Socio-demographic factors associated with hunger among food pantry users in Eastern Massachusetts.

To assess the determinants of hunger among food pantry users, the present study used a cross-sectional survey that included a modified Household Hunger Scale to quantify hunger. Mixed-effects logistic regression models were used to assess the relationship between hunger categories and various household socio-demographic and economic characteristics, such as age, race, household size, marital status and experience of any economic hardship. The survey was administered to food pantry users from June 2018 to August 2018 at various food pantries across Eastern Massachusetts with 611 food pantry users completing the questionnaire at any of the 10 food pantry sites. One-fifth (20⋅13 %) of food pantry users experienced moderate hunger and 19⋅14 % experienced severe hunger. Food pantry users who were single, divorced or separated; had less than a high school education; working part-time, unemployed or retired; or, who earned incomes less than $1000 per month were most likely to experience severe or moderate hunger. Pantry users who experienced any economic hardship had 4⋅78 the adjusted odds of severe hunger (95 % CI 2⋅49, 9⋅19), which was much larger than the odds of moderate hunger (AOR 1⋅95; 95 % CI 1⋅10, 3⋅48). Younger age and participation in WIC (AOR 0⋅20; 95 % CI 0⋅05-0⋅78) and SNAP (AOR 0⋅53; 95 % CI 0⋅32-0⋅88) were protective against severe hunger. The present study illustrates factors affecting hunger in food pantry users, which can help inform public health programmes and policies for people in need of additional resources. This is essential particularly in times of increasing economic hardships recently exacerbated by the COVID-19 pandemic.

The Shifting Public Health Landscape and Virtual Learning Environment: The Effectiveness of Practice-Based Teaching Delivered In-Person, Virtual, and Hybrid.

The pandemic necessitated teaching competencies that allow public health (PH) students to be immediately workforce ready. The shift to virtual learning provided an ideal time to consider pedagogies focused on applied learning opportunities, such as practice-based teaching (PBT). This multi-year, post-test evaluation of one PBT course explored differences in students' competency achievement immediately post-course with different modalities of delivery: fall 2019 in-person (n = 16), summer 2020 virtual (n = 8), and fall 2020 hybrid (n = 15). Using a variety of methods to assess across semesters, the study found virtual and hybrid learning environments resulted in equally high levels of competency achievement as in-person delivery. Regardless of course delivery, students reported, with no difference across semesters, PBT directly contributed to their workforce readiness, helped with acquisition of essential workforce skills such as problem-solving, leadership, and teamwork, and led to skill and knowledge acquisition they would not have achieved in a non-PBT course. The increased emphasis on virtual learning changed the higher education landscape and the need for students to be workforce-ready with the technical and professional skills demanded by the field and offered opportunity to redesign courses with an emphasis on applied opportunities. Virtually delivered PBT is an effective, adaptable, and sustainable pedagogy worth the investment.

Understanding the impacts of COVID-19 on the determinants of food insecurity: A state-specific examination.

This paper examines risk factors influencing food insecurity during the first year of the COVID-19 pandemic in a state in the U.S. heavily impacted by it and offers recommendations for multi-sector intervention. The U.S. Census Bureau Household Pulse Survey was analyzed to evaluate the impacts of COVID-19 on food security in Massachusetts from April 2020 through March 2021 using a study sample of 57,678 participants. Food security was defined as a categorical variable (food security, marginal food security, low food security, very low food security) and binary variable (food security and food insecurity). Known or suspected factors that contribute to it, such as childcare, education, employment, housing, and transportation were examined in multivariate logistic regression models. Data imputation methods accounted for missing data. Sociodemographic characteristics, including lower education level and living in a household with children, were determinants of food insecurity. Another factor that influenced food insecurity was economic hardships, such as unemployment, being laid off due to COVID-19, not working due to concerns about contracting or spreading COVID-19, or not having enough money to buy food. A third factor influencing food insecurity was food environment, such as lack of geographic access to healthy foods. Some of these factors have been exacerbated by the pandemic and will continue to impact food security. These should be addressed through a comprehensive approach with public health efforts considering all levels of the social ecological model and the context created by the pandemic.