This study aimed to evaluate and analyze LTCI policies of the first and second batches of pilot cities. Based on the principles of authoritativeness and comprehensiveness, relevant policies were searched and selected from the official government websites and other relevant websites, using the keywords “long-term care insurance” and “LTCI.” The selection criteria for the policies were: (a) issued by the relevant administrative departments of the first and second batches of pilot cities and based on national policies as a guide; (b) the main content around the establishment and implementation of the LTCI system and the expansion of its promotion; (c) issued from 2016 to 2024; (d) including departmental regulations, circulars, opinions, etc., excluding bulletins and approvals. A total of 79 policies were collected and screened for final inclusion in the analysis (see Supplementary Table S2 in Additional File 1), and the time of issuance of the two policies, the Guidance and the Expanding-Guidance, was used as the node, with 35 policies issued by the first batch of pilot cities in the first phase and 14 in the second phase, and 30 policies issued by the second batch of pilot cities all in the second phase.

Based on the specific content and characteristics of the LTCI policy, this study constructed the analytical framework of the LTCI policy from two dimensions: policy tools (X dimension) and policy ratings (Y dimension).

Policy tools are important means for the government to achieve policy objectives, and are the paths and ways to translate substantive objectives into specific behaviors, with the choice and application of policy tools being an important bridge connecting policy objectives and policy outcomes (33). Policy tools play an important role in implementing and developing the LTCI policy, which in turn promotes the equitable and effective provision of LTC services. The policy tools were classified by Rothwell and Zegweld into three categories: supply-based, demand-based, and environment-based, depending on the subject of influence (34). This classification is multidimensional and comprehensive, attaches importance to the government’s guiding function, and weakens the mandatory characteristics of policy tools, which is widely used in policy analyses of older adults care services, healthcare, and social insurance (34–36). Given the applicability of this classification and the achievements of the studies in related fields, this study classified the policy tools into supply-based tools, environment-based tools, and demand-based tools. Supply-based policy tools are used by the government to promote the implementation of the LTCI policy in various aspects with the help of infrastructure construction, technology support, talent development, information support, and financial support. Environment-based policy tools indirectly and implicitly influence the implementation of the LTCI policy through the penetration of external factors, which provide a favorable development environment for the implementation of LTCI by the government through tax incentives, strategic measures, regulatory control, institution building, target planning, and policy publicity. Demand-based policy tools refer to the efforts made by the government to stimulate the demand for LTCI by taking measures such as government purchasing, policy subsidies, service outsourcing, market cultivation, and demonstration pilots, to pull forward the healthy development of LTCI. Figure 1 and Table 2 present the details.

The PMC index model was proposed by Ruiz-Estrada et al. as a policy evaluation tool based on the Omnia Mobilis hypothesis. It combines traditional text mining methods with advanced mathematical tools to construct a quantitative evaluation model, focusing on the raw data within policy texts, with the aim of incorporating relevant variables as comprehensively as possible (37, 38). The function of the PMC index model is, on the one hand, to evaluate the internal consistency of an individual policy, and on the other hand, to reflect the overall evaluation and specifics of an individual policy through the PMC index and PMC-surface (39). The lower the score on the PMC index, the more concave the PMC-surface becomes, and the lower the score on an individual indicator, the more pronounced the concavity of the PMC-surface appears within that indicator’s dimension, which reflects both the overall policy situation and variations across different aspects. The PMC index model is one of the more advanced policy evaluation models currently available. It enables a relatively objective analysis of the internal strengths and weaknesses of a policy across multiple dimensions, and overcomes the issues of subjectivity and ambiguity present in previous policy evaluations. It has been widely applied in policy evaluations within the fields of healthcare and older adults care (40, 41). The PMC index model can be used to quantify and objectively analyze policy texts by constructing a set of evaluation indicators and balancing each indicator using binary without limiting the number and weight of the indicators, and according to the results of the analyses, policies can be classified into four ratings of perfect, good, acceptable and poor (42, 43). The PMC index model provides a more operational, objective and applicable tool for evaluating LTCI policies. Based on the construction of evaluation indicators, this study calculated the PMC index to assign ratings to each policy. According to the mean of each indicator, PMC-surface chats were plotted to analyze the overall status of 79 LTCI policies, as well as the internal consistency, strengths and weaknesses of policies across different phases. Figure 2 illustrates the specific steps of the PMC index model, and this study was conducted in accordance with these steps.

Firstly, with reference to the classification of variables proposed by Ruiz Estrada and the results of existing studies (44–46), and taking into account the specific content characteristics of the policy, 10 first-level variables and 46 s-level variables for the evaluation of 79 LTCI policies were identified, as detailed in Table 3. Secondly, the multi-input–output table is a data analysis framework for quantitatively calculating individual variables through multiple dimensions. In this study, the first- and second-level variables mentioned above were put into the multi-input–output table, as detailed in Table 4, and each variable was given the same weight and assigned a value using binary (0, 1). Further, the PMC index was calculated for each LTCI policy, which included the following aspects (47): (a) the second-level variables were assigned values according to Equations 1, 2; (b) the value of each first-level variable was calculated according to Equation 3, which was between 0 and 1; (c) the PMC index of each LTCI policy was calculated according to Equation 4, and the criteria for classifying the policies according to the value of the PMC index were 9 to 10 (perfect), 7 to 8.999 (good), 5 to 6.999 (acceptable), 0 to 4.999 (poor) (38, 48). Finally, the PMC-surface for the LTCI policy was drawn by calculating the PMC matrix. The first-level variable X₁₀ did not have any second-level variables, and each policy had a value of 1 in this variable. Considering the symmetry of the matrix and the balance of the surface, the first-level variable X₁₀ was removed from the PMC-surface drawing, and a matrix of size 3*3 was formed by the other 9 first-level variables. The formula for the matrix is shown in Equation 5.

t is the first-level variable, j is the second-level variable, and T (X_tj) is the number of second-level variables under the first-level variable.

The two-dimensional analytical framework constructed by combining policy tools (X dimension) and policy ratings (Y dimension) is shown in Figure 3.

Taking the 79 LTCI policies of the first and second batches of pilot cities as the sample for analysis, the specific contents of the policy texts were coded, categorized, counted for their frequency of use, and sorted and numbered according to region and chronological order. First, the policies were coded according to “policy number-policy chapter-specific unit of analysis,” for example, the code “1–1-1” indicated the first analytical unit in the first chapter of the first policy. Second, the policy codes were categorized according to the analytical framework of the policy tools, and the frequency of each categorized unit was counted.

A total of 2,914 coding units were obtained from the text coding of 79 LTCI policies in this study. The results of the analysis showed that the pilot cities in China preferred to combine three types of policy tools: supply-based, environment-based, and demand-based in their policy formulation. However, the degree to which these three types of policy tools were used varied significantly, revealing a pronounced structural unevenness. At all phases, the frequency of using the environment-based policy tools was the highest, accounting for more than 70% of the total, and under this policy tool, strategic measures were utilized the most, accounting for more than 50% of the total. Subsequently, the use of institution building and target planning was relatively common, each accounting for approximately 7%, while tax incentives and policy publicity were employed less frequently, each representing around 1%. This indicated variations in the use of the sub-policy tools within the environment-based policy tools, with the government showing a clear preference for implementing detailed arrangements for LTCI operations and placing greater emphasis on its development targets and principles. Conversely, there was a marked deficiency in investment financing and publicity guidance for LTCI.

Across all phases, the frequency of using supply-based policy tools ranged between 10 and 16%. The frequency of employing sub-policy tools, including infrastructure construction, talent development, information support, and financial support, all remained below 5%. Technology support was the least used, with a frequency of less than 1%. This indicated that policy support for the hardware and software facilities of older adults care institutions, as well as for professional nursing teams, was relatively limited. This situation was detrimental to the optimal allocation of care resources.

Demand-based policy tools maintained a frequency of use at around 10% across all phases. Among these, sub-tools such as policy subsidies and market cultivation were only used around 1%, while government purchasing was an even lower, at less than 1%. This was incompatible with the need for effective policy traction to stimulate demand during the initial phase of the LTCI system.

In terms of the different phases, the policies of the first batch of pilot cities in the second phase made the most use of environment-based policy tools, with a proportion of 79.310%, while the policies of the second batch of pilot cities made relatively less use of environment-based policy tools, with a proportion of 73.571%. Policies of the first batch of pilot cities in the second phase made the least use of supply-based policy tools, with a proportion of 10.345%, while policies of the second batch of pilot cities made relatively more use of supply-based policy tools, with a proportion of 16.026%. The proportion of demand-based policy tools used remained around 10% in all phases. Regarding sub-policy tools, within the environment-based policy tools, the percentage of strategic measures utilized decreased from 59.306 to 56.326%, the percentage of institutional building utilized decreased from 7.312 to 4.873%, while the percentage of policy publicity utilized increased from 0.739 to 1.500%. Within the supply-based policy tools, the use of infrastructure construction increased from 3.545 to 5.061%, talent development rose from 2.733 to 4.124%, and technology support was used significantly more times in the policies of the second batch of pilot cities, reaching eight times. This indicates that the second batch of pilot cities for LTCI have employed policy tools more rationally. The specific situation of various policy tools at different phases is shown in Table 5 and Figure 4.

The PMC index of each LTCI policy was calculated based on the PMC index model, and each policy was ranked and rated accordingly. The results of the PMC index for selected LTCI policies are demonstrated in Table 6, and the complete PMC index results for 79 LTCI policies can be found in Supplementary Table S3 in Additional File 1. The mean of the PMC index for the 79 policies was 7.701, the policy rating was good, and the mean of the variables X₁ ~ X₁₀ were 0.840, 0.307, 0.563, 0.808, 0.937, 0.924, 0.539, 0.873, 0.910, and 1. The mean of the seven variables, including policy nature, policy subject, policy objective, policy content, policy function, policy evaluation, and policy information disclosure, all were above 0.8. Compared with these, the mean of policy timeliness, policy level, and incentive and constraint were relatively low. Considering the specific variables, this showed that policy timeliness was relatively short, policy levels were predominantly normative documents and industry regulations, and the incentive and constraint effects of policies were not strong.

For the 35 policies of the first batch of pilot cities in the first phase, the mean of the PMC index was 7.683, with the policy rating of good, and the mean of the variables X₁ ~ X₁₀ were 0.795, 0.300, 0.579, 0.832, 0.933, 0.919, 0.588, 0.834, 0.903, and 1. The mean of the PMC index for this phase was lower than that of the overall 79 policies with a mean of 7.701, primarily due to lower scores in the variables of policy nature and policy function. Among these 35 policies, policies 14, 16, and 20 were rated as perfect with values of the PMC index of 9, 9, and 9.107, respectively. 21 policies were rated as good, and 11 policies were rated as acceptable. The PMC-surface charts of LTCI policies with different ratings were constructed based on the values of the PMC index (Figure 5). The vertical coordinate indicated the values of the PMC index, and the horizontal coordinate corresponded to the different dimensions of X₁ ~ X₉. These 35 policies overall exhibited moderate concavity. While the concavity of three variables, including policy timeliness, policy level, and incentive and constraint, was large, the concavity of other variables was relatively small. This suggested that these three variables represent key areas for future policy optimization. Furthermore, the three policies rated as perfect demonstrated the least concave, while the 11 policies rated as acceptable exhibited the most concave.

For the 14 policies of the first batch of pilot cities in the second phase, the mean of the PMC index was 7.577, with the policy rating of good, and the mean of variables X₁ ~ X₁₀ were 0.869, 0.268, 0.536, 0.837, 0.905, 0.940, 0.480, 0.829, 0.914, and 1. At all stages, the 14 policies in this phase exhibited the lowest mean of PMC index, primarily manifested in the shortest policy timeliness, the lowest policy level, unclear policy objective and function, and limited effectiveness in incentive and constraint. Of these 14 policies, 11 were rated as good, and two policies, 36 and 38, were rated as acceptable, with PMC index values of 6.730 and 6.873, respectively. Policy 43 had a PMC index value of 4.439, with a rating of poor. The PMC-surface charts of LTCI policies with different ratings were constructed based on the values of the PMC index (Figure 6). These 14 policies overall were moderate concavity, with one poor-rated policy being unacceptable concavity.

For the 30 policies of the second batch of pilot cities, the mean of the PMC index was 7.781, and the policies were rated as good. The means of variables X₁ ~ X₁₀ were 0.879, 0.333, 0.558, 0.766, 0.956, 0.922, 0.509, 0.940, 0.917, and 1. Across all phases, the 30 policies in this phase had the highest mean of PMC index, primarily due to high scores in the variables of policy nature, policy objective, and policy function, though the variable of policy subject scored relatively low. Among these 30 policies, 23 were rated as good and 6 were rated as acceptable. The PMC index value of policy 77 was 4.564, and the policy was rated as poor. The PMC-surface charts of LTCI policies with different ratings were constructed based on the values of the PMC index (Figure 7). These 30 policies were overall moderately concave, with one poor-rated policy exhibiting unacceptable concavity. Furthermore, significant concavity was observed across four variables, including policy timeliness, policy level, policy subject, and incentive and constraint, indicating that these four variables represent key areas for future policy optimization.

Overall, policies with ratings of perfect, good, and acceptable had the most use of environment-based policy tools, relatively less use of supply-based policy tools, and the least use of demand-based policy tools. Policies with a rating of poor were used 10 times for environment-based policy tools, 3 times for demand-based policy tools, and no supply-based policy tools were used.

Specifically, only three policies from the first batch of pilot cities in the first phase were rated as perfect, with the most frequent use of environment-based policy tools, at 75 times, and supply- and demand-based policy tools, at 12 and 17 times, respectively. Among the policies with a rating of perfect, the frequency of using the environment-based policy tools was 6.25 and 4.41 times higher than that of the supply- and demand-based policy tools, respectively.

Among the policies rated as good, policies of the first batch of pilot cities in the first phase used supply-based policy tools the least (9.601%), while policies of the second batch of pilot cities used supply-based policy tools relatively more (13.215%). Policies of the first batch of pilot cities in the second phase used the environment-based policy tools the most (74.848%), and policies of the first batch of pilot cities in the first phase used environment-based policy tools relatively less (52.511%). Demand-based policy tools were used most (9.736%) in the policies of the first batch of pilot cities in the second phase and relatively less (6.869%) in the policies of the first batch of pilot cities in the first phase. The frequency of using environment-based policy tools for good-rated policies was about 5 to 7 times as frequent as supply- and demand-based policy tools, and only policies rated as good in the second batch of pilot cities used environment-based policy tools 4.52 times more frequently than supply-based policy tools.

Of the policies with a rating of acceptable, supply-based policy tools were not used in the policies of the first batch of pilot cities in the second phase, and were used relatively more in the policies of the second batch of pilot cities (2.812%). Environment-based policy tools were most used in the policies of the first batch of pilot cities in the first phase (19.572%) and relatively less used in the policies of the first batch of pilot cities in the second phase (3.857%). Demand-based policy tools were least used in the policies of the first batch of pilot cities in the second phase (0.609%) and relatively more used in the policies of the first batch of pilot cities in the first phase (2.142%). In this rating of policies, the frequency of using environment-based policy tools in the first batch of pilot cities in the first phase was 12 and 9.14 times more frequent than supply-based and demand-based policy tools, respectively, while in the second batch of pilot cities, the frequency of using environment-based policy tools was 4.67 and 7.78 times more frequent than supply-based and demand-based policy tools, respectively.

Among the policies with a rating of poor, both the policies of the first batch of pilot cities in the second phase and the policies of the second batch of pilot cities did not use supply-based policy tools, and the use of environment-based policy tools was similar, accounting for 0.609 and 0.656%, respectively. Only the policies of the second batch of pilot cities used demand-based policy tools, accounting for 0.281%.

Taking the above findings together, although policies across all ratings tended toward the use of environment-based policy tools, policies with higher ratings exhibited a relatively lower propensity for environment-based policy tools, with greater balance in the use of various policy tools. Policies of the second batch of pilot cities, being of a higher rating than those of the first batch, also demonstrated relatively balanced utilization of policy tools. Further details are presented in Supplementary Table S4 in Additional File 1.

Analysis of policy texts from pilot cities for LTCI revealed that three types of policy tools were employed at all stages. However, environment-based policy tools were utilized most frequently, particularly in relation to sub-policy tools such as strategic measures, institutional building, and target planning. Supply-based and demand-based policy tools saw a lower frequency of use, indicating insufficient leverage of policy push and pull effects. Overall, though the policy tool usage patterns remained similar across stages, the proportions applied to each policy tool exhibited certain variations. The specific discussion and analysis of policy tools is as follows.

The frequency of using environment-based policy tools was the highest at all phases, reflecting the reliance of the government on environment-based policy tools. Zhang, Wu et al. analyzed LTCI policies in pilot regions of western and central China, similarly finding that governments preferred to employ environment-based policy tools (49, 50). The LTCI policy has been in the pilot stage, and the early policy needs to provide a clear institutional arrangement and development environment with the help of environment-based policy tools (51). However, excessive reliance on such policy tools is likely to result in LTCI policies becoming hollow in substance and failing to be effectively implemented in practice. Additionally, among the sub-policy tools, the use of strategic measures has been particularly high, followed by the relatively high use of institution building and target planning. This reflects the need for a series of detailed working arrangements, strengthened institutional norms and guidance, and clear development objectives for the pilot phases of LTCI, to provide a good environment for the orderly development of LTCI work (52). Instead, it has been used less for policy publicity and tax incentives. On the one hand, this is not conducive to the public fully understanding LTCI, affecting the rate of popularization and participation in LTCI. On the other hand, this is not conducive to the access of social capital to the LTCI market, and unfavorable to the broadening of investment and financing channels of LTCI, which makes it difficult to unleash the vitality of the market for LTCI (53).

Supply-based policy tools have given insufficient push to LTCI, with relatively little investment in infrastructure construction, talent development, information support, financial support, and even less investment in technology support. Zhou et al. also observed similar findings in their analysis of the policy text for LTCI in Nantong, Jiangsu Province, where supply-based policy tools were employed less frequently, and the technology support sub-policy tool was not utilized at all (22). This can lead to a series of problems, such as the insufficient supply of LTC services, low service levels, and a lack of professional caregivers, which are more prominent in rural areas (54, 55). Li et al. similarly noted in their research that rural areas faced more limited resources, with shortages of care practitioners and substandard LTC services, resulting in severe challenges to meeting the LTC needs of the disabled older adults in rural communities (56). Furthermore, China depends excessively on public health insurance funds and financial subsidies to finance LTCI, and lacks a multi-channel financing mechanism, which affects the stable and sustainable supply of funds (57). Li et al. also noted in their research that limited financing options and overreliance on public health insurance funds posed challenges threatening the sustained and effective development of LTCI (56).

On the one hand, an insufficient supply of funds reduces the salary level of LTC practitioners and limits training opportunities, thus aggravating the wastage of LTC practitioners and making it difficult to ensure the quality of care services. On the other hand, insufficient supply of funds limits investment in technology, information, infrastructure and other resources. Studies have shown that the older adults have poor technological literacy and low acceptance of intelligent services, and the application of intelligent information technology plays an important role in improving the efficiency and quality of LTC services, so there is a need to increase investment in information, technology and other resources to promote the safe and convenient use of intelligent services (58).

The pull effect of demand-based policy tools in enhancing the accessibility of LTCI and activating the demand for service targets has not been effectively brought into play, with insufficient efforts to cultivate the market (59), which was similar to the findings of Zhou, Zhang, Wu, et al. (22, 49, 50). Pilot cities have rarely used policy subsidies and government purchasing of sub-policy tools, which is related to factors such as the expansion of the scale of the older adults with disability in China, the increase in the demand for care, the insufficient inflow of social capital, and the increase in financial pressure on the government (60). The insufficient use of demand-based policy tools has also led to the failure to effectively meet the current demand for LTC. The current LTCI coverage in China is limited, primarily targeting severely physically disabled individuals who have been disabled for more than 6 months, while moderately, mildly physically disabled and intellectually disabled individuals are largely excluded from coverage (27). Among the multiple pilot cities surveyed by Wang et al., only Qingdao included those with intellectual disability within the coverage scope of its LTCI system (61). Additionally, the level of LTC service quality is relatively low, making it difficult to meet the demand for personalized care services, and the satisfaction with daily living care demands remains inadequate. There is a need to enhance the levels of treatment for disabled individuals. Wu et al. similarly recommend expanding the coverage of LTCI services to foster a people-centered care environment and meet the needs of the demand-side market (62).

Among the policies in the first batch of pilot cities in the first phase, the use of environment-based policy tools accounted for 77.622%, and in the policies of the second phase, the use of environment-based policy tools increased slightly to 79.310%, which reflected the increase in the use of regulatory control and policy publicity. Based on the policy experience in the first phase, the implementation of LTCI should emphasize the standardized management of stakeholders and the enhancement of policy authority, and the acceptance of and participation in LTCI by the demand side should also be enhanced through strengthened policy publicity (63). Compared with the first batch of pilot cities, the second batch of pilot cities have slightly decreased the use of environment-based policy tools, which can be seen in the decrease in the proportion of strategic measures and institution building. The main reason is that the policy experience of the first batch of pilot cities can provide a reference for the policy formulation of the second batch of pilot cities, which can reduce repetitive and restrictive program strategies and improve the flexibility of policies in the implementation process. In addition, the second batch of pilot cities have got a clearer plan for the LTCI, which balances guidance and flexibility in policy design to adapt to local realities (64). Compared with the first batch of pilot cities, the second batch of pilot cities made a relatively high proportion of use of supply-based policies, with a significantly higher proportion of infrastructure construction, talent development and technology support. This indicates that the LTCI pilots are increasingly focusing on the construction of hard and soft facilities and the building of talent teams in care organizations, to ensure that material and human resources are sufficient. The use of demand-based policy tools in the first and second batches of pilot cities was low and did not show significant fluctuating changes, suggesting the need to further improve the vitality of the LTCI market (65).

According to the analysis results of the PMC index model, the value of the PMC index for 79 LTCI policies was 7.701, with a policy rating of good. Among these, the policy ratings for the second batch of pilot cities were higher than those for the first batch. In terms of specific dimensions, the policies exhibited relatively short timeliness, lower policy levels, and room for improvement in incentive and constraint. However, it performed better in other dimensions such as policy objective and policy content. Furthermore, higher policy ratings corresponded with lower concavity in the PMC-surface. Detailed discussions and analyses of the policy ratings are presented below.

The values in terms of policy timeliness, policy level, and incentive and constraint were relatively low. LTCI policies have been in the pilot phase, and the timeliness of each policy is mainly short-term (1–3 years) and medium-term (3–5 years). At the same time, these policies can generally be used as normative documents and industry regulations, with a lack of more regulatory documents such as local laws and legislations, reducing the authority of the policies to a certain extent. Compared with countries such as Germany and Japan, which have already established corresponding legal systems for LTCI, the legislative and regulatory support system for LTCI in China is still relatively weak (66). This indicates that China lacks a unified top-level design for its LTCI policy, with significant variations among policy pilots in different areas, which is detrimental to the future unified management of the LTCI system. In addition, LTCI policies were currently weak in terms of incentive and constraint indicators, especially in terms of tax incentives, financial support, and government purchasing, which is not conducive to strengthening the supply of resources for the policies, promoting multi-party financing cooperation, and enhancing the market vitality on the demand side (53). Xue et al. conducted a PMC index model analysis on 36 LTCI policies issued at the central and provincial levels between 2016 and 2022. Their findings similarly rated the overall policy as good, while also identifying key developmental challenges as the lack of legal frameworks, insufficient effectiveness of incentive policy measures, and constraints on diversified funding channels (67).

LTCI policies had relatively high ratings in terms of policy nature, policy subject, policy objective, policy content, policy function, and policy evaluation, with mean values above 0.8. This can reflect the following aspects: first, LTCI policies have a better guiding role in prediction, recommendation, supervision, and description, and the policy objectives are clear, which helps to achieve the objectives of care guarantee for the disabled in the long-term, financial compensation, and the pilot extension of the policy (68), which was consistent with the findings of Xue et al. (67).

Second, LTCI policies cover a wide range of aspects such as financing, disability assessment, service provision, and treatment payment. The sustainable development of the LTCI depends on the policy design of the financing, assessment, safety provision and treatment segments, but the problems of a single financing channel and limited level of treatment need to be paid attention to (69). Wang et al. also pointed out in his research that broadening funding sources would enhance the fairness of LTCI financing (61).

Thirdly, the policy subjects involve the government, insurance companies, professional care organizations, social organizations, third-party assessment organizations, and the public, but the mechanism for deep cooperation and interaction among the subjects is still lacking. In addition, urban employees and urban and rural residents, as the general public, are to be fully included in the scope of LTCI coverage, but further studies have found that the coverage rate in rural areas in the pilot areas is relatively low, and the problem of imbalance between urban and rural areas in the development of LTCI is becoming increasingly prominent (70). Weng et al. noted in their research that older adults individuals in rural areas have faced poorer economic circumstances, lacked formal occupational safeguards, and received inadequate support from traditional family care models, with their carers themselves confronting greater challenges and pressures to sacrifice employment opportunities in order to provide care (63). The demand for LTC among the disabled older adults in rural areas is growing at a significantly higher rate than in urban areas. However, the majority of existing rehabilitation, medical, and healthcare facilities in China are concentrated in urban areas. This imbalance between supply and demand, coupled with the uneven distribution of resources, results in a poorer quality of life for the older adults with disability in rural areas, thereby further widening the wealth gap between urban and rural areas.

Fourthly, policies have positive functional value in terms of macro design, normative guidance, and service optimization. Taken together, the policies are formulated on well-founded, clear objectives and scientific programs, but there are relatively few measures to encourage incentives, supervision and constraints, so it is necessary to further improve the detailed nature of the policies (64). Li et al. also highlighted the need to further refine the LTC security and service system, detail policy development, and enhance the standard and quality of care services (8).

In terms of the phases, the mean of the PMC index of LTCI policies was highest in the second batch of pilot cities, second highest in the first batch of pilot cities in the first phase, and lowest in the first batch of pilot cities in the second phase. Regarding specific variables, the policies of the first batch of pilot cities in the first phase scored lower than the mean value, mainly in terms of the policy nature and function. This is related to the fact that LTCI policies did not significantly orient toward policy function in the early pilot phase. LTCI policies need to further clarify the policy nature and function in the course of practice, considering the care needs of the older adults and the accessibility of LTC services (71). The policies of the first batch of pilot cities in the second phase, as complementary policies to those of the first phase, scored relatively short in terms of timeliness, relatively low in terms of policy level, weak clarity in terms of policy objective, and left room for improvement in terms of incentive and constraint mechanisms and policy function. The second batch of pilot cities actively drew on the policy experience of the first batch of pilot cities, and improved their scores in terms of the policy nature, policy objective, and policy function (72). At the same time, the target of various policy measures has been made clearer, and the scope of policy subjects has been reduced accordingly, but the degree of closeness has been increased. Wang et al. also found in their research that compared to the first batch of pilot cities, the second batch of pilot cities saw a reduction in the scope of population coverage (61).

In addition, in terms of the PMC-surface of each rating policy, the PMC-surface of the perfect-rated policy was the least concave, and the values of the variables were relatively balanced. Hu et al. in their PMC index model analysis of older adults-related policies also noted that policies of higher rating were less prone to concavity (41). This has demonstrated that the perfect-rated policy has detailed provisions in all aspects of LTCI, such as policy targets, incentives and constraints, with comprehensive and substantive content, clear objectives and tasks, a well-defined functional nature, and few shortcomings, thereby effectively promoting the improvement of the LTCI. The PMC-surface of the poor-rated policy was the most concave, the values of the variables were at a low level, which can be blamed on the fact that the poor-rated policy is mainly a supplementary document for pilot projects and lacks substantive specific content. The degrees of PMC-surface concavity for the good- and acceptable-rated policies were in the middle of the above two, with a higher concavity in terms of policy timeliness, policy level, and incentive constraint variables compared with the perfect-rated policy. The concavity of the good-rated policy was lower than that of the acceptable-rated policy, and its concavity in terms of policy nature and policy function variables was lower than that of the acceptable-rated policy.

The overall analysis showed that higher-rated policies employed a more balanced use of the various policy tools. The specific analysis is as follows.

The results of the analysis showed that perfect-rated policies had relatively weak reliance on environment-based policy tools and a relatively high degree of evenness in the use of all types of policy tools, followed by good-rated policies. Acceptable-rated policies were less even for using policy tools than good-rated policies, and poor-rated policies were the least even for using policy tools. This suggests that the ratings of LTCI policies are correlated with the evenness of using the policy tools. The ratings of LTCI policies can be improved by optimizing the use of various policy tools. In addition, compared to the first batch of pilot cities, the second batch of pilot cities had relatively higher evenness in using policy tools for each rating policy. This suggests that LTCI policies are continuous and evolving, and that the second batch of pilot cities have actively learned from the lessons of the first batch of pilot cities, further optimizing the use of policy tools and improving policy ratings.

Based on the analysis of policy tools, although the second batch of pilot cities for LTCI demonstrated reduced reliance on environment-based policy tools, an overview of LTCI policies across all phases showed a continued preference for employing environment-based policy tools. In particular, the excessive use of three sub-policy tools, including strategic measures, institution building, and target planning, can lead to policies being more subject to traditional command-and-control administrative intervention during implementation, lacking flexibility for adjustment, and the implementation of policy content becoming mechanized and hollow. Consequently, the vital interests of the disabled older adults are not effectively protected. Conversely, the underutilized supply-based and demand-based policy tools has resulted in insufficient policy push and pull effects, limiting LTCI funding channels. This has also led to inadequate provision of hardware and software facilities alongside care professionals, insufficient activation of the demand-side market, and failure to fully meet the diverse, multi-tiered LTC needs of the disabled older adults. Therefore, the three types of policy tools should be used rationally, and the structure of sub-policy tools should be optimized. The specific recommendations are as follows.

Regarding environment-based policy tools, more attention should be paid to the effectiveness of three sub-policy tools, namely strategic measures, institution building, and target planning, while optimizing and upgrading those already overused to reduce the deployment of duplicative or ineffective policy tools. Meanwhile, efforts should be made to continuously explore and better utilize sub-policy tools such as policy publicity and tax incentives. For instance, publicity could be actively employed to raise awareness among disabled older adults individuals and their families about LTCI and encourage their acceptance of LTC services. For stakeholders involved in LTCI, preferential measures such as tax relief could be offered to attract greater social capital participation in related services. Through the rational deployment of policy tools, the government can both foster a conducive environment for the development of LTCI and promote its flexible, efficient implementation, thereby reducing reliance on traditional administrative intervention.

In response to the insufficient use of LTCI policies in supply-based policy tools, it is recommended that the government increase infrastructure investment, improve the training mechanism for LTC practitioners, enhance the support of informatization such as big data, and pay particular attention to the application of innovative internet technologies in LTCI management. It can be embedded in a standardized and scientific operation network through professional organizations, talents, capital and technology, to activate all kinds of resources and effectively promote the professional capacity of the supply of resources for LTC services.

To address the insufficient pull of demand-based policy tools, greater emphasis should be placed on utilizing such tools as government purchasing, market cultivation and policy subsidies. Market mechanisms should be introduced into the LTCI sector to fully leverage market and social forces, thereby fostering a socialized care services market capable of meeting diverse and multi-tiered LTC needs.

Analysis of the variables within the PMC index model has shown that the second phase of LTCI pilot cities has optimized policy nature and function. However, across all phases, LTCI policies predominantly take the form of normative documents and industry regulations, with relatively short policy timelines. This could lead to institutional fragmentation, potentially trapping LTCI development in short-termism while exacerbating regional and urban–rural disparities in coverage. The absence of incentive measures such as financial input, tax incentives, financial support, and talent training would lead to severe challenges for the sustainability of the LTCI fund, insufficient supply of professional care services. Consequently, public trust in LTCI would diminish, and the contradiction between increasing care demands of the disabled older adults and insufficient supply would persistently worsen. Specific recommendations are as follows.

Firstly, the central government should strengthen the top-level design of LTCI policies, make concerted efforts to establish a highly operational national implementation plan for LTCI, and expedite the issuance of a unified, reasonably funded, standardized, and integrated urban–rural national regulatory framework for LTCI. Local governments should continuously refine policy frameworks and implementation guidelines in accordance with central government policies. By prioritizing funding mechanisms and service optimization, they should vigorously develop the LTCI service market, continually improve the detailed design of the system, enhance policy effectiveness and enforceability, and standardize the operation of the system.

Secondly, it should leverage incentive policy measures to guide social forces, organizations and capital into the LTCI market in an orderly manner, building upon multi-channel funding sources, including individuals, government subsidies and charitable institutions, while offering multifaceted preferential policies and establishing incentive and constraint mechanisms to foster the sustainable development of LTCI. Concurrently, the government should increase investment in infrastructure, refine training mechanisms for LTC practitioners, and enhance support for information technology. This will fully leverage the critical role of “Internet Plus” and big data in LTCI services, enabling precision and intelligent care provision.

Furthermore, the government should expand the coverage of LTCI to gradually achieve comprehensive coverage across both urban and rural areas. On the one hand, the government should conduct policy publicity on LTCI in rural and remote areas, enabling more residents to understand, recognize and progressively participate in the system. On the other hand, the government should guide professional institutions to extend their services into rural and remote areas, and encourage more charitable organizations and local professionals in rural areas to engage in the LTCI sector, thereby providing increased care resources for the disabled older adults in these areas.

In summary, the government should optimize and upgrade policies that have been overused, and continuously explore and make use of more policy tools. Meanwhile, the LTCI policy can gradually extend its effective time and improve its authority. The incentive and constraint elements should also be strengthened to increase the supply of supporting policy resources, and to motivate the potential demand for LTCI among the service targets. In addition, the ratings of the policy can be improved by optimizing the use of policy tools and enhancing the guidance of the policy on the development of LTCI.

Although this study has its advantages, it also has some limitations. Firstly, there are various ways of classifying policy tools, and in this study, the supply-, environment-, and demand-based classification has been selected according to the content characteristics of the policy text. In future research, we can attempt to analyze the use of policy tools more comprehensively by choosing other ways of classifying policy tools, such as authoritative tools, capability-based tools, and learning tools. Second, the PMC index model has set the same weight for each evaluation variable. Although the refined and quantified difference in the setting of the second-level variables can feed the weights into the first-level variables to a certain extent, it does not reflect the weighting information in a complete and refined way. This consequently limits its effectiveness in identifying relationships between different evaluation indicators and significant relative differences. In future research, we will consider using methods such as the Analytic Hierarchy Process (AHP) to assign weights to the indicators at all levels, so as to evaluate the policy more objectively by reflecting the relative importance of each variable.