Dynamics of COVID-19 progression and the long-term influences of measures on pandemic outcomes

A controversy about the Swedish strategy of dealing with COVID-19 during the early period is how decision-making was based on evidence, which refers to data and data analysis. During the earliest period of the pandemic, the Swedish decision-making was based on subjective perspective. However, when more data became available, the decision-making stood on mathematical and descriptive analyses. The mathematical analysis aimed to model the condition for herd immunity while the descriptive analysis compared different measures without adjustment of population differences and updating pandemic situations. Due to the dubious interpretations of these analyses, a mild measure was adopted in Sweden upon the arrival of the second wave, leading to a surge of poor public health outcomes compared to the other Nordic countries (Denmark, Norway, and Finland). In this article, using data available during the first wave, we conduct longitudinal analysis to investigate the consequence of the shred of evidence in the Swedish decision-making for the first wave, where the study period is between January 2020 and August 2020. The design is longitudinal observational study. The linear regressions based on the Poisson distribution and the binomial distribution are employed for the analysis. We found that the early Swedish measure had a long-term and significant effect on general mortality and COVID-19 mortality and a certain mitigating effect on unemployment in Sweden during the first wave; here, the effect was measured by an increase of general deaths, COVID-19 deaths or unemployed persons under Swedish measure relative to the measures adopted by the other Nordic countries. These pieces of statistical evidence were not studied in the mathematical and descriptive analyses but could play an important role in the decision-making at the second wave. In conclusion, a timely longitudinal analysis should be part of the decision-making process for containing the current pandemic or a future one.

The SARS-CoV-2 virus is a public health emergency. Social distancing is a key approach to slowing disease transmission. However, more evidence is needed on its efficacy, and little is known on the types of areas where it is more or less effective. We obtained county-level data on COVID-19 incidence and mortality during the first wave, smartphone-based average social distancing (0–5, where higher numbers indicate more social distancing), and census data on demographics and socioeconomic status. Using generalized linear mixed models with a Poisson distribution, we modeled associations between social distancing and COVID-19 incidence and mortality, and multiplicative interaction terms to assess effect modification. In multivariable models, each unit increase in social distancing was associated with a 26% decrease (p < 0.0001) in COVID-19 incidence and a 31% decrease (p < 0.0001) in COVID-19 mortality. Percent crowding, minority population, and median household income were all statistically significant effect modifiers. County-level increases in social distancing led to reductions in COVID-19 incidence and mortality but were most effective in counties with lower percentages of black residents, higher median household incomes, and with lower levels of household crowding.

County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States

Background:COVID-19 is an infectious disease that has killed more than 246,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection.Objectives:We evaluated whether greenness is related to COVID-19 incidence and mortality in the United States.Methods:We downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order.Results:An increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density. Discussion: Exposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.

Background: During the COVID-19 outbreak, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection. We evaluated whether greenness is related to COVID-19 incidence and mortality in the United States.Methods: We downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order.Findings: An increase of 0·1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate. Associations with COVID-19 incidence were stronger in counties with high population density and high median home values, and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with high percentages of Black residents, high median home value, and higher population density.Interpretation: Exposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.Funding: this study was funded by R01HL150119 and R01ES028033Declaration of Interests: The authors declare that we have no conflicts of interest.

Impact of short-term exposure to ambient air pollutants and meteorological factors on COVID-19 incidence and mortality: A retrospective study from Dammam, Saudi Arabia

COVID-19 is reported as an extremely contagious disease with common symptoms of fever, dry cough, sore throat, and tiredness. The published literature on incidence and gender-wise prevalence of COVID-19 is scarce in Pakistan. Therefore, the present study was designed to compare the distribution, incubation period and mortality rate of COVID-19 among the male and female population of district Attock. The data were collected between 01 April 2020 and 07 December 2020 from the population of district Attock, Pakistan. A total of 22,962 individuals were screened and 843 were found positive for RT-qPCR for SARS-CoV-2. The confirmed positive cases were monitored carefully. Among the positive cases, the incidence of COVID-19 was 61.7% among males and 38.2% among females. The average recovery period of males was 18.89±7.75 days and females were 19±8.40 days from SARS-CoV-2. The overall mortality rate was 8.06%. The death rate of male patients was significantly higher (P<0.05) compared to female patients. Also, the mortality rate was higher (P<0.05) in male patients of 40-60 years of age compared to female patients of the same age group. Moreover, the mortality rate significantly increased (P<0.05) with the increase of age irrespective of gender. In conclusion, the incidence and mortality rate of COVID-19 is higher in males compared to the female population. Moreover, irrespective of gender the mortality rate was significantly lower among patients aged <40 years.

Does prior exposure to immune checkpoint inhibitors treatment affect incidence and mortality of COVID-19 among the cancer patients: The systematic review and meta-analysis

We aimed to estimate associations between COVID-19 incidence and mortality with neighbourhood-level immigration, race, housing, and socio-economic characteristics. We conducted a population-based study of 28,808 COVID-19 cases in the provincial reportable infectious disease surveillance systems (Public Health Case and Contact Management System) which includes all known COVID-19 infections and deaths from Ontario, Canada reported between January 23, 2020 and July 28, 2020. Residents of congregate settings, Indigenous communities living on reserves or small neighbourhoods with populations <1,000 were excluded. Comparing neighbourhoods in the 90th to the 10th percentiles of socio-demographic characteristics, we estimated the associations between 18 neighbourhood-level measures of immigration, race, housing and socio-economic characteristics and COVID-19 incidence and mortality using Poisson generalized linear mixed models. Neighbourhoods with the highest proportion of immigrants (relative risk (RR): 4.0, 95%CI:3.5-4.5) and visible minority residents (RR: 3.3, 95%CI:2.9-3.7) showed the strongest association with COVID-19 incidence in adjusted models. Among individual race groups, COVID-19 incidence was highest among neighbourhoods with the high proportions of Black (RR: 2.4, 95%CI:2.2-2.6), South Asian (RR: 1.9, 95%CI:1.8-2.1), Latin American (RR: 1.8, 95%CI:1.6-2.0) and Middle Eastern (RR: 1.2, 95%CI:1.1-1.3) residents. Neighbourhoods with the highest average household size (RR: 1.9, 95%CI:1.7-2.1), proportion of multigenerational families (RR: 1.8, 95%CI:1.7-2.0) and unsuitably crowded housing (RR: 2.1, 95%CI:2.0-2.3) were associated with COVID-19 incidence. Neighbourhoods with the highest proportion of residents with less than high school education (RR: 1.6, 95%CI:1.4-1.8), low income (RR: 1.4, 95%CI:1.2-1.5) and unaffordable housing (RR: 1.6, 95%CI:1.4-1.8) were associated with COVID-19 incidence. Similar inequities were observed across neighbourhood-level sociodemographic characteristics and COVID-19 mortality. Neighbourhood-level inequities in COVID-19 incidence and mortality were observed in Ontario, with excess burden experienced in neighbourhoods with a higher proportion of immigrants, racialized populations, large households and low socio-economic status.

Impact of covid-19 on patients in radiotherapy oncology departaments in Spain

Background: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has emerged as a worldwide public health emergency. Social distancing currently represents the primary public health approach to slowing disease transmission. However, to date, little evidence on the efficacy of these policies have been available, and nothing is known on the types of areas where these policies have been more or less effective in reducing incidence and mortality. Methods: We obtained county-level data on COVID-19 incidence and mortality, cell-phone based average social distancing (0-5, where higher numbers indicate more social distancing), and Census data on demographics and socioeconomic status. Using generalized linear mixed models with a Poisson distribution accounting for counties nested within states, we modeled associations between county-level social distancing and COVID-19 incidence and mortality. We used multiplicative interaction terms to determine if associations varied by demographics or socioeconomic status. Findings: In multivariable adjusted models, each unit increase in social distancing was associated with a 26% decrease (pInterpretation: County-level measures of social distancing lead to reductions in COVID-19 incidence and mortality, but was most effective in counties with lower percentages of black residents, higher median household incomes, and in counties with lower levels of household crowding. Funding: JEH and GA were supported by NIH/NIEHS P30 ES000002. TV was supported by NIH/NIDDK K01 DK125612. MDW was supported by CDC/NIOSH R01 OH011773 and the Brigham Research Institute Fund to Sustain Research Excellence.Declaration of Interests: Missing.

COVID-19 vaccination, incidence, and mortality rates among individuals with mental disorders in South Korea: A nationwide retrospective study.

Background: The magnitude of the impact of COVID-19 is dependent on social, demographic, health, nutrition and even environmental factors. These factors act individually and synergistically to impact the incidence, mortality and morbidity of COVID-19. We aimed to evaluate the variables contributing individually to COVID-19 incidence and mortality utilizing techniques to minimize the effects of interaction between these factors. Method: Data regarding 88 variables for 195 countries over three years were extracted from The Health Nutrition and Population Statistics database and aggregated into a consolidated median. Outliers were eliminated and variables having a completeness of more than 70% were selected. The analysis was done separately for the incidence and mortality of COVID19. Principal component Analysis (PCA) and Elastic net regression were used to identify the most important single variables. The significant variables of the PCA which explained the most variance were identified. Subsequently, variables with the highest importance (using normalized ranked regression coefficients) in the Elastic Net model were selected and the intersecting set of variables common to both models was considered as predictors affecting incidence and mortality of COVID-19. Result: The study revealed communities with a high prevalence of anaemia has a negative correlation with COVID-19 incidence which was furthermore, interestingly seen in multiple age groups. Diphtheria, Tetanus and Pertussis (DTP) Immunization in children was also found to have a negative linear correlation. Conclusion: A negative individual association was seen between anaemia (in multiple age groups) and DTP immunization in children with the incidence and mortality of COVID 19.

Rationale: The COVID-19 pandemic has disproportionately impacted racial/ethnic minority and socioeconomically disadvantaged groups in the United States, including at-risk populations within Southeastern Pennsylvania. We sought to determine whether neighborhood-level health, demographic, and socioeconomic characteristics in Southeastern Pennsylvania were associated with COVID-19 incidence and mortality at the zip code and municipality level, thereby establishing whether neighborhood-level disparities mirror individual-level ones. Methods: Cumulative zip code- and municipality-level data on COVID-19 cases and deaths were obtained from the public data hubs of 5 counties in Southeast Pennsylvania, and those of individual long-term care facilities (LTCFs) were obtained from the Pennsylvania Department of Health. For corresponding geographic areas, demographic and socioeconomic status variables were obtained from the American Community Survey, and data on the health status and behaviors of local residents were obtained from the Southeastern Pennsylvania Household Health Survey. COVID-19 cases and deaths reported by LTCFs were excluded from area-aggregated counts. Multivariable quasi-Poisson models with offsets for population counts were created to determine whether neighborhood-level variables were associated with COVID-19 incidence and mortality. Before adjusted incidence rate ratios were calculated, such models included individual predictors that were significantly associated (p<0.05) with COVID-19 outcomes and excluded highly collinear terms as determined by having variance inflation factors greater than 3. Results: Among 208 zip codes and municipalities that had complete data, the COVID-19 cumulative incidence through July 24, 2020 ranged from 0 to 331.9 per 10,000 residents, and the COVID-19 mortality rate ranged from 0 to 1.0 per 10,000 residents. Among 45 neighborhood-level variables considered, 5 were independently associated with COVID-19 incidence (p<0.01): 1) the proportion of residents aged 65 years or older (incidence rate ratio [IRR] = 1.341, 95%-CI: 1.147-1.567 for a 10% increase), 2) population density (IRR = 1.002, 95%-CI: 1.001-1.003 for a 100 people/square kilometer increase), 3) the proportion of individuals eating 3 or more servings of fruits/vegetables daily (IRR = 0.891, 95%-CI: 0.836-0.950 for a 10% increase), 4) average median house value (IRR = 0.989, 95%-CI: 0.980-0.994 for a $10,000 USD increase), and 5) the proportion of 2-person households (IRR = 0.997, 95%-CI: 0.995-0.999 for a 10% increase). The proportion of individuals aged 65 years or older was the only factor independently associated with COVID-19 mortality (IRR = 2.59, 95%-CI: 1.55-4.41 for a 10% increase). Conclusions: Neighborhood-level data can help identify specific needs of vulnerable populations and inform policies to address health disparities related to COVID-19.

Objectives. To examine and compare how 4 indices of population-level social disadvantage-the Social Vulnerability Index (SVI), the Area Deprivation Index (ADI), the COVID-19 Community Vulnerability Index (CCVI), and the Minority Health-Social Vulnerability Index (MH-SVI)-are associated with COVID-19 outcomes. Methods. Spatial autoregressive models adjusted for population density, urbanicity, and state fixed effects were used to estimate associations of county-level SVI, MH-SVI, CCVI, and ADI values with COVID-19 incidence and mortality. Results. All 4 disadvantage indices had similar positive associations with COVID-19 incidence. Each index was also significantly associated with COVID-19 mortality, but the ADI had a stronger association than the CCVI, MH-SVI, and SVI. Conclusions. Despite differences in component measures and weighting, all 4 of the indices we assessed demonstrated associations between greater disadvantage and COVID-19 incidence and mortality. Public Health Implications. Our findings suggest that each of the 4 disadvantage indices can be used to assist public health leaders in targeting ongoing first-dose and booster or third-dose vaccines as well as new vaccines or other resources to regions most vulnerable to negative COVID-19 outcomes, weighing potential tradeoffs in their political and practical acceptability. (Am J Public Health. 2022;112(11):1584-1588. https://doi.org/10.2105/AJPH.2022.307018).