Identification of Factors Influencing Out-of-county Hospitalizations in the New Cooperative Medical Scheme

Overview

Journal Curr Med Sci

Specialty General Medicine

Date 2019 Oct 16

PMID 31612406

Citations 2

Authors

Wan-Rong Lu

Wen-Jie Wang

Chen Li

Huang-Guo Xiong

Yi-Lei Ma

Mi Luo

Hong-Yu Peng

Zong-Fu Mao

Ping Yin

Affiliations

Soon will be listed here.

Abstract

Throughout the duration of the New Cooperative Medical Scheme (NCMS), it was found that an increasing number of rural patients were seeking out-of-county medical treatment, which posed a great burden on the NCMS fund. Our study was conducted to examine the prevalence of out-of-county hospitalizations and its related factors, and to provide a scientific basis for follow-up health insurance policies. A total of 215 counties in central and western China from 2008 to 2016 were selected. The total out-of-county hospitalization rate in nine years was 16.95%, which increased from 12.37% in 2008 to 19.21% in 2016 with an average annual growth rate of 5.66%. Its related expenses and compensations were shown to increase each year, with those in the central region being higher than those in the western region. Stepwise logistic regression reveals that the increase in out-of-county hospitalization rate was associated with region (X1), rural population (X2), per capita per year net income (X3), per capita gross domestic product (GDP) (X4), per capita funding amount of NCMS (X5), compensation ratio of out-of-county hospitalization cost (X6), per time average in-county (X7) and out-of-county hospitalization cost (X8). According to Bayesian network (BN), the marginal probability of high out-of-county hospitalization rate was as high as 81.7%. Out-of-county hospitalizations were directly related to X8, X3, X4 and X6. The probability of high out-of-county hospitalization obtained based on hospitalization expenses factors, economy factors, regional characteristics and NCMS policy factors was 95.7%, 91.1%, 93.0% and 88.8%, respectively. And how these factors affect out-of-county hospitalization and their interrelationships were found out. Our findings suggest that more attention should be paid to the influence mechanism of these factors on out-of-county hospitalizations, and the increase of hospitalizations outside the county should be reasonably supervised and controlled and our results will be used to help guide the formulation of proper intervention policies.

Citing Articles

Impacts of unifying urban and rural residents' medical insurance on the hospitalisation expenses of rural patients in eastern China: an interrupted time series analysis.

Qin Z, Liu S, Zhou M, Chen L, Huang W, Shen L BMJ Open. 2023; 13(5):e067198.

PMID: 37230522 PMC: 10230915. DOI: 10.1136/bmjopen-2022-067198.

Long-Term Impact of COVID-19 on Hospital Visits of Rural Residents in Guangdong, China: A Controlled Interrupted Time Series Study.

Zhong W, Yin R, Pan Y, Zhang X, Renzaho A, Ling L Int J Environ Res Public Health. 2022; 19(20).

PMID: 36293836 PMC: 9603214. DOI: 10.3390/ijerph192013259.

References

Guo N, Iversen T, Lu M, Wang J, Shi L . Does the new cooperative medical scheme reduce inequality in catastrophic health expenditure in rural China?. BMC Health Serv Res. 2017; 16(1):653. PMC: 5214924. DOI: 10.1186/s12913-016-1883-7. View

Moscelli G, Siciliani L, Gutacker N, Gravelle H . Location, quality and choice of hospital: Evidence from England 2002-2013. Reg Sci Urban Econ. 2016; 60:112-124. PMC: 5063539. DOI: 10.1016/j.regsciurbeco.2016.07.001. View

Meng Q, Xu K . Progress and challenges of the rural cooperative medical scheme in China. Bull World Health Organ. 2014; 92(6):447-51. PMC: 4047801. DOI: 10.2471/BLT.13.131532. View

Ogunlesi T, Olanrewaju D . Socio-demographic factors and appropriate health care-seeking behavior for childhood illnesses. J Trop Pediatr. 2010; 56(6):379-85. DOI: 10.1093/tropej/fmq009. View

Agrahari R, Foroushani A, Docking T, Chang L, Duns G, Hudoba M . Applications of Bayesian network models in predicting types of hematological malignancies. Sci Rep. 2018; 8(1):6951. PMC: 5934387. DOI: 10.1038/s41598-018-24758-5. View

Kaewprag P, Newton C, Vermillion B, Hyun S, Huang K, Machiraju R . Predictive models for pressure ulcers from intensive care unit electronic health records using Bayesian networks. BMC Med Inform Decis Mak. 2017; 17(Suppl 2):65. PMC: 5506589. DOI: 10.1186/s12911-017-0471-z. View

Subramanian S, Kawachi I . Whose health is affected by income inequality? A multilevel interaction analysis of contemporaneous and lagged effects of state income inequality on individual self-rated health in the United States. Health Place. 2005; 12(2):141-56. DOI: 10.1016/j.healthplace.2004.11.001. View

Zhang X, Tian X, Cheng Y, Feng Z, Wang L, Southerland J . Health disparities among the western, central and eastern rural regions of China after a decade of health promotion and disease prevention programming. J Huazhong Univ Sci Technolog Med Sci. 2015; 35(4):606-614. DOI: 10.1007/s11596-015-1478-2. View

Babiarz K, Miller G, Yi H, Zhang L, Rozelle S . New evidence on the impact of China's New Rural Cooperative Medical Scheme and its implications for rural primary healthcare: multivariate difference-in-difference analysis. BMJ. 2010; 341:c5617. PMC: 6173169. DOI: 10.1136/bmj.c5617. View

10.

Sperotto A, Molina J, Torresan S, Critto A, Marcomini A . Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective. J Environ Manage. 2017; 202(Pt 1):320-331. DOI: 10.1016/j.jenvman.2017.07.044. View

11.

Pan X, Xu J, Meng Q . Integrating social health insurance systems in China. Lancet. 2016; 387(10025):1274-5. DOI: 10.1016/S0140-6736(16)30021-6. View

12.

Zhang L, Li S, Yi H, dIntignano L, Ding Y . Correlation Between New Cooperative Medical Scheme Policy Design and Catastrophic Medical Payment: Evidence From 25 Counties in Rural China. Asia Pac J Public Health. 2015; 28(1):26-38. DOI: 10.1177/1010539515612907. View

13.

Sun M, Shen J, Li C, Cochran C, Wang Y, Chen F . Effects of China's New Rural Cooperative Medical Scheme on reducing medical impoverishment in rural Yanbian: An alternative approach. BMC Health Serv Res. 2016; 16(1):422. PMC: 4994392. DOI: 10.1186/s12913-016-1660-7. View

14.

Yang X, Gao J, Zhou Z, Yan J, Lai S, Xu Y . Assessing the Effects of the New Cooperative Medical Scheme on Alleviating the Health Payment-Induced Poverty in Shaanxi Province, China. PLoS One. 2016; 11(7):e0157918. PMC: 4933374. DOI: 10.1371/journal.pone.0157918. View

15.

Dai B . Does China's new cooperative medical scheme promote rural elders' access to healthcare services in relation to chronic conditions?. Int Health. 2014; 7(1):32-41. PMC: 4351354. DOI: 10.1093/inthealth/ihu050. View

16.

Hanson K, Yip W, Hsiao W . The impact of quality on the demand for outpatient services in Cyprus. Health Econ. 2004; 13(12):1167-80. DOI: 10.1002/hec.898. View

17.

Yip W, Hsiao W . Non-evidence-based policy: how effective is China's new cooperative medical scheme in reducing medical impoverishment?. Soc Sci Med. 2008; 68(2):201-9. DOI: 10.1016/j.socscimed.2008.09.066. View

18.

Wang H, Su M, Fang P, Xu J . Analysis on Medical Expenses of Hypertensive Inpatients in Urban Areas from 2010 to 2013-Evidence from Two Provinces in South of China. Curr Med Sci. 2018; 38(4):741-748. DOI: 10.1007/s11596-018-1939-5. View

19.

Dai T, Hu H, Na X, Li Y, Wan Y, Xie L . Effects of New Rural Cooperative Medical Scheme on Medical Service Utilization and Medical Expense Control of Inpatients: A 3-year Empirical Study of Hainan Province in China. Chin Med J (Engl). 2016; 129(11):1280-4. PMC: 4894036. DOI: 10.4103/0366-6999.182842. View

20.

Hall D, Le Q . Use of Bayesian networks in predicting contamination of drinking water with E. coli in rural Vietnam. Trans R Soc Trop Med Hyg. 2017; 111(6):270-277. DOI: 10.1093/trstmh/trx043. View