Biophysical and socioeconomic state and links of deltaic areas vulnerable to climate change: Volta (Ghana), Mahanadi (India) and Ganges-Brahmaputra-Meghna (India and Bangladesh)

We examine the similarities and differences of specific deltaic areas in parallel, under the project DEltas, vulnerability and Climate Change: Migration and Adaptation (DECCMA). The main reason for studying Deltas is their potential vulnerability to climate change and sea level rise, which generates important challenges for livelihoods. We provide insights into the current socioeconomic and biophysical states of the Volta Delta (Ghana), Mahanadi Delta (India) and Ganges-Brahmaputra-Meghna (India and Bangladesh). Hybrid methods of input-output (IO) construction are used to develop environmentally extended IO models for comparing the economic characteristics of these delta regions with the rest of the country. The main sources of data for regionalization were country level census data, statistics and economic surveys and data on consumption, trade, agricultural production and fishing harvests. The Leontief demand-driven model is used to analyze land use in the agricultural sector of the Delta and to track the links with final demand. In addition, the Hypothetical Extraction Method is used to evaluate the importance of the hypothetical disappearance of a sector (e.g., agriculture). The results show that, in the case of the Indian deltas, more than 60% of the cropland and pasture land is devoted to satisfying demands from regions outside the delta. While in the case of the Bangladeshi and Ghanaian deltas, close to 70% of the area harvested is linked to internal demand. The results also indicate that the services, trade and transportation sectors represent 50% of the GDP in the deltas. Still, agriculture, an activity directly exposed to climate change, plays a relevant role in the deltas'' economies-we have estimated that the complete disappearance of this activity would entail GDP losses ranging from 18 to 32%

Problematic smartphone and social media use among Bangladeshi college and university students amid COVID-19: the role of psychological well-being and pandemic related factors

Background: Smartphone and social media use are an integral part of our daily life. Currently, the impact of excessive smartphone and social media use during the COVID-19 pandemic is poorly understood. The present study aimed to investigate problematic smartphone use (PSPU) and problematic social media use (PSMU) among Bangladeshi college and university students during the COVID-19 pandemic. Methods: A cross-sectional study was carried out involving 5,511 Bangladeshi college and university students (male: 58.9%; mean age: 21.2 years [SD = 1.7]; age range: 18–25) during the social-distancing in the COVID-19 pandemic (July 2020). A self-reported survey containing questions regarding socio-demographic, lifestyle, and home quarantine activities along with four psychometric scales was completed by participants. Results: The mean scores of PSPU and PSMU were 20.8 ± 6.8 (out of 36) and 14.7 ± 4.8 (out of 30). Based on a hierarchical regression analysis, PSPU and PSMU were positively associated with lower age, poor sleep, social media use, watching television, anxiety, and depression. Additionally, PSMU was linked to being female, living with nuclear family, having urban residence, irregular physical exercise, poor engagement with academic studies, and avoiding earning activities, whilst being male, being married, living with lower-income family, and alcohol consumption were linked to PSMU. Conclusions: The findings indicate that PSPU and PSMU were linked to poor psychological well-being (i.e., anxiety and depression) and other factors (especially lower age, poor sleep) during the pandemic, further suggesting the need for interventions including virtual awareness programs among college and university students

Diagnosis of chronic conditions with modifiable lifestyle risk factors in selected urban and rural areas of Bangladesh and sociodemographic variability therein

<p>Abstract</p> <p>Background</p> <p>Bangladesh suffers from a lack of healthcare providers. The growing chronic disease epidemic's demand for healthcare resources will further strain Bangladesh's limited healthcare workforce. Little is known about how Bangladeshis with chronic disease seek care. This study describes chronic disease patients' care seeking behavior by analyzing which providers diagnose these diseases.</p> <p>Methods</p> <p>During 2 month periods in 2009, a cross-sectional survey collected descriptive data on chronic disease diagnoses among 3 surveillance populations within the International Center for Diarrheal Disease Research, Bangladesh (ICDDR, B) network. The maximum number of respondents (over age 25) who reported having ever been diagnosed with a chronic disease determined the sample size. Using SAS software (version 8.0) multivariate regression analyses were preformed on related sociodemographic factors.</p> <p>Results</p> <p>Of the 32,665 survey respondents, 8,591 self reported having a chronic disease. Chronically ill respondents were 63.4% rural residents. Hypertension was the most prevalent disease in rural (12.4%) and urban (16.1%) areas. In rural areas chronic disease diagnoses were made by MBBS doctors (59.7%) and Informal Allopathic Providers (IAPs) (34.9%). In urban areas chronic disease diagnoses were made by MBBS doctors (88.0%) and IAP (7.9%). Our analysis identified several groups that depended heavily on IAP for coverage, particularly rural, poor and women.</p> <p>Conclusion</p> <p>IAPs play important roles in chronic disease care, particularly in rural areas. Input and cooperation from IAPs are needed to minimize rural health disparities. More research on IAP knowledge and practices regarding chronic disease is needed to properly utilize this potential healthcare resource.</p

Socioeconomic Inequalities in Newborn Care During Facility and Home Deliveries: A Cross Sectional Analysis of Data from Demographic Surveillance Sites in Rural Bangladesh, India and Nepal

Background: In Bangladesh, India and Nepal, neonatal outcomes of poor infants are considerably worse than those of better-off infants. Understanding how these inequalities vary by country and place of delivery (home or facility) will allow targeting of interventions to those who need them most. We describe socio-economic inequalities in newborn care in rural areas of Bangladesh, Nepal and India for all deliveries and by place of delivery. Methods: We used data from surveillance sites in Bangladesh, India and from Makwanpur and Dhanusha districts in Nepal, covering periods from 2001 to 2011. We used literacy (ability to read a short text) as indicator of socioeconomic status. We developed a composite score of nine newborn care practices (score range 0–9 indicating infants received no newborn care to all nine newborn care practices). We modeled the effect of literacy and place of delivery on the newborn care score and on individual practices. Results: In all study sites (60,078 deliveries in total), use of facility delivery was higher among literate mothers. In all sites, inequalities in newborn care were observed: the difference in new born care between literate and illiterate ranged 0.35–0.80. The effect of literacy on the newborn care score reduced after adjusting for place of delivery (range score difference literate-illiterate: 0.21–0.43). Conclusion: Socioeconomic inequalities in facility care greatly contribute to inequalities in newborn care. Improving newborn care during home deliveries and improving access to facility care are a priority for addressing inequalities in newborn care and newborn mortality

A penalized four-dimensional variational data assimilation method for reducing forecast error related to adaptive observations

Four-dimensional variational (4D-Var) data assimilation method is used to find the optimal initial conditions by minimizing a cost function in which background information and observations are provided as the input of the cost function. The optimized initial conditions based on background error covariance matrix and observations improve the forecast. The targeted observations determined by using methods such as adjoint sensitivity, observation sensitivity, or singular vectors may further improve the forecast. In this paper, we are proposing a new technique-consisting of a penalized 4D-Var data assimilation method that is able to reduce the forecast error significantly. This technique consists in penalizing the cost function by a forecast aspect defined over the verification domain at the verification time. The results obtained using the penalized 4D-Var method show that the initial condition is optimally estimated, thus resulting in a better forecast by significantly reducing the forecast error over the verification domain at verification time