Hepatitis A Outbreaks Associated With the Opioid Epidemic in Kentucky Counties, 2017–2018

2020 ◽  
Vol 110 (9) ◽  
pp. 1332
Author(s):  
Natalie DuPre ◽  
Lyndsey Blair ◽  
Sarah Moyer ◽  
E. Francis Cook ◽  
Bert Little ◽  
...  
Keyword(s):  
Income Inequality ◽  
Manufacturing Industry ◽  
Hepatitis A ◽  
Principal Component ◽  
County Level ◽  
Opioid Epidemic ◽  
Residential Instability ◽  
Relative Risks ◽  
Data Surveillance ◽  
Disability Income

Objectives. To describe county-level socioeconomic profiles associated with Kentucky’s 2017–2018 hepatitis A outbreak that predominately affected communities affected by the opioid epidemic. Methods. We linked county-level characteristics on socioeconomic and housing variables to counties’ hepatitis A rates. Principal component analysis identified county profiles of poverty, education, disability, income inequality, grandparent responsibility, residential instability, and marital status. We used Poisson regression to estimate adjusted relative risks (RRs) and 95% confidence intervals (CIs). Results. Counties with scores reflecting an extremely disadvantaged profile (RR = 1.21; 95% CI = 0.99, 1.48) and greater percentage of nonmarried men, residential instability, and income inequality (RR = 1.15; 95% CI = 0.94, 1.41) had higher hepatitis A rates. Counties with scores reflecting more married adults, residential stability, and lower income inequality despite disability, poverty, and low education (RR = 0.77; 95% CI = 0.59, 1.00) had lower hepatitis A rates. Counties with a higher percentage of workers in the manufacturing industry had slightly lower rates (RR = 0.97; 95% CI = 0.94, 1.00). Conclusions. As expected, impoverished counties had higher hepatitis A rates. Evaluation across the socioeconomic patterns highlighted community-level factors (e.g., residential instability, income inequality, and social structures) that can be collected to augment hepatitis A data surveillance and used to identify higher-risk communities for targeted immunizations.

Abstract P636: Social Determinants of Stroke Hospitalization and Mortality in the United States

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Randhir Sagar Yadav ◽  
Durgesh Chaudhary ◽  
Shima Shahjouei ◽  
Jiang Li ◽  
Vida Abedi ◽  
...  
Keyword(s):  
United States ◽  
Income Inequality ◽  
Social Determinants ◽  
Hemorrhagic Stroke ◽  
The United States ◽  
Care Physician ◽  
County Level ◽  
Medicare Beneficiaries ◽  
Level Data ◽  
Control And Prevention

Introduction: Stroke hospitalization and mortality are influenced by various social determinants. This ecological study aimed to determine the associations between social determinants and stroke hospitalization and outcome at county-level in the United States. Methods: County-level data were recorded from the Centers for Disease Control and Prevention as of January 7, 2020. We considered four outcomes: all-age (1) Ischemic and (2) Hemorrhagic stroke Death rates per 100,000 individuals (ID and HD respectively), and (3) Ischemic and (4) Hemorrhagic stroke Hospitalization rate per 1,000 Medicare beneficiaries (IH and HH respectively). Results: Data of 3,225 counties showed IH (12.5 ± 3.4) and ID (22.2 ± 5.1) were more frequent than HH (2.0 ± 0.4) and HD (9.8 ± 2.1). Income inequality as expressed by Gini Index was found to be 44.6% ± 3.6% and unemployment rate was 4.3% ± 1.5%. Only 29.8% of the counties had at least one hospital with neurological services. The uninsured rate was 11.0% ± 4.7% and people living within half a mile of a park was only 18.7% ± 17.6%. Age-adjusted obesity rate was 32.0% ± 4.5%. In regression models, age-adjusted obesity (OR for IH: 1.11; HH: 1.04) and number of hospitals with neurological services (IH: 1.40; HH: 1.50) showed an association with IH and HH. Age-adjusted obesity (ID: 1.16; HD: 1.11), unemployment (ID: 1.21; HD: 1.18) and income inequality (ID: 1.09; HD: 1.11) showed an association with ID and HD. Park access showed inverse associations with all four outcomes. Additionally, population per primary-care physician was associated with HH while number of pharmacy and uninsured rate were associated with ID. All associations and OR had p ≤0.04. Conclusion: Unemployment and income inequality are significantly associated with increased stroke mortality rates.

IMPACTS OF FINANCIAL INCLUSION ON POVERTY AND INCOME INEQUALITY IN DEVELOPING ASIA

2021 ◽  
pp. 1-17
Author(s):  
WARATTAYA CHINNAKUM
Keyword(s):  
Developing Countries ◽  
Income Inequality ◽  
Low Income ◽  
Financial Services ◽  
Trade Openness ◽  
Generalized Method Of Moments ◽  
Principal Component ◽  
Financial Inclusion ◽  
Poor Households ◽  
Low Income Groups

This study investigates the impacts of financial inclusion on poverty and income inequality in 27 developing countries in Asia during 2004–2019 based on a composite financial inclusion index (FII) constructed using principal component analysis (PCA). The generalized method of moments (GMM) was employed for the estimation. The results show that financial inclusion can influence the reduction in both poverty and income inequality. The empirical findings also reveal the contribution of such control variables as economic growth in decreasing income disparity and trade openness in helping improve the standard of living of poor households despite its tendency to co-vary with income inequality. The present empirical evidence supporting the role of financial inclusion in reducing poverty and income inequality in developing countries has led to a policy implication that financial sector development should focus on the availability, usage, and depth of credit to cover all poor households or low-income groups to help improve their access to financial services, enable them to increase their income, and reduce the income gap between poor and rich households.

scholarly journals Psychological Mechanisms Linking County-Level Income Inequality to Happiness in China

2018 ◽  
Vol 15 (12) ◽  
pp. 2667
Author(s):  
Jiaoli Cai ◽  
Li Zhang ◽  
Yulin Zhao ◽  
Peter Coyte
Keyword(s):  
Structural Equation Modeling ◽  
Income Inequality ◽  
Structural Equation ◽  
General Social Survey ◽  
Negative Relationship ◽  
Living Standards ◽  
Equation Modeling ◽  
County Level ◽  
Psychological Mechanisms ◽  
The Impact

Background In China, income levels and living standards have improved significantly, but many Chinese citizens still do not feel any happier. This phenomenon may be attributed to increased income inequality. Methods Using data from the 2013 Chinese General Social Survey (CGSS), we employed multilevel structural equation modeling (MSEM) to investigate the impact of county-level income inequality on individual-level happiness in China and multilevel mediation analysis with structural equation modeling (MMSEM) to explore the mechanisms through which income inequality impacted happiness. Results A negative relationship between income inequality and happiness was found. The negative association between them was explained by two psychological mechanisms, i.e., fairness and trust. The findings explained a “Chinese puzzle,” i.e., why people do not feel happier despite improved income and living standards. Conclusions Our findings may provide a reference for policy makers to implement policies designed to improve individual happiness. What is important now is to reduce income inequality, and to potentially improve perceptions of fairness and trust in China.

Degradation Assessment of Reciprocating Seal Using Support Vector Regression

2019 ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Jon Keegan ◽  
Zahed Siddique
Keyword(s):  
Support Vector Regression ◽  
Friction Force ◽  
Manufacturing Industry ◽  
Oil And Gas ◽  
Principal Component ◽  
Oil And Gas Industry ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Failure Data ◽  
Set Up

Abstract Reciprocating seal located directly on the rod/piston of a reciprocating equipment is used for preventing leakage and reducing wear between two parts that are in relative motion. Degradation assessment of reciprocating seal is extremely important in the manufacturing industry to avoid fatal breakdown of reciprocating equipment and machines. In this paper, we have proposed a data-driven prognostics approach using friction force to predict the degradation of reciprocating seal using Support Vector Regression. Statistical time domain features are extracted from friction force signal to reduce the complexity of raw data. Principal Component Analysis is used to fuse the relevant features and remove the redundant features from the process. Based on the selected features, a Support Vector Regression model is then built and trained for the prediction of seal degradation. A Grid search method is used to tune the hyperparameters in the SVR model. Run-to-failure data collected from an experimental test set-up is used to validate the proposed methodology. The study findings indicate that a small set of relevant features which can represent the pattern related to degradation is sufficient to have a high prediction accuracy. The seal tested for this study comes from oil and gas industry, but the proposed method can be implemented in any industry with reciprocating equipment and machines.

scholarly journals Comparison of two statistical approaches to predict all-cause mortality by dietary patterns in German elderly subjects

2005 ◽  
Vol 93 (5) ◽  
pp. 709-716 ◽  
Author(s):  
Kurt Hoffmann ◽  
Heiner Boeing ◽  
Paolo Boffetta ◽  
Gabriele Nagel ◽  
Philippos Orfanos ◽  
...  
Keyword(s):  
Dietary Patterns ◽  
Saturated Fat ◽  
Principal Component ◽  
Energy Sources ◽  
Elderly Subjects ◽  
Monounsaturated Fat ◽  
Reduced Rank Regression ◽  
Rank Regression ◽  
Relative Risks ◽  
All Cause Mortality

Dietary patterns are comprehensive variables of dietary intake appropriate to model the complex exposure in nutritional research. The objectives of this study were to identify dietary patterns by applying two statistical methods, principal component analysis (PCA) and reduced rank regression (RRR), and to assess their ability to predict all-cause mortality. Motivated by previous studies we chose percentages of energy from different macronutrients as response variables in the RRR analysis. We used data from 9356 German elderly subject enrolled in the European Prospective Investigation into Cancer and Nutrition study. The first RRR pattern, subjects which explained 30·8 % of variation in energy sources and especially much variation in intake of saturated fat, monounsaturated fat and carbohydrates was a significant predictor of all-cause mortality. The pattern score had high positive loadings in all types of meat, butter, sauces and eggs, and was inversely associated with bread and fruits. After adjustment for other known risk factors, the relative risks from the lowest to highest quintiles of the first RRR pattern score were 1·0, 1·01, 0·96, 1·32, 1·61 (P for trend: 0·0004). In contrast, the first two PCA patterns explaining 19·7 % of food intake variation but only 7·0 % of variation in energy sources were not related to mortality. These results suggest that variation in macronutrients is meaningful for mortality and that the RRR method is more appropriate than the classic PCA method to identify dietary patterns relevant to mortality.

scholarly journals The Impacts of Globalization on Inequality in the Post-COVID-19 World: Evidence From China

2021 ◽  
Vol 9 ◽  
Author(s):  
Tsun Se Cheong ◽  
Yanrui Wu ◽  
Michal Wojewodzki ◽  
Ning Ma
Keyword(s):  
International Trade ◽  
Income Inequality ◽  
Empirical Studies ◽  
County Level ◽  
Primary Industry ◽  
Industry Sector ◽  
Secondary Industry ◽  
Per Se ◽  
Industry Sectors ◽  
The Impact

Empirical studies suggest that globalization (FDI and international trade) has been greatly affected by the COVID-19 and related anti-pandemic measures imposed by governments worldwide. This paper investigates the impact of globalization on intra-provincial income inequality in China and the data is based on the county level. The findings reveal that FDI is negatively associated with intra-provincial inequality, intra-provincial inequality increases as the primary industry sector (agriculture) declines. The result also finds that the increase in inequality stems not from the development in the tertiary or secondary industry sectors per se, but the unevenness in the distribution of these sectors.

scholarly journals The Relationship Between Employees’ Job Satisfaction and Organizational Culture in Korea’s Manufacturing Industry: Focusing on Online Training Participation as a Moderator

2020 ◽  
Vol 13 (4) ◽  
pp. 13
Author(s):  
Hae Na Kim
Keyword(s):  
Job Satisfaction ◽  
Organizational Culture ◽  
Manufacturing Industry ◽  
Principal Component ◽  
Online Training ◽  
Hierarchical Regression ◽  
Manufacturing Companies ◽  
Market Cultures ◽  
The Relationship

<p class="a">This study intends to address the relationship between job satisfaction of employees and organizational culture in Korea’s manufacturing industry. In particular, this research addresses the role of online training participation as a moderator for the relationship between organizational culture and job satisfaction. Principal component analysis and hierarchical regression analysis were applied using the Korean Human Capital Corporate Dataset. The result of this study indicates higher job satisfaction under Clan culture or Adhocracy and Market cultures. Also, online training participation can enhance employees' job satisfaction and online training participation has a moderating effect for Adhocracy and Market cultures and job satisfaction. Therefore, the manufacturing companies of Korea need to build Adhocracy and Market cultures and to encourage online training participation for employees' higher job satisfaction.</p>

scholarly journals State-Level Income Inequality and County-Level Social Capital in Relation to Individual-Level Depression in Middle-Aged Adults: A Lagged Multilevel Study

2020 ◽  
Vol 17 (15) ◽  
pp. 5386
Author(s):  
Saloni Dev ◽  
Daniel Kim
Keyword(s):  
Social Capital ◽  
Income Inequality ◽  
State Level ◽  
Social Determinant ◽  
County Level ◽  
Middle Aged ◽  
Effective Prevention ◽  
Individual Level ◽  
The Us ◽  
Middle Aged Adults

In the US, the incidence of depression and suicide have followed escalating trends over the past several years. These trends call for greater efforts towards identifying their underlying drivers and finding effective prevention strategies and treatments. One social determinant of health that plausibly influences the risk of depression is income inequality, the gap between the rich and poor. However, research on this association is still sparse. We used data from the National Longitudinal Survey of Youth 1979 and the US Census to investigate the multilevel lagged associations of state-level income inequality with the individual-level odds of depression in middle-aged adults, controlling for state- and individual-level factors. We also examined the independent associations of county-level social capital with depression and explored whether it mediated the income inequality relationship. Higher income inequality at the state level predicted higher odds of individual-level depression nearly 2 decades later [OR for middle vs. lowest tertile of income inequality = 1.35 (95% CI: 1.02, 1.76), OR for highest vs. lowest tertile = 1.34 (95% CI: 1.01, 1.78)]. This association was stronger among men than women. Furthermore, there was evidence that county-level social capital independently predicted depression and that it mediated the income inequality association. Overall, our findings suggest that policies attenuating levels of income inequality at the US state level and that leverage social capital may protect against one’s likelihood of developing depression.

An Econometric Analysis of the Links Between Income Inequality and Economic Growth

2009 ◽  
pp. 130
Author(s):  
Yohannes G. Hailu ◽  
Tesfa G. Gebremedhin ◽  
Randall W. Jackson
Keyword(s):  
Economic Growth ◽  
Income Inequality ◽  
Empirical Evidence ◽  
Growth Model ◽  
Capita Income ◽  
Regional Growth ◽  
County Level ◽  
Income Inequalities ◽  
Per Capita ◽  
The Relationship

This study investigates temporal demographic changes and income inequalities, and more importantly the relationship between income inequality and economic growth inWest Virginia. Departing from earlier studies, a regional growth model is utilized and empirically tested using county level West Virginia data (1990-2000). Results suggest that per-capita income change is positively related to population and employment changes but negatively related to income inequality. This empirical evidence indicates that higher income inequality can potentially hinder economic growth.

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