[Perinatal care regionalization and geographical distribution model in the Islamic Republic of Iran]

During the past decade, neonatal mortality rate in Iran has not decreased satisfactorily. Regionalization of perinatal care services is a potential solution to improve the access of those in need to the best quality care within economic and administrative constraints. This study aims to develop a framework for optimized and efficient distribution of perinatal care services at different levels of care provision. We utilized small area analysis in an iterative process to divide the country into service areas of Perinatal Care Regions [PCRs], to distribute three levels of perinatal services and hospital beds in PCRs, to minimize patients' traveling distances, and to fit the facilities to the needs while incurring minimum changes to the current administrative borders and available infra-structure. We divided the country into 33 PCRs. A total of 1256 level-III [Neonatal Intensive Care Unit] beds and 3768 level-II neonatal beds were required in the country and distributed to the districts. One level-Ill district was designated as the center for each PCR. Sixty one districts were identified as level-III and 104 as level-II. Level-I and Ib districts were allocated to the nearest next level districts. Our proposed model decreased the average distance of districts from the center from 125 to 109 km. The average distance and the distance weighted by population of the districts from the PCR center also reduced to 79 and 42 km, respectively. Our model reduced the distance between levels of care provision and balanced the care facilities with population needs at the district level Implementing this model requires resources. It may encounter some resistance in practice. Such resistance should be tackled with setting regulations, monitoring, training, advocacy, and appropriate incentives. A sustainable national regionalization model should be developed centrally, and customized to the specific needs and circumstances of each region

[Determination of effectvie factors on preeclampsia severity; the application of classification and regression trees]

Despite advances in medical sciences, preeclampsia and eclampsia are still among chief causes of maternal mortality worldwide. In this study, we used classification and regression trees to investigate the role of certain inherent and maternity care factors in severe preeclampsia. This study was done on 1643 pregnant women admitted at 4 hospitals in Iran with one of the 53 maternity complaints were enrolled in this study during 2005. Variables of socioeconomic status, history of pregnancy and diseases, health care visits numbers awareness of warning signs, and the body mass index before pregnancy were recorded in the analysis model as predictors, and preeclampsia severity was entered as the dependent variable. A non-parametric method, known as the classification and regression tree was used to predict the studied consequence. Model validation was done using subsets of the study sample. The results were compared with logistic regression analysis. The incidence of preeclampsia among the studied patients was 5.2%. In model 1, variables of frequent headaches and epigastric pain during pregnancy, the number of previous pregnancies, and the amount of maternal care received were predictive of severe preeclampsia. In model 2, only frequent headaches and the number of previous pregnancies were found predictive. Sensitivity for model 1 and 2 was 47.8% and 39.1%, respectively, and specificity was 96.8% and 93.6%, respectively. In logistic regression analysis, only frequent headache was related to severe preeclampsia [OR=2.5, CI 95%: 1.3-5.0]. This study showed that using of variables that can be measured during maternity care visits to predict severe preeclampsia. Regarding the simple interpretation of tree models and their application in clinical decision making, which can be used in different levels of the health care system