The Comparison of Various Types of Health Insurance in the Healthcare Utilization, Costs and Catastrophic Health Expenditures among Middle-Aged and Older Chinese Adults
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Variable Specifications
2.2.1. Dependent Variables
2.2.2. Types of Health Insurance
2.2.3. Covariates
2.3. Statistical Analysis
3. Results
3.1. Population Characteristics
3.2. Health Care Utilization and Costs
3.3. Two-Part Model
3.4. Catastrophic Health Expenditures
4. Discussion
5. Strengths and Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Variables | Outpatient Care | Inpatient Care | CHE | ||||
---|---|---|---|---|---|---|---|
Utilization | Total Costs | OPP Costs | Utilization | Total Costs | OPP Costs | Logit | |
Logit | OLS | OLS | Logit | OLS | OLS | ||
Health insurance (Uninsured as reference) | |||||||
UEMI | 0.048 † (0.027) | 0.731 * (0.339) | −0.211 (0.378) | 0.122 ** (0.019) | 0.475 † (0.273) | −0.976 ** (0.302) | 0.014(0.021) |
URMI | 0.047 (0.030) | 0.428 (0.328) | 0.165 (0.353) | 0.115 ** (0.024) | 0.356 (0.270) | −0.686 * (0.303) | 0.033(0.025) |
NCMI | 0.043 * (0.017) | 0.259 (0.219) | −0.047 (0.226) | 0.086 ** (0.013) | 0.149 (0.230) | −0.851 ** (0.244) | 0.004 (0.015) |
URRMI | 0.043 * (0.020) | 0.307 (0.255) | −0.092 (0.259) | 0.086 ** (0.015) | 0.357 (0.251) | −0.649 * (0.266) | 0.005 (0.017) |
Other insurance | 0.111 ** (0.042) | 0.696 † (0.364) | −1.085 * (0.509) | 0.093 ** (0.028) | 0.532 † (0.282) | −1.236 * (0.535) | −0.006 (0.028) |
Health insurance (UEMI as reference) | |||||||
Uninsured | −0.048 † (0.027) | −0.731 * (0.339) | 0.211 (0.378) | −0.122 ** (0.019) | −0.475 † (0.273) | 0.976 ** (0.302) | −0.014 (0.021) |
URMI | −0.001 (0.023) | −0.303† (0.180) | 0.376 (0.250) | −0.007 (0.020) | −0.119 (0.140) | 0.290 (0.204) | 0.019 (0.022) |
NCMI | −0.004 (0.025) | −0.472 † (0.281) | 0.164 (0.313) | −0.036 * (0.018) | −0.326 * (0.149) | 0.125 (0.183) | −0.009 (0.017) |
URRMI | −0.005 (0.023) | −0.424 † (0.242) | 0.120 (0.286) | −0.036 † (0.019) | −0.118 (0.153) | 0.327 † (0.192) | −0.008 (0.018) |
Other insurance | 0.064 (0.051) | −0.035 (0.372) | −0.874 (0.663) | −0.029 (0.028) | 0.058 (0.160) | −0.260 (0.444) | −0.020 (0.026) |
Predisposing factors | |||||||
Age | −0.002 ** (<0.001) | −0.005 (0.004) | −0.016 ** (0.005) | 0.002 ** (<0.001) | −0.002 (0.003) | −0.001 (0.005) | <0.001 (<0.001) |
Male | 0.007 (0.009) | 0.198 * (0.100) | 0.133 (0.114) | 0.050 **(0.010) | 0.247 ** (0.058) | 0.123 (0.082) | 0.011 (0.007) |
Married | 0.004 (0.011) | 0.119 (0.117) | 0.050 (0.141) | 0.015 (0.009) | 0.059 (0.071) | 0.354 ** (0.131) | −0.021 * (0.009) |
Education | |||||||
Primary school and below | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Secondary school | −0.006 (0.011) | 0.091 (0.096) | 0.242 (0.138) | −0.003 (0.009) | −0.071 (0.066) | −0.045 (0.100) | −0.002 (0.007) |
College and above | 0.072 † (0.041) | −0.395 (0.263) | −0.681 (0.462) | −0.050 ** (0.018) | −0.172 (0.204) | −0.495 (0.394) | −0.028 (0.026) |
Retired | 0.012 (0.008) | 0.401 ** (0.087) | 0.480**(0.125) | 0.037 ** (0.009) | 0.273 ** (0.069) | 0.111 (0.099) | 0.030 ** (0.007) |
Having social activities | 0.034 ** (0.009) | −0.055 (0.084) | −0.074 (0.104) | 0.006 (0.007) | −0.075 (0.054) | −0.077 (0.084) | −0.001 (0.007) |
Current smoking | −0.036 ** (0.010) | −0.104 (0.103) | −0.104 (0.129) | −0.041 ** (0.008) | −0.223 ** (0.070) | −0.338 ** (0.122) | −0.027 ** (0.008) |
Current drinking | −0.029 ** (0.009) | −0.041 (0.102) | 0.020 (0.132) | −0.049 ** (0.007) | −0.221 ** (0.070) | −0.441 ** (0.125) | −0.027 ** (0.007) |
Having physical exercise | 0.031 ** (0.010) | −0.332 * (0.127) | −0.301 * (0.150) | −0.011 (0.011) | −0.185 * (0.080) | −0.213 † (0.134) | −0.023 * (0.011) |
Enabling factors | |||||||
Residents | |||||||
Urban residents | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Rural residents | 0.009 (0.024) | 0.218 (0.238) | 0.513 † (0.267) | 0.031 * (0.015) | 0.070 (0.129) | −0.059 (0.177) | 0.025 † (0.013) |
Rural migrants | −0.004 (0.022) | 0.167 (0.173) | 0.495 † (0.256) | 0.008 (0.015) | 0.086 (0.140) | 0.073 (0.197) | 0.006 (0.015) |
Per capital household expenditure | |||||||
Quartile 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Quartile 2 | 0.004 (0.009) | 0.224 * (0.098) | 0.090 (0.124) | 0.023 ** (0.009) | 0.270 ** (0.065) | 0.269 * (0.115) | −0.079 ** (0.010) |
Quartile 3 | 0.008 (0.010) | 0.376 ** (0.094) | 0.426 ** (0.120) | 0.051 ** (0.010) | 0.472 ** (0.067) | 0.546 ** (0.109) | −0.093 ** (0.011) |
Quartile 4 | 0.043 ** (0.011) | 0.676 ** (0.125) | 0.810 ** (0.151) | 0.071 ** (0.010) | 0.896 ** (0.084) | 1.183 ** (0.118) | −0.108 ** (0.011) |
Area | |||||||
West | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Central | −0.013 (0.010) | 0.137 (0.101) | 0.023 (0.114) | −0.020 * (0.009) | 0.106 (0.067) | 0.092 (0.103) | −0.018 * (0.009) |
East | −0.001 (0.013) | −0.061 (0.124) | −0.360 * (0.140) | −0.056 ** (0.009) | 0.254 ** (0.073) | 0.272 * (0.116) | −0.014 (0.010) |
Health need factors | |||||||
Any chronic disease | |||||||
No | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
One | 0.020 (0.017) | 0.475 † (0.263) | 0.805 * (0.317) | 0.039 ** (0.009) | −0.077 (0.126) | 0.074 (0.233) | 0.016 † (0.010) |
Two or more | 0.073 ** (0.013) | 0.538 * (0.217) | 0.683 ** (0.260) | 0.113 ** (0.009) | −0.063 (0.115) | 0.108 (0.215) | 0.054 ** (0.008) |
With any ADL | 0.034 ** (0.010) | −0.021 (0.092) | 0.016 (0.105) | 0.038 ** (0.009) | 0.053 (0.064) | 0.069 (0.084) | 0.031 ** (0.009) |
With any IADL | −0.003 (0.009) | 0.030 (0.079) | 0.143 (0.106) | 0.008 (0.009) | 0.007 (0.080) | −0.042 (0.101) | −0.003 (0.008) |
Self-reported health status | |||||||
Very good/Good | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Fair | 0.044 ** (0.009) | 0.090 (0.115) | 0.205 (0.152) | 0.046 ** (0.008) | −0.005 (0.086) | −0.048 (0.128) | 0.016 * (0.008) |
Poor/Very poor | 0.126 ** (0.016) | 0.368* (0.150) | 0.394 * (0.185) | 0.126 ** (0.011) | 0.218 * (0.087) | 0.287* (0.125) | 0.080 ** (0.009) |
References
- National Bureau of Statistics of China. Seventh National Census; National Bureau of Statistics of China: Beijing, China, 2021.
- Ren, J.; Ding, D.; Wu, Q.; Liu, C.; Hao, Y.; Cui, Y.; Sun, H.; Ning, N.; Li, Y.; Kang, Z.; et al. Financial Affordability, Health Insurance, and Use of Health Care Services by the Elderly: Findings from the China Health and Retirement Longitudinal Study. Asia Pac. J. Public Health 2019, 31, 510–521. [Google Scholar] [CrossRef] [PubMed]
- Smith, J.P.; Strauss, J.; Zhao, Y. Healthy Aging in China. J. Econ. Ageing 2014, 4, 37–43. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Manqie, C.; Yumei, L. Suggestions on solving poverty alleviation caused by poverty caused by rural diseases. China Stat. 2017, 8, 22–24. [Google Scholar]
- Gong, C.H.; Kendig, H.; He, X. Factors predicting health services use among older people in China: An analysis of the China Health and Retirement Longitudinal Study 2013. BMC Health Serv. Res. 2016, 16, 63. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, Z.; Li, X.; Chen, M.; Si, L. Social health insurance, healthcare utilization, and costs in middle-aged and elderly community-dwelling adults in China. Int. J. Equity Health 2018, 17, 17. [Google Scholar] [CrossRef] [PubMed]
- Guindon, G.E. The impact of health insurance on health services utilization and health outcomes in Vietnam. Health Econ. Policy Law 2014, 9, 359–382. [Google Scholar] [CrossRef]
- Yu, H. Universal health insurance coverage for 1.3 billion people: What accounts for China’s success? Health Policy 2015, 119, 1145–1152. [Google Scholar] [CrossRef] [Green Version]
- Liu, G.G.; Vortherms, S.A.; Hong, X. China’s Health Reform Update. Annu. Rev. Public Health 2017, 38, 431–448. [Google Scholar] [CrossRef] [Green Version]
- Tan, S.Y.; Wu, X.; Yang, W. Impacts of the type of social health insurance on health service utilisation and expenditures: Implications for a unified system in China. Health Econ. Policy Law 2019, 14, 468–486. [Google Scholar] [CrossRef] [Green Version]
- WHO. Health Financing: A Strategy for the African Region Report of the Regional Director; Regional Committee for Africa: Addis Ababa, Ethiopia, 2006. [Google Scholar]
- Wagstaff, A.; Lindelow, M. Can insurance increase financial risk? The curious case of health insurance in China. J. Health Econ. 2008, 27, 990–1005. [Google Scholar] [CrossRef]
- Fu, X.; Sun, N.; Xu, F.; Li, J.; Tang, Q.; He, J.; Wang, D.; Sun, C. Influencing factors of inequity in health services utilization among the elderly in China. Int. J. Equity Health 2018, 17, 144. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Han, J.; Zhang, X.; Meng, Y. Out-Patient Service and in-Patient Service: The Impact of Health Insurance on the Healthcare Utilization of Mid-Aged and Older Residents in Urban China. Risk Manag. Healthc. Policy 2020, 13, 2199–2212. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.; Chen, Y.; Pan, T.; Liu, X.; Hu, H. The comparison of healthcare utilization inequity between URRBMI and NCMS in rural China. Int. J. Equity Health 2019, 18, 90. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhao, Y.; Hu, Y.; Smith, J.P.; Strauss, J.; Yang, G. Cohort profile: The China Health and Retirement Longitudinal Study (CHARLS). Int. J. Epidemiol. 2014, 43, 61–68. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhao, Y.; Strauss, J.; Chen, X.; Wang, Y.; Gong, J.; Meng, Q.; Wang, G.; Wang, H. China Health and Retirement Longitudinal Study Wave 4 User’s Guide; National School of Development, Peking University: Beijing, China, 2020. [Google Scholar]
- Cylus, J.; Thomson, S.; Evetovits, T. Catastrophic health spending in Europe: Equity and policy implications of different calculation methods. Bull. World Health Organ. 2018, 96, 599–609. [Google Scholar] [CrossRef]
- Zhao, Y.; Atun, R.; Oldenburg, B.; McPake, B.; Tang, S.; Mercer, S.W.; Cowling, T.E.; Sum, G.; Qin, V.M.; Lee, J.T. Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in China: An analysis of population-based panel data. Lancet Glob. Health 2020, 8, e840–e849. [Google Scholar] [CrossRef]
- Jiang, Y.; Ni, W. Association between Supplemental Private Health Insurance and Burden of Out-of-Pocket Healthcare Expenditure in China: A Novel Approach to Estimate Two-Part Model with Random Effects Using Panel Data. Risk Manag. Healthc. Policy 2020, 13, 323–334. [Google Scholar] [CrossRef] [Green Version]
- Andersen, R.M. Revisiting the behavioral model and access to medical care: Does it matter? J. Health Soc. Behav. 1995, 36, 1–10. [Google Scholar] [CrossRef]
- Liu, W.; Grunwald, G.K.; Ho, P.M. Two-part models for cost with zeros to decompose effects of covariates on probability of cost, mean nonzero cost, and mean total cost. Stat. Med. 2019, 38, 2767–2782. [Google Scholar] [CrossRef]
- Farewell, V.T.; Long, D.L.; Tom, B.D.M.; Yiu, S.; Su, L. Two-Part and Related Regression Models for Longitudinal Data. Annu. Rev. Stat. Appl. 2017, 4, 283–315. [Google Scholar] [CrossRef] [Green Version]
- Li, X.; Zhang, W. The impacts of health insurance on health care utilization among the older people in China. Soc. Sci. Med. 2013, 85, 59–65. [Google Scholar] [CrossRef] [PubMed]
- Fan, G.; Deng, Z.; Wu, X.; Wang, Y. Medical insurance and health equity in health service utilization among the middle-aged and older adults in China: A quantile regression approach. BMC Health Serv. Res. 2020, 20, 553. [Google Scholar] [CrossRef] [PubMed]
- Blumenthal, D.; Hsiao, W. Lessons from the East—China’s Rapidly Evolving Health Care System. N. Engl. J. Med. 2015, 372, 1281–1285. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhou, S.; Huang, T.; Li, A.; Wang, Z. Does universal health insurance coverage reduce unmet healthcare needs in China? Evidence from the National Health Service Survey. Int. J. Equity Health 2021, 20, 43. [Google Scholar] [CrossRef]
- Fu, L.; Wang, Y.; He, L. Factors Associated with Healthy Ageing, Healthy Status and Community Nursing Needs among the Rural Elderly in an Empty Nest Family: Results from the China Health and Retirement Longitudinal Study. Healthcare 2020, 8, 317. [Google Scholar] [CrossRef]
- Krobot, K.J.; Miller, W.C.; Kaufman, J.S.; Christensen, D.B.; Preisser, J.S.; Ibrahim, M.A. The disparity in access to new medication by type of health insurance—Lessons from Germany. Med. Care 2004, 42, 487–491. [Google Scholar] [CrossRef]
- Ngorsuraches, S.; Ungsupanit, J. The relationship between health insurance type and costs of prescribed drugs. Value Health 2004, 7, 651. [Google Scholar] [CrossRef] [Green Version]
- Liu, Q.; Liu, J.; Sui, S. Public Medical Insurance and Healthcare Utilization and Expenditures of Older with Chronic Diseases in Rural China: Evidence from NRCMS. Int. J. Environ. Res. Public Health 2020, 17, 7683. [Google Scholar] [CrossRef]
- Zhang, A.; Nikoloski, Z.; Mossialos, E. Does health insurance reduce out-of-pocket expenditure? Heterogeneity among China’s middle-aged and elderly. Soc. Sci. Med. 2017, 190, 11–19. [Google Scholar] [CrossRef] [Green Version]
- Chen, J.; Yu, H.; Dong, H. Effect of the new rural cooperative medical system on farmers’ medical service needs and utilization in Ningbo, China. BMC Health Serv. Res. 2016, 16, 593. [Google Scholar] [CrossRef] [Green Version]
- Chatterjee, C.; Chandra Nayak, N.; Mahakud, J. Magnitude and determinants of inpatient health expenditure among the elderly in India. Int. J. Health Plan. Manag. 2021; online ahead of print. [Google Scholar] [CrossRef]
- Islek, D.; Kilic, B.; Akdede, B.B. Out-of-pocket health expenditures in patients with bipolar disorder, anxiety, schizophrenia and other psychotic disorders: Findings from a study in a psychiatry outpatient clinic in Turkey. Soc. Psychiatry Psychiatr. Epidemiol. 2018, 53, 151–160. [Google Scholar] [CrossRef] [PubMed]
- Abrokwah, S.O.; Moser, C.M.; Norton, E.C. The effect of social health insurance on prenatal care: The case of Ghana. Int. J. Health Care Financ. Econ. 2014, 14, 385–406. [Google Scholar] [CrossRef] [PubMed]
- Yu, L.; Lang, J. Diagnosis-related Groups (DRG) pricing and payment policy in China: Where are we? Hepatobiliary Surg. Nutr. 2020, 9, 771–773. [Google Scholar] [CrossRef] [PubMed]
- Zou, K.; Li, H.Y.; Zhou, D.; Liao, Z.J. The effects of diagnosis-related groups payment on hospital healthcare in China: A systematic review. BMC Health Serv. Res. 2020, 20, 112. [Google Scholar] [CrossRef]
- Tao, W.; Zeng, Z.; Dang, H.; Li, P.; Chuong, L.; Yue, D.; Wen, J.; Zhao, R.; Li, W.; Kominski, G. Towards universal health coverage: Achievements and challenges of 10 years of healthcare reform in China. BMJ Glob. Health 2020, 5, e002087. [Google Scholar] [CrossRef] [Green Version]
- Xu, K.; Evans, D.B.; Kawabata, K.; Zeramdini, R.; Klavus, J.; Murray, C.J. Household catastrophic health expenditure: A multicountry analysis. Lancet 2003, 362, 111–117. [Google Scholar] [CrossRef]
- Ma, M.; Li, Y.; Wang, N.; Wu, Q.; Shan, L.; Jiao, M.; Fu, X.; Li, H.; Sun, T.; Yi, B.; et al. Does the medical insurance system really achieved the effect of poverty alleviation for the middle-aged and elderly people in China? Characteristics of vulnerable groups and failure links. BMC Public Health 2020, 20, 435. [Google Scholar] [CrossRef] [Green Version]
- Liu, S.; Coyte, P.C.; Fu, M.; Zhang, Q. Measurement and determinants of catastrophic health expenditure among elderly households in China using longitudinal data from the CHARLS. Int. J. Equity Health 2021, 20, 62. [Google Scholar] [CrossRef]
- Wang, N.; Xu, J.; Ma, M.; Shan, L.; Jiao, M.; Xia, Q.; Tian, W.; Zhang, X.; Liu, L.; Hao, Y.; et al. Targeting vulnerable groups of health poverty alleviation in rural China- what is the role of the New Rural Cooperative Medical Scheme for the middle age and elderly population? Int. J. Equity Health 2020, 19, 161. [Google Scholar] [CrossRef]
- Kim, S.; Kwon, S. Impact of the policy of expanding benefit coverage for cancer patients on catastrophic health expenditure across different income groups in South Korea. Soc. Sci. Med. 2015, 138, 241–247. [Google Scholar] [CrossRef]
- Wagstaff, A.; van Doorslaer, E. Catastrophe and impoverishment in paying for health care: With applications to Vietnam 1993–1998. Health Econ. 2003, 12, 921–934. [Google Scholar] [CrossRef] [PubMed]
- Su, T.T.; Kouyate, B.; Flessa, S. Catastrophic household expenditure for health care in a low-income society: A study from Nouna District, Burkina Faso. Bull. World Health Organ. 2006, 84, 21–27. [Google Scholar] [CrossRef] [PubMed]
- Buigut, S.; Ettarh, R.; Amendah, D.D. Catastrophic health expenditure and its determinants in Kenya slum communities. Int. J. Equity Health 2015, 14, 46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Amaya Lara, J.L.; Ruiz Gómez, F. Determining factors of catastrophic health spending in Bogota, Colombia. Int. J. Health Care Financ. Econ. 2011, 11, 83–100. [Google Scholar] [CrossRef] [PubMed]
- King, G.; Gakidou, E.; Imai, K.; Lakin, J.; Moore, R.T.; Nall, C.; Ravishankar, N.; Vargas, M.; Téllez-Rojo, M.M.; Avila, J.E.; et al. Public policy for the poor? A randomised assessment of the Mexican universal health insurance programme. Lancet 2009, 373, 1447–1454. [Google Scholar] [CrossRef] [Green Version]
- Azzani, M.; Roslani, A.C.; Su, T.T. Determinants of Household Catastrophic Health Expenditure: A Systematic Review. Malays. J. Med. Sci. 2019, 26, 15–43. [Google Scholar] [CrossRef]
UEMI | NCMI | URMI | URRMI | |
---|---|---|---|---|
Date Started | 1994 | 2003 | 2007 | 2016 |
Target population | Urban employee | Rural residents | Urban residents without formal employment | Urban residents without formal employment and rural residents |
Enrolment | Mandatory | Voluntary at household level but could be enforced once the county joins the NCMI | Voluntary | Voluntary |
Reimbursement rate, ceiling and deductibles | Set by the city governments. The rates depend largely on the types of health providers | Set by the county government. The rates depend largely on the types of health providers | Set by the city government, but these rates are different for children, elderly and other urban residents. They also depend on the types of health providers | Set by the county governments. The rates depend largely on the types of health providers |
Covered services | Inpatient services, catastrophic outpatient services, some prevention care services | Inpatient services, catastrophic outpatient services, some prevention care services | Mainly cover inpatient services and catastrophic outpatient services | Inpatient services and outpatient services |
Variables | Total (N = 15,936) n (col%) | No Insurance (n1 = 481) n (col%) | UEMI (n2 = 1972) n (col%) | URMI (n3 = 597) n (col%) | NCMI (n4 = 10,639) n (col%) | URRMI (n5 = 1985) n (col%) | Other Insurance (n6 = 262) n (col%) | p-Value |
---|---|---|---|---|---|---|---|---|
Predisposing factors | ||||||||
Age, mean (SD) | 62.3 (9.9) | 64.6 (11.9) | 62.7 (10.1) | 61.7 (10.0) | 62.0 (9.7) | 62.3 (9.8) | 66.1 (10.7) | <0.001 |
Male | 7478 (46.9) | 204 (42.4) | 1079 (54.7) | 228 (38.2) | 4913 (46.2) | 914 (46.0) | 140 (53.4) | <0.001 |
Married | 13,606 (85.4) | 331 (68.8) | 1736 (88.0) | 504 (84.4) | 9140 (85.9) | 1686 (84.9) | 209 (79.8) | <0.001 |
Education | <0.001 | |||||||
Primary school and below | 10,638 (66.8) | 401 (83.4) | 539 (27.3) | 275 (46.1) | 7879 (74.1) | 1425 (71.8) | 119 (45.4) | |
Secondary school | 5010 (31.4) | 80 (16.6) | 1221 (61.9) | 305 (51.1) | 2744 (25.8) | 557 (28.1) | 103 (39.3) | |
College and above | 288 (1.8) | 0 (0.0) | 212 (10.8) | 17 (2.8) | 16 (0.2) | 3 (0.2) | 40 (15.3) | |
Retired | 5694 (35.7) | 195 (40.5) | 1172 (59.4) | 371 (62.1) | 3174 (29.8) | 623 (31.4) | 159 (60.7) | <0.001 |
Having social activities | 8425 (52.9) | 217 (45.1) | 1415 (71.8) | 349 (58.5) | 5236 (49.2) | 1045 (52.6) | 163 (62.2) | <0.001 |
Current smoking | 4306 (27.0) | 143 (29.7) | 498 (25.3) | 127 (21.3) | 2978 (28.0) | 499 (25.1) | 61 (23.3) | <0.001 |
Current drinking | 4184 (26.3) | 108 (22.5) | 601 (30.5) | 135 (22.6) | 2706 (25.4) | 564 (28.4) | 70 (26.7) | <0.001 |
Having physical exercise | 14,369 (90.2) | 395 (82.1) | 1845 (93.6) | 546 (91.5) | 9582 (90.1) | 1761 (88.7) | 240 (91.6) | <0.001 |
Enabling factors | ||||||||
Residents | <0.001 | |||||||
Urban residents | 2628 (16.5) | 37 (7.7) | 1569 (79.6) | 485 (81.2) | 209 (2.0) | 196 (9.9) | 132 (50.4) | |
Rural residents | 11,489 (72.1) | 401 (83.4) | 244 (12.4) | 44 (7.4) | 9142 (85.9) | 1557 (78.4) | 101 (38.5) | |
Rural migrants | 1819 (11.4) | 43 (8.9) | 159 (8.1) | 68 (11.4) | 1288 (12.1) | 232 (11.7) | 29 (11.1) | |
Per capital household expenditure | <0.001 | |||||||
Quartile 1 (~850.6 USD) | 3986 (25.0) | 163 (33.9) | 66 (3.3) | 63 (10.6) | 3165 (29.7) | 496 (25.0) | 33 (12.6) | |
Quartile 2 (850.6~1592.3 USD) | 3983 (25.0) | 120 (24.9) | 270 (13.7) | 127 (21.3) | 2881 (27.1) | 543 (27.4) | 42 (16.0) | |
Quartile 3 (1592.3~2895.2 USD) | 3983 (25.0) | 111 (23.1) | 584 (29.6) | 177 (29.6) | 2530 (23.8) | 509 (25.6) | 72 (27.5) | |
Quartile 4 (2895.2 USD) | 3984 (25.0) | 87 (18.1) | 1052 (53.3) | 230 (38.5) | 2063 (19.4) | 437 (22.0) | 115 (43.9) | |
Area | <0.001 | |||||||
West | 5204 (32.7) | 184 (38.3) | 544 (27.6) | 167 (28.0) | 3686 (34.6) | 553 (27.9) | 70 (26.7) | |
Central | 5294 (33.2) | 135 (28.1) | 670 (34.0) | 315 (52.8) | 3623 (34.1) | 469 (23.6) | 82 (31.3) | |
East | 5438 (34.1) | 162 (33.7) | 758 (38.4) | 115 (19.3) | 3330 (31.3) | 963 (48.5) | 110 (42.0) | |
Health need factors | ||||||||
Any chronic disease | <0.001 | |||||||
No | 3094 (19.4) | 101 (21.0) | 380 (19.3) | 409 (20.6) | 109 (18.3) | 2052 (19.3) | 43 (16.4) | |
One | 3755 (23.6) | 135 (28.1) | 386 (19.6) | 446 (22.5) | 134 (22.4) | 2602 (24.5) | 52 (19.8) | |
Two or more | 9087 (57.0) | 245 (50.9) | 1206 (61.2) | 1130 (56.9) | 354 (59.3) | 5985 (56.3) | 167 (63.7) | |
With any ADL | 2559 (16.1) | 120 (24.9) | 158 (8.0) | 277 (14.0) | 83 (13.9) | 1879 (17.7) | 42 (16.0) | <0.001 |
With any IADL | 3619 (22.7) | 191 (39.7) | 186 (9.4) | 413 (20.8) | 107 (17.9) | 2677 (25.2) | 45 (17.2) | <0.001 |
Self-reported health status | <0.001 | |||||||
Very good/Good | 3750 (23.5) | 117 (24.3) | 600 (30.4) | 508 (25.6) | 165 (27.6) | 2298 (21.6) | 62 (23.7) | |
Fair | 7599 (47.7) | 206 (42.8) | 1017 (51.6) | 942 (47.5) | 287 (48.1) | 5009 (47.1) | 138 (52.7) | |
Poor/Very poor | 4587 (28.8) | 158 (32.8) | 355 (18.0) | 535 (27.0) | 145 (24.3) | 3332 (31.3) | 62 (23.7) |
Variables | Total (N = 15,936) n (col%) | No Insurance (n1 = 481) n (col%) | UEMI (n2 = 1972) n (col%) | URMI (n3 = 597) n (col%) | NCMI (n4 = 10,639) n (col%) | URRMI (n5 = 1985) n (col%) | Other Insurance (n6 = 262) n (col%) | p-Value |
---|---|---|---|---|---|---|---|---|
Outpatient care | ||||||||
Utilization in last month | 2466 (15.5) | 59 (12.3) | 329 (16.7) | 94 (15.7) | 1642 (15.4) | 302 (15.2) | 40 (15.3) | 0.290 |
Total cost, mean (SD), RMB | 195.0 (1106.3) | 161.5 (1165.6) | 298.6 (1405.2) | 166.5 (865.4) | 176.3 (1033.8) | 180.3 (985.8) | 411.8 (2121.4) | 0.110 |
OOP cost, mean (SD), RMB | 126.1 (680.1) | 161.5 (1165.6) | 145.3 (668.3) | 117.0 (494.8) | 121.3 (668.5) | 119.2 (592.9) | 186.5 (953.9) | 0.240 |
Reimbursement rate, mean (SD), % | 18.4 (30.6) | 0 (0.0) | 35.8 (39.6) | 14.4 (27.0) | 15.3 (27.5) | 18.1 (28.9) | 41.7 (42.2) | <0.001 |
Inpatient care | ||||||||
Utilization in last year | 2480 (15.6) | 41 (8.5) | 385 (19.5) | 108 (18.1) | 1618 (15.2) | 284 (14.3) | 44 (16.8) | <0.001 |
Total cost, mean (SD), RMB | 2333.7 (104,15.6) | 1000.1 (6116.6) | 4149.5 (14,935.8) | 3409.2 (14,326.5) | 1992.2 (9098.3) | 2356.7 (11,057.7) | 2361.5 (7890.0) | <0.001 |
OOP cost, mean (SD), RMB | 1210 (6154.7) | 1000.1 (6116.6) | 1741.7 (7859.0) | 1836.4 (8241.3) | 1085.1 (5635.2) | 1254.0 (6421.3) | 916.2 (3284.2) | <0.001 |
Reimbursement rate, mean (SD), % | 47.5 (29.0) | 0.0 (0.0) | 59.2 (27.5) | 44.6 (26.1) | 46.3 (28.4) | 45.8 (28.2) | 54.9 (31.2) | <0.001 |
Catastrophic health expenditures | ||||||||
Yes | 2051 (12.9) | 76 (15.8) | 178 (9.0) | 70 (11.7) | 1450 (13.6) | 254 (12.8) | 23 (8.8) | <0.001 |
Variables | Outpatient Care | Inpatient Care | CHE | ||||
---|---|---|---|---|---|---|---|
Utilization | Total Costs | OOP Costs | Utilization | Total Costs | OOP Costs | Logit | |
Logit | OLS | OLS | Logit | OLS | OLS | ||
Health insurance (Uninsured as reference) | |||||||
UEMI | 0.048 † (0.027) | 0.731 * (0.339) | −0.211 (0.378) | 0.122 ** (0.019) | 0.475 † (0.273) | −0.976 ** (0.302) | 0.014 (0.021) |
URMI | 0.047 (0.030) | 0.428 (0.328) | 0.165 (0.353) | 0.115 ** (0.024) | 0.356 (0.270) | −0.686 * (0.303) | 0.033 (0.025) |
NCMI | 0.043 * (0.017) | 0.259 (0.219) | −0.047 (0.226) | 0.086 ** (0.013) | 0.149 (0.230) | −0.851 ** (0.244) | 0.004 (0.015) |
URRMI | 0.043 * (0.020) | 0.307 (0.255) | −0.092 (0.259) | 0.086 ** (0.015) | 0.357 (0.251) | −0.649 * (0.266) | 0.005 (0.017) |
Other insurance | 0.111 ** (0.042) | 0.696 † (0.364) | −1.085 * (0.509) | 0.093 ** (0.028) | 0.532 † (0.282) | −1.236 * (0.535) | −0.006 (0.028) |
Health insurance (UEMI as reference) | |||||||
Uninsured | −0.048 † (0.027) | −0.731 * (0.339) | 0.211 (0.378) | −0.122 ** (0.019) | −0.475 † (0.273) | 0.976 ** (0.302) | −0.014 (0.021) |
URMI | −0.001 (0.023) | −0.303 † (0.180) | 0.376 (0.250) | −0.007 (0.020) | −0.119 (0.140) | 0.290 (0.204) | 0.019 (0.022) |
NCMI | −0.004 (0.025) | −0.472 † (0.281) | 0.164 (0.313) | −0.036 * (0.018) | −0.326 * (0.149) | 0.125 (0.183) | −0.009 (0.017) |
URRMI | −0.005 (0.023) | −0.424 † (0.242) | 0.120 (0.286) | −0.036 † (0.019) | −0.118 (0.153) | 0.327 † (0.192) | −0.008 (0.018) |
Other insurance | 0.064 (0.051) | −0.035 (0.372) | −0.874 (0.663) | −0.029 (0.028) | 0.058 (0.160) | −0.260 (0.444) | −0.020 (0.026) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chen, S.; Lin, Z.; Fan, X.; Li, J.; Xie, Y.-J.; Hao, C. The Comparison of Various Types of Health Insurance in the Healthcare Utilization, Costs and Catastrophic Health Expenditures among Middle-Aged and Older Chinese Adults. Int. J. Environ. Res. Public Health 2022, 19, 5956. https://doi.org/10.3390/ijerph19105956
Chen S, Lin Z, Fan X, Li J, Xie Y-J, Hao C. The Comparison of Various Types of Health Insurance in the Healthcare Utilization, Costs and Catastrophic Health Expenditures among Middle-Aged and Older Chinese Adults. International Journal of Environmental Research and Public Health. 2022; 19(10):5956. https://doi.org/10.3390/ijerph19105956
Chicago/Turabian StyleChen, Sha, Zhiye Lin, Xiaoru Fan, Jushuang Li, Yao-Jie Xie, and Chun Hao. 2022. "The Comparison of Various Types of Health Insurance in the Healthcare Utilization, Costs and Catastrophic Health Expenditures among Middle-Aged and Older Chinese Adults" International Journal of Environmental Research and Public Health 19, no. 10: 5956. https://doi.org/10.3390/ijerph19105956