Using Log-Linear Models and Odd Ratios to Determine Outcome of Admissions in a Hospital

September 7, 2018


The purpose of this study is to determine the outcome of patients’ admission in the medical ward (male, female and paediatrics) in the Central Regional Hospital

The specific objectives of the study are as follows:to determine association between Outcome of admission(alive and death), Year of admission conditioning on the type of Medical Ward, to determine association between Medical Ward and Outcome of admission(alive and death) conditioning Year of admission, to determine association between Year of admission, Medical Ward and Outcome of admission(alive and death) and determine associations among variables by means of log-linear model Statistics and likelihood ratios

The statistical tools used in analyzing the data were SPSS Micro soft excel and SAS. The Outcome of admission variable was categories into two levels; the medical ward had three levels and year variable had five levels and is ordinal. The model approach based on log-linear models was used. In this case, the homogeneous association was tested by comparing the saturated model (SM) and a model assuming homogeneity (HAM). The conditional independence was checked by comparing the HAM model with different models assuming conditional independence. The best model was chosen to conduct further analysis on the effect of medical ward on the outcome of admission while controlling by year.

It realized that total number of patients admitted in the hospital was twelve thousand four hundred and twenty six (12426) for the 5 year period. Ten thousand seven hundred and sixty one (10761) were discharged (alive); representing 86.6%. Also one thousand six hundred and sixty five died; representing 13.4% for the same five year period.

We observed that the female medical ward recorded highest patients’ death in the hospital from 2006 to 2010.

In this model, the conditional likelihood ratio between any two variable are identical. We therefore concluded that the best model is the saturated log-linear model.


Log-linear models, Odd ratios, Admissions, Medical Wards.


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Author Details

Patrick Kwabena Amoakoh