Internationally studied parameters related to the COVID-19 pandemic in nursing homes: a scoping review.

Abstract

<h4>Background</h4>Nursing homes were severely affected by the COVID-19 pandemic. Standardised parameters are essential to understand and monitor the unintended consequences of pandemic control measures and changes in work processes. In this scoping review, we aimed to identify COVID-19-related parameters studied in nursing homes that could form a minimum data set suitable for database development. We focused on the perspectives of all interest-holders: facilities, residents, their relatives and nursing home staff.<h4>Methods</h4>We searched MEDLINE and CINAHL and included quantitative studies published in English since the beginning of the pandemic (2020 to 2024). The extracted parameters were initially categorised according to five dimensions: pandemic-related data, facility level, staff level, residents and relatives. Within each dimension, the original terms were compared and inductively organised into (sub-)categories based on conceptual similarities, with synonymous terms subsequently standardised.<h4>Results</h4>From 82 included articles, 96 different parameters related to COVID-19 in nursing homes were identified. Infection and mortality rates emerged as the pandemic-related data most often reported, particularly within this dimension but also across all dimensions. However, we found a broad range of resident-related parameters. Our most often identified facility-related parameters include the number of staff and the provision of personal protective equipment. Staff-related parameters most often studied were personal burden and stress. Only a few parameters (n = 9) were considered for relatives.<h4>Conclusions</h4>The diversity of the reported parameters indicates that a comprehensive database is required to adequately assess a pandemic situation in this vulnerable population. In terms of pandemic preparedness, our overview of the reported parameters offers a basis for the development of country- and context-specific data capture approaches.