Analyzing comorbidity index for cancer patients during management using semi Markov chain process

Abstract

Globally, cancer is a high health burden, as it’s the fifth leading cause of death, in the year 2022, a study by the WHO showed there were an estimated 20 million new cases, and the number of deaths totaled 9.7 million. In a group of 5 people, 1 person develops cancer in their lifetime, approximately 1:12 women and 1:9 men die from cancer. The management of cancer patients is complicated by the presence of comorbidities, which can significantly affect transition between time of diagnosis and treatment initiation. The data used in the study was sourced from Cancer regional registry. It entailed the following details of the patients: age, gender, cancer staging, pre-existing and post-existing health condition(s), and cancer subtype. Patients were grouped according to the stage in which they were diagnosed. Stage I and II are the early stages, while Stage III and IV are the late stages. The most prevalent cancer type in males is prostate cancer, while in females it is breast cancer, with hypertension as the commonest comorbid condition. Most of the cancer diagnoses were in late stage, that’s stage III and stage IV. This distribution showed that a significant proportion of patients were diagnosed at advanced stages (III and IV), which could affect treatment options and prognosis. Cardiovascular conditions were associated with cancer progression, treatment method, and overall survival. Patients diagnosed at later stages (III-IV) received systemic therapies as their treatment type. There is need to continue the study in the future by prospective researchers in terms of size and variability of the participants investigated at several centers in order to enhance inclusivity. This would be efficient by the use of longitudinal designs so that it monitors disease progression, its response and also model survival rates too.