Transmission dynamics and control of COVID-19 in Chile,March-October, 2020

Since the detection of the first case of COVID-19 in Chile on March 3^rd, 2020, a total of 513,188 cases, including ~14,302 deaths have been reported in Chile as of November 2^nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile’s incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96 (95% CI: 0.95, 0.98) as of November 2^nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7^th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.     

Low-impact social distancing interventions to mitigate local epidemics of SARS-CoV-2

Many jurisdictions implemented intensive social distancing to suppress SARS-CoV-2 transmission. The challenge now is to mitigate the ongoing COVID-19 epidemic without overburdening economic and social activities. An agent-based model simulated the population of King County, Washington. SARS-CoV-2 transmission probabilities were estimated by fitting simulated to observed hospital admissions. Interventions considered included encouraging telecommuting, reducing contacts to high-risk persons, and reductions to contacts outside of the home, among others. Removing all existing interventions would result in nearly 42,000 COVID-19 hospitalizations between June 2020 and January 2021, with peak hospital occupancy exceeding available beds 6-fold. Combining interventions is predicted to reduce total hospitalizations by 48% (95% CI, 47–49%), with peak COVID-19 hospital occupancy of 70% of total beds. Targeted school closures can further reduce the peak occupancy. Combining low-impact interventions may mitigate the course of the COVID-19 epidemic, keeping hospital burden within the capacity of the healthcare system.     

Attitudes toward COVID-19 vaccines in Chinese college students

Background: Vaccination is an important preventative measure against the coronavirus disease 19 (COVID-19) pandemic. To implement vaccination and immunization programs effectively, it is essential to investigate public attitudes toward COVID-19 vaccines. This study examined the attitudes of Chinese college students toward COVID-19 vaccines and their associated factors. Methods: A cross-sectional study was conducted in college students nationwide from December 27, 2020 to January 18, 2021. Attitudes toward COVID-19 vaccines and acceptance of future vaccination programs were assessed. Results: Totally, 2,881 college students participated in this survey; of them, 76.3% (95% CI: 74.8% - 77.9%) were willing to accept a COVID-19 vaccine in the future. Multiple logistic analysis revealed that students living in urban (OR=1.409, 95% CI: 1.152 - 1.724, p=0.001) and those studying health-related courses (OR=1.581, 95% CI: 1.291 - 1.935, p<0.001) were more likely to have a positive attitude toward COVID-19 vaccines. In addition, those who were worried about being infected with COVID-19 (very much vs no, OR=1.690, 95% CI: 1.212-2.356, p=0.002), heard previously about COVID-19 vaccines (OR=1.659, 95% CI: 1.268-2.170, p<0.001), believed that vaccines are safe (Yes vs No, OR=3.570, 95% CI: 1.825-6.980), thought that vaccines can protect people from being infected with COVID-19 (Yes vs No, OR=1.957, 95% CI: 1.286-2.979, p=0.002), and had encouraged their family and friends to have a vaccine (Yes vs No, OR=17.745, 95% CI: 12.271-25.660, p<0.001) had higher acceptance of COVID-19 vaccination. Conclusions: A high rate of acceptance of COVID-19 vaccines was found among Chinese college students. However, vaccine uptake may be reduced by concerns about vaccine safety and efficacy. Alleviating these concerns and enhancing public confidence in vaccines are crucial for future immunization programs against the COVID-19 pandemic.     

COVID-19 and excess mortality in the United States: A county-level analysis

BackgroundCoronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics.Methods and findingsIn this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics.ConclusionsIn this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.

Andrew Stokes and co-workers report a county-level analysis of excess deaths owing to COVID-19 in the United States.     

Social determinants of mortality from COVID-19: A simulation study using NHANES

BackgroundThe COVID-19 epidemic in the United States is widespread, with more than 200,000 deaths reported as of September 23, 2020. While ecological studies show higher burdens of COVID-19 mortality in areas with higher rates of poverty, little is known about social determinants of COVID-19 mortality at the individual level.Methods and findingsWe estimated the proportions of COVID-19 deaths by age, sex, race/ethnicity, and comorbid conditions using their reported univariate proportions among COVID-19 deaths and correlations among these variables in the general population from the 2017–2018 National Health and Nutrition Examination Survey (NHANES). We used these proportions to randomly sample individuals from NHANES. We analyzed the distributions of COVID-19 deaths by race/ethnicity, income, education level, and veteran status. We analyzed the association of these characteristics with mortality by logistic regression. Summary demographics of deaths include mean age 71.6 years, 45.9% female, and 45.1% non-Hispanic white. We found that disproportionate deaths occurred among individuals with nonwhite race/ethnicity (54.8% of deaths, 95% CI 49.0%–59.6%, p < 0.001), individuals with income below the median (67.5%, 95% CI 63.4%–71.5%, p < 0.001), individuals with less than a high school level of education (25.6%, 95% CI 23.4% –27.9%, p < 0.001), and veterans (19.5%, 95% CI 15.8%–23.4%, p < 0.001). Except for veteran status, these characteristics are significantly associated with COVID-19 mortality in multiple logistic regression. Limitations include the lack of institutionalized people in the sample (e.g., nursing home residents and incarcerated persons), the need to use comorbidity data collected from outside the US, and the assumption of the same correlations among variables for the noninstitutionalized population and COVID-19 decedents.ConclusionsSubstantial inequalities in COVID-19 mortality are likely, with disproportionate burdens falling on those who are of racial/ethnic minorities, are poor, have less education, and are veterans. Healthcare systems must ensure adequate access to these groups. Public health measures should specifically reach these groups, and data on social determinants should be systematically collected from people with COVID-19.

In this simulation study, Benjamin Seligman and colleagues explore socio-demographic factors associated with COVID-19 deaths in the US.     

Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level

The Islamic Republic of Iran reported its first COVID-19 cases by 19^th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19^th to March 18^th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran’s I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.     

Excess years of life lost to COVID-19 and other causes of death by sex,neighbourhood deprivation,and region in England and Wales during 2020: A registry-based study

BackgroundDeaths in the first year of the Coronavirus Disease 2019 (COVID-19) pandemic in England and Wales were unevenly distributed socioeconomically and geographically. However, the full scale of inequalities may have been underestimated to date, as most measures of excess mortality do not adequately account for varying age profiles of deaths between social groups. We measured years of life lost (YLL) attributable to the pandemic, directly or indirectly, comparing mortality across geographic and socioeconomic groups.Methods and findingsWe used national mortality registers in England and Wales, from 27 December 2014 until 25 December 2020, covering 3,265,937 deaths. YLLs (main outcome) were calculated using 2019 single year sex-specific life tables for England and Wales. Interrupted time-series analyses, with panel time-series models, were used to estimate expected YLL by sex, geographical region, and deprivation quintile between 7 March 2020 and 25 December 2020 by cause: direct deaths (COVID-19 and other respiratory diseases), cardiovascular disease and diabetes, cancer, and other indirect deaths (all other causes). Excess YLL during the pandemic period were calculated by subtracting observed from expected values. Additional analyses focused on excess deaths for region and deprivation strata, by age-group. Between 7 March 2020 and 25 December 2020, there were an estimated 763,550 (95% CI: 696,826 to 830,273) excess YLL in England and Wales, equivalent to a 15% (95% CI: 14 to 16) increase in YLL compared to the equivalent time period in 2019. There was a strong deprivation gradient in all-cause excess YLL, with rates per 100,000 population ranging from 916 (95% CI: 820 to 1,012) for the least deprived quintile to 1,645 (95% CI: 1,472 to 1,819) for the most deprived. The differences in excess YLL between deprivation quintiles were greatest in younger age groups; for all-cause deaths, a mean of 9.1 years per death (95% CI: 8.2 to 10.0) were lost in the least deprived quintile, compared to 10.8 (95% CI: 10.0 to 11.6) in the most deprived; for COVID-19 and other respiratory deaths, a mean of 8.9 years per death (95% CI: 8.7 to 9.1) were lost in the least deprived quintile, compared to 11.2 (95% CI: 11.0 to 11.5) in the most deprived. For all-cause mortality, estimated deaths in the most deprived compared to the most affluent areas were much higher in younger age groups, but similar for those aged 85 or over. There was marked variability in both all-cause and direct excess YLL by region, with the highest rates in the North West. Limitations include the quasi-experimental nature of the research design and the requirement for accurate and timely recording.ConclusionsIn this study, we observed strong socioeconomic and geographical health inequalities in YLL, during the first calendar year of the COVID-19 pandemic. These were in line with long-standing existing inequalities in England and Wales, with the most deprived areas reporting the largest numbers in potential YLL.

In a registry-based study, Evangelos Kontopantelis and colleagues examine the excess years of life lost to COVID-19 and other causes of death by sex, neighbourhood deprivation and region in England & Wales during 2020.     

Disparities in distribution of COVID-19 vaccines across US counties: A geographic information system–based cross-sectional study

BackgroundThe US Centers for Disease Control and Prevention has repeatedly called for Coronavirus Disease 2019 (COVID-19) vaccine equity. The objective our study was to measure equity in the early distribution of COVID-19 vaccines to healthcare facilities across the US. Specifically, we tested whether the likelihood of a healthcare facility administering COVID-19 vaccines in May 2021 differed by county-level racial composition and degree of urbanicity.Methods and findingsThe outcome was whether an eligible vaccination facility actually administered COVID-19 vaccines as of May 2021, and was defined by spatially matching locations of eligible and actual COVID-19 vaccine administration locations. The outcome was regressed against county-level measures for racial/ethnic composition, urbanicity, income, social vulnerability index, COVID-19 mortality, 2020 election results, and availability of nontraditional vaccination locations using generalized estimating equations.Across the US, 61.4% of eligible healthcare facilities and 76.0% of eligible pharmacies provided COVID-19 vaccinations as of May 2021. Facilities in counties with >42.2% non-Hispanic Black population (i.e., > 95th county percentile of Black race composition) were less likely to serve as COVID-19 vaccine administration locations compared to facilities in counties with <12.5% non-Hispanic Black population (i.e., lower than US average), with OR 0.83; 95% CI, 0.70 to 0.98, p = 0.030. Location of a facility in a rural county (OR 0.82; 95% CI, 0.75 to 0.90, p < 0.001, versus metropolitan county) or in a county in the top quintile of COVID-19 mortality (OR 0.83; 95% CI, 0.75 to 0.93, p = 0.001, versus bottom 4 quintiles) was associated with decreased odds of serving as a COVID-19 vaccine administration location.There was a significant interaction of urbanicity and racial/ethnic composition: In metropolitan counties, facilities in counties with >42.2% non-Hispanic Black population (i.e., >95th county percentile of Black race composition) had 32% (95% CI 14% to 47%, p = 0.001) lower odds of serving as COVID administration facility compared to facilities in counties with below US average Black population. This association between Black composition and odds of a facility serving as vaccine administration facility was not observed in rural or suburban counties. In rural counties, facilities in counties with above US average Hispanic population had 26% (95% CI 11% to 38%, p = 0.002) lower odds of serving as vaccine administration facility compared to facilities in counties with below US average Hispanic population. This association between Hispanic ethnicity and odds of a facility serving as vaccine administration facility was not observed in metropolitan or suburban counties.Our analyses did not include nontraditional vaccination sites and are based on data as of May 2021, thus they represent the early distribution of COVID-19 vaccines. Our results based on this cross-sectional analysis may not be generalizable to later phases of the COVID-19 vaccine distribution process.ConclusionsHealthcare facilities in counties with higher Black composition, in rural areas, and in hardest-hit communities were less likely to serve as COVID-19 vaccine administration locations in May 2021. The lower uptake of COVID-19 vaccinations among minority populations and rural areas has been attributed to vaccine hesitancy; however, decreased access to vaccination sites may be an additional overlooked barrier.

Inmaculada Hernandez and colleagues investigate the disparities in early-phase distribution of COVID-19 Vaccines across U.S. Counties.     

An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020–2021

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with R_t<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.     

Focus: Health Equity: COVID-19 Healthcare Inequity: Lessons Learned from Annual Influenza Vaccination Rates to Mitigate COVID-19 Vaccine Disparities

The COVID-19 pandemic has infected 33 million Americans and resulted in more than 600,000 deaths as of late Spring 2021. Black, Indigenous, and Latinx (BIL) people are disproportionately infected, hospitalized, and dying. Effective vaccines were rapidly developed and have been widely available in the United States since their initial rollout in late 2020-early 2021 but vaccination rates in BIL communities have remained low compared with non-BIL communities. Limited access to the vaccine, lack of customized information, and mistrust of the medical system, all contribute to vaccine hesitancy and low vaccination rates. Regrettably, COVID-19 is not the only vaccine-preventable illness with racial/ethnic inequities. Similar inequities are seen with the seasonal influenza vaccine. We review the racial/ethnic health disparities in COVID-19 illness and vaccination rates and what inequities contribute to these disparities. We use evidence from the seasonal influenza vaccination efforts to inform potential strategies to attenuate these inequities. The development of effective and sustainable strategies to improve vaccination rates and reduce factors that result in health inequities is essential in managing current and future pandemics and promoting improved health for all communities.     

Focus: Health Equity: Financial Protection Indexes and the Iranian Health Transformation Plan: A Systematic Review

Background: On May 5, 2014, the Iranian Ministry of Health and Medical Education launched the Health Transformation Plan (HTP) as a major healthcare reform to curb out-of-pocket (OOP) expenses and protect people from catastrophic health expenditures (CHEs). Therefore, in this study, we conducted a comprehensive literature search with the aim of systematically investigating the impacts of HTP on OOP and CHE after the implementation of the plan. Method: Web of Science, PubMed, Scopus, Embase, and Iranian bibliographic thesauri and repositories such as MagIran, Elmnet, and Scientific Information Database were searched. Studies published between May 2014 and December 2020 that reported the impact of HTP on the financial indicators under investigation in this study (OOP and CHEs) that were conducted in Iran. Estimated pooled change both for OOP and CHEs was calculated as effect size utilizing meta-analytical techniques. Also, heterogeneity among studies was assessed with the I² statistics. Results: Seventeen studies were included, nine of which evaluated the OOP index, six studies assessed the CHEs index, and two studies examined both the OOP and CHEs indexes. The OOP was found to decrease after the implementation of the HTP (with an estimated decrease of 13.02% (95% CI: 9.09-16.94). Also, CHEs experienced a decrease of 5.80% (95% CI: 3.85-7.74). Conclusion: The findings show that the implementation of HTP has reduced health costs. In this regard and in order to keep reducing the costs that many people are unable to pay, the government and other organizations involved in the health system should provide sustainable financial resources in order to continue running HTP. However, there remain gaps and weaknesses that can be solved through discussion with all the actors involved.     

Assessment of risk management and control measures against coronavirus disease

This article presents the COVID-19 situation and control measures taken by the Government of Pakistan. Two waves of pandemic are faced globally and similar in the study area. We have investigated the risk management decision in two phases. Primarily, strict lockdown was observed from March 2020 to July 2020 and smart lockdown was enforced from August 2020 to December 2020. It has been studied that during strict lockdown, COVID cases reduced gradually but reopening of institutes and smart lockdown strategy resulted gradual increase in confirmed cases and death rates. During first wave of COVID-19 in Pakistan, a total confirmed number of patients of COVID-19 were 263,496 till 18th of July 2020 with total deaths of 5,568 people and 204,276 recoveries, while total number of COVID-19 patients reached 555,511 till 9th of February 2021 with total deaths of 12,026 people. Province of Sindh was affected badly with total number of 251,434 COVID-19 cases followed by Punjab Province with total number of 161,347 COVID-19 till 9th of February 2020.     

Diagnosis-wide analysis of COVID-19 complications: an exposure-crossover study

BACKGROUND:Many studies reporting coronavirus disease 2019 (COVID-19) complications have involved case series or small cohorts that could not establish a causal association with COVID-19 or provide risk estimates in different care settings. We sought to study all possible complications of COVID-19 to confirm previously reported complications and to identify potential complications not yet known.METHODS:Using United States health claims data, we compared the frequency of all International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis codes occurring before and after the onset of the COVID-19 pandemic in an exposure-crossover design. We included patients who received a diagnosis of COVID-19 between Mar. 1, 2020, and Apr. 30, 2020, and computed risk estimates and odds ratios (ORs) of association with COVID-19 for every ICD-10-CM diagnosis code.RESULTS:Among 70 288 patients with COVID-19, 69 of 1724 analyzed ICD-10-CM diagnosis codes were significantly associated with COVID-19. Disorders showing both strong association with COVID-19 and high absolute risk included viral pneumonia (OR 177.63, 95% confidence interval [CI] 147.19–214.37, absolute risk 27.6%), respiratory failure (OR 11.36, 95% CI 10.74–12.02, absolute risk 22.6%), acute kidney failure (OR 3.50, 95% CI 3.34–3.68, absolute risk 11.8%) and sepsis (OR 4.23, 95% CI 4.01–4.46, absolute risk 10.4%). Disorders showing strong associations with COVID-19 but low absolute risk included myocarditis (OR 8.17, 95% CI 3.58–18.62, absolute risk 0.1%), disseminated intravascular coagulation (OR 11.83, 95% CI 5.26–26.62, absolute risk 0.1%) and pneumothorax (OR 3.38, 95% CI 2.68–4.26, absolute risk 0.4%).INTERPRETATION:We confirmed and provided risk estimates for numerous complications of COVID-19. These results may guide prognosis, treatment decisions and patient counselling.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel strain of coronavirus that has been identified as the cause of the coronavirus disease 2019 (COVID-19) pandemic. As of Nov. 20, 2020, more than 50 million people have received a diagnosis of COVID-19 globally.¹ The clinical spectrum of disease is wide and can range from symptoms typical of the common cold to respiratory failure and death.² Most patients have mild symptoms and can be managed as outpatients, but as many as 20% have a severe form of the disease requiring admission to hospital, commonly presenting with hypoxia secondary to pneumonia.³Studies also show that COVID-19 is associated with a wide variety of nonrespiratory sequelae, including endothelial, thrombotic, cardiac, inflammatory, neurologic and other complications. ^4–9 Whether these associations are causal is not well established, as many of these findings originate from case reports, which are prone to publication bias and cannot provide risk estimates, or from cohort studies that often do not provide relative risk estimates.An alternative strategy for identifying potential complications of COVID-19 is studying all possible complications as captured in International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10 CM) diagnosis codes, which allows for the discovery of unreported complications and can confirm previously identified ones. The objective of our study was to analyze all diagnoses associated with COVID-19, to identify those that could be complications of the disease and to present both the absolute risk and relative odds of any complications identified.     

Fish ACE2 is not susceptible to SARS-CoV-2

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has reshaped our daily life and caused > 4 million deaths worldwide (https://covid19.who.int/). Although lockdown and vaccination have improved the situation in many countries, imported cases and sporadic outbreaks pose a constant stress to the prevention and control of COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent responsible for COVID-19, has a positive-sense single-stranded RNA genome of 30 kb (Coronaviridae Study Group of the International Committee on Taxonomy of Viruses, 2020). We and other groups have demonstrated that the SARS-CoV-2 could use the angiotensin-converting enzyme 2 (ACE2) as cell receptor, including orthologs of a broad range of animal species such as human, bats, ferrets, pigs, cats, and dogs (Hoffmann et al., 2020; Zhou et al., 2020; Liu et al., 2021). Although the evolutionary origin of SARS-CoV-2 can be linked to the discoveries of diverse coronaviruses related to SARS-CoV-2 in wild animals such as bats (Zhou et al., 2020; Wacharapluesadee et al., 2021) and pangolins (Liu et al., 2019; Lam et al., 2020), the direct origin of SARS-CoV-2 in humans remains unknown. In China, several sporadic outbreaks of COVID-19 in 2020 were linked to food in cold chain sold at trade markets, including salmon meat (http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml) (Yang et al., 2020). The detection of SARS-CoV-2 RNA on the surface of frozen meat for as long as 20 days has also been reported (Feng et al., 2021). A concern regarding the potential role of fish in SARS-CoV-2 transmission has also been raised. Therefore, we investigated the susceptibility of fish ACE2 to SARS-CoV-2.     

Income differences in COVID-19 incidence and severity in Finland among people with foreign and native background: A population-based cohort study of individuals nested within households

BackgroundAlthough intrahousehold transmission is a key source of Coronavirus Disease 2019 (COVID-19) infections, studies to date have not analysed socioeconomic risk factors on the household level or household clustering of severe COVID-19. We quantify household income differences and household clustering of COVID-19 incidence and severity.Methods and findingsWe used register-based cohort data with individual-level linkage across various administrative registers for the total Finnish population living in working-age private households (N = 4,315,342). Incident COVID-19 cases (N = 38,467) were identified from the National Infectious Diseases Register from 1 July 2020 to 22 February 2021. Severe cases (N = 625) were defined as having at least 3 consecutive days of inpatient care with a COVID-19 diagnosis and identified from the Care Register for Health Care between 1 July 2020 and 31 December 2020. We used 2-level logistic regression with individuals nested within households to estimate COVID-19 incidence and case severity among those infected.Adjusted for age, sex, and regional characteristics, the incidence of COVID-19 was higher (odds ratio [OR] 1.67, 95% CI 1.58 to 1.77, p < 0.001, 28.4% of infections) among individuals in the lowest household income quintile than among those in the highest quintile (18.9%). The difference attenuated (OR 1.23, 1.16 to 1.30, p < 0.001) when controlling for foreign background but not when controlling for other household-level risk factors. In fact, we found a clear income gradient in incidence only among people with foreign background but none among those with native background. The odds of severe illness among those infected were also higher in the lowest income quintile (OR 1.97, 1.52 to 2.56, p < 0.001, 28.0% versus 21.6% in the highest quintile), but this difference was fully attenuated (OR 1.08, 0.77 to 1.52, p = 0.64) when controlling for other individual-level risk factors—comorbidities, occupational status, and foreign background. Both incidence and severity were strongly clustered within households: Around 77% of the variation in incidence and 20% in severity were attributable to differences between households. The main limitation of our study was that the test uptake for COVID-19 may have differed between population subgroups.ConclusionsLow household income appears to be a strong risk factor for both COVID-19 incidence and case severity, but the income differences are largely driven by having foreign background. The strong household clustering of incidence and severity highlights the importance of household context in the prevention and mitigation of COVID-19 outcomes.

Sanni Saarinen and colleagues explore the association between income differences and COVID-19 incidence and severity among people with foreign and native background in Finland.     

Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey: A repeated cross-sectional study

BackgroundDuring the Coronavirus Disease 2019 (COVID-19) pandemic, the United Kingdom government imposed public health policies in England to reduce social contacts in hopes of curbing virus transmission. We conducted a repeated cross-sectional study to measure contact patterns weekly from March 2020 to March 2021 to estimate the impact of these policies, covering 3 national lockdowns interspersed by periods of less restrictive policies.Methods and findingsThe repeated cross-sectional survey data were collected using online surveys of representative samples of the UK population by age and gender. Survey participants were recruited by the online market research company Ipsos MORI through internet-based banner and social media ads and email campaigns. The participant data used for this analysis are restricted to those who reported living in England. We calculated the mean daily contacts reported using a (clustered) bootstrap and fitted a censored negative binomial model to estimate age-stratified contact matrices and estimate proportional changes to the basic reproduction number under controlled conditions using the change in contacts as a scaling factor. To put the findings in perspective, we discuss contact rates recorded throughout the year in terms of previously recorded rates from the POLYMOD study social contact study.The survey recorded 101,350 observations from 19,914 participants who reported 466,710 contacts over 53 weeks. We observed changes in social contact patterns in England over time and by participants’ age, personal risk factors, and perception of risk. The mean reported contacts for adults 18 to 59 years old ranged between 2.39 (95% confidence interval [CI] 2.20 to 2.60) contacts and 4.93 (95% CI 4.65 to 5.19) contacts during the study period. The mean contacts for school-age children (5 to 17 years old) ranged from 3.07 (95% CI 2.89 to 3.27) to 15.11 (95% CI 13.87 to 16.41). This demonstrates a sustained decrease in social contacts compared to a mean of 11.08 (95% CI 10.54 to 11.57) contacts per participant in all age groups combined as measured by the POLYMOD social contact study in 2005 to 2006. Contacts measured during periods of lockdowns were lower than in periods of eased social restrictions. The use of face coverings outside the home has remained high since the government mandated use in some settings in July 2020. The main limitations of this analysis are the potential for selection bias, as participants are recruited through internet-based campaigns, and recall bias, in which participants may under- or overreport the number of contacts they have made.ConclusionsIn this study, we observed that recorded contacts reduced dramatically compared to prepandemic levels (as measured in the POLYMOD study), with changes in reported contacts correlated with government interventions throughout the pandemic. Despite easing of restrictions in the summer of 2020, the mean number of reported contacts only returned to about half of that observed prepandemic at its highest recorded level. The CoMix survey provides a unique repeated cross-sectional data set for a full year in England, from the first day of the first lockdown, for use in statistical analyses and mathematical modelling of COVID-19 and other diseases.

In a repeated cross-sectional study, Amy Gimma and colleagues study social contact patterns in the context of lockdown periods and government interventions in England during the first year of the COVID-19 pandemic.     

Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies

Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a “stay at home” order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020 on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. We find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Our work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.     

Colonoscopy screening and surveillance disparities during the COVID-19 pandemic

BackgroundThe COVID-19 pandemic has increased barriers to accessing preventive healthcare. This study identifies populations disproportionately underrepresented in screening and surveillance colonoscopies during the COVID-19 pandemic.MethodsIn this single-center cohort study, colonoscopy procedures were reviewed during 6-month intervals before the pandemic (July 1, 2019 - December 31, 2019) and during the pandemic (July 1, 2020 - December 31, 2020 and January 1, 2021 - June 30, 2021). 7095 patients were categorized based on procedure indication, demographics, Charlson Comorbidity Index and Social Vulnerability Index (SVI). Statistics performed using VassarStats.Results2387 (2019) colonoscopies pre-pandemic and 2585 (2020) and 2123 (2021) during the pandemic were identified. There was a decrease in colonoscopies performed during months when COVID-19 cases peaked. The total number of average CRC risk patients presenting for first colonoscopy declined during the pandemic: 232 (10 %) pre-pandemic to 190 (7 %) in 2020, 145 (7 %) in 2021 (p < 0.001). Fewer of these patients presented from highly vulnerable communities, SVI > 0.8, during the pandemic, 39 in 2019 vs 16 in 2020 and 22 in 2021. Of all screening and surveillance patients, fewer presented from communities with SVI > 0.8 during the pandemic, 106 in 2019 versus 67 in 2020 and 77 in 2021.ConclusionIt is important to address the decline in CRC preventive care during this pandemic among average CRC risk first-time screeners and vulnerable community patients. An emphasis on addressing social determinants of health and establishing patients in gastroenterology clinics is imperative to promote future health in these populations.