Background: The COVID-19 pandemic caused disruptions to healthcare resource utilization (HCRU) globally, complicating longitudinal analyses that span the COVID era and introducing potential bias to traditional HCRU analyses.
Objectives: To review published studies quantifying the relative change in HCRU across different disease areas and to summarize applicable methods to account for this data shift.
Methods: A systematic search on HCRU during the pandemic was conducted via PubMed. Inclusion criteria included: English language, multi-year pre- and post- pandemic observational HCRU data, and use of modeling methods.
Results: Fifteen of 521 articles identified met inclusion criteria and represented data from pre-pandemic (1/2014 to 3/2020) and pandemic (3/2020-3/2023) periods across various cardiovascular, endocrinological, neurodegenerative, oncological and mental health conditions. Across all conditions, outpatient and emergency department visits typically displayed a sharp decline during the early phase of the pandemic, followed by a recovery period for some to pre-pandemic levels by Months 6-12. Inpatient visits experienced a minor decline. The magnitude of disruptions was more substantial for some conditions, such as multiple sclerosis, major depressive disorder, and breast cancer, than for others. Modelling methods included interrupted time series analyses using segmented regression models, with or without autoregressive integrated moving average. To account for different waves of the pandemic, linear splines were implemented to account for nonlinear trends in HCRU during the pandemic period.
Conclusions: The impact of COVID on HCRU disruptions varies by type and disease area, ranging from small reductions with a quick recovery to fundamental shifts in care delivery, resulting in long-term change in HCRU levels. This implies that a one-size-fits-all approach to accounting for COVID-era bias is insufficient. While traditional methods may be suitable at times, the variety of more sophisticated models available to study COVID-era HCRU interruptions could be adapted to best leverage data from this time period in HCRU analyses.
