scholarly journals Causal Analysis of Health Interventions and Environments for Influencing the Spread of COVID-19 in the United States of America

2020 ◽  
Author(s):  
zhouxuan Li ◽  
Tao Xu ◽  
Kai Zhang ◽  
Hong-Wen Deng ◽  
Eric Boerwinkle ◽  
...  
Keyword(s):  
Risk Factor ◽  
Unemployment Rate ◽  
Additive Model ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
The United States ◽  
Health Interventions ◽  
Health Crisis ◽  
The Us ◽  
Pharmaceutical Interventions

As of August 27, 2020, the number of cumulative cases of COVID-19 in the US exceeded 5,863,363 and included 180,595 deaths, thus causing a serious public health crisis. Curbing the spread of Covid-19 is still urgently needed. Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1000 people, workplaces, tests done/1000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1000 people, mobility trends for places of residence (residential), retail and test capacity were the most significant risk factor for the new cases of COVID-19 in 23, 7, 6, 5, 4, 2, 1 and 1 states, respectively, and that active cases/1000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1000 people, transit stations, mobility trends (transit) , tests done/1000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the most significant risk factor for the deaths of COVID-19 in 17, 10, 4, 4, 3, 2, 2, 2, 1, 1, 1, 1 states, respectively. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

scholarly journals Causal Analysis of Health Interventions and Environments for Influencing the Spread of COVID-19 in the United States of America

2021 ◽  
Vol 6 ◽  
Author(s):  
Zhouxuan Li ◽  
Tao Xu ◽  
Kai Zhang ◽  
Hong-Wen Deng ◽  
Eric Boerwinkle ◽  
...  
Keyword(s):  
Risk Factor ◽  
Unemployment Rate ◽  
Additive Model ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
The United States ◽  
Accurate Estimate ◽  
Health Interventions ◽  
Mobility Data ◽  
Pharmaceutical Interventions

Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1,000 people, workplaces, tests done/1,000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1,000 people, mobility trends for places of residence (residential), retail and test capacity were the popular significant risk factor for the new cases of COVID-19, and that active cases/1,000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1,000 people, transit stations, mobility trends (transit), tests done/1,000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the popular significant risk factor for the deaths of COVID-19. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

scholarly journals MON-585 Racial/Ethnic Contribution and Metabolic Factors of NAFLD/NASH in the US Population: Data from NNANES III

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Theodore C Friedman ◽  
Magda Shaheen ◽  
Dulcie Kermah ◽  
Deyu Pan ◽  
Katrina Schrode ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Liver Enzyme ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Healthy Eating Index ◽  
C Peptide ◽  
The Us ◽  
Race Ethnicity ◽  
Racial Ethnic

Abstract Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver condition. It is manifested by hepatic steatosis (HS) that can progress to non-alcoholic steatohepatitis (NASH), and even liver failure. Interestingly, it is marked by racial/ethnic disparities, with a high prevalence in Hispanics. We aimed to identify the risk factors for these chronic conditions in the US. To this end, we analyzed data from NHANES III (1988-1994) using multiple or multinomial logistic regression considering the design and sample weight. HS was identified by ultrasound. NAFLD was defined as HS in the absence of viral hepatitis or excessive use of alcohol or hepatotoxic drugs. The NAFLD population was further divided into those with NASH (defined by the HAIR score), or with simple NAFLD. The prevalence of HS was 19.8%, 16.6%, and 27.9%; of NAFLD was 17.8%, 14.7%, and 25.5%; and of NASH was 3.2%, 2.5%, and 5.1% in non-Hispanic Whites, non-Hispanic Blacks and Hispanics, respectively. Race/ethnicity was a significant predictor of HS, NAFLD and NASH, with Hispanics having the highest odds for all conditions, and non-Hispanic Blacks having the lowest odds relative to Whites (p<0.05). Other significant risk factors for all three conditions were older age, higher BMI, abnormal levels of C-peptide, and elevated serum glucose and triglycerides (p<0.05). HOMA insulin resistance was associated with HS and NAFLD (p<0.05). While smoking status was not associated with HS (p>0.05), current smokers had lower odds of NAFLD & NASH than non-smokers (p<0.05). Elevation of the liver enzyme aspartate aminotransferase was a significant risk factor of HS, while elevation of the liver enzyme alanine transaminase was a significant risk factor of NAFLD. Elevation in the levels of both liver enzymes was predictive of NASH (p<0.05). Although we included physical activity relative to national recommendation variable and the Healthy Eating Index (a measure of diet quality) in our analyses, neither of these factors was a predictor of any of the liver conditions (p>0.05). Our results showed an independent association between race/ethnicity and HS, NAFLD, and NASH, whereby Hispanics had the highest odds for every condition relative to non-Hispanic Whites. Providers should consider the race/ethnicity of their patients when evaluating the risk for NAFLD and NASH, and also be aware of the other risk factors, such as BMI and levels of C-peptide, glucose, and triglycerides.

scholarly journals Differential impact of non-pharmaceutical public health interventions on COVID-19 epidemics in the United States

2020 ◽  
Author(s):  
Xiaoshuang Liu ◽  
Xiao Xu ◽  
Guanqiao Li ◽  
Xian Xu ◽  
Yuyao Sun ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Decision Making ◽  
The United States ◽  
School Closure ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Health Crisis ◽  
The Us ◽  
Individualized Decision

Abstract Background: The widespread pandemic of novel coronavirus disease 2019 (COVID-19) poses an unprecedented global health crisis. In the United States (US), different state governments have adopted various combinations of non-pharmaceutical public health interventions (NPIs), such as non-essential business closures and gathering bans, to mitigate the epidemic from February to April, 2020. Quantitative assessment on the effectiveness of NPIs is greatly needed to assist in guiding individualized decision making for adjustment of interventions in the US and around the world. However, the impacts of these approaches remain uncertain.Methods: Based on the reported cases, the effective reproduction number (Rt) of COVID-19 epidemic for 50 states in the US was estimated. Measurements on the effectiveness of nine different NPIs were conducted by assessing risk ratios (RRs) between R t and NPIs through a generalized linear model (GLM). Results: Different NPIs were found to have led to different levels of reduction in Rt. Stay-at-home contributed approximately 51% (95% CI 46%-57%), wearing (face) masks 29% (15%-42%), gathering ban (more than 10 people) 19% (14%-24%), non-essential business closure 16% (10%-21%), declaration of emergency 13% (8%-17%), interstate travel restriction 11% (5%-16%), school closure 10% (7%-14%), initial business closure 10% (6%-14%), and gathering ban (more than 50 people) 7% (2%-11%).Conclusions: This retrospective assessment of NPIs on Rt has shown that NPIs played critical roles on epidemic control in the US in the past several months. The quantitative results could guide individualized decision making for future adjustment of NPIs in the US and other countries for COVID-19 and other similar infectious diseases.

scholarly journals Differential impact of non-pharmaceutical public health interventions on COVID-19 epidemics in the United States

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaoshuang Liu ◽  
Xiao Xu ◽  
Guanqiao Li ◽  
Xian Xu ◽  
Yuyao Sun ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Decision Making ◽  
The United States ◽  
School Closure ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Health Crisis ◽  
The Us ◽  
Individualized Decision

Abstract Background The widespread pandemic of novel coronavirus disease 2019 (COVID-19) poses an unprecedented global health crisis. In the United States (US), different state governments have adopted various combinations of non-pharmaceutical public health interventions (NPIs), such as non-essential business closures and gathering bans, to mitigate the epidemic from February to April, 2020. Quantitative assessment on the effectiveness of NPIs is greatly needed to assist in guiding individualized decision making for adjustment of interventions in the US and around the world. However, the impacts of these approaches remain uncertain. Methods Based on the reported cases, the effective reproduction number (Rt) of COVID-19 epidemic for 50 states in the US was estimated. Measurements on the effectiveness of nine different NPIs were conducted by assessing risk ratios (RRs) between Rt and NPIs through a generalized linear model (GLM). Results Different NPIs were found to have led to different levels of reduction in Rt. Stay-at-home contributed approximately 51% (95% CI 46–57%), wearing (face) masks 29% (15–42%), gathering ban (more than 10 people) 19% (14–24%), non-essential business closure 16% (10–21%), declaration of emergency 13% (8–17%), interstate travel restriction 11% (5–16%), school closure 10% (7–14%), initial business closure 10% (6–14%), and gathering ban (more than 50 people) 7% (2–11%). Conclusions This retrospective assessment of NPIs on Rt has shown that NPIs played critical roles on epidemic control in the US in the past several months. The quantitative results could guide individualized decision making for future adjustment of NPIs in the US and other countries for COVID-19 and other similar infectious diseases.

scholarly journals Differential impact of non-pharmaceutical public health interventions on COVID-19 epidemics in the United States

2020 ◽  
Author(s):  
Xiaoshuang Liu ◽  
Xiao Xu ◽  
Guanqiao Li ◽  
Xian Xu ◽  
Yuyao Sun ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Decision Making ◽  
The United States ◽  
School Closure ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Health Crisis ◽  
The Us ◽  
Individualized Decision

Abstract Background: The widespread pandemic of novel coronavirus disease 2019 (COVID-19) poses an unprecedented global health crisis. In the United States (US), different state governments have adopted various combinations of non-pharmaceutical public health interventions (NPIs), such as non-essential business closures and gathering bans, to mitigate the epidemic from February to April, 2020. Quantitative assessment on the effectiveness of NPIs is greatly needed to assist in guiding individualized decision making for adjustment of interventions in the US and around the world. However, the impacts of these approaches remain uncertain. Methods: Based on the reported cases, the effective reproduction number (B) of COVID-19 epidemic for 50 states in the US was estimated. Measurements on the effectiveness of nine different NPIs were conducted by assessing risk ratios (RRs) between a and NPIs through a generalized linear model (GLM). Results: Different NPIs were found to have led to different levels of reduction in c. Stay-at-home contributed approximately 51% (95% CI 46%-57%), wearing (face) masks 29% (15%-42%), gathering ban (more than 10 people) 19% (14%-24%), non-essential business closure 16% (10%-21%), declaration of emergency 13% (8%-17%), interstate travel restriction 11% (5%-16%), school closure 10% (7%-14%), initial business closure 10% (6%-14%), and gathering ban (more than 50 people) 7% (2%-11%). Conclusions: This retrospective assessment of NPIs on k has shown that NPIs played critical roles on epidemic control in the US in the past several months. The quantitative results could guide individualized decision making for future adjustment of NPIs in the US and other countries for COVID-19 and other similar infectious diseases.

Physical Illness and Suicide Risk in Rural Residents of Contemporary China

Crisis ◽  
2014 ◽  
Vol 35 (5) ◽  
pp. 330-337 ◽  
Author(s):  
Cun-Xian Jia ◽  
Lin-Lin Wang ◽  
Ai-Qiang Xu ◽  
Ai-Ying Dai ◽  
Ping Qin
Keyword(s):  
Risk Factor ◽  
Family Income ◽  
Rural China ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Health Condition ◽  
Physical Illness ◽  
Rural Residents ◽  
Increased Risk ◽  
Study Population

Background: Physical illness is linked with an increased risk of suicide; however, evidence from China is limited. Aims: To assess the influence of physical illness on risk of suicide among rural residents of China, and to examine the differences in the characteristics of people completing suicide with physical illness from those without physical illness. Method: In all, 200 suicide cases and 200 control subjects, 1:1 pair-matched on sex and age, were included from 25 townships of three randomly selected counties in Shandong Province, China. One informant for each suicide or control subject was interviewed to collect data on the physical health condition and psychological and sociodemographic status. Results: The prevalence of physical illness in suicide cases (63.0%) was significantly higher than that in paired controls (41.0%; χ2 = 19.39, p < .001). Compared with suicide cases without physical illness, people who were physically ill and completed suicide were generally older, less educated, had lower family income, and reported a mental disorder less often. Physical illness denoted a significant risk factor for suicide with an associated odds ratio of 3.23 (95% CI: 1.85–5.62) after adjusted for important covariates. The elevated risk of suicide increased progressively with the number of comorbid illnesses. Cancer, stroke, and a group of illnesses comprising dementia, hemiplegia, and encephalatrophy had a particularly strong effect among the commonly reported diagnoses in this study population. Conclusion: Physical illness is an important risk factor for suicide in rural residents of China. Efforts for suicide prevention are needed and should be integrated with national strategies of health care in rural China.

The Nexus Between Johannesburg Stock Exchange and the United States Unemployment Rate Announcement: An Impulse Response Analysis

2018 ◽  
Vol 10 (3(J)) ◽  
pp. 160-168
Author(s):  
Misheck Mutize ◽  
Victor Virimai Mugobo
Keyword(s):  
United States ◽  
South African ◽  
Impulse Response ◽  
Unemployment Rate ◽  
Stock Prices ◽  
Stock Exchange ◽  
The United States ◽  
Response Analysis ◽  
Johannesburg Stock Exchange ◽  
The Us

The study explores the relationship between the unemployment rate in the United States and South Africa’s stock prices from the beginning of 2013 to the last day 2017. The objective of this paper is to examine the impact of the US unemployment rate announcement on the South African financial market. Results of Impulse Response analysis show that there is a very minimal impact from the US unemployment announcement to South Africa’s stock prices which disappears within two days of the announcement. In addition, the Johannesburg stock exchange index marginally responds to own shocks, which marginally fades away within two days. These findings imply that the changes in the US employment policies have a direct ripple effect on the South African macroeconomic environment, its investing public sentiments and corporate confidence on the future prospects of businesses.

scholarly journals Higher premature atrial complex burden from the Holter examination predicts poor cardiovascular outcome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ting-Chun Huang ◽  
Po-Tseng Lee ◽  
Mu-Shiang Huang ◽  
Pei-Fang Su ◽  
Ping-Yen Liu
Keyword(s):  
Risk Factor ◽  
Dose Response ◽  
Cox Model ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Cardiovascular Death ◽  
Specific Model ◽  
Distribution Model ◽  
All Cause Mortality ◽  
Quartile Group

AbstractPremature atrial complexes (PACs) have been suggested to increase the risk of adverse events. The distribution of PAC burden and its dose–response effects on all-cause mortality and cardiovascular death had not been elucidated clearly. We analyzed 15,893 patients in a medical referral center from July 1st, 2011, to December 31st, 2018. Multivariate regression driven by ln PAC (beats per 24 h plus 1) or quartiles of PAC burden were examined. Older group had higher PAC burden than younger group (p for trend < 0.001), and both genders shared similar PACs distribution. In Cox model, ln PAC remained an independent risk factor for all-cause mortality (hazard ratio (HR) = 1.09 per ln PAC increase, 95% CI = 1.06‒1.12, p < 0.001). PACs were a significant risk factor in cause-specific model (HR = 1.13, 95% CI = 1.05‒1.22, p = 0.001) or sub-distribution model (HR = 1.12, 95% CI = 1.04‒1.21, p = 0.004). In ordinal PAC model, 4th quartile group had significantly higher risk of all-cause mortality than those in 1st quartile group (HR = 1.47, 95% CI = 1.13‒1.94, p = 0.005), but no difference in cardiovascular death were found in competing risk analysis. In subgroup analysis, the risk of high PAC burden was consistently higher than in low-burden group across pre-specified subgroups. In conclusion, PAC burden has a dose response effect on all-cause mortality and cardiovascular death.

scholarly journals THU0442 GOUT AND HEART FAILURE IN THE US: A NATIONAL PERSPECTIVE

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 458.2-458
Author(s):  
G. Singh ◽  
M. Sehgal ◽  
A. Mithal
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Confidence Intervals ◽  
Burden Of Illness ◽  
Average Length ◽  
Private Insurance ◽  
Significant Risk ◽  
Case Fatality ◽  
The United States ◽  
The Us

Background:Heart failure (HF) is the eighth leading cause of death in the US, with a 38% increase in the number of deaths due to HF from 2011 to 2017 (1). Gout and hyperuricemia have previously been recognized as significant risk factors for heart failure (2), but there is little nationwide data on the clinical and economic consequences of these comorbidities.Objectives:To study heart failure hospitalizations in patients with gout in the United States (US) and estimate their clinical and economic impact.Methods:The Nationwide Inpatient Sample (NIS) is a stratified random sample of all US community hospitals. It is the only US national hospital database with information on all patients, regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. We examined all inpatient hospitalizations in the NIS in 2017, the most recent year of available data, with a primary or secondary diagnosis of gout and heart failure. Over 69,800 ICD 10 diagnoses were collapsed into a smaller number of clinically meaningful categories, consistent with the CDC Clinical Classification Software.Results:There were 35.8 million all-cause hospitalizations in patients in the US in 2017. Of these, 351,735 hospitalizations occurred for acute and/or chronic heart failure in patients with gout. These patients had a mean age of 73.3 years (95% confidence intervals 73.1 – 73.5 years) and were more likely to be male (63.4%). The average length of hospitalization was 6.1 days (95% confidence intervals 6.0 to 6.2 days) with a case fatality rate of 3.5% (95% confidence intervals 3.4% – 3.7%). The average cost of each hospitalization was $63,992 (95% confidence intervals $61,908 - $66,075), with a total annual national cost estimate of $22.8 billion (95% confidence intervals $21.7 billion - $24.0 billion).Conclusion:While gout and hyperuricemia have long been recognized as potential risk factors for heart failure, the aging of the US population is projected to significantly increase the burden of illness and costs of care of these comorbidities (1). This calls for an increased awareness and management of serious co-morbid conditions in patients with gout.References:[1]Sidney, S., Go, A. S., Jaffe, M. G., Solomon, M. D., Ambrosy, A. P., & Rana, J. S. (2019). Association Between Aging of the US Population and Heart Disease Mortality From 2011 to 2017. JAMA Cardiology. doi:10.1001/jamacardio.2019.4187[2]Krishnan E. Gout and the risk for incident heart failure and systolic dysfunction. BMJ Open 2012;2:e000282.doi:10.1136/bmjopen-2011-000282Disclosure of Interests: :Gurkirpal Singh Grant/research support from: Horizon Therapeutics, Maanek Sehgal: None declared, Alka Mithal: None declared

Sign in / Sign up

Export Citation Format

Share Document