Estimating the household drought driven food insecurity using system dynamics model: The case of afar national regional state of Ethiopia

Spatial exploration of non-resilience to food insecurity, its association with COVID-19 and household coping strategies in East Gojjam districts, Northwest Ethiopia, 2020

The coronavirus disease-2019 (COVID-19) pandemic has posed a significant multifaceted threat to the global community. Ethiopia, as a Sub-Saharan African country, is suffering from chronic food insecurity, and the emergence of such a pandemic will exacerbate the situation. As a result, this study investigated the spatial variation of non-resilience to food insecurity, its relationship with COVID-19, and household coping strategies to become resilient in the long run among households in the East Gojjam Zone of Northwest Ethiopia. From September 22 to December 24, 2020, an agro-ecological-based cross-sectional study of 3532 households was conducted to assess the spatial distribution and associated factors of non-resilience to household food insecurity. The enumeration areas (EAs) and households were chosen using a multistage sampling technique. Data were gathered using a semi-structured questionnaire and checklist using an Android device loaded with an Open Data Kit (ODK) template. Binary logistic regression was used to identify the specific factors associated with household non-resilience to food insecurity. A thematic analysis was conducted to investigate the opportunities and challenges of resilience for household food insecurity. Nearly two-thirds (62.5%) of the households were farmers, 67.9% lived in rural areas, and nearly three-quarters (73.8%) earned less than or equal to ETB 2100 per month. Males headed more than four-fifths of the households (81.7%). We found that nearly two-thirds of the households (60.02%), 95% CI 58.40, 61.64) were food insecure. After bivariate logistic regression, we found that households who were divorced (AOR = 2.54 (1.65, 3.87)), daily laborers (AOR = 2.37 (1.15, 4.87)), government employees (AOR = 2.06 (1.05, 4.05)), residents of highland and hot areas (AOR = 11.5 (5.37, 16.77)) and lowland areas (AOR = 1.35 (1.02, 3.15)) were frustrated by COVID-19 (AOR = 1.23 (1.02, 1.50)) and price inflation (1.89 (AOR = 1.42, 2.56))) were at higher odds of being non-resilient to household food insecurity at a 95% confidence level. Geospatial hot spot analysis revealed that Kurar kebele (the lowest government administrative unit) in Dejen District and Debre Markos town were the red-hotspot areas of household non-resilience to food insecurity. Less than a quarter of the households attempted to cope with food insecurity by adjusting their food consumption, while more than 60% of the households chose none of the coping strategies tested. According to the thematic analysis, the degree of poverty (lack of asset ownership), the COVID-19 pandemic, farm decreased variety, and low crop productivity were identified as challenges to coping with the hardship of resilience to food insecurity. During the COVID-19 pandemic and public emergency, the proportion of households that were unprepared for food insecurity reached its peak. It was recognized that a segment of the population with low economic capacity was more vulnerable to food insecurity and less resilient. Tough developmental gains will be undermined in this case. As a result, each responsible body and stakeholder should develop and implement solid corrective plans for the local context.

Risk Factors of Mid-upper Arm Circumference (MUAC) Based Child Malnutrition in the Flood-affected Areas of Pakistan: A Cross-sectional Study

Low- and middle-income countries are usually at high risk of malnutrition. Not only that but the prevalence of malnutrition is much higher. It is important to evaluate the determinants of malnutrition in flood-affected areas of Pakistan. The present study examined the prevalence and risk factors of MUAC-based child malnutrition in flood-hit regions of Khyber Pakhtunkhwa, Pakistan. Multi-stage sampling was employed to select 656 households. Finally, 298 children of 6-59 months were selected. MUAC, an independent anthropometric parameter, was used to investigate the nutritional status of children. An automated logistic regression model was used to identify the risk factors of MUAC-based malnutrition. The prevalence of MUAC-based malnutrition was found 46%, including 40.5% females and 52.1% males. More than 90% of people had improved water quality and soap hand washing facility. Almost 17% of respondents had no toilet facility. Through automated logistic model, child age, maternal age, family size, income level, mother education, water quality, toilet facility were the significant determinants (P < .05) of MUAC-based undernutrition in flood affecting the area. The findings suggest that MUAC-based malnutrition can be minimized in flood-hit areas by targeting the listed risk factors. Community-based awareness programs regarding guidance on nutrition might be a key to reducing malnutrition in the target areas.