[Risk factors, diseases and health care acceptance in perinatology: a predictive model of hospital use].

BACKGROUND

The goal of our study was to develop a predictive model of resource use for pregnancy and perinatal care based on the knowledge of the distribution of risk factors in a given population of pregnant women.

METHODS

Data recorded in Outcome of Pregnancy Certificates (CIG) from 11 voluntary maternities of the district of Seine-Saint-Denis allowed us to identify those pathologies that were predictive of premature births and prenatal 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 expected rates with their confidence intervals.

RESULTS

Among singletons, malformations, diabetes, toxemia, intra-uterin growth retardation, premature rupture of membranes covered 25% of all pregnancies but explained 64% of maternal hospitalizations; 90% of all mothers hospitalized 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 classes had a normal pregnancy and delivery.

CONCLUSIONS

In a given population, the distribution of risk levels is predictive of the incidence of disease per class. Then, given the length of stay of mothers per class, the rate of transfer of babies and the length of stay in postnatal care, we can simulate bed occupancy and compute bed capacities. The precision of the model is globally good, despite the relatively modest size of our initial data base: it will improve with the use of the model and the expected more widespread availability of data in France.