© 1995 by Institute of Mathematics and its Applications
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Projecting utilization of hospital in-patient days in The Netherlands: A time-series analysis
Centre of Public Health Forecasting, National Institute of Public Health and Environmental Protection Rijksinstituut voor Volksgezondheid en Milieuhygiëne; RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
Centre for Mathematical Methods, National Institute of Public Health and Environmental Protection Rijksinstituut voor Volksgezondheid en Milieuhygiëne; RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
The object was to model and project utilization of hospital in-patient days for selected diseases in The Netherlands. We used sex- and age-specific standardized monthly utilization of hospital in-patient days during 1980–90 for lung cancer, diabetes, coronary heart disease, stroke, and pneumonia. These data were supplied by the Health Care Information Centre (Stichting Informatiecentrum voor de Gezondheids-zorg). We applied Box-Jenkins time-series analysis seasonal autoregressive integrated moving-average (SARIMA) models. Estimated models are tested by considering the Portmanteau test and the Akaike information criterion. SARIMA models give an adequate representation of hospital-in-patient-days utilization for the major sex and age classes of most selected diseases. Poor modelling results are obtained for diabetes in all sex and age groups and in elderly women with coronary heart disease or with stroke. Seasonality is an important factor in most of the models that we have estimated, particularly for utilization of pneumonia and stroke patients. The major trends in standardized in-patient days are downward, and projected 1995 levels of standardized utilization are below the 1990 levels for all the selected diseases. Population-based projections for 1995 are lower than the 1990 projections only for lung cancer and diabetes. The adequacy of the SARIMA models appears to be sensitive with respect to the parameter in the Portmanteau test. We discuss two possible explanatory developments for in-patient-days utilization: (i) developments in the provision of hospital care, and (ii) epidemiological developments. The selected diseases showed a decreasing mean duration of stay in 1980–90. Only for coronary heart disease did a rise in discharges in the same period outweigh this trend. We assessed contrasts between published epidemiological developments and the trends in in-patient-days utilization. Possible explanations concern shifts from in-patient to out-patient care and changes in treatment. Finally, complementary to our SARIMA models, the investigation of future in-patient days utilization by means of scenario analytic appoaches remains important.
Keywords: time series; SARIMA model; hospital care; projection; health-care planning