Community Attitude and Associated Factors towards People with Mental Illness among Residents of Worabe Town, Silte Zone, Southern Nation's Nationalities and People's Region, Ethiopia.

BackgroundMental illnesses worldwide are accompanied by another pandemic, that of stigma and discrimination. Public understanding about mental illnesses and attitudes towards people with mental illness play a paramount role in the prevention and treatment of mental illness and the rehabilitation of people with mental illness.ObjectiveTo assess community attitude and associated factors towards people with mental illness.MethodsCommunity based cross-sectional study was conducted from April 28 to May 28, 2014. Quantitative data were collected through interview from 435 adults selected using simple random sampling. Data were collected using community attitude towards mentally ill (CAMI) tool to assess community attitude towards people with mental illness and associated factors. Multiple linear regression analysis was performed to identify predictors of community attitude towards people with mental illness and the level of significance association was determined by beta with 95% confidence interval and P less than 0.05.ResultsThe highest mean score was on social restrictiveness subscale (31.55±5.62). Farmers had more socially restrictive view (β = 0.291, CI [0.09, 0.49]) and have less humanistic view towards mentally ill (β = 0.193, CI [-0.36, -0.03]). Having mental health information had significantly less socially restrictive (β = -0.59, CI [-1.13, -0.05]) and less authoritarian (β = -0.10, CI [-1.11, -0.06]) view towards mentally ill but respondents who are at university or college level reported to be more socially restrictive (β = 0.298, CI [0.059, 0.54]). Respondents whose age is above 48 years old had significantly less view of community mental health ideology (β = -0.59, CI [-1.09, -0.08]).Conclusion and recommendationResidents of Worabe town were highly socially restrictive but less authoritarian. There was high level of negative attitude towards people with mental illness along all the subscales with relative variation indicating a need to develop strategies to change negative attitude attached to mental illness in Worabe town at community level

Prevalence of Common Mental Disorders and Associated Factors among People with Glaucoma Attending Outpatient Clinic at Menelik II Referral Hospital, Addis Ababa, Ethiopia.

BackgroundThe burden of blindness from glaucoma is high. Therefore, people suffering from a serious eye disease such as glaucoma, which can lead to blindness, usually have an emotional disturbance on the patient. Untreated psychiatric illness is associated with increased morbidity and increased costs of care.ObjectiveThis study aimed to assess prevalence of common mental disorders and associated factors among people with Glaucoma attending Menelik II referral hospital, Addis Ababa, Ethiopia, 2014.MethodsInstitution based Cross-sectional study design was conducted in the Department of Ophthalmology Menelik II Referral Hospital from April 10 to May 15, 2014. 423 participants who had undergone through investigation, examination and diagnosed as patients of glaucoma were selected randomly from the glaucoma clinic. Data were collected through face to face interview using Self Reporting Questionnaire consisted of 20 items. Study subjects who scored ≥11 from SRQ-20 were considered as having common mental disorders. Bivariate and multivariable logistic regression analysis with 95% CI were done and variables with PResultsFour hundred five patients with glaucoma were included in our study with response rate of 95.7% and 64.5% were males. The average age was 59±13.37 years. Common mental disorders were observed in 23.2% of Glaucoma patients. It is quite obvious that levels of CMDs were high among patients with glaucoma. There was a significant association between age, sex, chronic physical illness, income and duration of illness at P Conclusion and recommendationSymptoms of common mental disorders were the commonest comorbidities among patients with glaucoma. It will be better to assess and treat Common mental disorders as a separate illness in patients with glaucoma

High aboveground carbon stock of African tropical montane forests

Tropical forests store 40–50 per cent of terrestrial vegetation carbon1. However, spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests2. Owing to climatic and soil changes with increasing elevation3, AGC stocks are lower in tropical montane forests compared with lowland forests2. Here we assemble and analyse a dataset of structurally intact old-growth forests (AfriMont) spanning 44 montane sites in 12 African countries. We find that montane sites in the AfriMont plot network have a mean AGC stock of 149.4 megagrams of carbon per hectare (95% confidence interval 137.1–164.2), which is comparable to lowland forests in the African Tropical Rainforest Observation Network4 and about 70 per cent and 32 per cent higher than averages from plot networks in montane2,5,6 and lowland7 forests in the Neotropics, respectively. Notably, our results are two-thirds higher than the Intergovernmental Panel on Climate Change default values for these forests in Africa8. We find that the low stem density and high abundance of large trees of African lowland forests4 is mirrored in the montane forests sampled. This carbon store is endangered: we estimate that 0.8 million hectares of old-growth African montane forest have been lost since 2000. We provide country-specific montane forest AGC stock estimates modelled from our plot network to help to guide forest conservation and reforestation interventions. Our findings highlight the need for conserving these biodiverse9,10 and carbon-rich ecosystems

The global abundance of tree palms

Aim Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location Tropical and subtropical moist forests. Time period Current. Major taxa studied Palms (Arecaceae). Methods We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co‐occurring non‐palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long‐term climate stability. Life‐form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non‐tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above‐ground biomass, but the magnitude and direction of the effect require additional work. Conclusions Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests

High above-ground carbon stock of African tropical montane forests

Tropical forests store 40–50 per cent of terrestrial vegetation carbon1. However, spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests2. Owing to climatic and soil changes with increasing elevation3, AGC stocks are lower in tropical montane forests compared with lowland forests2. Here we assemble and analyse a dataset of structurally intact old-growth forests (AfriMont) spanning 44 montane sites in 12 African countries. We find that montane sites in the AfriMont plot network have a mean AGC stock of 149.4 megagrams of carbon per hectare (95% confidence interval 137.1–164.2), which is comparable to lowland forests in the African Tropical Rainforest Observation Network4 and about 70 per cent and 32 per cent higher than averages from plot networks in montane2,5,6 and lowland7 forests in the Neotropics, respectively. Notably, our results are two-thirds higher than the Intergovernmental Panel on Climate Change default values for these forests in Africa8. We find that the low stem density and high abundance of large trees of African lowland forests4 is mirrored in the montane forests sampled. This carbon store is endangered: we estimate that 0.8 million hectares of old-growth African montane forest have been lost since 2000. We provide country-specific montane forest AGC stock estimates modelled from our plot network to help to guide forest conservation and reforestation interventions. Our findings highlight the need for conserving these biodiverse9,10 and carbon-rich ecosystems

Prevalence of depression and associated factors among Somali refugee at Melkadida camp, Southeast Ethiopia: a cross-sectional study.

BackgroundPsychological distress, psychosomatic complaints and clinical mental disorders such as depression and post-traumatic stress disorder are highly prevalent among refugees than other populations. Even though there were several studies done on mental health of refugees globally, there is very few in Ethiopia regarding the mental health of these vulnerable populations. Thus we aimed at determining the prevalence of depression and identifying determinants of depression among refugees.MethodsA community based cross-sectional multistage survey with 847 adult refugees was conducted in May 2014 at Melkadida camp, Southeast Ethiopia. Data were collected by face to face interviews on socio demographic by using structured questionnaire, level of exposure to trauma by Harvard Trauma Questionnaire and depression symptoms by using Patient Health Questionnaire. Data entry and clearance were carried out by EpInfo version 7 and analysis was carried out by Statistical Package for Social Sciences version-20 software package. Data was examined using descriptive statistics and logistic regression, odds ratios and 95 % confidence intervals.ResultOver one third (38.3 %) of respondents met the symptoms criteria for depression. Gender, marital status, displaced previously as refugee, witnessing murderer of family or friend, lack of house or shelter and being exposed to increased number of cumulative traumatic events were significantly associated with depression among Somali refugees in Melkadida camp.ConclusionThe study revealed a relatively high prevalence of depression episode among refugees. Being female, divorced, deprived of shelter and witnessing the murder of family are most determinants of depression in refugees. Strengthening the clinical set up and establishing good referral linkage with mental health institutions is strongly recommended

Attributing the 2017 Bangladesh floods from meteorological and hydrological perspectives

In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents, for the first time, an attribution of this precipitation-induced flooding to anthropogenic climate change from a combined meteorological and hydrological perspective. Experiments were conducted with three observational datasets and two climate models to estimate changes in the extreme 10-day precipitation event frequency over the Brahmaputra basin up to the present and, additionally, an outlook to 2 C warming since pre-industrial times. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed. In all three observational precipitation datasets the climate change trends for extreme precipitation similar to that observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model ensemble shows a significant positive influence of anthropogenic climate change, whereas the other large ensemble model simulates a cancellation between the increase due to greenhouse gases (GHGs) and a decrease due to sulfate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than in precipitation, but the 95% confidence intervals still encompass no change in risk. Extending the analysis to the future, all models project an increase in probability of extreme events at 2 °C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation and being more likely by a factor of about 1.5 for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: We find the change in risk to be greater than 1 and of a similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available or as an additional measure to confirm qualitative conclusions. Besides this, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols

Attributing the 2017 Bangladesh floods from meteorological and hydrological perspectives

In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents, for the first time, an attribution of this precipitation-induced flooding to anthropogenic climate change from a combined meteorological and hydrological perspective. Experiments were conducted with three observational datasets and two climate models to estimate changes in the extreme 10-day precipitation event frequency over the Brahmaputra basin up to the present and, additionally, an outlook to 2 C warming since pre-industrial times. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed. In all three observational precipitation datasets the climate change trends for extreme precipitation similar to that observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model ensemble shows a significant positive influence of anthropogenic climate change, whereas the other large ensemble model simulates a cancellation between the increase due to greenhouse gases (GHGs) and a decrease due to sulfate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than in precipitation, but the 95% confidence intervals still encompass no change in risk. Extending the analysis to the future, all models project an increase in probability of extreme events at 2 °C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation and being more likely by a factor of about 1.5 for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: We find the change in risk to be greater than 1 and of a similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available or as an additional measure to confirm qualitative conclusions. Besides this, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols

Attributing the 2017 Bangladesh floods from meteorological and hydrological perspectives

In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents, for the first time, an attribution of this precipitation-induced flooding to anthropogenic climate change from a combined meteorological and hydrological perspective. Experiments were conducted with three observational datasets and two climate models to estimate changes in the extreme 10-day precipitation event frequency over the Brahmaputra basin up to the present and, additionally, an outlook to 2 C warming since pre-industrial times. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed. In all three observational precipitation datasets the climate change trends for extreme precipitation similar to that observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model ensemble shows a significant positive influence of anthropogenic climate change, whereas the other large ensemble model simulates a cancellation between the increase due to greenhouse gases (GHGs) and a decrease due to sulfate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than in precipitation, but the 95% confidence intervals still encompass no change in risk. Extending the analysis to the future, all models project an increase in probability of extreme events at 2 °C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation and being more likely by a factor of about 1.5 for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: We find the change in risk to be greater than 1 and of a similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available or as an additional measure to confirm qualitative conclusions. Besides this, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols