N Mahdavi, M Movahedi, A Khosravi, Y Mehrabi, M Karami, ,
Volume 8, Issue 3 (12-2012)
Abstract
Background and Objectives: Due to the importance of mortality
statistics for planning, setting priorities and equal allocation of health
services in population it is essential to assess quality of reporting
mortality data in health systems. The aim of this study was to evaluate the
completeness and accuracy of the Iranian Vital Horoscope reports for maternal
and the under-five mortality (U5M) in rural areas through its comparison with
other data sources in Iran.
Methods: The mortality data of Vital
Horoscope reported from 30 selected cities over country was compared with the
related data obtained from other data sources including Vital Horoscope's
Fieldwork reports, Death Registration System and Maternal Mortality
Surveillance System of Ministry of Health and Medical Education.
Results: Overall completeness of Vital
Horoscope's Fieldwork reports for U5M in rural areas was about % 62.1. In terms
of cause of death in children under-five,estimated sensitivity values were %
47.2 (95% CI: 22.9-72.2), % 66.6(95% CI: 22.7-95.7), %78.2 (95% CI: 64.3-89.3)for respiratory
infections, diarrhea and vomiting, and injuries-burning and poisoning respectively.
The vital horoscope reports had 12.5% misclassification in determining the cause of maternal death.
Conclusion: Our findings indicate the Vital Horoscope's data might
need some corrections because of underestimating of the mortality indicators.
The comparison of this source with Death Registration System report for causes
of death in children under-five (reported by Vital Horoscope) suggests that the
vital horoscope might have suboptimal quality.
Normal
0
false
false
false
EN-US
X-NONE
AR-SA
N Mahdavi, M Movahedi, A Khosravi, Y Mehrabi, M Karami,
Volume 8, Issue 3 (12-2012)
Abstract
Background and Objectives: Due to the importance of mortality statistics for planning, setting priorities and equal allocation of health services in population it is essential to assess quality of reporting mortality data in health systems. The aim of this study was to evaluate the completeness and accuracy of the Iranian Vital Horoscope reports for maternal and the under-five mortality (U5M) in rural areas through its comparison with other data sources in Iran.
Methods: The mortality data of Vital Horoscope reported from 30 selected cities over country was compared with the related data obtained from other data sources including Vital Horoscope's Fieldwork reports, Death Registration System and Maternal Mortality Surveillance System of Ministry of Health and Medical Education.
Results: Overall completeness of Vital Horoscope's Fieldwork reports for U5M in rural areas was about % 62.1. In terms of cause of death in children under-five,estimated sensitivity values were % 47.2 (95% CI: 22.9-72.2), % 66.6(95% CI: 22.7-95.7), %78.2 (95% CI: 64.3-89.3)for respiratory infections, diarrhea and vomiting, and injuries-burning and poisoning respectively. The vital horoscope reports had 12.5% misclassification in determining the cause of maternal death.
Conclusion: Our findings indicate the Vital Horoscope's data might need some corrections because of underestimating of the mortality indicators. The comparison of this source with Death Registration System report for causes of death in children under-five (reported by Vital Horoscope) suggests that the vital horoscope might have suboptimal quality.
M Rezaei, N Fakhri, S Shahsavari, F Rajati,
Volume 15, Issue 4 (1-2020)
Abstract
Background and Objectives: Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder in pregnancy. In case of early detection, some of its complications can be prevented. The aim of this study was to investigate early prediction of GDM by logistic regression (LR), discriminant analysis (DA), decision tree (DT) and perceptron artificial neural network (ANN) and to compare these models.
Methods: The medical files of 420 pregnant women (2010-12) in Kermanshah health centers were evaluated using convenience sampling. Demographic data, pregnancy-related variables, lab tests results, and a diagnosis of GDM according to a fasting blood sugar level of 92 or more were collected from their files. After fitting the four models, the performance of the models was compared and according to the criteria of accuracy, sensitivity and specificity (based on the ROC curve), the superior model was introduced.
Results: Following the fitting of LR, DA, DT and perceptron ANN models, the following results were obtained. The accuracy of the above models was 0.81, 0.83, 0.78 and 0.83, respectively, the sensitivity of the models was 0.50, 0.63, 0.58 and 0.58, the specificity of the models was 0.96, 0.93, 0.87 and 0.94, and the area under the ROC curve was 0.86, 0.78, 0.73 and 0.87, respectively.
Conclusion: In predicting and categorizing the presence of GDM, the ANN model had a lower error rate and a higher area under the ROC curve compared to other models. It can be concluded that this model offers better predictions and is closer to reality than other models.
