Risk factors, diseases and health care: A predictive model of the use of hospital care in perinatality in France

Background: The goal of our study was to develop a predictive model of reso urce use for pregnancy and perinatal care based on the knowledge of the dis tribution of risk factors in a given population of pregnant women. Methods. Data recorded in Outcome of Pregnancy Certificates (CIG) from 11 v oluntary maternities of the district of Seine-Saint-Denis allowed us to ide ntify those pathologies that were predictive of premature births and prenat al hospitalization of mothers. We built a classification of disease states and of risk level. A logistic regression using disease states as dependent variables and risk levels as independent variables allowed us to compute ex pected rates with their confidence intervals. Results: Among singletons, malformations, diabetes, toxemia, intra-uterin g rowth retardation, premature rupture of membranes covered 25% of all pregna ncies but explained 64% of maternal hospitalizations; 90% of all mothers ho spitalized and with delivery before 37 weeks gestation had at least one of these disease states, But 85% of the women who did not belong to disease cl asses had a normal pregnancy and delivery. Conclusions: In a given population, the distribution of risk levels is pred ictive of the incidence of disease per class. Then, given the length of sta y of mothers per class, the rate of transfer of babies and the length of st ay in postnatal care, we can simulate bed occupancy and compute bed capacit ies. The precision of the model is globally good, despite the relatively mo dest size of our initial data base: it will improve with the use of the mod el and the expected more widespread availability of data in France.