Abstract
This paper discusses models for the impact on patient referral flows from homes to hospitals of reconfigurations of emergency hospital services. Such system change might involve new hospital sites, or expanded bed numbers at some sites, together with possible closure of emergency units at other hospitals. In terms of a gravity model for the flows of patients, this corresponds to removing a destination or adding a new one. While retaining a gravity type approach to this problem, the relevance of supply weighting by distance within the broader accessibility concept is stressed since the independence from irrelevant alternatives property is generally inapplicable. This accessibility based approach may be implemented as a general linear model, with a Poisson outcome, and a Bayesian estimation and predictive approach is adopted. In the context of patient hospitalisations, this facilitates prediction of new patient flows following hospital reconfiguration. A UK based case study of small residential areas and hospitals in North East London and Essex is presented within the context of a review of emergency hospital siting in London.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
T. Bailey and A. Gatrell, Interactive Spatial Data Analysis (Longman, London, 1995).
J. Barnett, Reply to “Measuring potential accessibility to general practitioner services in urban areas: a methodological note” by A.E. Joseph, New Zealand Geographer 37 (1981) 33–34.
M. Baxter, Quasi-likelihood estimation and diagnostics for spatial interaction models, Environment and Planning 17A (1985) 1627–1635.
G. Box and G. Tiao, Bayesian Inference in Statistical Analysis (Wiley, 1992).
B. Carlin and T. Louis, Bayes and Empirical Bayes methods for Data Analysis (Chapman and Hall, London, 1996).
R. Carr-Hill, G. Hardman, S. Martin, S. Peacock, T. Sheldon and P. Smith, A formula for dsitributing NHS Revenues based on the small area use of hospital beds, University of York, Centre for Health Economics, Discussion Paper (1994).
I. Currim, Predictive testing of consumer choice models not subject to independence of irrelevant alternatives, J. Marketing Res. 19(2) (1982) 208–222.
Department of Health, Health Services in London – a strategic review, HMSO (1998).
A. Fotheringham, Spatial flows and spatial patterns, Environment and Planning 16A (1984) 519–543.
S. Fotheringham, Migration and spatial structure: the development of the competing destinations model, in: Migration Models: Macro and Micro Approaches, eds. J. Stillwell and P. Congdon (Belhaven Press, 1991) pp. 57–72.
A. Gelman, J. Carlin, H. Stern and D. Rubin, Bayesian Data Analysis (Chapman and Hall, 1995).
G. Gustaffson, The role of proximity in the use of hospital emergency departments, Sociology of Health and Illness 2 (1980) 202–214.
D. Ingram, D. Clarke and R. Murdie, Distance and the decision to visit an Emergency Department, Social Science and Medicine 12 (1978) 55–62.
A. Joseph and D. Phillips, Accessibility and Utilization: Geographical Perspectives on Health Care Delivery (Harper and Row, New York, 1984).
P. Knox, The intraurban ecology of primary medical care: patterns of accessibility and their policy implications, Environment and Planning 10A (1978) 415–435.
P. Laud and J. Ibrahim, Predictive model selection, J. R. Stat. Soc., Ser. B 57(1) (1995) 247–262.
A. Lowy, B. Kohler and J. Nicholl, Attendance at accident and emergency departments; unnecessary or inappropriate? Journal of Public Health Medicine 16 (1994) 134–140.
L. Mayhew, Urban Hospital Location (George Allen and Unwin, London, 1986).
P. Milner, J. Nicholl and B. Williams, Variation in demand for accident and emergency departments in England from 1974 to 1985, J. Epid. Comm. Health 42 (1988) 274–278.
National Health Service Executive, Quality and Performance in the NHS: High Level Performance Indicators, HMSO (1999).
National Health Service Executive, Modernising the NHS in London, HMSO (1999).
A.E. Raftery, Bayesian model selection in social research, Sociological Methodology 25 (1995) 111–163.
A.E. Raftery, Hypothesis testing and model selection via posterior simulation, in: Markov Chain Monte Carlo in Practice, eds.W. Gilks, S. Richardson and D. Spiegelhalter (CRC Press, Boca Raton, FL, 1996).
E. Rising and R. Segall, The calibration of an origin constrained gravity model to predict patient flow, in: Proc. of 3rd Int. Conf. on Systems Science in Health Care, Munich, Germany, 16–20 July 1984 (Springer, 1984).
Royal College of Surgeons, Management of Patients in major Injuries, Report of Working Party (Royal College of Surgeons, London, 1988).
R. Segall, Deterministic mathematical modelling for the spatial allocation of multi-categorical resources, Journal of the Operational Research Society 43 (1992) 579–589.
A. Sen and A. Smith, Gravity Models of Spatial Interactin Behaviour (Springer, Berlin, 1995).
D. Spiegelhalter, A. Thomas, N. Best and W. Gilks, BUGS: Bayesian Inference using Gibbs sampling, version 0.50, MRC Biostatistics Unit, Cambridge CB2 2SR (1995).
D. Spiegelhalter, N. Best and B. Carlin, Bayesian deviance, the effective number of parameters, and the comparison of arbitrarily complex models, manuscript, MRC Biostatistics Unit, Cambridge CB2 2SR (1998).
A. Taket, Equity and access: exploring the effects of hospital location on the population served – a case study of strategic planning, J. Operational Research Society 40 (1989) 1001–1010.
A. Taket and L. Mayhew, Interactions between the supply of and demand for hospital services in London, Omega 9 (1981) 519–526.
G. Upton, The exploratory analysis of survey data using log-linear models, The Statistician 40 (1991) 169–182.
A. Wilson, A statistical theory of spatial distribution models, Transportation Research 1 (1967) 253–269.
G. Zipf, The P1P2/D hypothesis on the intercity movement of persons, American Sociological Review 11 (1946) 677–686.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Congdon, P. The Development of Gravity Models for Hospital Patient Flows under System Change: A Bayesian Modelling Approach. Health Care Management Science 4, 289–304 (2001). https://doi.org/10.1023/A:1011894312001
Issue Date:
DOI: https://doi.org/10.1023/A:1011894312001