Lan Lan1, Yue Cai2, Tao Zhang1, Ruixian Wu2, Ming Xue2, Qun Meng3. 1. West China School of Public Health, Sichuan University, Chengdu 610041, China. 2. Center for Health Statistics and Information, National Health and Family Planning Commission, Beijing 100032, China. 3. West China School of Public Health, Sichuan University, Chengdu 610041, China; Center for Health Statistics and Information, National Health and Family Planning Commission, Beijing 100032, China.
Abstract
OBJECTIVE: We analyzed the proportion of cancer-caused hospitalization expenses in total hospitalization expenses from national authoritative data and explored influencing factors of the proportion so as to provide effective data information for more rational utilization of health resources. METHODS: Two-level lineal regression model was used to explore influencing factors of ratios of the cancer inpatient expenditure over the total inpatient expenditure of hospitals in China in 2015. A total of 40.76 million inpatient medical records were used to generate the outcome variables, while the explanatory variables were from hospital information database and China Health and Family Planning Statistical Yearbook and literatures. RESULTS: Inpatient expenditure pattern for cancer (IEPC) varied largely across provinces, ranging from 3.03% to 19.61%. The major sources of variability were from the differences of hospital level and number of beds. There was homogeneity within a province, while heterogeneity between the provinces. Rising one level of the hospital led to the increase of 0.475 natural logarithm units of IEPC averagely. The number of beds increasing 1,000 each made the natural logarithm of IEPC increase one unit averagely. CONCLUSIONS: Our study showed that a considerable proportion of IEPC variation could be explained by the differences of hospital level and number of beds. It implied that it is possible to estimate disease-specific ratio of inpatient expense taking into account key influencing factors in China. Furthermore, this study is an input to economic and financial analyses and provides evidence for future study on the national economic burden of cancer.
OBJECTIVE: We analyzed the proportion of cancer-caused hospitalization expenses in total hospitalization expenses from national authoritative data and explored influencing factors of the proportion so as to provide effective data information for more rational utilization of health resources. METHODS: Two-level lineal regression model was used to explore influencing factors of ratios of the cancer inpatient expenditure over the total inpatient expenditure of hospitals in China in 2015. A total of 40.76 million inpatient medical records were used to generate the outcome variables, while the explanatory variables were from hospital information database and China Health and Family Planning Statistical Yearbook and literatures. RESULTS: Inpatient expenditure pattern for cancer (IEPC) varied largely across provinces, ranging from 3.03% to 19.61%. The major sources of variability were from the differences of hospital level and number of beds. There was homogeneity within a province, while heterogeneity between the provinces. Rising one level of the hospital led to the increase of 0.475 natural logarithm units of IEPC averagely. The number of beds increasing 1,000 each made the natural logarithm of IEPC increase one unit averagely. CONCLUSIONS: Our study showed that a considerable proportion of IEPC variation could be explained by the differences of hospital level and number of beds. It implied that it is possible to estimate disease-specific ratio of inpatient expense taking into account key influencing factors in China. Furthermore, this study is an input to economic and financial analyses and provides evidence for future study on the national economic burden of cancer.
Authors: Hellen Gelband; Rengaswamy Sankaranarayanan; Cindy L Gauvreau; Susan Horton; Benjamin O Anderson; Freddie Bray; James Cleary; Anna J Dare; Lynette Denny; Mary K Gospodarowicz; Sumit Gupta; Scott C Howard; David A Jaffray; Felicia Knaul; Carol Levin; Linda Rabeneck; Preetha Rajaraman; Terrence Sullivan; Edward L Trimble; Prabhat Jha Journal: Lancet Date: 2015-11-11 Impact factor: 79.321
Authors: Nancy L Keating; Mary Beth Landrum; Haiden A Huskamp; Elena M Kouri; Holly G Prigerson; Deborah Schrag; Paul K Maciejewski; Mark C Hornbrook; David A Haggstrom Journal: Health Serv Res Date: 2016-01-22 Impact factor: 3.402