Perinatal mortality and associated risk factors: a case control study

BACKGROUND: Perinatal mortality is reported to be five times higher in developing than in developed nations. Little is known about the commonly associated risk factors for perinatal mortality in Southern Nations National Regional State of Ethiopia. METHODS: A case control study for perinatal mortality was conducted in University hospital between 2008 and 2010. Cases were stillbirths and early neonatal deaths. Controls were those live newborns till discharged from the hospital. Subgroup binary logistic regression analyses were done to identify associated risk factors for perinatal mortality, stillbirths and early neonatal deaths. RESULTS: A total of 1356 newborns (452 cases and 904 controls) were included in this analysis. The adjusted perinatal mortality rate was 85/1000 total delivery. Stillbirths accounted for 87% of total perinatal mortality. The proportion of hospital perinatal deaths was 26%. Obstructed labor was responsible for more than one third of perinatal deaths. Adjusted odds ratios revealed that obstructed labor, malpresentation, preterm birth, antepartum hemmorrhage and hypertensive disorders of pregnancy were independent predictors for high perinatal mortality. In the subgroup analysis, among others, obstructed labor and antepartum hemorrhage found to have independent association with both stillbirths and early neonatal deaths. CONCLUSION: The perinatal mortality rate was more than two fold higher than the estimated national perinatal mortality;and obstructed labor, malpresentation, preterm birth, antepartum hemmorrhage and hypertensive disorders of pregnancy were independent predictors. The reason for the poor progress of labor and developing obstructed labor is an area of further investigation.Keywords: Case control, early neonatal death, Ethiopia, obstructed labor, perinatal mortality, stillbirthEthiopian Journal of Health Sciences vol 22 (3) 201

Knowledge Discovery from Satellite Images for Drought Monitoring in Food Insecure Areas

Attributed to climatic change and uncertainty of weather conditions, drought has become a recurrent phenomenon. It is manifested by erratic and uncertain rainfall distribution in rainfall dependent farming areas. The hitherto methods of monitoring drought employed conventional methods that rely on availability of metrological data. The objectives of this research were to: 1) identify the critical factors for efficiently implementing geo-spatial information for drought monitoring, 2) develop a new approach for extracting knowledge from satellite imageries for real time drought monitoring in food insecure areas, and 3) validate and calibrate the new approach for national and regional applications. For this research, satellite data from MSG and NOAA AVHRR were used. The preliminary results confirmed that real time MSG satellite data can be used for monitoring drought in food insecure areas. The output of this research helps decision makers in taking the appropriate actions in time for saving millions of lives in drought affected areas using advanced satellite technology

Using Satellite Images for Drought Monitoring: A Knowledge Discovery Approach

The main objective of this research was to develop a new concept and approach to extract knowledge from satellite imageries for near real-time drought monitoring. The near real-time data downloaded from the Atlantic Bird satellite were used to produce the drought spatial distribution. Our results showed that approximately 40% of the observed areas exhibited negative deviation. In this study, the possibility of using the near real-time spatio-temporal Meteosat Second Generation (MSG) data for drought monitoring in food insecure areas of Ethiopia was tested, and promising results were obtained. The output of this research is expected to assist decision makers in taking timely and appropriate action in order to save millions of lives in drought-affected areas

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Accurate measurement of rainfall is vital to analyze the spatial and temporal patterns of precipitation at various scales. However, the conventional rain gauge observations in many parts of the world such as Ethiopia are sparse and unevenly distributed. An alternative to traditional rain gauge observations could be satellite-based rainfall estimates. Satellite rainfall estimates could be used as a sole product (e.g., in areas with no (or poor) ground observations) or through integrating with rain gauge measurements. In this study, the potential of a newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall product has been evaluated in comparison to rain gauge data over the Upper Blue Nile basin in Ethiopia for the period of 2000 to 2015. In addition, the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT 3) and the African Rainfall Climatology (ARC 2) products have been used as a benchmark and compared with CHIRPS. From the overall analysis at dekadal (10 days) and monthly temporal scale, CHIRPS exhibited better performance in comparison to TAMSAT 3 and ARC 2 products. An evaluation based on categorical/volumetric and continuous statistics indicated that CHIRPS has the greatest skills in detecting rainfall events (POD D0.99, 1.00) and measure of volumetric rainfall (VHID1.00, 1.00), the highest correlation coefficients (r D0.81, 0.88), better bias values (0.96, 0.96), and the lowest RMSE (28.45mmdekad 1, 59.03mmmonth 1) than TAMSAT 3 and ARC 2 products at dekadal and monthly analysis, respectively. CHIRPS overestimates the frequency of rainfall occurrence (up to 31% at dekadal scale), although the volume of rainfall recorded during those events was very small. Indeed, TAMSAT 3 has shown a comparable performance with that of the CHIRPS product, mainly with regard to bias. The ARC 2 product was found to have the weakest performance underestimating rain gauge observed rainfall by about 24 %. In addition, the skill of CHIRPS is less affected by variation in elevation in comparison to TAMSAT 3 and ARC 2 products. CHIRPS resulted in average biases of 1.11, 0.99, and 1.00 at lower (\u3c 1000ma.s.l.), medium (1000 to 2000ma.s.l.), and higher elevation (\u3e 2000ma.s.l.), respectively. Overall, the finding of this validation study shows the potentials of the CHIRPS product to be used for various operational applications such as rainfall pattern and

LINKING SEASONAL PREDICTIONS TO DECISION-MAKING AND DISASTER MANAGEMENT IN THE GREATER HORN OF AFRICA

Seventy-six participants, including experts from seven countries from the Greater Horn of Africa (GHA) and project coinvestigators from the United States, met to discuss experimental seasonal prediction models and products for the GHA, to engage decision-makers and users in the assessment of hydroclimatic information requirements, and to use feedback to build a framework to support decision-making and disaster management. In pre- and postworkshop surveys, workshop participants were asked how the utility of forecasts to decision-makers might be improved. Their recommendations are presente

Establishing a multicenter longitudinal clinical cohort Study in Ethiopia: Advanced Clinical Monitoring of Antiretroviral Treatment Project

Background: The purpose of this paper is to describe the establishment of the  Advanced Clinical Monitoring of ART Project in Ethiopia for monitoring and  evaluation of the longitudinal effectiveness of the ART program and to show the opportunities it presents. This cohort was established in response to the 2005 call by WHO for establishing additional mechanisms for stronger monitoring of ART and the need for creating the platform to generate evidence to guide the care given for the ever increasing number of patients on ART in Ethiopia.Method: A participatory and multi-stage process which started from a consensus building workshop and steered by a mother protocol as well as guiding documents which dictated the degree of engagement and expectations was followed. The primary and secondary aims of the study were agreed upon. A multi-site longitudinal observational clinical cohort was established by a consortium of stakeholders including seven Ethiopian medical schools and their affiliated referral hospitals, John Hopkins University, Ethiopian Public Health Institute, Ministry of Science and Technology, US Centers for Disease Prevention and Control - CDC-Ethiopia, and the Federal Ministry of Health. Adult and adolescent cohorts covering the age range of 14+ years) and pediatric cohorts covering those below age 14 years were the two main cohorts. During the initial recruitment of these cohorts information was extracted from existing documents for a total of 2,100 adult participants. In parallel, a prospective cohort of 1,400 adult and adolescent patients were enrolled for ART initiation and follow-up. Using similar recruitment procedures, a total of 120 children were enrolled in each of retrospective and prospective cohorts. Replacement of participants were made in subsequent years based on lost follow up and death rates to maintain adequacy of the sample to be followed-up.Achievements: Between January 2005 and August 2013 a total of 4,339 patients were followed for a median of 41.6 months and data on demographic characteristics, baseline and ongoing clinical features, hospitalization history, medication and laboratory information were collected. 39,762 aliquots and 25,515 specimens of plasma and dry-blood-spots respectively were obtained and stored longitudinally from October 2009 to August 2013. The project created a research platform for researchers, policy and decision makers. Moreover, it encouraged local and international investigators to identify and answer clinically and programmatically relevant research questions using the available data and specimens. Calls for concept notes paired with multiple trainings to stimulate investigators to conduct analyses further boosted the potential for doing research.Conclusions: A comprehensive and resourceful mechanism for scientific inquiry was established to support the national HIV/ART program. With meaningful involvement and defined roles, establishment of a study, which involved multiple institutions and investigators, was possible. Since ACM is the largest multi-site clinical cohort of patients on antiretroviral treatment in Ethiopia---which can be used for research and for improving clinical management---considering options to sustain the project is crucial. Key Words: Ethiopia, HIV clinical cohort, Antiretroviral therapy, Establishing Longitudinal Cohort Study, ART Monitoring and Evaluatio

Monitoring Residual Soil Moisture and Its Association to the Long-Term Variability of Rainfall over the Upper Blue Nile Basin in Ethiopia

Monitoring soil moisture and its association with rainfall variability is important to comprehend the hydrological processes and to set proper agricultural water use management to maximize crop growth and productivity. In this study, the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture product was applied to assess the dynamics of residual soil moisture in autumn (September to November) and its response to the long-term variability of rainfall in the Upper Blue Nile Basin (UBNB) of Ethiopia from 1992 to 2017. The basin was found to have autumn soil moisture (ASM) ranging from 0.09–0.38 m3/m3, with an average of 0.26 m3/m3. The ASM time series resulted in the coe_cient of variation (CV) ranging from 2.8%–28% and classified as low-to-medium variability. In general, the monotonic trend analysis for ASM revealed that the UBNB had experienced a wetting trend for the past 26 years (1992–2017) at a rate of 0.00024 m3/m3 per year. A significant wetting trend ranging from 0.001 to 0.006 m3/m3 per year for the autumn season was found. This trend was mainly showed across the northwest region of the basin and covers about 18% of the total basin area. The spatial patterns and variability of rainfall and ASM were also found to be similar, which implies the strong relationship between rainfall and soil moisture in autumn. The spring and autumn season rainfall explained a considerable portion of ASM in the basin. The analyses also signified that the rainfall amount and distribution impacted by the topography and land cover classes of the basin showed a significant influence on the characteristics of the ASM. Further, the result verified that the behavior of ASM could be controlled by the loss of soil moisture through evapotranspiration and the gain from rainfall, although changes in rainfall were found to be the primary driver of ASM variability over the UBNB

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., a = 0.01, a = 0.05, and a = 0.1). Thus, the AWS based SCA model with a = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when a value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions