Spatial Optimization to Improve COVID-19 Vaccine Allocation.

Early distribution of COVID-19 vaccines was largely driven by population size and did not account for COVID-19 prevalence nor location characteristics. In this study, we applied an optimization framework to identify distribution strategies that would have lowered COVID-19 related morbidity and mortality. During the first half of 2021 in the state of Missouri, optimized vaccine allocation would have decreased case incidence by 8% with 5926 fewer COVID-19 cases, 106 fewer deaths, and 4.5 million dollars in healthcare cost saved. As COVID-19 variants continue to be identified, and the likelihood of future pandemics remains high, application of resource optimization should be a priority for policy makers.

Assessing nursing home care quality through Bayesian networks.

This article demonstrates how Bayesian networks can be employed as a tool to assess the quality of care in nursing homes. For the data sets analyzed, the proposed model performs comparably to existing quantitative assessment models. In addition, a Bayesian network approach offers several unique advantages. The structure and parameters of a Bayesian network provide rich insight into the multidimensional aspects of the quality of care. Bayesian networks can be used as a guide in implementing limited resources by identifying information that would be most relevant to an assessment. Finally, Bayesian networks provide a straightforward framework for integrating nursing home care quality research that is conducted in various locations and for various purposes.

Nursing home care quality: insights from a Bayesian network approach.

PURPOSE: The purpose of this research is twofold. The first purpose is to utilize a new methodology (Bayesian networks) for aggregating various quality indicators to measure the overall quality of care in nursing homes. The second is to provide new insight into the relationships that exist among various measures of quality and how such measures affect the overall quality of nursing home care as measured by the Observable Indicators of Nursing Home Care Quality Instrument. In contrast to many methods used for the same purpose, our method yields both qualitative and quantitative insight into nursing home care quality. DESIGN AND METHODS: We construct several Bayesian networks to study the influences among factors associated with the quality of nursing home care; we compare and measure their accuracy against other predictive models. RESULTS: We find the best Bayesian network to perform better than other commonly used methods. We also identify key factors, including number of certified nurse assistant hours, prevalence of bedfast residents, and prevalence of daily physical restraints, that significantly affect the quality of nursing home care. Furthermore, the results of our analysis identify their probabilistic relationships. IMPLICATIONS: The findings of this research indicate that nursing home care quality is most accurately represented through a mix of structural, process, and outcome measures of quality. We also observe that the factors affecting the quality of nursing home care collectively determine the overall quality. Hence, focusing on only key factors without addressing other related factors may not substantially improve the quality of nursing home care.