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\begin{document}

\title{The effect of pairing assistance under medical alliance policy on
healthcare utilization for patients with chronic diseases in rural China}



\author[1]{Haoqing Tang}%
\author[1]{Mingyue Li}%
\author[2]{He Zhu}%
\author[1]{Xiaoran Cheng}%
\author[2]{Xiaoyun Liu}%
\affil[1]{Peking University School of Public Health Department of Health Policy and Management}%
\affil[2]{Peking University}%


\vspace{-1em}



  
  \date{June 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Pairing assistance (PA) of health professionals between county hospitals
and township health centers is one of the key components of the reform
of medical alliances in China to strengthen the development of health
workforce in primary health care (PHC). This study aims to examine the
effect of PA on healthcare utilization for patients with chronic
diseases in rural areas. Two waves of National Health Services Survey
(2013 and 2018) were used. A total of 13893 and 22725 rural residents
with chronic diseases were included in the 2013 and 2018 waves,
respectively. Multiple logistic regressions were used to examine the
associations between PA and outpatient and inpatient service utilization
in PHC. Chow test was used to examine the difference between PA in two
models. Among rural patients with chronic diseases, two-week outpatient
visits increased from 22.69\% to 27.54\%, and annual hospitalization
admission increased from 20.72\% in 2013 to 25.44\%. PA was associated
with a significant decrease in outpatient visits (p\textless{}0.001) in
2018 after controlling for individual and county characteristics.
Patients in PA counties were 1.45 times (95\% CI 1.10-1.90) more likely
to use PHC outpatient care in 2013, but the difference disappeared in
2018 (OR=0.85, 95\% CI 0.71-1.01). PA did not reverse the downward trend
in the share of PHC outpatient visits. PA under medical alliances in
China provides a potential model for building integrated people-centered
health systems for other low- and middle-income countries.%
\end{abstract}%



\sloppy


\textbf{Title page}

\textbf{Title:} The effect of pairing assistance under medical alliance
policy on healthcare utilization for patients with chronic diseases in
rural China

\textbf{Corresponding author:}

He Zhu; China Center for Health Development Studies, Peking University,
38 Xueyuan Road, Haidian District, Beijing, 100191, P. R. China; Email:

\emph{he.zhu@pku.edu.cn}

\textbf{Each authors affiliation and qualification}

Mingyue Li \textbf{(First author)}

\emph{Affiliation:} Department of Health Policy and Management, School
of Public Health, Peking University, Beijing, China; China Center for
Health Development Studies, Peking University, Beijing, China. Email:
\emph{mingyueli@pku.edu.cn}

He Zhu \textbf{(corresponding author)}

\emph{Affiliation:} China Center for Health Development Studies, Peking
University, Beijing, China. Email: \emph{he.zhu@pku.edu.cn}

Haoqing Tang

\emph{Affiliation:} Department of Health Policy and Management, School
of Public Health, Peking University, Beijing, China; China Center for
Health Development Studies, Peking University, Beijing,
China.\emph{Email:} \emph{haoqingtang@stu.pku.edu.cn}

Xiaoran Cheng

\emph{Affiliation:} Department of Health Policy and Management, School
of Public Health, Peking University, Beijing, China; China Center for
Health Development Studies, Peking University, Beijing,
China.\emph{Email:} \emph{shawnchan0113@163.com}

Xiaoyun Liu

\emph{Affiliation:} China Center for Health Development Studies, Peking
University, Beijing, China.\emph{Email:} \emph{xiaoyunliu@pku.edu.cn}

\textbf{Word count:} 3159

\textbf{No. of Tables:} 4

\textbf{No. of Figures:} 0

\textbf{The effect of pairing assistance under medical alliance policy
on healthcare utilization for patients with chronic diseases in rural
China}

\subsection*{Abstract}

{\label{abstract}}

Pairing assistance (PA) of health professionals between county hospitals
and township health centers is one of the key components of the reform
of medical alliances in China to strengthen the development of health
workforce in primary health care (PHC). This study aims to examine the
effect of PA on healthcare utilization for patients with chronic
diseases in rural areas. Two waves of National Health Services Survey
(2013 and 2018) were used. A total of 13893 and 22725 rural residents
with chronic diseases were included in the 2013 and 2018 waves,
respectively. Multiple logistic regressions were used to examine the
associations between PA and outpatient and inpatient service utilization
in PHC. Chow test was used to examine the difference between PA in two
models. Among rural patients with chronic diseases, two-week outpatient
visits increased from 22.69\% to 27.54\%, and annual hospitalization
admission increased from 20.72\% in 2013 to 25.44\%. PA was associated
with a significant decrease in outpatient visits (p\textless{}0.001) in
2018 after controlling for individual and county characteristics.
Patients in PA counties were 1.45 times (95\% CI 1.10-1.90) more likely
to use PHC outpatient care in 2013, but the difference disappeared in
2018 (OR=0.85, 95\% CI 0.71-1.01). PA did not reverse the downward trend
in the share of PHC outpatient visits. PA under medical alliances in
China provides a potential model for building integrated people-centered
health systems for other low- and middle-income countries.

\textbf{Key words:} pairing assistance, medical alliance, health care
utilization, chronic disease

\subsection*{Introduction}

{\label{introduction}}

Primary health care (PHC) is the cornerstone of any health system and a
catalyst for achieving Sustainable Development Goals (SDGs) and
Universal Health Coverage (UHC)\textsuperscript{1} \textsuperscript{2}.
The availability of qualified primary care healthcare workers is a key
step in improving PHC. Many countries have implemented interventions to
address the lack of health workers in primary care settings, especially
in rural and remote areas \textsuperscript{3}. China's reform of the
health system continued to invest efforts in PHC (in 2009, China
launched a huge and complex health reform to provide equal access and
eliminate financial risk for all residents) \textsuperscript{4}.
However, challenges remain for PHC. The share of outpatient visits in
PHC facilities decreased from 71\% in 2005 to 57\% in
2018\textsuperscript{5}.

To enhance the capacity of PHC facilities in rural areas and respond to
fragmentation of care throughout the health system, China issued
guidelines for building a hierarchical health system in which different
levels of hospitals would provide care according to designated scopes in
2015 and the government encouraged counties to explore medical alliances
in various forms \textsuperscript{6}. Pairing up physicians to PHC
facilities (mainly township health centers) is one of the major and
common strategies in different medical alliances to provide care and
guide PHC doctors to improve their ability \textsuperscript{7}. The
pairing of doctors has been a strategy for staffing the health workforce
in rural areas \textsuperscript{3, 8}. Currently, pairing doctors is
given priority in salary and promotion as incentives\textsuperscript{9}.
Medical alliances are the most important initiative to operationalize a
hierarchical health delivery system so that PHC facilities can be
supported and improved and care across levels of hospitals can be
coordinated and integrated. Currently, a tight medical alliance that
shares unified responsibilities, resources, risks, and economic
interests has become the policy direction. Under the medical alliance,
township health centers and village clinics can pair up with county
hospitals and receive support and training. To date, progress in
promoting unified administrative management, such as drugs and
consumables, procurement, and payment, still varies greatly in different
regions\textsuperscript{10}.

Pairing up with county hospitals is expected to improve PHC facilities
and benefit patients through several channels. First, pairing assistance
can increase the supply of rural health workers, which could temporarily
relieve the shortage of rural health workers \textsuperscript{11}.
Second, the pairing assistance can improve the capacity of the rural
health workforce, which will improve PHC in the long run and increase
the utilization of PHC \textsuperscript{12}. Third, for patients,
pairing assistance can help them enjoy high-quality care in PHC
facilities, shorter travel costs, and fewer payments.

Despite the nationwide implementation of medical alliances, little is
known about the actual effect of pairing up with county hospitals to
rural PHC facilities. This study aims to examine the effect of
partnership with county hospitals on the utilization of healthcare for
chronic disease patients in rural areas in the context of the
construction of medical alliances throughout the country.

\subsection*{Materials and methods}

{\label{materials-and-methods}}

\emph{Data source and study sample}

Data came from the 2013 and 2018 National Health Services Survey (NHSS)
waves in China\textsuperscript{13, 14}. The NHSS is a nationally
representative sample conducted every five years by the Center for
Health Statistics and Information, National Health Commission of China.
The NHSS covers 31 provinces/autonomous regions/ municipalities and
adopts a multistage cluster random sampling method to ensure
representativeness for different regions and the whole country. The NHSS
is the largest comprehensive investigation of information on operational
characteristics of county hospitals and PHC facilities (i.e., township
health centers and village clinics) and on health status, healthcare
need and demand, healthcare utilization, and financial burden of
healthcare services in China. In 2013 and 2018, the NHSS sampled 156
counties with two county hospitals and five township health centers in
each county, respectively. The NHSS received ethical approval from the
National Statistics Bureau of China\textsuperscript{15}.

In this study, we focus on a subsample of patients with chronic diseases
aged 15 or older in rural areas from the 2013 and 2018 NHSS waves.
People who answered yes to at least one of the following three questions
were defined as having a chronic disease: 1) Have you been diagnosed
hypertension? 2) Have you been diagnosed with diabetes mellitus? 3) Do
you have other diagnosed chronic diseases? Rural patients were
identified by hukou status (i.e., the Chinese household registration
method). We excluded counties that did not match between the 2013 and
2018 waves because their status of pairing assistance cannot be
identified. People under 15 years of age were excluded to reduce the
heterogeneity of the sample. We also dropped observations with missing
values in important variables. Finally, the study sample included 13893
and 22725 patients with chronic diseases in rural areas who lived in the
sampling counties in 2013 and 2018, respectively.

\emph{Study variables}

\emph{Outcomes}

We adopted three outcome variables for healthcare utilization that are
widely used in China's context and in accordance with the existing
literature, including outpatient visit, inpatient admission, outpatient
visit in PHC facilities, and hospital admission within counties.

First, outpatient visit was defined as seeing a doctor in the last two
weeks, which was based on the question in the survey ``Have you seen a
doctor in a clinic or a hospital outpatient department due to illness in
the past two weeks before the survey?''.

Second, inpatient admission was defined as receiving inpatient care in
the past year, which was based on the question ``Have you been
hospitalized due to illness, physical examination, delivery, and other
reasons in the past year before the survey?''.

Third, outpatient visit in PHC facilities was based on the question
``Where was your first visit in the past two weeks before the survey?''

Lastly, hospital admission inside counties was based on the question
``Where were you admitted to?'', and those who answered ``my county''
were recognized.

\emph{Independent variable}

Our main independent variable was the status of pairing assistance (PA)
of a county. We assigned a dichotomous variable for all people that
denoted whether they are in a PA county or not. If a county hospital
sent more than 30\% (\textgreater{}30\%) of its practicing physicians to
paired-up PHC facilities within the medical alliance, this country was
coded as having PA. If a count-level hospital report that there is no
establishment of a medical alliance or send 30\% or less ([?]30\%) of its
practicing physicians to its medical alliance, this county was coded as
having no pairing assistance (NPA). The medical alliance policy was
gradually implemented in different regions, so some counties were still
in the process of building alliance when the NHSS survey started in
2018. The distribution of the PA proportion was highly skewed, and thus
we used a considerable proportion of doctors to define PA county that
represented the county has built a relatively more stable PA mechanism
within their medical alliances. It should be noted that the medical
alliance was launched between 2013 and 2018, but due to data
constraints, the definition of PA was generated from the NHSS 2018 wave.

\emph{Covariates}

The covariates included characteristics at the individual and county
level. Individual-level characteristics included age group (15-34,
35-59, [?]60 years old), gender (male, female), marital status (married,
unmarried), educational level (primary school or below, junior middle
school, senior middle school, college degree or above), annual household
income, self-rated health (very poor, poor, fair, good, very good),
diabetes mellitus (yes, no), hypertension (yes, no), and health
insurance (yes, no). County-level characteristics included population
size, county gross domestic product (GDP), and health human resources
(ratio of physicians with bachelor's degree or higher in the total
number of licensed physicians). We selected these covariates to include
relevant sociodemographic, health status and health system
characteristics that may confound or mediate the relationship between PA
and healthcare utilization.

\emph{Data analysis}

We first used descriptive analysis to describe the characteristics of
the study sample. Then multiple logistic regressions were used to
examine the associations of PA with outpatient visit, inpatient
admission, outpatient visit in PHC facilities and inpatient admission
inside counties. Individual and county characteristics were controlled.
Lastly, we compared PA estimates between 2013 and 2018 using a fully
interactive model to examine changes in PA impact, and the Chow test was
used to compare the differences\textsuperscript{16, 17}. Two-sided
p-values less than 0.05 were considered statistically significant. Stata
15.0 (Stata Corp LP, College Station, TX, USA) was used to perform all
analyzes.

\subsection*{Results}

{\label{results}}

The characteristics of the study sample are presented in Table 1.
Overall, the majority of rural patients with chronic diseases were
female, aged 60 years or older, married and had a low education level of
primary school or below, consistent with the 2013 and 2018 waves. In
2018, 37.9\% of middle-aged 35-59-year-olds in rural areas had chronic
diseases, which was greater than 28.5\% in 2013. The majority of rural
patients with chronic diseases were covered by health insurance. We also
presented the characteristics of all rural residents in two waves of
survey (Supplementary file, Table S1).

\textbf{Table 1.} Characteristics of the study sample\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Characteristics & 2013 & 2013 & 2018 & 2018\tabularnewline
& N & Column \% & N & Column \%\tabularnewline
Total & 13893 & & 22725 &\tabularnewline
Gender & & & &\tabularnewline
Female & 7854 & 56.51 & 12530 & 55.13\tabularnewline
Male & 6039 & 43.50 & 10195 & 44.87\tabularnewline
Age in years & & & &\tabularnewline
15-34 & 134 & 0.97 & 522 & 2.32\tabularnewline
35-59 & 3952 & 28.49 & 8597 & 37.85\tabularnewline
[?]60 & 9807 & 70.54 & 13606 & 59.83\tabularnewline
Marital status & & & &\tabularnewline
Unmarried & 2567 & 18.54 & 3801 & 16.68\tabularnewline
Married & 11326 & 81.47 & 18924 & 83.33\tabularnewline
Education & & & &\tabularnewline
Primary school and below & 9357 & 67.37 & 14691 & 64.39\tabularnewline
Junior middle school & 3621 & 25.98 & 6288 & 27.83\tabularnewline
Senior middle school & 915 & 6.66 & 1746 & 7.80\tabularnewline
Household income (thousand CNY)/Mean (SD) & 2.84 & 2.48 & 3.14 &
3.06\tabularnewline
Self-rated health status & & & &\tabularnewline
Very poor & 197 & 1.42 & 694 & 3.05\tabularnewline
Very poor & 844 & 6.08 & 2126 & 9.32\tabularnewline
Fair & 3701 & 26.67 & 8592 & 37.85\tabularnewline
Good & 6300 & 45.37 & 8431 & 37.16\tabularnewline
Very good & 2845 & 20.47 & 2873 & 12.65\tabularnewline
Diabetes & & & &\tabularnewline
Yes & 1752 & 12.64 & 3456 & 15.22\tabularnewline
No & 12141 & 87.36 & 19269 & 84.79\tabularnewline
Hypertension & & & &\tabularnewline
Yes & 8212 & 59.20 & 12710 & 55.91\tabularnewline
No & 5681 & 40.81 & 10015 & 44.09\tabularnewline
Health insurance & & & &\tabularnewline
Yes & 13768 & 99.11 & 22421 & 98.66\tabularnewline
No & 125 & 0.90 & 304 & 1.34\tabularnewline
\bottomrule
\end{longtable}

Notes: Data sources: NHSS 2013 and 2018 wave; Mean and standard
deviation were reported for household income (thousand CNY).

Table 2 presents the healthcare utilization pattern in the study sample.
The proportion of outpatient visits among rural patients with chronic
diseases increased from 22.69\% in 2013 to 27.54\%, while annual
hospitalization admission increased from 20.72\% in 2013 to 25.44\% in
2018. Meanwhile, fewer patients visited PHC facilities, and the share of
PHC outpatient visits decreased from 79.02\% in 2013 to 75.43\% in 2018.
The share of inpatient admissions inside counties was 86.6\% in 2018. We
also present healthcare care utilization for all rural residents in the
survey (Supplementary file, Table S2).

\textbf{Table 2.} Healthcare utilization of rural residents with chronic
diseases\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Characteristics & 2013 & & 2018 &\tabularnewline
& N & Column \% & N & Column \%\tabularnewline
Outpatient visits & & & &\tabularnewline
Yes & 3152 & 22.69 & 6259 & 27.54\tabularnewline
No & 10741 & 77.31 & 16466 & 72.46\tabularnewline
Inpatient visits & & & &\tabularnewline
Yes & 11015 & 20.72 & 5782 & 25.44\tabularnewline
No & 11015 & 79.28 & 16943 & 74.56\tabularnewline
Setting of outpatient visit & & & &\tabularnewline
Primary health care facilities & 2376 & 79.02 & 4412 &
75.43\tabularnewline
Secondary/tertiary hospitals & 631 & 20.98 & 1437 & 24.57\tabularnewline
Location of inpatient visit & & & &\tabularnewline
Outside the county & \selectlanguage{ngerman}·· & \selectlanguage{ngerman}·· & 775 & 13.40\tabularnewline
Inside the county & \selectlanguage{ngerman}·· & \selectlanguage{ngerman}·· & 5007 & 86.60\tabularnewline
\bottomrule
\end{longtable}

Note: data for location of inpatient visit was not collected in 2013.

Table 3 presents the effect of PA on outpatient and inpatient
admissions. PA had a significant retention effect on outpatient
utilization and also retained inpatient admission to some extent. In
2018, patients with chronic diseases in PA counties were 10\% (OR=0.90,
95\% CI 0.83-0.98) less likely to use outpatient care than in NPA
counties. For comparison, OR for PA was 1.31 (95\% CI 1.16-1.47) in
2013, indicating that patients with chronic diseases in PA places were
1.31 times more likely to use outpatient care in 2013. For inpatient
admissions, patients in PA counties were more likely to use inpatient
care in 2013 (OR=1.22, 95\% CI 1.07 - 1.39), and patients in PA and NPA
counties did not show differences in inpatient admissions in 2018,
although the Chow test showed that the change in PA coefficients was not
statistically significant (p=0.052).

\textbf{Table 3.} The effect of pairing assistance on healthcare
utilization for rural patients with chronic diseases\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
Characteristics & Outpatient visits & Outpatient visits & Outpatient
visits & Outpatient visits & & Inpatient visits & Inpatient visits &
Inpatient visits & Inpatient visits\tabularnewline
& 2013 & 2013 & 2018 & 2018 & & 2013 & 2013 & 2018 & 2018\tabularnewline
& OR & 95\% CI & OR & 95\% CI & & OR & 95\% CI & OR & 95\%
CI\tabularnewline
Pairing assistance (PA) & 1.31*** & (1.16 - 1.47) & 0.90* & (0.83 -
0.98) & & 1.22** & (1.07 - 1.39) & 1.04 & (0.94 - 1.14)\tabularnewline
\emph{Personal Characteristics} & & & & & & & & &\tabularnewline
Gender (ref. female) & 0.87** & (0.80 - 0.95) & 0.94 & (0.88 - 1.01) & &
1.08 & (0.97 - 1.20) & 1.02 & (0.94 - 1.11)\tabularnewline
Age (ref. 15-34) & & & & & & & & &\tabularnewline
35-59 & 1.50 & (0.92 - 2.45) & 1.20 & (0.95 - 1.52) & & 0.60 & (0.34 -
1.07) & 0.91 & (0.69 - 1.20)\tabularnewline
[?]60 & 1.41 & (0.87 - 2.29) & 1.17 & (0.92 - 1.48) & & 0.92 & (0.52 -
1.62) & 1.29 & (0.98 - 1.71)\tabularnewline
Marital status (ref. unmarried) & 0.99 & (0.88 - 1.12) & 1.08 & (0.98 -
1.19) & & 1.13 & (0.98 - 1.31) & 1.14* & (1.01 - 1.28)\tabularnewline
Education (ref. primary school and below) & Education (ref. primary
school and below) & Education (ref. primary school and below) &
Education (ref. primary school and below) & Education (ref. primary
school and below) & Education (ref. primary school and below) &
Education (ref. primary school and below) & Education (ref. primary
school and below) & Education (ref. primary school and below) &
Education (ref. primary school and below)\tabularnewline
Junior middle school & 1.11 & (0.99 - 1.24) & 1.08 & (0.99 - 1.17) & &
1.04 & (0.91 - 1.20) & 1.00 & (0.90 - 1.11)\tabularnewline
Senior middle school & 1.13 & (0.93 - 1.38) & 1.03 & (0.89 - 1.18) & &
1.17 & (0.92 - 1.49) & 1.01 & (0.84 - 1.21)\tabularnewline
College degree and above & \selectlanguage{ngerman}·· & \selectlanguage{ngerman}·· & 0.93 & (0.51 - 1.71) & & 3.12 &
(0.60 - 16.29) & 0.59 & (0.23 - 1.53)\tabularnewline
Self-rated health (ref. very poor) & & & & & & & & &\tabularnewline
Poor & 0.83 & (0.53 - 1.29) & 1.25 & (0.96 - 1.61) & & 1.11 & (0.71 -
1.75) & 1.14 & (0.86 - 1.50)\tabularnewline
Fair & 0.81 & (0.54 - 1.22) & 1.09 & (0.86 - 1.38) & & 0.74 & (0.48 -
1.13) & 0.71** & (0.55 - 0.91)\tabularnewline
Good & 0.75 & (0.50 - 1.13) & 0.83 & (0.65 - 1.04) & & 0.44*** & (0.29 -
0.68) & 0.41*** & (0.32 - 0.53)\tabularnewline
Very good & 0.66 & (0.43 - 1.00) & 0.70** & (0.55 - 0.90) & & 0.33*** &
(0.21 - 0.51) & 0.28*** & (0.21 - 0.37)\tabularnewline
Household income & 0.96*** & (0.94 - 0.98) & 1.00 & (0.98 - 1.01) & &
1.03* & (1.00 - 1.05) & 1.00 & (0.99 - 1.02)\tabularnewline
Medical insurance (ref. uninsured) & 0.86 & (0.54 - 1.38) & 1.05 & (0.76
- 1.46) & & 2.41* & (1.22 - 4.75) & 1.68 & (0.98 - 2.88)\tabularnewline
\emph{County characteristics} & & & & & & & & &\tabularnewline
Area (ref. eastern China) & & & & & & & & &\tabularnewline
Central China & 1.05 & (0.92 - 1.19) & 0.88* & (0.79 - 0.97) & & 1.65***
& (1.41 - 1.93) & 1.24*** & (1.09 - 1.40)\tabularnewline
Western China & 1.00 & (0.87 - 1.15) & 0.98 & (0.89 - 1.09) & & 1.44***
& (1.23 - 1.70) & 1.11 & (0.98 - 1.27)\tabularnewline
County GDP (million CNY) & 1.06*** & (1.03 - 1.08) & 1.01 & (0.99 -
1.03) & & 0.99 & (0.96 - 1.02) & 1.00 & (0.98 - 1.02)\tabularnewline
Ratio of those with bachelor's degree or above in licensed physicians &
0.99*** & (0.98 - 0.99) & 1.00* & (0.99 - 1.00) & & 1.00 & (1.00 - 1.01)
& 1.00 & (1.00 - 1.00)\tabularnewline
Beds for emergency and ICU & 1.00 & (1.00 - 1.00) & 1.00*** & (1.00 -
1.01) & & 1.00 & (1.00 - 1.00) & 1.00** & (1.00 - 1.01)\tabularnewline
Observations & 13,877 & & 22,716 & & & 13,887 & & 22,716
&\tabularnewline
Chow test (PA in 2013 vs PA in 2018) & Chi-square value=24.42 &
Chi-square value=24.42 & P\textless{}0.001 & & & Chi-square value=3.78 &
Chi-square value=3.78 & P=0.052 &\tabularnewline
\bottomrule
\end{longtable}

Notes: *** p\textless{}0.001, ** p\textless{}0.01, * p\textless{}0.05;
OR, odds ratio; CI, confidence interval; ICU, intensive care unit of the
hospital; CNY, Chinese yuan renminbi.

Table 4 presents the effect of PA on places of healthcare utilization.
PA did not transfer more patients to PHC facilities. Rural patients with
chronic diseases in PA counties were more likely to use PHC outpatient
care in 2013 (OR=1.45, 95\% CI 1.10-1.90), but the significance
disappeared in 2018 (OR=0.85, 95\% CI 0.71-1.01), which indicated that
PA could not contribute to the transfer of patients with chronic
diseases to PHC facilities (p=0.001). The OR for PA was 1.72 (95\% CI
1.32-2.24), indicating that inpatients in PA counties were more likely
to choose hospitals inside counties. PA and NPA counties did not have
any differences in the use of PHC outpatient care in 2018 after
controlling covariates, but PA counties were associated with increased
odds of using inpatient care within counties.

\textbf{Table 4.} The effect of pairing assistance on places of
healthcare utilization for rural patients with chronic diseases\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Characteristics & Outpatient visits in PHC & Outpatient visits in PHC &
Outpatient visits in PHC & Outpatient visits in PHC & & Inpatient visits
inside the county & Inpatient visits inside the county\tabularnewline
& 2013 & 2013 & 2018 & 2018 & & 2018 & 2018\tabularnewline
& OR & 95\% CI & OR & 95\% CI & & OR & 95\% CI\tabularnewline
Pairing assistance & 1.45** & (1.10 - 1.90) & 0.85 & (0.71 - 1.01) & &
1.72*** & (1.32 - 2.24)\tabularnewline
\emph{Personal Characteristics} & & & & & & &\tabularnewline
Gender (ref. female) & 0.98 & (0.79 - 1.20) & 0.98 & (0.84 - 1.14) & &
1.10 & (0.89 - 1.36)\tabularnewline
Age (ref. 15-34) & & & & & & &\tabularnewline
35-59 & 3.39** & (1.43 - 8.06) & 1.13 & (0.71 - 1.80) & & 1.50 & (0.85 -
2.67)\tabularnewline
[?]60 & 6.11*** & (2.55 - 14.65) & 1.64* & (1.03 - 2.61) & & 1.92* & (1.08
- 3.42)\tabularnewline
Marital status (ref. unmarried) & 0.86 & (0.64 - 1.15) & 0.82 & (0.64 -
1.05) & & 0.82 & (0.61 - 1.10)\tabularnewline
Education (ref. primary school and below) & Education (ref. primary
school and below) & Education (ref. primary school and below) &
Education (ref. primary school and below) & Education (ref. primary
school and below) & Education (ref. primary school and below) &
Education (ref. primary school and below) & Education (ref. primary
school and below)\tabularnewline
Junior middle school & 1.12 & (0.87 - 1.45) & 0.83* & (0.70 - 0.99) & &
0.54*** & (0.42 - 0.69)\tabularnewline
Senior middle school & 1.18 & (0.75 - 1.85) & 0.74* & (0.55 - 0.99) & &
0.54** & (0.36 - 0.82)\tabularnewline
College degree and above & \selectlanguage{ngerman}·· & \selectlanguage{ngerman}·· & 1.75 & (0.38 - 8.14) & & 0.27 &
(0.06 - 1.21)\tabularnewline
Self-rated health (ref. very poor) & & & & & & &\tabularnewline
Poor & 0.73 & (0.27 - 1.96) & 1.69* & (1.05 - 2.71) & & 0.97 & (0.54 -
1.74)\tabularnewline
Fair & 1.46 & (0.56 - 3.80) & 2.06** & (1.33 - 3.17) & & 1.13 & (0.67 -
1.91)\tabularnewline
Good & 1.71 & (0.66 - 4.44) & 2.58*** & (1.66 - 4.00) & & 1.23 & (0.72 -
2.10)\tabularnewline
Very good & 2.68 & (1.00 - 7.22) & 2.80*** & (1.75 - 4.48) & & 1.64 &
(0.88 - 3.07)\tabularnewline
Household income & 0.89*** & (0.85 - 0.93) & 0.96** & (0.93 - 0.99) & &
0.99 & (0.95 - 1.02)\tabularnewline
Medical insurance (ref. uninsured) & 0.80 & (0.29 - 2.22) & 1.68 & (0.75
- 3.75) & & 0.47 & (0.15 - 1.52)\tabularnewline
\emph{County characteristics} & & & & & & &\tabularnewline
Area (ref. eastern China) & & & & & & &\tabularnewline
Central China & 0.55*** & (0.40 - 0.77) & 0.77* & (0.63 - 0.95) & &
1.46* & (1.08 - 1.98)\tabularnewline
Western China & 0.67* & (0.47 - 0.96) & 1.03 & (0.84 - 1.28) & & 0.70* &
(0.51 - 0.96)\tabularnewline
County GDP (million CNY) & 0.86*** & (0.81 - 0.91) & 0.87*** & (0.84 -
0.91) & & 0.99 & (0.93 - 1.05)\tabularnewline
Ratio of those with bachelor's degree or above in licensed physicians &
1.01* & (1.00 - 1.02) & 1.00 & (0.99 - 1.00) & & 1.00 & (1.00 -
1.01)\tabularnewline
Beds for emergency and ICU & 1.00 & (0.99 - 1.00) & 1.00 & (1.00 - 1.01)
& & 1.00 & (0.99 - 1.01)\tabularnewline
Observations & 3,007 & & 5,849 & & & 5,782 &\tabularnewline
Chow test (PA in 2013 vs PA in 2018) & Chi-square value=10.43 &
Chi-square value=10.43 & P=0.001 & & & \selectlanguage{ngerman}·· &\tabularnewline
\bottomrule
\end{longtable}

Notes: *** p\textless{}0.001, ** p\textless{}0.01, * p\textless{}0.05;
OR, odds ratio; CI, confidence interval; ICU, intensive care unit of the
hospital; CNY, Chinese yuan renminbi.

\subsection*{Discussion}

{\label{discussion}}

This paper examined the effect of PA on PHC facilities on outpatient and
inpatient utilization among rural patients with chronic diseases in the
context of the development of medical alliances in China. There were two
key findings in this study. First, PA contained the rising outpatient
and inpatient utilization for rural patients with chronic diseases in
China. Second, PA did not appear to transfer more rural patients with
chronic diseases to PHC facilities. This study added evidence to the
ongoing reform of the medical alliance in China and could provide
implications for other countries that planned similar vertical
integration of health facilities.

The fragmentation of the health system has been a worldwide problem that
leads to inefficiency, ineffectiveness, inequality, depersonalization,
and commercialization \textsuperscript{18}. The World Health
Organization has called for an integrated people-centered health system
to improve health and health care for all people, and building strong
PHC-based systems has been emphasized greatly \textsuperscript{19}.
Countries around the world are exploring different strategies of
integrated care models, such as Australia \textsuperscript{20}. PA under
the national medical alliance policy in China is an exploratory
initiative that strives to ensure appropriate training and intersectoral
action in health and coordination of comprehensive services for all
conditions across disciplines.

Our findings indicated that PA had a positive impact on containing the
rising outpatient and inpatient utilization in China. The overuse of
healthcare services has been recognized globally as a problem. Studies
have suggested that the overuse of various services ranged from
approximately 1 to 80\% in the United States \textsuperscript{21}. The
overuse of healthcare services has become an increasingly serious
problem in China and has attracted more attention \textsuperscript{22,
23}. When health insurance coverage is universal, the moral hazard that
providers overprovide and patients overuse healthcare services arises,
resulting in a waste of resources and massive health
expenditures\textsuperscript{23}. PA under medical alliances has
positive effects on containing the increased utilization of outpatient
and inpatient care, which would help save unnecessary utilization and
contain increasing medical expenditure in the long run.

Under-resourced PHC and lack of health professionals are key barriers to
integrated care \textsuperscript{24}. PA in China's medical alliance
policy proposed a promising solution. Doctors were sent to the lower
level PHC facilities allied to county hospitals to not only provide
medical services directly to rural patients, but also provide training
in treatment, recovery and skills in specific diseases to PHC
doctors\textsuperscript{25, 26}. The capabilities of PHC doctors could
be improved after PA. Patients with chronic diseases usually have more
frequent healthcare utilization and require continuous treatment and
management to prevent complications and early death\textsuperscript{27}.
Under the medical alliance policy, prevention services are tailored to
routine care for patients with chronic diseases, which could help to
reduce unnecessary healthcare utilization and increase resource
efficiency.

The share of outpatient PHC visits among total outpatients decreased
significantly between 2013 and 2018. This phenomenon has also been
documented in other studies \textsuperscript{5, 23}. PA under medical
alliances did not reverse the downward trend of the share of PHC
outpatient visits until now. This finding was consistent with a recent
study that found that the performance of township health centers had not
improved significantly \textsuperscript{28}. In medical alliances,
county hospitals used to be medical facilities with more resources, but
now they were empowered to also have administrative functions over PHC
facilities. Within a county, county hospitals have the advantage of
monopoly that will be further reinforced by building medical
alliances\textsuperscript{12}. Without competition pressure, medical
alliances will have no continuous motivation to reform and innovate.
Paired physicians attracted and transferred more patients to county
hospitals through their contact with patients. The financing schemes of
township health centers and county hospitals are different -- township
health centers are fully funded by government funding but county
hospitals are only partially funded and still need to earn most of the
revenue by providing services. Therefore, PA doctors who belong to
county hospitals have incentives to attract more patients to their
hospitals. Future policies on medical alliance should pay attention to
the risk of the monopoly of county hospitals, especially in counties
where local government health departments had a weak power of
coordination and supervision. The benefit package could be delivered to
the medical alliance to incentivize prevention services and utilization
of PHC.

Without data for 2013, we cannot determine whether pairing assistance
contributed to retaining patients within counties because it could be
due to differences between counties with respect to implementing medical
alliances; for example, wealthy local governments with platform support
are more likely to seek innovation in building medical
alliances\textsuperscript{29}. The increase in the share of visits
within counties is more a comprehensive effect of medical alliance
rather than pairing assistance alone. A study that examined the first
three provinces that implemented medical alliances found that the
counties that implemented medical alliances all had a large increase in
the share of inpatient admission within counties \textsuperscript{30}.

This study was subject to several limitations. First, the NHSS was a
repeated cross-sectional survey. Although we controlled sociodemographic
characteristics, health status, medical insurance and county
characteristics, we cannot eliminate the effect of unobserved individual
characteristics. Second, the establishment of medical alliances involves
multiple policies simultaneously, for example, enhancing the capacity of
county hospitals. Therefore, changes in the outcomes might also contain
the effect of other policies. Third, our definition for PA might not
reflect the assistance quality. Services among paired doctors from
different medical alliances in China varied. Future research could
explore better definitions and measurements for PA to strengthen the
evidence base.

\subsection*{Conclusions}

{\label{conclusions}}

Pairing assistance from county hospitals with PHC facilities has been a
major strategy in medical alliances to improve the healthcare delivery
system. This study examined the effect of pairing assistance on rural
patients with chronic diseases as vulnerably frequent PHC users, and we
found that pairing assistance contained the increasing outpatient visits
and inpatient admissions. Pairing assistance did not reverse the
downward trend in the share of PHC outpatient visits. Pairing assistance
under medical alliances provides a potential path toward integrated
people-centered health systems for other low- and middle-income
countries. However, the government should pay attention to avoiding the
potential monopoly of county hospitals.

\subsection*{}

{\label{section}}

\subsection*{Declarations}

{\label{declarations}}

\textbf{Ethical approval and consent to participate:} The study has been
approved by the National Statistics Bureau of China and was performed in
accordance with the Declaration of Helsinki. Written informed consent
was obtained from all participants prior to questionnaire
administration.

\textbf{Consent for publication:} not applicable

\textbf{Data availability:} The data underlying this article will be
shared upon reasonable request to the corresponding author.

\textbf{Competing interests:} The authors declare that they have no
conflict of interest.

\textbf{Funding:} The authors received no financial support for the
research, authorship and publication of this article.

\textbf{Author contributions:} H.Z. conceived of the study. H.Z. and
X.L. designed the study. H.Z. and M.L. created the analysis plan. M.L.
processed the dataset and conducted the analysis. M.L. drafted the
article. H.Z, X.L, H.T. and X.C. revised the paper and contributed to
critical revision of the article.

\textbf{Acknowledgements:} The authors would like to thank the China
Center for Health Statistics and Information, who collected data and
assisted with data access for the study.

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