Objective: To develop and validate the Italian Health Search Morbidity (HSM) Index to adjust health care costs in general practice. Methods: The study population comprised 1,076,311 patients registered in the Health Search CSD Longitudinal Patient Database between January 1, 2008, and December 31, 2010. We randomly selected 538,254 and 538,057 patients to form the development and validation cohorts, respectively. To ensure model convergence, 5% of the aforementioned cohorts were selected randomly to create development and validation samples. The outcome was the total direct health care costs covered by the national health system. Interaction between age and sex, chronic diseases, and acute diseases were entered in a multilevel generalized linear latent mixed model with random intercepts (province of residence and general practitioner) to identify determinants associated with increased or decreased costs. The estimated coefficients were linearly combined to create the HSM Index for individual patients. The score was applied to the validation sample, and measures of predictive accuracy, explained variance, and the observed/predicted ratio were computed to evaluate the model's accuracy. Results: The mean yearly cost was 414.57 per patient, and the HSM Index had a median value of 5.08 (25th-75th range 4.44-5.98). 'I'he HSM Index explained 50.17% of the variation in costs. Concerning calibration, in 80% of the populadon, the margin of error in the estimation of costs was around 10%. Conclusions: The HSM Index is a reliable case-mix system that could be implemented in general practice for costs adjustment This tool should ensure fairer scrutiny of resource use and allocation of budgets among general practitioners.

Development and Validation of a Score for Adjusting Health Care Costs in General Practice

TRIFIRO', Gianluca;
2015-01-01

Abstract

Objective: To develop and validate the Italian Health Search Morbidity (HSM) Index to adjust health care costs in general practice. Methods: The study population comprised 1,076,311 patients registered in the Health Search CSD Longitudinal Patient Database between January 1, 2008, and December 31, 2010. We randomly selected 538,254 and 538,057 patients to form the development and validation cohorts, respectively. To ensure model convergence, 5% of the aforementioned cohorts were selected randomly to create development and validation samples. The outcome was the total direct health care costs covered by the national health system. Interaction between age and sex, chronic diseases, and acute diseases were entered in a multilevel generalized linear latent mixed model with random intercepts (province of residence and general practitioner) to identify determinants associated with increased or decreased costs. The estimated coefficients were linearly combined to create the HSM Index for individual patients. The score was applied to the validation sample, and measures of predictive accuracy, explained variance, and the observed/predicted ratio were computed to evaluate the model's accuracy. Results: The mean yearly cost was 414.57 per patient, and the HSM Index had a median value of 5.08 (25th-75th range 4.44-5.98). 'I'he HSM Index explained 50.17% of the variation in costs. Concerning calibration, in 80% of the populadon, the margin of error in the estimation of costs was around 10%. Conclusions: The HSM Index is a reliable case-mix system that could be implemented in general practice for costs adjustment This tool should ensure fairer scrutiny of resource use and allocation of budgets among general practitioners.
2015
case-mix
costs adjustment
health care costs
HSM Index
Adolescent
Adult
Aged
Budgets
Chronic Disease
Comorbidity
Cost-Benefit Analysis
Databases
Factual
Female
General Practice
Health Care Rationing
Health Services Needs and Demand
Health Services Research
Humans
Italy
Linear Models
Male
Middle Aged
Models
Economic
National Health Programs
Needs Assessment
Primary Health Care
Reproducibility of Results
Time Factors
Young Adult
Health Care Costs
Health Policy
Public Health
Environmental and Occupational Health
Medicine (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1039387
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