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Impact of family doctor contracted
services on the health of migrants:
a cross-sectional study in China
Sijia Liu & Jiajing Hu
This study investigates the impact of the family doctor contracted service system on the health of
migrants in China, utilizing data from the 2018 China Migrants Dynamic Survey (CMDS). The study
employs a double machine learning model to estimate the eect of family doctor contracted services
(FDCS) on migrants’ self-rated health (MSRH). The sample consists of 137,851 migrants, with family
doctor service contract status, health education, and health records as key variables. To address
potential endogeneity issues, an instrumental variable approach using the regional family doctor
contracting rate was implemented. Mediation analysis was conducted to examine the roles of health
education and health records in this relationship. The ndings indicate that FDCS signicantly improve
MSRH. This positive eect is robust across various machine learning models, including Lassocv,
Random Forest, and Gradient Boost. The instrumental variable approach conrms the validity of these
results, mitigating concerns about endogeneity. Mediation analysis reveals that the positive impact
of FDCS on MSRH is fully mediated by health education and health records, highlighting their critical
roles in enhancing health outcomes. The FDCS play a crucial role in improving the health of migrants by
providing continuous and comprehensive care. Enhanced health education and eective health records
management are signicant pathways through which these services exert their positive eects. Policy
recommendations include expanding access to family doctor services, enhancing health education
programs, and improving health records management to optimize healthcare delivery for migrants.
Future research should consider longitudinal studies to further validate these ndings and explore their
applicability to specic subgroups or regions.
Keywords Migrant population, Self-rated health, Family doctor contracted services, Health education,
Health records, China migrant dynamic survey, Double machine learning
Since the implementation of China’s reform and opening-up policy, internal migration has surged due to relaxed
economic and household registration controls1,2. Rapid urbanization policies in the 21st century have further
fueled this growth. In China, migrants typically relocate from their registered residences to other cities within
the country3. According to China’s population census data, the proportion of migrants to the total population
was under 2% in 19904, but by the seventh population census in 2020, it had skyrocketed to 34.9%5.
Providing healthcare services for this large migrant population has become a critically important task.
Migrants oen work in labor-intensive industries and live in impoverished conditions, exposing them to
heightened health risks such as infectious diseases, occupational ailments, and mental health disorders6. Over
recent decades, China’s social security has been linked to household registration, obligating migrants to access
basic medical insurance through their registered residence, oen leading to reluctance in seeking healthcare
services7,8.
Furthermore, healthcare disparities signicantly impact migrants, who are frequently excluded from
healthcare benets oered by local communities and grassroots medical facilities9,10. Despite government eorts
to mitigate healthcare inequality and improve service accessibility11, the healthcare plight of migrants remains a
pressing issue. China’s healthcare system operates on a three-tier structure, with secondary and tertiary hospitals
equipped with advanced medical technologies and specialist physicians12. Consequently, residents oen
distrust primary healthcare institutions, preferring to seek medical care in large hospitals13. is preference is
exacerbated by the perceived high time and nancial costs associated with primary care, leading migrants to
resort to self-diagnosis or forego treatment altogether14. us, establishing an hierarchical medical system poses
School of Health Management, Inner Mongolia Medical University, Hohhot 010100, China. email:
hujiajing@immu.edu.cn
OPEN
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a formidable challenge15, particularly in ensuring that migrants can access high-quality healthcare services at
the grassroots level.
e Chinese government introduced the family doctor contracted service (FDCS) system in 2013 and fully
promoted it by 2016 to promote primary healthcare utilization. Family doctor contracting involves an agreement
between a general practitioner and a family, aimed at assisting the family in managing daily health issues and
providing routine healthcare services16. is contractual relationship oers migrants a cost-eective means
to access healthcare services17, enhancing medical service accessibility18 and reducing health risks19. Family
doctors provide health management services such as assessments, guidance, and education, which improve
migrants’ health literacy by imparting health knowledge and thereby enhancing their health outcomes. Health
education plays a crucial role as it directly inuences health literacy20–22, a key determinant of health behaviors
and outcomes23–25. Meanwhile, health records provide a systematic approach to tracking and managing health
information, ensuring continuous and coordinated care26,27.
However, the FDCS system in China lacks adequate medical insurance support, reasonable incentive
mechanisms, and objective evaluation methods, diering from international practices28. erefore, studying
the impact of FDCS on migrant health is essential. Previous studies have primarily focused on the relationship
between FDCS and the health status of the general population29,30, neglecting to explore the mechanisms
through which these services aect migrant health.
is study aims to expand on previous research by examining the causal relationship between China’s FDCS
system and migrant health status. Specically, it investigates (1) whether FDCS enhance the health status of
migrants and (2) whether health education and health records mediate the relationship between these services
and migrant health status. By addressing these research questions comprehensively, this study aims to provide
valuable insights for enhancing the FDCS system.
Literature review
e FDCS system in China, established as a crucial part of primary healthcare in 2016, emphasizes continuity
and comprehensiveness in care by providing basic medical, public health, and health management services to
residents. Research has shown that access to primary healthcare can signicantly improve health outcomes31,
particularly through enhanced access to preventive care32. Studies from various countries indicate that FDCS,
by providing regular health check-ups33, chronic disease management34, and personalized care35, contributes
positively to individuals’ health. While research specic to FDCS and health outcomes in China is scarce,
previous studies underscore its essential role in improving population health. For instance, Zhang et al. found
a signicant association between FDCS and objective health outcomes among older Chinese adults36. Xu et
al. observed that individuals with type 2 diabetes who participated in FDCS had a signicantly lower risk of
complications compared to non-participants37. Additionally, Lai et al. reported that individuals utilizing FDCS
exhibited higher health-related quality of life (HRQoL) than those without access to these services33.
ese studies emphasize the role of FDCS in enhancing population health, however, this association’s
relevance to internal migrants remains unexplored. Due to the motivations driving migration in China—
such as employment, education, and family care—healthier individuals are more likely to become migrants38.
Nevertheless, migrants face unique health risks due to limited access to healthcare services and reduced
opportunities to establish relationships with healthcare providers. Current studies on migrant health have
primarily focused on barriers to healthcare access, including economic constraints and limited insurance
coverage39–41.
An essential aspect of family doctors’ roles involves providing health education and establishing continuous
health records for patients. Extensive research suggests that eective health education can signicantly improve
health literacy, enhancing individuals’ capacity to utilize healthcare systems, adhere to health management
recommendations, and adopt preventive health measures42,43. Conversely, health records play a critical role in
maintaining continuity of care; studies indicate that maintaining health records enables healthcare providers to
access accurate patient histories, thus allowing them to oer personalized healthcare solutions44.
While existing research provides valuable insights into the role of FDCS, there remains a gap in understanding
how FDCS inuences the health outcomes of migrants. e literature lacks in-depth exploration of the
mechanisms, such as health education and health record management, through which FDCS aects health
outcomes. Additionally, previous research has limited focus on migrants, and the role of FDCS in improving
migrants’ health demands thorough investigation.
Theoretical framework
e conceptual foundation for this analysis is the Health Belief Model (HBM), a widely used psychological
model that explains health-related behaviors through individual beliefs and perceived barriers, benets, and
susceptibility. e HBM has been widely used in the eld of public health to understand and predict health-
related behaviors, particularly in studies focused on disease prevention45, health promotion46, and chronic
disease management46. By integrating the HBM, this study seeks to understand how FDCS can inuence health
outcomes through improved health literacy and healthcare engagement among migrants. is framework, as
shown in Fig.1, illustrates how the FDCS impacts MSRH through two mediating factors.
FDCS represents an initiative to provide continuous, personalized healthcare to individuals, addressing both
preventive and chronic health needs. For migrant populations, who oen face barriers to healthcare access and
continuity, FDCS oers a valuable opportunity to mitigate health disparities. However, the extent to which these
services impact self-rated health depends on various cognitive and structural factors, which are captured by the
HBM components.
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Health education interventions are central to FDCS, aiming to increase migrants’ awareness of their health
risks and the benets of preventive behaviors. According to the HBM, health education inuences perceived
susceptibility (awareness of personal health risks), perceived severity (understanding of the consequences of
poor health), and perceived benets (beliefs about the eectiveness of healthcare practices). By enhancing these
perceptions, health education encourages healthier behaviors, which ultimately improve self-rated health.
Establishing and maintaining health records allows for consistent health monitoring and better care
management, thereby addressing perceived barriers (reducing access diculties), enhancing self-ecacy
(empowering migrants to manage their health), and providing cues to action (prompting regular healthcare
interactions). ese HBM components facilitate more proactive health behaviors and reinforce the benets of
engaging in regular health assessments.
Materials and methods
Data source
e China Migrants Dynamic Survey (CMDS), initiated by the National Health Commission of China in 2009,
aims to comprehensively understand various aspects of migrants’ conditions, including their mobility patterns,
employment and social security status, income and expenditure, and utilization of basic public health services.
is study focuses on analyzing the impact of the family doctor contracted service system on the health status
of Chinese migrants and exploring potential pathways of inuence. e 2018 CMDS dataset is particularly
suited to this study’s objectives because it introduced questions about family doctor contract services for the rst
time. is enables a thorough investigation into how these services aect migrants’ health status. erefore, the
2018 CMDS dataset was selected for this research. e survey’s sampling process occurred in three stages: rst,
township-level units were sampled from 32 provincial-level administrative units; second, village committees
were selected from these township units, with sampling probabilities proportional to their population sizes;
third, migrants for the survey were chosen from designated village committees.
Study population
e CMDS conducted in 2018 encompassed 32 provincial-level administrative units. e respondents were
individuals who had lived at the sampling point for more than one month and were not registered as local
residents in the area, with a requirement of being over 15 years old. ose temporarily residing in places such
as stations, ports, hotels, hospitals, and students were excluded from the sample. e initial sample size was
152,000, covering various aspects such as family composition, income and expenditure, employment status,
health, and public services.
In this study, we restrict the sample to respondents who lived in the inow region for at least six months and
had no missing key variables. Specically, we exclude respondents who lived in the inow region for less than six
months (13045 respondents), resulting in 138,955 migrants being retained from the 2018 sample. Aer removing
respondents with missing data on monthly household income (299 respondents), basic medical insurance (395
respondents) and age (410 respondents), 137,851 migrants are included in the nal sample form CMDS 2018.
Measures
Dependent variables
Self-rated health status is commonly used to assess the health status of populations and has been shown to be
signicantly associated with health risk behaviors, disease status, mortality rates, and other health outcomes47–50.
It serves as a good proxy indicator of actual health status51, and can serve as a global measure of health status
in the general population52. erefore, we utilized self-rated health status to evaluate the health condition of
Chinese migrants. All respondents were asked to assess their own health status on a scale of 1–4, including
Fig. 1. eoretical framework of the impact of FDCS on MSRH using the health belief model. Noted:
eoretical framework. Author’s analysis.
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“unable to take care of oneself”, “unhealthy but able to take care of oneself”, “basically healthy”, and “healthy”.
Following the approach of previous studies53, we dichotomized the dependent variable, migrants’ self-rated
health (MSRH), to a binary scale where 0 represents ”unhealthy” and “unable to take care of oneself” and 1
represents “healthy” and “basically healthy”.
Independent variables
In this study, the variable “Have you ever signed up with a family doctor?” from the CMDS was used to identify
migrants who had contracted family doctor. Respondents who answered “uncertain” were classied as not having
signed a contract. is categorization was guided by focus on understanding the health literacy advantages
linked with clear, formalized engagements with family doctors. erefore, individuals responding as “uncertain”
were considered as not having accessed these benets. is approach ensures that our analysis primarily captures
the eects of family doctor services on health outcomes among migrants.
Mediation variables and control variables
e mediating variables in this study are health education and health records. Health education is assessed by
inquiring whether respondents had received health education in the past year, categorized as 0 (no) or 1 (yes).
ese health education programs were initiated by the Chinese government in 2009 to provide basic public
health services to the entire population. However, due to objective regional disparities, many residents have
not fully accessed these basic public health services. FDCS may increase the channels through which migrants
obtain health education. Health records are assessed by whether respondents have established a health record,
categorized as 0 (no) or 1 (yes). Establishing health records can enhance the continuity and quality of healthcare
services received by migrants. Both mediating variables are considered binary, with values indicating the
presence or absence of health education and health records.
To account for various factors potentially inuencing MSRH, this study utilizes previous research ndings
to categorize control variables into demographic characteristics, socioeconomic features, and migration
characteristics54–57. Demographic characteristics and socioeconomic status signicantly inuence health status,
as these attributes aect an individual’s susceptibility to infectious diseases and their access to healthcare services.
e demographic characteristics considered include gender (0 = female, 1 = male), age, ethnicity (0 = major ity,
1 = minority), education level (1 = have not attended school, 2 = middle or primary school, 3 = high s chool,
4 = college and above), marital status (0 = no, 1 = yes), and household registration (0 = other, 1 = agricultural
household registration). Age is divided into six groups: < 45 years, 45–65 years, and > 65 years. Socioeconomic
features include participation in basic medical insurance (0 = no, 1 = yes), monthly household income, and
employment status (0 = unemployed, 1 = employed). Monthly household income is specically categorized into
six classes: < CNY 4000), CNY 4001–6000, CNY 6001–8000), and > CNY 8000. Migration time (0 = less than
6 years, 1 = over 6 years) and migration type (1 = across provinces, 2 = across cities within a province, 3 = across
county within a city).
Double machine learning model
Currently, major causal inference methods, such as the dierence-in-dierences method, propensity score
matching, and regression discontinuity design, rely heavily on strict assumptions, typically assuming linear
relationships between the dependent and independent variables. is assumption can result in model
misspecication, leading to endogeneity issues and biased estimates. In addition, these methods have certain
limitations in dealing with high-dimensional control variables. To address this challenge, Chernozhukov et al.
proposed a double machine learning method to estimate treatment eects, which not only relaxes the assumption
of linear relationships between variables but also considers high-dimensional control variables simultaneously58.
In this study, we employ the double machine learning method to estimate the impact of the contracted family
doctor system on migrant health. e structural equations of the double machine model are dened as follows:
Y=θ·D+l(W)+U, E [U|W, F DOC ]=0,
(1)
D=m(W)+V, E [V|W]=0,
(2)
Here, Y represents MSRH, D indicates whether migrants have signed contracts with family doctors, W denotes
the vector of control variables, and U and V represent random disturbance terms, with θ indicating the
treatment eect. According to Eqs.(1) and (2), the control variable vector W aects the independent variable
D and the dependent variable Y through functions
l(W)
and
m(W)
, respectively. It is important to note that
functions
l(W)
and
m(W)
do not have strict assumptions and are referred to as nuisance functions. ese
functions are used to estimate the correlations between the dependent variable and control variables, as well as
between the independent variable and control variables. We employ machine learning methods to estimate the
nonparametric eects of control variables.
To estimate the treatment eect θ, machine learning algorithms are rst used to predict D and Y based on the
control variables. en, the residuals of the two models are calculated:
˜
Y=Y−l(W)
(3)
˜
D=
D
−
m
(
W
)
(4)
Finally, by regressing the residuals of the two models, the treatment eect θ is estimated:
˜
Y=θ·D+U
(5)
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In the double machine learning model, by using machine learning models to estimate
∼
l(W)
and
∼
m(W)
separately, regularization bias can be avoided. Furthermore, the K-fold cross-validation method is
employed to prevent overtting issues. Specically, the data are randomly divided into four folds, and nuisance
functions
l(W)
and
m(W)
are estimated on three folds, followed by the use of these two nuisance models to
estimate the treatment eect on the fourth fold.
Statistical analysis
First, chi-square tests were conducted to examine the statistical dierences at the bivariate level. Second, a double
machine learning model59 was employed to assess the impact of family doctor contracted services on MSRH. To
address endogeneity issues stemming from omitted variable bias, reverse causality, or selection bias, the family
doctor contracting rate in the migrants’ city of residence was used as an instrumental variable. e partially
linear IV model58,59 within the double machine learning framework was utilized to estimate the treatment eects.
Given that the dependent variable, independent variable, and mediators are all binary, a logit model is
required for estimation. However, coecients or odds ratios cannot be directly compared across logit models due
to rescaling bias60. erefore, the Karlson/Holm/Breen (KHB) method61,62 was employed to test for mediation
eects. e KHB method allows for the comparison of coecients and the distribution of data within nested
logit models.
e KHB method was applied to investigate the potential mediating roles of health education and health
records in the relationship between family doctor contracted services and MSRH. is method decomposes the
total eect of the variables into direct and indirect eects, calculating the proportion of the total eect explained
by the primary mediators.
Statistical analyses and data management were performed using Stata version 17 and Python version 3.1. A
p-value of < 0.05 (two-tailed) was considered statistically signicant.
Results
Statistical description and bivariate analysis
Table1 presents the descriptive statistics for the sample. e sample comprises 137,851 migrants, among whom
3,042 self-report unhealth. Of the total, 12.35% have signed contracts for family doctor services. e average
age of the migrants is 37.25 years. Within the sample, 51.25% are male and 83.70% are married. Over 90%
of the sample belong to the Han ethnic group, with most having educational backgrounds at the middle or
primary school levels. Additionally, 68.34% have agricultural household registrations, and 93.83% have medical
insurance. Moreover, 81.68% of the migrants have received health education, and 47.82% have established health
records.
The impact of the FDCS on MSRH
In this section, we employed a double machine learning model to estimate the impact of FDCS on MSRH. A
single machine learning model may result in simulation errors. erefore, we utilized three dierent models to
estimate the treatment eect, namely, Lassocv, Random Forest, and Gradient Boost. Table2 displays the results
estimated by dierent models. e results indicate that the FDCS has a positive impact on MSRH.
Endogenous analysis
In this study, the chosen instrumental variable is the family doctor contracting rate in the city where the migrant
resides. For a variable to be a valid instrument, it must satisfy both the relevance and exogeneity conditions.
In many regions of China, the family doctor signing rate has been adopted as a key performance indicator for
primary healthcare services. is has led to the phenomenon of “signing without commitment,” where the mere
act of signing a contract does not necessarily translate into actual service63. As a result, the regional signing
rate may directly increase the likelihood of each individual signing a family doctor contract, but it does not
directly improve the health outcomes of the population. We conducted a weak instrument test using the two-
stage least squares (2SLS) method to address concerns regarding the strength of the instrument. e rst-stage
regression results demonstrate a signicant F-statistic of 3918.32 (p < 0.001) and a minimum eigenvalue statistic
of 40,372.1, which far exceeds the critical values suggested for weak instrument tests. ese results conrm that
the instrumental variable chosen for FDCS is suciently strong, meeting the relevance criterion and providing
robust support for its validity.
Following this, the partially linear IV model within the double machine learning framework was employed to
estimate the instrumental variable technique, utilizing Lassocv, Random Forest, and Gradient Boost to estimate
the treatment eects. Table3 presents the estimation results. e coecients for the family doctor contracting
service are signicant at the 1% level across all models. e estimated treatment eects closely align with the
baseline results, indicating that even aer addressing the endogeneity issue, the impact of the family doctor
contracting service on MSRH remains signicant.
Robustness analysis
To mitigate the possible data imbalance in the sample, we employed two robustness checks: bootstrap resampling
and propensity score matching (PSM). ese methods were implemented to verify the robustness of our main
ndings given the inherent data imbalance.
Bootstrap sampling was used to create multiple resampled datasets, allowing us to repeatedly draw samples
from the original data. is approach generates condence intervals based on empirical distribution, which
helps to address issues arising from unequal group sizes. Specically, we performed 1,000 bootstrap replications,
drawing an equal number of samples from the “healthy” group as in the “unhealthy” group for each replication.
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is process provided robust standard errors and allowed us to evaluate whether the observed relationship
between FDCS and MSRH remained consistent.
In addition to bootstrap sampling, PSM was used to address observed imbalances between groups. By
estimating the probability of receiving FDCS (propensity score) based on a set of covariates, we matched
Var iable
MSRH
χ2
Healthy Unhealthy
Family doctor contracted service
Ye s 16,563 455 19.61***
No 118,246 2587
Gender 89.67***
Female 65,455 1741
Male 69,354 1301
Age group 7943.76***
< 45 87,320 393
45–65 42,945 1667
> 65 4544 982
Ethnicity 87.93***
Majority 123,676 2646
Minority 11,133 396
Education level 3630.90***
Have not attended school 2938 543
Middle or primary school 74,552 2096
High school 30,368 311
College and above 26,951 92
Marital status 30.83***
Ye s 112,723 2658
No 22,086 384
Household registration 21.27***
Agricultural household registration 92,015 2196
Other 42,794 846
Basic medical insurace 11.98***
Ye s 126,541 2809
No 8268 233
Monthly household income 2274.88***
< 4000 29,799 1770
4001–6000 38,328 658
6001–8000 27,009 316
> 8000 39,673 298
Employment status 5759.34***
Unemployed 20,875 2047
Employed 113,934 995
Migration time 492.34***
Less than 6 years 74,448 1064
Over 6 years 60,361 1978
Migration range 186.50***
Across provinces 67,285 1185
Across cities within a province 44,842 1109
Across county within a city 22,682 748
Health education 411.29***
Ye s 110,546 2057
No 24,263 985
Health records 37.00***
Ye s 64,633 1289
No 70,176 1753
Tot a l 134,809 3042
Tab le 1. Statistical description and bivariate analysis. Note: *** p < 0.01.
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individuals in the treatment group (those with FDCS) to similar individuals in the control group (those without
FDCS). We employed three commonly used matching techniques (nearest neighbor matching, radius matching,
and kernel matching) to assess the robustness of our results.
Table4 presents the results from both the bootstrap analysis and propensity score matching. Both methods
produced similar results, providing evidence that our ndings are robust and not inuenced by sample imbalance.
Analysis of heterogeneity
To further analyze the impact of FDCS on health in detail, we analyzed heterogeneity in terms of age and
monthly household income, with results presented in Table5. e results indicate that FDCS has a signicant
positive impact on MSRH for individuals over 45 years old, while no signicant eect was found for younger
individuals under 45. Additionally, FDCS shows a signicant positive association with MSRH in lower-income
groups with monthly household incomes below 6000 yuan. However, for income levels above 6000 yuan, the
eect was non-signicant, suggesting that FDCS has a more pronounced impact on health for middle-aged,
elderly, and lower-income groups.
Mediation eect analysis
is section presents the analysis of the mediating eects of health education and health records on the
relationship between family doctor contracted services and MSRH.
Variables Coecient Standard error P
Age group
< 45 0.1232 0.1529 0.420
45–65 0.2564 0.0809 0.002
> 65 0.1475 0.0613 0.016
Monthly household income
< 4000 0.2337 0.7338 0.002
4000–6000 0.2691 0.1194 0.024
6001–8000 0.2106 0.1901 0.268
> 8000 0.0346 0.2022 0.864
Tab le 5. Estimation results of heterogeneity analysis. Note:e models were adjusted for all control variables
mentioned in the Materials and methods section.
Model Coecient Standard error P
Bootstrap Sampling 0.1723 0.5857 0.003
PSM - Nearest Neighbor 0.1921 0.0758 0.011
PSM - Radius Matching 0.1654 0.0736 0.025
PSM - Kernel Matching 0.2108 0.0710 0.003
Tab le 4. Estimation results of robustness analysis. Note: e models were adjusted for all control variables
mentioned in the Materials and methods section.
Model Coecient Standard error Condence interval PMean squared error (MSE)
Lassocv 0.0799 0.0067 [0.0666,0.0932] < 0.001 0.007
Random Forest 0.0847 0.0090 [0.0671,0.1023] < 0.001 0.009
Gradient Boost 0.0948 0.0079 [0.0793,0.1102] < 0.001 0.008
Tab le 3. Estimation results of the impact of FDCS on MSRH using instrumental variable approach.
Model Value Standard error Condence interval PMean squared error (MSE)
Lassocv 0.0225 0.0033 [0.0160,0.0289] < 0.001 0.003
Random Forest 0.0271 0.0033 [0.0205,0.0336] < 0.001 0.003
Gradient Boost 0.0255 0.0032 [0.0192,0.0317] < 0.001 0.003
Tab le 2. Estimation results of the impact of FDCS on MSRH using double machine learning.
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As shown in Table6, the results indicate that FDCS signicantly increase the likelihood of receiving health
education and having a health record. Additionally, health education and health records signicantly improve
MSRH. However, the direct eect of FDCS on MSRH is not statistically signicant when health education and
health records are included in the model. is indicates that health education and health records play a complete
mediating role in the model, and the impact of FDCS on MSRH is entirely through the two mediating variables.
Table7 presents the total impact of FDCS on MSRH decomposed into direct and indirect eects. e total
eect of FDCS on MSRH is statistically signicant. e direct eect becomes non-signicant, indicating that
the relationship between FDCS and MSRH is fully mediated by health education and health records, consistent
with the results from the logit model. e indirect eects through health education and health records are both
signicant, demonstrating their roles as mediators. Health education mediates 38.26% of the total eect, while
health records account for 57.36% of the mediation eect. When combined, health education and health records
mediate 29.25% (M1) and 50.75% (M2) of the total eect.
Discussion
e primary objective of this study is to investigate the role of the FDCS, promoted by the Chinese government, in
improving the health of migrants. Our ndings, as shown in Table2, indicate that these services have a signicant
positive eect on MSRH. is positive impact is consistently observed across various double machine learning
models, including Lassocv, Random Forest, and Gradient Boost. e robustness of these results underscores the
importance of FDCS in enhancing health outcomes among migrants. Several factors might contribute to this
positive eect. Firstly, family doctors oer continuous and comprehensive care, which is particularly benecial
for managing chronic conditions64 and preventing acute health issues65,66. Secondly, these services oen include
personalized health advice and follow-up67, encouraging healthier behaviors and better management of existing
health conditions25. irdly, FDCS signicantly enhance migrants’ health literacy, providing them with more
comprehensive and continuous medical care68.
e results are consistent with previous studies that have demonstrated the benets of primary care services in
improving health outcomes and reducing health disparities among vulnerable populations30,69,70. e consistent
positive impact across various models underscores the robustness of our ndings and highlights the crucial role
of family doctor services in managing the health of migrants.
To address potential endogeneity issues, we utilized an instrumental variable approach, using the regional
family doctor contracting rate as an instrument. As presented in Table3, the results indicate that the positive
impact of FDCS on MSRH remains signicant even aer accounting for endogeneity. is suggests that the
observed relationship is not inuenced by unobserved confounders, further validating the eectiveness of these
services. Additionally, we employed bootstrap sampling and PSM model to test robustness, and the results
indicate that the positive eect of FDCS on MSRH is reliable.
e results were heterogeneous in terms of age and income levels. First, FDCS has a signicant impact on the
health of migrants over 45 years old. e negative correlation between health and age has been demonstrated
in prior research, with middle-aged and older adults facing a higher risk of chronic disease, where primary care
shows positive eects in chronic disease management. Second, FDCS exhibits a signicant positive eect for
migrants with monthly incomes below CNY 6,000, while this relationship is not signicant for higher-income
groups. is may be due to the ability of higher-income individuals to access health services through additional
channels, thereby diminishing the eect of FDCS.
Items Health education & Health records Health education Health records
Total eect 1.17*** 1.19** 1.17***
Direct eect 0.92 1.05 0.98
Indirect eect 1.27*** 1.13*** 1.20***
Mediation proportion/% M1: 43.09
M2: 56.91
Tab le 7. Decomposition of the mediating eects of health education and health records on the relationship
between FDCS and MSRH. Note:**p < 0.05, *** p < 0.01. e models were adjusted for all control variables
mentioned in the Materials and methods section.
Variables
Health education Heath records MSRH
OR 95%CI OR 95%CI OR 95%CI OR 95%CI
FDCS 5.42*** [5.04–5.85] 25.89*** [24.13–27.76] 1.18*** [1.06–1.33] 0.92 [0.81–1.04]
Health education 1.10*** [1.06–1.12]
Health records 1.30*** [1.18–1.43]
Tab le 6. Analysis of the mediating eects of health education and health records on the relationship between
FDCS and MSRH. Note:**p < 0.05, *** p < 0.01. e models were adjusted for all control variables mentioned
in the Materials and methods section.
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Content courtesy of Springer Nature, terms of use apply. Rights reserved
e mediation analysis indicates that health education and health records signicantly mediate the relationship
between FDCS and MSRH. Specically, FDCS substantially increase the likelihood of receiving health education
and having health records, which subsequently positively inuence MSRH. When these mediators are included,
the direct eect of FDCS on MSRH becomes non-signicant, demonstrating full mediation.
ese ndings suggest that the benets of family doctor contracted services are largely achieved through
enhancing health literacy and improving health records management, thus supporting migrants’ health outcomes.
Health education serves as a crucial mechanism by equipping migrants with knowledge and skills to adopt
healthier lifestyles, improving their awareness of disease prevention and self-management practices, which in
turn boosts their health perception. On the other hand, health records contribute by systematically tracking and
recording health information, facilitating regular monitoring of health indicators and timely interventions. is
organized health data enables family doctors to oer personalized health guidance, detect potential health issues
early, and adjust care strategies to meet individual needs. Previous research has also highlighted the importance
of health education and comprehensive health records in improving health outcomes53,71,72. Our study builds on
this understanding by emphasizing their specic roles within the context of family doctor services for migrants.
Based on the ndings, several policy recommendations emerge to enhance the impact of family doctor contracted
services on migrant health outcomes. Firstly, expanding access to these services should be a priority. Policymakers
can achieve this by increasing subsidies and providing incentives to attract doctors to underserved areas. Integrating
family doctor contracted services more closely with existing public health systems would also improve accessibility
for migrants, ensuring they receive continuous and comprehensive healthcare.Secondly, given the signicant role of
health education in mediating the relationship between family doctor services and health outcomes, enhancing these
programs is crucial. Tailoring health education materials to the cultural needs of migrant populations and training
family doctors to eectively deliver health education could substantially improve health literacy and self-management
skills among migrants20,22. Furthermore, improving health records management is essential for optimizing healthcare
delivery. Policymakers should invest in robust health information technologies and systems that facilitate the creation,
storage, and sharing of comprehensive health records. is would ensure continuity of care and enable healthcare
providers to make informed decisions based on complete patient histories.
Lastly, addressing endogeneity in health services research is vital for developing evidence-based policies.
Researchers should employ rigorous methods, such as instrumental variable approaches, to accurately estimate
treatment eects. is approach will provide policymakers with reliable evidence on the eectiveness of
interventions like family doctor contracted services, guiding future healthcare policy decisions eectively.
is study contributes valuable insights into the impact of family doctor contracted services on migrant
health outcomes, employing rigorous methodologies and large-scale data analysis. One of the strengths lies
in our use of a double machine learning model and instrumental variable techniques to address endogeneity
issues, ensuring robust estimation of treatment eects. Additionally, by examining the mediating role of health
education and health records, we provide a nuanced understanding of the pathways through which family
doctor services inuence health outcomes among migrants.
However, there are limitations to consider. e reliance on self-reported health data and a cross-sectional design
restricts our ability to denitively establish causality. Future research could benet from longitudinal studies to validate
these ndings over time. Using self-rated health to represent health outcomes may be biased, and more health outcome
indicators need to be used in the future to strengthen the reliability of the impact of FDCS on MSRH. Moreover, while
our study covers a broad demographic range of migrants, generalizability to specic subgroups or regions may be
limited. Despite these limitations, our ndings underscore the importance of family doctor contracted services in
enhancing migrant health, oering valuable implications for health policy and practice.
Conclusion
FDCS played a positive role in improving the MSRH in China, with particularly notable eects among lower-
income groups and older individuals. Health education and health record maintenance, as key components
of FDCS, contribute signicantly to this positive impact. It is recommended that the government implement
strategies such as outreach and incentives to encourage more migrants to contract with family doctors, fostering
a stronger focus on migrant health.
Data availability
e data that support the ndings of this study are available from China’s National Health Commission, but re-
strictions apply to their availability. ese data were used under license for the current study and are not publicly
available. However, data can be requested from the corresponding author JIAJING HU, who will facilitate the
request with the permission of China’s National Health Commission.
Received: 7 August 2024; Accepted: 25 November 2024
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Acknowledgements
We thank the Migrant Population Service Center, National Health Commission P.R. China, very much for pro-
viding the data of CMDS 2018.
Author contributions
S.L.: conceptualization, methodology, soware, formal analysis, investigation, writing—original dra prepara-
tion; J.H.: conceptualization, resources, writing—review and editing, supervision. All authors provided critical
revisions to the manuscript and approved the nal version for submission.
Funding
is research was funded by the Youth Natural Science Foundation of Inner Mongolia, grant number 2022QN07001,
and the Project for Young Scientists Foundation of Inner Mongolia Medical University, grant number YKD-
2021QN038.
Declarations
Ethics approval and consent to participate
e data (CMDS) used in this study was a secondary dataset from a publicly accessible source and have
acquired the consent of all individuals who participated in the survey process. All methods were performed in
accordance with the relevant guidelines and regulations.
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Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to J.H.
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