Simulation analysis of an adjusted gravity model for hospital admissions robust to incomplete data

BMC Medical Research Methodology

Research

Marlies Van der Wee¹  Sofie Verbrugge¹  Timo Latruwe¹  Didier Colle¹  Pieter Vanleenhove²  Kwinten Michielsen² 

[1] Department of Information Technology, Ghent University, Technology Lane, 9052, Ghent, Belgium;HICT, MeetDistrict, 9000, Ghent, Belgium;

关键词: Hospital admissions estimation;    Gravity model;    Healthcare planning;    Huff Model;   

DOI  :  10.1186/s12874-023-02033-0

 received in 2022-08-31, accepted in 2023-09-09,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundGravity models are often hard to apply in practice due to their data-hungry nature. Standard implementations of gravity models require that data on each variable is available for each supply node. Since these model types are often applied in a competitive context, data availability of specific variables is commonly limited to a subset of supply nodes.MethodsThis paper introduces a methodology that accommodates the use of variables for which data availability is incomplete, developed for a health care context, but more broadly applicable. The study uses simulated data to evaluate the performance of the proposed methodology in comparison with a conventional approach of dropping variables from the model.ResultsIt is shown that the proposed methodology is able to improve overall model accuracy compared to dropping variables from the model, and that model accuracy is considerably improved within the subset of supply nodes for which data is available, even when that availability is sparse.ConclusionThe proposed methodology is a viable approach to improve the performance of gravity models in a competitive health care context, where data availability is limited, and especially where a the supply nodes with complete data are most relevant for the practitioner.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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