Scholarly commentary on “Influence of Health-Seeking Behavior on Treatment Initiation of Cancer Patients at a Tertiary Care Hospital in Varanasi”

Dear Editor, We read with keen interest the article by Kesh et al.,[1] titled “Influence of Health-Seeking Behavior on Treatment Initiation of Cancer Patients at a Tertiary Care Hospital in Varanasi” (Indian J Community Med. 2025;50:445–52). The study provides valuable insights into the health-seeking behaviors associated with delayed cancer treatment initiation in a low-resource, tertiary care setting in India. The emphasis on sociocultural determinants and patient navigation pathways is highly relevant to ongoing efforts to improve early diagnosis and access to care. However, we would like to offer a few constructive observations that may further enrich future work in this important area. First, the study successfully identifies statistical associations between delay and variables such as self-medication, prior symptom awareness, and screening attendance. However, a critical omission is the absence of a validated behavioral framework (such as Andersen’s Behavioral Model of Health Services Use or the Health Belief Model) to guide variable selection and interpretation. Anchoring the study design in such a framework would have enabled a more theory-driven understanding of individual and contextual factors influencing delay, facilitating comparison with global literature.[2,3] Second, while the authors effectively categorize delays as those exceeding 60 days, the classification lacks the granularity that would distinguish between patient-related and system-related delays. The use of time-stamped events (e.g. symptom recognition date, first consultation, biopsy, and definitive diagnosis) would allow a more nuanced dissection of barriers at different stages of the care cascade—an approach increasingly emphasized in cancer control programs worldwide.[4] Moreover, the study employs logistic regression analysis but does not report on the model fit statistics or multicollinearity diagnostics. The robustness of the adjusted odds ratios reported—particularly those with wide confidence intervals (e.g. AOR for self-medication, 3.57 [1.16–11.02])—could have been improved with sensitivity analyses or penalized regression models to address potential overfitting, especially in the presence of multiple interrelated explanatory variables.[5] Additionally, the study would have benefited from qualitative triangulation, especially given the high prevalence of sociocultural and perceptual factors in determining delay. Previous studies in similar settings have shown that in-depth interviews can illuminate misconceptions, gender roles, and fear of stigma—elements that are difficult to quantify but central to behavior change interventions. Lastly, the authors rightfully highlight the low participation in screening programs, yet the absence of geographic access or facility availability data limits interpretability. Whether poor attendance, driven by structural barriers or knowledge gaps, remains unclear. Integrating geospatial data or proximity indices could add actionable value for policymakers designing community-based interventions. In conclusion, Kesh et al.[1] have delivered a crucial contribution to understanding cancer health-seeking behavior in India. We hope that incorporating behavioral theory, process-level delay metrics, and mixed-methods approaches in future studies will further enhance the translational potential of this line of research. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.