What this is
- This research examines health service utilization among Chinese rural migrant workers enrolled in the New Cooperative Medical Scheme (NCMS).
- It identifies socio-economic factors contributing to inequalities in access to health services.
- Using a multilevel regression approach, the study analyzes data from a nationally representative sample to quantify disparities.
Essence
- Rural migrant workers with NCMS face significant inequalities in health service utilization, with lower probabilities for outpatient (6.32%) and inpatient (5.9%) services compared to the general population.
Key takeaways
- Health service utilization among rural migrant workers with NCMS is inadequate, with outpatient and inpatient probabilities at 6.32% and 5.9%, respectively. These rates are lower than those for the general population, indicating systemic barriers.
- Socio-economic factors such as gender, marital status, and economic level significantly influence health service utilization, highlighting that poorer individuals face greater barriers to accessing care.
- The study reveals a pro-poor inequality in health service utilization, suggesting that efforts to improve access should focus on the most disadvantaged groups among rural migrant workers.
Caveats
- The cross-sectional design limits the ability to draw causal inferences regarding the factors influencing health service utilization among rural migrant workers.
- The study's sample size may not fully represent the current population of rural migrant workers, potentially affecting the generalizability of the findings.
AI simplified
Background
Chinese rural migrant workers (also called “nongmingong”) have made a great contribution to the rapid urbanization and industrialization in China. However, the socially marginalized living condition in their urban residence caused by their dual identities, rural residents defined by the Chinese household registration system (hukou) working in urban areas, is hindering them to use public health services, which are more accessible for local urban residents. New Rural Cooperative Medical Scheme (NCMS), launched in 2003, has remarkably facilitated Chinese residents’ utilization of health services with a range of approaches, such as increasing the reimbursement ratio and upgrading the facility in primary medical institutions. However, rural migrant workers with NCMS still cannot be effectually protected against economic risks of diseases. The State Council called for the integration of the basic urban and rural medical insurance system in January 2016, but the newly launched Urban–Rural Resident Basic Medical Insurance (URBMI) has not been implemented thoroughly in China after its introduction. Therefore, it is meaningful to understand how to guide rural residents with URBMI or covered by the NCMS to seek medical treatment.
Previous studies have investigated the inequality in health service utilization, specifically focusing on NCMS. Che et al. [1] comparatively studied the inpatient situation in the NCMS pilot and non-pilot counties, and their findings showed that NCMS could eliminate the inequality of inpatient and inpatient expenses for rural residents but only to a limited extent. Han et al. [2] discovered that rural residents with lower income were more disadvantaged in using health services since the implementation of NCMS. Fang et al. [3] found that rural residents with higher income experienced a higher participation rate of NCMS, and NCMS promoted equality in health service utilization. Guo et al. [4] found that NCMS played a certain role in improving the incidence of compensation, but its effect was still limited in eliminating the economic burden of the rural residents. Li et al. [5] highlighted that the inequalities in the total cost and out-of-pocket cost of both outpatient and inpatient were evident among rural migrant workers with NCMS, and health service needs of the rural migrant workers with NCMS should be fully considered. With the goal of reducing inequality initiated by UN, the Chinese government focused on the healthcare service inequality by carrying out policies regarding basic public services. However, systematic research on the inequality in health service utilization of Chinese rural migrants with NCMS is far from sufficient.
There is abundant research on health service utilization and the impact factors of Chinese rural migrant workers’ health-seeking behaviors. For example, Peng et al. [6] studied the influence of socio-demographic characteristics on rural migrant workers’ decision to seek health care services when they fell ill and found that household monthly income per capita and daily working hours were directly proportional to their medical visiting rate. In addition, their results showed that health-seeking behaviors of rural migrants were significantly associated with their insurance coverage. Zhao et al. [7] found that the outpatient rate of middle-aged rural migrant workers in four weeks was 13.7% and its determinants included gender, marital status, income level, household size, the place of insurance enrollment, and self-assessed health (SAH). NCMS in China has obtained remarkable achievements through many difficulties and many rural migrant workers have been benefited. However, very little literature has explored whether the expected equality has been achieved and to what extent the inequality of health service utilization exists among rural migrant workers with NCMS.
This study involves three dimensions of Andersen model’s original version: predisposition, factors that enable or impede, and need for care. The Andersen model is a useful theoretical analysis framework with a wide range of variables to explain individual’s health service utilization [8–11]. The Andersen model (2013 Version) emphasizes the dynamics of and displays a conceptual model of health services use, namely, how contextual characteristics, individual characteristics, health behavior, and the health outcomes affect health service utilization. Some studies [12] have adopted the original Andersen model to explore the influencing factors on the health services utilization of rural migrants and have found that the current healthcare delivery system was not conducive for rural migrants to seek appropriate health services. However, few empirical studies in China have applied the Andersen Model (2013 Version) regarding its dynamic nature [13]. Most of related studies [6, 7] that have conducted descriptive or regression analysis could not fully display the unequal distribution of health service utilization among rural migrant workers with NCMS. In addition, the existing health services in China cannot satisfy the increasing needs of rural migrant workers, which were often neglected. The reason behind this mismatch was rarely explored. Further investigation on the contributors of inequality in health service utilization among rural migrant workers with NCMS is required. Hence, it is important that the needs of rural migrant workers with NCMS related to health service utilization are better grasped.
This study sought to explore the health service utilization of Chinese rural migrant workers by posing two major questions: 1) What are the factors that influence the health service utilization of rural migrant workers with NCMS? 2) Is there inequality in the health service utilization of rural migrant workers with NCMS? If the inequality exists, to what extent? Our findings can not only facilitate the mobility of rural migrant workers with NCMS, but also provide insights for improving health services to vulnerable groups.
Methods
Data
Screening process of sample in our study)
Measurement
Our study focused on the health service utilization of rural migrant workers participating in NCMS. Two questions in CLDS 2016 were used (originally in Chinese).
Question 1: Have you visited the clinic at least once in two weeks?
Question 2: Have you been admitted to the hospital during the past 12 months when you were sick or injured?
In this study, we adopted dummy variables with the value 1 if the respondent answered “yes”, and 0 if “no”.
Predictors
To analyze the factors associated with health services utilization, we selected the predictors based on the Andersen Model (2013 Version). Our study only concerned how health services utilization is determined by four dynamics. In the Chinese socio-cultural context, we simplified the analysis framework considering the availability of data and the purposes of our study. We set parameters for the following variables of the conceptual framework:Individual characteristics: age group (50 ~ 60, 61 and above), gender (male, female), living arrangement (living with spouse, living without spouse), educational attainment (below primary school, primary school, middle school and above), technical certificate (yes, no), type of industry (professional technician/clerical staff, service staff, manufacturing and construction, freelancer), type of employer (party/government institutions and state/collective-owned enterprises, private/foreign/joint venture, self-employed and freelancer), migration distance (within the county/district, cross the county/district), working hour (moderate labor, excessive labor [5]), income quintiles (poorest, poorer, middle, richer, richest), injury insurance (yes, no), number of friends (≤ 5, 6 ~ 10, ≥ 11), SAH (good, fair, poor).health behavior: smoking (yes, no), alcohol use (yes, no), regular exercise per month (yes, no).health outcome: the sense of fairness (unhappy, fair, happy).contextual characteristics: the proportion of ethnic minorities (per capaita in the community) service quality index of the community, region (east, central, west), city level which reflecting the political rule, socio-economic development and the policy-oriented factors in China (below sub-provincial city, sub-provincial city and above), service quality index of the city, health index of the community, the number of medical institutions per 10,000 people in the community, the number of medical institutions per 10,000 people in the city, the number of hospital beds per 10,000 people in the city, and the number of doctors per 10,000 people in the city.
Multilevel regression approach
We used the nationally representative date in this study, which shown a obvious hierarchical structure. To capture within-group and between-group correlations in calculation, we estimated a series of three-level regression approaches, in which rural migrant workers with NCMS were nested within communities and cities because the data showed a hierarchical structure of “city-community-rural migrant workers with NCMS”. As noticed by Neuhaus et al. [15] and Snijders et al. [16], in a multilevel context, the relationships at the cluster level, measured by the between-cluster effects, can be very different from the relationships at the micro-level, measured by the within-cluster effects. For instance, rural migrant workers with NCMS in the same city or community may have the same city characteristics or community characteristics. Furthermore, due to similar living environment, the differences between rural migrant workers with NCMS living in the same community is less than those living in different communities. Those violates the classical assumption of the independence of error term in a single level regression model and the “mean square deviation” of city-level or community-level. When data are sampled in multi-level, failing to consider the clustering of the observations and ignoring the hierarchical structure of the data can lead to false inferences being drawn from the data.
Intra-class Correlation Coefficient (ICC) is the ratio of the between-group variance to the total variance, representing the degree of variation between groups. The calculation formula of ICC is as follows: 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ICC}=\frac{{\sigma }_{u0}^{2}}{{\sigma }_{u0}^{2}+{\sigma }_{e0}^{2}}$$\end{document} ICC = σ u 0 2 σ u 0 2 σ e 0 2 +
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma}_{u0}^{2}$$\end{document}σu02 presents the between-group variance and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma }_{e0}^{2}$$\end{document}σe02 presents the within-group variance. When ICC is closer to 0, the rural migrant workers with NCMS in the group tend to be independent, which represents that the multilevel model can be simplified to a fixed-effect model; when the ICC is closer to 1, the difference between groups is larger than that within the group. When ICC is significantly larger than 0.059, multilevel regression models should be considered [17]. In addition, decreases in variance and model fit statistics (for example, AIC and BIC) indicate a good performance[18]. When the dependent variable is a binary variable, a linear approximation method in the generalized linear model needs to be used.
On the model establishment, the basic operation steps of multilevel models are as listed below: First, establish a null model, which is also known as an unconditional two-level model, to check the hierarchical structure of the data. ICC can be utilized to judge whether it can be used for analysis the multi-level data. Secondly, include variables representing the fixed effects to expand the null model to observe the significance of high-level explanatory variables. Thirdly, include the explanatory variable in level 1. The random slope of level 1 can be tested to adjust the effect of the level of rural migrant workers with NCMS.
The three-level logistic regression model is expressed as follows: 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{logit}\left(\frac{{P}_{ijk}}{1-{P}_{ijk}}\right)={\beta x}_{ijk}+{\gamma w}_{jk}+{\tau z}_{k}+{\mu }_{jk}+{v}_{k}$$\end{document} logit P ijk 1 - P ijk = + + + + β x ijk γ w jk τ z k μ jk v k
where i, j, and k represent level 3-city, level 2-community, and level 1-rural migrant workers with NCMS. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{ijk}$$\end{document}xijk, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${w}_{jk}$$\end{document}wjk and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${z}_{k}$$\end{document}zk represent the explanatory variables of level 1-rural migrant workers with NCMS, level 2-community, and level 3-city, respectively. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upgamma$$\end{document}γ, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ represent the estimated value of the regression coefficient of the explanatory variable at each level. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mu }_{jk}$$\end{document}μjk and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{k}$$\end{document}vk represent the residuals of level 2-community and level 3-city, respectively.
The three-level regression model is expressed as follows: 3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{ijk}={\beta x}_{ijk}+{\gamma w}_{jk}+{\tau z}_{k}+{\mu }_{jk}+{v}_{k}$$\end{document} y ijk β x ijk γ w jk τ z k μ jk v k = + + + +
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{ijk}$$\end{document}yijk is a continuous dependent variable. i, j, and k represent level 3-city, level 2-community, and level 1-rural migrant workers with NCMS.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{ijk}$$\end{document}xijk,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${w}_{jk}$$\end{document}wjk, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${z}_{k}$$\end{document}zk represent the explanatory variables of level 1-rural migrant workers with NCMS, level 2-community, and level 3-city, respectively. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upgamma$$\end{document}γ, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ represent the estimated value of the regression coefficient of the explanatory variable at each level. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mu }_{jk}$$\end{document}μjk and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{k}$$\end{document}vk represent the residuals of level 2-community and level 3-city, respectively. The three-level regression model in our study addressed the first question.
Concentration index and decomposition
The inequality of health service utilization across socio-economic groups was estimated using a concentration index (CI). The CI is defined as twice the area between the concentration curve and the line of equality. When it takes values between -1 and 1, where a positive value indicates that a variable is more concentrated among richer rural migrant workers with NCMS and a negative value indicates less [19, 20]. The formula for computing the CI is:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{CI}=\frac{2}{\mu } \mathrm{cov }\left({y}_{i},{R}_{i}\right)$$\end{document}CI=2μcovyi,Ri
where CI is the concentration index of health service utilization of rural migrant workers with NCMS,is the health service utilization indicators, μ is the mean of health service utilization, andis the fractional rank in the economic status distribution. The inequalities in two-week outpatient probability and inpatient probability among rural migrant workers with NCMS were measured by CIs. The CIs helped us to measure the degree of inequality in health service utilization of rural migrant workers with NCMS, which addressed the second question. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{i}$$\end{document} y i \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{i}$$\end{document} R i
Decomposition methods can quantify each determinant’s specific contribution to the measured income-related inequality while controlling for other determinants, providing a basis for prioritizing interventions [21, 22]. The decomposition shows how each determinant’s separate contribution to explained income-related inequality can be decomposed into its elasticity and its income-related inequality. That is, each contribution is the product of the sensitivity of health service utilization with respect to that factor and the degree of income-related inequality in that factor. The decomposition of the CI clarified the need of health service utilization, to prepare for a further answer to the third question. As the probability of health service utilization is a dummy variable, a generalized linear model with binomial distribution and identity link was employed. The regression model is as follows:5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{y}={\alpha }^{m}+{\sum }_{j}{\beta }_{j}^{m}{x}_{j}+\varepsilon$$\end{document}y=αm+∑jβjmxj+ε
where y is the health service utilization indicator,is the partial effects (i.e., dy/dxj) of each variable and evaluated at sample means,is the constant term in the regression equation,is the error term. Calculating the CI of Eq. () and the decomposition of the CI could be specified as: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{j}^{m}$$\end{document} β m j \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\alpha }^{m}$$\end{document} α m \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upvarepsilon$$\end{document} ε 2 6 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{CI}={\sum }_{j}({{\beta }_{j}^{m}}\left/ {\mu}\right.){C}_{j}+G{C}_{\varepsilon }$$\end{document} CI G = + ∑ j C j C ε ( ) β m j μ
whereis the mean of the dependent variable,is the concentration index for,is the elasticity ofin health service utilization of rural migrant workers with NCMS, and G is the elasticity ofin health service utilization. The contribution ofis defined as the product of the elasticity ofin health service utilization and the CI of. The large elasticity of health service utilization with respect to these factors is responsible for their large contribution to the CI of health service utilization. The positive contribution of one factor indicated the factor widened the pro-rich (pro-poor) inequality, and vice versa. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document} μ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${C}_{j}$$\end{document} C j \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{j}$$\end{document} x j \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\beta }_{j}^{m}}\!\left/ \!{\mu }\right.$$\end{document} β m j μ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{j}$$\end{document} x j \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upvarepsilon$$\end{document} ε \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{j}$$\end{document} x j \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{j}$$\end{document} x j \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{j}$$\end{document} x j
To clarify the need for health service utilization, the horizontal inequality index (HI) was calculated considering the need for health service utilization among rural migrant workers with NCMS. In this study, HI of health service utilization was measured by deducting the contributions of unavoidable variables (such as gender, age, and SAH) from the overall CI. A positive (negative) HI also indicated the pro-rich (pro-poor) inequality. The results of HI are also conducive to the second question. The formula is as follows: 7 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{HI}=\mathrm{CI}-{\textstyle\sum_j}(\beta_j^mx_{ji}/\mu)C_j$$\end{document} HI CI = - ∑ j ( / ) β j m x ji μ C j
presents the partial regression coefficient of the variable of health service needs.andpresent the mean and the CI of health service need.presents the mean of y. The need variables of health service utilization in our study were age, gender and SAH. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{j}^{m}$$\end{document} β m j \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{j}$$\end{document} x j \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${C}_{j}$$\end{document} C j \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document} μ
All analyses were performed with STATA 15.0 (StataCorp LP., College Station, TX, USA). The probability, a p-value of less than 0.05 was considered statistically significant. We used the “mean replacement method” to deal with missing data as less than 15% of the data were missing for each variable in our analysis.
Results
| Variables | Number/Mean | Percentage/SD |
|---|---|---|
| Outcome Variables | ||
| Two-week outpatient | ||
| Yes† | 210 | 6.32 |
| No | 3112 | 93.68 |
| Inpatient probability | ||
| Yes† | 196 | 5.9 |
| No | 3126 | 94.1 |
| Individual characteristics | ||
| Age group | ||
| 15 ~ 36† | 1303 | 39.22 |
| 36 ~ 50 | 1199 | 36.09 |
| 50 ~ 64 | 820 | 24.68 |
| Gender | ||
| Men† | 1910 | 57.5 |
| Women | 1412 | 42.5 |
| Living arrangement | ||
| Live with spouse† | 500 | 15.05 |
| Live without spouse | 2822 | 84.95 |
| Educational attainment | ||
| Below primary school† | 923 | 27.78 |
| Primary school | 1619 | 48.74 |
| Middle school and above | 780 | 23.48 |
| Technical certificate | ||
| Yes† | 422 | 12.7 |
| No | 2900 | 87.3 |
| Type of industry | ||
| Professional technician/Clerical staff† | 248 | 7.47 |
| Service stuff | 1177 | 35.43 |
| Manufacturing and construction | 1041 | 31.34 |
| Freelancer | 856 | 25.77 |
| Type of unit | ||
| Party/government/state-owned† | 300 | 9.03 |
| Collective enterprises and institutions | 1327 | 39.95 |
| Self-employed and freelance | 1695 | 51.02 |
| Working hours | ||
| Moderate labor† | 1471 | 44.28 |
| Excessive labor | 1851 | 55.72 |
| Place of work | ||
| In the county/district† | 2721 | 81.91 |
| Across the county/district | 601 | 18.09 |
| Income quintiles | 664 | 19.99 |
| Poorest† | 665 | 20.02 |
| Poorer | 664 | 19.99 |
| Middle | 665 | 20.02 |
| Richer | 664 | 19.99 |
| Richest | 664 | 19.99 |
| Injury insurance | ||
| Yes† | ||
| No | 293 | 8.82 |
| number of friends | 3029 | 91.18 |
| < = 5† | ||
| 6 ~ 10 | 1904 | 57.31 |
| > = 11 | 811 | 24.41 |
| SAH | 607 | 18.27 |
| Good† | ||
| Fair | 2285 | 68.78 |
| Poor | 837 | 25.2 |
| health behavior | ||
| Smoke | ||
| Yes† | 1192 | 35.88 |
| No | 2130 | 64.12 |
| Alcohol use | ||
| Yes† | 831 | 25.02 |
| No | 2491 | 74.98 |
| Regular exercise every month | ||
| Yes† | 818 | 24.62 |
| No | 2504 | 75.38 |
| Health outcome | ||
| Sense of happiness | ||
| Unhappy† | ||
| Fair | 215 | 6.47 |
| Happy | 1014 | 30.52 |
| Contextual characteristic | ||
| Proportion of ethnic minorities | 1 | 0.006 |
| Per capita in the community | 1 | 2.02 × 10–4 |
| Region | ||
| East† | 2074 | 62.43 |
| Middle | 639 | 19.24 |
| West | 609 | 18.33 |
| City level | ||
| Sub-provincial city and above | 570 | 17.16 |
| Other | 2752 | 82.84 |
| Number of medical institutions for 10,000 people in the community | 5.6 | 18.48 |
| Number of medical institutions for 10,000 people in the city | 2601.65 | 4597.18 |
| Number of doctors for 10,000 people in the city | 7.48 | 12.24 |
| Number of beds for 10,000 people in the city | 0.7 | 1.33 |
| Health index of the community | 54.34 | 19.24 |
| Service quality index of the community | 83.94 | 44.33 |
| Urban service quality index | -0.05 | 0.64 |
| Intercept | 0.07 | 0.24 |
| Variables | Two-week outpatient service | Inpatient service | |||
|---|---|---|---|---|---|
| OR | SE | OR | SE | ||
| Fixed effects | |||||
| Intercept | -2.877*** | 0.105 | -2.796*** | 0.092 | |
| Random effects | |||||
| Community level variance | 0.35 | 0.141 | 0.065 | 0.124 | |
| Personal level parameter | 1 | 0 | 1 | 0 | |
| Variables | Two-week outpatient service | Inpatient service | |||
|---|---|---|---|---|---|
| OR | SE | OR | SE | ||
| Fixed effects | |||||
| Intercept | -2.873*** | 0.112 | -2.815*** | 0.094 | |
| Random effects | |||||
| City level variance | 0.243 | 0.129 | 4.07 × 10–33 | 2.18 × 10–17 | |
| Community level variance | 0.036 | 0.147 | 0.067 | 0.129 | |
| Personal level parameter | 1 | 0 | 1 | 0 | |
| Variables | Two-week outpatient probability | Inpatient probability | ||
|---|---|---|---|---|
| OR | SE | OR | SE | |
| Individual characteristics | ||||
| Age group | ||||
| 15 ~ 36† | ||||
| 36 ~ 50 | 1.01 | 0.219 | 0.724 | 0.152 |
| 50 ~ 64 | 1.001 | 0.247 | 1.197 | 0.263 |
| Gender | ||||
| Men† | ||||
| Women | 0.865 | 0.182 | 1.753* | 0.398 |
| Living arrangement | ||||
| Live with spouse† | ||||
| Live without spouse | 0.539** | 0.119 | 1.231 | 0.31 |
| Educational attainment | ||||
| Below primary school† | ||||
| Primary school | 1.363 | 0.269 | 1.277 | 0.242 |
| Middle school and above | 1.087 | 0.291 | 1.14 | 0.287 |
| Technical certificate | ||||
| Yes† | ||||
| No | 0.967 | 0.261 | 0.679 | 0.164 |
| Type of industry | ||||
| Professional technician/Clerical staff† | ||||
| Service stuff | 0.862 | 0.305 | 1.404 | 0.553 |
| Manufacturing and construction | 0.72 | 0.267 | 1.721 | 0.698 |
| Freelancer | 0.532 | 0.219 | 1.378 | 0.606 |
| Type of unit | ||||
| Party/government/state-owned† | ||||
| Collective enterprises and institutions | 1.107 | 0.369 | 1.273 | 0.43 |
| Self-employed and freelance | 1.318 | 0.459 | 1.234 | 0.434 |
| Working hours | ||||
| Moderate labor† | ||||
| Excessive labor | 1.091 | 0.18 | 0.92 | 0.145 |
| Place of work | ||||
| In the county/district† | ||||
| Across the county/district | 0.867 | 0.127 | 1.689** | 0.416 |
| Income quintiles | ||||
| Poorest† | ||||
| Poorer | 0.884 | 0.23 | 1.037 | 0.239 |
| Middle | 0.695 | 0.186 | 0.761 | 0.191 |
| Richer | 0.824 | 0.316 | 1.025 | 0.256 |
| Richest | 0.612 | 0.182 | 1.151 | 0.303 |
| Injury insurance | ||||
| Yes† | ||||
| No | 0.616 | 0.169 | 1.289 | 0.405 |
| number of friends | ||||
| < = 5† | ||||
| 6 ~ 10 | 1.061 | 0.22 | 0.79 | 0.156 |
| > = 11 | 0.506 | 0.17 | 1.054 | 0.22 |
| SAH | ||||
| Good† | ||||
| Fair | 3.947*** | 0.889 | 2.462*** | 0.442 |
| Poor | 14.608*** | 3.182 | 8.280*** | 1.872 |
| health behavior | ||||
| Smoke | ||||
| Yes† | ||||
| No | 1.429 | 0.312 | 0.332 | 0.331 |
| Alcohol use | ||||
| Yes† | ||||
| No | 1.168 | 0.264 | 0.363 | 0.292 |
| Regular exercise every month | ||||
| Yes† | ||||
| No | 0.759 | 0.137 | 0.109 | 0.252 |
| Health outcome | ||||
| Sense of happiness | ||||
| Unhappy† | ||||
| Fair | 0.894 | 0.236 | 0.956 | 0.293 |
| Happy | 0.697 | 0.184 | 1.267 | 0.372 |
| Contextual characteristic | ||||
| Proportion of ethnic minorities | 1.005 | 0.004 | 1.006 | 0.003 |
| Per capita in the community | 1 | 1.10 × 10–5 | 0.929 | 9.65 × 10–5 |
| Region | ||||
| East† | ||||
| Middle | 1.351 | 0.402 | 0.876 | 0.204 |
| West | 1.062 | 0.345 | 0.934 | 0.246 |
| City level | ||||
| Sub-provincial city and above | ||||
| Other | 1.345 | 0.39 | 1.166 | 0.26 |
| Number of medical institutions for 10,000 people in the community | 2.32 × 10–30 | 2.17 × 10–28 | 0.994 | 0.008 |
| Number of medical institutions for 10,000 people in the city | 1.011 | 0.097 | 1.025 | 0.084 |
| Number of doctors for 10,000 people in the city | 1 | 0.003 | 0.989 | 0.002 |
| Number of beds for 10,000 people in the city | 0.991 | 0.006 | 0.995 | 0.005 |
| Health index of the community | 0.892 | 0.145 | 0.994 | 0.128 |
| Service quality index of the community | 1.089 | 0.129 | 1.078 | 0.113 |
| Urban service quality index | 0.973 | 0.209 | 0.963 | 0.178 |
| Intercept | 0.108 | 0.089 | 0.015** | 0.013 |
| Economic Quintiles | Two-week outpatient probability | Inpatient probability | ||
|---|---|---|---|---|
| Number | Percentage (%) | Number | Percentage (%) | |
| Poorest | 56 | 9.74 | 42 | 7.3 |
| Poorer | 38 | 6.61 | 43 | 7.48 |
| Middle | 36 | 6.25 | 28 | 4.86 |
| Richer | 38 | 6.61 | 31 | 5.39 |
| Richest | 18 | 3.13 | 36 | 6.26 |
| All | 210 | 196 | ||
| Two-week outpatient probability | Inpatient probability | |||
|---|---|---|---|---|
| dy/dx | Contributions/% | dy/dx | Contributions/% | |
| 36 ~ 50 | 0.001 | -0.26 | -0.076 | 5.22 |
| 50 ~ 64 | 0.069 | 5.82 | 0.03 | 3.2 |
| Women | 0.167 | 85.41 | 0.217 | 53.85 |
| Live without spouse | -0.315 | 27.17 | 0.137 | -2.84 |
| Primary school | 0.27 | -13.98 | 0.1 | -5.44 |
| Middle school and above | -0.019 | 3.44 | 0.014 | -1.23 |
| Having technical certificate | 0.161 | -2.57 | -0.432 | -13.31 |
| Service stuff | 0.296 | -10.41 | 0.133 | -9.63 |
| Manufacturing and construction | 0.382 | 6.01 | 0.143 | 1.49 |
| Freelancer | 0.271 | 2.34 | 0.121 | 10.05 |
| Collective enterprises and institutions | -0.234 | -7.79 | 0.074 | 0.59 |
| Self-employed and freelance | -0.488 | 0.54 | -0.003 | 0.07 |
| Excessive labor | 0.14 | -28 | -0.017 | 1.06 |
| Across the county/district | -0.225 | 8.91 | 0.233 | 9.65 |
| Poorer | 0.042 | 9.16 | -0.027 | -11.57 |
| Middle | -0.026 | 7.32 | -0.016 | 2.53 |
| Richer | 0.077 | -36.47 | -0.014 | 9.73 |
| Richest | 0.036 | -10.78 | 0.04 | -45.53 |
| Having Injury insurance | -0.968 | -9.12 | -0.051 | -1.6 |
| Number of friends 6 ~ 10 | -0.093 | 13.22 | -0.051 | 2.51 |
| Number of friends > = 11 | 0.127 | -10.82 | 0.004 | -0.54 |
| Fair SAH | -0.086 | 6.93 | 0.233 | 22.03 |
| Poor SAH | 0.11 | 54.76 | 0.226 | 89.01 |
| No Smoking | -0.118 | -14.83 | -0.106 | -13.5 |
| No alcohol use | 0.044 | 6.62 | -0.096 | -8.47 |
| No regular exercise every month | -0.389 | -5.87 | -0.209 | -6.07 |
| Fair happiness | 0.149 | 5.43 | -0.031 | -0.95 |
| Happy | 0.313 | -14.15 | 0.085 | -3.17 |
| Proportion of ethnic minorities | -0.005 | -0.39 | 0.03 | 5.67 |
| Per capita in the community | 0.024 | -9.43 | 0.011 | -2.29 |
| Middle | 0.093 | -8.02 | 0.061 | -4.27 |
| West | -0.015 | -7.75 | -0.019 | -1.2 |
| Below Sub-provincial city | 0.614 | 5.62 | 0.166 | 0.73 |
| Number of medical institutions for 10,000 people in the community | -0.086 | 11.04 | 0.009 | 0.5 |
| Number of medical institutions for 10,000 people in the city | 0.152 | -2.85 | -0.025 | 1.43 |
| Number of beds for 10,000 people in the city in the city | -0.677 | 44.98 | 0.015 | -0.57 |
| Health index of the community | -0.01 | -7.69 | 0 | -1.07 |
| Service quality index of the community | -0.008 | -13.2 | 0.004 | -1.48 |
| Service quality index of the city | -0.024 | -35.79 | 0.006 | 4.17 |
| Two-week outpatient probability | Inpatient probability | ||||
|---|---|---|---|---|---|
| CI | Contributions/% | CI | Contributions/% | ||
| CI | -0.02 | 100 | -0.072 | 100 | |
| Need | -0.008 | 40.8 | -0.125 | 173.3 | |
| Economy | -0.003 | 14.57 | 0.032 | -44.84 | |
| Other | -0.007 | 31.87 | 0.031 | -42.95 | |
| residual | -0.003 | 12.76 | -0.01 | 14.49 | |
| HI | -0.012 | 0.053 | |||
Discussion
Reducing inequalities has been widely recognized as a major objective of health policies in China, and has become a growing concern of the public. As major medical insurance for rural migrant workers, NCMS has improved their medical treatment. Although China has the largest scale of population migration, there are few studies on the inequality in health service utilization among rural migrant workers with NCMS. To gain a better understanding of health service utilization regarding rural migrant workers with NCMS, we selected and used predictors from the Andersen Model (2013 Version) in the Chinese socio-cultural context to enhance its explanatory power when applied to empirical studies. Our results were conducive to an objective and comprehensive understanding of the health service utilization of rural migrant workers with NCMS.
The Fifth Chinese National Health Service Survey [23] showed that the two-week outpatient and inpatient probabilities of Chinese residents were 8.17% and 7.78% respectively. The China Health and Retirement Longitudinal Study [6] showed that the four-week outpatient probability was 13.7%, and the average cost was 400.3 yuan. The Chinese government made a comprehensive deployment of the milestone strategy of “Healthy China” to prioritize people’s health by integrating health into all policies [24]. Our results shown that the two-week outpatient probability (6.32%) and inpatient probability (5.90%) for rural migrant workers with NCMS were much lower than those for the general population as determined in CLDS 2016 (6.38% and 7.52% respectively). It can be seen that the health service utilization of rural migrant workers with NCMS was lower than that of the overall Chinese labor force. The Chinese government should spare more coordinated and comprehensive efforts to ensure people’s equal access to health services, especially that of rural migrant workers. In line with previous studies [5, 25], the current health service system discouraged rural migrant workers from seeking appropriate care of good quality. Combined with this factor and others, such as: the lack of specific implementation rules for NCMS, low income, frequent job changes, and high work intensity, have led to poor health service utilization by those workers.
In addition to offering a reasonable and reliable analysis framework for explaining health service utilization and its inequality among rural migrant workers with NCMS in China, this study also has significance in terms of application the Andersen Model (2013 Version) in the field of health in China. Our results revealed a variety of variables associated with the two-week outpatient probability and inpatient probability among rural migrant workers with NCMS in China. Therefore, we should pay attention to the health education of rural migrant workers and guide them to take regular physical exercise. It was found that rural migrant workers with stable jobs and income tended to use more health services. In addition, employers should approve sick leaves for those who need medical treatment to receive timely treatment. Our study, as well as previous studies [26], revealed that the inpatient probability is unequally distributed among the income spectrum. Our analyses provide evidence for the existence of pro-poor inequality that the poor are more likely to utilize health services. According to previous studies [27], rural migrant workers with higher economic status had better SAH, resulting in less need for hospitalization.
Adopting the theory of equal opportunity, we fully considered the need of rural migrant workers with NCMS for health services. The inequality in two-week inpatient probability, compared with the inequality in two-week outpatient probability, was found to be lower. This is related to the hospitalized compensation plan in China. Rural migrant workers are generally at a disadvantage in the labor market, and some of them are engaged in physical labor with high work intensity. Although the quality of health services for rural migrant workers improved, the predicaments faced by those workers have not been eliminated and their demand for health services has not been met. Since the 19th CPC National Congress in 2016, the Chinese government has been given increasing focus to the health needs of rural migrant workers, but joint efforts by society are still needed to improve their health in the long run.
The results highlight that gender, marital status, economic level, SAH, number of hospital beds per 10,000 urban population, and urban service quality index are the main contributing factors in relation to the inequality in two-week outpatient probability. The family support provided [28] helped improve the health status of rural migrant workers, thus reducing the probability of their seeking medical treatment. Poor SAH increased inequality in favor of a higher two-week outpatient probability for rural migrant workers with a lower economic status. Most rural migrant workers obtain a higher income by engaging in intensive physical work, so healthier rural migrant workers with NCMS are more likely to obtain a higher income. Gender, economic level, and SAH are the key indicators for the inequalities in the inpatient probability. The childbirth needs of female rural migrant workers for childbearing age exacerbate inequality. General SAH increases the inequality of inpatient probability of migrant workers with a lower income. Those findings are consistent with the existing research [29]. The proportion of inpatient reimbursement was higher compared to outpatient reimbursement. The urban service quality index and the number of hospital beds per 10,000 in the cities reflect that cities attach great importance to the livelihood of people, which can promote the level of medical insurance and thus help to improve the medical treatment of rural migrant workers. This results in an increasing recognition that reducing inequality in healthcare service utilization is a critical issue to address.
This study also have several limitations. First, due to the cross-sectional analysis, the determination of time precedence or causal inferences cannot be solved. More studies are needed to further explore a causal inference for specific key factors. Second, considering the availability of data, our study does not allow complete testing of the Chinese construction for the Andersen model. Third, the limited sample size for the rural migrant workers could not represent the most current statistics and may lead to underestimated or overestimated regarding results.
Conclusion
In conclusion, our study sheds light on the inequalities in the health service utilization of rural migrant workers with NCMS in China. Our findings provide evidence for the pro-poor inequality in regard to two-week outpatient and inpatient probabilities. These findings illustrate the main determinants of inequality in health service utilization and highlight the important influencing factors–- gender, marital status, economic status, SAH, number of beds per 10,000 population, and the urban service quality index. Our study found that if we do not take the health service needs into account, we may overestimate or underestimate the inequality in the health service utilization of rural migrant workers with NCMS. Thus, it is essential to involve rural migrant workers’ needs for offering better-designed health services to rural migrant workers.