Modeling hospital catchment areas in pediatric oncology using an empirically parameterized extended Huff-model

Area of Science:

• Health Services Research
• Spatial Analysis
• Pediatric Oncology

Background:

• Specialized pediatric oncology centers face challenges balancing centralization with equitable patient access.
• Accurate methods are needed to define hospital catchment areas and understand travel flow determinants.
• This study focuses on pediatric oncology in Bavaria, Germany, as a model system.

Purpose of the Study:

• To develop and validate an extended, empirically calibrated Huff model for pediatric oncology hospital catchment areas.
• To integrate structural hospital characteristics and travel time into spatial access modeling.
• To assess the impact of staffing changes on patient distribution.

Main Methods:

• Analyzed 3,320 pediatric oncology cases (2014-2023) from the German Childhood Cancer Registry.
• Employed an extended Huff model incorporating hospital capacity, quality indicators, spatial clustering, and travel time.
• Estimated model parameters using maximum likelihood and evaluated performance using Mean Absolute Percentage Error (MAPE).

Main Results:

• The final model achieved a MAPE of 5.85% and R² of 0.89, with positive effects for capacity/quality and negative for clustering.
• A scenario simulating a 20% nurse staffing reduction at Munich hospitals showed decreased patient shares (-2.0% to -2.5%) and gains at Augsburg (+3.5%) and Regensburg (+1.3%).
• Results highlight spatial interdependencies and the sensitivity of patient flows to hospital resources.

Conclusions:

• The extended Huff model accurately represents pediatric oncology hospital catchment areas and patient travel.
• The framework offers a transparent method for assessing accessibility and spatial hospital interdependencies.
• This approach supports informed regional health planning and workforce policy in specialized care.