Partograph utilization and associated factors among obstetric care providers in North Shoa Zone, Central Ethiopia: a cross sectional study

Background: Globally, prolonged and obstructed labor contributed to 8% of maternal deaths which can be reduced by proper utilization of partograph during labor.Methods: An Institution based cross-sectional study was conducted in June, 2013 on 403 obstetric care providers. A pre-tested and structured questionnaire was used to collect data. Data was entered to EpiInfo version 3.5.1 statistical package and exported to SPSS version 20.0 for further analysis. Logistic regression analyses were used to see the association of different variables.Results: Out of 403 obstetric care providers, 40.2% utilized partograph during labor.Those who were midwives by profession were about 8 times more likely to have a consistent utilization of the partograph than general practitioners (AOR=8. 13, 95% CI: 2.67, 24.78). Similarly, getting on job training (AOR=2. 86, 95% CI: 1.69, 4.86), being knowledgeable on partograph (AOR=3. 79, 95% CI: 2.05, 7.03) and having favorable attitude towards partograph (AOR=2. 35, 95% CI: 1.14, 4.87) were positively associated with partograph utilization.Conclusion: Partograph utilization in labor monitoring was found to be low. Being a midwife by profession, on job training, knowledge and attitude of obstetric care providers were factors affecting partograph utilization. Providing on job training for providers would improve partograph utilization.Keywords: Ethiopia, obstetric care providers, partograp

Spatial Distribution of Diarrhoea and Microbial Quality of Domestic Water during an Outbreak of Diarrhoea in the Tshikuwi Community in Venda, South Africa

Total microbial quality assessment and geographical information system were used for evaluating the quality of water and the spatial distribution of diarrhoea cases in Tshikuwi, a rural community in South Africa, during an outbreak of diarrhoea. The water-abstraction points included two groundwater storage tanks, namely Tank 1 and Tank 2 and the Khandanama river. Indicator microbial counts for total coliforms, faecal coliforms, enterococci, and heterotrophic bacteria exceeded the limit for no risk as stipulated by the South African water-quality guidelines for domestic use for Tank 1 and the Khandanama river. Vibrio, Salmonella, and Shigella species were prevalent in the Khandanama river. The spatial distribution of diarrhoea cases showed a hot-spot of diarrhoea cases close to Tank 1 and the Khandanama river. Results of chi-square analysis showed that the proportion of infection from each water source was different or that infection depends on the type of water source (α=0.05). The demonstrated spatial clustering of diarrhoea cases might have been influenced by the poor microbial quality of water used from Tank 1 and the Khandanama river. The results further highlight the urgent need of water-treatment facilities and monitoring of water quality in rural communities of South Africa

Global, regional, and national levels of maternal mortality, 1990–2015 : a systematic analysis for the Global Burden of Disease Study 2015

Background In transitioning from the Millennium Development Goal to the Sustainable Development Goal era, it is imperative to comprehensively assess progress toward reducing maternal mortality to identify areas of success, remaining challenges, and frame policy discussions. We aimed to quantify maternal mortality throughout the world by underlying cause and age from 1990 to 2015. Methods We estimated maternal mortality at the global, regional, and national levels from 1990 to 2015 for ages 10–54 years by systematically compiling and processing all available data sources from 186 of 195 countries and territories, 11 of which were analysed at the subnational level. We quantifi ed eight underlying causes of maternal death and four timing categories, improving estimation methods since GBD 2013 for adult all-cause mortality, HIVrelated maternal mortality, and late maternal death. Secondary analyses then allowed systematic examination of drivers of trends, including the relation between maternal mortality and coverage of specifi c reproductive health-care services as well as assessment of observed versus expected maternal mortality as a function of Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Findings Only ten countries achieved MDG 5, but 122 of 195 countries have already met SDG 3.1. Geographical disparities widened between 1990 and 2015 and, in 2015, 24 countries still had a maternal mortality ratio greater than 400. The proportion of all maternal deaths occurring in the bottom two SDI quintiles, where haemorrhage is the dominant cause of maternal death, increased from roughly 68% in 1990 to more than 80% in 2015. The middle SDI quintile improved the most from 1990 to 2015, but also has the most complicated causal profi le. Maternal mortality in the highest SDI quintile is mostly due to other direct maternal disorders, indirect maternal disorders, and abortion, ectopic pregnancy, and/or miscarriage. Historical patterns suggest achievement of SDG 3.1 will require 91% coverage of one antenatal care visit, 78% of four antenatal care visits, 81% of in-facility delivery, and 87% of skilled birth attendance. Interpretation Several challenges to improving reproductive health lie ahead in the SDG era. Countries should establish or renew systems for collection and timely dissemination of health data; expand coverage and improve quality of family planning services, including access to contraception and safe abortion to address high adolescent fertility; invest in improving health system capacity, including coverage of routine reproductive health care and of more advanced obstetric care—including EmOC; adapt health systems and data collection systems to monitor and reverse the increase in indirect, other direct, and late maternal deaths, especially in high SDI locations; and examine their own performance with respect to their SDI level, using that information to formulate strategies to improve performance and ensure optimum reproductive health of their population.GBD 2015 Maternal Mortality Collaborators ...Nicholas J Kassebaum ... Azmeraw T Amare ... Liliana G Ciobanu ... James Hancock ... Ratilal Lalloo ... Yohannes Adama Melaku ... John Nelson Opio ... G A Tessema ... et.al

NbSe3: Effect of Uniaxial Stress on the Threshold Field and Fermiology

We have measured the effect of uniaxial stress on the threshold field ET for the motion of the upper CDW in NbSe3. ET exhibits a critical behavior, ET ~ (1 - e/ec)^g, wher e is the strain, and ec is about 2.6% and g ~ 1.2. This ecpression remains valid over more than two decades of ET, up to the highest fields of about 1.5keV/m. Neither g nor ec is very sensitive to the impurity concentraction. The CDW transition temperature Tp decreases linearly with e at a rate dTp/de = -10K/%, and it does not show any anomaly near ec. Shubnikov de-Haas measurements show that the extremal area of the Fermi surface decreases with increasing strain. The results suggest that there is an intimate relationship between pinning of the upper CDW and the Fermiology of NbSe3.Comment: 4 pages, 5 figure

A robust SNP barcode for typing Mycobacterium tuberculosis complex strains

Strain-specific genomic diversity in the Mycobacterium tuberculosis complex (MTBC) is an important factor in pathogenesis that may affect virulence, transmissibility, host response and emergence of drug resistance. Several systems have been proposed to classify MTBC strains into distinct lineages and families. Here, we investigate single-nucleotide polymorphisms (SNPs) as robust (stable) markers of genetic variation for phylogenetic analysis. We identify ~92k SNP across a global collection of 1,601 genomes. The SNP-based phylogeny is consistent with the gold-standard regions of difference (RD) classification system. Of the ~7k strain-specific SNPs identified, 62 markers are proposed to discriminate known circulating strains. This SNP-based barcode is the first to cover all main lineages, and classifies a greater number of sublineages than current alternatives. It may be used to classify clinical isolates to evaluate tools to control the disease, including therapeutics and vaccines whose effectiveness may vary by strain type

Low sensitivity of a urine LAM-ELISA in the diagnosis of pulmonary tuberculosis

<p>Abstract</p> <p>Background</p> <p>The development and evaluation of rapid and accurate new diagnostic tools is essential to improve tuberculosis (TB) control in developing countries. In a previous study, the first release of a urine LAM-ELISA by Chemogen (Portland, USA) has been evaluated with a promising sensitivity and specificity for the diagnosis of pulmonary TB. In the present study, the now commercially available assay has been clinically assessed regarding its diagnostic value alone and in combination with clinical co-factors.</p> <p>Methods</p> <p>The test was applied to two urine samples from 291 consecutively enrolled Tanzanian patients with suspected pulmonary tuberculosis. The participants were subsequently assigned to classification groups according to microbiological, clinical and radiological findings at recruitment and during a maximum follow up period of 56 days.</p> <p>Results</p> <p>Only 35 out of 69 pulmonary TB cases -confirmed by smear microscopy and/or solid culture and/or liquid culture- showed at least one positive LAM-ELISA result (sensitivity 50.7%). The sensitivity was noticeably higher in females (66.7%) and in HIV positive participants (62.0%). The specificity amounted to 87.8% and was determined in participants with negative results in all microbiological tests and with sustained recovery under antibiotic treatment at day 56. Correlation with urinalysis revealed that proteinuria was significantly and positively associated with LAM-positivity (<it>P </it>= 0.026).</p> <p>Conclusion</p> <p>This commercially available generation of LAM-ELISA does not appear to be useful as an independent diagnostic test for pulmonary tuberculosis. The question whether the assay is suitable as a supplemental device in the diagnosis of HIV-associated TB, requires further investigations.</p

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression