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

Abstract

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

CLC Number:

R197.1

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.

Figures/Tables 5

Table 1

Table 2

The binary Logistic regression for the relationship between health insurance and the utilization of health services of migrants with chronic diseases"

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)

Table 2

Table 3

Table 4

Table 5

The binary Logistic regression for the relationship between health insurance and the utilization of health services of migrants with chronic diseases and different levels of HRP"

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)

Table 5

References 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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Impact of medical insurance on public health services utilization of floating population with chronic disease and the moderating role of health risk perception

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