Effects of Internal and External Factors on Hospital Data Breaches: Quantitative Study.

BACKGROUND: Health care data breaches are the most rapidly increasing type of cybercrime; however, the predictors of health care data breaches are uncertain. OBJECTIVE: This quantitative study aims to develop a predictive model to explain the number of hospital data breaches at the county level. METHODS: This study evaluated data consolidated at the county level from 1032 short-term acute care hospitals. We considered the association between data breach occurrence (a dichotomous variable), predictors based on county demographics, and socioeconomics, average hospital workload, facility type, and average performance on several hospital financial metrics using 3 model types: logistic regression, perceptron, and support vector machine. RESULTS: The model coefficient performance metrics indicated convergent validity across the 3 model types for all variables except bad debt and the factor level accounting for counties with >20% and up to 40% Hispanic populations, both of which had mixed coefficient directionality. The support vector machine model performed the classification task best based on all metrics (accuracy, precision, recall, F1-score). All the 3 models performed the classification task well with directional congruence of weights. From the logistic regression model, the top 5 odds ratios (indicating a higher risk of breach) included inpatient workload, medical center status, pediatric trauma center status, accounts receivable, and the number of outpatient visits, in high to low order. The bottom 5 odds ratios (indicating the lowest odds of experiencing a data breach) occurred for counties with Black populations of >20% and <40%, >80% and <100%, and >40% but <60%, as well as counties with ≤20% Asian or between 80% and 100% Hispanic individuals. Our results are in line with those of other studies that determined that patient workload, facility type, and financial outcomes were associated with the likelihood of health care data breach occurrence. CONCLUSIONS: The results of this study provide a predictive model for health care data breaches that may guide health care managers to reduce the risk of data breaches by raising awareness of the risk factors.

The Health Status of the US Veterans: A Longitudinal Analysis of Surveillance Data Prior to and during the COVID-19 Pandemic.

Chronic diseases affect a disproportionate number of United States (US) veterans, causing significant long-term health issues and affecting entitlement spending. This longitudinal study examined the health status of US veterans as compared to non-veterans pre- and post-COVID-19, utilizing the annual Center for Disease Control and Prevention (CDC) behavioral risk factor surveillance system (BRFSS) survey data. Age-adjusted descriptive point estimates were generated independently for 2003 through 2021, while complex weighted panel data were generated from 2011 and onward. General linear modeling revealed that the average US veteran reports a higher prevalence of disease conditions except for mental health disorders when compared to a non-veteran. These findings were consistent with both pre- and post-COVID-19; however, both groups reported a higher prevalence of mental health issues during the pandemic years. The findings suggest that there have been no improvements in reducing veteran comorbidities to non-veteran levels and that COVID-19 adversely affected the mental health of both populations.

An Exploratory Analysis of the Association Between Hospital Labor Costs and the Quality of Care.

Introduction: The competent healing touch of a caregiver is a critical component to the care patients receive. The more skilled the provider, the higher the likelihood outcomes will be delivered in a safe and effective manner. Unfortunately, in recent years, hospitals in the United States have faced immense financial pressures that are threatening their economic sustainability and patients' access to care in the future. Through the recent COVID-19 pandemic, the cost of delivering healthcare has continued to escalate, while the demand for patient care has exceeded many hospitals' capacity. Most troubling is the impact that the pandemic has had on the healthcare workforce, which has resulted in many hospitals struggling to fill vacancies at ever-increasing cost - all while under immense pressure to deliver quality patient care. What remains uncertain is whether the increase in labor costs has been matched with a commensurate rise in the quality of care or if quality has deteriorated as the labor force mix has changed to include more contract and temporary staff. Thus, in the enclosed study, we sought to determine what association, if any, exists between hospitals' cost of labor and the quality of care delivered. Methods and Models: Drawing from a representative national sample of nearly 3214 short-term acute care hospitals' common quality measures from the year 2021, we examined the labor cost-quality relationship via multivariate linear and logistic regression and found there is a persistent negative association across all quality outcome variables studied. Discussion: These findings lead us to believe simply paying more for hospital labor does not, by itself, ensure a positive patient outcome. We contend cautious use of temporary staff, measured adoption of short-term financial incentives, and robust staff development all should be considered as features of future workforce planning.

The Predictive Factors of Hospital Bankruptcy-An Exploratory Study.

The United States healthcare industry has witnessed a number of hospitals declare bankruptcy. This has a meaningful impact on local communities with vast implications on access, cost, and quality of care available. In our research, we seek to determine what contemporary structural and operational factors influence a bankruptcy outcome, and craft predictive models to guide healthcare leaders on how to best avoid bankruptcy in the future. In this exploratory study we performed, a single-year cross-sectional analysis of short-term acute care hospitals in the United States and subsequently developed three predictive models: logistic regression, a linear support vector machine (SVM) model with hinge function, and a perceptron neural network. Data sources include Definitive Healthcare and Becker's Hospital Review 2019 report with 3121 observations of 32 variables with 27 observed bankruptcies. The three models consistently indicate that 18 variables have a significant impact on predicting hospital bankruptcy. Currently, there is limited literature concerning financial forecasting models and knowledge detailing the factors associated with hospital bankruptcy. By having tailored knowledge of predictive factors to establish a sound financial structure, healthcare institutions at large can be empowered to take proactive steps to avoid financial distress at the organizational level and ensure long-term financial viability.

College students' willingness to accept COVID-19 vaccines.

Objective To assess college students' willingness to accept COVID-19 vaccines and the factors that influence their decisions. Participants: Traditional (aged 18-23) undergraduate students at a university in central Texas. Methods: An online survey was administered in fall 2020 to 614 students stratified by sex and race/ethnicity. Results: 40.9% of students planned to take the vaccine as soon as possible, 37.1% eventually, 11.4% only if required, and 10.6% did not intend to be vaccinated. Analyses indicated that gender, major/minor, political affiliation, receiving a flu shot in the preceding 12 months, perception of risk for COVID-19, and vaccine hesitancy were all associated with willingness to accept COVID-19 vaccines. Conclusion: Results confirm that no one-size-fits-all approach to promoting COVID-19 vaccination among college students is possible. Instead, administrators interested in increasing vaccine uptake should address concerns of specific groups, while also utilizing the prosocial beliefs of college students (e.g., being vaccinated will protect others).

Geospatial analysis as a tool to identify target areas for Chagas disease education for healthcare providers.

Chagas Disease (CD) is a neglected zoonotic disease of the Americas. It can be fatal if not diagnosed and treated in its early stages. Using geospatial and sensitivity analysis, this study focuses on understanding how to better allocate resources and educational information to areas in the United States, specifically Texas, that have the potential for increased risk of CD cases and the associated costs of addressing the disease. ICD-9 and 10 inpatient hospital diagnostic codes were used to illustrate the salience of potentially missed CD diagnoses (e.g., cardiomyopathic diagnoses) and where these are occurring with more frequency. Coding software along with GIS and Microsoft Excel 3D mapping were used to generate maps to illustrate where there may be a need for increased statewide surveillance and screening of populations at greater risk for CD. The CD cases reported to the Texas Department of State Healthcare Services (TxDSHS) are not homogenously dispersed throughout the state but rather, reveal that the incidences are in clusters and primarily in urban areas, where there is increased access to physician care, CD research and diagnostic capabilities.

Testing Kissick's Iron Triangle-Structural Equation Modeling Analysis of a Practical Theory.

BACKGROUND/PURPOSE: The purpose of this research is to determine if the tradeoffs that Kissick proposed among cost containment, quality, and access remain as rigidly interconnected as originally conceived in the contemporary health care context. Although many have relied on the Kissick model to advocate for health policy decisions, to our knowledge the model has never been empirically tested. Some have called for policy makers to come to terms with the premise of the Kissick model tradeoffs, while others have questioned the model, given the proliferation of quality-enhancing initiatives, automation, and information technology in the health care industry. One wonders whether these evolutionary changes alter or disrupt the originality of the Kissick paradigms themselves. METHODS: Structural equation modeling (SEM) was used to evaluate the Kissick hypothetical relationships among the unobserved constructs of cost, quality, and access in hospitals for the year 2018. Hospital data were obtained from Definitive Healthcare, a subscription site that contains Medicare data as well as non-Medicare data for networks, hospitals, and clinics (final n = 2766). RESULTS: Reporting significant net effects as defined by our chosen study variables, we find that as quality increases, costs increase, as access increases, quality increases, and as access increases, costs increase. Policy and Practice Implications: Our findings lend continued relevance to a balanced approach to health care policy reform efforts. Simultaneously bending the health care cost curve, increasing access to care, and advancing quality of care is as challenging now as it was when the Kissick model was originally conceived.

Deep Vision for Breast Cancer Classification and Segmentation.

(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss function gradient with respect to the image that maximizes the classification probability. This gradient is then re-mapped back to the original images, highlighting the areas of the original image that are most influential for classification (perhaps masses or boundary areas). (3) Results: initial classification results were 97% accurate, 99% specific, and 83% sensitive. Gradient techniques for unsupervised region of interest mapping identified areas most associated with the classification results clearly on positive mammograms and might be used to support clinician analysis. (4) Conclusions: deep vision techniques hold promise for addressing the overdiagnoses and treatment, underdiagnoses, and automated region of interest identification on mammography.

A Report Card on Prevention Efforts of COVID-19 Deaths in US.

COVID-19 (otherwise known as coronavirus disease 2019) is a life-threatening pandemic that has been combatted in various ways by the government, public health officials, and health care providers. These interventions have been met with varying levels of success. Ultimately, we question if the preventive efforts have reduced COVID-19 deaths in the United States. To address this question, we analyze data pertaining to COVID-19 deaths drawn from the Centers for Disease Control and Prevention (CDC). For this purpose, we employ incidence rate restricted Poisson (IRRP) as an underlying analysis methodology and evaluate all preventive efforts utilized to attempt to reduce COVID-19 deaths. Interpretations of analytic results and graphical visualizations are used to emphasize our various findings. Much needed modifications of the public health policies with respect to dealing with any future pandemics are compiled, critically assessed, and discussed.

Accreditation and Certification: Do They Improve Hospital Financial and Quality Performance?

The relationship between healthcare organizational accreditation and their leaders' professional certification in healthcare management is of specific interest to institutions of higher education and individuals in the healthcare management field. Since academic program accreditation is one piece of evidence of high-quality education, and since professional certification is an attestation to the knowledge, skills, and abilities of those who are certified, we expect alumni who graduated from accredited programs and obtained professional certification to have a positive impact on the organizations that they lead, compared with alumni who did not graduate from accredited programs and who did not obtain professional certification. The authors' analysis examined the impact of hiring graduates from higher education programs that held external accreditation from the Commission on Accreditation of Healthcare Management Education (CAHME). Graduates' affiliation with the American College of Healthcare Executives (ACHE) professional healthcare leadership organization was also assessed as an independent variable. Study outcomes focused on these graduates' respective healthcare organization's performance measures (cost, quality, and access) to assess the researchers' inquiry into the perceived value of a CAHME-accredited graduate degree in healthcare administration and a professional ACHE affiliation. The results from this study found no effect of CAHME accreditation or ACHE affiliation on healthcare organization performance outcomes. The study findings support the need for future research surrounding healthcare administration professional graduate degree program characteristics and leader development affiliations, as perceived by various industry stakeholders.

Determinants of Diabetes Disease Management, 2011-2019.

This study estimated the effects of Medicaid Expansion, demographics, socioeconomic status (SES), and health status on disease management of diabetes over time. The hypothesis was that the introduction of the ACA and particularly Medicaid Expansion would increase the following dependent variables (all proportions): (1) provider checks of HbA1c, (2) provider checks of feet, (3) provider checks of eyes, (4) patient education, (5) annual physician checks for diabetes, (6) patient self-checks of blood sugar. Data were available from the Behavioral Risk Factor Surveillance System for 2011 to 2019. We filtered the data to include only patients with diagnosed non-gestational diabetes of age 45 or older (n = 510,991 cases prior to weighting). Linear splines modeled Medicaid Expansion based on state of residence as well as implementation status. Descriptive time series plots showed no major changes in proportions of the dependent variables over time. Quasibinomial analysis showed that implementation of Medicaid Expansion had a statistically negative effect on patient self-checks of blood sugar (odds ratio = 0.971, p < 0.001), a statistically positive effect on physician checks of HbA1c (odds ratio = 1.048, p < 0.001), a statistically positive effect on feet checks (odds ratio = 1.021, p < 0.001), and no other significant effects. Evidence of demographic, SES, and health status disparities existed for most of the dependent variables. This finding was especially significant for HbA1c checks by providers. Barriers to achieving better diabetic care remain and require innovative policy interventions.

Exploring Health Outcomes for U.S. Veterans Compared to Non-Veterans from 2003 to 2019.

The physical demands on U.S. service members have increased significantly over the past several decades as the number of military operations requiring overseas deployment have expanded in frequency, duration, and intensity. These elevated demands from military operations placed upon a small subset of the population may be resulting in a group of individuals more at-risk for a variety of debilitating health conditions. To better understand how the U.S Veterans health outcomes compared to non-Veterans, this study utilized the U.S. Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) dataset to examine 10 different self-reported morbidities. Yearly age-adjusted, population estimates from 2003 to 2019 were used for Veteran vs. non-Veteran. Complex weights were used to evaluate the panel series for each morbidity overweight/obesity, heart disease, stroke, skin cancer, cancer, COPD, arthritis, mental health, kidney disease, and diabetes. General linear models (GLM's) were created using 2019 data only to investigate any possible explanatory variables associated with these morbidities. The time series analysis showed that Veterans have disproportionately higher self-reported rates of each morbidity with the exception of mental health issues and heart disease. The GLM showed that when taking into account all the variables, Veterans disproportionately self-reported a higher amount of every morbidity with the exception of mental health. These data present an overall poor state of the health of the average U.S. Veteran. Our study findings suggest that when taken as a whole, these morbidities among Veterans could prompt the U.S. Department of Veteran Affairs (VA) to help develop more effective health interventions aimed at improving the overall health of the Veterans.

Association of Electronic Health Record Vendors With Hospital Financial and Quality Performance: Retrospective Data Analysis.

BACKGROUND: Electronic health records (EHRs) are a central feature of care delivery in acute care hospitals; however, the financial and quality outcomes associated with system performance remain unclear. OBJECTIVE: In this study, we aimed to evaluate the association between the top 3 EHR vendors and measures of hospital financial and quality performance. METHODS: This study evaluated 2667 hospitals with Cerner, Epic, or Meditech as their primary EHR and considered their performance with regard to net income, Hospital Value-Based Purchasing Total Performance Score (TPS), and the unweighted subdomains of efficiency and cost reduction; clinical care; patient- and caregiver-centered experience; and patient safety. We hypothesized that there would be a difference among the 3 vendors for each measure. RESULTS: None of the EHR systems were associated with a statistically significant financial relationship in our study. Epic was positively associated with TPS outcomes (R2=23.6%; ß=.0159, SE 0.0079; P=.04) and higher patient perceptions of quality (R2=29.3%; ß=.0292, SE 0.0099; P=.003) but was negatively associated with patient safety quality scores (R2=24.3%; ß=-.0221, SE 0.0102; P=.03). Cerner and Epic were positively associated with improved efficiency (R2=31.9%; Cerner: ß=.0330, SE 0.0135, P=.01; Epic: ß=.0465, SE 0.0133, P<.001). Finally, all 3 vendors were associated with positive performance in the clinical care domain (Epic: ß=.0388, SE 0.0122, P=.002; Cerner: ß=.0283, SE 0.0124, P=.02; Meditech: ß=.0273, SE 0.0123, P=.03) but with low explanatory power (R2=4.2%). CONCLUSIONS: The results of this study provide evidence of a difference in clinical outcome performance among the top 3 EHR vendors and may serve as supportive evidence for health care leaders to target future capital investments to improve health care delivery.

Competency assessment of an undergraduate program using a third-party, objective pre-post examination.

BACKGROUND: Assessing competencies or program learning outcomes in educational programs is often a leadership challenge. This case study reports medical education program's efforts to document undergraduate competency attainment using a pre-post, third-party, objective testing service that allows for inter-university comparison, a testing service that is being adopted by some certification and accrediting bodies. METHODS: Students completed a pre-test after program acceptance and a post-test at the end of the last didactic semester (1.5 years later) just prior to their required internships. Scores and subscores were evaluated using t-tests (Holm-adjusted p-values). MANOVA models of sub-competency difference scores were also evaluated. RESULTS: Results indicate competency improvement for each of the 12 areas based on the n = 55 student sample, (p < .001 for all scores). These improvements were independent of ethnicity, age, gender, and grades. The average student improved by 12.85 points (95% CI of 10.52 to 15.18) with the largest improvements in strategic planning and leadership competency areas (21.30 and 18.33 percentage points, respectively). CONCLUSIONS: The third-party pre-post has some face validity given that student performance improved after completing a related curriculum as would be expected. Congruent with earlier studies, we find that repeated testing helps document competency attainment and that a single method for assessment is insufficient. We further document limitations of this 3d-party exam.

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018.

BACKGROUND: Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and explanatory models to account for hospitals' number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. METHODS: The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnostic-related groups (DRGs) depict areas of high incidence. State- and county-level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts are estimated. RESULTS: The incidence of heart failure has increased over time with the highest intensities in the East and center of the country; however, several Northern states have seen large increases since 2016. The best predictive model for the number of diagnoses (hospital unit of analysis) was an extremely randomized tree ensemble (predictive R2 = 0.86). The important variables in this model included workload metrics and hospital type. State-level spatial lag models using first-order Queen criteria were best at estimating heart failure admission rates (R2 = 0.816). At the county level, OLS was preferred over any GIS model based on Moran's I and resultant R2; however, none of the traditional models performed well (R2 = 0.169 for the OLS). Gradient-boosted tree models predicted 36% of the total sum of squares; the most important factors were facility workload, mean cash on hand of the hospitals in the county, and mean equity of those hospitals. Online interactive maps at the state and county levels are provided. CONCLUSIONS: Heart failure and associated expenditures are increasing. Costs of DRGs in the study increased $61 billion from 2016 through 2018. The increase in the more expensive DRG 291 outpaced others with an associated increase of $92 billion. With the increase in demand and steady-state supply of cardiologists, the costs are likely to balloon over the next decade. Models such as the ones presented here are needed to inform healthcare leaders.

Assessing the effectiveness of Chagas disease education for healthcare providers in the United States.

BACKGROUND: Chagas disease is a zoonotic infection caused by the parasite Trypanosoma cruzi, which affects an estimated 8-11 million people globally. Chagas disease is almost always associated with poverty in rural areas and disproportionately impacts immigrants from Latin America living in the United States. Approximately 20-30% of people who are infected with Chagas disease will develop a chronic form of the infection that can be fatal if left untreated. Chagas disease is vastly underestimated in the United States, often goes undiagnosed and is not well understood by most U.S. healthcare providers. One of the most important ways at reducing barriers to improving diagnostics of Chagas disease in the U.S. is giving healthcare providers the most up-to-date information and access to leading experts. METHODS: An online webinar was conducted for healthcare providers, veterinarians and public health professionals using Chagas disease expert panelists. Pre and post tests were administered to participants (n = 57) to determine the efficacy in raising awareness and to determine key focus areas for improving knowledge. A Wilcoxon rank-sum was used for non-parametric variables equivalent and for questions that assessed knowledge the McNemar's Chi-Square test was used. RESULTS: There were statistically significant learning increases in multiple categories including transmission (p = <.001), clinical presentation (p = 0.016), diagnostics (p = <.001), and treatment (p = <.001). CONCLUSION: Providing easily accessible learning opportunities using validated testing and evaluations should be further developed for rural healthcare providers in the U.S. as well as healthcare providers serving under represented populations such as immigrants. There is a clear lack of knowledge and awareness surrounding Chagas disease in the United States and just by raising awareness and providing education on the topic, lives will be saved.

Predictors of Death Rate during the COVID-19 Pandemic.

Coronavirus (COVID-19) is a potentially fatal viral infection. This study investigates geography, demography, socioeconomics, health conditions, hospital characteristics, and politics as potential explanatory variables for death rates at the state and county levels. Data from the Centers for Disease Control and Prevention, the Census Bureau, Centers for Medicare and Medicaid, Definitive Healthcare, and USAfacts.org were used to evaluate regression models. Yearly pneumonia and flu death rates (state level, 2014-2018) were evaluated as a function of the governors' political party using a repeated measures analysis. At the state and county level, spatial regression models were evaluated. At the county level, we discovered a statistically significant model that included geography, population density, racial and ethnic status, three health status variables along with a political factor. A state level analysis identified health status, minority status, and the interaction between governors' parties and health status as important variables. The political factor, however, did not appear in a subsequent analysis of 2014-2018 pneumonia and flu death rates. The pathogenesis of COVID-19 has a greater and disproportionate effect within racial and ethnic minority groups, and the political influence on the reporting of COVID-19 mortality was statistically relevant at the county level and as an interaction term only at the state level.

Obesity and Morbidity Risk in the U.S. Veteran.

The obesity epidemic in the United States has been well documented and serves as the basis for a number of health interventions across the nation. However, those who have served in the U.S. military (Veteran population) suffer from obesity in higher numbers and have an overall disproportionate poorer health status when compared to the health of the older non-Veteran population in the U.S. which may further compound their overall health risk. This study examined both the commonalities and the differences in obesity rates and the associated co-morbidities among the U.S. Veteran population, utilizing data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS). These data are considered by the Centers for Disease Control and Prevention (CDC) to be the nation's best source for health-related survey data, and the 2018 version includes 437,467 observations. Study findings show not only a significantly higher risk of obesity in the U.S. Veteran population, but also a significantly higher level (higher odds ratio) of the associated co-morbidities when compared to non-Veterans, including coronary heart disease (CHD) or angina (odds ratio (OR) = 2.63); stroke (OR = 1.86); skin cancer (OR = 2.18); other cancers (OR = 1.73); chronic obstructive pulmonary disease (COPD) (OR = 1.52), emphysema, or chronic bronchitis; arthritis (OR = 1.52), rheumatoid arthritis, gout, lupus, or fibromyalgia; depressive disorders (OR = 0.84), and diabetes (OR = 1.61) at the 0.95 confidence interval level.

Online Simulation Model to Estimate the Total Costs of Tobacco Product Waste in Large U.S. Cities.

Tobacco product waste (TPW) is one of the most ubiquitous forms of litter, accumulating in large amounts on streets, highways, sidewalks, beaches, parks, and other public places, and flowing into storm water drains, waste treatment plants, and solid waste collection facilities. In this paper, we evaluate the direct and indirect costs associated with TPW in the 30 largest U.S. cities. We first developed a conceptual framework for the analysis of direct and indirect costs of TPW abatement. Next, we applied a simulation model to estimate the total costs of TPW in major U.S. cities. This model includes data on city population, smoking prevalence rates, and per capita litter mitigation costs. Total annual TPW-attributable mean costs for large US cities range from US$4.7 million to US$90 million per year. Costs are generally proportional to population size, but there are exceptions in cities that have lower smoking prevalence rates. The annual mean per capita TPW cost for the 30 cities was US$6.46, and the total TPW cost for all 30 cities combined was US$264.5 million per year. These estimates for the TPW-attributable cost are an important data point in understanding the negative economic externalities created by cigarette smoking and resultant TPW cleanup costs. This model provides a useful tool for states, cities, and other jurisdictions with which to evaluate a new economic cost outcome of smoking and to develop new laws and regulations to reduce this burden.

Health Disparities and Cardiovascular Disease.

The number one leading cause of death in 2017 for Americans was cardiovascular disease (CVD), and health disparities can exacerbate risks. This study evaluates the 2018 Behavioral Risk Factor Surveillance System (BRFSS) (n = 437,436) to estimate population risks for behavioral, socio-economic, psychological, and biological factors. A general linear model with a quasi-binomial link function indicated higher risks for the following groups: smokers (odds ratio, OR = 0.688), individuals with higher body mass index scores (OR = 1.023), persons unable to work (OR = 2.683), individuals with depression (OR = 1.505), workers who missed more days due to mental issues (OR = 1.12), the elderly, males (OR = 1.954), those in race categories "indigenous Americans, Alaskan non-Hispanics", "Black Hispanics," or "other, non-Hispanic," and individuals with lower income. Surprisingly, increased consumption of alcohol was not found to be a risk factor as in other studies. Additional study of alcohol risk factors is needed. Further, Black non-Hispanics were associated with lower rates of CVD/MI (myocardial infarction), a finding that is supported by recent evidence of more unhealthy behaviors in other races. The results of this study highlight 2018 CVD/MI disparities based on the BRFSS and suggest the need for additional policy interventions including education and providing increased access to health care for the disadvantaged. The principles of beneficence and justice require policy interventions such as these.