OTHR-29. Exploring delays in diagnosis for children with brain and spinal cord tumors: a neuro-oncologist, epidemiologist and national nonprofit establish a consortium to discover the nature and extent of the problem in the United States

Abstract

Abstract Childhood brain and spinal cord tumors are the second most common pediatric cancer and the leading cause of disease-related mortality among children between one and nineteen years of age (Johnson & Cullen, 2014). To date, literature fails to acknowledge the perceived delay in diagnosis from a caregivers’ and providers’ perspective as it relates to disparities of care for a child with a brain or spinal cord tumor. Delay in diagnosis is a term used widely to define the time interval from onset of symptoms to confirmed diagnosis (Mullen, Barr & Franco, 2021). Our experience in caring for children with CNS tumors has demonstrated a disconnect in perception of delay in diagnosis between caregivers and providers. Furthermore, to our knowledge, there is no standardized data collection tool to assess delays in diagnosis and to enable the exploration of their relationship to the patient journey from time of diagnosis through long-term outcomes of patients with brain and spinal cord tumors. Timing of diagnosis of a brain or spinal cord tumor in a pediatric patient may be a predictor of clinical outcomes (Wilne, Dineen, Dommett, Chu & Walker, 2013). Additionally, the length of time from symptom onset to diagnosis may exacerbate caregiver distress which, in turn, impacts clinical outcomes. Associated social determinants of health, perceptions of credibility (e.g., not being believed about symptoms), and negative interactions with health care providers may contribute to inequalities of healthcare related to delays in diagnosis. This presentation will explore themes and key factors contributing to delays in pediatric brain and spinal cord tumor diagnoses developed through the work of our consortium. The goal of our work is to develop metrics and survey items that will be used to quantify sources of delays to improve our understanding and promote greater timeliness in diagnoses (Scott, Walter, Webster, Sutton, &Emery, 2013).