The relationship between effectiveness and costs measured by a risk-adjusted case-mix system: multicentre study of Catalonian population data bases.
BACKGROUND
The main objective of this study is to measure the relationship between morbidity, direct health care costs and the degree of clinical effectiveness (resolution) of health centres and health professionals by the retrospective application of Adjusted Clinical Groups in a Spanish population setting. The secondary objectives are to determine the factors determining inadequate correlations and the opinion of health professionals on these instruments.


METHODS
We will carry out a multi-centre, retrospective study using patient records from 15 primary health care centres and population data bases. The main measurements will be: general variables (age and sex, centre, service [family medicine, paediatrics], and medical unit), dependent variables (mean number of visits, episodes and direct costs), co-morbidity (Johns Hopkins University Adjusted Clinical Groups Case-Mix System) and effectiveness.The totality of centres/patients will be considered as the standard for comparison. The efficiency index for visits, tests (laboratory, radiology, others), referrals, pharmaceutical prescriptions and total will be calculated as the ratio: observed variables/variables expected by indirect standardization.The model of cost/patient/year will differentiate fixed/semi-fixed (visits) costs of the variables for each patient attended/year (N = 350,000 inhabitants). The mean relative weights of the cost of care will be obtained. The effectiveness will be measured using a set of 50 indicators of process, efficiency and/or health results, and an adjusted synthetic index will be constructed (method: percentile 50).The correlation between the efficiency (relative-weights) and synthetic (by centre and physician) indices will be established using the coefficient of determination. The opinion/degree of acceptance of physicians (N = 1,000) will be measured using a structured questionnaire including various dimensions.


METHODS
multiple regression analysis (procedure: enter), ANCOVA (method: Bonferroni's adjustment) and multilevel analysis will be carried out to correct models. The level of statistical significance will be p < 0.05.
Sicras-Mainar
Antoni
A
Directorate of Planning, Badalona Serveis Assistencials SA, Badalona, Barcelona, Spain. asicras@bsa.cat
Navarro-Artieda
Ruth
R
Blanca-Tamayo
Milagrosa
M
Velasco-Velasco
Soledad
S
Escribano-Herranz
Esperanza
E
Llopart-López
Josep Ramon
JR
Violan-Fors
Concepción
C
Vilaseca-Llobet
Josep Maria
JM
Sánchez-Fontcuberta
Encarna
E
Benavent-Areu
Jaume
J
Flor-Serra
Ferran
F
Aguado-Jodar
Alba
A
Rodríguez-López
Daniel
D
Prados-Torres
Alejandra
A
Estelrich-Bennasar
Jose
J
D000328:Adult; D000553:Ambulatory Care; D000704:Analysis of Variance; D003365:Costs and Cost Analysis; D003953:Diagnosis-Related Groups / Q000191:economics*; D005260:Female; D017048:Health Care Costs; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D011320:Primary Health Care / Q000191:economics*; D012044:Regression Analysis; D012189:Retrospective Studies; D020379:Risk Adjustment; D013030:Spain; D011795:Surveys and Questionnaires