Chronic conditions kill 41 million people each year, which accounts for 74% of all deaths globally and a majority of deaths are from developing countries [1]. The surge in chronic conditions may lead to increases in disease burden, overwhelmed healthcare facilities, and strained productivity [2, 3]. The UN has targeted to reduce the deaths from chronic conditions to one-third from 2015 to 2030 [2, 3]. Access to primary care, early detection, preventive measures, and treatment is crucial to achieving this target [4]. Among these measures, access to primary care could be the most cost-effective way to reduce deaths.

Primary care incorporates six core functions: accessibility, first contact, continuity, comprehensiveness, coordination, and patient-centered care [5, 6]. All these elements are coequally essential for the intervention against all chronic diseases. However, among these six core functions, continuity is important to improve the prevention of chronic illnesses, ensure early detection [7], reduce hospitalizations and readmissions [8, 9], and efficiently manage patients’ health information [10]. Compared to a new primary care practitioner, a family clinician can effectively diagnose the patients' symptoms based on the most up-to-date, relevant patient history and experience in evaluating patient-specific needs. All these play an important role in minimizing diagnosis to treatment time [10]. The continuity of primary care is defined as the continuation (frequency of the visits being repeated over time) and coordinated care (consistency of treatment plan with appropriately organized information) along with the cooperative relationship between the patients and their providers [11]. The emphasis of continuity is to lessen the healthcare system burden, specifically, to reduce readmission, stop exhausting hospital resources, and reduce traumatic experiences and expensive hospitalization [3, 9, 12, 13]. The issue related to the continuity of primary care has drawn the attention of many researchers worldwide. For example, some researchers found an association between continuity of care and emergency department use. Other investigators reported that higher continuity of care substantially reduced the use of emergency health care for the elderly [14]. Continuity of primary care is reported to be helpful with the early detection of chronic conditions, such as diabetes [15], asthma [16], hypertension [17], and cancer [7, 18] in many studies. For example, early diagnosis increases the survival of cancer patients. Women who have a long-term patient-provider relationship with the same medical clinician were more likely to undergo screening for different types of cancer, such as breast or cervical cancer. An evidence-based study conducted mostly in Canada and Taiwan area reported that higher continuity of care led to a lower rate of hospitalization and was associated with escalated satisfaction among patients suffering from chronic illnesses [19].

China is the largest developing country in terms of population. According to World Health Organization (WHO) report published in 2015, the proportion of people aged higher than 60 years would double in the next 20 years in China [20]. As a result, the disease burden in China would lean toward chronic diseases. Chronic diseases, such as ischemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), and type 2 diabetes, account for 80% of death of people aged higher than 60 years [21]. To resolve this complex situation and divert the increasing pressure on the healthcare system, the Government of China has decided to adopt a target of restructuring the country's healthcare system by establishing stronger primary healthcare services [22]. Even though a significant amount of financial investment and accommodative policies to promote primary care have been adopted, many challenges still prevail [23]. Among these challenges and barriers, lack of educational and occupational training for the general practitioner [24, 25], insufficient coordination between different levels of the healthcare system, and a gap in the continuity of care are top-ranked issues [23]. Hence, taking necessary actions to improve the continuity of primary care facilities is a must. However, there is a lack of literature to specifically address this problem in countries with huge populations, such as China. In this research, we investigate the relationship between the continuity of primary care and the pattern of chronic conditions in Guangdong, China, which would potentially provide evidence for health professionals, researchers, and policymakers to improve the healthcare system.

To our knowledge, this is the first study done in China that incorporates quantitative measurements on continuity of care using a specific question from our survey as the proxy for encounter-level data from electronic health records (EHRs). The survey questions included information regarding the length of the relationship with the primary care physician, which facilitated categorizing the continuity into 1-year and 3-year time intervals (k1, k3), which usually could only be calculated using EHRs. Additionally, we asked the participants about their number of chronic conditions to evaluate the association between the number of chronic conditions and continuity, which was not reported in previous works, such as the recent study by Zhang et al. [26]. The primary goal of this research is to investigate the effect of the number of chronic conditions on continuity based on our survey. Our results can add to the current knowledge regarding primary care services. In addition, we evaluate whether the relationship between chronic conditions and continuity is different across different residency statuses.

The rest of the paper is organized as follows; Section 2 describes the data collection approach and analytical methods, including the study design and corresponding measures of the analysis. Section 3 reports the results of the analysis. Section 4 discusses the results, summarizes our key findings, and reports the study's limitations. Finally, section 5 presents the conclusions.

Chronic conditions had a significant effect on the continuity in terms of dichotomized visit-based outcome (k1, k3) and the continuity domain score. Our results indicated the agreement between the visit-based measures (k1, k3) and the comprehensive continuity score. Besides, an increase in the number of chronic conditions significantly increased the chance of continuity of PCPs. Our results were consistent with previously published studies in China. A recent cross-sectional study conducted in Hangzhou, China reported that chronic conditions (yes vs. no) were a significant contributing factor to continuity in urban areas of China [26]. The study also found that continuity with their general practitioners (GPs) was normally overlooked by patients. A recent study on 700 participants from an urban area of Beijing assessed the relationship between continuity and patients’ preferences in their relationships with their PCPs. This study concluded that the relationship preferences of patients are associated with their experience of continuity of care, and patients’ strong preference for free choice of doctors is not aligned with relational continuity with primary care, a desirable feature of cost-effective healthcare systems [31]. The GPs in China can normally take care of patients with different types of chronic diseases, so patients with multiple chronic conditions may have weaker preferences for free choice of providers. This may partially explain the stronger continuity with PCPs/GPs among patients with more chronic conditions in China.

Fig. (2). Marginal plot for the effect of the number of chronic conditions.

Our study also examined the relationship using a 3-year continuity indicator (k3) and found no significant association between the chronic condition and k3. This implies that long-term continuity is less sensitive to chronic conditions than short-term continuity. In addition, it was also found that the residency status of the patients did not significantly modify the association between chronic conditions and continuity.

Compared to patients without any chronic conditions, patients with chronic conditions had a long relationship with their PCP and perceived a higher continuity score, indicating that they might have more trust in and dependency on their PCP, which, in turn, benefits their health [32]. Also, it was found that a PCP-patient relationship served as a mediating factor in the link between continuity of primary care and chronic conditions. This pattern indicates that having a high number of chronic conditions increases the likelihood of having a primary care contract service, which ultimately increases continuity with the same PCP. Detailed results from the mediation analysis can be found in supplementary materials.

Participants with more than one chronic condition tend to require special attention and specific patient-centered treatment adjustment. Such a treatment plan may require health practitioners to have a thorough understanding of the patient's medical history. Switching primary care practitioners may be inconvenient and inefficient in case of disease detection and follow-up treatment. High use of specialty care may strain clinicians' ability to coordinate care effectively, especially for patients with chronic conditions. This was asserted by an interview-based cross-sectional study conducted on Medicare enrollees with select chronic conditions in an integrated healthcare delivery system in Washington, USA [33] that used encounter data and measured continuity of care for 1 year by applying the Bice-Boxerman measure of continuity [34]. Among high-risk patients with acute and chronic diseases, continuity of care is most important as it will lower the hospitalization cost while enhancing coordinated care and ensuring timely treatment. Patients with chronic disease reported higher continuity, while those who lived in rural areas, had higher education, poorer mental health status, no regular provider, and who were employed reported lower continuity based on a survey data analysis conducted in Ontario, Canada [35], where measurement of continuity was a proportional score calculated from the responses of the participants.

The presence of multiple chronic conditions is linked to an increase in clinic visits (usually outpatients). The relationship between PCP and their patients becomes stronger as the frequency of outpatient visits rises. This long-lasting, reliable relationship makes it possible for PCP to continuously gather patient data, develop a more thorough understanding of patient's needs, make it easier to identify different patient needs, offer comprehensive patient services, and refer patients to more suitable secondary or tertiary care facilities [36-39]. Improved communication between PCP and their patients would encourage the interaction mechanism between continuity of services and other primary care core values. This may reinforce the relationship between providers and patients and provide cumulative benefits for patients as well as lower hospital admission rates.

Another issue highlighted in this study is that we are able to use the continuity parameters proposed by Smedby et al. [40]. Most of the previous studies done in China used their own questionnaires and the score associated with these questionnaires was used as primary outcomes [27, 32, 41]. There is not any universal technique to calculate the continuity scores, which makes it difficult to compare different studies. To measure continuity of care, Smedbly et. al. in their paper [40] proposed visit-based measures, which consider the number of visits within a specifically defined measurement period. The measurement of known provider continuity (k) represents the number of visits within the continuity-determining period (CDP). To be specific, k =1, refers that the primary care provider of the current visit also visited at some other point of time within the continuity determining period, if not, k is considered 0. In this study, we utilized this measure and modified it to consider for continuity of 1 year (k1), where the measurement period (MP) was a 1-year interval before the current visit, and continuity for at least 3 years (k3), where the measurement period (MP) was 3 years before the current visit.

Fig. (3). Measurement of continuity of care.

Fig. (3) illustrates how the continuity of care is calculated in this paper. Here, A and B represent the PCP. The horizontal line represents time with an interval of 1 year and 3 years. Here, for provider A, k1 = 0, since A appears among the primary care practitioners seen within less than a year of MP. On the contrary, k1 = 1 for provider B since the length of the continuity was more than 1 year. The survey questionnaire used in this study facilitated the information necessary for using this theory to measure continuity of care. The question utilized in this regard was “How long have you been seeing the primary care physician” with four response options, less than 1 year, 1-3 years, 3-5 years, and more than 5 years. After re-cording these responses to less than a year and more than a year, we obtained a similar outcome variable that measures continuity of 1 year or more with the same primary care practitioner and an outcome variable that measures continuity of 3 years or more with the same PCP.

In developing countries like China, obtaining real-time encounter data from electronic health records is difficult. Most of the continuity measurements were obtained from surveys. In this study, we attempted to incorporate quantitative measurements on continuity of care using specific survey questions as a proxy to the encounter data. However, using the proxy may lead to a biased estimate. In addition, this study may be subject to several other biases. First, as this is a cross-sectional study, it will not be appropriate to make causal inferences based on the results. Second, the survey is conducted on data solely based on urban areas in Guangdong province. Thus, this study may lose generalizability. Third, the selection procedure of the survey participants was not randomized so it was a convenient sample. Despite that, the sample size used for the analysis purpose was relatively large with no missing data; this would potentially correct some types of biases mentioned above.

Our results indicated that the number of chronic conditions is significantly associated with patients' continuity with their PCPs. The odds of visiting the same PCP were higher if the patient suffered from more chronic conditions. The status of residency did not significantly modify this association. Furthermore, the long-term contract between patients and PCPs could moderate the effect of chronic conditions on continuity. Since the relationship between chronic conditions and continuity can reinforce the relationship between providers and patients cumulatively, we recommend policymakers to make policies that facilitate patients with chronic conditions to use primary care services and make a contract with their PCPs.

N.H. and S.C. designed the study, determined the methodology, and selected the analytical tools. selected the methodology and software; N.H. performed validation; S.C performed the formal analysis. An investigation was done by S.C., M.Z, L.K, and N.H. Data were collected by M,Z, and L.K. Furthermore, S.C. contributed to data curation. S.C. participated in writing – original draft preparation. Writing – reviewing and editing were done by N.H. S.C. contributed to visualization. N.H. contributed to supervision and project administration. All authors have read and agreed to the published version of the manuscript