| Literature DB >> 32373337 |
Yalan Zhu1, Chunping Liu2, Luwen Zhang3, Quan Fang1, Shuang Zang4, Xin Wang1.
Abstract
BACKGROUND: In the past two decades, chronic non-communicable diseases have become the leading disease burden, and cardio-cerebrovascular diseases (CCVD) are the main causes of death in chronic diseases. It has become the focus of global public health attention, in this study, System of Health Accounts 2011 (SHA 2011) is used to calculate health expenditure, discuss its economic burden, and put forward countermeasures.Entities:
Mesh:
Year: 2020 PMID: 32373337 PMCID: PMC7182356 DOI: 10.7189/jogh.10.010802
Source DB: PubMed Journal: J Glob Health ISSN: 2047-2978 Impact factor: 4.413
Composition of cardio-cerebrovascular diseases of current health expenditure (CHE)
| Service function | CHE of cardio- cerebrovascular diseases (100 million Yuan) | Proportion (%) | CHE (100 million Yuan) | Proportion (%) |
|---|---|---|---|---|
| Treatment: | 29.47 | 73.94 | 215.27 | 69.56 |
| -Outpatient | 7.58 | 19.01 | 77.83 | 25.15 |
| -Inpatient | 21.90 | 54.93 | 137.44 | 44.41 |
| Medical supplies | 5.92 | 14.86 | 59.63 | 19.26 |
| Ancillary services | 0.05 | 0.13 | 1.23 | 0.4 |
| Preventive services | 3.60 | 9.03 | 27.66 | 8.94 |
| Governance, health administration and financing management | 0.81 | 2.04 | 5.68 | 1.84 |
Figure 1Allocation of outpatient and inpatient expenditures.
Figure 2International Classification of Diseases (ICD) classification of current curative expenditure (CCE).
Figure 3Age distribution of current curative expenditure (CCE).
Financing scheme for inpatient and outpatient
| Service function | Public financing scheme (million Yuan) | Voluntary financing scheme (million Yuan) | Family health expenditure (million Yuan) | ||
|---|---|---|---|---|---|
| Outpatient | 171.49 | 116.01 | 30.02 | 24.3 | 416.16 |
| Inpatient | 1019.17 | 166.66 | 170.67 | 97.88 | 735.26 |
Figure 4Flow of financing in different institutions.
Regression analysis of influencing factors of hospitalization expenditure
| Unstandardization coefficient | Standardization coefficient | T | Sig | ||
|---|---|---|---|---|---|
| Constant | -6380.878 | 892.879 | -7.146 | <0.001 | |
| Stayday | 1224.077 | 11.192 | 0.526 | 109.374 | <0.001 |
| Surgery | 4.326 | 0.088 | 0.246 | 49.014 | <0.001 |
| Age | -17.344 | 5.995 | -0.038 | -2.893 | 0.004 |
| Insurance | 374.696 | 41.818 | 0.043 | 8.960 | <0.001 |
| Gender | 1164.691 | 145.747 | 0.038 | 7.991 | <0.001 |
| Institution level | 534.202 | 215.493 | 0.033 | 2.479 | 0.013 |
*B = unstandardization regression coefficient. SE = Standard Error. Beta = standardization regression coefficient. t = t test value (t-statistic). Sig = significance (p) of coefficients.
Spearman correlation analysis among variables*
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
|---|---|---|---|---|---|---|---|---|
| Hospitalization expenses | 1.000 | |||||||
| Stayday | 0.639‡ | 1.000 | ||||||
| Surgery | 0.328‡ | -0.034‡ | 1.000 | |||||
| Insurance | -0.057‡ | -0.117‡ | 0.052‡ | 1.000 | ||||
| Institution level | -0.086‡ | 0.014† | -0.355‡ | -0.220‡ | 1.000 | |||
| Gender | 0.069‡ | 0.020‡ | 0.024‡ | -0.088‡ | -0.002 | 1.000 | ||
| Age | 0.060‡ | 0.009 | 0.261‡ | 0.196‡ | -0.745‡ | -0.024‡ | 1.000 |
*For surgery: no surgery = 0, and surgery = 1. For insurance status (sorted by reimbursement ratio): urban employees’ basic medical insurance = 1, urban residents’ basic medical insurance = 2, new rural cooperative medical care or urban and rural medical insurance = 3, self-funded = 4. For institution level: provincial hospital = 1, municipal hospital = 2, district and county hospital = 3, For gender: female = 1, male = 2.
†P < 0.05.
‡P < 0.01.