(1) Background: Salmonella infections are a major cause of illnesses in the United States. Each year around 450 people die from the disease and more than 23,000 people are hospitalized. Salmonella outbreaks are commonly associated with eggs, meat and poultry. In this study, a quantitative risk assessment model (QRAM) was developed to determine Salmonella infections in broiler chicken. (2) Methods: Data of positive Salmonella infections were obtained from the United States Department of Agriculture (USDA) and the Centers for Disease Control and Prevention (CDC) Foodborne Disease Outbreak Surveillance System, in addition to published literature. The Decision Tools @RISK add-in software was used for various analyses and to develop the QRAM. The farm-to-fork pathway was modeled as a series of unit operations and associated pathogen events that included initial contamination at the broiler house (node 1), contamination at the slaughter house (node 2), contamination at retail (node 3), cross-contamination during serving and cooking (node 4), and finally the dose–response model after consumption. (3) Results: QRAM of Salmonella infections from broiler meat showed highest contribution of infection from the retail node (33.5%). (4) Conclusions: This QRAM that predicts the risk of Salmonella infections could be used as a guiding tool to manage the Salmonella control programs

Approximately 2150 adults die every day in the U.S. from Cardiovascular Diseases (CVD) and another 115 deaths are attributed to opioid-related causes. Studies have found conflicting results on the relationship between opioid therapy and the development of cardiovascular diseases. This study examined whether an association exists between the use of prescription opioid medicines and cardiovascular diseases, using secondary data from the National Hospital Ambulatory Medical Care Survey (NAMCS) 2015 survey. Of the 1829 patients, 1147 (63%) were male, 1762 (98%) above 45 years of age, and 54% were overweight. The rate of cardiovascular diseases was higher among women [(p < 0.001), 95% CI: 0.40–0.51]. The covariates were age, race/ethnicity, sex, diabetes mellitus, hyperlipidemia, and hypertension; and were adjusted. Diabetes mellitus, hyperlipidemia, and hypertension were significant predictors of CVD [(p < 0.001, 95% CI: 0.57–0.78); (p < 0.001, 95% CI: 0.34–0.44); (p < 0.001, 95% CI: 0.49–0.59)]. There was no significant association between prescription opioid medication use and coronary artery disease [first opioid group p = 0.34, Prevalence Odds Ratio (POR): 1.39, 95% CI: 0.71–2.75; second opioid group: p = 0.59, POR: 1.20, 95% CI: 0.61–2.37, and third opioid group: p = 0.62, POR: 0.85, 95% CI: 0.45–1.6]. The results of this study further accentuate the conflicting results in literature. Further research is recommended, with a focus on those geographical areas where high prevalence of cardiovascular diseases exists.

Foodborne diseases continue to impact human health and the economy. The COVID-19 pandemic has dramatically affected the food system from production to consumption. This project aims to determine the impact of the COVID-19 pandemic on the spread of foodborne diseases and the factors that may have contributed, including environmental, behavioral, political, and socioeconomic. Data for this study were collected from The Foodborne Diseases Active Surveillance Network (FoodNet) for 2015-2020. FoodNet personnel located at state health departments regularly contact the clinical laboratories in Connecticut (CT), Georgia (GA), Maryland (MD), Minnesota (MN), New Mexico (NM), Oregon (OR), Tennessee (TN), and selected counties in California (CA), Colorado (CO), and New York (NY). Data were analyzed using SAS to determine the changes in rates of foodborne pathogens reported in FoodNet before and during the COVID-19 pandemic in the ten reporting states. Results of the study showed a significant decline in the incidences of foodborne diseases ranging between 25% and 60%. A geographical variation was also observed between California and states with the highest decline rate of foodborne illnesses. Policies and restrictions, in addition to environmental and behavioral changes during the COVID-19 pandemic, may have reduced rates of foodborne diseases.

The SARS-CoV-2 virus responsible for the COVID-19 pandemic continues to spread worldwide, with over half a billion cases linked to over 6 million deaths globally. COVID-19 has impacted populations unequally based on income, age, race, sex, and geographical location. This study aimed to characterize COVID-19 incidence and death rate trends in six states of the southern region of the USA and to understand the demographic and racial differences in its incidence and death rates. Data for the study were collected from the COVID-19 Data tracker of the Centers for Disease Control and Prevention for the following southern states: Alabama (AL), Florida (FL), Georgia (GA), Louisiana (LA), Mississippi (MS), and Tennessee (TN). The results showed a significant geographical variation in the COVID-19 cases and related deaths. Significant variations in COVID-19 cases and death rates were observed among different races and ethnic groups. The highest number of COVID-19 cases were observed among the Hispanic and Black populations, and the highest death rates were found among non-Hispanic Blacks and Whites. The southern states included in this paper showed a high number of COVID-19 cases and high death rates during the study period. These increased rates may result from the low socioeconomic status and large minority populations.

Background: Foodborne diseases are a major source of concern in USA. These diseases are a burden on public health and significantly contribute to the cost of health care. There is an urgent need to understand the contributing factors for such outbreaks, especially in Mississippi (MS), an agricultural state with low socioeconomic status. Methods: Secondary data for the current study were obtained from the Mississippi State Department of Health (MSDH) Epidemiology department for the study period 2010–2018. Data were for individuals with reported foodborne diseases cases. The data were analyzed to determine the pathogens’ trend over time, the highest contributing pathogens to foodborne diseases, the significant geographical variation, and any significant differences in rates based on demographic variables. Results: Salmonella was the highest contributing pathogen to foodborne disease in MS. The study showed a seasonal variation in the trends of pathogens and a geographical variation, and no racial differences in the incidents of the foodborne diseases was observed. Conclusions: Incidence rates of foodborne illness remain high in the state of Mississippi. A better understanding of high levels of foodborne infections caused by Salmonella, Shigella, and Campylobacter resulting from cultural food handling practices or socioeconomic factors will allow to provide guidelines and food safety preventive measures.

The COVID-19 pandemic has created a severe upheaval in the U.S., with a particular burden on the state of Mississippi, which already has an exhausted healthcare burden. The main objectives of this study are: (1) to analyze the county-level COVID-19 cases, deaths, and vaccine distribution and (2) to determine the correlation between various social determinants of health (SDOH) and COVID-19 vaccination coverage. We analyzed COVID-19-associated data and county-level SDOH factors in 82 counties of Mississippi. The cumulative COVID-19 and socio-demographic data variables were grouped into feature and target variables. The statistical and exploratory data analysis (EDA) was conducted using Python 3.8.5. The correlation between the target and feature variables was performed by Pearson Correlation analysis. The heat Map Correlation Matrix was visually presented to illustrate the correlation between each pair of features and each target variable. Results indicated that people of Asian descent had the highest vaccination coverage of 77% fully vaccinated compared to 52%, 46%, 42% and 25% for African Americans, Whites, Hispanics, and American Indians/Alaska Natives, respectively. The county-level vaccination rate was significantly higher among the minority populations than the White population. It was observed that COVID-19 cases and deaths were positively correlated with per capita income and negatively correlated with the percentage of persons without a high school diploma (age 25+). This study strongly demonstrates that different SDOH factors influence the outcome of the COVID-19 vaccination rate, which also affects the total number of COVID-19 cases and deaths. Vaccine promotion should be given to all populations regardless of race and ethnicity to achieve uniform acceptance. Therefore, statewide policy recommendations focusing on specific community needs should help achieve health equity in COVID-19 vaccination management.