Application of quartile regression method in influencing factor analysis for inpatient cost of adult leukemia
Song Zhen1,2,3, Li Changping4, Cui Zhuang4, Ma Jun4, Liu Yi1,2,3, Ma Yueshen1,2,3, Wang Jinyu1,2,3,Shi Jun1,2,3
1 Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China;
2 Chinese State Key Laboratory of Ex〖JP2〗perimental Hematology, Tianjin 300020, China;
3 National Clinical Research Center for Hematological Diseases, Tianjin 300020, China;
4 School of Public Health, Tianjin Medical University, Tianjin 300020, China
Abstract：Objective To analyze the influencing factors of hospitalization expenses of leukemia patients insured in Tianjin by quartile regression method, and to provide a reference for effective rapid analysis of medical expenses and further reform and improvement of medical insurance system.Methods Totally 5 044 cases diagnosed as with leukemia were collected from Tianjin urban residents basic medical insurance database system from 2003 to 2013, and the quartile regression method was used to analyze the influencing factors of cost.Results Compared with the multiple linear regression, the quartile regression model was more comprehensive in terms of analytical power and estimation results through data modeling and analysis. The results of the 25th, 50th and 75th quartile regression model showed that age and gender had different effects on the total hospitalization cost in different quartiles, that is, the relationship was not linear（P＜0.05）. There was a significant difference in the cost of the patients with different gender, age, type of personnel, operation or not, and hospitalization days (P＜0.05).Conclusion The quartile regression method can describe the overall condition distribution of explained variables more completely, and also analyze how explanatory variables affect the different quartiles of the explanatory variables. Factors such as gender, age, type of personnel, operation or not, hospitalization days and so on can affect the total hospitalization cost of different quartiles.
［1］JULIUSSON G,HOUGH R.Leukemia［J］.Prog Tumor Res,2016,43:87-100.
［2］SUNG H, FERLAY J, SIEGEL R L,et al.Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries［J］.CA:Cancer J Clin,2021,71(3):209-249.
［3］薛明.2010—2017年我国白血病次均费用分析［J］.中国卫生统计,2019,36(2):196-199.
［4］BLANKART C R, KOCH T, LINDER R, et al.Cost of illness and economic burden of chronic lymphocytic leukemia［J］.Orphanet J Rare Dis, 2013, 8:32.
［5］KOENKER R.Quantile regression［M］.New York: Cambridge University Press,2005.
［6］胡良平.分位数模型回归分析［J］.四川精神卫生,2018,31(4):296-301.
［7］KOENKER R, BASSETT G.Regression quantiles［J］.Econometrica, 1978, 46(1):33.
［8］CADE B S, NOON B R.A gentle introduction to quantile regression for ecologists［J］.Front Ecol Environ, 2003, 1(8):412-420.
［9］吴双.1241例白血病流行特征及直接经济负担评价［D］.兰州:兰州大学,2014.
［10］吴双,常锐霞,席亚明,等.2003—2012年白血病患者住院费用变化及影响因素分析［J］.中国卫生信息管理杂志,2015,12(3):308-314.
［11］马跃申,李长平,崔壮,等.天津市参保脑出血患者住院费用及影响因素分析［J］.中国慢性病预防与控制,2012,20(6):678-680.
［12］杨秀玲.山东省某教学医院急性髓系白血病住院费用及其影响因素研究［D］.济南:山东大学,2015.
［13］WU X H, YAN T Y, LIU Y H, et al.Nosocomial infections among acute leukemia patients in China: An economic burden analysis［J］.Am J Infect Control, 2016, 44(10):1123-1127.
