Floods and health in Gambella region, Ethiopia: a qualitative assessment of the strengths and weaknesses of coping mechanisms

BACKGROUND: Floods are the most frequent and devastating type of natural disaster worldwide, causing unprecedented deaths, diseases, and destruction of property and crops. Flooding has a greater impact in developing countries due to lack of sufficient disaster management structures and a lack of economic resources. OBJECTIVE: This study was conducted with the aim of contributing to the knowledge base of development strategies that reduce flood-related health risks in developing countries. The study focused particularly on assessing the flood risks and health-related issues in the Gambella region of Ethiopia; with the intent of producing relevant information to assist with the improvements in the efficacy of the current flood coping strategies in the region. METHODS: Data were gathered through interviews with 14 officers from different government and non-governmental organizations and a questionnaire survey given to 35 flood victims in Itang woreda. A qualitative approach was applied and the data were analyzed using content analysis. RESULTS: It was found that flooding is a common problem in Gambella region. The findings also indicate that the flood frequency and magnitude has increased rapidly during the last decade. The increase in floods was driven mainly by climate change and changes in land use, specifically deforestation. The reported main impacts of flooding on human health in Gambella region were deaths, injuries, and diseases such as malaria and diarrhea. Another notable consequence of flooding was crop destruction and subsequent malnutrition. CONCLUSIONS: Three weaknesses that were identified in the current coping strategies for flood-related health impacts in Gambella region were a lack of flood-specific policy, absence of risk assessment, and weak institutional capacity. This study recommends new policy approaches that will increase the effectiveness of the current flood coping strategies to sustainably address the impact of flooding on human health

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

Background: Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods: Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results: Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions: This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors

Climatic variables and malaria transmission dynamics in Jimma town, South West Ethiopia

<p>Abstract</p> <p>Background:-</p> <p>In Ethiopia, malaria is seasonal and unstable, causing frequent epidemics. It usually occurs at altitudes < 2,000 m above sea level. Occasionally, transmission of malaria occurs in areas previously free of malaria, including areas > 2,000 m above sea level. For transmission of malaria parasite, climatic factors are important determinants as well as non-climatic factors that can negate climatic influences. Indeed, there is a scarcity of information on the correlation between climatic variability and malaria transmission risk in Ethiopia in general and in the study area in particular. Therefore, the aim of this study was to determine the level of correlation between meteorological variables and malaria cases.</p> <p>Methods: -</p> <p>Time-series analysis was conducted using data on monthly meteorological variables and monthly total malaria in Jimma town, south west Ethiopia, for the period 2000-2009. All the data were entered and analyzed using SPSS-15 database program. Spearman correlation and linear regression analysis were used to asses association between the variables.</p> <p>Results: -</p> <p>During last ten years (2000-2009), a fluctuating trend of malaria transmission was observed with <it>P.vivax </it>becoming predominant species. Spearman correlation analysis showed that monthly minimum temperature, total rainfall and two measures of relative humidity were positively related with malaria but monthly maximum temperature negatively related. Also regression analysis suggested that monthly minimum (p = 0.008), monthly maximum temperature (p = 0.013) and monthly total rainfall (p = 0.040), at one month lagged effect, were significant meteorological factors for transmission of malaria in the study area.</p> <p>Conclusion: -</p> <p>Malaria incidences in the last decade seem to have a significant association with meteorological variables. In future, prospective and multidisciplinary cooperative research involving researchers from the fields of parasitology, epidemiology, botany, agriculture and climatology is necessary to identify the real effect of meteorological factors on vector- borne diseases like malaria.</p

Understanding the economic, socio-cultural, and environmental impacts of resettlement projects

Studies from the Global South on resettlement have focused on compensatory issues, with a little emphasis on socio-cultural, economic, and environmental impacts. The present study therefore focused on exploring the socio-cultural, economic, and environmental impacts of the Bui-Dam project in five resettled communities in Ghana. We used mixed methods to analyse household and qualitative in-depth interviews with institutions. We found that while resettlement projects offer positive benefits, they also yield negative impacts on the socio-cultural, economic, and environmental aspects of the resettled population. It is therefore important that policymakers carefully review and systematically integrate these concerns into resettlement plans