Analyzing the Correlations between the Uninsured and Diabetes Prevalence Rates in Geographic Regions in the United States

The increasing prevalence of diagnosed diabetes has drawn attentions of researchers in recently years. Research has been done in finding the correlations between diabetes prevalence with socioeconomic factors, obesity, social behaviors and so on. Since 2010, diabetes preventive services have been covered under health insurance plans in order to reduce diabetes burden and control the increasing of diabetes prevalence. In this study, a hierarchical clustering model is proposed by using Expectation-Maximization algorithm to investigate the correlations between the uninsured and diabetes prevalence rates in 3142 counties in United States for years from 2009 to 2013. The results identified geographic disparities in the uninsured and diabetes prevalence rates of individual years and over consecutive years

Preventable hospitalization among type 2 diabetes patients in Kentucky before and after medicaid expansion 2010-2017.

Objectives: 1) Analyze county level variation in T2DM-PH rates in Kentucky before ACA (2010-2013) and after the ACA (2014-2017). 2) Analyze the relationship between county level socioeconomic factors (income per capita, percentage of uninsured people, percent of urban population, primary care and general preventive offices, population aged 65 and above, median age, household income, percentage in poverty, and unemployment rate ) and county level T2DM-PH rates before (2010-2013) and after (2014-2017) ACA implementation in Kentucky. Method: This research was conducted in two phases: Phase one of this study estimated the county-level PH variation among T2DM patients across eight years (2010-2017), four years (2010-2013) before the Medicaid expansion and the next four years (2014-2017) after the implementation of Medicaid expansion to estimate the ACA impact on health outcomes among T2DM patients in Kentucky. The second phase focused on objective number two, to analyze and compare the socioeconomic factors association with T2DM-PH rates Previ and Post-Medicaid expansion. All county level socioeconomic factors and T2DM-PH rates were extracted from the AHRF data (2010-2017) and merged with Kentucky Hospital Inpatient Discharge Databases (KID) (2010-2017) to estimate and compare the correlations pre- and post-Medicaid expansion. Results: When the overall T2DM-PH rates Pre- and Post-ACA were assessed, a significant reduction (8.38%) in T2DM-PH discharges rates was found in the period of the postexpansion (P = 0.001). However, The spatial statistics analysis revealed significant spatial clustering of counties with similar high rates of T2DM-PH in the southeastern region before and after the expansion. These Counties with cluster type high-high (HH) had high positive z-score, positive Moran’s Index values and p-value2) of the variation in socioeconomic factors. PC1 loaded with wealth variables, whereas PC2 laded with poverty variables. While counties with high PC1 scores were in the northern region of the State, counties with high PC2 were mainly in the southeastern region Pre- and Post-ACA. The regression coefficients show that there is a positive association between PC2 and county level T2DMPH rates in Kentucky. The scaled slope (B) indicates the degree to which the T2DM-PH rate changes with a one-unit change in PC2 Pre-ACA (B=0.972, SE=0313, p=0.002) and Post- ACA (B=1.01, SE=0.218, p=0.001). Conclusion: The Medicaid expansion was associated with reduced T2DM-PH rates at county level in Kentucky. The Medicaid expansion affected the health coverage, but not the economic expansion. Extremely disadvantaged rural counties in southeast Kentucky scored highest on the socioeconomic deprivation profile component (PC2) and was significantly associated with high T2DM-PH rates (

Analyzing the Language of Food on Social Media

We investigate the predictive power behind the language of food on social media. We collect a corpus of over three million food-related posts from Twitter and demonstrate that many latent population characteristics can be directly predicted from this data: overweight rate, diabetes rate, political leaning, and home geographical location of authors. For all tasks, our language-based models significantly outperform the majority-class baselines. Performance is further improved with more complex natural language processing, such as topic modeling. We analyze which textual features have most predictive power for these datasets, providing insight into the connections between the language of food, geographic locale, and community characteristics. Lastly, we design and implement an online system for real-time query and visualization of the dataset. Visualization tools, such as geo-referenced heatmaps, semantics-preserving wordclouds and temporal histograms, allow us to discover more complex, global patterns mirrored in the language of food.Comment: An extended abstract of this paper will appear in IEEE Big Data 201

The Relationship between Access to Healthcare and Heart Disease Mortality in Kentucky, 2012-2014

Cardiovascular disease is the cause for a large portion of the mortalities in the United States. Furthermore, Kentucky has one of the highest heart disease mortality rates out of all 50 U.S. States. This study hypothesized that heart disease mortality in Kentucky counties has a positive relationship with lack of accessibility to healthcare. Furthermore, this ecological study hypothesized that because there is no available usual source of a healthcare providers and ultimately a lack of accessibility to receive diagnostic/preventative services for this population, the high rate of cardiovascular disease mortality in Kentucky is dependent on access to healthcare services. This study examined various other factors that may also have an impact on heart disease mortality such as lifestyle factors and sociodemographic factors. Obesity, smoking, and hypertension are known to contribute to heart disease mortality and were held constant in this analysis. Sociodemographic factors such as household income, age, gender, and race were also held constant as independent variables in a regression analysis. Data collected from the U.S. Census Bureau as well as the CDC were used to conduct a multivariate regression analysis. This analysis was performed to determine significance and examine the relationship of healthcare access to heart disease mortality. Although significance was not found between the two main variables of interest, this study affirmed what previous research has found which is that smoking, obesity, and old age are positively correlated with cardiovascular disease. Future researchers may look to determine significance by examining the insurance status of those who have heart disease and following up with them regarding their insurance status and usual source to ultimately see what preventative services were performed

Effects of Medicaid Expansion on Diabetes Related Hospital Utilization in Appalachia

The problem that we face as a nation is the increasing cases of opiate overdoses (CDC, 2019). Regulations vary across state lines regarding patient needs and prescribing regulations. The current study addresses closing the gaps in opioid use disorder. The overarching research question for this study is—How are Narcan policies related to the drug’s utilization? Other questions in this study will be explored through analysis of national claims data. The study population consist of beneficiaries who have received a prescription for Narcan in 2016. The data includes Narcan prescriptions across state lines as well as the Narcan access law. Using the MarketScan Commercial Database we look at patient claims from states that do not have a Narcan access law and states with a Narcan access law. The study included a total of 3,756,833 prescriptions for naloxone and opioids (14,210, 0.38%), naloxone only (1660, 0.04%), and opioids only (3,740,963, 99.6%) provided to privately insured individuals in 2016. In total, 7448 Naloxone prescriptions by State Policy Status were dispensed in 2016. The odds of receiving a Naloxone prescription in access law states presented 40% greater than the states without the access law in 2016. This study will add to the literature concerning the misuse of prescription and illicit opioids

Analysis of COVID-19 cases' spatial dependence in US counties reveals health inequalities

On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran I = 0.174 and 0.264, p < 0.0001). A similar result was found for the global spatial correlation between the percentage of Black American and deaths due to COVID-19 at the county level in the U.S. (Moran I = 0.264, p < 0.0001). There was no significant spatial correlation between the Hispanic population and COVID-19 cases and deaths; however, a higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases (Moran I = –0.203, p < 0.0001) and deaths (Moran I = –0.137, p < 0.0001) from the disease. This study showed significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran I = 0.08, p < 0.0001) and deaths (Moran I = 0.15, p < 0.0001), respectively. These findings provide more detail into the interplay between the infectious disease and healthcare-related characteristics of the population. Only by understanding these relationships will it be possible to mitigate the rate of spread and severity of the disease

Geospatial perspectives on the intersection of chronic disease and COVID-19

This collection of articles in Preventing Chronic Disease (PCD) brings together scientists and practitioners from the breadth of public health and the social sciences to demonstrate how geospatial perspectives can contribute to understanding and addressing the intersection of chronic disease and COVID-19, a respiratory disease caused by the SARS-CoV-2 virus. The COVID-19 pandemic has affected chronic disease in many complex ways. Early in the pandemic, it became clear that people with chronic conditions and those in older age groups were at the highest risk for COVID-19 hospitalization and death (1\u20133). Racial and ethnic minority populations experienced disproportionately worse health outcomes (4). Pandemic-related disruptions to the health care system and individuals\u2019 concerns about health care\u2013related exposures affected chronic disease management: in-person visits for people with chronic conditions declined, supply chain disruptions led to shortages of medications, and the number of cancer screenings, treatments, and surgeries declined in the United States (5\u20137). More recent evidence suggests that COVID-19 may exacerbate existing chronic diseases and increase the risk of developing new chronic conditions, such as diabetes in adults (8,9), type 1 diabetes in children (10), neurological disorders (11), dementia (12), mental illness (13), and cardiovascular disease (14). In addition, an estimated one-half of COVID-19 survivors worldwide continue to have COVID-related health problems 6 months or more after recovery from the acute infection, making \u201clong COVID\u201d our newest and still largely unresearched chronic disease (15). Finally, social and economic inequities underlie disparities in incidence of both chronic diseases and COVID-19, an intersection that has been labeled a syndemic, defined as the \u201cpresence of 2 or more disease states that adversely interact with each other, negatively affecting the mutual course of each disease trajectory, enhancing vulnerability, and which are made more deleterious by experienced inequities\u201d (16).Space and place are key elements of individual and population health \u2014 social and environmental determinants of health are embedded within place, and health outcomes and inequities typically exhibit strong geographic variation (17,18). Thus, geospatial perspectives, which address aspects of space and place, play a key role in the public health response to the COVID-19 pandemic and its intersection with chronic disease (19,20). Here, we consider geospatial perspectives to include the broad swath of geospatial data, analytical techniques, and technologies encompassed in the field of geographic information science and technology (GIS&T) (21). Geospatial data on disease incidence and mortality, available at the individual address level or aggregated to small areas, allow us to understand the geographic distribution of COVID-19 and the chronic disease burden and their spatial coincidence with other measures. Geospatial data can also capture community-level socioeconomic characteristics, such as indicators of race, ethnicity, and class, which serve to illuminate interrelated disparities in the incidence of COVID-19 and chronic disease.Publication date from document properties.Geospatial-Perspective_508.pdfGeospatial Perspectives on the Intersection of Chronic Disease and COVID-19 / Mennis J, Matthews KA, Huston SL. Geospatial Perspectives on the Intersection of Chronic Disease and COVID-19. Prev Chronic Dis 2022;19:220145. -- Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic / Smith CD, Mennis J. Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic. Prev Chronic Dis 2020;17:200246. -- Enabling Hotspot Detection and Public Health Response to the COVID-19 Pandemic / Foraker R, Landman J, Lackey I, Haslam MD, Antes AL, Goldfarb D. Enabling Hotspot Detection and Public Health Response to the COVID-19 Pandemic. Prev Chronic Dis 2022;19:210425. -- Variation in Risk of COVID-19 Infection and Predictors of Social Determinants of Health in Miami\u2013Dade County, Florida / Moise IK. Variation in Risk of COVID-19 Infection and Predictors of Social Determinants of Health in Miami\u2013Dade County, Florida. Prev Chronic Dis 2020;17:200358. -- Mapping Chronic Disease Risk Factors With ArcGIS Online in Support of COVID-19 Response in Florida/DuClos C, Folsom J, Joiner J, Jordan M, Reid K, Bailey M, et al. Mapping Chronic Disease Risk Factors With ArcGIS Online in Support of COVID-19 Response in Florida. Prev Chronic Dis 2021;18:200647. -- A Spatio-Demographic Perspective on the Role of Social Determinants of Health and Chronic Disease in Determining a Population\u2019s Vulnerability to COVID-19 / Embury J, Tsou MH, Nara A, Oren E. A Spatio-Demographic Perspective on the Role of Social Determinants of Health and Chronic Disease in Determining a Population\u2019s Vulnerability to COVID-19. Prev Chronic Dis 2022;19:210414. -- The Town-Level Prevalence of Chronic Lung Conditions and Death From COVID-19 Among Older Adults in Connecticut and Rhode Island / Jansen T, Man Lee C, Xu S, Silverstein NM, Dugan E. The Town-Level Prevalence of Chronic Lung Conditions and Death From COVID-19 Among Older Adults in Connecticut and Rhode Island. Prev Chronic Dis 2022;19:210421. -- Mapping EBT Store Closures During the COVID-19 Pandemic in a Low-Income, Food-Insecure Community in San Diego / Lowery BC, Swayne MR, Castro I, Embury J. Mapping EBT Store Closures During the COVID-19 Pandemic in a Low-Income, Food-Insecure Community in San Diego. Prev Chronic Dis 2022;19:210410. -- Expansion of Grocery Delivery and Access for Washington SNAP Participants During the COVID-19 Pandemic /Beese S, Amram O, Corylus A, Graves JM, Postma J, Monsivais P. Expansion of Grocery Delivery and Access for Washington SNAP Participants During the COVID-19 Pandemic. Prev Chronic Dis 2022;19:210412. -- Disparities in Internet Access and COVID-19 Vaccination in New York City / Michaels IH, Pirani SJ, Carrascal A. Disparities in Internet Access and COVID-19 Vaccination in New York City. Prev Chronic Dis 2021;18:210143. -- Association Between Population Mobility Reductions and New COVID-19 Diagnoses in the United States Along the Urban\u2013Rural Gradient, February\u2013April, 2020 / Li X, Rudolph AE, Mennis J. Association Between Population Mobility Reductions and New COVID-19 Diagnoses in the United States Along the Urban\u2013Rural Gradient, February\u2013April, 2020. Prev Chronic Dis 2020;17:200241.20221162

Ancestral ties, civic structure and health in the United States

This study links three streams of literature to explore the relationship between county-level European ancestry, civic structure and health outcomes. Research has shown that areas with high civic structure have better health outcomes compared to those areas low in civic structure. Studies also point out that some communities with higher population densities of certain ancestries have more civic structure than others. Researchers have also found some evidence that ethnic density is related to better mental or physical health. These mechanisms are tested on structural measures, such as county-level civic structure and ancestry (not race or ethnicity) to determine if they are associated with self-reported good health, obesity and diabetes diagnoses. Data was extracted from several publically available sources such as the U.S. Census Bureau, Centers for Disease Control and Prevention\u27s Behavioral Risk Factor Surveillance System (BRFSS), the University of Wisconsin\u27s County Health Rankings, Rupasingha and Goetz index, and the Economic Research Services\u27 Environmental Food Atlas. The data were compared across two different periods in time; early and late 2000s. This study finds that counties high in civic structure have higher self-reported good health, but it does not consistently show lower obesity and diabetes diagnoses. Further, civic structure added very little or in some cases no explained variance to the models. Norwegian and German ancestries were associated with higher civic structure, but they were not consistently related to better health outcomes. Ethnic density is associated with better health outcomes, but the results are not consistent. Further work should investigate the cultural activities of ancestries, such as food, holidays or celebrations and its potentially related health implications

Application of a Novel Method for Assessing Cumulative Risk Burden by County

The purpose of this study is to apply the Human Security Index (HSI) as a tool to detect social and economic cumulative risk burden at a county-level in the state of Texas. The HSI is an index comprising a network of three sub-components or “fabrics”; the Economic, Environmental, and Social Fabrics. We hypothesized that the HSI will be a useful instrument for identifying and analyzing socioeconomic conditions that contribute to cumulative risk burden in vulnerable counties. We expected to identify statistical associations between cumulative risk burden and (a) ethnic concentration and (b) geographic proximity to the Texas-Mexico border. Findings from this study indicate that the Texas-Mexico border region did not have consistently higher total or individual fabric scores as would be suggested by the high disease burden and low income in this region. While the Economic, Environmental, Social Fabrics (including the Health subfabric) were highly associated with Hispanic ethnic concentration, the overall HSI and the Crime subfabric were not. In addition, the Education, Health and Crime subfabrics were associated with African American racial composition, while Environment, Economic and Social Fabrics were not. Application of the HSI to Texas counties provides a fuller and more nuanced understanding of socioeconomic and environmental conditions, and increases awareness of the role played by environmental, economic, and social factors in observed health disparities by race/ethnicity and geographic region

Application of a Novel Method for Assessing Cumulative Risk Burden by County

The purpose of this study is to apply the Human Security Index (HSI) as a tool to detect social and economic cumulative risk burden at a county-level in the state of Texas. The HSI is an index comprising a network of three sub-components or “fabrics”; the Economic, Environmental, and Social Fabrics. We hypothesized that the HSI will be a useful instrument for identifying and analyzing socioeconomic conditions that contribute to cumulative risk burden in vulnerable counties. We expected to identify statistical associations between cumulative risk burden and (a) ethnic concentration and (b) geographic proximity to the Texas-Mexico border. Findings from this study indicate that the Texas-Mexico border region did not have consistently higher total or individual fabric scores as would be suggested by the high disease burden and low income in this region. While the Economic, Environmental, Social Fabrics (including the Health subfabric) were highly associated with Hispanic ethnic concentration, the overall HSI and the Crime subfabric were not. In addition, the Education, Health and Crime subfabrics were associated with African American racial composition, while Environment, Economic and Social Fabrics were not. Application of the HSI to Texas counties provides a fuller and more nuanced understanding of socioeconomic and environmental conditions, and increases awareness of the role played by environmental, economic, and social factors in observed health disparities by race/ethnicity and geographic region