Mapping Disparities in COVID-19: Determining the Demographic, Economic, Educational, Housing, Quality of Life, and Health Factors that Relate to Disparities in COVID-19 infections and Deaths

Background: Throughout the pandemic, minority groups, particularly African Americans and Hispanic/Latino Americans have experienced disproportionately high infection and death rates as compared to their white and Asian counterparts. Though this phenomenon could be attributed to high rates of pre-existing conditions in black and Hispanic communities, there are other underlying factors that cause such disparity. We set out to determine whether or not various demographic, economic, educational, health, housing, and quality of life indicators were correlated with higher rates of COVID-19 infection. Methods: We used USAFacts COVID-19 data to select the 150 United States counties with the highest infection rates. We then collected a series of data, courtesy of the U.S. Census Bureau, for each of those counties. While useful, county-level analysis failed to reveal inequality within counties (i.e. low-income areas nested inside a high-income county). In order to further understand minority health, we used Policy Map to collect a series of data for Chicago and New York City, and performed zip-code level analyses for each city. In order to explore societal indicators of minority health, we used descriptive statistics and statistical t-tests to compare the counties and zip codes with the highest white population to the counties and zip codes with the highest percentages of African Americans and Hispanic Americans. Finally, we created a series of scatterplots, studying the correlation between zip code level variables and COVID-19 infection rates. Results: Compared to the predominantly white counties and zip codes, areas with higher rates of African Americans and Hispanics demonstrated lower income levels, lower educational attainment, higher rates of certain pre-existing conditions (namely obesity and diabetes), lower rates of flu vaccination, lower self-rated health, lower insurance coverage, and minimal physical activity. Furthermore, larger household size, lower rates of yearly doctor’s visits, homeownership, and computer/internet access, and higher rates of unemployment, multiple jobholders, and public transport use were all related to higher COVID-19 rates among minority communities. Discussion: Underlying economic, educational, housing, and quality of life factors are associated with higher rates of COVID-19. Mitigating these underlying social determinants of disease could improve the health of minority groups disproportionately affected by COVID-19. Future research can work to further understand how these social indicators cause disease and should seek to uncover potential interventions to address these disparities

Gender Disparities in NLSY97: Educational Attainment and Income Level

This paper is designed to examine gender differences in education and their impact on education and occupation status, following the research Economic Rationale for Sex Differences in Education conducted by Janice Fanning Madden. Data was collected from the NLSY97 (National Longitudinal Survey) on American youth born between 1980-84. The sample originally included 8,984 respondents when first interviewed in 1997

Big Cities: Air Pollution and Human Health

Our project explores the growing issue of air pollution on urban environments throughout the United States. Through the use of the County Health Rankings data set, we investigated the connection amongst air pollution, health outcomes, and other socioeconomic and environmental risk factors

A Comparison Study among the High School Students in the US: Obesity and Overweight Rates among Racial Minorities—Stress, Dietary Behavior, Sports and Physical Activity Participation

Background: The Youth Risk Behavior Surveillance System (YRBSS) collects data on American youth primarily for assessing various health risk factors. This study aims to examine the relationship between demographic, and behavioral variables that we have considered as risk factors and obesity in American youth in 2015 and 2019. Methods: Data pertaining to obesity risk factors and BMI measurements were obtained from the YRBSS. Cross Tables and linear regression models were constructed and subsequent analyses were used to examine the correlation between risk factors and BMI. Two-sample t-tests were used to explore the difference between the 2015 and 2019 datasets. Results: Race and ethnicity were found to be significantly associated with a change in BMI from 2015 to 2019 alongside other behavioral risk factors such as fruit consumption, skipping breakfast, physical activity, involvement in sporting teams, sleeping hours, and academic performance. Conclusions: BMI is significantly affected by various risk factors. Ensuing research should examine the association of more factors with BMI

High-Risk Behavior of Students Carrying Weapon to School

The rise of personal and mental issues in students has been accompanied by an increase in the number of students carrying guns and the frequency of mass shootings at schools in the US. A recent study (Dong, 2021) pointed out that future researchers should focus on recognizing high-risk youths and tackle their issues early on in life most appropriately according to individual differences such as age and personal circumstances.This research discusses the contributing factors to youths carrying guns to schools and analyzes the high-risk behaviors of students. It was hypothesized that their personal issues such as their mental wellness, high-risk behavior, and attitude towards school correlate with their decision to carry weapons, particularly guns on school property. It is found that the group of students who do not feel safe at school and carry weapons has a higher percentage of students who are showing depression and suicidal symptoms than the overall group of students. These students also tend to get into fights in school or outside school more often. It is also found that students who experience dating violence (sexual or physical), who do binge drinking more often, who have higher sexual activities and higher number of sexual partners, students who were bullied, and those who have been threatened or injured by a weapon at school property with a weapon are also more likely to carry guns than those who are not. The results of the analysis also indicate that the feeling of being unsafe at school has a significant effect on whether students carry guns or not

Frequent mental distress among adults in the United States and its association with socio-demographic characteristics, unhealthy lifestyle, and chronic physical health status

Frequent mental distress (FMD) is a measure of poor mental health days for at least 14 days out of 30 days. It is one of the important dimensions of the health-related quality of life. The underlying causes of FMD are diverse. However, the issue has not been explored extensively due to the lack of reliable data on mental health. The aim of this study was to examine the level and trends of FMD among the adults of the United States (US) and identify the socio-demographic, lifestyles, and chronic health outcomes related correlates of FMD. The data for the study was obtained from the publicly available 2019 Behavioral Risk Factor Surveillance System (BRFSS) in the US, covering a large sample of 418,268 adult respondents from all the 50 states and participating territories. Respondents from each state and territory were identified by selecting the telephone number from the telephone directory following a systematic sampling design. To examine the trends in the prevalence of FMD, data from the 2010-2018 BRFSS were also utilized. Both descriptive and inferential statistical techniques, including multiple logistic regression models were employed to analyze the data. Results indicated that about 12% of the adults in the US experienced FMD, and the prevalence of FMD is increasing overtime. Females, students, adults aged below 35 years, multiracial, less educated, single, low income, and underweight individuals were found to have a higher risk of FMD. FMD was found to be significantly associated with unhealthy lifestyles and chronic health conditions. This study findings highlight the importance of interventions for mental health promotion and mental illness prevention, substance use prevention, screening and treatment services of FMD, and increased provision of resources to address social and economic determinants of FMD

Assessment of Surface Water Contamination from Coalbed Methane Fracturing-Derived Volatile Contaminants in Sullivan County, Indiana, USA

There is a growing concern over the contamination of surface water and the associated environmental and public health consequences from the recent proliferation in hydraulic fracturing in the USA. Petroleum hydrocarbon-derived contaminants of concern [benzene, toluene, ethylbenzene, and xylene (BTEX)] and various dissolved cations and anions were spatially determined in surface waters around 14 coalbed methane fracking wells in Sullivan County, IN, USA. At least one BTEX was detected in 69% of sampling sites (n=13) and 23% of sampling sites were found to be contaminated with all of the BTEX. Toluene was the most common BTEX compound detected across all sites, both upstream and downstream from coalbed methane fracking sites. The calcium (~60 ppm) and sulfates (~175 ppm) were the dominant cations and anions, respectively, in surface water around the fracking sites. This study represents the first report of BTEX contamination in surface water from coalbed methane hydraulic fracturing wells

Estimation of Qvf Measurement Error Models Using Empirical Likelihood Method

Predictor variables are often contaminated with measurement errors in statistical practice. This may be the case due to bad measurement apparatus or just because the true value of the variable cannot be measured precisely. In the framework of general regression models, measurement errors or misclassifications have very serious consequences in many cases as they lead to bias in the estimated parameters that does not disappear as the sample size goes to infinity. In most cases the estimated effect of the contaminated covariate is attenuated. There are some techniques, regression calibration, simulation extrapolation (SIMEX), and the score function method for correcting effect estimates in the presence of measurement error. These widely used approaches have some restricted applications in many situations, for example, SIMEX is a useful tool for correcting effect estimates in the presences of additive measurement error. The method is especially helpful for complex models with a simple measurement error structure. Score function method is employed only for linear measurement error models. In this dissertation, an inference method has been proposed that accounts for the presence of measurement error in the explanatory variables in both linear and nonlinear models. This approach relies on the consideration of the mean and variance function of the observed data and application of the empirical likelihood approach to those functions, which is referred to as quasi likelihood and variance function (QVF). This proposed approach provides the confidence intervals with high inclusion probability of the unknown regression parameters. Moreover, this method is computationally easy to employ to any measurement error model for correcting bias. In addition, general descriptions and comparisons of the existing methods and the suggested estimation framework with some applications in real life data are discussed. A simulation study is conducted to show the performance of the proposed estimation framework

Estimation of Qvf Measurement Error Models Using Empirical Likelihood Method

Predictor variables are often contaminated with measurement errors in statistical practice. This may be the case due to bad measurement apparatus or just because the true value of the variable cannot be measured precisely. In the framework of general regression models, measurement errors or misclassifications have very serious consequences in many cases as they lead to bias in the estimated parameters that does not disappear as the sample size goes to infinity. In most cases the estimated effect of the contaminated covariate is attenuated. There are some techniques, regression calibration, simulation extrapolation (SIMEX), and the score function method for correcting effect estimates in the presence of measurement error. These widely used approaches have some restricted applications in many situations, for example, SIMEX is a useful tool for correcting effect estimates in the presences of additive measurement error. The method is especially helpful for complex models with a simple measurement error structure. Score function method is employed only for linear measurement error models. In this dissertation, an inference method has been proposed that accounts for the presence of measurement error in the explanatory variables in both linear and nonlinear models. This approach relies on the consideration of the mean and variance function of the observed data and application of the empirical likelihood approach to those functions, which is referred to as quasi likelihood and variance function (QVF). This proposed approach provides the confidence intervals with high inclusion probability of the unknown regression parameters. Moreover, this method is computationally easy to employ to any measurement error model for correcting bias. In addition, general descriptions and comparisons of the existing methods and the suggested estimation framework with some applications in real life data are discussed. A simulation study is conducted to show the performance of the proposed estimation framework