Oncology services efficiency in the age of pandemic: A jackknife and bootstrap sensitivity analysis for robustness check of DEA scores.

Background: Developing countries face great challenges in health care because of changing disease dynamics and the increasing burden of chronic diseases such as cancers. Thus, effective operational design of health services is critical to better manage scarce health care resources. This study aimed to examine the spatial distribution of the efficiency of Turkey's oncology services.

Methods: Data was collected from the 2017 Public Hospitals Statistical Yearbook, and a total of 55 provinces with advanced centers for cancer were analyzed. This study applied Charnes, Cooper, and Rhodes's input-oriented data envelopment analysis (DEA) and performed jackknifing for robustness check of DEA scores.

Results: The iteration procedure generated four models. The final model included 38 decision-making units (DMUs), and 50 % of provinces were found to have efficient oncology services. The final model's average conventional efficiency score was 0.79. Next, bootstrapped DEA procedure was incorporated into the final model to gather bias-corrected efficiency scores. After applying the bootstrapping approach, efficiency scores are significantly improved and the difference between conventional and bias-corrected efficiency scores are statistically significant (U = 475; p < 0.05).

Conclusions: Geographic planning of cancer care services is a relevant principle in health operations design that requires specific health service configurations for preparedness of health crisis such as pandemic. The results highlighted that health policymakers must be aware of regional imbalances and eliminate them to provide advanced oncology care services for population groups in poor areas of the country.

Policy Summary: Health policy makers should prioritize a balanced geographical distribution of professional oncology services to provide vulnerable groups better access to critical care.