Applying the age-friendly-health system 4M paradigm to reframe climate-related disaster preparedness for nursing home populations

Winter Storm Uri Reduced Availability of Daily Direct-Care Nurse Staff in Texas Nursing Homes

We examined the effect of Winter Storm Uri on daily direct-care nurse staffing levels in 1,173 Texas nursing homes (NHs) from February 13th to 19th, 2021. We used data from Payroll-Based Journaling and the Care Compare website. Linear mixed effects models were used to estimate the linear and non-linear change in staffing. During Winter Storm Uri, Texas NHs experienced a linear decrease in daily staffing levels with subsequent non-linear increase for registered nurses (RNs; p < .001) and certified nursing assistants (CNAs; p < .001), and staffing increased linearly for licensed practical nurses (LPNs; p < .001). Compared to 1 week before the storm, Texas NHs reported lower staffing levels across all three types of staff, but most dramatically among LPNs and CNAs. In supplemental analyses, urban and lower quality NHs fared slightly poorer than rural and higher-quality NHs. Winter storms pose a particular challenge to NHs and their ability to maintain direct-care nurse staffing levels.

Impacts of household vulnerability on hurricane logistics evacuation under COVID-19: The case of U.S. Hampton Roads

Historical data suggest that when a severe tropical storm or hurricane impacts a community, the vulnerable segment of the population suffers the most severe consequences. With an increased aging population, it is crucial to understand how vulnerability alters evacuation behavior. Emergent variables such as fear of COVID-19 require additional exploration. People afraid of COVID-19 exposure may refuse to evacuate, exposing themselves unnecessarily. Differentiation is critical to evacuation logistics since it is needed to determine what proportion would stay in a local shelter, public or other, rather than evacuating or staying in their home and guide the logistics resource allocation process. This research uses data from a web and phone survey conducted in the Hampton Roads area of U.S. Virginia, with 2,200 valid responses to analyze the influence of social and demographic vulnerability factors and risk perception on evacuation decisions. This research contributes to the existing literature by developing a multinomial order logit model based on vulnerability factors and intended evacuation decisions, including staying at home, looking for a shelter, or leaving the Hampton Roads area. Findings show that race and risk perception are the variables that influence the decision-making process the most. Fear of COVID-19 transmission is also associated with an increased likelihood of leaving homes during evacuation. The variations in findings from previous studies are discussed regarding their implications for logistics emergency managers.