Rahnavard Z, Heidarnia A, Babaei Gh, Mahmoodi M, Khalkhali H,
Volume 59, Issue 5 (9-2001)
Abstract
Population growth has been one of the main anxieties of different countries planners so far. Background and purpose growth of population has always had various impacts on society in economical, social, health and even political fields and its cure is controlling population growth. In order to study the efficient factors upon unwanted children, 1527 married women in Tehran have been randomly selected and data from questionnaire was selected. In this study, effective factors such as couple's education level, couple's occupation, number of children, age of marriage, age of last pregnancy, having stillbirth, breast feeding period in last born and effect of sex of infant in family planning upon unwanted children have been studied. Results show that some factors like husband's age, number of children, age of first marriage, age of last pregnancy, husband occupation, having stillbirth, breast feeding period and effect of infant's sex in family planning increase the chance of unwanted children and some criteria like women age, woman's education, fist pregnancy age, woman occupation, decrease the chance of unwanted children. According to logistic regression model, women age is one of the most important effective factors and one year increment in woman's age increase the chance of unwanted child 0.89 more times. Other factors is the number of children that in return for increasing one child to family, the chance of un wanting become 116.8 more times. It seems families don't have enough knowledge about family planning measures and their usage. Breast feeding period in wives who have fed their last children for more than six months, is another important factor which increases the chance of unwanted child to 1.02 more times than woman who have fed their last children for less than six months.
Ahmad Shalbaf , Nasrin Amini, Hadi Choubdar, Mahdi Mahdavi, Atefeh Abedini, Reza Lashgari,
Volume 79, Issue 12 (3-2022)
Abstract
Background: Early prediction of the outcome situation of COVID-19 patients can decrease mortality risk by assuring efficient resource allocation and treatment planning. This study introduces a very accurate and fast system for the prediction of COVID-19 outcomes using demographic, vital signs, and laboratory blood test data.
Methods: In this analytic study, which is done from May 2020 to June 2021 in Tehran, 41 features of 244 COVID-19 patients were recorded on the first day of admission to the Masih Daneshvari Hospital. These features were categorized into eight different groups, demographic and patient history features, vital signs, and six different groups of laboratory blood tests including complete blood count (CBC), coagulation, kidney, liver, blood gas, and general. In this study, first, the significance of each of the extracted features and then the eight groups of features for prediction of mortality outcomes were considered, separately. Finally, the best combination of different groups of features was assessed. The statistical methods including the area under the receiver operating characteristic curve (AUC-ROC) based on binary Logistic Regression classification algorithm were used for evaluation.
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Results: The results revealed that red cell distribution width (RDW), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and mean corpuscular volume (MCV) in CBC features have the highest AUC with values of 85.29, 80.96, 79.94 and 79.70, respectively. Then, blood oxygen saturation level (SPO2) in vital features has a higher AUC with a value of 79.28. Moreover, combinations of features in the CBC group have the highest AUC with a value of 95.57. Then, coagulation and vital signs groups have the highest AUC with values of 85.20 and 83.84, respectively. Finally, triple combinations of features in CBC, vital signs, and coagulation groups have the highest AUC with the value of 96.54.
Conclusion: Our proposed system can be used as an assistant acceptable tool for triage of COVID-19 patients to determine which patient will have a higher risk for hospitalization and intensive care in medical environments.
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