Abstract
COVID-19 highlighted many shortcomings in the Canadian health system, however among all the chaos, long-term care facilities were hit the hardest hit. With an elderly population with increased susceptibility to infection, shared rooms and group living, and dependency for daily care resulting in frequent contact with staff and visitors, long-term care facilities are particularly vulnerable to outbreaks and severe disease outcomes. While keeping a facility completely closed to the outside would prevent the introduction of infection, residents frequently require additional care not already provided within a facility. Recognizing how long-term care residents move between health facilities is a crucial step to understanding how diseases can be introduced to a facility, and characterizing these movements is key to reduce unnecessary transfers.
We analyzed hospital admissions, emergency department visits and long-term care assessments of Winnipeg Long-term care residents from 2015 to 2021. Using bipartite social network analysis and ERGM, we analyzed the patterns of LTC resident movements to i) characterize patient movements, ii) determine how patient movement connects facilities, and iii) examine how movements have changed over time, particular during the COVID-19 pandemic.
This project is still in its analysis phase, so there are no results. However, the results of this analysis will help inform infection prevention and control policies according to how facilities connect, provide a more complete picture of how long-term residents health care needs have changed during the pandemic, and over time to inform health system needs, identify resident traits that may require preventative care plans to reduce excess between facility mobility and inform future disease models.