Measurement of the coupling coordination level and influencing factors of community healthcare services and older adults care services in China

Abstract

Background

With the continuous increase in morbidity among older adults and the rapid progression of population aging, the demand for healthcare services and older adults care services has grown rapidly. However, the traditional model characterized by the separation of healthcare and older adults care has become increasingly inadequate in meeting the diverse and expanding needs of the aging population. Therefore, promoting the coordinated development of healthcare services and older adults care services has emerged as an urgent and critical issue.

Method

Based on panel data from 31 provinces (including municipalities and autonomous regions) in China from 2014 to 2023, this study employs the entropy method, the coupling coordination degree model, and the geographic detector model to analyze the level of coordinated development and its key influencing factors between community-level healthcare services and older adults care services at the provincial scale. Specifically, the entropy method is used to determine indicator weights, the coupling coordination degree model is applied to evaluate the coordination level, and the geographic detector model is utilized to identify the main influencing factors.

Results

(1)The coupling coordination level between community healthcare services and older adults care services in China shows an overall upward trend, although the growth rate is uneven, exhibiting a dynamic evolution characterized by “rapid development–gradual slowdown–bottoming out and rebound–fluctuating advancement.”(2)Significant spatial disparities are observed in the coupling coordination level, gradually forming a diversified spatial pattern in which coastal regions take the lead, inland regions rise rapidly, and remote regions demonstrate substantial development potential.(3)The coupling coordination development of community healthcare services and older adults care services in China is jointly influenced by infrastructure, workforce, and service capacity. Among these factors, the number of healthcare institutions, the number of technical staff, the number of outpatient visits, the number of older adults care institutions, the number of certified social workers, and the number of community-based daytime care recipients play a dominant promoting role.

Conclusion

Based on the findings, this study recommends implementing dynamic regulatory strategies aligned with different development stages, formulating region-specific policies that account for spatial heterogeneity, and adopting targeted interventions focusing on key influencing factors, in order to systematically promote the high-quality coordinated development of healthcare services and older adults care services in China.

Introduction

With the continuous decline in China’s fertility rate, population aging has accelerated markedly. According to data released by National Bureau of Statistics of China, by 2023 the population aged 60 and above had reached 296.97 million, accounting for 21.1% of the total population [1]. At the same time, the persistently rising prevalence of chronic diseases among older adults has further intensified the demand for healthcare services. Data from the China Health Statistics Yearbook indicate that by 2023, the prevalence of chronic diseases among individuals aged 60 and above had reached 78.6%, with hypertension, diabetes, and cardiovascular and cerebrovascular diseases emerging as the primary threats to older adults health [2]. This distinctive national context—characterized by “aging before wealth” and “aging with disease”—poses severe challenges to China’s healthcare services and older adults care services systems [3]. However, the traditional service delivery framework has long been constrained by the problem of separating healthcare from older adults care. Medical institutions, limited by their functional positioning, are unable to provide long-term care services, while older adults care institutions, constrained by insufficient resources, struggle to meet professional medical needs. In response to these challenges, the Chinese government has continuously improved the policy framework for integrated healthcare and older adults care in recent years. In 2015, the General Office of the State Council of China issued the Guiding Opinions on Promoting the Integration of Healthcare and Older Adults Care Services, which for the first time formally introduced the concept of integrated healthcare and older adults care at the national level and clarified its basic principles and development goals, marking the official launch of China’s policy system in this field [4]. Subsequently, in 2019, the Department of Older Adults Health of the National Health Commission of China issued the Opinions on Further Promoting the Development of Integrated Healthcare and Older Adults Care, which, building on earlier practical experience, emphasized extending integrated services to the grassroots level and fostering community-based integrated healthcare and older adults care service institutions, thereby providing institutional guidance for development at the community level [5]. Building on this foundation, the National Health Commission issued the Guiding Opinions on Promoting the High-Quality Development of Integrated Healthcare and Older Adults Care Services in 2024, explicitly calling for the implementation of standardized diagnostic practices and industry norms, the strengthening of quality and safety management systems, and the advancement of integrated services toward greater standardization and refinement [6]. Against this policy backdrop, how to effectively promote the coordinated development of healthcare services and older adults care services has become an urgent and important issue requiring in-depth academic investigation.

Literature review

A systematic review of existing studies indicates that the coordinated development of healthcare services and older adults care services has attracted extensive attention in academic research. Current studies mainly focus on several core dimensions, including conceptual foundations, development models, practical challenges, and optimization pathways. Regarding the basic conceptualization, early research largely centered on critiques of the traditional model characterized by the separation of healthcare and older adults care, emphasizing the urgency of integrating medical services into the older adults care sector. In this stage, integrated healthcare and older adults care was preliminarily defined as a new type of service model that combines healthcare and older adults care functions. However, a unified definition had not yet been established, and discussions largely remained at the level of conceptual advocacy. As research has progressed, scholarly inquiry has gradually moved beyond normative advocacy toward multidimensional conceptual elaboration. From a demand-oriented perspective, integrated healthcare and older adults care is essentially a service innovation aimed at meeting the comprehensive needs of older adults, establishing a people-centered value foundation [7]. From the perspective of resource allocation, the core of integrated healthcare and older adults care lies in promoting the integration of healthcare resources into the older adults care service system through diversified mechanisms such as institutional embedding, contractual cooperation, and service extension, thereby maximizing the efficiency of resource utilization [8]. From a systems theory perspective, integrated healthcare and older adults care is not a simple aggregation of services, but rather a coordinated integration of the healthcare system and the older adults care system achieved through structural reorganization and functional realignment [6]. Driven by practical implementation, the connotation of integrated healthcare and older adults care has continued to expand. It has now evolved into a full-chain service system encompassing health management, rehabilitation nursing, long-term care, psychological support, and hospice care. Through a three-tier service network consisting of households, communities, and institutions, services are allocated in a multidimensional manner, effectively responding to the diverse and differentiated needs of the older adults population [9]. This evolution has laid a solid foundation for the coordinated development of healthcare services and older adults care services.

From the perspective of development models, China’s integrated healthcare and older adults care services have evolved into a pattern centered on diversified governance structures, primarily encompassing three typical models: institutional integration, community embedding, and contractual cooperation. Within the institutional integration model, two main forms can be further distinguished: “healthcare-led older adults care” and “older adults care–led healthcare.” The former refers to medical institutions—such as public hospitals and rehabilitation hospitals—leveraging their professional expertise and resource advantages to establish geriatric wards, rehabilitation wards, or nursing institutions oriented toward older adults [10]. This model is characterized by a well-established medical support system and smooth integration with health insurance schemes, enabling it to provide strong integrated healthcare and older adults care services for disabled older adults, the oldest-old population, and patients with multiple chronic conditions [11]. In contrast, the “older adults care–led healthcare” model refers to older adults care institutions of a certain scale independently establishing internal medical units, such as medical rooms, nursing stations, or rehabilitation centers. Its advantages lie mainly in timely service response and high convenience, allowing it to better meet the routine medical needs of older adults [12]. The community embedding model takes the community as the basic service unit and constructs a localized service network closely aligned with residents’ daily lives by organically integrating the functions of community health service centers (stations), day care centers, and older adults service stations [13]. This model offers high service accessibility and strong emotional connectedness, making it highly consistent with older adults’ widespread preference for aging in place [14]. The contractual cooperation model is based on formal contractual relationships and establishes standardized collaboration mechanisms between medical institutions and older adults care providers [15]. This model is characterized by low entry barriers, high organizational flexibility, and a short start-up cycle, enabling it to effectively compensate for the shortage of medical resources within the older adults care service system.

However, despite the practical advancement of diversified models of integrated healthcare and older adults care in China, a range of deep-seated development challenges persists. These challenges can be systematically categorized into four dimensions: institutional, organizational, resource-based, and technological. At the institutional level, systemic barriers severely constrain integrated development. The healthcare service system and the older adults care service system have long been administered by different governmental departments, resulting in insufficient policy coordination and the absence of a unified institutional framework. The most prominent contradiction lies in the weak alignment between the medical insurance payment system and the long-term care security system. Existing medical insurance policies primarily emphasize clinical diagnosis and treatment rather than long-term care services, making it difficult for many older adults care–led healthcare institutions to obtain stable reimbursement support [16]. As a result, these institutions face significant challenges in achieving sustainable development. At the organizational level, the lack of effective coordination mechanisms leads to service fragmentation. Cooperation between medical institutions and older adults care institutions often relies on loose arrangements such as project-based collaboration or contractual agreements, without establishing sustainable mechanisms for benefit sharing and risk sharing. Consequently, a large number of “nominal partnerships” emerge, which are difficult to translate into substantive service integration [17]. At the resource level, insufficient factor support undermines service sustainability, particularly in terms of financial resources and human capital. In terms of funding, government investment remains fragmented, and effective mechanisms for resource pooling have yet to be established [18]. Meanwhile, due to the long investment return cycle and unclear profit models associated with integrated healthcare and older adults care projects, private capital participation remains generally low, leaving many projects under sustained operational pressure [19]. With respect to human resources, the sector faces not only an overall shortage of personnel, but also a pronounced lack of interdisciplinary professionals who possess expertise in both medical care and older adults care [20]. More fundamentally, ambiguous career development pathways, low social recognition, and weak salary competitiveness have collectively created a vicious cycle in which talent is difficult to attract and retain, severely constraining service quality and long-term sustainability [21]. At the technological level, inconsistent data standards hinder efficiency improvement. Because healthcare services and older adults care services are managed within separate systems, significant disparities exist in data standards, resulting in pronounced “data silos [22].” This not only impedes effective information sharing, but also leads to difficulties in service coordination and delayed service responses, ultimately reducing the operational efficiency of the overall service system.

To address the practical development challenges of integrated healthcare and older adults care, scholars have proposed systematic optimization pathways from multiple dimensions, including institutional reform, organizational coordination, resource allocation, and technological innovation, with the aim of promoting a transition from fragmented service provision to systematic governance. At the institutional level, it is recommended to integrate the functions of multiple government departments—such as health, civil affairs, and medical insurance—by establishing cross-departmental coordination mechanisms for integrated healthcare and older adults care, and by developing unified standards for institutional access and service provision [23]. At the same time, reforms to medical insurance payment mechanisms should be advanced by linking disability assessment levels of older adults to care subsidies, and by gradually incorporating services such as rehabilitation nursing and long-term care into the scope of medical insurance reimbursement, thereby building a sustainable policy support system [24]. At the organizational level, deeper cooperation between medical institutions and older adults care institutions should be promoted through mechanisms such as equity-based partnerships and joint construction and management. These arrangements can facilitate the establishment of performance evaluation and risk-sharing mechanisms centered on user satisfaction, thereby enhancing the effectiveness of integrated service delivery [25]. At the resource level, the establishment of dedicated development funds for integrated healthcare and older adults care is proposed to improve the efficiency of public fund utilization through project-based and operational subsidies [26]. Simultaneously, efforts should be intensified to develop specialized training bases for integrated healthcare and older adults care professionals, expand relevant talent training programs, and provide supporting policies in areas such as housing and children’s education, in order to systematically strengthen human resource support [27]. At the technological level, unified data standards should be formulated to promote coordinated data sharing of key information—such as health records and care documentation—between healthcare services and older adults care services systems [28]. On this basis, intelligent service platforms integrating health monitoring, service scheduling, and management evaluation should be developed, leveraging digital technologies to enhance the overall efficiency of service provision [29].

In summary, a substantial body of research has examined the coordinated development of healthcare services and older adults care services, yielding a solid foundation of scholarly findings. However, much of the existing literature remains confined to theoretical discussions and lacks empirical analysis grounded in real-world development conditions. More importantly, the community—serving as the fundamental unit of urban governance and the primary arena of older adults’ daily activities—represents both the “last mile” of policy implementation and the “nerve endings” of service provision, thus occupying an irreplaceable strategic position. Nevertheless, quantitative studies conducted at the community level remain relatively scarce in the existing literature. In response to these gaps, this study takes communities across 31 provinces (including autonomous regions and municipalities) in China as the research focus. Using panel data spanning the period from 2014 to 2023, this study applies the entropy method, the coupling coordination degree model, and the geographic detector model to systematically measure the coupling coordination level between healthcare services and older adults care services, to reveal their spatiotemporal evolution patterns, and to identify the key influencing factors. This study aims to address existing research limitations and to provide empirical evidence and policy-relevant insights for promoting the coordinated development of healthcare services and older adults care services.

Methods

Data sources

At the level of analysis, this study focuses on the coordinated development of healthcare services and older adults care services at the community level. However, due to limitations in data availability, a unified nationwide community-level panel database has not yet been established in China. Accordingly, this study draws primarily on data from the China Statistical Yearbook, the China Health Statistics Yearbook, the China Civil Affairs Statistical Yearbook, as well as statistical yearbooks published by individual provinces, municipalities, and autonomous regions. It should be noted that the indicators related to community healthcare services and older adults care services reported in these yearbooks—such as the number of institutions and the number of beds—are explicitly defined and statistically calibrated at the community level. Although the data are aggregated and published at the provincial level, their underlying data-generating process is based on the aggregation of micro-level observations within administrative regions. Therefore, the data fundamentally reflect community-level conditions.

In terms of data completeness, a total of 4,480 observations were constructed for this study, with overall data coverage being relatively comprehensive. A small number of provinces exhibit missing values in certain years, with the proportion of missing data remaining below 5%, which is within an acceptable range. Based on an assessment of the missing data patterns, the missing values in this study are primarily classified as Missing at Random (MAR). To ensure temporal continuity and the plausibility of data values (non-negativity), linear interpolation was applied using SPSS software to impute the missing observations. Subsequently, a comparative analysis of data characteristics before and after interpolation was conducted, and no significant differences were identified. This indicates that the potential bias introduced by interpolation remains within a controllable range and is unlikely to materially affect the accuracy or robustness of the study’s findings.

Variable selection

To examine the coupling coordination development of community healthcare services and older adults care services in China, this study constructs a comprehensive evaluation indicator system based on the classic Input–Output Model proposed by Wassily Leontief [30]. The indicator system comprises three core dimensions: infrastructure, workforce, and service capacity. Among these dimensions, infrastructure and workforce jointly constitute the input dimension, representing fundamental production factors whose allocation levels directly determine the upper bound of potential service capacity. In contrast, service capacity functions as the output dimension and serves as an outcome variable, reflecting the effectiveness with which resource inputs are transformed into actual service provision. By adopting this input–output–oriented indicator framework, this study captures the overall development of healthcare services and older adults care services from both input and output perspectives, thereby laying a solid foundation for the subsequent identification and analysis of key influencing factors. A detailed explanation of each dimension is provided below.

Within the healthcare services system, infrastructure is measured using two indicators: the number of institutions and the number of beds. The number of institutions reflects the spatial coverage and accessibility of healthcare services [17], while the number of beds directly indicates the system’s service-carrying capacity [31]. Together, these two indicators capture the physical capacity of healthcare services and constitute a fundamental basis for assessing their potential service provision capability. Workforce is measured using three indicators: the number of management staff, the number of technical staff, and the number of support staff. Management staff are responsible for organizational coordination and operational management [32], technical staff (such as physicians and nurses) serve as the core executors of healthcare services [33], and support staff provide essential logistical support to ensure the normal operation of healthcare institutions [34]. Collectively, these personnel form the workforce system of healthcare services, and both their scale and structural composition are critical determinants of service quality. Service capacity is measured using the number of outpatient visits and the number of inpatient admissions. The number of outpatient visits reflects the routine service volume of healthcare services in disease diagnosis and treatment [35], while the number of inpatient admissions indicates the actual capacity of healthcare services to provide inpatient and nursing care [36]. Together, these indicators represent the output level of healthcare services.

Within the older adults care services system, infrastructure is measured using two indicators: the number of institutions and the number of beds. The number of institutions reflects the organizational layout of older adults care services [37], while the number of beds directly indicates the scale of service provision [38]. Together, these indicators provide an important basis for assessing the potential capacity of older adults care services. Workforce is measured using three indicators: the number of employees at year-end, the number of certified social workers, and the number of assistant social workers. The number of employees at year-end represents the total human resources engaged in older adults care services [39]. Certified social workers (such as care specialists and psychological counselors) constitute the professional backbone of older adults care services, as they possess specialized skills and are able to independently deliver professional services, including daily care and psychological support [40]. Assistant social workers primarily undertake supportive tasks, assisting in routine service delivery and community-based support activities [41]. Together, these groups form the workforce system of older adults care services, and both their scale and structural composition are key factors influencing service performance. Service capacity is measured using the number of community-based daytime care recipients and the number of community-based overnight care recipients. The number of daytime care recipients reflects the routine service volume of older adults care services at the community level [25], while the number of overnight care recipients indicates the actual capacity of older adults care services to provide long-term care [38]. Together, these indicators represent the output level of older adults care services. The specific results are shown in Table 1.

Table 1.

Comprehensive evaluation indicator system for the coupled and coordinated development of community healthcare services and older adults care services in China

Primary indicator	Secondary indicator	Tertiary indicator	Unit
Healthcare services	Infrastructure	Number of institutions	units
		Number of beds	beds
	Workforce	Number of management staff	persons
		Number of technical staff	persons
		Number of support staff	persons
	Service capacity	Number of outpatient visits	10,000 person-times
		Number of inpatient admissions	10,000 person-times
Older adults care services	Infrastructure	Number of institutions	units
		Number of beds	beds
	Workforce	Number of employees at year-end	persons
		Number of certified social workers	persons
		Number of assistant social workers	persons
	Service capacity	Number of community-based daytime care recipients	persons
		Number of community-based overnight care recipients	persons

Model selection

Entropy method

The entropy method is an objective weighting approach grounded in information entropy theory. Its core principle is to measure the amount of information contained in each indicator by assessing the degree of dispersion in the data. The greater the variability of an indicator, the smaller its entropy value and the larger the amount of information it provides; consequently, a higher weight should be assigned to that indicator in comprehensive evaluation. This method effectively addresses the question of how to objectively identify the relative importance of indicators within a multi-indicator system, making it well suited to the purposes of this study. Compared with traditional subjective weighting methods such as expert scoring, the entropy method determines indicator weights entirely based on the intrinsic characteristics of the data, thereby avoiding subjective interference and ensuring the objectivity of the evaluation results [36]. Accordingly, this study adopts the entropy method to objectively calculate the weights of indicators in the healthcare services and older adults care services systems, providing a robust data foundation for subsequent coupling coordination analysis. The specific calculation procedure consists of the following six steps.

Step 1: Data standardization

To eliminate differences in measurement units among the original indicators, this study applies Z-score standardization to preprocess the rawdata. Since all indicators in this study are positive indicators, no reverse transformation is required before standardization. Compared with min–max normalization, Z-score standardization reduces the influence of differences in scale and is therefore suitable for the present analysis.

Let denote the standardized value of the -th indicator for the -th sample, the original value, and and the sample mean and standard deviation of the -th indicator, respectively. The formula is as follows:

1

Step 2: Calculation of indicator proportions

To facilitate the entropy calculation,the standardized data are translated to positive values by adding a constant A, namely , where is used to ensure that all are positive. The proportion of the -th sample under the -th indicator is then calculated as:  

2

Step 3: Calculation of information entropy

The information entropy value of the -th indicator is calculated as follows, where is a normalization constant:

3

Step 4: Calculation of the difference coefficient

A larger entropy value indicates smaller variability in the indicator and, consequently, less information content. Therefore, the difference coefficient is defined to reflect the relative importance of each indicator. A larger implies greater importance in the comprehensive evaluation.

4

Step 5: Determination of indicator weights

Based on the difference coefficients, the final weight of the -th indicator is calculated. Accordingly, the weight vector for all evaluation indicators is objectively determined as .

5

Step 6: Calculation of the composite development index

After determining the weights of each indicator, the composite developmentindices of thetwo subsystemsare calculatedseparately. Specifically, the composite development index of thehealthcare services subsystem is denoted as , while that of the older adultscare services subsystem is denoted as . For the i-th region, the composite score ofsubsystem k is denoted as , where . The calculatioformula is asfollows:

6

Coupling and coordination degree model

The coupling coordination degree model is a classical analytical framework used to measure the interaction and coordinated development among two or more systems. It consists of three key indicators: the coupling degree, the coordination index, and the coupling coordination degree. This model effectively addresses the question of how to quantify and evaluate the level of coordinated development between systems, making it well suited to the objectives of this study. Compared with single-indicator evaluations or simple correlation analyses, the coupling coordination degree model overcomes the limitations of traditional approaches that focus solely on the independent development of individual systems while neglecting their dynamic interrelationships. By capturing interdependence and mutual constraints between systems, the model provides a more comprehensive understanding of coordination mechanisms [19]. Accordingly, this study applies the coupling coordination degree model to assess the coordinated development between the healthcare services system and the older adults care services system, with the aim of revealing the degree of coordination between the two subsystems. The specific calculation procedure includes the following steps.

Step 1: Calculation of the coupling degree (C)

The coupling degree measures the extent of interdependence and interaction between two systems. Its value ranges from 0 to 1, with a larger value indicating stronger interaction between the systems. The coupling degree is calculated as follows:

7

Step 2: Calculation of the coordination index (T)

The coordination index reflects the overall development level of the two systems. Here, denotes the composite development index of healthcare services, and denotes the composite development index of older adults care services. Parameters and are weighting coefficients. Given the equal importance of the two systems, this study sets . The coordination index is calculated as:

8

Step 3: Calculation of the coupling coordination degree (D)

The coupling coordination degree is a comprehensive indicator integrating both the coupling degree and the coordination index. It addresses the potential issue of “pseudo-coordination” that may arise when coupling degree values are high at low development levels, thereby providing a more accurate representation of coordinated development achieved through positive interaction between systems. The value of ranges from 0 to 1 and is calculated as:

9

Step 4: Classification of coordination levels

Following established practices in the literature on coupling coordination analysis, the coupling coordination degree is classified into ten categories to facilitate interpretation and to intuitively distinguish different types of coordinated development across regions. The specific classification criteria are presented in Table 2.

Table 2.

Classification standards for coupling coordination degree

D value	Coordination type	D value	Coordination type
[0.0 ~ 0.1)	Extreme disorder	[0.5 ~ 0.6)	Slight coordination
[0.1 ~ 0.2)	Severe disorder	[0.6 ~ 0.7)	Mild coordination
[0.2 ~ 0.3)	Moderate disorder	[0.7 ~ 0.8)	Moderate coordination
[0.3 ~ 0.4)	Mild disorder	[0.8 ~ 0.9)	High coordination
[0.4 ~ 0.5)	Slight disorder	[0.9 ~ 1.0]	Extreme coordination

Geographical detector model

The geographic detector model is a statistical method grounded in the theory of spatial stratified heterogeneity. Its core objective is to quantify the explanatory power of influencing factors on the spatial distribution pattern of a system by calculating the q statistic. Compared with traditional statistical methods, the geographic detector model does not rely on linear assumptions, and its results reflect associations rather than causal relationships [42]. In this study, the factor detector component of the geographic detector model is employed to comprehensively assess the independent explanatory power of each influencing factor on the coupling coordination degree between healthcare services and older adults care services. For continuous independent variables, K-means clustering is applied in SPSS to discretize the data, with each variable classified into six categories (). This approach aims to minimize within-group variance while maximizing between-group variance, thereby generating relatively robust stratification results. The q statistic is calculated as follows:

10

where denotes the explanatory power of the factor detector; represents the strata of the explanatory variable ; and denote the number of samples in the study area and in stratum , respectively; and and represent the variance of the dependent variable in the entire study area and within stratum , respectively. The value of ranges from 0 to 1, with larger values indicating stronger explanatory power of the corresponding factor.

Results

This study first applies the entropy method to determine the weights of indicators in the coupling coordination development of community healthcare services and older adults care services in China. Subsequently, the coupling coordination degree model is employed to quantitatively evaluate the level of coupling coordination development between community healthcare services and older adults care services. The results are presented in Table 3.

Table 3.

Coupling and coordinated development index of community healthcare services and older adults care services in China

	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
National	0.100	0.274	0.377	0.494	0.503	0.539	0.732	0.813	0.862	0.995
Beijing	0.177	0.172	0.300	0.253	0.361	0.829	0.918	0.910	0.995	0.955
Tianjin	0.298	0.478	0.541	0.590	0.603	0.607	0.753	0.688	0.314	0.794
Hebei	0.100	0.236	0.359	0.433	0.463	0.509	0.710	0.738	0.726	0.995
Shanxi	0.161	0.158	0.393	0.509	0.619	0.665	0.699	0.835	0.816	0.979
Inner Mongolia	0.211	0.137	0.356	0.372	0.323	0.336	0.777	0.771	0.847	0.995
Liaoning	0.185	0.157	0.370	0.633	0.681	0.605	0.766	0.873	0.791	0.979
Jilin	0.243	0.606	0.465	0.276	0.272	0.591	0.995	0.663	0.617	0.669
Heilongjiang	0.315	0.399	0.578	0.677	0.571	0.478	0.547	0.795	0.314	0.995
Shanghai	0.208	0.164	0.367	0.429	0.701	0.638	0.853	0.935	0.980	0.992
Jiangsu	0.205	0.303	0.207	0.633	0.651	0.683	0.833	0.852	0.624	0.972
Zhejiang	0.132	0.172	0.350	0.527	0.613	0.739	0.772	0.795	0.785	0.902
Anhui	0.408	0.210	0.385	0.592	0.425	0.234	0.637	0.885	0.978	0.952
Fujian	0.100	0.217	0.400	0.480	0.504	0.384	0.910	0.888	0.936	0.995
Jiangxi	0.266	0.460	0.547	0.655	0.679	0.754	0.897	0.730	0.547	0.315
Shandong	0.100	0.226	0.345	0.536	0.408	0.378	0.728	0.747	0.830	0.995
Henan	0.100	0.375	0.307	0.455	0.417	0.351	0.660	0.759	0.870	0.995
Hubei	0.100	0.341	0.480	0.493	0.557	0.597	0.505	0.757	0.783	0.995
Hunan	0.176	0.174	0.399	0.477	0.251	0.393	0.860	0.920	0.974	0.989
Guangdong	0.100	0.302	0.471	0.515	0.642	0.734	0.860	0.957	0.979	0.950
Guangxi	0.115	0.167	0.281	0.371	0.395	0.370	0.917	0.921	0.968	0.994
Hainan	0.173	0.216	0.499	0.582	0.206	0.582	0.895	0.910	0.891	0.948
Chongqing	0.100	0.294	0.348	0.307	0.339	0.443	0.772	0.852	0.978	0.952
Sichuan	0.100	0.227	0.389	0.454	0.512	0.554	0.690	0.748	0.826	0.995
Guizhou	0.185	0.257	0.371	0.477	0.230	0.403	0.832	0.886	0.894	0.936
Yunnan	0.100	0.465	0.730	0.755	0.811	0.672	0.805	0.847	0.924	0.995
Tibet	0.393	0.245	0.376	0.457	0.183	0.384	0.612	0.596	0.646	0.995
Shaanxi	0.217	0.270	0.197	0.168	0.344	0.410	0.793	0.877	0.824	0.995
Gansu	0.100	0.310	0.469	0.425	0.450	0.477	0.743	0.758	0.710	0.995
Qinghai	0.326	0.198	0.275	0.492	0.423	0.577	0.697	0.657	0.277	0.995
Ningxia	0.314	0.482	0.486	0.420	0.528	0.490	0.535	0.546	0.315	0.853
Xinjiang	0.218	0.326	0.446	0.444	0.305	0.289	0.486	0.359	0.695	0.994

Analysis of the temporal characteristics of the coupling and coordinated development of community healthcare and care services for older people in China

Overall, the coupling coordination level between community healthcare services and older adults care services in China exhibits a clear upward trend, although the growth rate is uneven. The dynamic evolution can be characterized as a process of “rapid development–gradual slowdown–bottoming out and rebound–fluctuating advancement.” Specifically, during the rapid development stage (2014–2015), the coupling coordination degree increased sharply from 0.100 to 0.274, with a compound growth rate of 174%, representing the largest increase across all stages. This indicates that the coordinated development of community healthcare services and older adults care services progressed extremely rapidly during this period, marking a golden phase of accelerated growth. During the gradual slowdown stage (2015–2018), the coupling coordination degree rose from 0.274 to 0.503, with a compound growth rate of 22.4%, ranking as the second-highest growth rate across the entire period. This suggests that although the pace of coordinated development slowed and entered a bottleneck phase, a relatively high growth momentum was still maintained. In the bottoming-out and rebound stage (2018–2020), the coupling coordination degree increased from 0.503 to 0.732, with a compound growth rate of 20.64%, indicating a period of steady growth. This stage reflects a renewed upward trajectory in the coordinated development of community healthcare services and older adults care services, with growth becoming more stable and sustained. During the fluctuating advancement stage (2020–2023), the coupling coordination degree further increased from 0.732 to 0.995, with a compound growth rate of 10.77%, representing a phase of optimization and adjustment. It is noteworthy that growth during this stage was not smooth but exhibited notable fluctuations. Specifically, the increase from 0.732 to 0.813 corresponded to a compound growth rate of 11.07%, constituting the first rapid growth interval within this stage. This was followed by a slowdown from 0.813 to 0.862, with the compound growth rate declining to 6.03%, indicating signs of growth fatigue. In the final sub-period, however, the coupling coordination degree rose again from 0.862 to 0.955, with the compound growth rate rebounding to 15.43%. Overall, despite considerable fluctuations in growth rates, this stage demonstrates a sustained upward trend, reflecting the uneven yet progressive development of community healthcare services and older adults care services in China. The detailed results are illustrated in Fig. 1.

Fig. 1.

Temporal evolution of the coupling coordination development of community healthcare services and older adults care services in China (2014–2023). Note: The horizontal axis represents the year (2014–2023). The primary vertical axis denotes the coupling coordination degree (dimensionless, ranging from 0 to 1), corresponding to the bar chart. The secondary vertical axis represents the annual growth rate (%, ranging from 0 to 200), corresponding to the line chart. The bar chart illustrates the national-level coupling coordination development level for each year, while the line chart reflects the year-on-year relative growth rate. The analysis is conducted at the national level (excluding Hong Kong, Macao, and Taiwan). Model outputs are derived from the entropy method–coupling coordination degree model, and growth rates are calculated based on first-order differences of the coupling coordination degree between consecutive years

Analysis of the spatial characteristics of the coupling and coordinated development of community healthcare and care services for older people in China

The coupling coordination level between community healthcare services and older adults care services in China exhibits pronounced spatial imbalance, gradually forming a diversified spatial development pattern characterized by leading coastal regions, rapidly rising inland regions, and remote regions with substantial development potential. Specifically, Guangdong and Shanghai, as representative coastal regions, record mean coupling coordination degrees of 0.6510 and 0.6267, respectively, both reaching the level of mild coordination. These regions serve as leading forces driving improvements in the national coupling coordination development of community healthcare services and older adults care services. Shanxi and Anhui, representing inland regions, exhibit mean coupling coordination degrees of 0.5834 and 0.5706, respectively, corresponding to the level of slight coordination, and have gradually emerged as rapidly growing areas in the national landscape. In contrast, Tibet Autonomous Region and Xinjiang Uyghur Autonomous Region, as representative remote regions, display relatively low mean coupling coordination degrees of 0.4887 and 0.4562, respectively, remaining at the level of slight disorder. Nevertheless, these regions are expected to gradually release their development potential in the future and contribute new momentum to the overall coordinated development of community healthcare services and older adults care services nationwide. The detailed results are illustrated in Fig. 2.

Fig. 2.

Spatial variation of the coupling coordination development of community healthcare services and older adults care services in China (2014–2023). Note: This map is produced based on the China Standard Map Service System, with map approval number (2023) 2767. The color gradient represents different levels of the coupling coordination degree, as indicated in the legend, while gray areas denote regions with missing data. The analysis is conducted at the provincial level (excluding Hong Kong, Macao, and Taiwan), with a map scale of 1:1000 km. Model outputs are derived from the entropy method–coupling coordination degree model, and the coupling coordination degree is classified into five levels according to the empirical distribution. English labels in the map correspond to the pinyin names of provincial-level administrative regions and are provided for geographic reference

Analysis of the influencing factors of the coupling and coordinated development of community healthcare and care services for older people in China

The coupling coordination development of community healthcare services and older adults care services in China is jointly influenced by infrastructure, workforce, and service capacity. Among these factors, the number of healthcare institutions, the number of technical staff, the number of outpatient visits, the number of older adults care institutions, the number of certified social workers, and the number of community-based daytime care recipients play a dominant promoting role. Specifically, within the healthcare services subsystem, infrastructure is measured using the number of institutions () and the number of beds (), among which the number of institutions () exhibits the highest explanatory power, with a q value of 0.9420. Workforce is measured using the number of management staff (), the number of technical staff (), and the number of support staff (), among which the number of technical staff () shows the highest q value of 0.7565. Service capacity is measured using the number of outpatient visits () and the number of inpatient admissions (), with the number of outpatient visits () yielding the highest q value of 0.8669.Within the older adults care services subsystem, infrastructure is measured using the number of institutions () and the number of beds (), among which the number of institutions () has the highest q value of 0.9430. Workforce is measured using the number of employees at year-end (), the number of certified social workers (), and the number of assistant social workers (), with the number of certified social workers () exhibiting the strongest explanatory power (q = 0.9600). Service capacity is measured using the number of community-based daytime care recipients () and the number of community-based overnight care recipients (), among which the number of community-based daytime care recipients () shows the highest q value of 0.9485. The detailed results are presented in Fig. 3.

Fig. 3.

Analysis of influencing factors on the coupling coordination development of community healthcare services and older adults care services in China (2014–2023). Note: The horizontal axis represents the influencing factors , and the vertical axis represents the q value (ranging from 0 to 1). A larger q value indicates stronger explanatory power of the corresponding factor on the coupling coordination degree. Different colors are used to distinguish indicator hierarchy types. The analysis is conducted at the national level (excluding Hong Kong, Macao, and Taiwan). Model outputs are derived from the factor detector component of the geographic detector model

Discussion

Main findings

The coupling coordination development of community healthcare services and older adults care services in China exhibits a clear dynamic evolution that is closely associated with different stages of policy implementation and institutional development. During the rapid development stage (2014–2015), policy innovation served as the primary driving force. With accelerated population aging and a continuous rise in morbidity, social demand for integrated healthcare and older adults care services expanded rapidly. In response, the State Council issued the Guiding Opinions on Promoting the Integration of Healthcare and Older Adults Care Services in 2015, which formally established integrated healthcare and older adults care as a national development model for the first time. This policy proposed several key implementation pathways, including promoting cooperation between medical institutions and older adults care institutions, supporting the establishment of medical units within older adults care facilities, and extending healthcare services to the community level [31]. These measures initially facilitated coordinated development between the two systems. However, during the gradual slowdown stage (2016–2017), policy implementation encountered substantial obstacles. Although top-level policy design continued to improve, local supporting measures lagged behind. Many county-level administrative units failed to formulate detailed implementation guidelines, resulting in weak inter-institutional coordination. By the end of 2017, only 11,800 formal cooperation agreements had been established nationwide between healthcare institutions and older adults care institutions, accounting for merely 0.1% of all healthcare institutions [7]. This mismatch between rapidly growing demand and insufficient service supply led to policy inefficiency and, in some regions, even resource misallocation, which significantly constrained coordinated development. Entering the bottoming-out and rebound stage (2018–2020), breakthroughs were achieved through institutional and governance innovations. In 2019, the National Health Commission issued the Opinions on Further Promoting the Development of Integrated Healthcare and Older Adults Care, which strengthened contract-based cooperation, simplified administrative approval procedures, encouraged private-sector participation, and enhanced information system support [9]. These measures accelerated sectoral development and led to substantial increases in both institutional numbers and bed capacity. By the end of 2020, a total of 5,857 integrated healthcare and older adults care institutions with dual qualifications had been established nationwide, providing 1.585 million beds, which markedly improved overall service supply capacity. During the fluctuating advancement stage (2020–2022), integrated healthcare and older adults care entered a phase of structural transformation. With the implementation of the 14th Five-Year Plan for National Aging Development, the development paradigm shifted from scale expansion toward quality-oriented improvement, as evidenced by a growing proportion of high-quality integrated institutions [9]. Nevertheless, this transformation was accompanied by dual challenges [9]. On the one hand, macroeconomic slowdown intensified fiscal constraints. Although national health expenditure reached RMB 8,484.67 billion in 2022, representing a year-on-year increase of 12.2%, the growth rate declined by 7% points compared with 2021, indicating tightening fiscal conditions. On the other hand, the emergence of the “new older adults” cohort born in the 1960s generated increasingly diversified and professionalized service demands, further exacerbating structural mismatches between supply and demand [43]. Against this backdrop, the sector gradually formed a complex and uneven development pattern. It should be emphasized that although the coupling coordination degree reached 0.995 in 2023—placing it in the “extreme coordination” category—this result reflects improvement within the analytical framework constructed in this study. It does not imply that healthcare services and older adults care services have achieved full integration or seamless coordination in practice. Even at higher coordination levels, regional disparities in institutional arrangements, unequal resource distribution, and imbalances in service quality remain objectively present.

The spatial imbalance in the coupling coordination development of community healthcare services and older adults care services in China is closely associated with regional differences in socioeconomic conditions, institutional capacity, and demographic characteristics. Coastal regions benefit from strong economic foundations, which provide solid support for the development of integrated healthcare and older adults care services. By 2023, the Yangtze River Delta region accounted for 24.4% of China’s total GDP. Robust economic capacity has attracted a large number of integrated institutions, many of which have improved service professionalism through initiatives such as joint training programs with medical universities and demand-oriented talent recruitment [44]. On this basis, the central government has established 28 pilot programs for integrated healthcare and older adults care in this region, accounting for 28.28% of all national pilots [45]. These initiatives have supported the sustained optimization of local service systems and have given rise to replicable models, such as the “Shanghai Model” and the “Jiangsu Experience,” which provide valuable references for nationwide development. However, recent policy adjustments have introduced short-term pressures. Fiscal support for demonstration institutions has gradually shifted from scale expansion to performance-oriented mechanisms, increasing transition costs for some providers. At the same time, rapid population aging in mega-cities has caused community-level demand to grow faster than service supply, amplifying supply–demand mismatches. This has led to temporary declines in coupling coordination levels in certain areas (e.g., Tianjin, where the index dropped from 0.688 in 2021 to 0.314 in 2022). Such fluctuations reflect short-term adjustments at advanced development stages rather than substantive deterioration of system functionality. Inland regions have experienced accelerated aging due to large-scale outmigration of younger populations. In response, local governments have adopted gradual and adaptive policy approaches. In the early stage, these regions drew on coastal experiences by introducing record-filing systems to improve administrative efficiency. For example, Taiyuan City in Shanxi Province eliminated prior administrative approval requirements for internal clinics and medical rooms within older adults care institutions, replacing them with record-based management. Institutions with complete documentation can now obtain a Medical Institution Practice License within five working days, reducing approval time by approximately 50% [46]. As implementation progressed, a full-cycle policy support system—including construction subsidies and operational support—was gradually established. These systematic interventions enhanced service supply capacity in the short term and contributed to the formation of integrated service networks in the long term, enabling inland regions to become increasingly important components of the national system. Nevertheless, as integrated healthcare and older adults care services were incorporated into stricter regulatory frameworks, policy priorities shifted from scale expansion toward service quality and operational standardization. During this transition, non-compliant institutions were rectified, merged, or removed from official statistics, leading to short-term declines in indicators such as institutional numbers and workforce size. Concurrently, continued population outflows prompted local governments to prioritize efficiency in public resource allocation, resulting in the consolidation of dispersed community resources. While such integration improves long-term efficiency, it often manifests as short-term contraction and temporary declines in coupling coordination levels (e.g., Qinghai Province, from 0.657 in 2021 to 0.277 in 2022). These changes represent transitional adjustment costs rather than a fundamental reversal of coordination trends. Remote regions, many of which are ethnic minority areas, are characterized by strong traditional cultures and religious practices. These contexts favor family-based care supplemented by neighborly support, resulting in low acceptance of community-based older adults care. Even in areas where community care is partially adopted, limited fiscal capacity constrains infrastructure quality and service diversity, making it difficult to meet heterogeneous needs of older adults. Moreover, sparse populations and vast geographic distances severely restrict healthcare accessibility. For instance, the number of medical institutions per 10,000 square kilometers in Tibet is only 1.31% of the national average, and in some pastoral areas of Xinjiang, service radii exceed 50 km [47]. These spatial barriers further intensify workforce outflows. Professional medical personnel are scarce, and qualified care workers are particularly limited. As a result, integrated healthcare and older adults care services in these regions often lack sustainable support mechanisms, leading to a vicious cycle of weak demand and inadequate supply. However, following the introduction of targeted national support policies, the central government has increased investment through transfer payments and paired assistance programs. These measures have led to rapid improvements in key indicators such as institutional numbers and workforce size. Local governments have also adopted adaptive service models, including mobile medical teams, which expand service coverage without substantially increasing staffing levels. Consequently, some regions have experienced short-term surges in coupling coordination indices (e.g., Xinjiang Uyghur Autonomous Region, from 0.359 in 2021 to 0.695 in 2022). It should be emphasized that such high values primarily reflect policy-driven “gap-filling effects” and localized improvements achieved through concentrated investment and service optimization. They do not indicate that remote regions have achieved comprehensive or stable high-level coordination. Significant development potential remains, particularly in terms of institutional sustainability and long-term service integration.

The coupling coordination development of community healthcare services and older adults care services in China is influenced by a combination of multiple factors, and the relative importance of each factor depends on the role it plays in the coordination process. Healthcare institutions, as the primary service providers within the integrated healthcare and older adults care system, directly determine the level of service accessibility through their numerical scale. In regions with high degrees of population aging, a denser distribution of medical institutions shortens service radii and enables faster response and more efficient utilization of healthcare resources. Technical personnel constitute the core workforce of integrated healthcare and older adults care services, and their scale directly shapes the level of professional service delivery. Beyond providing direct medical treatment, technical staff extend care from episodic disease treatment to continuous health management by offering personalized services such as chronic disease intervention and health monitoring. This shift facilitates the construction of a life-course-oriented and comprehensive health service system. The number of outpatient visits serves as a key indicator of resource utilization efficiency within the integrated service system and contains important information related to health management. High-frequency clinical interactions, supported by continuous medical records, enable a more comprehensive understanding of health trends among older adults. In community healthcare settings, they also allow for the early detection of potential risks, such as fluctuations in blood pressure or abnormal blood glucose levels. Through such proactive monitoring, health management can be advanced upstream, thereby reducing the incidence of major diseases at the source and strengthening health protection for the older adults population [47]. Older adults care institutions function as essential carriers of integrated healthcare and older adults care services, and their scale determines overall service capacity. It should be emphasized that not all older adults care institutions are qualified to deliver integrated services. Only those holding medical practice licenses are able to provide comprehensive services, including daily care, rehabilitation therapy, and chronic disease management [48]. These qualified institutions not only meet routine care needs but also facilitate the downward integration of high-quality medical resources, enabling more older adults to access convenient, high-quality, and sustainable integrated services. Certified social workers act as key coordinators within the integrated service system, and their scale directly affects overall service effectiveness. Through professional assessment tools and routine interpersonal engagement, social workers can accurately identify the diverse needs of older adults. At the same time, their training and experience within the healthcare system allow them to perform basic medical tasks such as blood pressure measurement, blood glucose monitoring, and essential medication management [49]. This dual professional capacity positions social workers as a critical linkage between healthcare services and older adults care services, playing an irreplaceable role in enhancing system-wide efficiency. Community-based daytime care represents an important indicator of integrated service development, as its frequency reflects the level of daily service activity. By providing daytime care, these services effectively alleviate the caregiving burden on families, allowing household members to allocate more time to work and daily life. Moreover, they promote the intensive use of service resources, reducing fragmentation and waste across healthcare and older adults care systems. As such, community-based daytime care has become a key entry point for improving the integrated healthcare and older adults care service system at the community level.

Innovations

This study conducts an in-depth investigation into the coupling coordination development of community healthcare services and older adults care services in China. Its innovations are mainly reflected in two aspects. First, it offers an innovative research perspective. Traditional studies have tended to treat healthcare services and older adults care services as two independent systems, examining their development separately. Such an isolated perspective overlooks the complex and close interconnections between the two under the framework of healthy aging, thereby constraining a comprehensive understanding of their coordinated development. To address this limitation, this study abandons the conventional fragmented approach and instead adopts a systems perspective, viewing healthcare services and older adults care services as an organically integrated whole with mutual interactions. This perspective not only helps to reveal the underlying mechanisms of coordinated development but also provides new theoretical support for policy design aimed at integrating healthcare and older adults care services. Second, the study demonstrates methodological innovation. Existing research on healthcare services and older adults care services has largely relied on qualitative or theoretical analysis, with limited quantitative examination of their coordinated development. This has restricted both the depth and scope of empirical inquiry. To bridge this gap, this study employs a combination of quantitative methods—including the entropy method, the coupling coordination degree model, and the geographic detector model—to systematically assess the level of coupling coordination development and to identify its key influencing factors. The application of this integrated methodological framework enables a comprehensive depiction of the dynamic development of community healthcare services and older adults care services in China and facilitates an in-depth analysis of their driving mechanisms, thereby substantially enhancing the scientific rigor and practical relevance of the research.

Limitations and future research directions

It is important to acknowledge several limitations of this study. First, the entropy method is used to construct the composite development indices for community healthcare services and older adults care services. As a composite index approach, the results are inevitably influenced by the selection of indicators and the weighting structure. Therefore, the estimated coupling coordination degree should be interpreted as a relative assessment within a specified indicator system and methodological framework, rather than as an absolute depiction of the true coordination status of the two systems. Second, this study employed the factor detector of the geographic detector model to evaluate the independent explanatory power of each influencing factor. It should be noted that the geographic detector reflects associations among variables rather than causal relationships. In practical analysis, interactions may exist among indicators; however, given the focus of this study, the interaction detector or variance inflation factor (VIF) test was not introduced to further examine interdependencies among variables. This constitutes a methodological limitation that could be addressed in future research. Third, due to data availability constraints, this study is based on provincial-level panel data. Although the selected indicators are explicitly defined to reflect community-level healthcare and older adults care services in terms of statistical scope and conceptual meaning, the empirical results still capture development characteristics at the provincial scale. Consequently, the findings cannot be directly generalized to individual communities. This ecological characteristic of macro-level data represents an objective scale-related limitation of the current analysis.

Future research may address these limitations in several ways. First, additional indicators capturing service quality, service efficiency, and service accessibility could be incorporated. By comparing multiple indicator schemes, future studies may systematically evaluate how different indicator combinations and weighting structures affect composite development indices, thereby improving the robustness of entropy-based measurements. Second, based on the geographic detector analysis, panel regression models could be employed to further examine the causal relationships between influencing factors and the coupling coordination degree, thereby enhancing the explanatory power of the research findings. In addition, interaction detectors within the geographic detector framework or multicollinearity diagnostics such as variance inflation factors could be introduced to analyze interactions among influencing factors, thus providing a more comprehensive understanding of the mechanisms underlying the coordinated development of community healthcare services and older adults care services. Third, as nationwide community-level data systems continue to improve, future research may seek to obtain and analyze finer-grained micro-level data. This would help overcome the limitations of provincial-level analysis in capturing intra-community heterogeneity and provide stronger empirical support for the formulation of more targeted and precise policies aimed at optimizing grassroots healthcare and older adults care service provision.

Conclusions

This study investigates the spatiotemporal evolution characteristics and key influencing factors of the coupling coordination development of community healthcare services and older adults care services in China over the period from 2014 to 2023, using data from 31 provinces, autonomous regions, and municipalities. The study applies the entropy method, coupling coordination degree model, and geographic detector model, and the following conclusions are drawn: (1) From a temporal perspective, the coupling coordination development of community healthcare services and older adults care services in China shows an overall upward trend, although the growth rate is uneven. The development follows a dynamic process of “rapid development—gradual slowdown—bottoming out and rebound—fluctuating advancement.”(2) From a spatial perspective, the coupling coordination development of community healthcare services and older adults care services in China exhibits significant spatial imbalance, gradually forming a diversified spatial development pattern, with coastal regions leading the way, inland regions rising rapidly, and remote regions possessing substantial development potential.(3) From an influencing factors perspective, the development is jointly affected by infrastructure, workforce, and service capacity. Key drivers include the number of healthcare institutions, the number of technical staff, the number of outpatient visits, the number of older adults care institutions, the number of certified social workers, and the number of community-based daytime care recipients.

Based on the above findings, and fully considering the limitations of the research methods in causal inference and the inherent uncertainty in quantitative results, this study provides the following targeted and conditional policy recommendations aimed at contributing to the collaborative development of community healthcare services and older adults care services in China.

Based on the characteristics of different stages in the integration of healthcare services and older adults care services in China, the following dynamic regulatory strategies are recommended: (1) During the rapid development phase, the focus should be on advancing top-level institutional design. A collaborative governance framework involving multiple departments such as health, civil affairs, and social security should be established to clarify each department’s role in integrated services. Key issues such as overlapping approval processes and poor coordination must be addressed. Additionally, policies encouraging social capital participation should be introduced, such as providing bed construction subsidies for private integrated healthcare and older adults care institutions and facilitating land supply through green channels. These measures will enhance the active involvement of social forces in integrated services. (2) During the gradual slowdown phase, policy focus should shift toward standardized development. Implementation guidelines should be formulated to define service content and establish a comprehensive service quality evaluation system. On this basis, a certification system for practitioners should be introduced, requiring nursing staff to be certified, and a continuing education credit system should be established to improve service standardization.(3) During the bottoming-out and rebound phase, emphasis should be placed on strengthening technology-driven innovation. Focusing on key technologies such as smart sensing and remote interaction will accelerate the development and application of smart terminals like home environment sensing systems and remote health monitoring platforms, enhancing the intelligence level of integrated services. It is important to emphasize that technological innovations must adhere to the principles of age-friendly design, improving service precision while maintaining necessary human service channels to ensure accessibility and avoid issues such as the “digital divide.“(4) During the fluctuating advancement phase, new sources of development momentum should be cultivated. By addressing the fragmentation between traditional health insurance and long-term care insurance, comprehensive plans that cover the full cycle of prevention, treatment, rehabilitation, and care should be developed, contributing to a comprehensive older adults health insurance system [49]. In addition, innovative “property + service” financial products, such as integrated healthcare REITs (Real Estate Investment Trusts), should be explored, alongside operational models such as rent-to-own and reverse mortgages [50]. This multidimensional collaborative innovation will not only cultivate new market growth areas but also effectively reduce the economic burden of family healthcare and older adults care expenses, ultimately fostering a sustainable and positive development pattern.

In light of the varying characteristics of different regions in the integrated development of healthcare and older adults care services in China, differentiated implementation plans are recommended: (1) For coastal regions, their role as a demonstration and leadership area should be fully leveraged. The primary task is to break down departmental barriers and establish a cross-departmental coordination mechanism to achieve the deep integration of healthcare and older adults care services. Taking Shanghai’s “Healthcare – Older Adults Care – Medical Insurance” three-network integration platform as an example, standardized data interfaces and protocols should be unified to enable real-time data sharing and business coordination, laying the foundation for resource integration. On this basis, the digital transformation of service models should be prioritized. Drawing from the successful experience of Zhejiang Province’s “Smart Older Adults Care” project, the application of digital tools such as remote diagnostic systems and smart monitoring devices should be promoted [51]. These tools not only extend the reach of healthcare services but also enhance the quality of healthcare provided in older adults care institutions. Once the foundational conditions are in place, the focus should shift to institutional output. Referring to the pilot experience of the long-term care insurance system in Qingdao, practical experiences should be systematically summarized to form a complete institutional framework, including assessment standards, service design, and settlement methods, providing a replicable model for nationwide implementation [8]. (2) For inland regions, local resource endowments should be fully utilized, and the development of specialized integrated healthcare and older adults care industries should be prioritized. Taking Jiangxi Province as an example, the traditional “Zhangbang” Chinese medicine processing technique should be utilized, systematically organizing local traditional Chinese medicine resources and selecting authentic medicinal herbs such as Eucommia ulmoides and Ziziphus jujuba. By aligning the development of these food therapy plans with seasonal variations, a distinctive “Chinese Medicine Food Therapy” service system targeting common older adults diseases can be developed [4]. Furthermore, to enhance service professionalism, it is essential to engage universities, institutions, and enterprises. Traditional Chinese medicine universities can provide technical support, older adults care institutions can implement services, and pharmaceutical enterprises can ensure the quality of medicinal herbs, thus creating a complete industrial chain. This regionally tailored development path not only avoids homogeneous competition with coastal areas but also fully activates the potential advantages of inland regions, providing sustainable endogenous driving forces for integrated healthcare and older adults care services. (3) For remote regions, special support policies should be formulated to build a healthcare and older adults care service system that aligns with local characteristics. Taking the Tibet Autonomous Region as an example, the “County-Town-Village Three-Tier Service Network” plan should continue to be implemented to improve grassroots medical and older adults care facilities, forming a basic service framework covering the entire region [52]. On this basis, innovative service delivery methods should be emphasized, combining mobile medical units with fixed service points. Leveraging unique methods, such as horseback medical teams, healthcare and older adults care services can be extended to pastoral areas, effectively addressing the “last mile” issue.

Targeted improvement measures for key factors affecting the integration of healthcare and older adults care services in China are recommended as follows: (1) In healthcare institutions, a hierarchical diagnosis and treatment network should be established. By promoting the distribution of high-quality resources from tertiary hospitals to primary care institutions, the diagnostic and treatment capabilities of grassroots medical institutions in addressing common older adults diseases should be enhanced. At the same time, standardized geriatrics departments should be set up in hospitals at or above the secondary level to form a specialized technical support system. Supporting measures, such as green channels for bidirectional referrals, remote medical consultations, and regular expert visits, should be implemented to ensure seamless coordination between healthcare institutions at all levels, ensuring that older adults patients receive appropriate continuous medical care. (2) In terms of technical personnel, a sound training and incentive mechanism should be established. Geriatrics should be introduced as a specialization in medical education, with expanded enrollment and the creation of a tiered, lifelong continuing education system for healthcare professionals. On this basis, policies such as preferential salary treatment and promotion opportunities should be implemented to create a favorable ecosystem for talent recruitment and retention, providing stable human resource support for integrated healthcare and older adults care services. (3) Regarding outpatient visits, the service process system should be optimized. Innovative models, such as specialized geriatric outpatient clinics and multidisciplinary joint diagnosis and treatment, should be established to create a full-process service system that covers pre-diagnosis, diagnosis, and post-diagnosis. Special attention should be given to extending services to communities and homes, effectively addressing common issues such as “repeated examinations and unclear medication” among the older adults, and ensuring seamless health management for older adults individuals. (4) In older adults care institutions, a differentiated development strategy should be implemented. Based on the aging characteristics of different regions, a targeted development approach should be established, with a focus on cultivating three types of specialized institutions: those for disabled older adults care, dementia care, and post-surgery rehabilitation. On this basis, a quality evaluation system should be developed, including service standards, staffing, and facility requirements, with dynamic, tiered management. By utilizing a market mechanism that promotes competition, institutions should be guided to optimize operational costs and improve professional service effectiveness. The goal is to establish a clear, distinctive, and reliable system of older adults care institutions that meets the diverse healthcare and older adults care needs of the older adults. (5) In terms of social workers, the professional workforce should be strengthened. Universities should establish a major in older adults social work, incorporating core courses such as Older Adults Social Work Practices, Older Adults Counseling Techniques, and Older Adults Welfare Policies and Regulations, thus increasing the supply of professional talent from the source. Furthermore, service capacity should be enhanced by requiring graduates to complete internships at specialized institutions for a specified period. The institution will then submit a practical ability assessment report, cultivating a truly professional older adults social work workforce. (6) Regarding community-based daytime care, efforts should focus on developing a “medical-nursing-social work” integrated service team. This team should consist of general practitioners, professional nurses, and social workers. General practitioners will provide medical guidance, nurses will handle daily care, and social workers will provide psychological support. By combining the expertise of these three professionals, integrated care will be provided to older adults individuals in the community, effectively addressing the fragmented nature of older adults care in daily life.

Authors’ contributions

HL was responsible for data analysis, interpreting the results, and drafting the manuscript. SL was responsible for conceptualization, data collection, and reviewing the manuscript. WW was responsible for funding support and reviewing the manuscript. All authors read and approved the final manuscript.

Funding

This study is funded by Project of Natural Science Foundation of Hunan Province (grant number: 2023JJ30313).Hunan Provincial Graduate Research Innovation Project (grant number CX20251057). Graduate Research Innovation Project of Hunan Agricultural University (grant number 2025xkc124).

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.