Linda Vo

Objective: In April 2020, the US Centers for Disease Control and Prevention (CDC) recommended community masking to prevent the transmission of SARS-CoV-2. Since then, a total of 39 US states and DC issued mask mandates. Despite CDC recommendations and supporting evidence that masking reduces COVID-19 community transmission, from January to June 20, 2021 states lifted their mask mandates for all individuals. This study examined the association between lifting state-issued mask mandates and mask-wearing behavior in 2021. Design: We estimated a difference-in-difference model, comparing changes in the likelihood for individuals to wear a mask in states that lifted their mask mandate relative to states that kept their mandates in place between February and June of 2021. Setting: Individuals were surveyed from across the United States. Participants: We used masking behavior data collected by the Porter Novelli View 360 + national surveys (N = 3459), and data from state-issued mask mandates obtained by CDC and the University of Nevada, Las Vegas. Main Outcomes: The outcome variable of interest was self-reported mask use during the 30 days prior to the survey data collection. Results: In the overall population, lifting mask mandates did not significantly influence mask-wearing behavior. Mask wearing did significantly decrease in response to the lifting of mask mandates among individuals living in rural counties and individuals who had not yet decided whether they would receive a COVID-19 vaccine. Conclusion: Policies around COVID-19 behavioral mitigation, specifically amongst those unsure about vaccination and in rural areas, may help reduce the transmission of COVID-19 and other respiratory viruses, especially in communities with low vaccination rates.

Individual and community-level COVID-19 mitigation policies can have effects beyond direct COVID-19 health outcomes, including social, behavioral, and economic outcomes. These social, behavioral, and economic outcomes can extend beyond the pandemic period and have disparate effects on populations. Public Health–Seattle & King County (PHSKC) built on the Centers for Disease Control and Prevention’s community mitigation strategy framework to create a local project tracking near–real-time data to understand factors affected by mitigation approaches, inform decision-making, and monitor and evaluate community-level disparities during the pandemic. This case study describes the framework and lessons learned from PHSKC’s collation, use, and dissemination of local data from 20 data sources to guide community and public health decision-making. Social, behavioral, economic, and health indicators were regularly updated and disseminated through interactive dashboards and products that examined data in the context of applicable policies. Data disaggregated by demographic characteristics and geography highlighted inequities, but not all datasets contained the same details; local surveys or qualitative data were used to fill gaps. Project outcomes included informing city and county emergency response planning related to implementation of financial and food assistance programs. Key lessons learned included the need to (1) build on existing processes and use automated processes and (2) partner with other sectors to use nontraditional public health data for active dissemination and data disaggregation and for real-time data contextualized by policy changes. This project provided programs and communities with timely, reliable data to understand where to invest recovery funding. A similar framework could position other health departments to examine social and economic effects during future public health emergencies.

Objective: The Centers for Disease Control and Prevention’s (CDC’s) Evaluation Fellowship Program is a 2-year fellowship that includes training, placement with a CDC program, and professional development funds. We evaluated whether the program contributed to CDC’s evaluation capacity, prepared fellows for evaluation work, and contributed to their career advancement during its first 10 years. Methods: We used a mixed-methods approach, including conducting an online survey and telephone interviews. External evaluators sent surveys to all 152 alumni and all 123 mentors who participated in the program from 2011 through 2020 (first 8 cohorts) and interviewed 9 mentors and 15 alumni. Results: A total of 110 alumni (72.4%) and 44 mentors (35.8%) completed surveys. Of 44 mentors, most agreed their fellow(s) contributed to their program’s overall evaluation capacity (90.9%) and its ability to do more evaluation (88.6%). Most (84.2%-88.1%) alumni agreed that the Evaluation Fellowship Program prepared them to apply the 6 skill sets that aligned with CDC’s Framework for Program Evaluation in Public Health. Support from the Fellowship office was significantly and positively correlated with performing evaluation tasks (β = 0.25; P = .004) and alumni obtaining their first job (β = 0.36; P < .001). Host program mentoring was significantly correlated with performing evaluation tasks (β = 0.27; P = .02) and alumni obtaining their first job (β = 0.34; P = .007). Conclusion: CDC’s Evaluation Fellowship Program has made progress toward building CDC’s evaluation capacity and preparing a public health workforce to use evaluation skills in various settings. A service-learning model that provides training and applied experiences could prepare a workforce to build evaluation capacity.

Objective: State-issued behavioral policy interventions (BPIs) can limit community spread of COVID-19, but their effects on COVID-19 transmission may vary by level of social vulnerability in the community. We examined the association between the duration of BPIs and the incidence of COVID-19 across levels of social vulnerability in US counties. Methods: We used COVID-19 case counts from USAFacts and policy data on BPIs (face mask mandates, stay-at-home orders, gathering bans) in place from April through December 2020 and the 2018 Social Vulnerability Index (SVI) from the Centers for Disease Control and Prevention. We conducted multilevel linear regression to estimate the associations between duration of each BPI and monthly incidence of COVID-19 (cases per 100 000 population) by SVI quartiles (grouped as low, moderate low, moderate high, and high social vulnerability) for 3141 US counties. Results: Having a BPI in place for longer durations (ie, ≥2 months) was associated with lower incidence of COVID-19 compared with having a BPI in place for <1 month. Compared with having no BPI in place or a BPI in place for <1 month, differences in marginal mean monthly incidence of COVID-19 per 100 000 population for a BPI in place for ≥2 months ranged from –4 cases in counties with low SVI to –401 cases in counties with high SVI for face mask mandates, from –31 cases in counties with low SVI to –208 cases in counties with high SVI for stay-at-home orders, and from –227 cases in counties with low SVI to –628 cases in counties with high SVI for gathering bans. Conclusions: Establishing COVID-19 prevention measures for longer durations may help reduce COVID-19 transmission, especially in communities with high levels of social vulnerability.