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Zeng S, Zhang Y, Guo C, Zhou X, He X. Big Data-Enabled Analysis of Factors Affecting Medical Expenditure in the Cerebral Infarction of a Developing City in Western China. Risk Manag Healthc Policy 2023; 16:2703-2714. [PMID: 38107438 PMCID: PMC10725695 DOI: 10.2147/rmhp.s438869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/03/2023] [Indexed: 12/19/2023] Open
Abstract
Purpose Cerebral infarction (CI) has been one of the leading causes of death in China since 2017, and controlling the medical expenses of this disease is an urgent issue for the Chinese government. This study aims to explore the important factors that affect the hospitalization expenses of CI patients and to provide a scientific basis for establishing a reasonable reimbursement mechanism and hospitalization expense standard for CI patients. Methods Data from 109,314 inpatients from the Healthcare Security Administration of Chengdu in western China from January 2016 to December 2018 were utilized. Descriptive statistical analysis was used for variable characteristic analysis. The Mann-Whitney test and Kruskal-Wallis test were used for single-factor analysis, and multiple linear stepwise regression was used for single-factor analysis and multiple-factor analysis. Results This study found that the average direct economic burden of CI in Chengdu was approximately 10,569 Chinese yuan (CNY), about 1450 US dollars, the average length of stay (LOS) was 14.47 days, the indirect economic burden was approximately 2817 CNY, and the total economic burden was 13,386 CNY for a CI inpatient. Gender, insurance type, grade of medical institution, the level of payment type, age, LOS, and complications and comorbidities (CCs) are the most important factors affecting CI medical costs. Conclusion Citizens should improve their lifestyle habits to reduce disease risk to avoid the associated medical and economic burdens. Hospitals should improve their medical technology to decrease the LOS and reduce direct medical costs. The government should actively promote the hierarchical diagnosis and treatment policy to reduce the waste of medical resources caused by low-acuity patients going to high-level hospitals for treatment. The National Healthcare Security Administration should optimize the medical insurance payment method and establish a corresponding mechanism to reduce the occurrence of excessive medical treatments such as overuse.
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Affiliation(s)
- Siyu Zeng
- School of Logistics, Chengdu University of Information Technology, Chengdu, Sichuan, People’s Republic of China
| | - Ying Zhang
- General Practice Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Chuijiang Guo
- School of Logistics, Chengdu University of Information Technology, Chengdu, Sichuan, People’s Republic of China
| | - Xia Zhou
- School of Logistics, Chengdu University of Information Technology, Chengdu, Sichuan, People’s Republic of China
| | - Xiaozhou He
- Business School, Sichuan University, Chengdu, Sichuan, People’s Republic of China
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陈 一, 胡 耀, 詹 宇, 孙 雅, 李 春, 辜 永, 曾 筱. [Effect of Short-Term Exposure to Air Pollutants on Hospital Admissions for End-Stage Renal Disease Patients Undergoing Hemodialysis]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:1176-1183. [PMID: 38162061 PMCID: PMC10752782 DOI: 10.12182/20231160504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Indexed: 01/03/2024]
Abstract
Objective To evaluate the association between short-term exposure to air pollutants of end-stage renal disease (ESRD) patients on maintenance hemodialysis and the number of daily hospital admissions. Methods The data on hospitalizations were obtained from the database of the municipal Urban Employees' Basic Medical Insurance and Urban Residents' Basic Medical Insurance of a city in Southwest China. Single and multiple pollutant generalized additive models were utilized to estimate the effect of air pollutants (CO, NO2, O3, PM10, PM2.5, and SO2) on patient admissions after the lag time of different numbers of days. In addition, subgroup analyses stratified by sex, age, PM2.5 and PM10 concentration thresholds, seasonality, and comorbidity status for cardiovascular diseases and hypertension were conducted. Results In the single pollutant models, the pollutants significantly associated with patient admissions and the corresponding lag time of the strongest association were as follows, every time CO increased by 0.1 mg/m3, there was a 2.39% increase (95% confidence interval [CI]: 0.96%-3.83%) in patient admissions after 7 days of lag time; every time NO2, O3, PM2.5, PM10, and SO2 increased by 10 μg/m3, patient admissions increased by 4.02% (95% CI: 1.21%-6.91%) after 7 days of lag time, 3.57% (95% CI: 0.78%-6.44%) after 0-4 days of lag time, 2.00% (95% CI: 1.07%-2.93%) after 6 days of lag time, 1.19% (95% CI: 0.51%-1.88%) after 7 days of lag time, and 8.37% (95% CI: 3.08%-13.93%) after 7 days of lag time, respectively. In the multiple pollutant model, every time O3 and PM2.5 increased by 10 μg/m3, there was an increase of 3.18% (95% CI: 0.34%-6.09%) in daily patient admissions after 0-4 days of lag time and an increase of 1.85% (95% CI: 0.44%-3.28%) after 7 days of lag time. Furthermore, subgroup analyses showed that seasonality, the severity of air pollution, and patients' comorbidities might be the effect modifiers for the association between ambient air pollution and hospital admissions in ESRD patients receiving maintenance hemodialysis. Conclusion Air pollution is closely associated with hospital admissions in ESRD patients undergoing maintenance hemodialysis and the strength of this association varies according to seasonality, the severity of air pollution, and patients' status of comorbidities.
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Affiliation(s)
- 一龙 陈
- 四川大学华西医院 肾脏内科与华西生物医学大数据中心 (成都 610041)Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
- 四川大学“医学+信息”中心 (成都 610041)Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
| | - 耀 胡
- 四川大学华西医院 肾脏内科与华西生物医学大数据中心 (成都 610041)Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
- 四川大学“医学+信息”中心 (成都 610041)Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
| | - 宇 詹
- 四川大学华西医院 肾脏内科与华西生物医学大数据中心 (成都 610041)Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 雅婧 孙
- 四川大学华西医院 肾脏内科与华西生物医学大数据中心 (成都 610041)Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
- 四川大学“医学+信息”中心 (成都 610041)Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
| | - 春漾 李
- 四川大学华西医院 肾脏内科与华西生物医学大数据中心 (成都 610041)Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
- 四川大学“医学+信息”中心 (成都 610041)Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
| | - 永红 辜
- 四川大学华西医院 肾脏内科与华西生物医学大数据中心 (成都 610041)Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
- 四川大学“医学+信息”中心 (成都 610041)Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
| | - 筱茜 曾
- 四川大学华西医院 肾脏内科与华西生物医学大数据中心 (成都 610041)Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
- 四川大学“医学+信息”中心 (成都 610041)Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
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Tang EJ, Zhou YM, Yang LL, Wang N, Jiang YX, Xiao H, Hu YG, Li DW, Li N, Huang QS, Du N, Li YF, Ji AL, Zhou LX, Cai TJ. The association between short-term ambient sulfur dioxide exposure and hospitalization costs of ischemic stroke: a hospital-based study in Chongqing, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:17459-17471. [PMID: 36194329 DOI: 10.1007/s11356-022-23254-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
Evidence of the short-term effects of ambient sulfur dioxide (SO2) exposure on the economic burden of ischemic stroke is limited. This study aimed to explore the association between short-term ambient SO2 exposure and hospitalization costs for ischemic stroke in Chongqing, the most populous city in China. The hospital-based study included 7271 ischemic stroke inpatients. Multiple linear regression models were used to estimate the association between SO2 concentration and hospitalization costs. Propensity score matching was used to compare the patients' characteristics when exposed to SO2 concentrations above and below 20 μg/m3. It is found that short-term SO2 exposure was positively correlated with the hospitalization costs of ischemic stroke. The association was more evident in males, people younger than 65, and people hospitalized in the cool seasons. Besides, among the components of hospitalization costs, medicine costs were most significantly associated with SO2. More interesting, the lower concentration of SO2, the higher costs associated with 1 μg/m3 SO2 change. Above all, SO2 was positively associated with hospitalization costs of ischemic stroke, even at its low levels. The measures to reduce the level of SO2 can help reduce the burden of ischemic stroke.
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Affiliation(s)
- En-Jie Tang
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Yu-Meng Zhou
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Li-Li Yang
- Department of Information, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China
| | - Nan Wang
- Medical Department, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China
| | - Yue-Xu Jiang
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
- Department of Nutrition and Food Hygiene, School of Public Health Guizhou Medical University, Guiyang, 550025, China
| | - Hua Xiao
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Yue-Gu Hu
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Da-Wei Li
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Na Li
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
- Department of Nutrition and Food Hygiene, School of Public Health Guizhou Medical University, Guiyang, 550025, China
| | - Qing-Song Huang
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
- Department of Nutrition and Food Hygiene, School of Public Health Guizhou Medical University, Guiyang, 550025, China
| | - Ning Du
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Ya-Fei Li
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Ai-Ling Ji
- Department of Preventive Medicine, Chongqing Medical and Pharmaceutical College, Chongqing, 401331, China
| | - Lai-Xin Zhou
- Medical Department, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Tong-Jian Cai
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
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