Investigating the association between mass transit adoption and COVID-19 infections in US metropolitan areas.

Urbanization introduces the threat of increased epidemic disease transmission resulting from crowding on mass transit. The coronavirus disease 2019 (COVID-19) pandemic, which has directly led to over 600,000 deaths in the US as of July 2021, triggered mass social distancing policies to be enacted as a key deterrent of widespread infections. Social distancing can be challenging in confined spaces required for transportation such as mass transit systems. Little is published regarding the degree to which mass transit system adoption effects impacted the rise of the COVID-19 pandemic in urban centers. Taking an ecological approach where areal data are the unit of observation, this national-scale study aims to measure the association between the adoption of mass transit and COVID-19 spread through confirmed cases in US metropolitan areas. National survey-based transit adoption measures are entered in negative binomial regression models to evaluate differences between areas. The model results demonstrate that mass transit adoption in US metropolitan areas was associated with the magnitude of outbreaks. Higher incidence of COVID-19 early in the pandemic was associated with survey results conveying higher transit use. Increasing weekly bus transit usage in metropolitan statistical areas by one scaled unit was associated with a 1.38 [95% CI: (1.25, 1.90)] times increase in incidence rate of COVID-19; a one scaled unit increase in weekly train transit usage was associated with an increase in incidence rate of 1.54 [95% CI: (1.42, 2.07)] times. These conclusions should inform early action practices in urban centers with busy transit systems in the event of future infectious disease outbreaks. Deeper understanding of these observed associations may also benefit modeling efforts by allowing researchers to include mathematical adjustments or better explain caveats to results when communicating with decision makers and the public in the crucial early stages of an epidemic.

An Examination of the Validity of the Health Risk Screening Tool: Predicting Mortality in People With Intellectual Disabilities.

BACKGROUND AND PURPOSE: The Health Risk Screening Tool (HRST) is a 22-item instrument specifically designed to assess the health risk of persons with developmental disabilities. The predictive validity of the HRST was investigated by examining its ability to predict mortality. METHODS: The sample consisted of 12,582 people with an intellectual or developmental disability residing in Georgia (U.S.). Data were analyzed using survival analysis (Kaplan-Meier estimate and Cox regression) and a binary logistic regression. RESULTS: All models supported the prognostic value of the six-level health risk classification. The Kaplan-Meier procedure showed clear separation among functions. The Cox proportional hazard regression revealed that hazard is inversely related to the health risk level, even after controlling for potential confounding by gender, ethnicity, and race. CONCLUSIONS: The HRST can predict mortality. Therefore, it can serve as a basis for establishing healthcare needs and determining nursing care acuity.

An Examination of the Validity of the Health Risk Screening Tool: Predicting Mortality in People With Intellectual Disabilities.

BACKGROUND AND PURPOSE: The Health Risk Screening Tool (HRST) is a 22-item instrument specifically designed to assess the health risk of persons with developmental disabilities. The predictive validity of the HRST was investigated by examining its ability to predict mortality. METHODS: The sample consisted of 12,582 people with an intellectual or developmental disability residing in Georgia (U.S.). Data were analyzed using survival analysis (Kaplan-Meier estimate and Cox regression) and a binary logistic regression. RESULTS: All models supported the prognostic value of the six-level health risk classification. The Kaplan-Meier procedure showed clear separation among functions. The Cox proportional hazard regression revealed that hazard is inversely related to the health risk level, even after controlling for potential confounding by gender, ethnicity, and race. CONCLUSIONS: The HRST can predict mortality. Therefore, it can serve as a basis for establishing healthcare needs and determining nursing care acuity.

The Vicodin abuse problem: A mathematical approach.

The prescription drug epidemic in the United States has gained attention in recent years. Vicodin, along with its generic version, is the country's mostly widely prescribed pain reliever, and it contains a narcotic component that can lead to physical and chemical dependency. The majority of Vicodin abusers were first introduced via prescription, unlike other drugs which are often experienced for the first time due to experimentation. Most abusers report obtaining their supply from a prescription, either their own or someone else's. Although the problem with prescription drug abuse is well known, there is no standard method of addressing the problem. To better understand how to do this, we develop and analyze a mathematical model of Vicodin use and abuse, considering only those patients who were initially prescribed the drug. Through global sensitivity analysis, we show that focusing efforts on abuse prevention rather than treatment has greater success at reducing the population of Vicodin abusers. Our results demonstrate that relying solely on rehabilitation and other treatment programs is not enough to combat the prescription drug problem in the United States. We anticipate that implementing preventative measures in both prescribers and patients will reduce the number of Vicodin abusers.