A Hierarchical Bayesian Model for Improving Short-Term Forecasting of Hospital Demand by Including Meteorological Information
Abstract
Summary: The effect of weather on health has been widely researched, and the ability to forecast meteorological events can offer valuable insights into the effect on public health services. In addition, better predictions of hospital demand that are more sensitive to fluctuations in weather can allow hospital administrators to optimize resource allocation and service delivery. Using historical hospital admission data and several seasonal and meteorological variables for a site near the hospital, the paper develops a novel Bayesian model for short-term prediction of the numbers of admissions categorized by several factors such as age group and sex. The model proposed is extended by incorporating the inherent uncertainty in the meteorological forecasts into the predictions for the number of admissions. The methods are illustrated with admissions data obtained from two moderately large hospital trusts in Cardiff and Southampton, in the UK, each admitting about 30000–50000 non-elective patients every year. The Bayesian model, computed by using Markov chain Monte Carlo methods, is shown to produce more accurate predictions of the number of hospital admissions than those obtained by using a 6-week moving average method which is similar to that widely used by hospital managers. The gains are shown to be substantial during periods of rapid temperature changes, typically during the onset of cold and highly variable winter weather.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Hospital services can be managed more efficiently if accurate short-term forecasts for emergency demand are available.
“Hospital services can be managed more efficiently if accurate short-term forecasts for emergency demand are available8,9.”
From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [8]
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879 · see citing works
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works
An Analysis of Transformations
Box, G. E. P., & Cox, D. R. (1964) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1964.tb00553.x · Cited by 15,059 · see citing works
Fine Particulate Air Pollution and Hospital Admission for Cardiovascular and Respiratory Diseases
Dominici, F., Peng, R. D., Bell, M. L., Pham, L. D., McDermott, A., Zeger, S. L., & Samet, J. M. (2006) · JAMA · doi:10.1001/jama.295.10.1127 · Cited by 2,640 · see citing works
Using Bayesian model averaging to calibrate forecast ensembles.
Raftery, A. E., Gneiting, T., Balabdaoui, F., & Polakowski, M. (2005) · Monthly Weather Review · doi:10.1175/mwr2906.1 · Cited by 1,915 · see citing works
An analysis of transformations (with discussion)
Box, G. E. P. (1964) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 1,891 · see citing works
Probabilistic Forecasts, Calibration and Sharpness
Gneiting, T., Balabdaoui, F., & Raftery, A. E. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00587.x · Cited by 1,783 · see citing works
Heat-related and cold-related deaths in England and Wales: who is at risk?
Hajat, S., Kovats, S. R., & Lachowycz, K. (2006) · Occupational and Environmental Medicine · doi:10.1136/oem.2006.029017 · Cited by 634 · see citing works
Excess winter mortality in Europe: a cross country analysis identifying key risk factors
Healy, J. (2003) · Journal of Epidemiology & Community Health · doi:10.1136/jech.57.10.784 · Cited by 623 · see citing works
Demand at the emergency department front door: 10‐year trends in presentations
Lowthian, J. A., Curtis, A. J., Jolley, D. J., Stoelwinder, J. U., McNeil, J. J., & Cameron, P. A. (2012) · The Medical Journal of Australia · doi:10.5694/mja11.10955 · Cited by 280 · see citing works
O’Brien, K. L., Walters, M. I., Sellman, J. S., Quinlisk, P., Regnery, H. L., Schwartz, B., & Dowell, S. F. (2000) · Clinical Infectious Diseases · doi:10.1086/313772 · Cited by 280 · see citing works
Fuentes, M., & Raftery, A. E. (2005) · Biometrics · doi:10.1111/j.0006-341x.2005.030821.x · Cited by 273 · see citing works
Bhaskaran, K., Hajat, S., Haines, A., Herrett, E. L., Wilkinson, P., & Smeeth, L. L. (2010) · BMJ · doi:10.1136/bmj.c3823 · Cited by 214 · see citing works
Forecasting Demand of Emergency Care
Jones, S., Joy, M., & Pearson, J. (2002) · Health Care Management Science · doi:10.1023/a:1020390425029 · Cited by 123 · see citing works
Hajat, S., & Haines, A. (2002) · International Journal of Epidemiology · doi:10.1093/ije/31.4.825 · Cited by 123 · see citing works
Glezen, W. P., Payne, A., Snyder, D. N., & Downs, T. (1982) · The Journal of Infectious Diseases · doi:10.1093/infdis/146.3.313 · Cited by 114 · see citing works
High-Resolution Space–Time Ozone Modeling for Assessing Trends
Sahu, S. K., Gelfand, A. E., & Holland, D. M. (2007) · Journal of the American Statistical Association · doi:10.1198/016214507000000031 · Cited by 111 · see citing works
Self-citationHousing standards and excess winter mortality: Table 1
Clinch, J. P., & Healy, J. D. (2000) · Journal of Epidemiology and Community Health · doi:10.1136/jech.54.9.719 · Cited by 97 · see citing works
Excess winter mortality: influenza or cold stress? Observational study
Donaldson, G. C., & Keatinge, W. R. (2002) · BMJ · doi:10.1136/bmj.324.7329.89 · Cited by 96 · see citing works
Zhu, L., Carlin, B. P., & Gelfand, A. E. (2003) · Environmetrics · doi:10.1002/env.614 · Cited by 75 · see citing works
Congdon, P. (2000) · Geographical Analysis · doi:10.1111/j.1538-4632.2000.tb00425.x · Cited by 50 · see citing works
Cowperthwaite, M. C., & Burnett, M. G. (2010) · Neurosurgery · doi:10.1227/neu.0b013e3181fe23a1 · Cited by 50 · see citing works
Improved space–time forecasting of next day ozone concentrations in the eastern US
Sahu, S. K., Yip, S., & Holland, D. M. (2008) · Atmospheric Environment · doi:10.1016/j.atmosenv.2008.10.028 · Cited by 43 · see citing works
Self-citationA survey of human biometeorology
Sargent, F. (1965) · International Journal of Biometeorology · doi:10.1007/bf02187303 · Cited by 40 · see citing works
The Benefits of Multianalysis and Poor Man’s Ensembles
Bowler, N. E., Arribas, A., & Mylne, K. R. (2008) · Monthly Weather Review · doi:10.1175/2008mwr2381.1 · Cited by 31 · see citing works
Shay (2000) · doi:10.1175/1520-0493(2000)128
Flagged first, then by citations
Cited by
Sorted by citations
Machine learning for real-time aggregated prediction of hospital admission for emergency patients
King, Z., Farrington, J., Utley, M., Kung, E., Elkhodair, S., Harris, S., Sekula, R., Gillham, J., Li, K., & Crowe, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00649-y · Cited by 87 · see citing works
- Authors
- Sahu, S. K., Baffour, B., Harper, P. R., Minty, J. H., & Sarran, C.
- Affiliations
- University of Southampton (GB)
- The University of Queensland (AU)
- Cardiff University (GB)
- Met Office (GB)
- Corresponding author
- Sahu, Sujit K.
- Venue
- Journal of the Royal Statistical Society Series A: Statistics in Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-04-23
- Volume
- 177
- Issue
- 1
- Pages
- 39-61
- ISSN
- 0964-1998 (ISSN-L)
- 1467-985X
- OpenAlex
- W2047452988
- Cited by
- 25
- References
- 26
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 2 · 2018: 1 · 2017: 3 · 2016: 1 · 2015: 2
- FWCI
- 0.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- University of Southampton
- Met Office
- Engineering and Physical Sciences Research Council (EP/H010637/1)
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
- Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- hospital demand forecasting, hospitalization, weather effects on health, Bayesian hierarchical model, short-term forecasting, temperature change, winter weather, hospital resource allocation, age and sex stratification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
