Perceptions of COVID-19 Risk: How Did People Adapt to the Novel Risk?

BACKGROUND: There is limited understanding of how risk perceptions changed as the US population gained experience with COVID-19. The objectives were to examine risk perceptions and determine the factors associated with risk perceptions and how these changed over the first 18 mo of the pandemic. METHODS: Seven cross-sectional online surveys were fielded between May 2020 and October 2021. The study included a population-weighted sample of 138,303 US adults drawn from a market research platform, with an average 68% cooperation rate. Respondents' risk perception of developing COVID in the next 30 days was assessed at each time point. We examined relationships between 30-day risk perceptions and various factors (including sociodemographic features, health, COVID-19 experience, political affiliation, and psychological variables). RESULTS: COVID risk perceptions were stable across the 2020 surveys and showed a significant decrease in the 2021 surveys. Several factors, including older age, worse health, high COVID worry, in-person employment type, higher income, Democratic political party affiliation (the relatively more liberal party in the United States), low tolerance of uncertainty, and high anxiety were strongly associated with higher 30-d risk perceptions in 2020. One notable change occurred in 2021, in that younger adults (aged 18-29 y) had significantly higher 30-d risk perceptions than older adults did (aged 65 y and older) after vaccination. Initial differences in perception by political party attenuated over time. Higher 30-d risk perceptions were significantly associated with engaging in preventive behaviors. LIMITATIONS: Cross-sectional samples, risk perception item focused on incidence not severity. CONCLUSIONS: COVID risk perceptions decreased over time. Understanding the longitudinal pattern of risk perceptions and the factors associated with 30-d risk perceptions over time provides valuable insights to guide public health communication campaigns. HIGHLIGHTS: The study assessed COVID-19 risk perceptions at 7 time points over 18 mo of the pandemic in large samples of US adults.Risk perceptions were fairly stable until the introduction of vaccines in early 2021, at which point they showed a marked reduction.Higher COVID-19 30-d risk perceptions were significantly associated with the preventive behaviors of masking, limiting social contact, avoiding restaurants, and not entertaining visitors at home.

Parental intentions to vaccinate children against COVID-19: Findings from a U.S. National Survey.

We examined parents' COVID-19 vaccination intentions for their children, reasons for not vaccinating, and the potential impact of a school/daycare vaccination requirement or pediatrician's recommendation on vaccination intentions. Two online surveys were conducted in June-July and September-October 2021, before pediatric COVID-19 vaccines were authorized for emergency use in children age < 12 years, with an internet-based, non-probability sample of U.S. adults. Respondents with children (age < 18 years) in the household were asked about their intention (likelihood) of vaccinating these children against COVID-19. Weighted Chi-square tests using a Rao-Scott correction were performed. Vaccinated (45.7 %) versus unvaccinated (6.9 %) parents were almost seven times more likely to have vaccinated their 12-17-year-old children against COVID-19. Approximately 58.4 % of respondents with unvaccinated children ages 2-11 years and 42.4 % of those with children < 2 years said they are "very" or "extremely likely" to vaccinate these children against COVID-19. Female parents were significantly more likely (p < .01 to p < .001) to express lower levels of COVID-19 vaccine intentions. Across all age groups of children unvaccinated against COVID-19, parental vaccine intentions increased with increased household income and education levels. COVID-19 vaccine side effects and safety concerns were primary reasons for not vaccinating children. Strategies including school vaccination requirements and recommendations from pediatricians were shown to increase parental COVID-19 vaccination intentions for some. More research is needed on factors that increase/hinder COVID-19 pediatric vaccine uptake.

Strategies to increase the intention to get vaccinated against COVID-19: Findings from a nationally representative survey of US adults, October 2020 to October 2021.

OBJECTIVES: We examined COVID-19 vaccination status, intention, and hesitancy and the effects of five strategies to increase the willingness of unvaccinated adults (≥18 years) to get a COVID vaccine. METHODS: Online surveys were conducted between October 1-17, 2020 (N = 14,946), December 4-16, 2020 (N = 15,229), April 8-22, 2021 (N = 14,557), June 17-July 6, 2021 (N = 30,857), and September 3-October 4, 2021 (N = 33,088) with an internet-based, non-probability opt-in sample of U.S. adults matching demographic quotas. Respondents were asked about current COVID-19 vaccination status, intention and hesitancy to get vaccinated, and reasons for vaccine hesitancy. Unvaccinated respondents were assigned to treatment groups to test the effect of five strategies (endorsements, changing social restrictions, financial incentives, vaccine requirements for certain activities, and vaccine requirements for work). Chi-square tests of independence were performed to detect differences in the response distributions. RESULTS: Willingness to be vaccinated (defined as being vaccinated or planning to be) increased over time from 47.6 % in October 2020 to 81.1 % in October 2021. By October 2021, across most demographic groups, over 75 % of survey respondents had been or planned to be vaccinated. In terms of strategies: (1) endorsements had no positive effect, (2) relaxing the need for masks and social distancing increased Intention to Get Vaccinated (IGV) by 6.4 % (p < 0.01), (3) offering financial incentives increased the IGV between 12.3 and 18.9 % (p <.001), (4) vaccine requirements for attending sporting events or traveling increased IGV by 7.8 % and 9.1 %, respectively (p = 0.02), and vaccine requirement for work increased IGV by 35.4 %. The leading causes (not mutually exclusive) for hesitancy were concerns regarding vaccine safety (52.5 %) or side effects (51.6 %), trust in the government's motives (41.0 %), and concerns about vaccine effectiveness (37.6 %). CONCLUSIONS: These findings suggest that multiple strategies may be effective and needed to increase COVID-19 vaccination among hesitant adults during the pandemic.

How local partisan context conditions prosocial behaviors: Mask wearing during COVID-19.

Does local partisan context influence the adoption of prosocial behavior? Using a nationwide survey of 60,000 adults and geographic data on over 180 million registered voters, we investigate whether neighborhood partisan composition affects a publicly observable and politicized behavior: wearing a mask. We find that Republicans are less likely to wear masks in public as the share of Republicans in their zip codes increases. Democratic mask wearing, however, is unaffected by local partisan context. Consequently, the partisan gap in mask wearing is largest in Republican neighborhoods, and less apparent in Democratic areas. These effects are distinct from other contextual effects such as variations in neighborhood race, income, or education. In contrast, partisan context has significantly reduced influence on unobservable public health recommendations like COVID-19 vaccination and no influence on nonpoliticized behaviors like flu vaccination, suggesting that differences in mask wearing reflect the publicly observable and politicized nature of the behavior instead of underlying differences in dispositions toward medical care.

Depressive Symptoms and Anxiety During the COVID-19 Pandemic: Large, Longitudinal, Cross-sectional Survey.

BACKGROUND: The COVID-19 pandemic has influenced the mental health of millions across the globe. Understanding factors associated with depressive symptoms and anxiety across 12 months of the pandemic can help identify groups at higher risk and psychological processes that can be targeted to mitigate the long-term mental health impact of the pandemic. OBJECTIVE: This study aims to determine sociodemographic features, COVID-19-specific factors, and general psychological variables associated with depressive symptoms and anxiety over 12 months of the pandemic. METHODS: Nationwide, cross-sectional electronic surveys were implemented in May (n=14,636), July (n=14,936), October (n=14,946), and December (n=15,265) 2020 and March/April 2021 (n=14,557) in the United States. Survey results were weighted to be representative of the US population. The samples were drawn from a market research platform, with a 69% cooperation rate. Surveys assessed depressive symptoms in the past 2 weeks and anxiety in the past week, as well as sociodemographic features; COVID-19 restriction stress, worry, perceived risk, coping strategies, and exposure; intolerance of uncertainty; and loneliness. RESULTS: Across 12 months, an average of 24% of respondents reported moderate-to-severe depressive symptoms and 32% reported moderate-to-severe anxiety. Of the sociodemographic variables, age was most consistently associated with depressive symptoms and anxiety, with younger adults more likely to report higher levels of those outcomes. Intolerance of uncertainty and loneliness were consistently and strongly associated with the outcomes. Of the COVID-19-specific variables, stress from COVID-19 restrictions, worry about COVID-19, coping behaviors, and having COVID-19 were associated with a higher likelihood of depressive symptoms and anxiety. CONCLUSIONS: Depressive symptoms and anxiety were high in younger adults, adults who reported restriction stress or worry about COVID-19 or who had had COVID-19, and those with intolerance of uncertainty and loneliness. Symptom monitoring as well as early and accessible intervention are recommended.

The Impact of COVID-19 on Routine Medical Care and Cancer Screening.

BACKGROUND: COVID-19 restrictions and fear dramatically changed the use of medical care. Understanding the magnitude of cancelled and postponed appointments and associated factors can help identify approaches to mitigate unmet need. OBJECTIVE: To determine the proportion of medical visits cancelled or postponed and for whom. We hypothesized that adults with serious medical conditions and those with higher anxiety, depressive symptoms, and avoidance-oriented coping would have more cancellations/postponements. DESIGN: Four nationally representative cross-sectional surveys conducted online in May, July, October, and December 2020. PARTICIPANTS: 59,747 US adults who completed 15-min online surveys. 69% cooperation rate. MEASURES: Physical and mental health visits and cancer screening cancelled or postponed over prior 2 months. Plan to cancel or postpone visits over the next 2 months. Relationship with demographics, medical conditions, local COVID-19 death rate, anxiety, depressive symptoms, coping, intolerance of uncertainty, and perceived COVID-19 risk. KEY RESULTS: Of the 58% (N = 34,868) with a medical appointment during the 2 months before the survey, 64% had an appointment cancelled or postponed in May, decreasing to 37% in December. Of the 41% of respondents with scheduled cancer screening, 20% cancelled/postponed, which was stable May to December. People with more medical conditions were more likely to cancel or postpone medical visits (OR 1.19 per condition, 95% CI 1.16, 1.22) and cancer screening (OR 1.20, 95% CI 1.15, 1.24). Race, ethnicity, and income had weak associations with cancelled/postponed visits, local death rate was unrelated, but anxiety and depressive symptoms were strongly related to cancellations, and this grew between May and December. CONCLUSIONS: Cancelled medical care and cancer screening were more common among persons with medical conditions, anxiety and depression, even after accounting for COVID-19 deaths. Outreach and support to ensure that patients are not avoiding needed care due to anxiety, depression and inaccurate perceptions of risk will be important.

Effects of Age, Gender, Health Status, and Political Party on COVID-19-Related Concerns and Prevention Behaviors: Results of a Large, Longitudinal Cross-sectional Survey.

BACKGROUND: With conflicting information about COVID-19, the general public may be uncertain about how to proceed in terms of precautionary behavior and decisions about whether to return to activity. OBJECTIVE: The aim of this study is to determine the factors associated with COVID-19-related concerns, precautionary behaviors, and willingness to return to activity. METHODS: National survey data were obtained from the Democracy Fund + UCLA Nationscape Project, an ongoing cross-sectional weekly survey. The sample was provided by Lucid, a web-based market research platform. Three outcomes were evaluated: (1) COVID-19-related concerns, (2) precautionary behaviors, and (3) willingness to return to activity. Key independent variables included age, gender, race or ethnicity, education, household income, political party support, religion, news consumption, number of medication prescriptions, perceived COVID-19 status, and timing of peak COVID-19 infections by state. RESULTS: The data included 125,508 responses from web-based surveys conducted over 20 consecutive weeks during the COVID-19 pandemic (comprising approximately 6250 adults per week), between March 19 and August 5, 2020, approved by the University of California, Los Angeles (UCLA) Institutional Review Board for analysis. A substantial number of participants were not willing to return to activity even after the restrictions were lifted. Weighted multivariate logistic regressions indicated the following groups had different outcomes (all P<.001): individuals aged ≥65 years (COVID-19-related concerns: OR 2.05, 95% CI 1.93-2.18; precautionary behaviors: OR 2.38, 95% CI 2.02-2.80; return to activity: OR 0.41, 95% CI 0.37-0.46 vs 18-40 years); men (COVID-19-related concerns: OR 0.73, 95% CI 0.70-0.75; precautionary behaviors: OR 0.74, 95% CI 0.67-0.81; return to activity: OR 2.00, 95% CI 1.88-2.12 vs women); taking ≥4 medications (COVID-19-related concerns: OR 1.47, 95% CI 1.40-1.54; precautionary behaviors: OR 1.36, 95% CI 1.20-1.555; return to activity: OR 0.75, 95% CI 0.69-0.81 vs <3 medications); Republicans (COVID-19-related concerns: OR 0.40, 95% CI 0.38-0.42; precautionary behaviors: OR 0.45, 95% CI 0.40-0.50; return to activity: OR 2.22, 95% CI 2.09-2.36 vs Democrats); and adults who reported having COVID-19 (COVID-19-related concerns: OR 1.24, 95% CI 1.12-1.39; precautionary behaviors: OR 0.65, 95% CI 0.52-0.81; return to activity: OR 3.99, 95% CI 3.48-4.58 vs those who did not). CONCLUSIONS: Participants' age, party affiliation, and perceived COVID-19 status were strongly associated with their COVID-19-related concerns, precautionary behaviors, and willingness to return to activity. Future studies need to develop and test targeted messaging approaches and consider political partisanship to encourage preventative behaviors and willingness to return to activities.

Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources: A Randomized Clinical Trial.

Importance: Accurate surgical scheduling affects patients, clinical staff, and use of physical resources. Although numerous retrospective analyses have suggested a potential for improvement, the real-world outcome of implementing a machine learning model to predict surgical case duration appears not to have been studied. Objectives: To assess accuracy and real-world outcome from implementation of a machine learning model that predicts surgical case duration. Design, Setting, and Participants: This randomized clinical trial was conducted on 2 surgical campuses of a cancer specialty center. Patients undergoing colorectal and gynecology surgery at Memorial Sloan Kettering Cancer Center who were scheduled more than 1 day before surgery between April 7, 2018, and June 25, 2018, were included. The randomization process included 29 strata (11 gynecological surgeons at 2 campuses and 7 colorectal surgeons at a single campus) to ensure equal chance of selection for each surgeon and each campus. Patients undergoing more than 1 surgery during the study's timeframe were enrolled only once. Data analyses took place from July 2018 to November 2018. Interventions: Cases were assigned to machine learning-assisted surgical predictions 1 day before surgery and compared with a control group. Main Outcomes and Measures: The primary outcome measure was accurate prediction of the duration of each scheduled surgery, measured by (arithmetic) mean (SD) error and mean absolute error. Effects on patients and systems were measured by start time delay of following cases, the time between cases, and the time patients spent in presurgical area. Results: A total of 683 patients were included (mean [SD] age, 55.8 [13.8] years; 566 women [82.9%]); 72 were excluded. Of the 683 patients included, those assigned to the machine learning algorithm had significantly lower mean (SD) absolute error (control group, 59.3 [72] minutes; intervention group, 49.5 [66] minutes; difference, -9.8 minutes; P = .03) compared with the control group. Mean start-time delay for following cases (patient wait time in a presurgical area), dropped significantly: 62.4 minutes (from 70.2 minutes to 7.8 minutes) and 16.7 minutes (from 36.9 minutes to 20.2 minutes) for patients receiving colorectal and gynecology surgery, respectively. The overall mean (SD) reduction of wait time was 33.1 minutes per patient (from 49.4 minutes to 16.3 minutes per patient). Improved accuracy did not adversely inflate time between cases (surgeon wait time). There was marginal improvement (1.5 minutes, from a mean of 70.6 to 69.1 minutes) in time between the end of cases and start of to-follow cases using the predictive model, compared with the control group. Patients spent a mean of 25.2 fewer minutes in the facility before surgery (173.3 minutes vs 148.1 minutes), indicating a potential benefit vis-à-vis available resources for other patients before and after surgery. Conclusions and Relevance: Implementing machine learning-generated predictions for surgical case durations may improve case duration accuracy, presurgical resource use, and patient wait time, without increasing surgeon wait time between cases. Trial Registration: ClinicalTrials.gov Identifier: NCT03471377.

Fatalities from COVID-19 are reducing Americans' support for Republicans at every level of federal office.

Between early March and 1 August 2020, COVID-19 took the lives of more than 150,000 Americans. Here, we examine the political consequences of the COVID-19 epidemic using granular data on COVID-19 fatalities and the attitudes of the American public. We find that COVID-19 has led to substantial damage for President Trump and other Republican candidates. States and local areas with higher levels of COVID-19 fatalities are less likely to support President Trump and Republican candidates for House and Senate. Our results show that President Trump and other Republican candidates would benefit electorally from a reduction in COVID-19 fatalities. This implies that a greater emphasis on social distancing, masks, and other mitigation strategies would benefit the president and his allies.