医疗保险对流动慢性病患者卫生服务利用的影响以及健康风险感知在其中的调节作用

doi:10.19723/j.issn.1671-167X.2026.01.021

摘要/Abstract

摘要：

目的: 流动人口中的慢性病患病率随社会老龄化的加剧不断增长，然而此类慢性病患者因其流动特性和异地就医的医疗保险(简称医保)报销问题，卫生服务利用不足，导致其健康状况较差。本研究从行为经济学角度分析医保对流动慢性病患者卫生服务利用的影响以及健康风险感知在其中的调节作用，以期对实现我国“健康中国”战略、“防治慢性病中长期规划”提供帮助。方法: 根据2017年中国流动人口动态监测调查数据(China Migrants Dynamic Survey，CMDS)，共获得流动慢性病患者有效样本5 640人。采用描述统计方法描述样本基本特征，利用二元Logistic回归模型分析流动慢性病患者不同类型的医保参与率情况与医疗服务利用之间的关联，并且分析健康风险感知在其中的调节作用。结果: 流动慢性病患者流入地的医保参与率为21.9%，仍有12.5%的流动慢性病患者在身体不适时未及时利用卫生服务。只有在流入地参保才能够显著提高流动慢性病患者在流入地的卫生服务利用，健康风险感知能够显著增强公费医保、城镇职工医保与流动慢性病患者卫生服务利用的关联。结论: 参与流入地医保和健康风险感知能够促进流动慢性病患者利用卫生服务，应去除医保制度的区隔阻碍，提高医保统筹层次，同时应加强流动人口慢性病防治的宣传教育，提高其健康风险感知的水平。此外，为提高卫生服务利用率，还应进一步合理配置医疗服务资源，对健康风险感知较低、卫生服务可及性差的跨省流动男性慢性病患者给予更多的关注。

关键词: 人口动力学, 慢性病, 医疗保险, 卫生服务, 健康风险感知

Abstract:

Objective: The prevalence rate of chronic diseases among the floating population is increasing with the acceleration of the aging society. However, factors such as the characteristics of mobility, and the problem of medical insurance reimbursement in different places lead to underutilization of health service and poor health. Therefore, this study analyzed the impact of medical insurance on health service utilization of floating chronic disease patients and the moderating role of health risk perception from the perspective of behavioral economics, such work could shed light on the realization of "Healthy China" strategy and "the Medium- and Long-term Planning for the Prevention and Treatment of Chronic Diseases". Methods: We selected the data of China Migrants Dynamic Survey (CMDS) in 2017. A total of 5 640 migrants with chronic diseases were selected. Descriptive statistics was used to describe the basic characteristics of the sample, and binary Logistic regression model was used to analyze the relationship between the different types of medical insurance participation rate and the utilization of medical services, and the moderating role of health risk perception in this relationship. Results: The participation rate of medical insurance was 21.9%. There was still 12.5% migrants with chronic diseases who did not utilize health services when they felt unwell. Only having medical insurance in inflowing area could increase the utilization of health services among migrants with chronic diseases. Health risk perception significantly positively moderated the association of medical insurance for urban employee or public medical insurance with health service utilization among migrants with chronic diseases. Conclusion: The medical insurance and health risk perception can promote the utilization of health services among migrants with chronic diseases. The government should break the barrier of the medical insurance system, improve the level of medical insurance, and strengthen the propaganda and education of chronic diseases prevention and treatment, so as to improve the level of health risk perception of migrants with chronic diseases. In addition, it is necessary to rationally allocate health service supplies and focus more on trans-provincial floating male patients with chronic diseases, who have low health risk perception and limited access to medical services, to improve health service utilization.

Key words: Population dynamics, Chronic disease, Medical insurance, Health services, Health risk perception

中图分类号:

R197.1

黄宁, 刘笑晗, 郭静. 医疗保险对流动慢性病患者卫生服务利用的影响以及健康风险感知在其中的调节作用[J]. 北京大学学报（医学版）, 2026, 58(1): 160-168.

Ning HUANG, Xiaohan LIU, Jing GUO. Impact of medical insurance on public health services utilization of floating population with chronic disease and the moderating role of health risk perception[J]. Journal of Peking University (Health Sciences), 2026, 58(1): 160-168.

图/表 5

表1

表2

医保与流动慢性病患者卫生服务利用的二元Logistic回归"

Items	Model 1		Model 2
Items	OR	95%CI	OR	95%CI
Age	0.997	(-0.016, 0.010)	0.999	(-0.014, 0.012)
Male	0.781^#	(-0.527, 0.034)	0.783^#	(-0.527, 0.038)
Hukou (Reference: non-rural hukou)	1.004	(-0.410, 0.417)	1.092	(-0.254, 0.430)
Married	0.998	(-0.516, 0.513)	1.017	(-0.495, 0.529)
Education (Reference: primary school and below)
Middle school	0.777	(-0.580, 0.075)	0.773	(-0.579, 0.065)
High school	0.940	(-0.504, 0.380)	0.887	(-0.555, 0.314)
University and above	0.485^*	(-1.315, -0.133)	0.451^**	(-1.392, -0.201)
Family income	0.933	(-0.204, 0.066)	0.933	(-0.203, 0.063)
Migrant scope (Reference: trans-provincial migration)
Intercity mobility	1.555^**	(0.121, 0.762)	1.506^*	(0.095, 0.725)
Inter county mobility	1.197	(-0.150, 0.509)	1.217	(-0.132, 0.524)
Migrant time	1.006	(-0.012, 0.024)	1.004	(-0.014, 0.022)
Health services accessibility (Reference: ≤15 min)
16-30 min	0.765	(-0.599, 0.064)	0.756^#	(-0.606, 0.047)
>30 min, ≤60 min	0.831	(-0.836, 0.467)	0.824	(-0.839, 0.451)
>60 min	0.468	(-1.826, 0.306)	0.469	(-1.827, 0.312)
Health risk perception (Reference: low)	1.657^**	(0.148, 0.862)	1.662^**	(0.151, 0.865)
Health insurance participation rates in all regions
New farming medical insurance	1.455	(-0.124, 0.874)
Medical insurance for urban and rural residents	1.542	(-0.317, 1.183)
Medical insurance for urban residents	1.226	(-0.492, 0.898)
Medical insurance for urban employees	1.111	(-0.366, 0.577)
Public medical insurance	1.754	(-0.393, 1.517)
Health insurance participation rate in the inflow area
New farming medical insurance in the inflow area			2.166^*	(0.176, 1.370)
Medical insurance for urban and rural residents in the inflow area			1.811	(-0.166, 1.354)
Medical insurance for urban residents in the inflow area			0.787	(-1.230, 0.750)
Medical insurance for urban employees in the inflow area			1.153	(-0.239, 0.524)
Public medical insurance in the inflow area			17.723^***	(1.164, 4.586)

表2

表3

表4

表5

不同健康风险感知的流动性慢性病患者医保与卫生服务利用关联的二元Logistic回归"

Items	Model 1-high HRP		Model 2-low HRP		Model 3-high HRP		Model 4-low HRP
Items	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Age	0.970^*	(−0.060, −0.000)	1.000	(−0.014, 0.014)	0.978	(−0.053, 0.008)	1.002	(−0.012, 0.016)
Male	0.872	(−0.716, 0.442)	0.765#	(−0.574, 0.038)	0.858	(−0.727, 0.419)	0.765#	(−0.579, 0.044)
Hukou	0.908	(−1.068, 0.876)	1.075	(−0.365, 0.510)	0.632	(−1.333, 0.417)	1.195	(−0.185, 0.542)
Married	1.320	(−0.546, 1.101)	0.869	(−0.729, 0.447)	1.458	(−0.446, 1.200)	0.886	(−0.710, 0.469)
Education (Reference: primary school and below)
Middle school	0.692	(−0.988, 0.252)	0.790	(−0.604, 0.132)	0.736	(−0.928, 0.316)	0.777	(−0.615, 0.111)
High school	0.611	(−1.646, 0.661)	1.004	(−0.476, 0.484)	0.789	(−1.440, 0.968)	0.918	(−0.561, 0.390)
University and above	0.165^^	(−3.153, −0.445)	0.555#	(−1.216, 0.038)	0.254#	(−2.786, 0.046)	0.495^*	(−1.339, −0.068)
Family income	0.899	(−0.342, 0.128)	0.919	(−0.252, 0.084)	0.921	(−0.302, 0.137)	0.916	(−0.253, 0.077)
Migrant scope (Reference: trans-provincial migration)
Intercity mobility	1.158	(−0.521, 0.814)	1.622^^	(0.126, 0.841)	1.112	(−0.515, 0.727)	1.569^*	(0.101, 0.800)
Inter county mobility	1.016	(−0.802, 0.835)	1.289	(−0.104, 0.612)	1.023	(−0.785, 0.830)	1.311	(−0.084, 0.626)
Migrant time	0.966^*	(−0.069, −0.001)	1.014	(−0.007, 0.034)	0.959^*	(−0.076, −0.009)	1.011	(−0.009, 0.032)
Health services accessibility (Reference: ≤15 min)
16–30 min	1.086	(−0.732, 0.897)	0.737#	(−0.663, 0.052)	1.218	(−0.673, 1.067)	0.728#	(−0.669, 0.034)
>30 min, ≤60 min	2.290	(−0.491, 2.148)	0.602	(−1.263, 0.247)	2.418	(−0.369, 2.134)	0.602	(−1.254, 0.239)
>60 min	255 930.338^^^*	(11.461, 13.445)	0.304#	(−2.398, 0.014)	383 036.363^^^*	(11.785, 13.927)	0.302^*	(−2.389, −0.005)
Health insurance participation rates in all regions
NFAR	1.246	(−1.259, 1.699)	1.432	(−0.166, 0.884)
URRAR	1.430	(−1.259, 1.974)	1.459	(−0.456, 1.212)
URAR	1.285	(−1.092, 1.594)	1.210	(−0.582, 0.963)
UEAR	3.712	(−0.615, 3.238)	0.988	(−0.503, 0.478)
PAR	12.655^*	(0.313, 4.764)	1.490	(−0.571, 1.369)
Health insurance participation rate in the inflow area
NFIA					2.531#	(−0.092, 1.949)	2.178^*	(0.053, 1.504)
URRIA					8.039^*	(0.005, 4.164)	1.601	(−0.324, 1.265)
URIA					1.677	(−0.416, 1.450)	0.745	(−1.393, 0.804)
UEIA					8.483^^	(0.569, 3.707)	1.097	(−0.297, 0.482)
PIA					67 989.343^^^*	(9.588, 12.666)	16.917^^	(1.116, 4.540)

表5

参考文献 22

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Items	Data, $\bar x \pm s$ or n (%)
Age/years	51.26 (12.0)
Gender (Reference: male)
Female	2 400 (42.6)
Male	3 240 (57.4)
Hukou (Reference: non-rural hukou)
Non-rural hukou	1 489 (26.4)
Rural hukou	4 151 (73.6)
Marital status (Reference: unmarried)
Unmarried	522 (9.3)
Married	5 118 (90.7)
Education (Reference: primary school and below)
Primary school and below	2 167 (38.4)
Middle school	2 207 (39.1)
High school	858 (15.2)
University and above	408 (7.2)
Family income/yuan	6 510.70±6 173.14
Migrant scope (Reference: trans-provincial migration)
Trans-provincial migration	2 661 (47.2)
Intercity mobility	1 850 (32.8)
Inter county mobility	1 129 (20.0)
Migrant time/years	9.38±7.75
Health services accessibility (Reference: ≤15 min)
≤15 min	4 489 (79.6)
16-30 min	987 (17.5)
>30 min, ≤60 min	135 (2.4)
>60 min	29 (0.5)
Health risk perception (Reference: low)
Low	4 295 (76.2)
High	1 345 (23.8)
Health services utilization	4 932 (87.5)
Health insurance participation in all regions	5 292 (93.8)
New farming medical insurance	3 398 (60.2)
Medical insurance for urban and rural residents	347 (6.2)
Medical insurance for urban residents	522 (9.3)
Medical insurance for urban employees	1 188 (21.1)
Public medical insurance	179 (3.2)
Health insurance participation in the inflow area	1 236 (21.9)
New farming medical insurance in the inflow area	220 (3.9)
Medical insurance for urban and rural residents in the inflow area	113 (2.0)
Medical insurance for urban residents in the inflow area	278 (4.9)
Medical insurance for urban employees in the inflow area	628 (11.1)
Public medical insurance in the inflow area	43 (0.8)

Covariates	Model 1		Model 2		Model 3		Model 4		Model 5
Covariates	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
HRP	2.115 ^^	(0.180, 1.318)	1.665 ^^	(0.140, 0.880)	1.704 ^^	(0.154, 0.913)	1.316	(-0.106, 0.655)	1.586 ^*	(0.100, 0.821)
NFAR	1.521	(-0.090, 0.930)	1.455	(-0.123, 0.874)	1.455	(-0.124, 0.874)	1.437	(-0.136, 0.861)	1.451	(-0.127, 0.872)
URRAR	1.545	(-0.321, 1.191)	1.564	(-0.370, 1.265)	1.542	(-0.318, 1.184)	1.514	(-0.338, 1.168)	1.536	(-0.322, 1.181)
URAR	1.233	(-0.492, 0.911)	1.226	(-0.491, 0.899)	1.288	(-0.499, 1.005)	1.211	(-0.499, 0.881)	1.222	(-0.493, 0.895)
UEAR	1.117	(-0.366, 0.586)	1.112	(-0.366, 0.578)	1.115	(-0.363, 0.581)	1.001	(-0.475, 0.477)	1.106	(-0.371, 0.573)
PAR	1.738	(-0.403, 1.508)	1.754	(-0.393, 1.517)	1.752	(-0.394, 1.515)	1.680	(-0.436, 1.474)	1.461	(-0.596, 1.354)
NFAR × HRP	0.669	(-1.130, 0.325)
URRAR × HRP			0.899	(-1.479, 1.267)
URAR × HRP						0.660	(-1.428, 0.597)
UEAR × HRP							4.074 ^*	(0.169, 2.640)
PAR × HRP									13.810^*	(0.342, 4.909)

Covariates	Model 1		Model 2		Model 3		Model 4		Model 5
Covariates	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
HRP	1.668 ^^	(0.150, 0.873)	1.651 ^^	(0.143, 0.860)	1.649 ^^	(0.138, 0.863)	1.548 ^*	(0.072, 0.803)	1.662 ^^	(0.151, 0.865)
NFIA	2.296 ^*	(0.117, 1.546)	2.167 ^*	(0.176, 1.371)	2.169 ^*	(0.177, 1.372)	2.180 ^*	(0.182, 1.377)	2.166 ^*	(0.176, 1.370)
URRIA	1.811	(-0.166, 1.354)	1.657	(-0.287, 1.297)	1.811	(-0.166, 1.354)	1.808	(-0.167, 1.352)	1.811	(-0.166, 1.354)
URIA	0.787	(-1.230, 0.750)	0.787	(-1.230, 0.750)	0.764	(-1.356, 0.816)	0.785	(-1.231, 0.746)	0.787	(-1.230, 0.750)
UEIA	1.154	(-0.239, 0.525)	1.153	(-0.239, 0.524)	1.151	(-0.241, 0.523)	1.095	(-0.298, 0.481)	1.153	(-0.239, 0.524)
PIA	17.721 ^^^*	(1.164, 4.586)	17.720 ^^^*	(1.164, 4.586)	17.710 ^^^*	(1.163, 4.585)	17.863 ^^	(1.164, 4.601)	17.056 ^^	(1.116, 4.557)
NFIA × HRP	0.783	(-1.476, 0.986)
URRIA × HRP			4.374	(-0.716, 3.667)
URIA × HRP					1.384	(-1.099, 1.749)
UEIA × HRP							9.069 ^^	(0.610, 3.799)
PIA × HRP									6.338.166 ^^^*	(6.824, 10.684)

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