Factors Influencing Healthcare-Seeking Behavior at Primary Care Institutions among Middle-Aged and Older Adults with Chronic Diseases: An Empirical Study Based on CHARLS

Factors Influencing Healthcare-Seeking Behavior at Primary Care Institutions among Middle-Aged and Older Adults with Chronic Diseases: An Empirical Study Based on CHARLS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Factors Influencing Healthcare-Seeking Behavior at Primary Care Institutions among Middle-Aged and Older Adults with Chronic Diseases: An Empirical Study Based on CHARLS Li-li ZHU, Hui HAN, Jia-ru XIE, Yu-han LI, Wen CAO, Zhi-ling ZHU, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8539451/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective Based on data from the China Health and Retirement Longitudinal Study (CHARLS), this study aims to explore the current situation and influencing factors regarding healthcare-seeking behavior at primary healthcare institutions among patients with chronic diseases, with the goal of providing references for enhancing the service capacity of these primary care facilities. Methods tudy subjects were selected from the 2018 CHARLS database, including individuals aged 45 and above with chronic diseases who had sought medical care within the past year. A multivariate Logistic regression model (using the backward stepwise regression method) was employed to analyze the factors influencing their choice of primary care institutions for medical visits. Results A total of 1,846 patients with chronic diseases were included in the study. Among them, 891 (48.27%) middle-aged and older chronic disease patients chose to seek care at primary healthcare institutions. Logistic regression analysis revealed the following influencing factors: Within the dimension of individual characteristics, educational level (odds ratio for high school/vocational school 0.543; 95% confidence intervals 0.354 to 0.834), type of medical insurance (odds ratio for urban medical insurance 1.873; 95% confidence intervals 1.254 to 2.798), type of residence (odds ratio for rural residence 2.057; 95% confidence intervals 1.524 to 2.775), and monthly per capita consumption level (odds ratio for low consumption level 1.443; 95% confidence intervals 1.132 to 1.840) were significant factors influencing the choice of primary healthcare institutions. Within the dimension of contextual characteristics, distance to the healthcare institution (odds ratio 0.974; 95% confidence intervals 0.965 to 0.984), total medical expenses (odds ratio 0.9998; 95% confidence intervals 0.9996 to 0.9999), and out-of-pocket expenses (odds ratio 0.9996; 95% confidence intervals 0.9994 to 0.9999) were significant influencing factors. Within the dimension of health behaviors, the type of healthcare institution visited (odds ratio for public institutions 0.156; 95% confidence intervals 0.116 to 0.210) was a significant factor. Within the dimension of health outcomes, mental health status (odds ratio for moderate-to-severe depressive symptoms 1.407; 95% confidence intervals 1.012 to 1.958) was a significant influencing factor. Conclusion The rate of primary care utilization among middle-aged and older adults with chronic diseases in China still has significant room for improvement. Key influencing factors include urban-rural disparities, the medical security system, and the accessibility of health services. It is recommended to enhance the primary care utilization rate through strategies such as optimizing the allocation of primary health resources, establishing a tiered medical insurance reimbursement mechanism, and promoting contracted family doctor services. Middle-aged and older adults Chronic diseases Primary care Healthcare institutions Healthcare-seeking behavior Figures Figure 1 Background With the acceleration of population aging and shifts in lifestyle patterns, chronic non-communicable diseases have emerged as a central challenge to China's public health system. As of 2021, chronic diseases accounted for 91% of all deaths and were responsible for 86.7% of the total disease burden, as measured by Disability-Adjusted Life Years (DALYs) [ 1 ] . According to statistics from the National Health Commission, the overall prevalence rate of chronic diseases stands at 34.29%. Middle-aged and older adults constitute the primary demographic affected by chronic diseases, with prevalence rates of 31.26% for those aged 45–54, 48.39% for those aged 55–64, and 62.33% for individuals aged 65 and above [ 2 ]. Primary healthcare institutions—including community health service centers (stations), street health clinics, township health centers, village clinics, outpatient departments, and private clinics (infirmaries)—serve as the "gatekeepers" within the tiered healthcare system. Their service capacity directly determines the effectiveness of chronic disease management at the grassroots level [ 2 ] . However, existing research on primary care utilization among middle-aged and older adults with chronic diseases remains scarce and is often limited to specific regions, with few nationwide studies examining the factors influencing their choice of primary healthcare institutions. Based on Andersen's Behavioral Model of Health Services Use and considering the current state of primary healthcare services in China, this study utilizes data from the China Health and Retirement Longitudinal Study (CHARLS) to analyze the healthcare-seeking behavior and influencing factors among middle-aged and older adults with chronic diseases at primary healthcare institutions. The findings aim to provide evidence-based references for enhancing the service capacity of these institutions. Method Data sources The data for this study were derived from the fourth round of national tracking data (2018) of the China Health and Retirement Longitudinal Study (CHARLS), conducted by the China Social Science Survey Center of Peking University. This project employs a multi-stage stratified probability sampling method and has covered 150 counties/districts and 450 villages/communities across 28 provincial-level administrative regions in China since its inception in 2011. The fourth round of the survey included a total of 19,816 middle-aged and older adults, making it representative of the national middle-aged and older population. The project has been approved by the Ethics Review Committee of Peking University (Project Approval No. IRB00001052-11015), and the dataset can be accessed upon application through the project's official website (http://charls.pku.edu.cn). Study Subjects Based on the 14 chronic diseases included in the CHARLS 2018 questionnaire (hypertension, dyslipidemia, diabetes, malignant tumors, chronic lung disease, chronic liver disease, heart disease, stroke, kidney disease, digestive system disease, emotional or psychiatric disorders, memory-related diseases, arthritis or rheumatism, asthma), the study subjects were screened according to the following inclusion criteria: 1. Age ≥ 45 years; 2. Diagnosed with at least one chronic disease; 3. Had at least one medical visit within 30 days prior to the survey. A total of 1,846 subjects were ultimately included in the study. The detailed screening flowchart is shown in Figure 1. Statistical analysis Data processing and analysis in this study were performed using R software (version 4.4.1), strictly adhering to the STROBE guidelines for reporting observational studies. Variables with more than 20% missing data were removed. For the remaining variables, under the assumption that missing values were missing at random, multiple imputation was conducted using the mice package in R [3] . Continuous data not conforming to a normal distribution were described as median (P25, P75) and analyzed using nonparametric tests. Categorical data were presented as frequencies and percentages, with between-group comparisons performed using the chi-square test. Variables with a significance level of P<0.05 in univariate analysis were included in the multivariate logistic regression model. The significance level was set at α=0.05. Results Self-Reported Healthcare-Seeking Behavior for Chronic Diseases Among the 1,846 study subjects, the median age was 62 (54, 70) years. This included 1,030 females (55.80%) and 816 males (44.20%). Regarding education level, 1,235 individuals (66.90%) had a primary school education or below, and 383 (20.75%) had a junior high school education. The majority, 1,541 subjects (83.48%), were married. In terms of residence, 1,332 individuals (72.16%) lived in rural areas. For medical insurance, 486 participants (80.50%) were covered by the Urban and Rural Resident Basic Medical Insurance. Concerning mental health status, 1,016 patients (55.04%) exhibited moderate-to-severe depressive symptoms, 390 (21.13%) had mild depressive symptoms, and 268 (14.52%) showed no significant depressive symptoms. Regarding healthcare-seeking behavior, 891 individuals (48.27%) chose to seek care at primary healthcare institutions. Furthermore, 1,390 patients (75.30%) opted for public medical institutions, and for 1,685 individuals (91.28%), the primary purpose of their visit was treatment. Among the study participants, 891 individuals (48.27%) had visited primary healthcare institutions within the past month. Based on the four dimensions of Andersen's Behavioral Model, the following findings were observed: In the individual characteristics dimension, statistically significant differences (P < 0.05) were found in the choice of primary healthcare institutions versus other types of medical facilities across factors including gender, age, educational level, marital status, place of residence, type of medical insurance, and monthly per capita consumption. In the contextual characteristics dimension, the distance to the healthcare facility, total medical expenses, and out-of-pocket costs showed statistically significant differences (P < 0.05). Patients who sought care at primary institutions, compared to those who used other healthcare facilities, had shorter travel distances and significantly lower total medical expenses as well as out-of-pocket costs. In the health behavior dimension, a statistically significant difference (P < 0.05) was observed between those who sought care at public versus private institutions. Among patients who visited private institutions, a higher proportion chose primary healthcare facilities. In the health outcome dimension, mental health status showed a statistically significant difference (P < 0.05). Patients with moderate-to-severe depressive symptoms had a higher rate of choosing primary healthcare institutions (60.94%) compared to other groups (see Table 1). Analysis of Factors Influencing Healthcare-Seeking at Primary Institutions Using whether patients chose primary healthcare institutions as the dependent variable (yes=1, no=0) and the 12 indicators with statistically significant differences from the univariate analysis as independent variables, a multivariate logistic regression analysis was performed. The specific variable assignments are detailed in Table 2. The results showed that factors within the dimensions of individual characteristics (educational level, type of medical insurance, type of residence, monthly per capita consumption level), contextual characteristics (distance to the institution, total medical expenses, and out-of-pocket costs), health behavior (type of healthcare institution visited), and health outcomes (mental health status) were statistically significant influencing factors (P < 0.05) for the choice of primary healthcare institutions by chronic disease patients (see Table 3). Specifically, residence in rural areas, enrollment in urban medical insurance, a low consumption level, and moderate-to-severe depression were identified as facilitating factors for seeking care at primary institutions. Conversely, a high school/vocational school education level, higher total and out-of-pocket medical expenses, greater distance to the healthcare facility, and choosing public medical institutions served as barriers for middle-aged and older patients in opting for primary care (see Table 3). Discussion The study revealed that among the 1,846 participants, 891 (48.27%) middle-aged and older chronic disease patients chose to seek medical care at primary healthcare institutions. This underscores the significant role of primary care services in the healthcare-seeking decisions of this population. Such a distribution pattern largely aligns with the "gatekeeper" function of primary healthcare in China's tiered medical system. Furthermore, it reflects how the current high volume of primary care visits is closely linked to regional economic development levels and the coverage of medical security systems. The choice of primary healthcare institutions by middle-aged and older adults with chronic diseases is influenced by multiple factors. The results of this study indicate that middle-aged and older adult patients with chronic diseases who reside in rural areas, are enrolled in the urban-rural resident basic medical insurance, or have lower consumption levels are more inclined to seek care at primary healthcare institutions. In contrast, those with a high school or vocational school education tend to prefer seeking care at secondary or tertiary hospitals. The findings of this study demonstrate that the type of residence influences the choice of primary care among middle-aged and older adults with chronic diseases, which is consistent with the research by Lu Cao [4] . This may be related to the accessibility and economic burden of primary care services in rural areas. Rural areas often have village clinics that are geographically closer and offer higher reimbursement rates, whereas urban residents have easier access to secondary or tertiary hospitals due to shorter distances and more convenient transportation, encouraging them to seek care locally. Therefore, for rural populations, it is essential to continue strengthening the advantages of primary care services and improve the quality of medical care. For urban populations, it is recommended that the government increase investment in the development of primary healthcare institutions, leveraging their geographical proximity and convenience to retain patients at the primary care level. The type of medical insurance is also a factor influencing primary care utilization among middle-aged and older adults with chronic diseases, aligning with the findings of Zeng Yanbing [5] . Individuals covered by the urban-rural resident basic medical insurance are more likely to seek care at primary institutions compared to those with employee medical insurance. This may be attributed to differences in reimbursement rates between the two insurance types at primary care facilities. The deductible for primary care under the urban-rural resident insurance is significantly lower than that for tertiary hospitals, whereas the deductible differences across healthcare levels for employee insurance are less pronounced. Additionally, the population covered by urban-rural resident insurance generally has lower incomes and is predominantly located in suburban or rural areas. This suggests a need to reform the reimbursement structure of employee medical insurance, potentially by incorporating "primary care visit incentives" into personal insurance accounts. This study also found that middle-aged and older adults with chronic diseases who have a high school or vocational school education are more inclined to seek care at secondary or tertiary hospitals, which differs slightly from the findings of Chen Xing [6] . Furthermore, the study revealed that individuals with lower consumption levels are more likely to utilize primary healthcare institutions. Research [7] indicates that disposable income is a prerequisite for consumption among the elderly. "Intergenerational income transfer," rooted in Confucian cultural traditions that emphasize family and heritage, leads many elderly individuals to allocate their income to their children for housing, marriage, and daily expenses. This intergenerational income transfer influences the consumption decisions and, consequently, the healthcare-seeking behavior of China's elderly population. This highlights the connection between the healthcare-seeking behavior of middle-aged and older adults and their family dynamics, underscoring the importance of strengthening family support for this group. It is recommended that the government actively improve the social security system and vigorously promote contracted family doctor services. Studies [8-9] have shown that enrolled family doctor services can enhance trust between doctors and patients, thereby attracting middle-aged and older adults with chronic diseases residing in urban areas, covered by employee medical insurance, and with higher consumption levels. The results of this study indicate that as the distance to healthcare institutions increases and as total medical expenses and out-of-pocket costs rise, the likelihood of middle-aged and older adults with chronic diseases choosing primary healthcare institutions decreases. This finding aligns with general observations on the unequal distribution of resources by domestic scholar Lai Yuqing [10] and the U.S. Centers for Disease Control and Prevention [11] , particularly in areas with scarce medical resources, such as rural and remote regions. Based on this, it is recommended to continue strengthening the development of primary healthcare institutions and advance the realization of the "15-minute basic healthcare service circle" goal mentioned in the "Healthy China 2030" blueprint [12] . Primary healthcare institutions with lower total medical expenses, especially lower out-of-pocket costs, are more likely to be preferred by patients, which is consistent with the findings of Zhang Jian [13] and Chen Cong [14] . The primary reasons for this preference are related to the economic burden patients can bear, as well as the convenience of accessing care. The results of this study show that middle-aged and older adults with chronic diseases have a higher tendency to seek care at primary healthcare institutions of a private nature. This may be because public medical institutions often suffer from resource constraints and offer a poorer patient experience, whereas private institutions at the primary level, such as private clinics, are often staffed by familiar doctors, leading to a better patient experience. Given that chronic diseases are characterized by a long disease course, frequent follow-up visits, and complex health management needs, patients with such conditions may place greater emphasis on the quality of their healthcare experience. Studies have indicated [15–16] that private medical institutions are capable of meeting diverse healthcare needs and that their integration into medical consortia can help reduce service costs and enhance access to integrated health information.. Therefore, it is recommended to promote and refine the development of private primary healthcare institutions, improve their medical insurance reimbursement systems, strengthen staff training to enhance service quality, and encourage their participation in healthcare consortiums for system integration and increased public trust. The study found that middle-aged and older adults with chronic diseases who are in a state of moderate-to-severe depression have a higher rate of seeking care at primary healthcare institutions, indicating that mental health status also influences healthcare-seeking behavior. Research [17] has shown that the prevalence of depression among middle-aged and older adults with chronic diseases in China is 40.7%, and the mental health of this group is poorer than that of the general population. Compared to other middle-aged and older adults with chronic diseases, those experiencing depression are more likely to feel anxious when dealing with their illnesses. For them, visiting familiar community health service centers or township hospitals is a more feasible choice compared to large hospitals that may require long travel distances and waiting times. This finding suggests that healthcare providers at primary institutions should pay greater attention to the mental health of this population. Therefore, the government should prioritize the mental health of middle-aged and older adults with chronic diseases, particularly their depressive symptoms, and include them as a key group for mental health monitoring. It is recommended to incorporate psychological counseling services into primary healthcare institutions, include mental health assessments as part of routine check-ups for this group, provide timely interventions, and integrate mental health education into health promotion activities. Staff at primary healthcare institutions should strive to enhance their psychological service capabilities, and individuals in this group should be encouraged to pay attention to their emotional well-being and manage negative emotions promptly. Conclusion In summary, this study based on the CHARLS 2018 database reveals that the utilization rate of primary healthcare institutions among patients with chronic diseases is 48.27%. Personal characteristics, contextual factors, health behaviors, and health outcomes collectively influence the choice of primary care among chronic disease patients, indicating that there is still room for further improvement in chronic disease management capacity at the primary healthcare level. This study, relying on the CHARLS questionnaire, primarily examined influencing factors at the individual patient level and did not incorporate family decision-making mechanisms (such as the opinions of primary caregivers or social support from children), which could be an important direction for future research. Declarations Funding This work was supported by the Education Department of Henan Province (24A320015), the People's Government of Xinxiang City (B24172), and the Postgraduate Education Reform and Quality Improvement Project of Henan Province (YJS2024JD25). Ethics approval and consent to participate This study is a secondary analysis of data from the China Health and Retirement Longitudinal Study (CHARLS). The CHARLS main survey received ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants in the original CHARLS survey provided written informed consent. The data used in this study are anonymous and publicly available. Consent for Publication Not applicable. Competing interests The authors declare no competing interests. References Liu HX, Yin P, Qi JL, et al. Burden of non-communicable diseases in China and its provinces, 1990-2021: Results from the Global Burden of Disease Study 2021 [J]. Chin Med J (Engl), 2024,137(19):2325-2333. National Health Commission of the People's Republic of China. 2023 China Health Statistics Yearbook [M]. China Union Medical University Press, 2023：236-237 Peng HY, Li YZ, Meng LJ. Research on Multiple Imputation Errors Based on Missing Rate and Missing Pattern of Data [J]. Statistics & Decision, 2022, 38(1): 20-24. Lu C, Guan T, Yao KQ, et al. The Status and Influencing Factors of Health Care Seeking at Grassroots Medical Institutions Among Middle-aged and Elderly Patients in China [J]. Medicine and Society,2020, 33(10): 59-64. Zeng YB, Yuan ZP, Fang Y. Healthcare Seeking Behavior among Chinese Older Adults:Patterns and Predicting Factors [J]. Chinese Journal of Health Statistics, 2020, 37(2): 199-205. Chen X, Han ZP, Wang YL, et al. Impact of basic public health service utilization on residents’ willingness to the first choice of medical institutions for treatment: an empirical analysis based on propensity score matching [J]. Modern Preventive Medicine, 2025, 52(6): 1079-1084. Wang SH, Li FY. Consumption Structure,Demanding Characteristics and Behavior Decision of Chinese Elder People [J]. Social Sciences of Beijing, 2021, (8): 119-128. Zhang JD, Chen XF, Mao XH, et al. Quality of Primary Care Services：a Perspective from Chronic Disease Patients [J]. Chinese General Practice, 2022, 25(19): 2391-2398. Han ZP, Chen X, Wang YL, et al. Effect of family doctor contract on the service utilization of residents’ primary medical and health institutions: based on propensity score matching method [J]. Modern Preventive Medicine, 2025, 52(6): 1074-1078+1143. Lai YQ, Wan Y, Mao J, et al. Medical seeking behavior and its influencing factors of the middle-aged and elderly in Tibet [J]. Modern Preventive Medicine, 2023, 50(1): 134-138. Federal Office of Rural Health Policy.Chronic Disease in Rural America[A/OL].(2025-03-24)[2025-05-15].https://www.ruralhealthinfo.org/topics/chronic-disease. The Central Committee of the Communist Party of China (CPC), General Office of the State Council of the People's Republic of China issued Outline of the Healthy China 2030 Plan [A/OL].(2025-03-24)[2025-05-15].http://www.gov.cn/zhengce/2016-10/25/content_5124174.htm. Zhang J, Cai JL, Huan YY, et al. China's Floating Population's Healthcare Utilization Choices and Influencing Factors [J]. Chinese General Practice, 2021, 24(16): 2008-2014. Chen C, Zhu HH. Influencing Factors of Grassroots Medical Care Seeking Behavior of Patients with Type 2 Diabetes Mellitus Who Received Contracted Family Doctor Services Based on Anderson Model [J]. Chinese General Practice, 2025, 28(7): 888-892. Liu QY, Li LQ, Cao SY, et al. The SWOT Analysis on Development of Non-government Medical Institutions in China [J].Chinese Journal of Social Medicine, 2019, 36(1): 4-7. Niu YD, Zhang L. Analysis in the Development and Problems of County Medical Alliance [J]. Chinese Health Economics, 2020, 39(2): 22-25. Wei X, Wang N, Wei Y, et al. Analysis of Depression Status and Influencing Factors in Middle-aged and Elderly Patients with Chronic Diseases in China:an Empirical Analysis Based on CHARLS Data [J]. Chinese General Practice, 2025, 28(11): 1303-1308. Tables Tables 1 to 3 are available in the supplementary files section Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor invited by journal 13 Jan, 2026 Editor assigned by journal 09 Jan, 2026 Submission checks completed at journal 09 Jan, 2026 First submitted to journal 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8539451","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588512788,"identity":"28003cdf-2aa6-428f-b6e7-2a641a8587a4","order_by":0,"name":"Li-li ZHU","email":"","orcid":"","institution":"Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Li-li","middleName":"","lastName":"ZHU","suffix":""},{"id":588512789,"identity":"07b7b7ee-c8e8-4422-ab6f-89bde4ea957a","order_by":1,"name":"Hui HAN","email":"","orcid":"","institution":"Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"HAN","suffix":""},{"id":588512791,"identity":"bf7cb150-12c5-4a1c-8676-86e45d4a99b7","order_by":2,"name":"Jia-ru XIE","email":"","orcid":"","institution":"Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia-ru","middleName":"","lastName":"XIE","suffix":""},{"id":588512795,"identity":"eb84530c-e09d-4bf3-ae0a-ddf5e09ba347","order_by":3,"name":"Yu-han LI","email":"","orcid":"","institution":"Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu-han","middleName":"","lastName":"LI","suffix":""},{"id":588512797,"identity":"40521154-85ff-486c-ba2f-02dde2574824","order_by":4,"name":"Wen CAO","email":"","orcid":"","institution":"Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"CAO","suffix":""},{"id":588512798,"identity":"296f31cc-5fc6-42c9-b9fe-42ea812579d1","order_by":5,"name":"Zhi-ling ZHU","email":"","orcid":"","institution":"Xinxiang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhi-ling","middleName":"","lastName":"ZHU","suffix":""},{"id":588512799,"identity":"7b413915-bc19-4fa0-ab34-94499bc57e5f","order_by":6,"name":"Ming-ming YU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYLCCjw0gkg1EMBOng3EmyVqYeUnSYnAjO3Wz7Y46eQbptjQJhgrrxAb2swcIaMnddjv3zGHDBpljxyQYzqQnNvDkJRChpe1AAoNEepsEY9vhxAYJHgPCWizb6qBa/hGrhbGNGagl7ZgEYwMRWiTPvN12s7ftsGGbzLFki4Rj6cZtPDn4tfAdz91242dbnTy/dJvhjQ811rL97Gfwa1E4AGWwSQCJBAZo9OAD8g0wlgQhpaNgFIyCUTBiAQDQsEWcZzU3swAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Ming-ming","middleName":"","lastName":"YU","suffix":""}],"badges":[],"createdAt":"2026-01-07 09:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8539451/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8539451/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102389380,"identity":"330064f7-6e9f-468a-b40f-8204600c9139","added_by":"auto","created_at":"2026-02-11 08:28:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60776,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Inclusion of Research Subjects\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8539451/v1/a63acc7956bbe7903e2c1f87.png"},{"id":102389382,"identity":"d5f0e807-a644-4e5f-9bd1-001129344708","added_by":"auto","created_at":"2026-02-11 08:28:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":432063,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8539451/v1/73cd3d6d-7c6b-4785-896e-fda0ba796cb7.pdf"},{"id":102389381,"identity":"a1d195dd-d73a-43da-bf92-6222308f4e96","added_by":"auto","created_at":"2026-02-11 08:28:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35620,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8539451/v1/12f9cff63f8f79edf37a46e3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Influencing Healthcare-Seeking Behavior at Primary Care Institutions among Middle-Aged and Older Adults with Chronic Diseases: An Empirical Study Based on CHARLS","fulltext":[{"header":"Background","content":"\u003cp\u003eWith the acceleration of population aging and shifts in lifestyle patterns, chronic non-communicable diseases have emerged as a central challenge to China's public health system. As of 2021, chronic diseases accounted for 91% of all deaths and were responsible for 86.7% of the total disease burden, as measured by Disability-Adjusted Life Years (DALYs) \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to statistics from the National Health Commission, the overall prevalence rate of chronic diseases stands at 34.29%. Middle-aged and older adults constitute the primary demographic affected by chronic diseases, with prevalence rates of 31.26% for those aged 45\u0026ndash;54, 48.39% for those aged 55\u0026ndash;64, and 62.33% for individuals aged 65 and above \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Primary\u003c/sup\u003e healthcare institutions\u0026mdash;including community health service centers (stations), street health clinics, township health centers, village clinics, outpatient departments, and private clinics (infirmaries)\u0026mdash;serve as the \"gatekeepers\" within the tiered healthcare system. Their service capacity directly determines the effectiveness of chronic disease management at the grassroots level\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. However, existing research on primary care utilization among middle-aged and older adults with chronic diseases remains scarce and is often limited to specific regions, with few nationwide studies examining the factors influencing their choice of primary healthcare institutions.\u003c/p\u003e \u003cp\u003eBased on Andersen's Behavioral Model of Health Services Use and considering the current state of primary healthcare services in China, this study utilizes data from the China Health and Retirement Longitudinal Study (CHARLS) to analyze the healthcare-seeking behavior and influencing factors among middle-aged and older adults with chronic diseases at primary healthcare institutions. The findings aim to provide evidence-based references for enhancing the service capacity of these institutions.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eData sources\u003c/p\u003e\n\u003cp\u003eThe data for this study were derived from the fourth round of national tracking data (2018) of the China Health and Retirement Longitudinal Study (CHARLS), conducted by the China Social Science Survey Center of Peking University. This project employs a multi-stage stratified probability sampling method and has covered 150 counties/districts and 450 villages/communities across 28 provincial-level administrative regions in China since its inception in 2011. The fourth round of the survey included a total of 19,816 middle-aged and older adults, making it representative of the national middle-aged and older population. The project has been approved by the Ethics Review Committee of Peking University (Project Approval No. IRB00001052-11015), and the dataset can be accessed upon application through the project\u0026apos;s official website (http://charls.pku.edu.cn).\u003c/p\u003e\n\u003cp\u003eStudy Subjects\u003c/p\u003e\n\u003cp\u003eBased on the 14 chronic diseases included in the CHARLS 2018 questionnaire (hypertension, dyslipidemia, diabetes, malignant tumors, chronic lung disease, chronic liver disease, heart disease, stroke, kidney disease, digestive system disease, emotional or psychiatric disorders, memory-related diseases, arthritis or rheumatism, asthma), the study subjects were screened according to the following inclusion criteria:\u003c/p\u003e\n\u003cp\u003e1. Age \u0026ge; 45 years;\u003c/p\u003e\n\u003cp\u003e2. Diagnosed with at least one chronic disease;\u003c/p\u003e\n\u003cp\u003e3. Had at least one medical visit within 30 days prior to the survey.\u003c/p\u003e\n\u003cp\u003eA total of 1,846 subjects were ultimately included in the study. The detailed screening flowchart is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData processing and analysis in this study were performed using R software (version 4.4.1), strictly adhering to the STROBE guidelines for reporting observational studies. Variables with more than 20% missing data were removed. For the remaining variables, under the assumption that missing values were missing at random, multiple imputation was conducted using the mice package in R \u003csup\u003e[3]\u003c/sup\u003e. Continuous data not conforming to a normal distribution were described as median (P25, P75) and analyzed using nonparametric tests. Categorical data were presented as frequencies and percentages, with between-group comparisons performed using the chi-square test. Variables with a significance level of P\u0026lt;0.05 in univariate analysis were included in the multivariate logistic regression model. The significance level was set at \u0026alpha;=0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSelf-Reported Healthcare-Seeking Behavior for Chronic Diseases\u003c/p\u003e\n\u003cp\u003eAmong the 1,846 study subjects, the median age was 62 (54, 70) years. This included 1,030 females (55.80%) and 816 males (44.20%). Regarding education level, 1,235 individuals (66.90%) had a primary school education or below, and 383 (20.75%) had a junior high school education. The majority, 1,541 subjects (83.48%), were married. In terms of residence, 1,332 individuals (72.16%) lived in rural areas. For medical insurance, 486 participants (80.50%) were covered by the Urban and Rural Resident Basic Medical Insurance. Concerning mental health status, 1,016 patients (55.04%) exhibited moderate-to-severe depressive symptoms, 390 (21.13%) had mild depressive symptoms, and 268 (14.52%) showed no significant depressive symptoms. Regarding healthcare-seeking behavior, 891 individuals (48.27%) chose to seek care at primary healthcare institutions. Furthermore, 1,390 patients (75.30%) opted for public medical institutions, and for 1,685 individuals (91.28%), the primary purpose of their visit was treatment.\u003c/p\u003e\n\u003cp\u003eAmong the study participants, 891 individuals (48.27%) had visited primary healthcare institutions within the past month. Based on the four dimensions of Andersen\u0026apos;s Behavioral Model, the following findings were observed:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eIn the individual characteristics dimension, statistically significant differences (P \u0026lt; 0.05) were found in the choice of primary healthcare institutions versus other types of medical facilities across factors including gender, age, educational level, marital status, place of residence, type of medical insurance, and monthly per capita consumption.\u003c/li\u003e\n \u003cli\u003eIn the contextual characteristics dimension, the distance to the healthcare facility, total medical expenses, and out-of-pocket costs showed statistically significant differences (P \u0026lt; 0.05). Patients who sought care at primary institutions, compared to those who used other healthcare facilities, had shorter travel distances and significantly lower total medical expenses as well as out-of-pocket costs.\u003c/li\u003e\n \u003cli\u003eIn the health behavior dimension, a statistically significant difference (P \u0026lt; 0.05) was observed between those who sought care at public versus private institutions. Among patients who visited private institutions, a higher proportion chose primary healthcare facilities.\u003c/li\u003e\n \u003cli\u003eIn the health outcome dimension, mental health status showed a statistically significant difference (P \u0026lt; 0.05). Patients with moderate-to-severe depressive symptoms had a higher rate of choosing primary healthcare institutions (60.94%) compared to other groups (see Table 1).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnalysis of Factors Influencing Healthcare-Seeking at Primary Institutions\u003c/p\u003e\n\u003cp\u003eUsing whether patients chose primary healthcare institutions as the dependent variable (yes=1, no=0) and the 12 indicators with statistically significant differences from the univariate analysis as independent variables, a multivariate logistic regression analysis was performed. The specific variable assignments are detailed in Table 2. The results showed that factors within the dimensions of individual characteristics (educational level, type of medical insurance, type of residence, monthly per capita consumption level), contextual characteristics (distance to the institution, total medical expenses, and out-of-pocket costs), health behavior (type of healthcare institution visited), and health outcomes (mental health status) were statistically significant influencing factors (P \u0026lt; 0.05) for the choice of primary healthcare institutions by chronic disease patients (see Table 3).\u003c/p\u003e\n\u003cp\u003eSpecifically, residence in rural areas, enrollment in urban medical insurance, a low consumption level, and moderate-to-severe depression were identified as facilitating factors for seeking care at primary institutions. Conversely, a high school/vocational school education level, higher total and out-of-pocket medical expenses, greater distance to the healthcare facility, and choosing public medical institutions served as barriers for middle-aged and older patients in opting for primary care (see Table 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study revealed that among the 1,846 participants, 891 (48.27%) middle-aged and older chronic disease patients chose to seek medical care at primary healthcare institutions. This underscores the significant role of primary care services in the healthcare-seeking decisions of this population. Such a distribution pattern largely aligns with the \"gatekeeper\" function of primary healthcare in China's tiered medical system. Furthermore, it reflects how the current high volume of primary care visits is closely linked to regional economic development levels and the coverage of medical security systems.\u003c/p\u003e\n\u003cp\u003eThe choice of primary healthcare institutions by middle-aged and older adults with chronic diseases is influenced by multiple factors.\u003c/p\u003e\n\u003cp\u003eThe results of this study indicate that middle-aged and older adult patients with chronic diseases who reside in rural areas, are enrolled in the urban-rural resident basic medical insurance, or have lower consumption levels are more inclined to seek care at primary healthcare institutions. In contrast, those with a high school or vocational school education tend to prefer seeking care at secondary or tertiary hospitals.\u003c/p\u003e\n\u003cp\u003eThe findings of this study demonstrate that the type of residence influences the choice of primary care among middle-aged and older adults with chronic diseases, which is consistent with the research by Lu Cao\u003csup\u003e[4]\u003c/sup\u003e. This may be related to the accessibility and economic burden of primary care services in rural areas. Rural areas often have village clinics that are geographically closer and offer higher reimbursement rates, whereas urban residents have easier access to secondary or tertiary hospitals due to shorter distances and more convenient transportation, encouraging them to seek care locally. Therefore, for rural populations, it is essential to continue strengthening the advantages of primary care services and improve the quality of medical care. For urban populations, it is recommended that the government increase investment in the development of primary healthcare institutions, leveraging their geographical proximity and convenience to retain patients at the primary care level.\u003c/p\u003e\n\u003cp\u003eThe type of medical insurance is also a factor influencing primary care utilization among middle-aged and older adults with chronic diseases, aligning with the findings of Zeng Yanbing\u003csup\u003e[5]\u003c/sup\u003e. Individuals covered by the urban-rural resident basic medical insurance are more likely to seek care at primary institutions compared to those with employee medical insurance. This may be attributed to differences in reimbursement rates between the two insurance types at primary care facilities. The deductible for primary care under the urban-rural resident insurance is significantly lower than that for tertiary hospitals, whereas the deductible differences across healthcare levels for employee insurance are less pronounced. Additionally, the population covered by urban-rural resident insurance generally has lower incomes and is predominantly located in suburban or rural areas. This suggests a need to reform the reimbursement structure of employee medical insurance, potentially by incorporating \"primary care visit incentives\" into personal insurance accounts.\u003c/p\u003e\n\u003cp\u003eThis study also found that middle-aged and older adults with chronic diseases who have a high school or vocational school education are more inclined to seek care at secondary or tertiary hospitals, which differs slightly from the findings of Chen Xing \u003csup\u003e[6]\u003c/sup\u003e. Furthermore, the study revealed that individuals with lower consumption levels are more likely to utilize primary healthcare institutions. Research \u003csup\u003e[7]\u003c/sup\u003e indicates that disposable income is a prerequisite for consumption among the elderly. \"Intergenerational income transfer,\" rooted in Confucian cultural traditions that emphasize family and heritage, leads many elderly individuals to allocate their income to their children for housing, marriage, and daily expenses. This intergenerational income transfer influences the consumption decisions and, consequently, the healthcare-seeking behavior of China's elderly population. This highlights the connection between the healthcare-seeking behavior of middle-aged and older adults and their family dynamics, underscoring the importance of strengthening family support for this group. It is recommended that the government actively improve the social security system and vigorously promote contracted family doctor services. Studies \u003csup\u003e[8-9]\u003c/sup\u003e have shown that enrolled family doctor services can enhance trust between doctors and patients, thereby attracting middle-aged and older adults with chronic diseases residing in urban areas, covered by employee medical insurance, and with higher consumption levels.\u003c/p\u003e\n\u003cp\u003eThe results of this study indicate that as the distance to healthcare institutions increases and as total medical expenses and out-of-pocket costs rise, the likelihood of middle-aged and older adults with chronic diseases choosing primary healthcare institutions decreases. This finding aligns with general observations on the unequal distribution of resources by domestic scholar Lai Yuqing\u003csup\u003e[10]\u003c/sup\u003e and the U.S. Centers for Disease Control and Prevention\u003csup\u003e\u0026nbsp;[11]\u003c/sup\u003e, particularly in areas with scarce medical resources, such as rural and remote regions. Based on this, it is recommended to continue strengthening the development of primary healthcare institutions and advance the realization of the \"15-minute basic healthcare service circle\" goal mentioned in the \"Healthy China 2030\" blueprint\u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePrimary healthcare institutions with lower total medical expenses, especially lower out-of-pocket costs, are more likely to be preferred by patients, which is consistent with the findings of Zhang Jian\u003csup\u003e[13]\u003c/sup\u003e and Chen Cong\u003csup\u003e[14]\u003c/sup\u003e. The primary reasons for this preference are related to the economic burden patients can bear, as well as the convenience of accessing care.\u003c/p\u003e\n\u003cp\u003eThe results of this study show that middle-aged and older adults with chronic diseases have a higher tendency to seek care at primary healthcare institutions of a private nature. This may be because public medical institutions often suffer from resource constraints and offer a poorer patient experience, whereas private institutions at the primary level, such as private clinics, are often staffed by familiar doctors, leading to a better patient experience. Given that chronic diseases are characterized by a long disease course, frequent follow-up visits, and complex health management needs, patients with such conditions may place greater emphasis on the quality of their healthcare experience. Studies have indicated\u003csup\u003e\u0026nbsp;[15–16]\u003c/sup\u003e that private medical institutions are capable of meeting diverse healthcare needs and that their integration into medical consortia can help reduce service costs and enhance access to integrated health information.. Therefore, it is recommended to promote and refine the development of private primary healthcare institutions, improve their medical insurance reimbursement systems, strengthen staff training to enhance service quality, and encourage their participation in healthcare consortiums for system integration and increased public trust.\u003c/p\u003e\n\u003cp\u003eThe study found that middle-aged and older adults with chronic diseases who are in a state of moderate-to-severe depression have a higher rate of seeking care at primary healthcare institutions, indicating that mental health status also influences healthcare-seeking behavior. Research \u003csup\u003e[17]\u003c/sup\u003e has shown that the prevalence of depression among middle-aged and older adults with chronic diseases in China is 40.7%, and the mental health of this group is poorer than that of the general population. Compared to other middle-aged and older adults with chronic diseases, those experiencing depression are more likely to feel anxious when dealing with their illnesses. For them, visiting familiar community health service centers or township hospitals is a more feasible choice compared to large hospitals that may require long travel distances and waiting times. This finding suggests that healthcare providers at primary institutions should pay greater attention to the mental health of this population. Therefore, the government should prioritize the mental health of middle-aged and older adults with chronic diseases, particularly their depressive symptoms, and include them as a key group for mental health monitoring. It is recommended to incorporate psychological counseling services into primary healthcare institutions, include mental health assessments as part of routine check-ups for this group, provide timely interventions, and integrate mental health education into health promotion activities. Staff at primary healthcare institutions should strive to enhance their psychological service capabilities, and individuals in this group should be encouraged to pay attention to their emotional well-being and manage negative emotions promptly.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study based on the CHARLS 2018 database reveals that the utilization rate of primary healthcare institutions among patients with chronic diseases is 48.27%. Personal characteristics, contextual factors, health behaviors, and health outcomes collectively influence the choice of primary care among chronic disease patients, indicating that there is still room for further improvement in chronic disease management capacity at the primary healthcare level. This study, relying on the CHARLS questionnaire, primarily examined influencing factors at the individual patient level and did not incorporate family decision-making mechanisms (such as the opinions of primary caregivers or social support from children), which could be an important direction for future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Education Department of Henan Province (24A320015), the People\u0026apos;s Government of Xinxiang City (B24172), and the Postgraduate Education Reform and Quality Improvement Project of Henan Province (YJS2024JD25).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a secondary analysis of data from the China Health and Retirement Longitudinal Study (CHARLS). The CHARLS main survey received ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants in the original CHARLS survey provided written informed consent. The data used in this study are anonymous and publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLiu HX, Yin P, Qi JL, et al. Burden of non-communicable diseases in China and its provinces, 1990-2021: Results from the Global Burden of Disease Study 2021 [J]. Chin Med J (Engl), 2024,137(19):2325-2333.\u003c/li\u003e\n \u003cli\u003eNational Health Commission of the People\u0026apos;s Republic of China. 2023 China Health Statistics Yearbook [M]. China Union Medical University Press, 2023：236-237\u003c/li\u003e\n \u003cli\u003ePeng HY, Li YZ, Meng LJ. Research on Multiple Imputation Errors Based on Missing Rate and Missing Pattern of Data [J]. Statistics \u0026amp; Decision, 2022, 38(1): 20-24.\u003c/li\u003e\n \u003cli\u003eLu C, Guan T, Yao KQ, et al. The Status and Influencing Factors of Health Care Seeking at Grassroots Medical Institutions Among Middle-aged and Elderly Patients in China [J]. Medicine and Society,2020, 33(10): 59-64.\u003c/li\u003e\n \u003cli\u003eZeng YB, Yuan ZP, Fang Y. Healthcare Seeking Behavior among Chinese Older Adults:Patterns and Predicting Factors [J]. Chinese Journal of Health Statistics, 2020, 37(2): 199-205.\u003c/li\u003e\n \u003cli\u003eChen X, Han ZP, Wang YL, et al. Impact of basic public health service utilization on residents\u0026rsquo; willingness to the first choice of medical institutions for treatment: an empirical analysis based on propensity score matching [J]. Modern Preventive Medicine, 2025, 52(6): 1079-1084.\u003c/li\u003e\n \u003cli\u003eWang SH, Li FY. Consumption Structure,Demanding Characteristics and Behavior Decision of Chinese Elder People [J]. Social Sciences of Beijing, 2021, (8): 119-128.\u003c/li\u003e\n \u003cli\u003eZhang JD, Chen XF, Mao XH, et al. Quality of Primary Care Services：a Perspective from Chronic Disease Patients [J]. Chinese General Practice, 2022, 25(19): 2391-2398.\u003c/li\u003e\n \u003cli\u003eHan ZP, Chen X, Wang YL, et al. Effect of family doctor contract on the service utilization of residents\u0026rsquo; primary medical and health institutions: based on propensity score matching method [J]. Modern Preventive Medicine, 2025, 52(6): 1074-1078+1143.\u003c/li\u003e\n \u003cli\u003eLai YQ, Wan Y, Mao J, et al. Medical seeking behavior and its influencing factors of the middle-aged and elderly in Tibet [J]. Modern Preventive Medicine, 2023, 50(1): 134-138.\u003c/li\u003e\n \u003cli\u003eFederal Office of Rural Health Policy.Chronic Disease in Rural America[A/OL].(2025-03-24)[2025-05-15].https://www.ruralhealthinfo.org/topics/chronic-disease.\u003c/li\u003e\n \u003cli\u003eThe Central Committee of the Communist Party of China (CPC), General Office of the State Council of the People\u0026apos;s Republic of China issued Outline of the Healthy China 2030 Plan [A/OL].(2025-03-24)[2025-05-15].http://www.gov.cn/zhengce/2016-10/25/content_5124174.htm.\u003c/li\u003e\n \u003cli\u003eZhang J, Cai JL, Huan YY, et al. China\u0026apos;s Floating Population\u0026apos;s Healthcare Utilization Choices and Influencing Factors [J]. Chinese General Practice, 2021, 24(16): 2008-2014.\u003c/li\u003e\n \u003cli\u003eChen C, Zhu HH. Influencing Factors of Grassroots Medical Care Seeking Behavior of Patients with Type 2 Diabetes Mellitus Who Received Contracted Family Doctor Services Based on Anderson Model [J]. Chinese General Practice, 2025, 28(7): 888-892.\u003c/li\u003e\n \u003cli\u003eLiu QY, Li LQ, Cao SY, et al. The SWOT Analysis on Development of Non-government Medical Institutions in China [J].Chinese Journal of Social Medicine, 2019, 36(1): 4-7.\u003c/li\u003e\n \u003cli\u003eNiu YD, Zhang L. Analysis in the Development and Problems of County Medical Alliance [J]. Chinese Health Economics, 2020, 39(2): 22-25.\u003c/li\u003e\n \u003cli\u003eWei X, Wang N, Wei Y, et al. Analysis of Depression Status and Influencing Factors in Middle-aged and Elderly Patients with Chronic Diseases in China:an Empirical Analysis Based on CHARLS Data [J]. Chinese General Practice, 2025, 28(11): 1303-1308.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Middle-aged and older adults, Chronic diseases, Primary care, Healthcare institutions, Healthcare-seeking behavior","lastPublishedDoi":"10.21203/rs.3.rs-8539451/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8539451/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eBased on data from the \u003cem\u003eChina Health and Retirement Longitudinal Study \u003c/em\u003e(CHARLS), this study aims to explore the current situation and influencing factors regarding healthcare-seeking behavior at primary healthcare institutions among patients with chronic diseases, with the goal of providing references for enhancing the service capacity of these primary care facilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e tudy subjects were selected from the 2018 CHARLS database, including individuals aged 45 and above with chronic diseases who had sought medical care within the past year. A multivariate Logistic regression model (using the backward stepwise regression method) was employed to analyze the factors influencing their choice of primary care institutions for medical visits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA total of 1,846 patients with chronic diseases were included in the study. Among them, 891 (48.27%) middle-aged and older chronic disease patients chose to seek care at primary healthcare institutions. Logistic regression analysis revealed the following influencing factors:\u003c/p\u003e\n\u003cp\u003eWithin the dimension of individual characteristics, educational level (odds ratio for high school/vocational school 0.543; 95% confidence intervals 0.354 to 0.834), type of medical insurance (odds ratio for urban medical insurance 1.873; 95% confidence intervals 1.254 to 2.798), type of residence (odds ratio for rural residence 2.057; 95% confidence intervals 1.524 to 2.775), and monthly per capita consumption level (odds ratio for low consumption level 1.443; 95% confidence intervals 1.132 to 1.840) were significant factors influencing the choice of primary healthcare institutions.\u003c/p\u003e\n\u003cp\u003eWithin the dimension of contextual characteristics, distance to the healthcare institution (odds ratio 0.974; 95% confidence intervals 0.965 to 0.984), total medical expenses (odds ratio 0.9998; 95% confidence intervals 0.9996 to 0.9999), and out-of-pocket expenses (odds ratio 0.9996; 95% confidence intervals 0.9994 to 0.9999) were significant influencing factors.\u003c/p\u003e\n\u003cp\u003eWithin the dimension of health behaviors, the type of healthcare institution visited (odds ratio for public institutions 0.156; 95% confidence intervals 0.116 to 0.210) was a significant factor.\u003c/p\u003e\n\u003cp\u003eWithin the dimension of health outcomes, mental health status (odds ratio for moderate-to-severe depressive symptoms 1.407; 95% confidence intervals 1.012 to 1.958) was a significant influencing factor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eThe rate of primary care utilization among middle-aged and older adults with chronic diseases in China still has significant room for improvement. Key influencing factors include urban-rural disparities, the medical security system, and the accessibility of health services. It is recommended to enhance the primary care utilization rate through strategies such as optimizing the allocation of primary health resources, establishing a tiered medical insurance reimbursement mechanism, and promoting contracted family doctor services.\u003c/p\u003e","manuscriptTitle":"Factors Influencing Healthcare-Seeking Behavior at Primary Care Institutions among Middle-Aged and Older Adults with Chronic Diseases: An Empirical Study Based on CHARLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 08:28:45","doi":"10.21203/rs.3.rs-8539451/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-20T13:39:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211520497586622751759649833180735693017","date":"2026-02-16T09:26:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325937134310616931030923809567748416611","date":"2026-02-09T11:40:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T07:03:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-13T12:23:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-09T07:52:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T07:51:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Primary Care","date":"2026-01-07T09:19:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3694f1af-f4ed-42cb-a339-2fabe385e192","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T08:28:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 08:28:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8539451","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8539451","identity":"rs-8539451","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}