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

Timo Latruwe (UGent) , Marlies Van der Wee (UGent) , Pieter Vanleenhove (UGent) , Kwinten Michielsen, Sofie Verbrugge (UGent) and Didier Colle (UGent)
(2023) BMC MEDICAL RESEARCH METHODOLOGY. 23(1).
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Abstract
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.
Keywords
Hospital admissions estimation, Gravity model, Healthcare planning, Huff Model

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MLA
Latruwe, Timo, et al. “Simulation Analysis of an Adjusted Gravity Model for Hospital Admissions Robust to Incomplete Data.” BMC MEDICAL RESEARCH METHODOLOGY, vol. 23, no. 1, 2023, doi:10.1186/s12874-023-02033-0.
APA
Latruwe, T., Van der Wee, M., Vanleenhove, P., Michielsen, K., Verbrugge, S., & Colle, D. (2023). Simulation analysis of an adjusted gravity model for hospital admissions robust to incomplete data. BMC MEDICAL RESEARCH METHODOLOGY, 23(1). https://doi.org/10.1186/s12874-023-02033-0
Chicago author-date
Latruwe, Timo, Marlies Van der Wee, Pieter Vanleenhove, Kwinten Michielsen, Sofie Verbrugge, and Didier Colle. 2023. “Simulation Analysis of an Adjusted Gravity Model for Hospital Admissions Robust to Incomplete Data.” BMC MEDICAL RESEARCH METHODOLOGY 23 (1). https://doi.org/10.1186/s12874-023-02033-0.
Chicago author-date (all authors)
Latruwe, Timo, Marlies Van der Wee, Pieter Vanleenhove, Kwinten Michielsen, Sofie Verbrugge, and Didier Colle. 2023. “Simulation Analysis of an Adjusted Gravity Model for Hospital Admissions Robust to Incomplete Data.” BMC MEDICAL RESEARCH METHODOLOGY 23 (1). doi:10.1186/s12874-023-02033-0.
Vancouver
1.
Latruwe T, Van der Wee M, Vanleenhove P, Michielsen K, Verbrugge S, Colle D. Simulation analysis of an adjusted gravity model for hospital admissions robust to incomplete data. BMC MEDICAL RESEARCH METHODOLOGY. 2023;23(1).
IEEE
[1]
T. Latruwe, M. Van der Wee, P. Vanleenhove, K. Michielsen, S. Verbrugge, and D. Colle, “Simulation analysis of an adjusted gravity model for hospital admissions robust to incomplete data,” BMC MEDICAL RESEARCH METHODOLOGY, vol. 23, no. 1, 2023.
@article{01HDR43NMRZ5C1EFEFQZ11QEN0,
  abstract     = {{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.}},
  articleno    = {{215}},
  author       = {{Latruwe, Timo and Van der Wee, Marlies and Vanleenhove, Pieter and Michielsen, Kwinten and Verbrugge, Sofie and Colle, Didier}},
  issn         = {{1471-2288}},
  journal      = {{BMC MEDICAL RESEARCH METHODOLOGY}},
  keywords     = {{Hospital admissions estimation,Gravity model,Healthcare planning,Huff Model}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{9}},
  title        = {{Simulation analysis of an adjusted gravity model for hospital admissions robust to incomplete data}},
  url          = {{http://doi.org/10.1186/s12874-023-02033-0}},
  volume       = {{23}},
  year         = {{2023}},
}
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