Abstract Background: There are numerous patients that are su ering with cancer disease in Ethiopia. However, as far as our knowledge concerned there was no any study that have been conducted to determine the factors and that account the spatial e ect even if Ethiopia has diversi ed geographical areas and variations due to various contributing factors for cancer disease particularly in West Amhara, Ethiopia and hence, this study also evaluated the spatial e ect on the cancer disease in addition to other common factors using a data obtained from FHRH. Objectives: The main objective of this study is to assess the distribution of cancer disease risk by taking into account the spatial e ect in terms of district and explore whether there is spatial autocorrelation of incidence of cancer disease between districts. It was also aimed to determine the e ects of some common clinical patients characteristics, complications of cancer disease and prognostic factor on current patient status, and hence, nally predict the patient status. Methods: The study was evaluated based on di erent statistical model such as intercept only logistic regression model, spatial ordinal logistic regression model and spatial mixed ordinal logistic regression. In the meantime, a model with good t of the data was identi ed by comparing goodness of t of the model using AIC and BIC. Of those models spatial mixed ordinal logistic regression model was relatively best model that manifest the data in the study, and hence, the e ect of patients characteristics including the spatial e ect on the patients status were estimated using spatial mixed OLR model. Results: In the exploratory data analysis to determine the incidence of cancer disease within each district the population at risk was standardized into same 100,000 population. Of patients included in the study and who have been taken treatment only 1.45% cured and 46.02% improved while the rest are no change and even worse status after they getting treatment. It was found that most of patients (104 cases, 25.06% of the total) present at hospital with high stage (IV) of cancer tumor. Conclusion: Patients without anemia complication at diagnosis was more likely to fall in the lower patient status category than patients with anemia complication during diagnosis. There was negative spatial e ect on the incidence of cancer indicates that districts with higher cancer incidence usually surrounded by districts with lower incidence. Prognostic factor (stage and grade of cancer tumor), complication of cancer disease such as anemia during diagnosis and treatment of patients given in the hospital had signi cant e ect on the patient status. Keywords: Incidence of cancer, Patient Status, Spatial (Mixed) OLR