Epidemiology and cost analysis for patients with oral cancer in a university hospital in China

<p>Abstract</p> <p>Background</p> <p>Although several studies have reported the direct cost of oral cancer (OC), little research has invested the factors that could influence the costs of OC patient. This study analyzes the epidemiological characteristics and the direct cost of OC. More specifically, the study examines the relationship between patients' medical costs and influencing factors of epidemiology.</p> <p>Methods</p> <p>All patients encountered from January 2007 to December 2007 at the School of Stomatology of the Fourth Military Medical University (FMMU) in China with diagnosis of oral cancer have been selected. Medical hospitalization days (MHD) and cost per patient (CPP) of the samples have been calculated for different patient groups, and the results have been compared using statistical methods.</p> <p>Results</p> <p>A total of 456 oral cancer patients have been selected in this study. The epidemical characteristics are as follows: female/male 176/280; squamous cell carcinoma (SCC)/adenocarcinoma/sarcoma/lymphoma/other types 246/127/40/27/16; stage I/II/III/IV 90/148/103/115; smoker/non-smoker 136/320; rural/urban patients 82/374. Of all the patients, 82.24% were over 40 years of age. Rural patients were significantly younger than urban patients. SCC was the majority histology in older patients, while sarcoma was more common in younger patients. 372 of the patients received treatment and 84 gave up any treatment after diagnosis. Treatment cost accounted for majority of the payment. The CPP and MHD of patients in late clinical stage were higher than that of patient in early stage.</p> <p>Conclusion</p> <p>Gender, smoking habit and age older than 40 years are the epidemiological risk factors for oral cancer. Lack of medicare, smoking habit, late clinical stage and SCC are the high economic factors for patient medical cost.</p

Oral squamous cell cancer: early detection and the role of alcohol and smoking

Objective: Oral squamous cell carcinoma has a remarkable incidence worldwide and a fairly onerous prognosis, encouraging further research on factors that might modify disease outcome. Data sources: A web-based search for all types of articles published was initiated using Medline/Pub Med, with the key words such as oral cancer, alcohol consumption, genetic polymorphisms, tobacco smoking and prevention. The search was restricted to articles published in English, with no publication date restriction (last update 2010). Review Methods: In this review article, we approach the factors for a cytologic diagnosis during OSCC development and the markers used in modern diagnostic technologies as well. We also reviewed available studies of the combined effects of alcohol drinking and genetic polymorphisms on alcohol-related cancer risk. Results: The interaction of smoking and alcohol significantly increases the risk for aero-digestive cancers. The interaction between smoking and alcohol consumption seems to be responsible for a significant amount of disease. Conclusion: Published scientific data show promising pathways for the future development of more effective prognosis. There is a clear need for new prognostic indicators, which could be used in diagnostics and, therefore a better selection of the most effective treatment can be achieved

A Novel Morbidity Prediction Model for Head and Neck Oncosurgery

The purpose of the study was to construct and validate a risk model to predict morbidity in head and neck oncosurgeries. Potential risk factors of 300 surgically treated head and neck cancer patients like age, sex, tumor site, TNM stage, duration of surgery, adjunctive treatment, comorbidities and alcohol and tobacco usage were analyzed. Postoperative complications were noted. We developed a logistic model to predict the probability of patients developing morbidity based on the statistically significant variables-duration of surgery, preoperative radiation and hypertension. The validity of the test was assessed by the c-index which were 0.79 (95% C.I 0.71–0.87) for the study set (250 patients) and 0.86(95% C.I 0.73–0.90) for the test set (50 patients). The correlation of observed to expected morbidity was 0.709 (P < 0.0001). We validated a risk model and constructed a simple chart that provides us an assessment of the risk of a patient of developing morbidity