Predictors of radiotherapy non-compliance in a large public cancer center in the Philippines.

Background: Non-compliance with radiotherapy (RT) is a critical barrier to effective cancer care, particularly in low- and middle-income countries like the Philippines. Despite a high national cancer burden, there is a lack of research on the specific factors driving RT non-compliance within the Philippine public health system. This study aimed to identify the independent predictors of non-compliance at a major public cancer center, to inform targeted interventions. Methods: This retrospective cohort study analysed the records of 448 patients with breast, cervical, head and neck, endometrial or rectal cancer who underwent curative intent RT at a large public cancer center in the Philippines between January 2022 and April 2024. Non-compliance was defined as missing two or more scheduled RT sessions. A hierarchical multivariable binary logistic regression model was used to identify independent predictors, assessing sociodemographic, clinical and seasonal/systemic factors in sequential blocks. Results: The overall non-compliance rate was 42.4%. The final multivariable model revealed that non-compliance was primarily driven by a convergence of clinical and systemic factors rather than patient demographics. The strongest predictors reflected clinical severity, specifically cancer type [cervical: odds ratio (OR) = 7.43; head and neck: OR = 3.54] and the need for a treatment replan (OR = 5.60). Systemic factors were also significant predictors, including an internal referral source (OR = 1.83) and treatment timing. Specifically, the risk of non-compliance increased for patients undergoing computed tomography simulation in the third quarter (July-September) and for those starting treatment in the fourth quarter (October-December), which are periods associated with regional climatic and socioeconomic pressures. Conclusion: In this Philippine public cancer center, RT non-compliance is driven by clinical vulnerability and dynamic systemic pressures, not static patient demographics. These findings highlight the need to shift from passive risk assessment to proactive, risk-stratified interventions. Implementing strategies such as patient navigation and support programs, adjusted for predictable seasonal pressures, can mitigate vulnerability, improve treatment adherence and ultimately enhance cancer outcomes in resource-constrained settings.