[Impact of health services delivery on hospital admission utilization in Brazil].
OBJECTIVE
To assess hospital services utilization in Brazil incorporating information about health services delivery.


METHODS
Data from the National Household Survey carried out by Brazilian Institute of Statistics and Geography (IBGE) in 1998 and from other sources were collected. Hierarchical models having the individual in the first level and the state of residence in the second level were used. Two models were separately adjusted for adults and children: logistic regression was used when to have been or not admitted was the response variable, and Poisson's regression was used when the number of admissions was the response variable. This last model was adjusted only for those individuals who had been admitted at least once.


RESULTS
The main determinant of hospital admissions was health need. Poor people were more likely to be admitted when controlling for health needs and enabling factors (health insurance coverage and regular health service). Only 1 to 3% of the variability in hospital admission utilization could be attributed to differences in services delivery at the state level. In the logistic models, the number of beds was positively associated and the number of doctors was negatively associated with the likelihood of admission. Poisson's models did not show any delivery variables associated with the likelihood of admission.


CONCLUSIONS
These results suggest a delivery-induced demand impact concerning hospital beds. The inverse association of the number of doctors and hospital admissions suggests the impact of outpatient care on hospital utilization.
Castro
Mônica Silva Monteiro
MS
Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil. mmcastro@uai.com.br
Travassos
Cláudia
C
Carvalho
Marilia Sá
MS
D000328:Adult; D000368:Aged; D001938:Brazil; D002648:Child; D005260:Female; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008297:Male; D016012:Poisson Distribution; D012959:Socioeconomic Factors