Intersectional factors associated with non-engagement in follow-up care among critical care survivors: A retrospective cohort study
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Introduction
Recovery for those who have survived a critical illness can be a long process, with many survivors facing physical, psychological and cognitive challenges that can lead to long-term reliance on health and social care services. Engagement with follow up services is therefore essential for critical care survivors, however some face greater barriers to engagement leading to worse health outcomes. Understanding these disparities in service engagement is a crucial first step in equitable healthcare provision.
Methods
Retrospective cohort study including adults aged ≥16 years discharged from a critical care unit and who survived to hospital discharge between April 2023 and December 2024. Eligible patients with valid contact details were invited to complete an electronic questionnaire post-discharge from critical care. The primary outcome was engagement with a follow up service, defined as completion of the electronic questionnaire. Demographic and clinical data were obtained from routine local data collection. The primary exposures were index of multiple deprivation and ethnic group. Associations were determined with adjusted logistic regression. Intersectional analysis was conducted to understand relationships more comprehensively.
Results
A total of 2191 patients with complete data and who survived to hospital discharge were included in the analysis. 639 (29%) patients engaged with the follow up service and 1552 (71%) patients did not engage. There was no statistical significance in age, sex, or clinical characteristics between those who engaged and those who did not. Non-engagement was significantly associated with higher deprivation (aOR = 0.72, CI = 0.5 – 0.89), Black ethnicity (aOR = 0.60, CI = 0.44 – 0.80), and frailty (aOR = 0.72, CI = 0.50 – 0.89). Intersectional analysis revealed subgroups with high rates of non-engagement, individuals of Asian ethnicity from less deprived areas.
Conclusions
The method of analysing disparities and factors for non-engagement can significantly affect which populations are revealed to be at risk. Applying an intersectional lens is essential for identifying groups who may be overlooked by single factor approaches. Understanding the size of these at-risk groups, rather than just their relative odds ratios, is vital for planning interventions to maximise impact. Failure to account for intersectionality risks designing interventions that are ineffective or inequitable, ultimately perpetuating existing disparities in recovery outcomes for critical care survivors.
