The Use Penalized Quasi Likelihood (PQL) to Estimate Multilevel Binary Logistic Model to Identify the Factors of Anemia

Abstract

Multilevel logistic regression analysis is used in many fields, including health, medical, geography, social and educational, where the researchers were interested in this analysis to identify and study the nature of the relationship between the behavior of the vocabulary or units of study, and the social, environmental and economic variables in different environments in which they live and belong, and in such hierarchical data. In this research, multilevel data was used, Data were taken on anemia in children, as the dependent variable represents anemia infections (infected - uninfected) from four hospitals affiliated to the Babylon Health Department, namely (Al-Hah General Teaching Hospital - Imam Al-Sadiq Hospital (PBUH) - Marjan General Teaching Hospital - Babel Women's and Children's Hospital ) which represents the third level in the analysis and according to the type of lobby (public - private), which represents the second level. (50) cases of anemia were taken from the general ward from the general ward and (20) cases from the private ward, and from Imam al-Sadiq Hospital ( P) Peace, (75) sick cases were taken from the general ward and (20) sick cases from the private ward. From Marjan General Teaching Hospital, (30) sick cases were taken from the private ward and (20) sick cases from the general ward, and (40) sick cases were taken from the general ward and (25) sick cases from the general ward, and these sick cases represent the level Third, in the multi-level analysis, so that the total number of disease cases is (290) cases, and the independent variables that can affect anemia were taken (sex, age, weight, occupation, marital status, smoking, academic achievement, place of residence, infection with other diseases, blood pressure). That is, the first level contains (10) independent variables. The logistic analysis of the multilevel binary was carried out using the NCSS 2022 program, using the method of possibility semi-penalty. has been reached Significance of the multi-level binary logistic regression model, as the p-value = 0.0015, which is less than the level of significance 1%, and achieved a high odds ratio of (0.7767). The variables (age - occupation - smoking - blood pressure) are not significant and the variables (weight - water source - place of residence - other diseases - wealth index) are significant.