Study setting and period
The study setting is China in 2014, with a fee-for-service payment system in health system. Analysis was conducted from May 2017 to December 2017.
Ethics approval
The study was approved by the Health Science Center Ethics Committee at Xi’an Jiaotong University, Shaanxi, China (approval number: 2015-644).
Study design and data source
The data was drawn from the China Labor-force Dynamics Survey (CLDS) conducted in 2014. CLDS is an open-access database and the first national longitudinal social survey targeted at the labor force in China, covering a series of topics, such as demographic characteristics, family, education, employment, work history, income, migration, and health (http://css.sysu.edu.cn/) [28,29]. A multistage stratified cluster random sampling method was used, and the subjects of CLDS are the laborers (all family members aged 15–64) randomly selected from 29 provinces in China. The survey is conducted every two years and has accumulated three waves of data now (2012, 2014, and 2016). All investigators were trained before investigation and were monitored during the investigation. Computer-assisted personal interviewing (CAPI) technology was adopted to control data quality. The study was performed in 2017 when we used the available 2014 wave for analysis, in which more than 800 investigators collected 401 village questionnaires, 14214 family questionnaires and 23594 individual questionnaires.
Occupation information of each family member was collected in CLDS, and the occupation was classified using code in the fifth National Census. By following previous studies and enlarging the sample size, we included all healthcare professionals for our analysis and defined affiliated individuals as there is any family member is healthcare professional (Appendix1). Not affiliated individuals were defined as no family member is healthcare professional. Finally, we identified 806 individuals affiliated with healthcare professionals and 22788 individuals not affiliated with healthcare professionals for analysis.
Variables
The CLDS collected information on outpatient use in the two weeks preceding the survey and inpatient use in one year preceding the survey to measure heath care utilization to avoid the recall bias in retrospective investigation [30]. Therefore, we generated four outcome variables, outpatient rate (0-No, 1-Yes), outpatient expenditure (continuous variable measured by CNY), inpatient rate (0-No, 1-Yes), and inpatient expenditure (continuous variable measured by CNY), for analysis.
A series of socio-demographic variables that might be associated with health care utilization was considered for inclusion in the matching. The variables were chosen based on a literature review and data availability (see detailed definition in Table 1) [4,8–10]. We have a set of general variables including a health status indicator (self-reported question, 1-Healthy, 0-Fair/Unhealthy), coverage of health insurance (1-Yes, 0-No), living in urban or rural areas (1-Urban, 0-Rural), age (1-Ages equal and over 60, 0-Aged less 60 ), gender (1-Male, 0-Female), educational attainment (0-Primary school, 1-Middle school, 2-High school and above), access to healthcare (log of time to the nearest medical facilities) and economic status (log of household consumption per capita) in the matching for health care utilization. In addition, we have outpatient hospital tier (0-Primary, 1-Non-primary), inpatient hospital tier (0-Primary, 1-Secondary, 2-Tertiary) and inpatient reason (0-Else, 1-Disease, 2-Rehabilitation, 3-Fertility) for expenditure analysis.
Analysis and Interpretation
We employed the coarsened exact matching (CEM) to better balance distributions of the covariates between the comparison groups and thereby reduce biases [31–34]. A key property of CEM, comparing with propensity score matching (PSM), is that CEM fixes the maximum imbalance through an ex ante choice specified by the user, i.e., the user decides how the observed characteristics are to be coarsened. The user does not need to further conduct balance checking or restrict data to common support as required by PSM [31–35]. The matching approach helped to identify the counterparts for patients affiliated with healthcare professionals, based upon the observable pre-treatment characteristics. The general covariates were included in matching for health care utilization and we further included the hospital tier in the matching for per-outpatient expenditure and the hospital tier and inpatient reason in the matching for yearly inpatient expenditure. Overall, we carried out three coarsened exact matching processes in the study.
After the matching, 7722 patients not affiliated with healthcare professionals and 677 patients affiliated were identified for further analysis in health care utilization, 387 patients not affiliated with healthcare professionals and 32 patients affiliated were identified for further analysis in per-outpatient expenditure, and 195 patients not affiliated with healthcare professionals and 31 patients affiliated were identified for further analysis in yearly inpatient expenditure. The balance check (Appendix 2) is reported to confirm that there is no statistical significance between the two groups.
Theoretically, with everything else equal, patients affiliated with healthcare professionals may use less healthcare and incur lower healthcare costs than patients not affiliated due to more information or higher health literacy. The difference in outcomes between the matched groups were regarded as supplied-induced demand and were accessed using 2-tailed t-tests and a significance threshold of P < 0.05. Furthermore, we checked the robustness of our results using weighted regression analysis. All analyses were performed in Stata version 13.0 (Stata Corp LP, College Station, Texas, USA).