Modelling of resource allocation to health care authorities in Stockholm county
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Modelling of resource allocation to health care authorities in Stockholm county. / Andersson, Paula; Varde, E; Diderichsen, Finn.
I: Health Care Management Science, Bind 3, Nr. 2, 2000, s. 141-9.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › fagfællebedømt
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TY - JOUR
T1 - Modelling of resource allocation to health care authorities in Stockholm county
AU - Andersson, Paula
AU - Varde, E
AU - Diderichsen, Finn
PY - 2000
Y1 - 2000
N2 - Since the Stockholm County Council introduced a system of purchasers and providers there has been a quest for population-based resource allocation models to allocate monies to purchasers of health care. In contrast to models used in Britain, Swedish models have been based on individual level data. This paper presents recent developments in the model used in Stockholm for all care except primary care, testing new statistical methods for compression and clustering of the matrices used and the effect of introducing diagnostic information in addition to the demographic and socio-economic information used before. We also show the effect of using more current data sources by replacing existing census variables with data from annually updated registers. Since the aim is to use the resource allocation models for prospective budgeting we test and evaluate the predictive power of the models one to two years ahead. Moreover, two calibration methods are compared: Cross-sectional modelling, based on data for one year only, versus prospective modelling, using population characteristics for one year and registered health-care costs for a following year. While models including diagnostic information are deemed valuable, the prospective models yield little improvement. Further, although it takes a combination of new variables to replace the census based model, the resulting model now implemented by Stockholm County Council has fewer estimated parameters.
AB - Since the Stockholm County Council introduced a system of purchasers and providers there has been a quest for population-based resource allocation models to allocate monies to purchasers of health care. In contrast to models used in Britain, Swedish models have been based on individual level data. This paper presents recent developments in the model used in Stockholm for all care except primary care, testing new statistical methods for compression and clustering of the matrices used and the effect of introducing diagnostic information in addition to the demographic and socio-economic information used before. We also show the effect of using more current data sources by replacing existing census variables with data from annually updated registers. Since the aim is to use the resource allocation models for prospective budgeting we test and evaluate the predictive power of the models one to two years ahead. Moreover, two calibration methods are compared: Cross-sectional modelling, based on data for one year only, versus prospective modelling, using population characteristics for one year and registered health-care costs for a following year. While models including diagnostic information are deemed valuable, the prospective models yield little improvement. Further, although it takes a combination of new variables to replace the census based model, the resulting model now implemented by Stockholm County Council has fewer estimated parameters.
KW - Adolescent
KW - Adult
KW - Aged
KW - Aged, 80 and over
KW - Child
KW - Child, Preschool
KW - Cluster Analysis
KW - Community Health Planning
KW - Diagnosis-Related Groups
KW - Forecasting
KW - Health Care Rationing
KW - Hospital Costs
KW - Humans
KW - Infant
KW - Middle Aged
KW - Models, Econometric
KW - National Health Programs
KW - Needs Assessment
KW - Registries
KW - Reproducibility of Results
KW - Residence Characteristics
KW - Socioeconomic Factors
KW - Sweden
U2 - 10.1023/a:1019045408441
DO - 10.1023/a:1019045408441
M3 - Journal article
C2 - 10780282
VL - 3
SP - 141
EP - 149
JO - Health Care Management Science
JF - Health Care Management Science
SN - 1386-9620
IS - 2
ER -
ID: 40344552