The Impact of Multimorbidities on Catastrophic Health Expenditures among Patients Suffering from Hypertension in China: An Analysis of Nationwide Representative Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Sources
2.2. Multimorbidities
2.3. Catastrophic Health Expenditure
2.4. Socioeconomic Status
2.5. Health Insurance
2.6. Variables
2.7. Statistical Analysis
2.8. Sensitivity Analyses
3. Results
3.1. Basic Characteristics of the Sample Population
3.2. Prevalence and Factors Associated with CHE
3.3. Impact of Age, Work Status and Combinations of Socioeconomic Groups and Health Insurance Schemes on the Relevance of Multimorbidities and CHE
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Stanaway, J.D.; Afshin, A.; Gakidou, E.; Lim, S.S.; Abate, D.; Abate, K.H.; Abbafati, C.; Abbasi, N.; Abbastabar, H.; Abd-Allah, F.; et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018, 392, 1923–1994. [Google Scholar] [CrossRef] [Green Version]
- NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in blood pressure from 1975 to 2015: A pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet 2017, 389, 37–55. [Google Scholar] [CrossRef] [Green Version]
- NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: A pooled analysis of 1201 population-representative studies with 104 million participants. Lancet 2021, 398, 957–980. [Google Scholar] [CrossRef]
- WHO. Hypertension Indicators for Improving Quality and Coverage of Services; World Health Organization: Geneva, Switzerland, 2021. [Google Scholar]
- National Statistics Bureau of China. Statistical Yearbook of China 2021; National Statistics Bureau of China: Beijing, China, 2021.
- Wong, M.C.S.; Wang, H.H.X.; Cheung, C.S.K.; Tong, E.L.H.; Sek, A.C.H.; Cheung, N.T.; Yan, B.P.Y.; Yu, C.M.; Griffiths, S.M.; Coats, A.J.S. Factors associated with multimorbidity and its link with poor blood pressure control among 223,286 hypertensive patients. Int. J. Cardiol. 2014, 177, 202–208. [Google Scholar] [CrossRef] [PubMed]
- Sarkar, C.; Dodhia, H.; Crompton, J.; Schofield, P.; White, P.; Millett, C.; Ashworth, M. Hypertension: A cross-sectional study of the role of multimorbidity in blood pressure control. BMC Fam. Pr. 2015, 16, 98. [Google Scholar] [CrossRef] [Green Version]
- Feng, L.; Jehan, I.; de Silva, H.A.; Naheed, A.; Farazdaq, H.; Hirani, S.; Kasturiratne, A.; Ranasinha, C.D.; Islam, M.T.; Siddiquee, A.T.; et al. Prevalence and correlates of cardiometabolic multimorbidity among hypertensive individuals: A cross-sectional study in rural South Asia-Bangladesh, Pakistan and Sri Lanka. BMJ Open 2019, 9, e030584. [Google Scholar] [CrossRef] [Green Version]
- WHO. The World Health Report 2008: Primary Health Care (Now More than Ever); World Health Organization: Geneva, Switzerland, 2008. [Google Scholar]
- Lloyd-Jones, D.M.; Evans, J.C.; Larson, M.G.; O’Donnell, C.J.; Wilson, P.W.; Levy, D. Cross-classification of JNC VI blood pressure stages and risk groups in the Framingham Heart Study. Arch. Intern. Med. 1999, 159, 2206–2212. [Google Scholar] [CrossRef] [Green Version]
- Lawes, C.M.; Vander Hoorn, S.; Rodgers, A. Global burden of blood-pressure-related disease, 2001. Lancet 2008, 371, 1513–1518. [Google Scholar] [CrossRef]
- Bao, X.Y.; Xie, Y.X.; Zhang, X.X.; Peng, X.; Huang, J.X.; Du, Q.F.; Wang, P.X. The association between multimorbidity and health-related quality of life: A cross-sectional survey among community middle-aged and elderly residents in southern China. Health Qual. Life Outcomes 2019, 17, 107. [Google Scholar] [CrossRef] [Green Version]
- Sara, H.H.; Chowdhury, M.A.B.; Haque, M.A. Multimorbidity among elderly in Bangladesh. Aging Med. (Milton) 2018, 1, 267–275. [Google Scholar] [CrossRef]
- Lee, J.T.; Hamid, F.; Pati, S.; Atun, R.; Millett, C. Impact of Noncommunicable Disease Multimorbidity on Healthcare Utilisation and Out-Of-Pocket Expenditures in Middle-Income Countries: Cross Sectional Analysis. PLoS ONE 2015, 10, e0127199. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Piette, J.D.; Kerr, E.A. The impact of comorbid chronic conditions on diabetes care. Diabetes Care 2006, 29, 725–731. [Google Scholar] [CrossRef] [Green Version]
- Jan, S.; Laba, T.L.; Essue, B.M.; Gheorghe, A.; Muhunthan, J.; Engelgau, M.; Mahal, A.; Griffiths, U.; McIntyre, D.; Meng, Q.; et al. Action to address the household economic burden of non-communicable diseases. Lancet 2018, 391, 2047–2058. [Google Scholar] [CrossRef]
- WHO. Global Health Observatory Data Repository: Out-of-Pocket Expenditure as Percentage of Current Health Expenditure (CHE) (%). Available online: https://apps.who.int/gho/data/view.main.GHEDOOPSCHESHA2011v?lang=en (accessed on 19 June 2022).
- Londoño Agudelo, E.; García Fariñas, A.; Pérez Ospina, V.; Taborda Pérez, C.; Villacrés Landeta, T.; Battaglioli, T.; Gómez Arias, R.; Van der Stuyft, P. Out-of-pocket expenditure for hypertension care: A population-based study in low-income urban Medellin, Colombia. Glob. Health Action 2020, 13, 1806527. [Google Scholar] [CrossRef] [PubMed]
- Vasan, R.S.; Beiser, A.; Seshadri, S.; Larson, M.G.; Kannel, W.B.; D’Agostino, R.B.; Levy, D. Residual lifetime risk for developing hypertension in middle-aged women and men: The Framingham Heart Study. JAMA 2002, 287, 1003–1010. [Google Scholar] [CrossRef] [PubMed]
- Bernardes, G.M.; Saulo, H.; Fernandez, R.N.; Lima-Costa, M.F.; Andrade, F.B. Catastrophic health expenditure and multimorbidity among older adults in Brazil. Rev. Saude Publica 2020, 54, 125. [Google Scholar] [CrossRef]
- Wagstaff, A.; Flores, G.; Hsu, J.; Smitz, M.F.; Chepynoga, K.; Buisman, L.R.; van Wilgenburg, K.; Eozenou, P. Progress on catastrophic health spending in 133 countries: A retrospective observational study. Lancet Glob. Health 2018, 6, e169–e179. [Google Scholar] [CrossRef] [Green Version]
- Wang, J.; Zhu, H.; Liu, H.; Wu, K.; Zhang, X.; Zhao, M.; Yin, H.; Qi, X.; Hao, Y.; Li, Y.; et al. Can the reform of integrating health insurance reduce inequity in catastrophic health expenditure? Evidence from China. Int. J. Equity Health 2020, 19, 49. [Google Scholar] [CrossRef] [Green Version]
- 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]
- Wang, Z.; Li, X.; Chen, M. Catastrophic health expenditures and its inequality in elderly households with chronic disease patients in China. Int. J. Equity Health 2015, 14, 8. [Google Scholar] [CrossRef] [Green Version]
- Zhao, Y.; Zhang, P.; Oldenburg, B.; Hall, T.; Lu, S.; Haregu, T.N.; He, L. The impact of mental and physical multimorbidity on healthcare utilization and health spending in China: A nationwide longitudinal population-based study. Int. J. Geriatr. Psychiatry 2021, 36, 500–510. [Google Scholar] [CrossRef] [PubMed]
- Si, Y.; Zhou, Z.; Su, M.; Ma, M.; Xu, Y.; Heitner, J. Catastrophic healthcare expenditure and its inequality for households with hypertension: Evidence from the rural areas of Shaanxi Province in China. Int. J. Equity Health 2017, 16, 27. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- 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] [PubMed]
- Owen, O.D.; Eddy, V.D.; Adam, W.; Magnus, L. Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and their Implementation; The World Bank: Washington, DC, USA, 2008; p. 2007. [Google Scholar]
- Chen, Z. CONCINDC: Stata Module to Calculate Concentration Index with both Individual and Grouped Data. Available online: http://econpapers.repec.org/software/bocbocode/S456802.htm (accessed on 19 June 2022).
- O’Donnell, O.; O’Neill, S.; Van Ourti, T.; Walsh, B. conindex: Estimation of concentration indices. Stata J. 2016, 16, 112–138. [Google Scholar] [CrossRef] [Green Version]
- Sancho-Mestre, C.; Vivas-Consuelo, D.; Alvis-Estrada, L.; Romero, M.; Usó-Talamantes, R.; Caballer-Tarazona, V. Pharmaceutical cost and multimorbidity with type 2 diabetes mellitus using electronic health record data. BMC Health Serv. Res. 2016, 16, 394. [Google Scholar] [CrossRef] [Green Version]
- Wang, H.H.; Wang, J.J.; Wong, S.Y.; Wong, M.C.; Li, F.J.; Wang, P.X.; Zhou, Z.H.; Zhu, C.Y.; Griffiths, S.M.; Mercer, S.W. Epidemiology of multimorbidity in China and implications for the healthcare system: Cross-sectional survey among 162,464 community household residents in southern China. BMC Med. 2014, 12, 188. [Google Scholar] [CrossRef]
- Zawudie, A.B.; Lemma, T.D.; Daka, D.W. Cost of Hypertension Illness and Associated Factors Among Patients Attending Hospitals in Southwest Shewa Zone, Oromia Regional State, Ethiopia. Clin. Outcomes Res. 2020, 12, 201–211. [Google Scholar] [CrossRef] [Green Version]
- Oyando, R.; Njoroge, M.; Nguhiu, P.; Kirui, F.; Mbui, J.; Sigilai, A.; Bukania, Z.; Obala, A.; Munge, K.; Etyang, A.; et al. Patient costs of hypertension care in public health care facilities in Kenya. Int. J. Health Plann. Manag. 2019, 34, e1166–e1178. [Google Scholar] [CrossRef]
- Zhang, X.; Xu, Q.; Guo, X.; Jing, Z.; Sun, L.; Li, J.; Zhou, C. Catastrophic health expenditure: A comparative study between hypertensive patients with and without complication in rural Shandong, China. BMC Public Health 2020, 20, 545. [Google Scholar] [CrossRef]
- Si, Y.; Zhou, Z.; Su, M.; Wang, X.; Lan, X.; Wang, D.; Gong, S.; Xiao, X.; Shen, C.; Ren, Y.; et al. Decomposing inequality in catastrophic health expenditure for self-reported hypertension household in Urban Shaanxi, China from 2008 to 2013: Two waves’ cross-sectional study. BMJ Open 2019, 9, e023033. [Google Scholar] [CrossRef] [Green Version]
- Liu, X.; Sun, X.; Zhao, Y.; Meng, Q. Financial protection of rural health insurance for patients with hypertension and diabetes: Repeated cross-sectional surveys in rural China. BMC Health Serv. Res. 2016, 16, 481. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, Q.; Liu, H.; Lu, Z.X.; Luo, Q.; Liu, J.A. Role of the new rural cooperative medical system in alleviating catastrophic medical payments for hypertension, stroke and coronary heart disease in poor rural areas of China. BMC Public Health 2014, 14, 907. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, Y.; Wu, Q.; Liu, C.; Kang, Z.; Xie, X.; Yin, H.; Jiao, M.; Liu, G.; Hao, Y.; Ning, N. Catastrophic health expenditure and rural household impoverishment in China: What role does the new cooperative health insurance scheme play? PLoS ONE 2014, 9, e93253. [Google Scholar] [CrossRef] [PubMed]
- Duan, W.; Zhang, W.; Wu, C.; Wang, Q.; Yu, Y.; Lin, H.; Liu, Y.; Hu, D. Extent and determinants of catastrophic health expenditure for tuberculosis care in Chongqing municipality, China: A cross-sectional study. BMJ Open 2019, 9, e026638. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, J.; Tan, X.; Qi, X.; Zhang, X.; Liu, H.; Wang, K.; Jiang, S.; Xu, Q.; Meng, N.; Chen, P.; et al. Minimizing the Risk of Catastrophic Health Expenditure in China: A Multi-Dimensional Analysis of Vulnerable Groups. Front. Public Health 2021, 9, 689809. [Google Scholar] [CrossRef] [PubMed]
- Liu, C.; Liu, Z.M.; Nicholas, S.; Wang, J. Trends and determinants of catastrophic health expenditure in China 2010-2018: A national panel data analysis. BMC Health Serv. Res. 2021, 21, 526. [Google Scholar] [CrossRef] [PubMed]
- McRae, I.; Yen, L.; Jeon, Y.-H.; Herath, P.M.; Essue, B. Multimorbidity is associated with higher out-of-pocket spending: A study of older Australians with multiple chronic conditions. Aust. J. Prim. Health 2013, 19, 144–149. [Google Scholar] [CrossRef]
- Sum, G.; Hone, T.; Atun, R.; Millett, C.; Suhrcke, M.; Mahal, A.; Koh, G.C.-H.; Lee, J.T. Multimorbidity and out-of-pocket expenditure on medicines: A systematic review. BMJ Glob. Health 2018, 3. [Google Scholar] [CrossRef] [Green Version]
- World Health Organization. The World Health Report 2000, Health Systems: Improving Performance; World Health Organization: Geneva, Switzerland, 2000. [Google Scholar]
- Lee, H.Y.; Oh, J.; Hoang, V.M.; Moon, J.R.; Subramanian, S.V. Use of high-level health facilities and catastrophic expenditure in Vietnam: Can health insurance moderate this relationship? BMC Health Serv. Res. 2019, 19, 318. [Google Scholar] [CrossRef]
- Pan, X.F.; Xu, J.; Meng, Q. Integrating social health insurance systems in China. Lancet 2016, 387, 1274–1275. [Google Scholar] [CrossRef] [Green Version]
- Tangcharoensathien, V.; Patcharanarumol, W.; Ir, P.; Aljunid, S.M.; Mukti, A.G.; Akkhavong, K.; Banzon, E.; Huong, D.B.; Thabrany, H.; Mills, A. Health-financing reforms in southeast Asia: Challenges in achieving universal coverage. Lancet 2011, 377, 863–873. [Google Scholar] [CrossRef]
- Limwattananon, S.; Tangcharoensathien, V.; Prakongsai, P. Catastrophic and poverty impacts of health payments: Results from national household surveys in Thailand. Bull. World Health Organ. 2007, 85, 600–606. [Google Scholar] [CrossRef] [PubMed]
- Somkotra, T.; Lagrada, L.P. Payments for health care and its effect on catastrophe and impoverishment: Experience from the transition to Universal Coverage in Thailand. Soc. Sci. Med. 2008, 67, 2027–2035. [Google Scholar] [CrossRef] [PubMed]
Hypertension Only N = 1143 (16.7%) | Hypertension and 1 Other Chronic Disease N = 1582 (23.1%) | Hypertension and 2 Other Chronic Diseases N = 1493 (21.8%) | Hypertension and 3 Other Chronic Diseases N = 1067 (15.6%) | Hypertension and ≥4 Chronic Diseases N = 1551 (22.7%) | Overall N = 6836 | p Value | |
---|---|---|---|---|---|---|---|
Age | <0.001 | ||||||
Age, years | 63.1 (10.5) | 64.4 (10.2) | 65.2 (10.0) | 65.3 (9.5) | 66.2 (9.3) | 64.9 (9.9) | |
Sex | <0.001 | ||||||
Male | 612 (53.5%) | 800 (50.6%) | 692 (46.3%) | 488 (45.7%) | 647 (41.7%) | 3239 (47.4%) | |
Female | 531 (46.5%) | 782 (49.4%) | 801 (53.7%) | 579 (54.3%) | 904 (58.3%) | 3597 (52.6%) | |
Education | 0.049 | ||||||
No education and primary school | 746 (65.3%) | 1063 (67.2%) | 1012 (67.8%) | 743 (69.6%) | 1107 (71.4%) | 4671 (68.3%) | |
Secondary school | 377 (33.0%) | 485 (30.7%) | 447 (29.9%) | 301 (28.2%) | 415 (26.8%) | 2025 (29.6%) | |
College and above | 20 (1.7%) | 34 (2.1%) | 34 (2.3%) | 23 (2.2%) | 29 (1.9%) | 140 (2.0%) | |
Work status | <0.001 | ||||||
Employed | 757 (66.3%) | 950 (60.1%) | 788 (52.8%) | 519 (48.7%) | 648 (41.9%) | 3662 (53.6%) | |
Jobless † | 5 (0.4%) | 1 (0.1%) | 3 (0.2%) | 2 (0.2%) | 6 (0.4%) | 17 (0.2%) | |
Unemployed | 356 (31.2%) | 580 (36.7%) | 649 (43.5%) | 496 (46.5%) | 800 (51.7%) | 2881 (42.2%) | |
Retired | 24 (2.1%) | 50 (3.2%) | 52 (3.5%) | 49 (4.6%) | 94 (6.1%) | 269 (3.9%) | |
Smoking status | <0.001 | ||||||
Non-smoker | 806 (70.6%) | 1160 (73.3%) | 1143 (76.6%) | 853 (79.9%) | 1252 (80.8%) | 5214 (76.3%) | |
Smoker | 336 (29.4%) | 422 (26.7%) | 350 (23.4%) | 214 (20.1%) | 297 (19.2%) | 1619 (23.7%) | |
Frequency of drinking | <0.001 | ||||||
>1 time/month | 368 (32.2%) | 421 (26.6%) | 359 (24.0%) | 251 (23.5%) | 253 (16.3%) | 1652 (24.2%) | |
<1 time/month | 80 (7.0%) | 96 (6.1%) | 96 (6.4%) | 86 (8.1%) | 97 (6.3%) | 455 (6.7%) | |
Never | 694 (60.8%) | 1065 (67.3%) | 1038 (69.5%) | 730 (68.4%) | 1199 (77.4%) | 4726 (69.2%) | |
Physical examination | <0.001 | ||||||
No | 745 (65.2%) | 956 (60.5%) | 842 (56.5%) | 554 (52.0%) | 781 (50.4%) | 3878 (56.8%) | |
Yes | 397 (34.8%) | 625 (39.5%) | 649 (43.5%) | 512 (48.0%) | 768 (49.6%) | 2951 (43.2%) | |
Health insurance | 0.020 | ||||||
No public health insurance | 42 (3.7%) | 65 (4.1%) | 49 (3.3%) | 34 (3.2%) | 55 (3.6%) | 245 (3.6%) | |
UEBMI | 140 (12.3%) | 220 (13.9%) | 243 (16.3%) | 171 (16.0%) | 256 (16.5%) | 1030 (15.1%) | |
URRBMI | 169 (14.8%) | 203 (12.8%) | 200 (13.4%) | 124 (11.6%) | 184 (11.9%) | 880 (12.9%) | |
URBMI | 51 (4.5%) | 65 (4.1%) | 59 (4.0%) | 45 (4.2%) | 87 (5.6%) | 307 (4.5%) | |
NRCMS | 728 (63.7%) | 1006 (63.6%) | 926 (62.1%) | 673 (63.1%) | 936 (60.4%) | 4269 (62.5%) | |
Other * | 12 (1.1%) | 22 (1.4%) | 15 (1.0%) | 19 (1.8%) | 31 (2.0%) | 99 (1.4%) | |
Socioeconomic status | <0.001 | ||||||
Quartile 1 (lowest) | 320 (28.0%) | 425 (26.9%) | 327 (21.9%) | 247 (23.2%) | 319 (20.6%) | 1638 (24.0%) | |
Quartile 2 | 332 (29.1%) | 407 (25.7%) | 398 (26.7%) | 252 (23.6%) | 396 (25.5%) | 1785 (26.1%) | |
Quartile 3 | 257 (22.5%) | 398 (25.2%) | 410 (27.5%) | 295 (27.7%) | 399 (25.7%) | 1759 (25.7%) | |
Quartile 4 (highest) | 232 (20.3%) | 351 (22.2%) | 358 (24.0%) | 272 (25.5%) | 437 (28.2%) | 1650 (24.2%) | |
Area | <0.001 | ||||||
East | 450 (39.4%) | 539 (34.1%) | 494 (33.1%) | 307 (28.8%) | 365 (23.5%) | 2155 (31.5%) | |
Central | 307 (26.9%) | 437 (27.6%) | 423 (28.3%) | 316 (29.6%) | 445 (28.7%) | 1928 (28.2%) | |
West | 316 (27.6%) | 507 (32.0%) | 469 (31.4%) | 367 (34.4%) | 591 (38.1%) | 2250 (32.9%) | |
Northeast | 70 (6.1%) | 99 (6.3%) | 107 (7.2%) | 77 (7.2%) | 150 (9.7%) | 503 (7.4%) | |
Number of family members | 0.002 | ||||||
Population | 2.8 (1.5) | 2.7 (1.4) | 2.7 (1.4) | 2.6 (1.4) | 2.6 (1.4) | 2.7 (1.4) | |
Impoverished | 0.251 | ||||||
No | 1037 (90.7%) | 1435 (92.6%) | 1345 (91.6%) | 960 (91.5%) | 1375 (90.5%) | 6152 (91.7%) | |
Yes | 87 (7.6%) | 114 (7.4%) | 123 (8.4%) | 89 (8.5%) | 145 (9.5%) | 558 (8.3%) |
Coef. (95% CI) | p Value | |
---|---|---|
Number of chronic diseases | 0.22 (0.19, 0.24) | <0.001 |
Age, per 5 years | 0.16 (0.13, 0.18) | <0.001 |
Sex | ||
Male | 1 (ref) | |
Female | 0.19 (0.10, 0.29) | 0.015 |
Education | ||
No education and primary school | 1 (ref) | |
Secondary school | −0.43 (−0.53, −0.32) | 0.048 |
College and above | −0.71 (−1.05, −0.36) | 0.143 |
Work status | ||
Employed | 1 (ref) | |
Jobless † | 0.05 (−0.91, 1.00) | 0.839 |
Unemployed | 0.46 (0.36, 0.56) | <0.001 |
Retired | 0.25 (0.00, 0.50) | 0.151 |
Smoking status | ||
Non-smoker | 1 (ref) | |
Smoker | −0.31 (−0.42, −0.20) | 0.014 |
Frequency of drinking | ||
>1 time/month | 1 (ref) | |
<1 time/month | 0.18 (−0.03, 0.39) | 0.296 |
Never | 0.50 (0.38, 0.61) | <0.001 |
Physical examination | ||
No | 1 (ref) | |
Yes | 0.19 (0.09, 0.28) | 0.074 |
Health insurance | ||
No basic medical insurance | 1 (ref) | |
UEBMI | −0.23 (−0.50, 0.05) | 0.782 |
URRBMI | 0.18 (−0.10, 0.47) | 0.005 |
URBMI | −0.02 (−0.36, 0.31) | 0.747 |
NRCMS | 0.12 (−0.14, 0.38) | 0.012 |
Other * | −0.24 (−0.71, 0.23) | 0.568 |
Socioeconomic status | ||
Quartile 1 (lowest) | 1 (ref) | |
Quartile 2 | −0.15 (−0.28, −0.02) | 0.115 |
Quartile 3 | −0.16 (−0.29, −0.02) | 0.039 |
Quartile 4 (highest) | −0.13 (−0.27, 0.00) | 0.161 |
Area | ||
East | 1 (ref) | |
Central | 0.13 (0.01, 0.26) | 0.206 |
West | 0.25 (0.13, 0.36) | 0.023 |
Northeast | 0.14 (−0.05, 0.34) | 0.307 |
Number of family members | −0.21 (−0.24, −0.17) | <0.001 |
Impoverished | ||
No | 1 (ref) | |
Yes | 0.61 (0.43, 0.79) | <0.001 |
Odds Ratio (95% CI) | Robust Standard Error | p Value | |
---|---|---|---|
Number of chronic diseases | 1.21 (1.18, 1.25) | 0.019 | <0.001 |
Age, per 5 years | 1.09 (1.06, 1.13) | 0.017 | <0.001 |
Sex | |||
Male | 1 (ref) | ||
Female | 0.85 (0.75, 0.97) | 0.056 | 0.015 |
Education | |||
No education and primary school | 1 (ref) | ||
Secondary school | 0.88 (0.78, 1.00) | 0.056 | 0.048 |
College and above | 0.75 (0.51, 1.10) | 0.143 | 0.143 |
Work status | |||
Employed | 1 (ref) | ||
Jobless † | 0.90 (0.33, 2.48) | 0.496 | 0.839 |
Unemployed | 1.23 (1.09, 1.39) | 0.074 | <0.001 |
Retired | 1.23 (0.93, 1.64) | 0.180 | 0.151 |
Smoking status | |||
Non-smoker | 1 (ref) | ||
Smoker | 0.84 (0.73, 0.97) | 0.059 | 0.014 |
Frequency of drinking | |||
>1 time/month | 1 (ref) | ||
<1 time/month | 1.13 (0.90, 1.41) | 0.131 | 0.296 |
Never | 1.33 (1.16, 1.53) | 0.093 | <0.001 |
Physical examination | |||
No | 1 (ref) | ||
Yes | 1.10 (0.99, 1.22) | 0.059 | 0.074 |
Health insurance | |||
No basic medical insurance | 1 (ref) | ||
UEBMI | 0.96 (0.70, 1.31) | 0.154 | 0.782 |
URRBMI | 1.55 (1.14, 2.10) | 0.245 | 0.005 |
URBMI | 1.06 (0.74, 1.52) | 0.199 | 0.747 |
NRCMS | 1.42 (1.08, 1.88) | 0.205 | 0.012 |
Other * | 0.86 (0.52, 1.44) | 0.232 | 0.568 |
Socioeconomic status | |||
Quartile 1 (lowest) | 1 (ref) | ||
Quartile 2 | 0.89 (0.77, 1.03) | 0.066 | 0.115 |
Quartile 3 | 0.86 (0.74, 0.99) | 0.064 | 0.039 |
Quartile 4 (highest) | 0.90 (0.77, 1.04) | 0.070 | 0.161 |
Area | |||
East | 1 (ref) | ||
Central | 1.09 (0.95, 1.24) | 0.073 | 0.206 |
West | 1.16 (1.02, 1.32) | 0.076 | 0.023 |
Northeast | 1.12 (0.90, 1.38) | 0.118 | 0.307 |
Number of family members | 0.83 (0.80, 0.86) | 0.017 | <0.001 |
Impoverished | |||
No | 1 (ref) | ||
Yes | 1.54 (1.28, 1.87) | 0.154 | <0.001 |
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Fu, Y.; Chen, M. The Impact of Multimorbidities on Catastrophic Health Expenditures among Patients Suffering from Hypertension in China: An Analysis of Nationwide Representative Data. Sustainability 2022, 14, 7555. https://doi.org/10.3390/su14137555
Fu Y, Chen M. The Impact of Multimorbidities on Catastrophic Health Expenditures among Patients Suffering from Hypertension in China: An Analysis of Nationwide Representative Data. Sustainability. 2022; 14(13):7555. https://doi.org/10.3390/su14137555
Chicago/Turabian StyleFu, Yu, and Mingsheng Chen. 2022. "The Impact of Multimorbidities on Catastrophic Health Expenditures among Patients Suffering from Hypertension in China: An Analysis of Nationwide Representative Data" Sustainability 14, no. 13: 7555. https://doi.org/10.3390/su14137555