COVID-19 and thyroid disease: An infodemiological pilot study

BACKGROUNDGoogle Trends searches for symptoms and/or diseases may reflect actual disease epidemiology. Recently, Google Trends searches for coronavirus disease 2019 (COVID-19)-associated terms have been linked to the epidemiology of COVID-19. Some studies have linked COVID-19 with thyroid disease.AIMTo assess COVID-19 cases per se vs COVID-19-associated Google Trends searches and thyroid-associated Google Trends searches.METHODSWe collected data on worldwide weekly Google Trends searches regarding “COVID-19”, “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)”, “coronavirus”, “smell”, “taste”, “cough”, “thyroid”, “thyroiditis”, and “subacute thyroiditis” for 92 wk and worldwide weekly COVID-19 cases'' statistics in the same time period. The study period was split in half (approximately corresponding to the preponderance of different SARS-COV-2 virus variants) and in each time period we performed cross-correlation analysis and mediation analysis.RESULTSSignificant positive cross-correlation function values were noted in both time periods. More in detail, COVID-19 cases per se were found to be associated with no lag with Google Trends searches for COVID-19 symptoms in the first time period and in the second time period to lead searches for symptoms, COVID-19 terms, and thyroid terms. COVID-19 cases per se were associated with thyroid-related searches in both time periods. In the second time period, the effect of “COVID-19” searches on “thyroid’ searches was significantly mediated by COVID-19 cases (P = 0.048).CONCLUSIONSearches for a non-specific symptom or COVID-19 search terms mostly lead Google Trends thyroid-related searches, in the second time period. This time frame/sequence particularly in the second time period (noted by the preponderance of the SARS-COV-2 delta variant) lends some credence to associations of COVID-19 cases per se with (apparent) thyroid disease (via searches for them).     

Utility of Facebook’s Social Connectedness Index in Modeling COVID-19 Spread: Exponential Random Graph Modeling Study

BackgroundThe COVID-19 (the disease caused by the SARS-CoV-2 virus) pandemic has underscored the need for additional data, tools, and methods that can be used to combat emerging and existing public health concerns. Since March 2020, there has been substantial interest in using social media data to both understand and intervene in the pandemic. Researchers from many disciplines have recently found a relationship between COVID-19 and a new data set from Facebook called the Social Connectedness Index (SCI).ObjectiveBuilding off this work, we seek to use the SCI to examine how social similarity of Missouri counties could explain similarities of COVID-19 cases over time. Additionally, we aim to add to the body of literature on the utility of the SCI by using a novel modeling technique.MethodsIn September 2020, we conducted this cross-sectional study using publicly available data to test the association between the SCI and COVID-19 spread in Missouri using exponential random graph models, which model relational data, and the outcome variable must be binary, representing the presence or absence of a relationship. In our model, this was the presence or absence of a highly correlated COVID-19 case count trajectory between two given counties in Missouri. Covariates included each county’s total population, percent rurality, and distance between each county pair.ResultsWe found that all covariates were significantly associated with two counties having highly correlated COVID-19 case count trajectories. As the log of a county’s total population increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 66% (odds ratio [OR] 1.66, 95% CI 1.43-1.92). As the percent of a county classified as rural increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 1% (OR 1.01, 95% CI 1.00-1.01). As the distance (in miles) between two counties increased, the odds of two counties having highly correlated COVID-19 case count trajectories decreased by 43% (OR 0.57, 95% CI 0.43-0.77). Lastly, as the log of the SCI between two Missouri counties increased, the odds of those two counties having highly correlated COVID-19 case count trajectories significantly increased by 17% (OR 1.17, 95% CI 1.09-1.26).ConclusionsThese results could suggest that two counties with a greater likelihood of sharing Facebook friendships means residents of those counties have a higher likelihood of sharing similar belief systems, in particular as they relate to COVID-19 and public health practices. Another possibility is that the SCI is picking up travel or movement data among county residents. This suggests the SCI is capturing a unique phenomenon relevant to COVID-19 and that it may be worth adding to other COVID-19 models. Additional research is needed to better understand what the SCI is capturing practically and what it means for public health policies and prevention practices.     

United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study

BackgroundThe emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends.ObjectiveWe aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States.MethodsWe retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data.ResultsWe observed a nonsignificant weak correlation (ρ= –0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models—for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ρ=0.643; 2019-2020: ρ=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ρ=0.746; 2019-2020: ρ=0.707).ConclusionsRelevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool.     

Changes in the incidence of seasonal influenza in response to COVID-19 social distancing measures: an observational study based on Canada’s national influenza surveillance system

ObjectivesSeasonal influenza is an acute respiratory infection that presents a significant annual burden to Canadians and the Canadian healthcare system. Social distancing measures that were implemented to control the 2019–2020 novel coronavirus outbreak were investigated for their ability to lessen the incident cases of seasonal influenza.MethodsWe conducted an ecological study using data from Canada’s national influenza surveillance system to investigate whether social distancing measures to control COVID-19 reduced the incident cases of seasonal influenza. Data taken from three separate time frames facilitated analysis of the 2019–2020 influenza season prior to, during, and following the implementation of COVID-19-related measures and enabled comparisons with the same time periods during three preceding flu seasons. The incidence, which referred to the number of laboratory-confirmed cases of specific influenza strains, was of primary focus. Further analysis determined the number of new laboratory-confirmed influenza or influenza-like illness outbreaks.ResultsOur results indicate a premature end to the 2019–2020 influenza season, with significantly fewer cases and outbreaks being recorded following the enactment of many COVID-19 social distancing policies. The incidence of influenza strains A (H3N2), A (unsubtyped), and B were all significantly lower at the tail end of the 2019–2020 influenza season as compared with preceding seasons (p = 0.0003, p = 0.0007, p = 0.0019).ConclusionSpecific social distancing measures and behaviours may serve as effective tools to limit the spread of influenza transmission moving forward, as they become more familiar.Supplementary InformationThe online version contains supplementary material available at 10.17269/s41997-021-00509-4.     

Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response

SettingCOVID-19 has highlighted the need for credible epidemiological models to inform pandemic policy. Traditional mechanisms of commissioning research are ill-suited to guide policy during a rapidly evolving pandemic. At the same time, contracting with a single centre of expertise has been criticized for failing to reflect challenges inherent in specific modelling approaches.InterventionThis report describes an alternative approach to mobilizing scientific expertise. Ontario’s COVID-19 Modelling Consensus Table (MCT) was created in March 2020 to enable rapid communication of credible estimates of the impact of COVID-19 and to accelerate learning on how the disease is spreading and what could slow its transmission. The MCT is a partnership between the province and academic modellers and consists of multiple groups of experts, health system leaders, and senior decision-makers. Armed with Ministry of Health data, the MCT meets once per week to share results from modelling exercises, generate consensus judgements of the likely future impact of COVID-19, and discuss decision-makers’ priorities.OutcomesThe MCT has enabled swift access to data for participants, a structure for developing consensus estimates and communicating these to decision-makers, credible models to inform health system planning, and increased transparency in public reporting of COVID-19 data. It has also facilitated the rapid publication of research findings and its incorporation into government policy.ImplicationsThe MCT approach is one way to quickly draw on scientific advice outside of government and public health agencies. Beyond speed, this approach allows for nimbleness as experts from different organizations can be added as needed. It also shows how universities and research institutes have a role to play in crisis situations, and how this expertise can be marshalled to inform policy while respecting academic freedom and confidentiality.     

Generalized anxiety and sleep quality among health care professionals during the COVID-19 pandemic: a cross-sectional study from a tertiary healthcare institution in Eastern India

ObjectivesWith the emergence of the coronavirus disease 2019 (COVID-19) pandemic, healthcare professionals (HCPs) have experienced high levels of stress and anxiety because of the high risk of infection for themselves and their families. This has led to acute sleep problems for HCP. This study was designed to assess the anxiety and sleep quality of HCPs during the COVID-19 pandemic.Methods This cross-sectional study analyzed 370 HCPs employed at All India Institute of Medical Sciences Patna over 3 months, using the standard Generalized Anxiety Disorder 7-item scale (GAD-7) for suspected GAD and the Pittsburgh Sleep Quality Index for sleep quality. Results were tabulated and multivariable binomial logistic regression analysis was done to determine the predictors of poor sleep. Significance was attributed to p<0.05.ResultsOf the 370 HCPs screened, 52 (14.1%; 95% confidence interval [CI], 10.8%–18.1%) were found to have GAD and 195 (52.7%; 95% CI, 47.5%–57.9%) were found to be poor sleepers. The presence of any addictive habit (adjusted odds ratio [AOR], 1.833; 95% CI, 1.12–2.8), unprotected contact with COVID-19 cases (AOR, 1.902; 95% CI, 1.1–3.3), and the presence of GAD (AOR, 5.57; 95% CI, 2.5–12.4) were found to be predictors of poor sleep quality among HCPs.ConclusionA significant proportion of HCPs were found to have suspected GAD and were poor sleepers. This highlights the need for measures to confront this problem.     

Estimating and Characterizing COVID-19 Deaths,Puerto Rico,March–July 2020

ObjectivesUsing the Council of State and Territorial Epidemiologists (CSTE) classification guidelines, we characterized coronavirus disease 2019 (COVID-19)–associated confirmed and probable deaths in Puerto Rico during March–July 2020. We also estimated the total number of possible deaths due to COVID-19 in Puerto Rico during the same period.MethodsWe described data on COVID-19–associated mortality, in which the lower bound was the sum of confirmed and probable COVID-19 deaths and the upper bound was excess mortality, estimated as the difference between observed deaths and average expected deaths. We obtained data from the Puerto Rico Department of Health COVID-19 Mortality Surveillance System, the Centers for Disease Control and Prevention’s National Electronic Disease Surveillance System Base System, and the National Center for Health Statistics.ResultsDuring March–July 2020, 225 COVID-19–associated deaths were identified in Puerto Rico (119 confirmed deaths and 106 probable deaths). The median age of decedents was 73 (interquartile range, 59-83); 60 (26.7%) deaths occurred in the Metropolitana region, and 140 (62.2%) deaths occurred among men. Of the 225 decedents, 180 (83.6%) had been hospitalized and 93 (41.3%) had required mechanical ventilation. Influenza and pneumonia (48.0%), sepsis (28.9%), and respiratory failure (27.1%) were the most common conditions contributing to COVID-19 deaths based on death certificates. Based on excess mortality calculations, as many as 638 COVID-19–associated deaths could have occurred during the study period, up to 413 more COVID-19–associated deaths than originally reported.ConclusionsIncluding probable deaths per the CSTE guidelines and monitoring all-cause excess mortality can lead to a better estimation of COVID-19–associated deaths and serve as a model to enhance mortality surveillance in other US jurisdictions.     

COVID-19’s impact on primary care and related mitigation strategies: A scoping review

BackgroundThe COVID-19 pandemic has had a substantial impact on primary care throughout Europe and globally.ObjectivesThis review aims to ascertain how the pandemic has impacted primary care service provision/patients and to examine strategies to mitigate these impacts.MethodsThe scoping review framework comprised a six-stage process developed by Arksey and O''Malley. The search process was guided by the Joanna Briggs Institute three-step search strategy and involved searching the PubMed, Embase, Scopus, CINAHL Plus, and Cochrane Library databases. The review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. A thematic analysis approach by Braun and Clarke was used to interpret the findings.ResultsThirty-two studies from 18 countries and six continents were included, 13 reported original research, three were reviews, and 16 were case reports reporting healthcare systems’ experiences of dealing with the pandemic. Emerging themes concerned the COVID-19 pandemic’s impact on primary care service provision and patients, the impact of the rapid transition to telemedicine due to COVID-19 on primary care, and strategies to mitigate the impact of COVID-19 on primary care (i.e. infection prevention and control measures, alternatives/modifications to traditional service delivery or workflow, government policy responses, and education).ConclusionThe COVID-19 pandemic has considerably impacted on primary care at both service and patient levels, and various strategies to mitigate these impacts have been described. Future research examining the pandemic’s ongoing impacts on primary care, as well as strategies to mitigate these impacts, is a priority.     

Prevention Measures for COVID-19 and Changes in Kawasaki Disease Incidence

BackgroundKawasaki disease is suspected to be triggered by previous infection. The prevention measures for coronavirus disease 2019 (COVID-19) have reportedly reduced transmission of certain infectious diseases. Under these circumstances, the prevention measures for COVID-19 may reduce the incidence of Kawasaki disease.MethodsWe conducted a retrospective study using registration datasets of patients with Kawasaki disease who were diagnosed in all 11 inpatient pediatric facilities in Yamanashi Prefecture. The eligible cases were 595 cases that were diagnosed before the COVID-19 pandemic (from January 2015 through February 2020) and 38 cases that were diagnosed during the COVID-19 pandemic (from March through November 2020). Incidence of several infectious disease were evaluated using data from the Infectious Disease Weekly Report conducted by the National Institute of Infectious Diseases.ResultsEpidemics of various infectious diseases generally remained at low levels during the first 9 months (March through November 2020) of the COVID-19 pandemic. Moreover, the incidence of COVID-19 was 50–80 times lower than the incidence in European countries and the United States. The total number of 38 cases with Kawasaki disease for the 9 months during the COVID-19 pandemic was 46.3% (−3.5 standard deviations [SDs] of the average [82.0; SD, 12.7 cases] for the corresponding 9 months of the previous 5 years. None of the 38 cases was determined to be triggered by COVID-19 based on their medical histories and negative results of severe acute respiratory syndrome coronavirus 2 testing at admission.ConclusionThese observations provide a new epidemiological evidence for the notion that Kawasaki disease is triggered by major infectious diseases in children.Key words: COVID-19, Kawasaki disease, retrospective database, infectious disease     

The Influence of Media Coverage and Governmental Policies on Google Queries Related to COVID-19 Cutaneous Symptoms: Infodemiology Study

BackgroundDuring COVID-19, studies have reported the appearance of internet searches for disease symptoms before their validation by the World Health Organization. This suggested that monitoring of these searches with tools including Google Trends may help monitor the pandemic itself. In Europe and North America, dermatologists reported an unexpected outbreak of cutaneous acral lesions (eg, chilblain-like lesions) in April 2020. However, external factors such as public communications may also hinder the use of Google Trends as an infodemiology tool.ObjectiveThe study aimed to assess the impact of media announcements and lockdown enforcement on internet searches related to cutaneous acral lesions during the COVID-19 outbreak in 2020.MethodsTwo searches on Google Trends, including daily relative search volumes for (1) “toe” or “chilblains” and (2) “coronavirus,” were performed from January 1 to May 16, 2020, with the United States, the United Kingdom, France, Italy, Spain, and Germany as the countries of choice. The ratio of interest over time in “chilblains” and “coronavirus” was plotted. To assess the impact of lockdown enforcement and media coverage on these internet searches, we performed an interrupted time-series analysis for each country.ResultsThe ratio of interest over time in “chilblains” to “coronavirus” showed a constant upward trend. In France, Italy, and the United Kingdom, lockdown enforcement was associated with a significant slope change for “chilblain” searches with a variation coefficient of 1.06 (SE 0.42) (P=0.01), 1.04 (SE 0.28) (P<.01), and 1.21 (SE 0.44) (P=0.01), respectively. After media announcements, these ratios significantly increased in France, Spain, Italy, and the United States with variation coefficients of 18.95 (SE 5.77) (P=.001), 31.31 (SE 6.31) (P<.001), 14.57 (SE 6.33) (P=.02), and 11.24 (SE 4.93) (P=.02), respectively, followed by a significant downward trend in France (–1.82 [SE 0.45]), Spain (–1.10 [SE 0.38]), and Italy (–0.93 [SE 0.33]) (P<.001, P=0.004, and P<.001, respectively). The adjusted R² values were 0.311, 0.351, 0.325, and 0.305 for France, Spain, Italy, and the United States, respectively, suggesting an average correlation between time and the search volume; however, this correlation was weak for Germany and the United Kingdom.ConclusionsTo date, the association between chilblain-like lesions and COVID-19 remains controversial; however, our results indicate that Google queries of “chilblain” were highly influenced by media coverage and government policies, indicating that caution should be exercised when using Google Trends as a monitoring tool for emerging diseases.     

COVID-19-Related Stress and Anxiety,Body Mass Index,Eating Disorder Symptomatology,and Body Image in Women from Poland: A Cluster Analysis Approach

To limit the spread of the novel coronavirus (COVID-19), many countries have introduced mandated lockdown or social distancing measures. Although these measures may be successful against COVID-19 transmission, the pandemic and attendant restrictions are a source of chronic and severe stress and anxiety which may contribute to the emergence or worsening of symptoms of eating disorders and the development of negative body image. Therefore, in this study, we aimed to: (1) classify different conditions associated with COVID-19-related stress, COVID-19-related anxiety, and weight status; and (2) analyze and compare the severity of dimensions typically related to eating disorders symptomatology and body image in individuals with different COVID-19-related stress, COVID-19-related anxiety, and weight status. Polish women (N = 671, M_age = 32.50 ± 11.38) completed measures of COVID-19-related stress and anxiety along with body dissatisfaction, drive for thinness, and bulimia symptomatology subscales of the Eating Disorders Inventory, and the appearance evaluation, overweight preoccupation, and body areas satisfaction subscales of the Multidimensional Body-Self Relations Questionnaire. The following four clusters were identified through cluster analysis: (a) Cluster 1 (N = 269), healthy body weight and low COVID-related stress (M = 3.06) and anxiety (M = 2.96); (b) Cluster 2 (N = 154), healthy body weight and high COVID-related stress (M = 5.43) and anxiety (M = 5.29); (c) Cluster 3 (N = 127), excess body weight and high COVID-related stress (M = 5.23) and anxiety (M = 5.35); (d) Cluster 4 (N = 121), excess body weight and low COVID-related stress (M = 2.69) and anxiety (M = 2.83). Our results showed that Clusters 3 and 4 had significantly greater body dissatisfaction and lower appearance evaluation and body areas satisfaction than Clusters 1 and 2. Cluster 3 also had a significantly higher level of drive for thinness, bulimia, and overweight preoccupation than Clusters 1 and 2. These preliminary findings may mean that the COVID-19 pandemic and attendant anxiety and stress caused by the pandemic are exacerbating symptoms of eating disorders and negative body image, with women with excess weight particularly at risk.     

Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data,Online News Articles,and Search Queries

BackgroundThe SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored.ObjectiveThe main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources.MethodsWe first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis.ResultsOur proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t₁₉₈=–8.722, P<.001; model 2, t₁₉₈=–5.000, P<.001, model 3, t₁₉₈=–1.882, P=.06; model 4, t₁₉₈=–4.644, P<.001; model 5, t₁₉₈=–4.488, P<.001). In Hubei province, our proposed model''s forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t₁₉₈=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks.ConclusionsOur approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies'' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.     

Public Preferences for Allocation Principles for Scarce Medical Resources in the COVID-19 Pandemic in Korea: Comparisons With Ethicists’ Recommendations

Objectives:The purpose of this study was to investigate public preferences regarding allocation principles for scarce medical resources in the coronavirus disease 2019 (COVID-19) pandemic, particularly in comparison with the recommendations of ethicists.Methods:An online survey was conducted with a nationally representative sample of 1509 adults residing in Korea, from November 2 to 5, 2020. The degree of agreement with resource allocation principles in the context of the medical resource constraints precipitated by the COVID-19 pandemic was examined. The results were then compared with ethicists’ recommendations. We also examined whether the perceived severity of COVID-19 explained differences in individual preferences, and by doing so, whether perceived severity helps explain discrepancies between public preferences and ethicists’ recommendations.Results:Overall, the public of Korea agreed strongly with the principles of “save the most lives,” “Koreans first,” and “sickest first,” but less with “random selection,” in contrast to the recommendations of ethicists. “Save the most lives” was given the highest priority by both the public and ethicists. Higher perceived severity of the pandemic was associated with a greater likelihood of agreeing with allocation principles based on utilitarianism, as well as those promoting and rewarding social usefulness, in line with the opinions of expert ethicists.Conclusions:The general public of Korea preferred rationing scarce medical resources in the COVID-19 pandemic predominantly based on utilitarianism, identity and prioritarianism, rather than egalitarianism. Further research is needed to explore the reasons for discrepancies between public preferences and ethicists’ recommendations.     

Predicting COVID-19 vaccination intention using protection motivation theory and conspiracy beliefs

BackgroundWhile COVID-19 vaccine uptake has been encouraging overall, some individuals are either hesitant towards, or refuse, the vaccine. Protection Motivation Theory (PMT) has been applied to influenza vaccine acceptance, but there is a lack of research applying PMT to COVID-19 vaccine acceptance. Additionally, prior research has suggested that coronavirus conspiracy beliefs and demographic factors may play a role in attitudes towards the vaccine. This study aimed to predict COVID-19 vaccination intention using PMT, coronavirus conspiracy beliefs, and demographic factors. Furthermore, vaccinated and unvaccinated individuals were compared in relation to their coronavirus conspiracy beliefs.MethodsAn online survey was administered to 382 (278 vaccinated, and 104 unvaccinated) individuals in the United Kingdom (77 males, 301 females, one non-binary/third gender, and three unstated). Respondents’ mean age was 43.78 (SD = 12.58).ResultsA hierarchical multiple linear regression was performed in three stages. Initially, four PMT constructs - severity, susceptibility, maladaptive response costs, and self-efficacy - emerged as significant predictors of COVID-19 vaccination intention. The final model accounted for 75% of the variance and retained two significant predictors from PMT - maladaptive response rewards and self-efficacy - alongside coronavirus conspiracy beliefs and age. An independent t-test established that unvaccinated individuals held greater coronavirus conspiracy beliefs than vaccinated ones.ConclusionsInterventions and campaigns addressing COVID-19 vaccine acceptance should employ strategies increasing individuals’ perceived severity of COVID-19, perceived susceptibility, and perceived ability to get vaccinated, while decreasing perceived rewards of not getting vaccinated. Additionally, coronavirus conspiracy beliefs should be addressed, as these appear to play a role for some vaccine-hesitant individuals.     

Identifying Communities at Risk for COVID-19–Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators

BackgroundAlthough it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health.ObjectiveThis study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning.MethodsWe developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19–related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health.ResultsUsing 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19–related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r²=0.87).ConclusionsThe COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19–related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.     

Vitamin D Deficiency and Comorbidities as Risk Factors of COVID-19 Infection: A Systematic Review and Meta-analysis

ObjectivesExtensive evidence links low vitamin D status and comorbidities with coronavirus disease 2019 (COVID-19) outcomes, but the results of published studies are contradictory. Therefore, we investigated the association of lower levels of vitamin D and comorbidities with the risk of COVID-19 infection.MethodsWe searched MEDLINE (via PubMed), Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov for articles published until August 20, 2021. Sixteen eligible studies were identified (386 631 patients, of whom 181 114 were male). We included observational cohort and case-control studies that evaluated serum levels of vitamin D in COVID-19-positive and COVID-19-negative patients. Mean differences (MDs) with 95% confidence intervals (CIs) were calculated.ResultsSignificantly lower vitamin D levels were found in COVID-19-positive patients (MD, −1.70; 95% CI, −2.74 to −0.66; p=0.001), but with variation by study design (case-control: −4.04; 95% CI, −5.98 to −2.10; p<0.001; cohort: −0.39; 95% CI, −1.62 to 0.84; p=0.538). This relationship was more prominent in female patients (MD, −2.18; 95% CI, −4.08 to −0.28; p=0.024) than in male patients (MD, −1.74; 95% CI, −3.79 to 0.31; p=0.096). Male patients showed higher odds of having low vitamin D levels (odds ratio [OR], 2.09; 95% CI, 1.38 to 3.17; p<0.001) than female patients (OR, 1.17; 95% CI, 0.74 to 1.86; p=0.477). Comorbidities showed inconsistent, but generally non-significant, associations with COVID-19 infection.ConclusionsLow serum vitamin-D levels were significantly associated with the risk of COVID-19 infection. This relationship was stronger in female than in male COVID-19 patients. Limited evidence was found for the relationships between comorbidities and COVID-19 infection, warranting large population-based studies to clarify these associations.     

COVID-19 outbreak and risk factors for infection in a taekwondo gym in the Republic of Korea

ObjectivesRelatively few studies have assessed risk factors for coronavirus disease 2019 (COVID-19) in public facilities used by children and adolescents. This study presents an analysis of a COVID-19 outbreak that occurred in a taekwondo gym in Korea, predominantly among children and adolescents, with the aim of providing insights on managing COVID-19 outbreaks in similar facilities.MethodsAll 108 taekwondo gym students and staff received COVID-19 tests. A survey and closed-circuit television analyses were used to identify risk factors. A univariate analysis was conducted, followed by multivariate logistic regression analysis with backward elimination for variables with a significance level <0.10 in the univariate analysis.Results COVID-19 was confirmed in 30 of 108 subjects at the taekwondo gym (attack rate, 27.8%). The outbreak started in an adult class student. This student transmitted the virus to the staff, who consequently transmitted the virus to adolescent students. In the univariate analysis, the relative risk for younger age (≤9 years) was 2.14 (95% confidence interval [CI], 1.01–4.54; p=0.054), and that for food consumption inside the gym was 2.12 (95% CI, 1.04–4.30; p=0.048). In the multivariate logistic regression analysis, the odds ratio for younger age was 2.96 (95% CI, 1.07–8.20; p=0.036), and that for food consumption inside the gym was 3.00 (95% CI, 1.10–8.17; p=0.032).Conclusion Food consumption inside the facility and young age were significant risk factors for COVID-19 transmission in this taekwondo gym. Food consumption should be prohibited in sports facilities, and infection prevention education for young students is also required.     

Provincial implementation supports for socio-demographic data collection during COVID-19 in Ontario’s public health system

SettingThe Ontario government implemented a regulatory change to mandate the collection of socio-demographic (SD) data for individuals who tested positive for COVID-19. This change was informed by evidence of COVID-19’s disproportionate impact on marginalized communities and calls for broader collection of SD data. Given the scarcity of similar efforts, there is a significant knowledge gap around implementing standardized SD data collection in public health settings.InterventionPublic Health Ontario provided collaborative support for the implementation of SD data collection, grounded in health equity principles, evidence, and best practices. We supported the addition of SD fields in Ontario’s COVID-19 data collection systems, issued data entry guidance, hosted webinars for training and learning exchange, and published a resource to support the data collection process. The current focus is on building sustainability and quality improvement through continued engagement of public health units.OutcomesBy November 28, 2020, almost 80% of COVID-19 cases had information recorded for at least one SD question (individual questions, range 46.8–67.0%). We hosted three webinars for the field, and the data collection resource was viewed almost 650 times. Practitioners continue to express needs for support on applying equity principles to data analysis and interpretation, and community engagement on data collection and use.ImplicationsSharing knowledge on responsive implementation supports in collaboration with the field and using current evidence and guidance will strengthen public health practice for SD data collection. Laying this groundwork will also improve the likelihood of success and sustainability of these equity-focused efforts.     

Low sensitivity of rapid tests detecting anti-CoV-2 IgG and IgM in health care workers’ serum for COVID-19 screening

Background: the sensitivity and specificity of a rapid antibody test were investigated for the screening of healthcare workers. Methods: the serum of 389 health care workers exposed to COVID-19 patients or with symptoms, were analysed. All workers underwent monthly the screening for SARS-CoV-2 with detection of viral RNA in nasopharyngeal swabs by RT-PCR. IgG antibody detection in serum was performed by Chemiluminescence Immunoassay (CLIA) and by the Rapid test (KHB diagnostic kit for SARS CoV-2 IgM/IgG antibody after a median of 7.6 weeks (25°-75° percentiles 6.6-11.5). Results: the rapid test resulted positive in 31/132 (23.5%), 16/135 (11.8%) and 0/122 cases in COVID-19 positive individuals, in those with only SARS-CoV-2 IgG antibodies and in those negative for both tests, respectively. Sensitivity was 17.6% (CI95% 13.2-22.7) and 23.5% (CI95% 16.5-31.6), and specificity was 100% (CI95% 97-100) and 100% (CI95% 97-100) considering Rapid test vs CLIA IgG or Rapid test vs SARS-CoV-2 positive RNA detection, respectively. Conclusion: the KHB Rapid test is not suitable for the screening of workers with previous COVID-19 infection.