In this cross-sectional study, we utilized the validated the PCAT-C to evaluate the quality of PHIs primary care from the patient's perspective. Building on these insights, we applied a regression model to dissect the determinants of PHIs primary care quality. Our findings underscore that, beyond the typical considerations of area, region, working status, family annual income, commercial medical insurance participation and seeking medical attention for illness, personal attributes such as the presence of adult children and macro-level indicators like the number of hospital beds per 10,000 population and per capita health expenditure as a percentage of GDP per capita, also exert a significant influence on PHIs primary care quality. Prior research often focused on a disease-centric approach to assess primary care quality, with a narrow sample scope confined to specific cities or regions. These studies typically examined a limited set of factors, often in isolation, lacking a comprehensiveness view. Our nationwide survey provides a representative snapshot of PHIs primary care quality in China, addressing a research gap regarding the impact of both personal and macro-level factors on PHIs primary care quality, and offering a valuable reference for quality enhancement initiatives.
Current Status of PHIs Primary Care Quality
In general, the PCAT scores reported by our respondents are lower than those observed among patients in the United States[32], a discrepancy that may stem from the relative underdevelopment of China's primary health care system, especially when juxtaposed with those of developed nations. The inclusion of nearly all Chinese provinces in our survey, where there is a marked disparity in medical resource allocation between central and western provinces and a few more developed areas such as Shanghai and Beijing, could also account for the lower PCAT scores. This observation aligns with a study conducted in Shanghai[17]. Notably, the Community-orientation dimension scored the lowest, indicating a significant discrepancy with other dimensions, consistent with findings from a study in Guangdong[33]. This suggests that respondents' experiences with community-based health services are less than optimal, potentially attributable to a shortfall in PHIs physician teams' understanding of community health needs and their ability to adapt to the cultural nuances of the communities they serve.
Factors Influencing PHIs Primary Care Quality
Our regression analysis identified nine significant factors affecting PHIs primary care quality: area, region, working status, family annual income, the presence of adult children, commercial medical insurance participation, seeking medical attention for illness, the number of hospital beds per 10,000 population in the province and per capita health expenditure as a percentage of GDP per capita (refer to Fig. 2). While factors such as region, working status, family annual income, commercial medical insurance participation and seeking medical attention for illness align with previous literature, the inclusion of area, the presence of adult children, the number of hospital beds per 10,000 population and per capita health expenditure as a percentage of GDP per capita in our study offer new insights. The following discussion will synthesize these findings with existing literature to provide a more nuanced understanding of these determinants.
Figure 2 An influencing model of primary care quality
Adult Children Enhance Patients' Primary Care Experiences
Our analysis indicates that patients with adult children tend to score higher on the PCAT, which may be due to their generally better health status. This superior health condition likely leads to a more positive primary care experience during PHI visits[18]. A study in Jilin Province's PHIs corroborates this, showing that patients who receive assistance and support enjoy a more satisfactory experience with PHIs[34]. Under the influence of China's traditional culture of filial piety, adult children provide extensive intergenerational support, including financial, caregiving, and emotional support. Research has demonstrated that active intergenerational exchanges between the elderly and their adult children foster a reciprocal and equitable exchange of economic support, daily care, and emotional comfort, which in turn improves the elderly's physical, mental, and social well-being[35, 36], leading to an enhanced primary care experience. A study from the United States echoes these findings[37]. Therefore, it is not surprising that patients with adult children report relatively higher PCAT scores.
Geographical Health Resource Allocation Affects Primary Care Experiences
Analysis of geographical influence shows that patients in central China achieve the highest PCAT scores, with those in the eastern and western regions scoring comparatively lower. This is consistent with previous research[38] that has highlighted disparities in health resource allocation and primary care quality across China's three major geographical areas. A Brazilian study also noted a significant consistency in primary care quality across different areas within a vast and varied country, although it did not explore the reasons for these disparities in depth[39]. Our study goes further by incorporating the health resource allocation of participants' provinces into the regression analysis, aiming to uncover the intrinsic factors contributing to the variance in primary care quality across areas. Interestingly, the analysis reveals that areas with a higher number of hospital beds per 10,000 population tend to have lower PCAT scores, which is counterintuitive. However, in China, the hospitalization rate in PHIs is typically low[40], as residents often prefer to visit higher-level hospitals directly, bypassing PHIs for both minor and severe illnesses[38, 41]. Furthermore, according to the "2022 China Health Statistics Yearbook," the proportion of PHIs beds within the total hospital bed count is often low, particularly in cities like Beijing and Shanghai. The expansion of higher-level hospitals as the number of beds per 10,000 population increases exacerbates the disparity in medical resource allocation between these hospitals and PHIs, potentially leading to a less favorable primary care experience for patients at PHIs. Conversely, the analysis shows that a higher ratio of per capita health expenditure to GDP per capita is associated with higher PCAT scores. This ratio is an indicator of the financial commitment to the health sector over a specific period and reflects the extent of government and societal emphasis on health and resident well-being. Evidently, in areas where a larger share of GDP is allocated to health expenditure, there is a greater investment in the health sector and a higher priority placed on resident health[42]. Consequently, individuals in these areas are more likely to access superior primary care services through PHIs, which translates into an improved primary care experience.
In conclusion, our study uncovers significant variations in the quality of primary care across different areas, predominantly influenced by the area's economic status and the government's financial investment in health care. This aligns with previous research[43, 44], which also rationalizes the higher quality of primary care experienced by patients in the eastern and central regions compared to those in the west. The highest PCAT scores in central China may be attributed to the most notable improvements in PHIs health resource allocation following the implementation of a tiered diagnosis and treatment system[38], leading to a more perceptible enhancement in the quality of primary care. This has resulted in a slightly better primary care experience in central China compared to other areas. It is imperative for China to consider refining its medical and health resource allocation mechanisms, with an emphasis on directing more resources to economically disadvantaged and remote areas. This approach will gradually improve regional equity, fostering balanced development across the nation.
Differential Impact Factors Across Dimensions of Primary Care Quality
The correlation between microscopic factors and the various dimensions of primary care quality observed in this study is in line with existing literature. Our regression analysis has pinpointed the number of licensed physicians per 10,000 population and the number of registered nurses per 10,000 population as influential factors for the First Contact dimension, echoing conclusions from a study in India[45]. Given that primary care providers are the entry point into the health care system, and in PHIs, these roles are mainly filled by licensed physicians, a higher physician-to-population ratio translates into a superior First Contact experience. Conversely, an overabundance of nurses might hinder swift and effective communication with doctors, potentially degrading the First Contact experience as the number of registered nurses per 10,000 population. The sole macro factor identified to influence Continuity is the number of licensed physicians per 10,000 population. Continuity, being closely tied to primary care providers, is about establishing long-term relationships that facilitate mutual understanding and align expectations and needs. The role of licensed physicians in PHIs is thus paramount in ensuring continuous care[46]. The macro factor for Coordination is limited to the number of beds per 10,000 population, as previously discussed. For Comprehensiveness, the macro factors include the number of licensed physicians per 10,000 population, the number of registered nurses per 10,000 population, the number of beds per 10,000 population, and per capita health expenditure as a percentage of GDP per capita. Comprehensiveness necessitates a broad range of services from PHIs, tailored to address all but the rarest health needs within the population. Achieving this requires significant investment in human, material, and financial resources, all of which have a bearing on the Comprehensiveness dimension. Family-centered care, which emphasizes the family's integral role in the assessment and treatment process, sees minimal impact from macro factors. Instead, the presence of adult children, a microscopic factor, significantly influences Family-centered care, with the highest impact coefficient among all dimensions. Lastly, the macro factors for Community-orientation parallel those for Comprehensiveness, recognizing the broader health care needs within the community and the characteristics that affect them[26]. Like Comprehensiveness, Community-orientation demands substantial resources from PHIs, thus all three areas of resources significantly influence Community-orientation.
Innovation
There are several innovative points in our research. Firstly, the coverage of the investigation in this research is larger and more comprehensive compared to previous studies. The cross-sectional investigation conducted in this research spans 22 provinces, 5 autonomous regions, and 4 municipalities directly under the Central Government, covering almost all provincial administrative regions in the country (except Hong Kong, Macao and Taiwan). Secondly, in contrast to previous disease-centered or task-oriented studies, this research incorporates micro and macro factors into the study of influencing factors of primary health care quality in PHIs for the first time, exploring more comprehensive influencing factors of primary health care. Furthermore, the macro factors in this research include a total of 5 factors in three parts: human resources, material resources and financial resources. The consideration of macro factors is more systematic and representative. These factors have scarcely appeared together in previous studies on the quality of primary health care.
Limitations
Our research has several limitations. The cross-sectional design, while effective for acquiring a large sample size, restricts the ability to establish causality. Longitudinal studies would provide a more robust analysis of cause-and-effect relationships. Additionally, our survey data relies entirely on patient self-reports, which are subject to recall and response biases and limit the inclusion of technical quality aspects of primary care. Future development of primary care assessment tools based on clinical data could mitigate recall bias and incorporate technical quality issues. Furthermore, given the economic, cultural, and health service development disparities across regions, our study may not encapsulate the entirety of China, despite an extensive dataset of over two thousand individuals. Nonetheless, our findings are instrumental in informing policy decisions regarding primary care, particularly in the context of an aging population, the Healthy China 2030 initiative, and the tiered diagnosis and treatment system in China.