The Baltimore Urban Food Distribution (BUD) App: Study Protocol to Assess the Feasibility of a Food Systems Intervention.

Low-income urban communities in the United States commonly lack ready access to healthy foods. This is due in part to a food distribution system that favors the provision of high-fat, high-sugar, high-sodium processed foods to small retail food stores, and impedes their healthier alternatives, such as fresh produce. The Baltimore Urban food Distribution (BUD) study is a multilevel, multicomponent systems intervention that aims to improve healthy food access in low-income neighborhoods of Baltimore, Maryland. The primary intervention is the BUD application (app), which uses the power of collective purchasing and delivery to affordably move foods from local producers and wholesalers to the city's many corner stores. We will implement the BUD app in a sample of 38 corner stores, randomized to intervention and comparison. Extensive evaluation will be conducted at each level of the intervention to assess overall feasibility and effectiveness via mixed methods, including app usage data, and process and impact measures on suppliers, corner stores, and consumers. BUD represents one of the first attempts to implement an intervention that engages multiple levels of a local food system. We anticipate that the app will provide a financially viable alternative for Baltimore corner stores to increase their stocking and sales of healthier foods, subsequently increasing healthy food access and improving diet-related health outcomes for under-resourced consumers. The design of the intervention and the evaluation plan of the BUD project are documented here, including future steps for scale-up. Trial registration #: NCT05010018.

Changes in prenatal care and vaccine willingness among pregnant women during the COVID-19 pandemic.

INTRODUCTION: Concerns about SARS-CoV-2 infection risk in health care settings have resulted in changes in prenatal care and birth plans, such as shifts to in-person visits and increased Cesarean delivery. These changes may affect quality of care and limit opportunities for clinicians to counsel pregnant individuals, who are at higher risk of severe COVID-19 disease and adverse pregnancy outcomes, about prevention and vaccination. METHODS: We conducted a cross-sectional online survey of United States adults on changes in prenatal care, COVID-19 vaccine willingness, and reasons for unwillingness to receive a vaccine. We summarized changes in access to care and examined differences in vaccine willingness between pregnant and propensity-score matched non-pregnant controls using chi-squared tests and multivariable conditional logistic regression. RESULTS: Between December 15-23, 2020, 8481 participants completed the survey, of which 233 were pregnant. Three-quarters of pregnant women (n = 186) experienced a change in prenatal care, including format of care (n = 84, 35%) and reduced visits (n = 69, 24%). Two-thirds experienced a change in birth plans, from a hospital birth to home birth (n = 45, 18%) or vaginal birth to a Cesarean delivery (n = 42, 17%). Although 40% of pregnant women (n = 78) were unwilling to receive COVID-19 vaccination, they had higher, though non-significant, odds of reporting willingness to receive vaccination compared to similar non-pregnant women (aOR 1.38, 95% CI: 0.95, 2.00). CONCLUSION: To support pregnant women through the perinatal care continuum, maternity care teams should develop protocols to foster social support, patient-centered education around infection prevention that focuses on improved risk perception, expected changes in care due to COVID-19, and vaccine effectiveness and safety.

COVID-19 risk perceptions of social interaction and essential activities and inequity in the USA: results from a nationally representative survey.

INTRODUCTION: SARS-CoV-2 has disproportionately affected disadvantaged communities across the USA. Risk perceptions for social interactions and essential activities during the COVID-19 pandemic may vary by sociodemographic factors. METHODS: We conducted a nationally representative online survey of 1592 adults in the USA to understand risk perceptions related to transmission of COVID-19 for social (eg, visiting friends) and essential activities (eg, medical visits or returning to work). We assessed relationships for activities using bivariate comparisons and multivariable logistic regression modelling, between responses of safe and unsafe, and participant characteristics. Data were collected and analysed in 2020. RESULTS: Among 1592 participants, risk perceptions of unsafe for 13 activities ranged from 29.2% to 73.5%. Large gatherings, indoor dining and visits with elderly relatives had the highest proportion of unsafe responses (>58%), while activities outdoor, accessing healthcare and going to the grocery store had the lowest (<36%). Older respondents were more likely to view social gatherings and indoor activities as unsafe but less likely for other activities, such as going to the grocery store and accessing healthcare. Compared with white/Caucasian respondents, black/African-American and Hispanic/Latino respondents were more likely to view activities such as dining and visiting friends outdoor as unsafe. Generally, men versus women, Republicans versus Democrats and independents, and individuals with higher versus lower income were more likely to view activities as safe. CONCLUSION: Evidence-based interventions should be tailored to sociodemographic differences in risk perception, access to information and health behaviours when implementing efforts to control the COVID-19 pandemic.

Rapid Real-time Tracking of Nonpharmaceutical Interventions and Their Association With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Positivity: The Coronavirus Disease 2019 (COVID-19) Pandemic Pulse Study.

BACKGROUND: Current mitigation strategies for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on the population-wide adoption of nonpharmaceutical interventions (NPIs). Monitoring the adoption of NPIs and their associations with SARS-CoV-2 infection history can provide key information for public health. METHODS: We sampled 1030 individuals in Maryland from 17-28 June 2020 to capture sociodemographically and geographically resolved information about NPI adoption and access to SARS-CoV-2 testing, and examine associations with self-reported SARS-CoV-2 positivity. RESULTS: Overall, 92% reported traveling for essential services and 66% visited friends/family. Use of public transport was reported by 18%. In total, 68% reported strict social distancing indoors and 53% reported strict masking indoors; indoor social distancing was significantly associated with age, and race/ethnicity and income were associated with masking. Overall, 55 participants (5.3%) self-reported ever testing positive for SARS-CoV-2, with strong dose-response relationships between several forms of movement frequency and SARS-CoV-2 positivity. In a multivariable analysis, a history of SARS-CoV-2 infection was negatively associated with strict social distancing (adjusted odds ratio [aOR] for outdoor social distancing, 0.10; 95% confidence interval, .03-.33). Only public transport use (aOR for >7 times vs never, 4.3) and visiting a place of worship (aOR for ≥3 times vs never, 16.0) remained significantly associated with SARS-CoV-2 infection after adjusting for strict social distancing and demographics. CONCLUSIONS: These results support public health messaging that strict social distancing during most activities can reduce SARS-CoV-2 transmission. Additional considerations are needed for indoor activities with large numbers of persons (places of worship and public transportation), where even NPIs may not be possible or sufficient.

Food insecurity is adversely associated with psychological distress, anxiety and depression during the COVID-19 pandemic.

Early COVID-19 pandemic data showed a spike in both food insecurity and poor mental health. The purpose of this study was to examine the relationship between food insecurity and mental health outcomes nine months after the start of the COVID-19 pandemic. A national survey of adults 18 years and older was administered in December 2020 (N = 8,355). Multivariable logistic models and post-estimation margins commands were used to show the predicted probability of mental health outcomes (psychological distress, anxiety, and depression) by food security status. The majority of participants (68.5%) reported high/marginal food security, 15.5% had low food security, and 16.0% had very low food security. There was a strong dose response relationship between food insecurity and higher psychological distress, anxiety and depression. Fewer than one in five adults with high/marginal food security screened positive for all three mental health outcomes, while more than two in five adults with low food security screened positive for psychological distress (39.9%), depression (41.7%) and anxiety (41.3%). Among adults with very low food security, nearly half screened positive for psychological distress (47.7%), depression (48.1%) and anxiety (49.4%). Younger adults had higher prevalence of psychological distress compared to older adults regardless of food security status. Food insecure adults, particularly young adults, have higher rates of psychological distress, anxiety, and depression than their food secure counterparts. Facilitating opportunities to connect at risk populations with food assistance and affordable mental healthcare should be prioritized as the pandemic continues and beyond.

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study.

The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

HIT-COVID, a global database tracking public health interventions to COVID-19.

The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national and local governments, including border restrictions, school closures, mandatory facemask use and stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmission is key to rational policy making in response to the current and future pandemics. In order to estimate the effectiveness of these interventions, detailed descriptions of their timelines, scale and scope are needed. The Health Intervention Tracking for COVID-19 (HIT-COVID) is a curated and standardized global database that catalogues the implementation and relaxation of COVID-19 related PHSM. With a team of over 200 volunteer contributors, we assembled policy timelines for a range of key PHSM aimed at reducing COVID-19 risk for the national and first administrative levels (e.g. provinces and states) globally, including details such as the degree of implementation and targeted populations. We continue to maintain and adapt this database to the changing COVID-19 landscape so it can serve as a resource for researchers and policymakers alike.