scholarly journals Regional Characteristics of the Second Wave of SARS-CoV-2 Infections and COVID-19 Deaths in Germany

2021 ◽  
Vol 18 (20) ◽  
pp. 10663
Author(s):  
Gabriele Doblhammer ◽  
Daniel Kreft ◽  
Constantin Reinke
Keyword(s):  
Low Socioeconomic Status ◽  
Gradient Boosting ◽  
County Level ◽  
Learning Approaches ◽  
Nursing Home Population ◽  
Death Rates ◽  
Individual Level ◽  
Regional Characteristics ◽  
Level Information ◽  
Second Wave

(1) Background: In the absence of individual level information, the aim of this study was to identify the regional key features explaining SARS-CoV-2 infections and COVID-19 deaths during the upswing of the second wave in Germany. (2) Methods: We used COVID-19 diagnoses and deaths from 1 October to 15 December 2020, on the county-level, differentiating five two-week time periods. For each period, we calculated the age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates by 155 indicators and identified the top 20 associations using Shap values. (3) Results: Counties with low socioeconomic status (SES) had higher infection and death rates, as had those with high international migration, a high proportion of foreigners, and a large nursing home population. The importance of these characteristics changed over time. During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates. (4) Machine learning approaches can reveal regional characteristics that are associated with high rates of infection and mortality.

scholarly journals The second wave of SARS-CoV-2 infections and COVID-19 deaths in Germany – driven by values, social status and migration background? A county-scale explainable machine learning approach

2021 ◽  
Author(s):  
Gabriele Doblhammer ◽  
Constantin Reinke ◽  
Daniel Kreft
Keyword(s):  
Gradient Boosting ◽  
Low Ses ◽  
County Level ◽  
Nursing Home Population ◽  
Death Rates ◽  
Individual Level ◽  
Shapley Values ◽  
And Migration ◽  
The Individual ◽  
Second Wave

ABSTRACTThere is a general consensus that SARS-CoV-2 infections and COVID-19 deaths have hit lower social groups the hardest, however, for Germany individual level information on socioeconomic characteristics of infections and deaths does not exist. The aim of this study was to identify the key features explaining SARS-CoV-2 infections and COVID-19 deaths during the upswing of the second wave in Germany.We considered information on COVID-19 diagnoses and deaths from 1. October to 15. December 2020 on the county-level, differentiating five two-week time periods. We used 155 indicators to characterize counties in nine geographic, social, demographic, and health domains. For each period, we calculated directly age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates with the 155 characteristics of the counties for each period. To explore the importance and the direction of the correlation of the regional indicators we used the SHAP procedure. We categorized the top 20 associations identified by the Shapley values into twelve categories depicting the correlation between the feature and the outcome.We found that counties with low SES were important drivers in the second wave, as were those with high international migration and a high proportion of foreigners and a large nursing home population. During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates.We concluded that risky working conditions with reduced opportunities for social distancing and a high chronic disease burden put populations in low-SES counties at higher risk of SARS-CoV-2 infections and COVID-19 deaths. In addition, noncompliance with Corona measures and spill-over effects from neighbouring counties increased the spread of the virus. To further substantiate this finding, we urgently need more data at the individual level.

scholarly journals Food Environments and Hepatocellular Carcinoma Incidence

2021 ◽  
Vol 18 (11) ◽  
pp. 5740
Author(s):  
Mimi Ton ◽  
Michael J. Widener ◽  
Peter James ◽  
Trang VoPham
Keyword(s):  
Hepatocellular Carcinoma ◽  
Food Environment ◽  
Variance Estimation ◽  
Cancer Registries ◽  
County Level ◽  
Individual Level ◽  
Retail Food Environment ◽  
Food Retailers ◽  
Environment Index ◽  
Rate Ratios

Research into the potential impact of the food environment on liver cancer incidence has been limited, though there is evidence showing that specific foods and nutrients may be potential risk or preventive factors. Data on hepatocellular carcinoma (HCC) cases were obtained from the Surveillance, Epidemiology, and End Results (SEER) cancer registries. The county-level food environment was assessed using the Modified Retail Food Environment Index (mRFEI), a continuous score that measures the number of healthy and less healthy food retailers within counties. Poisson regression with robust variance estimation was used to calculate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the association between mRFEI scores and HCC risk, adjusting for individual- and county-level factors. The county-level food environment was not associated with HCC risk after adjustment for individual-level age at diagnosis, sex, race/ethnicity, year, and SEER registry and county-level measures for health conditions, lifestyle factors, and socioeconomic status (adjusted IRR: 0.99, 95% CI: 0.96, 1.01). The county-level food environment, measured using mRFEI scores, was not associated with HCC risk.

scholarly journals Meta-analysis cum machine learning approaches address the structure and biogeochemical potential of marine copepod associated bacteriobiomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Balamurugan Sadaiappan ◽  
Chinnamani PrasannaKumar ◽  
V. Uthara Nambiar ◽  
Mahendran Subramanian ◽  
Manguesh U. Gauns
Keyword(s):  
Machine Learning ◽  
Iron Transport ◽  
Meta Analysis ◽  
Zooplankton Community ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
16S Rdna Gene ◽  
Marine Copepod ◽  
Form Of Life ◽  
First Time

AbstractCopepods are the dominant members of the zooplankton community and the most abundant form of life. It is imperative to obtain insights into the copepod-associated bacteriobiomes (CAB) in order to identify specific bacterial taxa associated within a copepod, and to understand how they vary between different copepods. Analysing the potential genes within the CAB may reveal their intrinsic role in biogeochemical cycles. For this, machine-learning models and PICRUSt2 analysis were deployed to analyse 16S rDNA gene sequences (approximately 16 million reads) of CAB belonging to five different copepod genera viz., Acartia spp., Calanus spp., Centropages sp., Pleuromamma spp., and Temora spp.. Overall, we predict 50 sub-OTUs (s-OTUs) (gradient boosting classifiers) to be important in five copepod genera. Among these, 15 s-OTUs were predicted to be important in Calanus spp. and 20 s-OTUs as important in Pleuromamma spp.. Four bacterial s-OTUs Acinetobacter johnsonii, Phaeobacter, Vibrio shilonii and Piscirickettsiaceae were identified as important s-OTUs in Calanus spp., and the s-OTUs Marinobacter, Alteromonas, Desulfovibrio, Limnobacter, Sphingomonas, Methyloversatilis, Enhydrobacter and Coriobacteriaceae were predicted as important s-OTUs in Pleuromamma spp., for the first time. Our meta-analysis revealed that the CAB of Pleuromamma spp. had a high proportion of potential genes responsible for methanogenesis and nitrogen fixation, whereas the CAB of Temora spp. had a high proportion of potential genes involved in assimilatory sulphate reduction, and cyanocobalamin synthesis. The CAB of Pleuromamma spp. and Temora spp. have potential genes accountable for iron transport.

scholarly journals Timing of social distancing policies and COVID-19 mortality: county-level evidence from the U.S.

2021 ◽  
Author(s):  
Catalina Amuedo-Dorantes ◽  
Neeraj Kaushal ◽  
Ashley N. Muchow
Keyword(s):  
New York ◽  
Political Ideology ◽  
Death Rate ◽  
The United States ◽  
County Level ◽  
Level Data ◽  
Level Information ◽  
Alternative Measures ◽  
The U.S ◽  
Pharmaceutical Interventions

AbstractUsing county-level data on COVID-19 mortality and infections, along with county-level information on the adoption of non-pharmaceutical interventions (NPIs), we examine how the speed of NPI adoption affected COVID-19 mortality in the United States. Our estimates suggest that adopting safer-at-home orders or non-essential business closures 1 day before infections double can curtail the COVID-19 death rate by 1.9%. This finding proves robust to alternative measures of NPI adoption speed, model specifications that control for testing, other NPIs, and mobility and across various samples (national, the Northeast, excluding New York, and excluding the Northeast). We also find that the adoption speed of NPIs is associated with lower infections and is unrelated to non-COVID deaths, suggesting these measures slowed contagion. Finally, NPI adoption speed appears to have been less effective in Republican counties, suggesting that political ideology might have compromised their efficacy.

scholarly journals Interpersonal Comparisons of the Good: Epistemic not Impossible

Utilitas ◽  
2015 ◽  
Vol 28 (3) ◽  
pp. 288-313 ◽  
Author(s):  
MATHEW COAKLEY
Keyword(s):  
Institutional Choice ◽  
Individual Level ◽  
Interpersonal Comparisons ◽  
Individual Good ◽  
Level Information ◽  
Action Policy ◽  
Social Mind ◽  
The Individual

To evaluate the overall good/welfare of any action, policy or institutional choice we need some way of comparing the benefits and losses to those affected: we need to make interpersonal comparisons of the good/welfare. Yet sceptics have worried either: (1) that such comparisons are impossible as they involve an impossible introspection across individuals, getting ‘into their minds’; (2) that they are indeterminate as individual-level information is compatible with a range of welfare numbers; or (3) that they are metaphysically mysterious as they assume the existence either of a social mind or of absolute levels of welfare when no such things exist. This article argues that such scepticism can potentially be addressed if we view the problem of interpersonal comparisons as fundamentally an epistemic problem – that is, as a problem of forming justified beliefs about the overall good based on evidence of the individual good.

scholarly journals 694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A271-A271
Author(s):  
Azizi Seixas ◽  
Nicholas Pantaleo ◽  
Samrachana Adhikari ◽  
Michael Grandner ◽  
Giardin Jean-Louis
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
The United States ◽  
Poor Sleep ◽  
County Level ◽  
Infection Rates ◽  
Insufficient Sleep ◽  
Death Rates ◽  
Rural Counties ◽  
The Relationship

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep <7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

scholarly journals What does and does not correlate with COVID-19 death rates

2020 ◽  
Author(s):  
Christopher R. Knittel ◽  
Bora Ozaltun
Keyword(s):  
Linear Regression ◽  
Public Transportation ◽  
Death Rate ◽  
Negative Binomial ◽  
Statistical Significance ◽  
County Level ◽  
Linear Regression Models ◽  
Death Rates ◽  
Socio Economic Variables ◽  
Home Values

AbstractWe correlate county-level COVID-19 death rates with key variables using both linear regression and negative binomial mixed models, although we focus on linear regression models. We include four sets of variables: socio-economic variables, county-level health variables, modes of commuting, and climate and pollution patterns. Our analysis studies daily death rates from April 4, 2020 to May 27, 2020. We estimate correlation patterns both across states, as well as within states. For both models, we find higher shares of African American residents in the county are correlated with higher death rates. However, when we restrict ourselves to correlation patterns within a given state, the statistical significance of the correlation of death rates with the share of African Americans, while remaining positive, wanes. We find similar results for the share of elderly in the county. We find that higher amounts of commuting via public transportation, relative to telecommuting, is correlated with higher death rates. The correlation between driving into work, relative to telecommuting, and death rates is also positive across both models, but statistically significant only when we look across states and counties. We also find that a higher share of people not working, and thus not commuting either because they are elderly, children or unemployed, is correlated with higher death rates. Counties with higher home values, higher summer temperatures, and lower winter temperatures have higher death rates. Contrary to past work, we do not find a correlation between pollution and death rates. Also importantly, we do not find that death rates are correlated with obesity rates, ICU beds per capita, or poverty rates. Finally, our model that looks within states yields estimates of how a given state’s death rate compares to other states after controlling for the variables included in our model; this may be interpreted as a measure of how states are doing relative to others. We find that death rates in the Northeast are substantially higher compared to other states, even when we control for the four sets of variables above. Death rates are also statistically significantly higher in Michigan, Louisiana, Iowa, Indiana, and Colorado. California’s death rate is the lowest across all states.It is important to understand that this research, and other observational analyses like it, only identify correlations: these relationships are not necessarily causal. However, these correlations may help policy makers identify variables that may potentially be causally related to COVID-19 death rates and adopt appropriate policies after understanding the causal relationship.

Abstract MP49: Application Of A Novel Assessment Of County-level Cardiovascular Health Profile And Its Association With County-level Disease Rates

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Neil P Rowen ◽  
Daniel Kim ◽  
Hannah P Rayala ◽  
Andrew H Reiter ◽  
Wayne D Rosamond
Keyword(s):  
Hospital Discharge ◽  
Cardiovascular Health ◽  
Circulatory System ◽  
Coastal Region ◽  
Blood Cholesterol ◽  
County Level ◽  
Individual Level ◽  
Discharge Rates ◽  
Novel Approach ◽  
Level Hospital

Background: The AHA’s definition of cardiovascular health (CVH) is based on seven metrics known as Life’s Simple 7 (LS7): smoking, diet, obesity, physical inactivity, high blood cholesterol, high blood pressure, and diabetes. Although used to evaluate CVH at the national and individual level, its use as a local county-level measure of CVH has not yet been studied. Our objective was to create a modification of LS7 using publicly available data to estimate county-level CVH and to determine its association with CVH outcomes in all 100 counties of North Carolina (NC). Methods and Results: Using data on all the LS7 metrics collected by the CDC, USDA, BRFSS, and Community Health Assessments, we created a Modified LS7 scoring system, calculated scores for all 100 counties in NC, and created a regression model that predicts county-level hospital discharge rates for diseases and disorders of the circulatory system (Figure 1). Modified LS7 scores ranged from 60.8 to 80.6 (median = 73.1, SD = 3.9). Hospital discharge rates per 100,000 population ranged from 753.4 to 2223.4 (median = 1345.6, SD = 328.7). We found a negative correlation (R-squared = 0.610) between Modified LS7 scores and county-level hospital discharge rates. Counties in the mountain and piedmont regions had significantly higher mean Modified LS7 scores (74.3, 95% CI: 73.5-75.2; 73.9, 95% CI: 72.8-75.0) and lower mean discharge rates (1167.1, 95% CI: 1074.7-1259.5; 1273.9, 95% CI: 1181.4-1366.2) than counties in the coastal plains region (70.7, 95% CI: 69.4-72.0; 1612.3, 95% CI: 1518.5-1706.1). Studentized residuals and leverage points were used to identify five low performing counties and two high performing counties of interest for further analyses. Conclusions: The coastal region of NC was found to have significantly higher CVH risk and poorer CVH outcomes compared to the piedmont and mountain regions. The Modified LS7 model provides a novel approach to examine county-level variation in CVH that had previously only been reported at the national, state or individual level.

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