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Background:The purpose of this study was to determine the prevalence and related factors of low birth weight (LBW) in the Southeast of Iran.Methods:This cross-sectional study was carried out in Kerman province. Data were collected from Iranian Maternal and Neonatal Network at public and private hospitals. All live births from March 2014 to March 2015 considered as the source population. The risk factors including maternal age, gravida, parity, abortion, pregnancy risk factors, maternal nationality, maternal education, maternity insurance, place of living, consanguinity, neonate sex, preterm labor, place of birth, delivery manager, and delivery type were compared between LBW and normal birth weight groups.Results:The prevalence of LBW was 9.4% in the present study. Preterm labor (odds ratio [OR]: 22.06; P < 0.001), neonate female sex (OR: 1.41; P < 0.001), low parity (OR: 0.85; P < 0.001), pregnancy age <18 years (OR: 1.26; P = 0.012), pregnancy age >35 years (OR: 1.21; P = 0.001), delivery by cesarean section (OR: 1.17; P = 0.002), pregnancy risk factors (OR: 1.67; P < 0.001), maternal illiteracy (OR: 1.91; P < 0.001), living in the rural area (OR: 1.19; P < 0.001), consanguineous (OR: 1.08; P = 0.025), and delivery by obstetrician (OR: 1.12; P = 0.029) were identified as significant factors associated with LBW in this study.Conclusions:Prevention of preterm labor, consanguineous marriage, pregnancy age <18 and >35 years old, and maternal medical risk factors are some critical interventions to reduce its burden. Increasing the access to high-quality health-care services in rural and deprived areas is another effective strategy for the prevention of LBW.

Enterprise architecture facilitates the alignment between different domains, such as business, applications and information technology. These domains must be described with description languages that best address the concerns of its stakeholders. However, current model-based enterprise architecture techniques are unable to integrate multiple descriptions languages either due to the lack of suitable extension mechanisms or because they lack the means to maintain the coherence, consistency and traceability between the representations of the multiple domains of the enterprise. On the other hand, enterprise architecture models are often designed and used for communication and not for automated analysis of its contents. Model analysis is a valuable tool for assessing the qualities of a model, such as conformance and completeness, and also for supporting decision making. This paper addresses these two issues found in model-based enterprise architecture: (1) the integration of domain description languages, and (2) the automated analysis of models. This proposal uses ontology engineering techniques to specify and integrate the different domains and reasoning and querying as a means to analyse the models. The utility of the proposal is shown through an evaluation scenario that involve the analysis of an enterprise architecture model that spans multiple domains.

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Context Although it is well-acknowledged that gestational diabetes mellitus (GDM) is associated with the increased risks of adverse pregnancy outcomes, the optimal strategy for screening and diagnosis of GDM are still a matter of debate. Objective This study was conducted to demonstrate the non-inferiority of less strict GDM-screening criteria compared to the strict International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria with respect to maternal and neonatal outcomes. Methods A cluster randomized non-inferiority field-trial was conducted on 35528 pregnant women; they were scheduled to have two phases of GDM-screening, based on five different pre-specified protocols including fasting plasma glucose in the first trimester with threshold of 5.1 mmol/L (92 mg/dL) (protocols A, D) or 5.6 mmol/L (100 mg/dL) (protocols B, C, E) and either a one-step (GDM is defined if one of the plasma glucose values is exceeded ( protocol A and C) or two or more exceeded values are needed (protocol B)) or two-step approach (protocol D, E) in the second trimester. Guidelines for treatment of GDM were consistent with all protocols. Primary outcomes of the study were the prevalence of macrosomia and primary cesarean-section (C-S). The null hypothesis that less strict protocols are inferior to protocol A (IADPSG) was tested with a non-inferiority margin effect (OR) of 1.7. Results The percentages of pregnant women diagnosed with GDM and assigned to protocols A, B, C, D and E were 21.9%, 10.5%, 12.1%, 19.4%, and 8.1%, respectively. Intention to treat (ITT) analyses satisfying the non-inferiority of the less strict protocols of B, C, D and E compared to protocol A. However, non-inferiority was not shown for primary C-section comparing protocol E vs A. The ORs (95% CI) for macrosomia and C-S were: B (1.01(0.95-1.08); 0.85(0.56- 1.28), C (1.03, (0.73-1.47); 1.16, (0.88-1.51)), D (0.89(0.68-1.17); 0.94, (0.61-1.44)) and E (1.05, (0.65-1.69); 1.33, (0.82-2.00)) versus A. There were no statistically significant differences in the adjusted odds of adverse pregnancy outcomes in the two-step compared to one-step screening approaches, considering multiplicity adjustment. Conclusions The IADPSG GDM-definition significantly increased the prevalence of GDM diagnosis. However, the less strict approaches were not inferior to other criteria in terms of adverse maternal and neonatal outcomes.