The coronavirus disease 2019 (COVID-19) pandemic profoundly affected food systems including food security. Understanding how the COVID-19 pandemic impacted food security is important to provide support and identify long-term impacts and needs. The National Food Access and COVID research Team (NFACT) was formed to assess food security over different US study sites throughout the pandemic, using common instruments and measurements. This study presents results from 18 study sites across 15 states and nationally over the first year of the COVID-19 pandemic. A validated survey instrument was developed and implemented in whole or part through an online survey of adults across the sites throughout the first year of the pandemic, representing 22 separate surveys. Sampling methods for each study site were convenience, representative, or high-risk targeted. Food security was measured using the USDA 6-item module. Food security prevalence was analyzed using ANOVA by sampling method to assess statistically significant differences. Respondents (n = 27,168) indicate higher prevalence of food insecurity (low or very low food security) since the COVID-19 pandemic, compared with before the pandemic. In nearly all study sites, there is a higher prevalence of food insecurity among Black, Indigenous, and People of Color (BIPOC), households with children, and those with job disruptions. The findings demonstrate lingering food insecurity, with high prevalence over time in sites with repeat cross-sectional surveys. There are no statistically significant differences between convenience and representative surveys, but a statistically higher prevalence of food insecurity among high-risk compared with convenience surveys. This comprehensive study demonstrates a higher prevalence of food insecurity in the first year of the COVID-19 pandemic. These impacts were prevalent for certain demographic groups, and most pronounced for surveys targeting high-risk populations. Results especially document the continued high levels of food insecurity, as well as the variability in estimates due to the survey implementation method.

States adopt minimum wages to improve workers’ economic circumstances and well-being. Many studies, but not all, find evidence of health benefits from higher minimum wages. This study used a rigorous “triple difference” strategy to identify the associations between state minimum wages and adult obesity, body mass index (weight (kg)/height (m)²), hypertension, diabetes, fair or poor health, and serious psychological distress. National Health Interview Survey data (United States, 2008–2015) on adults aged 25–64 years (n = 131,430) were linked to state policies to estimate the prevalence odds ratio or mean difference in these outcomes associated with a $1 increase in current and 2-year lagged minimum wage among less-educated adults overall and by sex, race/ethnicity, and age. In contrast to prior studies, there was no association between current minimum wage and health; however, 2-year lagged minimum wage was positively associated with the likelihood of obesity (prevalence odds ratio = 1.08, 95% confidence interval: 1.00, 1.16) and with elevated body mass index (mean difference = 0.27, 95% confidence interval: 0.04, 0.49). In subgroup models, current and 2-year lagged minimum wage were associated with a higher likelihood of obesity among male and non-White or Hispanic adults. The associations with hypertension also varied by sex and the timing of the exposure.

Typical diets include an assortment of unprocessed, processed, and ultra-processed foods, along with culinary ingredients. Linear programming (LP) can be used to generate nutritionally adequate food patterns that meet pre-defined nutrient guidelines. The present LP models were set to satisfy 22 nutrient standards, while minimizing deviation from the mean observed diet of the Seattle Obesity Study (SOS III) sample. Component foods from the Fred Hutch food frequency questionnaire comprised the market basket. LP models generated optimized 2000 kcal food patterns by selecting from all foods, unprocessed foods only, ultra-processed foods only, or some other combination. Optimized patterns created using all foods contained less fat, sugar, and salt, and more vegetables compared to the SOS III mean. Ultra-processed foods were the main sources of added sugar, saturated fat and sodium. Ultra-processed foods also contributed most vitamin E, thiamin, niacin, folate, and calcium, and were the main sources of plant protein. LP models failed to create optimal diets using unprocessed foods only and ultra-processed foods only: no mathematical solution was obtained. Relaxing the vitamin D criterion led to optimized diets based on unprocessed or ultra-processed foods only. However, food patterns created using unprocessed foods were significantly more expensive compared to those created using foods in the ultra-processed category. This work demonstrates that foods from all NOVA categories can contribute to a nutritionally adequate diet.

Summary: Background: In‐person assessments of physical activity (PA) and body weight can be burdensome for participants and cost prohibitive for researchers. This study examined self‐reported PA and weight accuracy and identified patterns of misreporting in a diverse sample.Methods: King, Pierce and Yakima county residents, aged 21–59 years (n = 728), self‐reported their moderate‐to‐vigorous PA (MVPA) and weight, in kilograms. Self‐reports were compared with minutes of bout‐level MVPA, from 3 days of accelerometer data, and measured weights. Regression models examined characteristics associated with underreporting and overreporting of MVPA and weight, the potential bias introduced using each measure and the relation between perceived and measured PA and weight.Results: MVPA underreporting was higher among males and college educated participants; however, there was no differential MVPA overreporting. Weight underreporting was higher among males, those age 40–49 years and persons with obesity. Weight overreporting was higher among Hispanic participants and those reporting stress, unhappiness and fair or poor health. The estimated PA–obesity relation was similar using measured and self‐reported PA but not self‐reported weight. Perceived PA and weight predicted measured values.Conclusion: Self‐reported PA and weight may be useful should objective measurement be infeasible; however, though population‐specific adjustment for differential reporting should be considered.