In China, patients increasingly choose to access already severely overcrowded higher level hospitals, leaving lower level facilities with low utilization rates. This situation undermines the effectiveness and efficiency of the health system. The situation tends to worsen despite policy measures aimed at improvement. We systematically review the factors affecting patient choice to synthesize scientific understanding of health system access in China. The review provides an evidence base for measures to direct patient flow towards lower level facilities.
We screened the peer-reviewed literature published from April 2009 to January 2016 that investigates Chinese patients’ choice of health care facilities at different levels and assessed 45 studies in total. We applied two structured forms to extract data on each study’s characteristics, methodology, and factors.
Results of data synthesis
The results identified four factor types: 1) patient, 2) provider, 3) context and 4) composite: combined patient, provider, and/or context attributes. Patient factors are mentioned the most, but the evidence on patient factors is often inconclusive. Evidence suggests that the provider factors ‘drug variety’ and ‘equipment’, and composite factor ‘perceived quality’, push patients from lower levels towards higher levels.
Underuse of primary care facilities and overcrowding of higher level facilities will likely be amplified by current demographic trends. Evidence suggests that improving drug availability, equipment and perceived quality of primary care services can improve the situation. Well-designed research that considers the interactions between factors is called for to better inform future interventions.
Citation: Liu Y, Kong Q, Yuan S, van de Klundert J (2018) Factors influencing choice of health system access level in China: A systematic review. PLoS ONE 13(8): e0201887. https://doi.org/10.1371/journal.pone.0201887
Editor: Massimo Ciccozzi, National Institute of Health, ITALY
Received: October 21, 2017; Accepted: July 24, 2018; Published: August 10, 2018
Copyright: © 2018 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was supported by China Scholarship Council [grant number 201507720036; URL: http://en.csc.edu.cn/; receiver: YL]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Since the turn of the millennium, the Chinese government has made unprecedented investments to improve its health system. Government spending on health care has grown tenfold to a total budget of 1,243 billion RMB in 2016 . By November 2016, the number of hospitals was increased to 29,000 and the number of primary care facilities amounted to 930,000 . Supply-side growth, however, continues to be outpaced by the growth in demand, particularly for higher level hospitals . The resulting overcrowding in higher level hospitals and low utilization of primary care facilities undermine the effectiveness and efficiency of the health system [4–7]. Here we review the scientific evidence for factors that influence the patient’s choice of health care access level, as a step toward developing evidence-based interventions to improve patient flow.
The Chinese health system defines hospitals as “medical institutions having more than 20 beds” and distinguished the hospital system in “3 levels and 10 classes of hospital system” [8,9] as shown in Fig 1. The general population is free to choose health care facilities without being restricted by a gatekeeping mechanism . In rural areas, township health centers (THCs) and village clinics offer grass roots primary care and public health services. In urban areas, these services are provided by community health centers (CHCs) and community health stations [5,11].
In the first 11 months of 2016, the number of primary care visits decreased by 0.6% to 3.93 billion , thus sustaining the low utilization rates of lower level facilities . Over the same period, the number of hospital visits increased by 5.6% compared to 2015, to a total of 2.89 billion . Moreover, patients in China increasingly access the health system at hospitals on level 2 and 3 , which has resulted in overcrowding of level 3 hospitals particularly. This is further illustrated by the “three longs and one short” phenomenon : long waiting time for registration, long waiting time to prepay the charges, long waiting time for the appointment with a doctor, but a short appointment duration. This situation has generated great patient discontent  and caused deterioration of the patient-doctor relationship .
The situation and corresponding challenges to effectiveness and efficiency may be further amplified by future societal developments such as increased welfare, expanded health insurance coverage, rapid urbanization, and aging of the population [16,17]. Therefore, in order to develop a sustainable, cost-effective health system, ongoing Chinese health system reforms target strengthening primary care facilities and directing patients toward the lower levels of care. Examples are the introduction of gradient reimbursement schemes [4,7,18] and the continuously increasing resources spending on primary care infrastructure [7,19].
Scientific understanding of the effect of such interventions is limited [12–14] and this effect depends highly on the influence on the access choices of the population. While some empirical [20,21] and theoretical studies [22–24] address this topic, scientific research focused on the influence of reform interventions on access choices is scarce. Moreover, the difficulty that actual reforms have in effectively directing access choice indicate that currently available theory and evidence may be insufficient to inform policy making. The apparent complexity of the relationships between reform intervention and access choice or health-seeking behavior calls for an empirical evidence base, which can facilitate the design and implementation of more effective interventions and help researchers develop empirically grounded theory. With these objectives, we present a systematic review of empirical evidence on factors influencing access level choice.
We conducted this systematic review in accordance with National Health Service Centre for Reviews and Dissemination Guidance for undertaking reviews in health care  (see S1 Appendix). We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  for reporting purposes.
We searched Embase, Medline, Web of Science, and Pubmed for English language articles, and three large Chinese databases (CNKI, VIP and Wanfang) for articles in Chinese. As the new round of health reform starting in April 2009  brought considerable change, we sought articles that investigated Chinese patients’ choice of health care access levels between April 2009 and January 2016. The detailed search strategies (see S1 Text) were executed by a medical librarian and the first author.
The following inclusion criteria were applied during study selection: (1) primary empirical studies; (2) research aimed at identifying factors that influence patients’ choice of health care facility access level, and how these factors affect the choice of level; (3) data collected after April of 2009; (4) study population is Chinese residents; (5) written in English or Chinese language; (6) published in a peer-reviewed journal.
Two authors (YL and one other, either QK or SY) screened each record independently. The first round of study selection was to screen titles and abstracts of primarily identified articles based on the inclusion criteria. In the case of disagreement between reviewers, the articles were included. In the second round, the full text of each selected article was assessed for eligibility using the inclusion criteria. Eligibility assessment discrepancies were discussed until consensus was reached. Twice, we found two articles reporting analysis of the same data. In both cases, we combined the findings and presented them under the earliest included article (reducing the number of studies from 47 to 45).
We developed a first form to extract the characteristics of each study by following the broad format of PICO (Population, Intervention, Comparison and Outcomes) guideline , and made necessary adaptations to the study characteristics by adding more information of interest. We then developed a second form to extract findings regarding the factors mentioned in each study. Factors were labeled by type (patient, provider and context); we also allowed new factor types. When including studies that considered patient choice with respect to provider facilities rather than the level of the provider facilities, we considered the facility level only.
Some included studies use qualitative methods, others use quantitative methods, and a third subset uses mixed methods. We thus conducted a narrative synthesis, which is a systematic review methodology that appropriately accommodates the heterogeneity of the included articles . For the quantitative results, we extracted only the information regarding associations reported as significant.
For each of the factors and choices reported, we extracted whether they were stated (e.g. in interviews or questionnaires) or revealed (e.g. on actual visits) given that revealed factors and choices may be considered to provide stronger evidence than stated factors and choices . Therefore, we distinguished four evidence types: a revealed factor for a revealed choice (RR), a stated factor for a revealed choice (RS), a stated factor for a stated choice (SS), and a revealed factor for a stated choice (SR). We provide further insight into the workings of each factor by identifying whether it positively or negatively affected choice for a certain level. To this purpose, we speak of attraction when a factor is positively associated with choice for a certain level, and of repulsion when the association is negative.
When synthesizing the data, we firstly considered whether the evidence reported in the studies was conclusive or inconclusive. Evidence is classified as conclusive if the research methods employed provide an unambiguous answer to the stated empirical research question (e.g. the hypothesis is accepted) . If the results of the included studies contradict each other, the review classifies them as inconsistent. Otherwise, they are considered to be consistent.
Characteristics of the included studies and quality assessment
As shown in Fig 2, we initially retrieved a total of 18,855 records. After removing duplicates and applying the inclusion criteria, we were left with a final set of 45 articles [23,24,32–74]. Table 1 shows the basic information of these articles and the results of the quality assessment.
For ease of exposition, Figs 3 and 4 summarizes the characteristics of the studies. Except for one quasi-experimental study, all studies are observational (n = 44). The data are collected mostly from questionnaires (n = 23). Other data sources include interviews (n = 12), registration databases (n = 10) and combinations of questionnaires and interviews (n = 10). The number of studies that take the general population as respondents (n = 20) is slightly larger than those with patients or service users as respondents (n = 15). 10 studies have both types of respondents. The reported sample size varied from 80 to 162,464. 14 Studies have a sample size of less than 1,000 individuals.
(A) Distribution of data sources. (B) Distribution of respondent types. (C) Distribution of sample sizes. (D) Evidence types. (E) Distribution of quality assessment scores. *The number in each slice of the pie chart indicates the number of studies with the corresponding attribute of interest.
A majority of the studies reports results on revealed factors, either for revealed choices (n = 23), or for stated choices (n = 18). 11 Studies report stated factors for stated choices and five studies report stated factors for revealed choices. The most frequently studied provinces are Guangdong (n = 11), Shandong (n = 6), Beijing (n = 4) and Sichuan (n = 4; including Chongqing). The MMAT quality score was 100% for 13 studies, 75% for 25 studies, 50% for six studies and 25% for one study.
Identified factors influencing patient’s choice
The factors identified in the studies are presented with brief notes in Table 2, and in detail in Table 3 and S2 Text. We found 15 patient factors, nine provider factors, and four context factors. In addition, we found six factors of a new type, which we call ‘composite factors’. These include attributes of more than one of the other three types of factors.
The most frequently indicated patient factors are age (n = 18 studies), health insurance status (n = 15 studies), income (n = 13 studies) and education (n = 11 studies). The most often found provider factors include drug availability (n = 13 studies), medical equipment (n = 8 studies), service price/cost-effectiveness (n = 7 studies) and service attitude (n = 6 studies). Context factors were reported less frequently: capitation/gatekeeping (n = 2 studies), freedom of service choice (n = 2 studies), salary reform on health workers (n = 1 study) and public campaign/interaction of social capital (n = 1 study). The most frequently identified composite factors are perceived quality of care (n = 16 studies), transportation convenience/distance (n = 9 studies) and reimbursement rate/insurance coverage (n = 7 studies).
Effects of identified factors on patient’s choice
Table 4 gives an overview of whether factors attracted or repulsed patients, and for which facility levels. The reader may first notice that the synthesized evidence on patient factors age, insurance status, pre-existing disease, disease severity, gender, marital status, and location of residence is inconclusive. For instance, there is evidence that older people are repulsed by both lower and higher level facilities while female patients are attracted by both lower and higher level facilities.
Patient factors positively associated with lower level attraction are: lower education level, retired patients/working for governments/peasants, and patients of the Han ethnicity. Attracting lower level provider factors are lower and unified drug price, service price, and good service attitude. Composite factors and context factors which cause lower level facilities to attract patients are the short distance to home, transportation convenience, implementation of capitation and gatekeeping, previous experience with provider, knowledge about CHCs or THCs, being exposed to publicity campaigns, and high social capital.
Repulsive patient factors for lower level facilities are health knowledge, habit of seeking help from higher level facilities, regular physical exercise, and high anxiety to seeing a doctor. The most repulsive provider factors for low level facilities are limited drug variety, obsolete medical equipment and discomfort. The limited service portfolio of lower level facilities is another repulsing factor. The composite factor perceived poor quality is frequently reported to repulse patients, although some studies report that patients consider lower level facilities to be reliable. Repulsing context factors for level facilities are complexity of the referral procedure, and limited freedom of choice following from general practitioner contracts. The implementation of salary reform at primary level facilities caused them to repulse.
The included studies provide little evidence for factors explicitly addressing access at higher level facilities. Patient factors that attract to higher levels are higher level of education, habit of seeking medical care at higher level facilities, and employment at a large enterprise. The purpose of seeking confirmation of disease diagnosis also stimulated patient flow towards higher level facilities. The most attractive provider factors are drug variety, medical equipment, and physical environment. Other than high price, patient crowding, and difficulty to see a doctor, we found no evidence on repulsion with regard to higher level facilities.
Main findings and interpretations
We first summarize the evidence on the factors influencing health system access level choice, thus outlining the contribution to the necessary advancement of scientific understanding and development of evidence-based interventions. In the process, we interpret the evidence in relation to previously reported literature and the ongoing reforms. A general reflection on relevant theory and policy is subsequently presented.
Patient factors are the most reported. Interestingly, while the patient factors age, health insurance status, income, education, pre-existing condition, and disease severity received most attention, the evidence for these factors is inconclusive. Thus, based on the review, for instance, we cannot conclude that elderly patients choose primary care more frequently, or less frequently.
The evidence on the factor education is conclusive. Better education is associated with accessing higher levels (as is further supported by the association between health literacy and access at higher levels). The evidence on income level and disease severity is almost conclusive. Most of the studies (12/13) found that people with higher income are more likely to choose higher level facilities. These findings suggest that inequality in the health system access persists . Geography may operate as an underlying factor, as patients from remote rural areas tend to have lower incomes and live further away from higher level facilities [75–77]. Evidence for the patient factor disease severity is also almost conclusive. Five out of six studies investigating disease severity reported that people with perceived minor diseases preferred lower level facilities, while people with more severe conditions preferred high access levels. This might be explained by the limited trust people attach to lower level facilities and might relate to the composite factor perceived quality discussed below.
The provider factors drug variety, equipment, followed by service price, and service attitude received the most attention. Limited drug variety and lack of equipment at lower level facilities cause patients to access higher levels. These findings echo earlier evidence that patients attach much importance to provider factors believed to be associated with effectiveness, i.e. clinical outcomes . In terms of the Structure-Process-Outcome model to explain quality of care developed by Donabedian , these factors relate to structures which patients appear to associate with poor outcomes  and hence cause lower levels to repulse . From a policy perspective, this suggests that interventions to improve the structure, for instance improving drug variety by extending the essential medicine list, or by investing in equipment, may help to direct patient flows toward the lower levels. The recent encouragement of health authorities to invest in independent regional diagnostic medical imaging centers  may result in similar effect.
Factors of the context type that influence patient choice mostly relate to gatekeeping and referral policies. The perceived high complexity of referral procedures, and limited freedom of access choice when registering with a general practitioner cause lower levels to repulse. This suggests that policy interventions to improve ease of referral can help direct patient flows towards lower levels.
This systematic review has produced a new factor type: composite factors, including such factors as perceived quality of care, transportation convenience, travel distance, and reimbursement rate that are frequently reported to influence access choice both in China and elsewhere (e.g., in relation to bypassing nearby facilities [81,82]). Factors are classified as composite when they relate to combinations of patient attributes, provider attributes and/or context attributes.
Current reforms are intended to direct patient flow by changes in coverage and diversifying reimbursement rates . Interestingly, we found that when the reimbursement rate or coverage became more generous, patients tended to choose higher level facilities more frequently, even when lower level reimbursement changes were larger. Apparently, copayment reductions at higher levels have more effect than relatively higher reductions at lower level facilities. This is congruent with patient factor findings where higher income and education are positively associated with access at higher levels. These results may suggest an underlying affordability factor to be at work, causing patients who can afford it to choose access at higher levels. However, our review did not reveal any results on the relationships between factors. Current understanding of (and evidence for) interactions among factors is poor. While this identifies a relevant area for future research, it also calls for modesty when deriving policy implications from this review.
As a more general reflection, our results reveal that most of the evidence is in regard to factors that push patients away from the lower levels (repulsion) and cause them to seek care at higher levels. Lack of drug variety, (obsolete) medical equipment, and perceived poor quality are the most important among such factors. Hence our review indicates that for many Chinese citizens, the lower levels are not the ‘first point of access’ that primary care is intended to be according to the Declaration of Alma Ata , which explicitly mentions primary health care to “form an integral part of a country’s health system, of which it is the central function and main focus” and “first level of contact of individuals, the family and community with the national health system”. The identified factors and evidence allow for some corresponding theoretical interpretation for this finding.
Classifying factors as attracting or repulsing relates to push and pull factor theory, as for instance considered by Bansal et al.  to explain why people migrate to other countries or switch service providers. While they focus on provider related push and pull factors, their framework also includes other (mooring) factors which relate to the person (patient) and context . Herzberg  considers push and pull factors to explain why employees leave their employer organization. He relates the factors to Maslow’s needs hierarchy  and considers push factors to be more fundamental as they relate to basic physiological and safety needs.
Building on these related theories, we may interpret provider related factors such as drug variety, equipment, and perceived quality to push patients away from the (default) primary care, because primary care facilities are not trusted to safely address basic patient health needs. It may also explain why disease severity pushes toward higher level facilities, as more severe diseases form a larger threat to basic needs. Moreover it suggests that patients who can afford will often choose access at higher levels, as indicated by the evidence on the factors higher income, education, and reimbursement.
Reasoning along these lines, one may deduce that further economic development, and more generous reimbursement will increase the number of patients who can afford to access higher levels, thus pushing an even larger population away from primary care and to overcrowded high level hospitals. The evidence on the patient flow data in 2016  provided in the introduction supports these arguments. From a policy perspective, this stresses the importance of lower level ability to provide safe health services for fundamental health needs, and to be trusted to refer to when required to address fundamental health needs.
As the context of health policy changes rapidly in China [16,89] and new developments advance rapidly (e.g. encouragement of private hospitals  and innovations such as e-consults [91,92]), the validity of some of the evidence provided by this systematic review reduces over time.
Second, most of the evidence is derived from observational designs without adjustment for confounders or consideration of interactions among factors. Hence, our review delivers little evidence that demonstrates causal relationships between factors and choice. Likewise, the designs of the included studies varied considerably, preventing us from presenting synthesized findings on effect sizes, as might be obtained through meta-analysis when enough high quality quantitative studies are available. Obviously, effect sizes forms an important direction for future research as well.
Eastern China is overrepresented in the included studies. This calls for caution when applying the findings nationwide, or in Western Chinese contexts and other under-studied regions. In addition, it calls for further research in other parts of China.
The present problem in the Chinese health system of overcrowding in higher level hospitals and underuse of lower level facilities is driven by patient access choices. However, current scientific evidence on the factors influencing patient access choices is limited. This systematic review reveals that higher income, higher education, and urbanization are associated with access at high levels. As urbanization and income are increasing in China, as is the education level, our results suggest that current problems may worsen, and may further threaten the effectiveness and efficiency of health services in China.
Patients appear to be pushed towards higher level facilities by the perceived inability of lower level facilities to address basic health needs. This inability is predominantly expressed by the factors lack of drug variety, obsolete equipment and perceived poor quality. From a policy viewpoint, our results suggest that improving lower level structures and quality perceptions of lower level institutions, in combination with a trusted referral system, may promote access at lower levels. This can help the primary care to regain its intended central function and improve the Chinese health system at large.
As the identified evidence is inconsistent for many identified factors, it is likely that contextual factors are not yet well understood, and that interactions between factors play a role. As of yet, these interactions have not received attention. Moreover, effect sizes remain uncertain, and very little evidence exists for western China. Therefore, the scientific evidence base to support policy interventions aiming to promote the utilization of primary care facilities in China deserves extension.
S2 Text. Detail description of identified factors influencing patient’s choice.
S3 Text. Background information on the Chinese health system.
The authors thank Fang Wang (Institute of Medical Information, Chinese Academy of Medical Sciences) and Judith Gulpers (Erasmus University Rotterdam) for advice on screening Chinese articles, Gusta Drenthe (Erasmus University Rotterdam) and Wichor Bramer (Erasmus Medical Center) for help with the literature search.
- 1. Ministry of finance of the people’s republic of china. National General Public Budget Expenditure 2016. 2016; Available at: http://yss.mof.gov.cn/2016czys/201603/t20160325_1924496.html. Accessed 24March, 2017.
- 2. Statistics Information Center of National Health and Family Planning Commission. Quantity of National Health Service Facilities in November 2016. 2017; Available at: http://www.moh.gov.cn/mohwsbwstjxxzx/s7967/201702/0a644a51bfc347ccab43fb1766aa5089.shtml. Accessed 24March, 2017.
- 3. Wang Y, Fang M, Wang Y. How to decrease violence against doctors in China? Int J Cardiol 2016;211:66. pmid:26977580
- 4. Meng Q, Fang H, Liu X, Yuan B, Xu J. Consolidating the social health insurance schemes in China: towards an equitable and efficient health system. The Lancet 2015;386(10002):1484–1492.
- 5. Eggleston K, Ling L, Qingyue M, Lindelow M, Wagstaff A. Health service delivery in China: a literature review. Health Econ 2008;17(2):149–165. pmid:17880024
- 6. Hu S, Tang S, Liu Y, Zhao Y, Escobar M, De Ferranti D. Reform of how health care is paid for in China: challenges and opportunities. The Lancet 2008;372(9652):1846–1853.
- 7. Li X, Lu J, Hu S, Cheng K, De Maeseneer J, Meng Q, et al. The primary health-care system in China. The Lancet 2017;390(10112):2584–2594.
- 8. National Health and Family Planning Commission of the People's Republic of China. Interim Measures for Hospital Acreditation. 2011; Available at: http://www.nhfpc.gov.cn/zwgkzt/wsbysj/201109/53040.shtml. Accessed 31March, 2017.
- 9. National Health and Family Planning Commission of the People's Republic of China. Basic standards for medical institutions (for trial). 1994; Available at: http://www.nhfpc.gov.cn/yzygj/s3577/200804/13cac302fc0a422b80de18612f6d3d9a.shtml. Accessed 31March, 2017.
- 10. Eggleston K. Health Care for 1.3 Billion: An Overview of China’s Health System (January 9, 2012). Stanford Asia Health Policy Program Working Paper No. 28. Available at SSRN: https://ssrn.com/abstract = 2029952 or https://doi.org/10.2139/ssrn.2029952.
- 11. National Health and Family Planning Commission of the People's Republic of China. What services can be provided by primary health care institutions? 2009; Available at: http://www.nhfpc.gov.cn/tigs/s9664/200904/81df89f6981842dd9e47d2ff0951bac3.shtml. Accessed 31March, 2017.
- 12. Statistics Information Center of National Health and Family Planning Commission. National Medical Services Summary from January to November in 2016. 2017; Available at: http://www.moh.gov.cn/mohwsbwstjxxzx/s7967/201702/79b6d9e3bf9e40e6a8efa1328b80ada9.shtml. Accessed 24March, 2017.
- 13. Chao J, Lu B, Zhang H, Zhu L, Jin H, Liu P. Healthcare system responsiveness in Jiangsu Province, China. BMC Health Services Research 2017;17(1):31. pmid:28086950
- 14. Li H, Qian D, Griffiths S, Chung RY, Wei X. What are the similarities and differences in structure and function among the three main models of community health centers in China: a systematic review. BMC health services research 2015;15(1):504.
- 15. Correspondence. Violence against doctors in China. Lancet 2014;384(9945):744–745.
- 16. Yip WC, Hsiao WC, Chen W, Hu S, Ma J, Maynard A. Early appraisal of China's huge and complex health-care reforms. The Lancet 2012;379(9818):833–842.
- 17. Meng Q, Xu L, Zhang Y, Qian J, Cai M, Xin Y, et al. Trends in access to health services and financial protection in China between 2003 and 2011: a cross-sectional study. The Lancet 2012;379(9818):805–814.
- 18. Pan J, Qin X, Li Q, Messina JP, Delamater PL. Does hospital competition improve health care delivery in China? China Economic Review 2015;33:179–199.
- 19. Wu D, Lam TP, Lam KF, Zhou XD, Sun KS. Health reforms in china: the public’s choices for first-contact care in urban areas. Fam Pract 2017;34(2):194–200. pmid:28122845
- 20. Qian D, Lucas H, Chen J, Xu L, Zhang Y. Determinants of the use of different types of health care provider in urban China: A tracer illness study of URTI. Health Policy 2010;98(2):227–235.
- 21. Brown PH, Theoharides C. Health-seeking behavior and hospital choice in China's new cooperative medical system. Health Econ 2009;18(S2):S47–S64.
- 22. Xiong J, Xia x, Cheng X, Li Y. A SEM-based Study on Influence Factors of Hospital Choice. The Chinese Health Service Management 2012(2):84–87.
- 23. Chen Y, Zhao C, Dai T. The impact to the satisfaction and health seeking intention of out-patients after implementing essential medicine system. Chinese Journal of Health Policy 2013(04):26–30.
- 24. Zhou H, Ye C, Zhu B, Wang R, Peng Z, Wang B, et al. Multilevel Model Analysis on Health Seeking Behavior of Patients with Chronic Diseases in Shenzhen City. Chinese Journal of Social Medicine 2011;28(4):249–251.
- 25. Centre for reviews, dissemination (CRD). Systematic reviews: CRD's guidance for undertaking reviews in health care: Centre for Reviews and Dissemination; 2009.
- 26. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS med 2009;6(7):e1000097. pmid:19621072
- 27. Mark TL, Swait J. Using stated preference and revealed preference modeling to evaluate prescribing decisions. Health Econ 2004;13(6):563–573. pmid:15185386
- 28. OCEBM Levels of Evidence Working Group. The Oxford 2011 Levels of Evidence. 2011; Available at: http://www.cebm.net/index.aspx?o = 5653. Accessed 18July, 2018.
- 29. Pluye P, Gagnon M, Griffiths F, Johnson-Lafleur J. A scoring system for appraising mixed methods research, and concomitantly appraising qualitative, quantitative and mixed methods primary studies in Mixed Studies Reviews. Int J Nurs Stud 2009;46(4):529–546. pmid:19233357
- 30. Pace R, Pluye P, Bartlett G, Macaulay AC, Salsberg J, Jagosh J, et al. Testing the reliability and efficiency of the pilot Mixed Methods Appraisal Tool (MMAT) for systematic mixed studies review. Int J Nurs Stud 2012;49(1):47–53. pmid:21835406
- 31. Pluye P, Robert E, Cargo M, Bartlett G, O’Cathain A, Griffiths F, et al. Proposal: A mixed methods appraisal tool for systematic mixed studies reviews. 2011; Available at: http://mixedmethodsappraisaltoolpublic.pbworks.com. Accessed 01March, 2016.
- 32. Powell-Jackson T, Yip WC, Han W. Realigning demand and supply side incentives to improve primary health care seeking in rural China. Health Econ 2015;24(6):755–772. pmid:24807650
- 33. Jing L, Bai J, Sun X, Zakus D, Lou J, Li M, et al. NRCMS capitation reform and effect evaluation in Pudong New Area of Shanghai. International Journal of Health Planning & Management 2015;31(3):e131–e157.
- 34. Jing L, Shu Z, Sun X, Chiu JF, Lou J, Xie C. Factors influencing patients' contract choice with general practitioners in Shanghai: a preliminary study. Asia-Pacific Journal of Public Health 2015 Mar;27(2 Suppl):77S–85S. pmid:25503975
- 35. Dong X, Liu L, Cao S, Yang H, Song F, Yang C, et al. Focus on vulnerable populations and promoting equity in health service utilization—an analysis of visitor characteristics and service utilization of the Chinese community health service. BMC Public Health 2014;14:503. pmid:24884542
- 36. Li H, Chung RY, Wei X, Mou J, Wong SY, Wong MC, et al. Comparison of perceived quality amongst migrant and local patients using primary health care delivered by community health centres in Shenzhen, China. BMC Family Practice 2014;15:76. pmid:24779564
- 37. Wang HH, Wang JJ, Wong SY, Wong MC, Li FJ, Wang PX, et al. Epidemiology of multimorbidity in China and implications for the healthcare system: cross-sectional survey among 162,464 community household residents in southern China. BMC Med 2014;12:188. pmid:25338506
- 38. He H, Wang C, Zhen Q, Gu Y, Zhong L, Yu Y, et al. Community health service utilization among patients with chronic disease in Jilin province. Chinese Journal of Public Health 2014(10):1247–1249.
- 39. Bao L. Investigation on the Utilization of Rural Residents in the Background of the New Rural Cooperative Medical System in Ankang. Medicine and Society 2013(08):9–11.
- 40. Wang Y, Ma J, Huang A, Gong S. Investigation on Utilization of Community Health Service and Satisfactory Degree among Community Population in Jinan. Chinese Journal of Public Health Management 2011(04):374–375.
- 41. Ji R, Zhu Z, Wang Y, Liu L, Sun G, Yang J. Demand and Utilization of Community Health Service for Middle and Old Age Residents Registered With Different Types of Medical Insurances in Beijing. Chinese General Practice 2015(16):1968–1971.
- 42. Guo Z, Xu L, Sun L, Liu T, Zhao W, Zhang Q, et al. Analysis on the Behavior of Rural Residents' Choice of Village Clinic in Shandong Province. Chinese Journal of Public Health 2012(05):690–692.
- 43. Jin Q, Han W, Zhang W. Medical demand of participants in new rural CMS and the relating factors. Chinese Rural Health Service Administration 2011(09):894–896.
- 44. Huang X, Wang Q, Gu X, Xiang G, Li T, Mao Z. Research on Influence of New Rural Cooperative Medical Scheme Out-patient Reimbursement Plan on Choosing Medical Institutions. Chinese Health Economics 2012(04):48–51.
- 45. Li L, Mao J, Lin J, Weng Y, Li Y, Zhao J, et al. The Influencing Factors of Medical Care - seeking Intention and the Use of Community Health Service Among Residents in Guangzhou. Chinese General Practice 2015(01):100–104.
- 46. Xia X, Ren Y, Yang X, Zhang Y, Wei X. Study on initial diagnosis will and influencing factors in residents' community——An example in Nanchong. Soft Science of Health 2015(10):619–622.
- 47. Yao W, Lin Y, Zhong W, Wu M, Li J, Chen X. Investigation on the Willingness of Initial visit of Community Residents in Guangdong Province. Soft Science of Health 2014(09):602–606.
- 48. Gong X, Cao X. Analysis of rural residents' willingness of getting care in township hospitals and its influence factors. Chinese Health Service Management 2011(11):854–855.
- 49. Zhang X, Yao W, Zhao X, Wei H. Investigation of Community Residents' Intention of Visiting Doctor and Community Health Service in Urban Areas of Urumqi. Chinese General Practice 2014(13):1538–1542.
- 50. Wang Z, Yang H, Gao B, Li N. Behavior of and satisfaction to first medical consultation among community ill residents in Chengdu city during a two-week period. Chinese Journal of Public Health 2014(11):1473–1476.
- 51. Xie Y, Dai T, Zhu K, Li C. Analysis of residents' willingness to select community doctor as gatekeeper and its determinants. Chinese General Practice 2010(15):1621–1624.
- 52. Zhuang X, Zhu K, Yu H, Song Y. study on the prevalence rates of health services need, utilization and its influencing factors in resident in Haizhu District, Guangzhou City. CHINESE PRIMARY HEALTH CARE 2011(03):7–9.
- 53. Cheng SMSN, Zhao J, Bai JMSN, Zang X. Continuity of Care for Older Adults with Chronic Illness in China: An Exploratory Study. Public Health Nursing 2015 July/August;32(4):298–306. pmid:25308128
- 54. Zhou XD, Li L, Hesketh T. Health system reform in rural China: voices of healthworkers and service-users. Soc Sci Med 2014 Sep;117:134–141. pmid:25063969
- 55. Wang H, Liu Y, Zhu Y, Xue L, Dale M, Sipsma H, et al. Health Insurance Benefit Design and Healthcare Utilization in Northern Rural China. Plos One 2012 NOV 21 2012;7(11):e50395. pmid:23185616
- 56. Zhang X, Chen L, Mueller K, Yu Q, Liu J, Lin G. Tracking the effectiveness of health care reform in China: A case study of community health centers in a district of Beijing. Health Policy 2011 MAY 2011;100(2–3):181–188.
- 57. Jiang Y, Wang Y, Zhang L, Li Y, Wang X, Ma S. Access to healthcare and medical expenditure for the middle-aged and elderly: observations from China. PLoS One 2013 May 15;8(5):e6458923691252.
- 58. He P, Liu B, Sun Q, Zuo G, Li K. Comparative analysis on inpatient flow and medical expenditure of New Rural Cooperative Medical Scheme before and after the essential medicines system reform in Anhui province. Chinese Journal of Health Policy 2011(11):19–24.
- 59. Tian L, Zhao F, Yang H, Yang L, Yang L. The change tendency of visits among patients with new rural cooperative medical scheme after the implementation of essential medicines system in a city of Yunnan Province. Chinese Journal of Health Policy 2012(11):27–32.
- 60. Luo F, Yao L, Chen K, Liu Z, Wu S, Li Y, et al. Study on the distribution and medical expense of inpatients under the new medical reform: using a county of Hubei as case study. Chinese Health Economics 2015(02):60–62.
- 61. Guo M, Wu Q, Li Y, Hao Y, Huang Z, Jiao M, et al. Analysis of the change tendency of inpatient flow and hospital cost burden among rural residents under new rural cooperative medical scheme. Chinese Hospital Management 2015(01):72–74.
- 62. Chen Q, Yin A, Han Z, Liang Z, Wei S. Study on Choice of medical institution of rural inpatients with distributable diseases in Shandong Province. Chinese Health Economics 2013(07):56–58.
- 63. Zhang L, Wang Z, Qian D, Ni J. Effects of changes in health insurance reimbursement level on outpatient service utilization of rural diabetics: evidence from Jiangsu Province, China. BMC Health Serv Res 2014;14:185. pmid:24758602
- 64. Zeng P, Que W, Li Y, Peng S, Ye D, Chen S. National Essential Drug System and Treatment Flow in Community Health Centers of Shajing Street. CHINESE PRIMARY HEALTH CARE 2012(04):26–27.
- 65. Kuang L, Liang Y, Mei J, Zhao J, Wang Y, Liang H, et al. Family practice and the quality of primary care: a study of Chinese patients in Guangdong Province. Fam Pract 2015 Oct;32(5):557–563. pmid:26232722
- 66. Liu GG, Chen Y, Qin X. Transforming rural health care through information technology: an interventional study in China. Health Policy & Planning 2013 December;29(8):975–985.
- 67. Tang L. The patient's anxiety before seeing a doctor and her/his hospital choice behavior in China. BMC Public Health 2012;12:1121. pmid:23270526
- 68. Zeng J, Shi L, Zou X, Chen W, Ling L. Rural-to-Urban Migrants' Experiences with Primary Care under Different Types of Medical Institutions in Guangzhou, China. PLoS ONE [Electronic Resource] 2015;10(10):e0140922.
- 69. Yang H, Huang X, Zhou Z, Wang HHX, Tong X, Wang Z, et al. Determinants of initial utilization of community healthcare services among patients with major non-communicable chronic diseases in South China. PLoS ONE 2014;9(12):e116051. pmid:25545636
- 70. Zhou Z, Wang C, Yang H, Wang X, Zheng C, Wang J. Health-related quality of life and preferred health-seeking institutions among rural elderly individuals with and without chronic conditions: A population-based study in Guangdong Province, China. BioMed Res Int 2014;2014:Article ID 192376.
- 71. Zhao Y, Zhang T. Analysis of Influence Factors of Rural Residents' Choice of Selecting Care Providers during Two-Week Prevalence in Tongzhou District of Beijing. Chinese Health Quality Management 2012(02):69–72.
- 72. Wang X, Zhang M, Hel L, Huang X, Gao J, Zhang L. Analysis of Urban and Rural Residents’ Desire and Hospitalizing Behavior on Drugs Zero -Profit Policy. Chinese Hospital Management 2012(05):10–13.
- 73. Wei M, Xiao J. Study on influencing factors and countermeasures analyses of choosing a different medical institution by patients. Chinese Health Service Management 2014(04):259–261.
- 74. Ma T, Zhao J, Sun J, Wang C, Zhu X, Ren J, et al. Survey on awareness rate and utilization rate of residents in community health service in Ningbo. Chin Rural Health Service Administration 2015;35(7):869–871.
- 75. Jin C, Cheng J, Lu Y, Huang Z, Cao F. Spatial inequity in access to healthcare facilities at a county level in a developing country: a case study of Deqing County, Zhejiang, China. International journal for equity in health 2015;14(1):67.
- 76. Zhou Z, Su Y, Gao J, Campbell B, Zhu Z, Xu L, et al. Assessing equity of healthcare utilization in rural China: results from nationally representative surveys from 1993 to 2008. International journal for equity in health 2013;12(1):34.
- 77. Zhang X, Kanbur R. Spatial inequality in education and health care in China. China economic review 2005;16(2):189–204.
- 78. Donabedian A. Evaluating the quality of medical care. Milbank Mem Fund Q 1966;44(3):166–206.
- 79. Victoor A, Delnoij DM, Friele RD, Rademakers JJ. Determinants of patient choice of healthcare providers: a scoping review. BMC health services research 2012;12(1):272.
- 80. National Health and Family Planning Commission of the People's Republic of China. Notice of Printing and Distributing the Basic Standards and Management Regulations (Trial) of Medical Diagnostic Imaging Center issued by National Health and Family Planning Commission. 2016; Available at: http://www.moh.gov.cn/yzygj/s3593g/201608/6622dba2c35f4c88ac05c09ee29f877f.shtml. Accessed 24March, 2017.
- 81. Escarce JJ, Kapur K. Do patients bypass rural hospitals? Determinants of inpatient hospital choice in rural California. J Health Care Poor Underserved 2009;20(3):625–644. pmid:19648694
- 82. Varkevisser Marco, van der Geest Stéphanie A., Schut Frederik T.. Do patients choose hospitals with high quality ratings? Empirical evidence from the market for angioplasty in the Netherlands. J Health Econ 2012(2):371–378. pmid:22425770
- 83. The General Office of the State Council of the People's Republic of China. Guidance on promoting the construction of hierarchical medical system issued the General Office of the State Council. 2015; Available at: http://www.gov.cn/zhengce/content/2015-09/11/content_10158.htm. Accessed 24March, 2017.
- 84. International Conference on Primary Health Care. Declaration of Alma-Ata. 1978; Available at: http://www.who.int/publications/almaata_declaration_en.pdf?ua = 1. Accessed March27, 2017.
- 85. Bansal HS, Taylor SF, St. James Y. “Migrating” to new service providers: Toward a unifying framework of consumers’ switching behaviors. Journal of the Academy of Marketing Science 2005;33(1):96–115.
- 86. Moon B. Paradigms in migration research: exploring 'moorings' as a schema. Prog Hum Geogr 1995;19(4):504–524. pmid:12347395
- 87. Herzberg FI. Work and the nature of man. Oxford, England: World; 1966.
- 88. Maslow AH. A theory of human motivation. Psychol Rev 1943;50(4):370.
- 89. Yip W, Hsiao WC. What drove the cycles of Chinese health system reforms? Health Systems & Reform 2015;1(1):52–61.
- 90. The General Office of the State Council of the People's Republic of China. Notice of the General Office of the State Council on Issuing Several Policies and Measures for Promoting the Accelerated Development of Private Medical Institutions. 2015; Available at: http://www.gov.cn/zhengce/content/2015-06/15/content_9845.htm. Accessed 24March, 2017.
- 91. Yu W, Yu X, Hu H, Duan G, Liu Z, Wang Y. Use of hospital appointment registration systems in China: a survey study. Glob J Health Sci 2013;5(5):193–201. pmid:23985121
- 92. Li K, Naganawa S, Wang K, Li P, Kato K, Li X, et al. Study of the cost-benefit analysis of electronic medical record systems in general hospital in China. J Med Syst 2012;36(5):3283–3291. pmid:22212632