中国医患纠纷关注度时空差异特征及影响因素

doi:10.16381/j.cnki.issn1003-207x.2021.1866

Abstract

Abstract:

In recent years， the occurrence of disputes between doctors and patients has aroused great public concern， which not only wastes public attention but also hurts the promotion of the "Health China" strategy. Although the media and the public have reported and paid some attention to the issue of doctor-patient disputes， it is difficult to directly reflect the actual level of attention paid to doctor-patient disputes. Therefore， what are the characteristics of attention to doctor-patient disputes in China？ What are the factors driving the evolution of attention to doctor-patient disputes？ To explore the question of increasing attention to doctor-patient disputes and regional differences in China， a variety of methods is used to present an empirical study on spatiotemporal differences and influencing factors of attention to doctor-patient disputes in China.Based on the Baidu Index of doctor-patient disputes from 2011 to 2019， the Dagum Gini coefficient， kernel density estimation， and spatial Moran index are used to analyze the regional differences， distribution dynamics， and spatial agglomeration characteristics. It also uses OLS estimation of the panel data model and Oaxaca Blinder decomposition to explore the driving factors and sources of regional differences in doctor-patient dispute attention.The results indicate that the degree of attention to doctor-patient disputes has increased as a whole， showing a decreasing and more obvious unbalanced pattern from east to west. The inter-provincial difference is the main source of attention and imbalance， among which the East-West difference is the largest and the Northeast-Central difference is the smallest. Attention has significant spatial correlation characteristics， and it forms a high-high attention “club” in the Yangtze River Delta， Fujian， Shandong， and Henan. Cultural literacy， the burden of patients， social governance level， informatization， the regional economic level， and the number of media reports have a significant positive impact on attention， while geographical distance has a negative inhibitory effect. The above results are still robust after excluding extreme samples and spatial econometric analysis. Besides， the level of social governance and the burden on patients are the main sources of regional and high-low level differences in attention.The value of this study is to provide policy implications for the correct guidance and management of the spread of public opinion on doctor-patient disputes. It also can help people to understand the regional differences and evolution of attention to doctor-patient disputes in China.

Key words: doctor-patient disputes, attention, spatiotemporal characteristics, influencing factors, Oaxaca Blinder decomposition

CLC Number:

G212

Jusheng Liu,Zhiping Qiu,Changrui Yu. Spatiotemporal Differences and Influencing Factors of Attention to Doctor-patient Disputes in China[J]. Chinese Journal of Management Science, 2024, 32(8): 322-334.

Figures/Tables 11

References 39

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变量	Mean	Std.Dev.	Max	VIF
dpd	0.344	0.215	1.000	4.33^#
pdgp	0.254	0.179	1.000	7.23
num	0.035	0.124	1.000	1.73
sga	0.276	0.158	1.000	8.91
ict	0.430	0.226	1.000	5.26
rcl	0.879	0.155	1.000	1.51
pexp	0.236	0.170	1.000	6.71
age	0.461	0.204	1.000	1.58
dis	0.330	0.240	1.000	1.70

省份	2019年	均值	增长率(%)	省份	2019年	均值	增长率(%)
北京	21788(3)	27962	2.833	重庆	9484(18)	12869	1.886
天津	5044(23)	9919	-4.145	四川	20928(5)	24836	3.373
河北	15441(9)	19365	2.436	贵州	8816(20)	10686	6.398
上海	18816(8)	23524	2.640	云南	10207(17)	12873	3.055
江苏	27919(2)	30871	4.919	西藏	406(31)	314	11.414
浙江	19778(6)	26527	1.773	陕西	10383(16)	15023	-0.711
福建	13948(11)	18118	1.240	甘肃	4587(24)	7836	-2.457
山东	21301(4)	25836	1.616	青海	955(30)	1486	-5.889
广东	30843(1)	31929	7.250	宁夏	1089(29)	2499	-11.746
海南	2152(28)	3429	-5.143	新疆	3685(27)	7001	-3.793
山西	9449(19)	13333	-0.192	辽宁	10799(15)	14207	1.200
安徽	13162(13)	17820	-0.158	吉林	4516(25)	8032	-1.802
江西	8580(21)	13180	-5.179	黑龙江	6683(22)	11271	-1.226
河南	18821(7)	23419	1.660	东部	17703	21748	2.886
湖北	15222(10)	19760	0.131	中部	13092	17620	-0.624
湖南	13315(12)	18207	-1.448	西部	7198	9730	1.465
内蒙古	4473(26)	7665	0.323	东北	7333	11170	-0.235
广西	11362(14)	13674	3.499	全国	11740	15273	1.508

类别	组别	2011年	2015年	2019年
基尼系数	全国	0.300	0.341	0.373
	东部	0.187	0.229	0.269
	东北部	0.137	0.151	0.190
	中部	0.085	0.128	0.146
	西部	0.355	0.394	0.421
	东部-东北部	0.237	0.282	0.327
	东部-中部	0.160	0.232	0.261
	东部-西部	0.344	0.387	0.417
	东北部-中部	0.174	0.173	0.212
	东北部-西部	0.317	0.351	0.385
	中部-西部	0.324	0.330	0.352
	东部东北部-中西部	0.187	0.229	0.269
差异来源	区域间	0.182	0.201	0.211
	超密度	0.054	0.062	0.074
	区域内	0.064	0.078	0.088
差异贡献率%	区域间%	60.651	58.920	56.628
	超密度%	17.964	18.161	19.743
	区域内%	21.385	22.919	23.630

时间	全局 Moran's I				局部 Moran's I
时间	W1	W2	W3	W4	高-高型集聚区
2011年	0.373^***	0.112^***	0.177^*	0.093^***	沪、苏、浙、皖、闽、赣、鲁、豫、鄂、湘和粤
2011年	(3.399)	(4.140)	(1.892)	(3.361)	沪、苏、浙、皖、闽、赣、鲁、豫、鄂、湘和粤
2015年	0.218^**	0.072^***	0.188^**	0.061^**	沪、苏、浙、闽、鲁和豫
2015年	(2.122)	(3.021)	(2.006)	(2.520)	沪、苏、浙、闽、鲁和豫
2019年	0.187^*	0.056^***	0.141	0.040^**	沪、苏、浙、鲁和豫
2019年	(1.876)	(2.570)	(1.590)	(1.990)	沪、苏、浙、鲁和豫

变量	(1)	(2)	(3)	(4)	(5)
变量	zdpd	zdpd	zdpd	zdpd	zdpd
pgdp	0.255^***	0.212^***	0.216^***	0.164^**	0.191^**
pgdp	(0.067)	(0.068)	(0.068)	(0.073)	(0.074)
num	0.194^***	0.172^***	0.159^**	0.167^***	0.166^***
num	(0.060)	(0.063)	(0.063)	(0.059)	(0.058)
sga		0.266^**	0.245^*	0.238^*	0.238^*
sga		(0.129)	(0.129)	(0.129)	(0.130)
ict		0.108	0.124	0.228^**	0.231^*
ict		(0.101)	(0.102)	(0.115)	(0.120)
rcl			0.436^***	0.448^***	0.458^***
rcl			(0.134)	(0.128)	(0.131)
pexp				0.341^**	0.293^*
pexp				(0.169)	(0.170)
age					0.014
age					(0.060)
dis					-0.189^**
dis					(0.080)
Constant	0.273^***	0.165^***	-0.220^*	-0.340^**	-0.291^*
Constant	(0.019)	(0.050)	(0.130)	(0.143)	(0.162)
R²	0.944	0.946	0.948	0.949	0.950

Spatiotemporal Differences and Influencing Factors of Attention to Doctor-patient Disputes in China

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变量	(1)截尾1%	(2)截尾5%	(3)缩尾1%	(4)缩尾5%
pgdp	0.196^***	0.199^**	0.190^**	0.203^***
pgdp	(0.075)	(0.079)	(0.074)	(0.078)
num	0.167^***	0.173^***	0.166^***	0.164^***
num	(0.059)	(0.063)	(0.058)	(0.060)
sga	0.238^*	0.267^**	0.237^*	0.248^*
sga	(0.132)	(0.135)	(0.130)	(0.135)
ict	0.236^*	0.238^*	0.231^*	0.242^*
ict	(0.122)	(0.127)	(0.120)	(0.124)
rcl	0.452^***	0.437^***	0.455^***	0.476^***
rcl	(0.130)	(0.138)	(0.128)	(0.138)
pexp	0.299^*	0.345^*	0.292^*	0.307^*
pexp	(0.173)	(0.182)	(0.170)	(0.176)
age	0.020	0.003	0.016	0.015
age	(0.061)	(0.063)	(0.059)	(0.061)
dis	-0.190^**	-0.201^**	-0.187^**	-0.188^**
dis	(0.080)	(0.084)	(0.079)	(0.081)
Constant	-0.294^*	-0.295^*	-0.288^*	-0.323^*
Constant	(0.162)	(0.172)	(0.160)	(0.170)
R²	0.949	0.947	0.950	0.947

变量	地理临近矩阵W1		地理距离矩阵W2		地理经济距离矩阵
变量	(1)SAR	(2)SEM	(3)SAR	(4)SEM	(5)SAR-W3	(6)SAR-W4
pgdp	0.190^***	0.219^***	0.176^***	0.210	0.188^***	0.187^***
pgdp	(0.067)	(0.075)	(0.064)	(0.154)	(0.068)	(0.068)
num	0.182^***	0.166^***	0.141^***	0.166^*	0.176^***	0.171^***
num	(0.057)	(0.055)	(0.054)	(0.090)	(0.053)	(0.053)
sga	0.251^**	0.228^**	0.266^**	0.256^**	0.267^**	0.255^**
sga	(0.117)	(0.116)	(0.114)	(0.113)	(0.119)	(0.118)
ict	0.232^**	0.241^**	0.265^**	0.262	0.243^**	0.237^**
ict	(0.109)	(0.110)	(0.105)	(0.282)	(0.109)	(0.110)
rcl	0.416^***	0.436^***	0.324^***	0.393	0.438^***	0.455^***
rcl	(0.109)	(0.110)	(0.0905)	(0.670)	(0.111)	(0.114)
pexp	0.294*	0.262^*	0.325^**	0.324^**	0.331^**	0.322^**
pexp	(0.161)	(0.159)	(0.149)	(0.156)	(0.158)	(0.158)
age	0.013	0.034	0.031	0.037	0.013	0.012
age	(0.054)	(0.056)	(0.050)	(0.211)	(0.054)	(0.055)
dis	-0.220^***	-0.210^***	-0.198^**	-0.218	-0.192^***	-0.193^**
dis	(0.077)	(0.075)	(0.079)	(0.177)	(0.073)	(0.076)
Constant	-0.188	-0.148	-0.213^*	-0.120	-0.204^*	-0.204^*
Constant	(0.124)	(0.110)	(0.120)	(0.670)	(0.119)	(0.119)
空间效应	0.043^**	0.045^*	0.016^***	0.021	0.0004^**	0.048^***
空间效应	(0.017)	(0.025)	(0.004)	(0.109)	(0.0002)	(0.003)
Hausman检验	41.93^***	54.97^***	23.44^***	39.13^***	53.62^***	20.96^***
LM检验	LM统计量
no lag	9.283^***		13.068^***		3.250^*	18.679^***
robust no lag	6.467^***		42.797^***		4.690^**	1159.30^***
no error	3.419^*		0.012		0.001	1.696
robust no error	0.602		29.741^***		2.441	1142.31^***

变量	东部东北部	中西部	关注度较高水平	关注度较低水平
变量	(1)	(2)	(3)	(4)
pgdp	0.048	-0.015	-0.100	0.204^**
pgdp	(0.098)	(0.122)	(0.092)	(0.080)
num	0.127^**	0.339	0.095^*	0.341
num	(0.058)	(0.361)	(0.048)	(0.304)
sga	-0.068	0.680^***	0.209	0.031
sga	(0.129)	(0.229)	(0.179)	(0.119)
ict	0.170	0.398^***	0.466^***	0.061
ict	(0.174)	(0.145)	(0.149)	(0.107)
rcl	-0.065	0.493^***	0.607^**	0.382^***
rcl	(0.409)	(0.126)	(0.266)	(0.128)
pexp	0.281	0.098	1.009^***	0.278^*
pexp	(0.251)	(0.216)	(0.280)	(0.156)
age	-0.016	-0.079	-0.033	-0.067
age	(0.084)	(0.106)	(0.070)	(0.065)
dis	-0.196	0.022	-0.200^**	-0.135
dis	(0.123)	(0.126)	(0.082)	(0.151)
Constant	0.075	-0.242	0.557^*	-0.063
Constant	(0.428)	(0.162)	(0.301)	(0.151)
R²	0.950	0.953	0.948	0.935
Obs	117	162	144	135

变量	东部东北部地区（对照组）对中部西部地区（基准组）
	禀赋差异(Explained)		系数差异(Unexplained)
	(1)结果	(2)占比(%)	(3)结果	(4)占比(%)
pgdp	0.0014	0.880	0.015	9.318
pgdp	(0.020)	0.880	(0.027)	9.318
num	0.007^*	4.192	-0.002	-1.418
num	(0.004)	4.192	(0.003)	-1.418
sga	0.096^***	60.993	0.121^***	76.940
sga	(0.035)	60.993	(0.040)	76.940
ict	0.081^*	51.022	-0.048	-30.495
ict	(0.042)	51.022	(0.077)	-30.495
rcl	0.012	7.506	-0.513	-324.994
rcl	(0.043)	7.506	(0.353)	-324.994
pexp	0.089^*	56.393	0.044	27.840
pexp	(0.052)	56.393	(0.052)	27.840
age	-0.0007	-0.459	0.032	20.262
age	(0.007)	-0.459	(0.055)	20.262
dis	-0.028^*	-17.929	-0.077	-48.515
dis	(0.017)	-17.929	(0.064)	-48.515
Constant	/	/	0.069	43.611
Constant	/	/	(0.451)	43.611
双向固定效应	YES	132.868	YES	31.985
总体解释量Total	0.466^***	295.465	-0.309^***	-195.465
总体解释量Total	(0.070)	295.465	(0.069)	-195.465
两地区DPD差异	0.436^***	61.048	0.278^***	38.952
两地区DPD差异	(0.022)	61.048	(0.014)	38.952

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[2]	CAI Wen-wu, LU Jing, ZHAO Yu-yang. The Asymmetric Driving Effect of Investors’ Attention Frequency on Trading under Different Market Situations——An Empirical Analysis Based on Data on Users’ Behavior in Securities Service Mobile Applications [J]. Chinese Journal of Management Science, 2023, 31(4): 11-25.
[3]	GAO Ming, LIU Chao, TANG Jia-fu, SUN Si-jing, ZOU Guang-yu. Lifetime Decay Prediction of Fuel Cell Based on Attention Neural Network [J]. Chinese Journal of Management Science, 2023, 31(3): 155-166.
[4]	Shi-feng LIU,Lai-song KANG,Da-qing GONG. An Event POI Recommendation System for Groups in EBSN [J]. Chinese Journal of Management Science, 2023, 31(12): 301-310.
[5]	Yong-ji ZHANG,Tian-xiong LI,Zhi SU,Qiong HUANG. Fund Size, Investor Attention and Fund Performance Persistence [J]. Chinese Journal of Management Science, 2023, 31(12): 57-68.
[6]	Xiao-lei WANG,Ren MU,Li ZHOU,Jin-quan DONG. An Evaluation Method of Input-output Performance of Decision Making Units Considering Efficiency and Effect [J]. Chinese Journal of Management Science, 2023, 31(10): 128-135.
[7]	ZHANG Yan-hui, GAO Yun-fan. The Effect of Online Reputation on Consumer Attention: Based on the Empirical Analysis of DianPing.com [J]. Chinese Journal of Management Science, 2022, 30(8): 277-286.
[8]	QU Hui, SHEN Wei. Investor Attention and Covariance Forecasting in China’s Stock Markets——A Study Based on the MHAR Type Models [J]. Chinese Journal of Management Science, 2022, 30(7): 9-19.
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变量	DPD较高地区(对照组)对DPD较低地区(基准组)
	禀赋差异(Explained)		系数差异(Unexplained)
	(1)结果	(2)占比(%)	(3)结果	(4)占比(%)
pgdp	-0.010	-3.172	0.057^**	18.090
pgdp	(0.012)	-3.172	(0.025)	18.090
num	0.006^*	2.032	-0.002	-0.482
num	(0.003)	2.032	(0.002)	-0.482
sga	0.015^*	4.867	0.063	20.178
sga	(0.009)	4.867	(0.039)	20.178
ict	0.034^**	10.960	0.088	27.949
ict	(0.017)	10.960	(0.056)	27.949
rcl	0.052^**	16.638	0.853^***	272.504
rcl	(0.026)	16.638	(0.265)	272.504
pexp	0.059^**	18.853	0.199^***	63.537
pexp	(0.026)	18.853	(0.060)	63.537
age	-0.013	-4.158	-0.0006	-0.193
age	(0.011)	-4.158	(0.034)	-0.193
dis	-0.053^*	-16.956	-0.031	-9.779
dis	(0.029)	-16.956	(0.085)	-9.779
Constant	/	/	0.770^**	245.847
Constant	/	/	(0.368)	245.847
双向固定效应	YES	4.399	YES	-571.115
总体解释量Total	0.105^**	33.464	0.208^***	66.536
总体解释量Total	(0.047)	33.464	(0.0463)	66.536
高低组DPD 差异	0.496^***	73.069	0.183^***	26.931
高低组DPD 差异	(0.015)	73.069	(0.010)	26.931