethiopia are historical problems due to natural- and human-made disasters, which affect a wide range of areas at a higher magnitude with adverse effects on the overall health of households. In Ethiopia, the problem is wider with higher magnitude. Moreover, this geographical distribution of this challenge remains unexplored regarding the effects of cultures and shocks, despite previous case studies suggesting the effects of shocks and other factors. Hence, this study aims to assess the geographic distribution of corrected-food insecurity levels (FCSL) across zones and explore the comprehensive effects of diverse factors on each level of a household's food insecurity.MethodThis study analyzes three-term household-based panel data for years 2012, 2014, and 2016 with a total sample size of 11505 covering the all regional states of the country. An extended additive model, with empirical Bayes estimation by modeling both structured spatial effects using Markov random field or tensor product and unstructured effects using Gaussian, was adopted to assess the spatial distribution of FCSL across zones and to further explore the comprehensive effect of geographic, environmental, and socioeconomic factors on the locally adjusted measure.ResultDespite a chronological decline, a substantial portion of Ethiopian households remains food insecure (25%) and vulnerable (27.08%). The Markov random field (MRF) model is the best fit based on GVC, revealing that 90.04% of the total variation is explained by the spatial effects. Most of the northern and south-western areas and south-east and north-west areas are hot spot zones of food insecurity and vulnerability in the country. Moreover, factors such as education, urbanization, having a job, fertilizer usage in cropping, sanitation, and farming livestock and crops have a significant influence on reducing a household's probability of being at higher food insecurity levels (insecurity and vulnerability), whereas shocks occurrence and small land size ownership have worsened it.ConclusionChronically food insecure zones showed a strong cluster in the northern and south-western areas of the country, even though higher levels of household food insecurity in Ethiopia have shown a declining trend over the years. Therefore, in these areas, interventions addressing spatial structure factors, particularly urbanization, education, early marriage control, and job creation, along with controlling conflict and drought effect by food aid and selected coping strategies, and performing integrated farming by conserving land and the environment of zones can help to reduce a household's probability of being at higher food insecurity levels.</p

Prevalence of trachoma at sub-district level in Ethiopia : determining when to stop mass azithromycin distribution

To eliminate blinding trachoma, the World Health Organization emphasizes implementing the SAFE strategy, which includes annual mass drug administration (MDA) with azithromycin to the whole population of endemic districts. Prevalence surveys to assess impact at the district level are recommended after at least 3 years of intervention. The decision to stop MDA is based on a prevalence of trachomatous inflammation follicular (TF) among children aged 1-9 years below 5% at the sub-district level, as determined by an additional round of surveys limited within districts where TF prevalence is below 10%. We conducted impact surveys powered to estimate prevalence simultaneously at the sub-district and district in two zones of Amhara, Ethiopia to determine whether MDA could be stopped.; Seventy-two separate population-based, sub-district surveys were conducted in 25 districts. In each survey all residents from 10 randomly selected clusters were screened for clinical signs of trachoma. Data were weighted according to selection probabilities and adjusted for correlation due to clustering.; Overall, 89,735 residents were registered from 21,327 households of whom 72,452 people (80.7%) were examined. The prevalence of TF in children aged 1-9 years was below 5% in six sub-districts and two districts. Sub-district level prevalence of TF in children aged 1-9 years ranged from 0.9-76.9% and district-level from 0.9-67.0%. In only one district was the prevalence of trichiasis below 0.1%.; The experience from these zones in Ethiopia demonstrates that impact assessments designed to give a prevalence estimate of TF at sub-district level are possible, although the scale of the work was challenging. Given the assessed district-level prevalence of TF, sub-district-level surveys would have been warranted in only five districts. Interpretation was not as simple as stopping MDA in sub-districts below 5% given programmatic challenges of exempting sub-districts from a highly regarded programme and the proximity of hyper-endemic sub-districts

Sample description in South Wollo and South Gondar by <i>woreda</i> (district) and sub-district evaluation unit (EU).

<p>*Sub-district evaluation unit (EU).</p><p>** excluding 1.8% and 0.4% of households in South Wollo and South Gondar for which consent was not given.</p

Surveyed communities in South Gondar and South Wollo zones of Amhara National Regional State, Ethiopia.

<p>Surveyed communities in South Gondar and South Wollo zones of Amhara National Regional State, Ethiopia.</p