Characterizing Healthcare Delays and Interruptions in the US During the COVID-19 Pandemic: Data from an Internet-Based Cross-Sectional Survey (Preprint)

BACKGROUND The COVID-19 pandemic has broader geographic spread and potentially longer lasting effects than previous disasters. Necessary preventive precautions for the transmission of COVID-19 resulted in delays of in-person healthcare services, especially at the outset of the pandemic. OBJECTIVE Among a US sample, we examined the rates of delays (defined as cancellations and postponements) to healthcare at the outset of the pandemic and characterized the reasons for delays. METHODS As part of an internet-based survey distributed on social media in April 2020, we asked a US-based convenience sample of 2,570 participants about delays to their healthcare due to the COVID-19 pandemic. Participant demographics and self-reported worries about general health and the COVID-19 pandemic were explored as potent determinants of healthcare delays. Along with all delays, we focused on three main types of delays as the primary outcomes in the study: 1) dental, 2) preventive, and 3) diagnostic care. For each outcome, we used bivariate statistical tests (t-tests and chi-square tests) and multiple logistic regression models to study which factors are associated with healthcare delays. RESULTS The top reported barrier to receiving healthcare was fear of COVID-19 infection (34%). Almost half (n = 1,227, 48%) of participants reported experiencing healthcare delays. Among those who experienced healthcare delays and further clarified the type of delay they experienced, the top three reported types of care affected included: 1) dental (n = 351, 38%); 2) preventive (n = 269, 29%); and 3) diagnostic (n = 151, 16%) care. Logistic regression models show that age, gender, sexual identity, education, and self-reported worry about general health are significantly associated with experiencing healthcare delays overall. Self-reported worry about general health was negatively related to experiencing delays in dental care, while this predictor was positively associated with delays in diagnostic testing in the logistic regression model. Additionally, age was positively associated with delays in diagnostic testing. No factors remained significant in the multiple logistic regression for delays in preventive care, although race was marginally significant (p=.056), with people of color reporting experiencing less delays than their white counter parts. CONCLUSIONS Lessons learned from the initial surge of COVID-19 cases can help inform systemic mitigation strategies for potential future disruptions. This research addresses the demand-side of healthcare delays by exploring the determinants of delays. More research on healthcare delays during the pandemic is needed, including the short- and long-term impacts on patient-level outcomes including mortality, morbidity, mental health, quality of life, and experience of pain. CLINICALTRIAL