Data source

We collected the data from Wenzhou in 2016. Wenzhou was chosen because it was a pilot city to the implement of the HMS [16]. This status attached importance to the leverage of medical insurance in HMS. In addition, medical insurance policy practices in Wenzhou were standard throughout China. The copayment rate was the proportion that patients must pay after receiving health care services. The Urban Resident Basic Medical Insurance (URBMI) policy from Wenzhou in 2016 established a copayment rate for outpatient services of 50% at PHC institutions, 60% at secondary, and 75% at tertiary hospitals. For inpatient services, the PHC institution copayment rate was 10%. It was 20% at secondary and 25% at tertiary hospitals.

This study used a multistage stratified cluster random sampling method. First, four regions were selected from Wenzhou in the sample areas based on their levels of economic development: Ouhai, Ruian, Yongjia, and Taishun. Next as cross-sectional survey samples, four representative streets/towns were randomly selected from each region, and two committees/villages were randomly selected from each street/town. Then households in each committee/village were randomly selected by a table of random numbers from sampling distance. Finally, we sampled 30 households per community for our investigation.

The inclusion criteria for respondents were (1) individuals over the age of 15, (2) individuals who knew the details of the survey content and agreed to participate, and (3) individuals who were able to express their ideas clearly and voluntarily. The exclusion criteria for respondents were (1) individuals with visual impairment, and (2) individuals with delirium, dementia, and other mental disorders, and those with no interest in participating.

Face-to-face interviews were conducted to gather the data based on the multistage stratified cluster random sampling method. The survey questionnaire was divided into two dimensions: basic characteristics and the influence of the multitiered copayment system on healthcare-seeking behavior in current and hypothetical situations. Interviews were conducted with all family members in the selected households. (Following strategies of previous studies [17, 18], individuals over 15 years old who could understand the questionnaire and respond were chosen to guarantee survey reliability.) Finally, 960 households, 10 streets, and six towns were selected from the four regions for our sample. We distributed a total of 1,854 questionnaires, received 1,831 valid responses and deleted two missing values in this study. All data collected was cleaned and analyzed via EpiData and SPSS with double-checking.

Dependent variables

The dependent variables included whether offering the multitiered copayment system would influence the public’s selection of PHC institutions in two situations, including 1) the current situation of the large gap in the quality of healthcare services and 2) the hypothetical situation of a reduced gap in the quality of healthcare services in the future.

The following questions were presented in the questionnaire: Does the current offering of the multitiered copayment system affect your selection of PHC institutions? Moreover, under the hypothetical situation of a reduced gap in the quality of healthcare services, will the offer of the multitiered copayment system still influence your selection of PHC institutions? The alternative responses were “have effect,” “no effect,” and “uncertainty.”

Explanatory variables

Independent variables

The Anderson Model describes healthcare service use in behavioral terms [19]. It is a fully verified and recognized theoretical framework that aims to understand the determinants of healthcare service utilization [20]. The model emphasizes contextual and individual determinants [19]. According to the Anderson Model, independent variables were examined across three dimensions: predisposing, enabling, and need variables. Each dimension has different effects on access to healthcare services.

Predisposing variables: represent whether people’s healthcare needs and social status affect their healthcare-seeking behavior [19, 21, 22]. The predisposing variables in this study include age, education, marital status, and employment status.

Enabling variables: represent financial and social factors that affect people’s healthcare-seeking behavior [19]. The enabling variables in this paper included social support [23, 24], types of insurance [17, 23,24,25], distance to a medical care facility [24, 25], and household income [24, 25]. Social support was assessed by asking respondents if they could receive help from others (i.e., family, friends, colleagues, neighbors) when they needed it. There were three possible responses: “absolutely,” “occasionally,” and “not at all.” Based on participants’ responses, we classified their social support level as “good,” “medium,” or “poor.” Household income was reported based on 2015 values. As shown in Table 2, we distinguished four regions (Ouhai, Ruian, Taishun, Yongjia), and collapsed the data into quartiles for analysis (Q1/4 = “poor,” Q2/4 = “medium,” Q3/4 = “good”).

Table 2 Respondents’ household income in 2015 by region and quartile (unit: yuan)

Needs variables: represent people’s perception of their general health, functional status, and how the severity of their diseases affects their healthcare-seeking behavior [19]. The needs variables in this study include types of chronic diseases [17, 24] and self-rated health status [17, 23, 24]. The self-rated health status score was the respondents’ assessment of their health status on the day of the survey. The score ranged from 0 to 100. We classified total scores into quartiles [26]. (good: 91–100, medium: 70–90, poor: 0–69).

Time variable

It is generally accepted that a large gap exists in healthcare services between the higher-tier hospitals and PHC institutions in China. Thus, we defined the existing gaps in the quality of healthcare services between the higher-tier hospitals and PHC institutions as the pre-change phase. The hypothetical situation of a reduced gap in the quality of healthcare services in the future between the higher-tier hospitals and PHC institutions was defined as the post-change phase.

Statistical analysis

This study used the hypothetical quality improvement scenario to elicit people’s hypothetical behaviors. We used SPSS version 26 to conduct the analysis. A descriptive statistical analysis was performed first to introduce the effect of the multitiered copayment system pre- and post-change. Next, a chi-square analysis and Fisher’s exact test were used to examine the differences between variables. The variables associated with p = 0.2 and below were entered into a multinomial logistic regression model to explore the factors affecting people’s selection of PHC institutions pre- and post-change [27].

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