| Literature DB >> 33115445 |
Jialong Chen1, Liuna Yang1, Zhenzhu Qian2, Mingwei Sun1, Honglin Yu1, Xiaolei Ma2, Chonghua Wan3, Yunbin Yang4.
Abstract
BACKGROUND: This study compares and analyzes the differences of residents' medical economic burden in different economic levels, explores the factors for improving the equity of health services in Guangdong, China.Entities:
Keywords: Aging; Different economic levels; Equity; Medical economic burden
Mesh:
Year: 2020 PMID: 33115445 PMCID: PMC7594465 DOI: 10.1186/s12913-020-05817-y
Source DB: PubMed Journal: BMC Health Serv Res ISSN: 1472-6963 Impact factor: 2.655
General information of 7 indicators
| Variables | Max | Min | Mean ± SEM |
|---|---|---|---|
| Proportion of health care expenditure to per capita consumer expenditure(%) | 7.77(Maoming city) | 3.46(Chaozhou city) | 5.42 ± 1.36 |
| Per capita GDP(yuan) | 167,411.15(Shenzhen city) | 24,031.6(Meizhou) | 65,641.65 ± 43,277.34 |
| Proportion of population over 65 years old (%) | 10.35(Meizhou city) | 1.79(Shenzhen city) | 7.35 ± 2.56 |
| Proportion of urban population to permanent population (%) | 100(Shenzhen city) | 40.8(Maoming) | 64.86 ± 19.67 |
| Proportion of expenditure for medical and health care to local government general budgetary expenditure(%) | 16.42(Jieyang city) | 2.03(Dongguang city) | 10.74 ± 3.89 |
| Number of medical technical personnel per 1000 permanent population | 9.82(Guangzhou city) | 3.5(Shanwei city) | 5.77 ± 1.65 |
| Per capita disposable income of permanent households (yuan) | 48,695(Shenzhen city) | 17,654.1(Jieyang city) | 26,777.72 ± 11,493.39 |
Similarity Matrix for Cluster Analysis of Indicators
| Proportion of Health Care Expenditure to Per Capita Consumer Expenditure (%) | Per Capita Gross Domestic Product (yuan) | Proportion of Population Over 65 Years (%) | Proportion of Urban Population to Permanent Population (%) | Proportion of Expenditure for Medical and Health Care to Local Government General Budgetary Expenditure (%) | Number of Medical Technical Personnel Per 1000 Permanent Population | Per Capita Disposable Income of Permanent Households (yuan) | |
|---|---|---|---|---|---|---|---|
| Proportion of Health Care Expenditure to Per Capita Consumer Expenditure (%) | 1.000 | −.456 | .487 | −.578 | .276 | −.153 | −.447 |
| Per Capita Gross Domestic Product (yuan) | −.456 | 1.000 | −.756 | −.668 | .777 | ||
| Proportion of Population Over 65 Years (%) | .487 | −.756 | 1.000 | .674 | −.315 | ||
| Proportion of Urban Population to Permanent Population (%) | −.578 | 1.000 | −.739 | .563 | |||
| Proportion of Expenditure for Medical and Health Care to Local Government General Budgetary Expenditure (%) | .276 | −.668 | .674 | −.739 | 1.000 | −.515 | −.760 |
| Number of Medical Technical Personnel Per 1000 Permanent Population | −.153 | .777 | −.315 | .563 | −.515 | 1.000 | .723 |
| Per Capita Disposable Income of Permanent Households (yuan) | −.447 | −.760 | .723 | 1.000 |
Fig. 1Dendrogram of Cluster Analysis of Indicators
Classification results of cities by cluster analysis
| City | Clusters |
|---|---|
| 1:Guangzhou | 1 |
| 2:Shenzhen | 1 |
| 3:Zhuhai | 1 |
| 4:Shantou | 2 |
| 5:Foshan | 2 |
| 6:Shaoguan | 2 |
| 7:Heyuan | 3 |
| 8:Meizhou | 3 |
| 9:Huizhou | 2 |
| 10:Shanwei | 2 |
| 11:Dongguan | 1 |
| 12:Zhongshan | 3 |
| 13:Jiangmen | 2 |
| 14:Yangjiang | 2 |
| 15:Zhanjiang | 3 |
| 16:Maoming | 3 |
| 17:Zhaoqing | 3 |
| 18:Qingyuan | 3 |
| 19:Chaozhou | 2 |
| 20:Jieyang | 2 |
Fig. 2Dendrogram of Cluster Analysis of Cities
One-way ANOVA of five selected indicators
| Sum of Squares | df | Mean Square | F | Sig. | ||
|---|---|---|---|---|---|---|
| Proportion of Health Care Expenditure to Per Capita Consumer Expenditure (%) | Between Groups | 27.565 | 2 | 13.783 | 29.910 | .000 |
| Within Groups | 7.834 | 17 | .461 | |||
| Total | 35.399 | 19 | ||||
| Per Capita Gross Domestic Product (yuan) | Between Groups | 21,941,875,551.721 | 2 | 10,970,937,775.861 | 13.670 | .000 |
| Within Groups | 13,643,762,457.801 | 17 | 802,574,262.224 | |||
| Total | 35,585,638,009.522 | 19 | ||||
| Proportion of Population Over 65 Years (%) | Between Groups | 61.955 | 2 | 30.977 | 8.291 | .003 |
| Within Groups | 63.514 | 17 | 3.736 | |||
| Total | 125.469 | 19 | ||||
| Proportion of Expenditure for Medical and Health Care to Local Government General Budgetary Expenditure (%) | Between Groups | 141.400 | 2 | 70.700 | 8.173 | .003 |
| Within Groups | 147.061 | 17 | 8.651 | |||
| Total | 288.460 | 19 | ||||
| Number of Medical Technical Personnel Per 1000 Permanent Population | Between Groups | 23.311 | 2 | 11.656 | 6.907 | .006 |
| Within Groups | 28.686 | 17 | 1.687 | |||
| Total | 51.997 | 19 | ||||
The means of five indicators in three clusters of cities
| Clusters | Proportion of Health Care Expenditure to Per Capita Consumer Expenditure (%) | Per Capita Gross Domestic Product (yuan) | Proportion of Population Over 65 Years (%) | Proportion of Expenditure for Medical and Health Care to Local Government General Budgetary Expenditure (%) | Number of Medical Technical Personnel Per 1000 Permanent Population | |
|---|---|---|---|---|---|---|
| 1 | Mean | 4.2350 | 131,643.6628 | 3.9400 | 5.4375 | 7.9125 |
| Std. Deviation | .67060 | 35,547.38186 | 2.30527 | 3.12486 | 2.00086 | |
| 2 | Mean | 4.7300 | 51,934.8097 | 7.7911 | 12.2178 | 5.0911 |
| Std. Deviation | .76274 | 27,364.83806 | 1.74819 | 2.70893 | 1.35889 | |
| 3 | Mean | 7.0086 | 45,549.3076 | 8.7486 | 11.8914 | 5.4314 |
| Std. Deviation | .55231 | 25,371.36998 | 1.96308 | 3.13741 | .56322 | |
| Total | Mean | 5.4285 | 65,641.6546 | 7.3560 | 10.7475 | 5.7745 |
| Std. Deviation | 1.36495 | 43,277.34183 | 2.56975 | 3.89642 | 1.65430 | |
Fig. 3Linear regression models