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Budget impact analysis of high-priced orphan medicinal products intended for the treatment of rare diseases in China: evidence from a densely populated metropolis of Chengdu

Abstract

Background

An increasing number of orphan medicinal products (OMPs) are being included in social health insurance schemes, significantly improving access to medicines for patients with rare diseases. However, high-priced OMPs are still not covered, primarily due to health equity controversies and inadequate data systems required for economic evaluation. The aim of this study was to estimate the burden of drug expenditures and the size of the reimbursement budget required for high-priced OMPs from the perspectives of society and healthcare payers.

Methods

The study performed a budget impact analysis using data from multiple sources to estimate the reimbursement budget for high-priced OMPs in Chengdu, a densely populated metropolis in China. The budget analysis consisted of three main elements: the number of patients, the price of drugs, and the simulated policy scenario. By adjusting the combinations of these elements, the budget fluctuations for payers were estimated. Furthermore, the study predicted the budget trend for the next three years to validate its sustainability.

Results

The analysis indicated that 98 rare disease patients in Chengdu required high-priced OMPs in 2019. This suggested a projected budget of CNY 179 million for these patients without reimbursement policies, from a societal perspective. Under six assumed policy scenarios, this budget ranged from CNY 32 million to CNY 156 million. Over the next three years, the annual budget was estimated to range from CNY 200 million to CNY 1.303 billion.

Conclusion

Integration of multi-source data helps to obtain more scientifically reliable results on budget impacts. The study found that the budgetary impacts of high-priced OMPs on society and payers are relatively limited. Health policymakers can choose appropriate reimbursement strategies based on financial affordability among a diverse mix of elements. The results of related studies provided insights for optimizing the allocation of health resources and improving patient access to medications.

Peer Review reports

Background

Rare diseases (RDs) are characterized by their extremely low incidence but often result in life-threatening outcomes for patients. Consequently, patients and their families typically face significant financial burdens and psychological stress [1]. Orphan medicinal products (OMPs) are widely used as the primary treatment for RDs; however, currently, only 5% of all RDs have effective treatments [2]. The scarcity of OMPs, combined with drug companies’ pricing strategies to recoup research and development costs during periods of market exclusivity, has led to exceptionally high prices for these medications [3]. This also directly impacted patients’ adherence to long-term treatment plans [4]. To alleviate the financial burden induced by medical costs for rare disease patients, many countries have enacted legislative support and implemented drug funding programs that fully or partially reimburse the costs of OMPs, aiming to improve healthcare outcomes of rare disease patients [5].

It is noteworthy that reimbursement decisions made by policymakers largely depend on clinical efficacy and safety, as well as evidence-based implications from health economic evaluations. However, the cost-effectiveness assessments traditionally used in the drug market are usually not applicable to orphan drugs for several complex reasons. First, the number of patients with rare diseases is extremely small, and inadequate sample sizes make cost-effectiveness assessments challenging. Second, the high development costs of orphan drugs result in expensive per-unit costs, with some studies showing that the cost for rare disease drugs is 4.8 times higher per capita compared to non-rare disease drugs [6]. This can lead to polarized benefit assessment results. Furthermore, rare diseases are often associated with severity and lethality, which complicates the assessment of benefit indicators such as improved quality of life. Therefore, budget impact analysis has been introduced as a complementary method to cost-effectiveness analysis, providing a different perspective on the economic impact of medicines to facilitate health-related decision-making.

Several studies in the literature have focused on budget impact analyses for OMPs, particularly in European countries and the United States (US) [7,8,9,10]. These studies estimated the budgetary impact of OMPs and the share of OMPs-induced costs within drug markets based on factors such as drug costs, sales, and the number of newly approved drugs [11,12,13]. For instance, Mestre-Ferrandiz et al. [14] analyzed the budget sustainability of OMPs in eight European countries and found that the share of OMPs-induced expenditure within general drug markets increased yearly. However, they noted that concerns about OMPs-related expenditures might be alleviated by changes in patient group composition and market conditions resulting from the introduction of new generics. In another study, Divino et al. [9] examined OMPs expenditures in the US drug market and reported a gradual increase in the budget for OMPs at a relatively slow pace, suggesting a minimal impact on the overall budget sustainability. Conversely, a Korean study by Lee et al. [15] analyzed the budgetary impact of OMPs and suggested that the rapid growth in OMPs-related expenditures could potentially threaten the overall reimbursement budget. Therefore, appropriate budget management by the government is critically needed at the level of health administration.

China has also increased its awareness of issues related to the costs of OMPs. As of 2018, the government has facilitated the inclusion of 29 types of OMPs in the national social health insurance program through a pricing negotiation process conducted by the National Healthcare Security Administration [16]. However, many market-authorized OMPs have failed to be reimbursed under the current social health insurance program due to poor cost-effectiveness evaluations, making the unaffordability of these OMPs a significant obstacle for rare disease patients seeking effective treatments [17, 18]. Specifically, the difficulty of including high-priced OMPs in health insurance catalogs is due, on the one hand, to the contradiction between the limited health insurance funds and the diverse needs of patients, making the reimbursement of high-priced medicines often controversial in terms of fairness. On the other hand, there is a high degree of uncertainty in the budget for OMPs reimbursement. This has created a vicious cycle where high-priced drugs are not used, diagnosed patients cannot receive treatment, clinical data are deficient, budget estimation becomes difficult, compensation policies are not implemented, and patients face a high out-of-pocket burden. Therefore, there is a need to conduct a budget impact analysis of orphan drugs to estimate the financial outlay required to inform policymakers. The testing and development of relevant reimbursement strategies could be extended to national contexts and to OMPs in general, thus setting the stage for broader healthcare improvements.

Most studies on OMPs in China have focused on the availability of drugs in the markets, individuals’ affordability of OMPs, and comparisons of drug-related policies through reviews [19, 20]. To date, no study in China has assessed the budget for OMPs in rare disease treatments from either the societal or healthcare payers’ perspectives. This study focuses on the following objectives: i) understanding the demand for high-priced OMPs by collecting data from rare disease patients; ii) estimating the scale of spending on treatment for high-priced OMPs based on information about market-supplied drugs; iii) determining the compensation budget range for high-priced OMPs based on designed reimbursement schemes; and iv) analyzing the effects of changes in each parameter on the reimbursement budget over time and exploring the sustainability of drug reimbursement policies. By analyzing various simulation scenarios, this study aims to provide policymakers with detailed budget projections and support the design of policies to improve patient access to OMPs.

Methods

Study area

The study focused on the densely populated metropolis of Chengdu, China. Chengdu, the capital city of Sichuan province, had a GDP per capita of approximately CNY 103,386 in 2019. In the same year, the city’s population exceeded 16 million, making Chengdu one of the most populous cities in China, with a rapidly growing urbanization rate of 74.41% [21]. As a metropolitan city in southwest China, Chengdu was well-equipped with abundant medical resources and had a well-established social health insurance system [22]. Chengdu also had a wide range of identified rare disease types and numerous cases for epidemiological analysis [23]. However, compared to other pilot cities that had implemented OMPs reimbursement policies, the health administration in Chengdu was relatively underexplored regarding reimbursement strategies for rare disease patient populations. Therefore, there was an urgent need to conduct a budget impact analysis to inform policy-making. In this context, the budget study of Chengdu provided a reference for the support of high-priced OMPs in China.

Eight hospitals in Chengdu were selected for the study to collect patient data during the survey phase. The on-site surveys at these hospitals were completed in two rounds. The first round was based on purposive sampling, and data were collected from four tertiary public hospitals designated by the National Health Commission for the Rare Disease Treatment Collaborative Network [24]. However, the interviews with medical staff during the first round revealed that patients with RDs also visited hospitals outside the collaborative network. Consequently, a second round was conducted at four additional hospitals in Chengdu that had admission cases for patients diagnosed with the studied types of RDs. Of these, three were tertiary public hospitals and one was a private hospital. Therefore, a large portion of the data we collected on patients with rare diseases came from cases diagnosed with rare diseases in 2019 at eight hospitals in Chengdu City, which were visited during the fieldwork phase.

Study design and variables

The study’s estimate of the reimbursement budget for high-priced OMPs was based on drug expenditures for patients with rare diseases, supplemented by simulated policy scenarios. In this context, information on drug expenditures specifically related to individual patients and drug use, which in this study primarily corresponded to the number of patients with rare diseases, and the dosage and price of the drugs used. Thus, the variables directly related to the reimbursement budget were the number of patients, the price of medication, the dose of medication used, and the simulated policy scenarios. Our study focused on the impact of changes in these variables on the budget. Since the dosage of drug use was primarily determined by the patient’s age, weight, and other characteristics, which were essentially deterministic, they were not considered as the main variables. Regarding the other three variables, the study considered a variety of standard settings and combinations of variables. The identification of high-priced OMPs and rare diseases was explained in more detail below, followed by a presentation of the different standardized settings for variables related to the reimbursement budget.

Target rare disease types

According to the definition of rare diseases in China and the availability of drugs for patients, OMPs with extremely high prices and significant financial burdens on patients were identified as the focus of this study. The types of RDs and their corresponding drugs were selected based on the following criteria: i) The RDs had to be among the 121 diseases listed in the Directory of RDs (1st edition) [25]; ii) Drugs intended for the selected types of RDs were only used for the treatment of RDs and had been officially authorized by the National Medical Products Administration before December 31, 2019; iii) The OMPs for which no reimbursement policy had been implemented, specifically those excluded from the National Basic Medical Insurance Drug Formulary List, were the subjects of this study; iv) The drugs had no other alternatives; and v) The drugs would potentially pose catastrophic health expenditures for the family. According to WHO’s definition [26], an OMP was considered to have great potential to induce catastrophic household health expenditures when its cost exceeded 40% of the household’s per capita disposable income. First, the study calculated the annual household disposable income based on the per capita disposable income and the average number of people per household in Chengdu in 2019. The data on relevant demographic and economic indicators were obtained from the Sichuan Statistical Yearbook. Second, the threshold value for household available health expenditure was calculated based on the 40% threshold associated with catastrophic health expenditure. Third, the estimated annual medication expenditure for each rare disease patient was calculated. Finally, a household was judged to have incurred catastrophic health expenditures by comparing its expenditures on medications to the calculated thresholds. Figure 1 detailed the selection process. Following this process, this study identified nine RDs, including Atypical hemolytic uremic syndrome, Paroxysmal nocturnal hemoglobinuria, Fabry disease, Gaucher disease, Glycogen storage disease, Hemophilia A with inhibitors, Mucopolysaccharidosis IVA, Spinal muscular atrophy, and Tetrahydrobiopterin deficiency. Information on high-priced OMPs corresponding to the target RD types could be found in Supplementary file 1.

Fig. 1
figure 1

The selection of target rare disease types

Collection of patient data

The number of patients was a crucial component in estimating OMPs expenditures from a societal perspective, which in turn was a prerequisite for determining reimbursement budgets. Therefore, based on the results of disease identification above, patients with rare diseases in Chengdu during this period were used as the study population, with the study period spanning from January 1st to December 31st, 2019. For the description of the current status of rare diseases, previous studies primarily used sampling methods to collect patient data [19, 27, 28]. Some scholars noted that the total number of cases for each disease remained low, making it difficult to draw meaningful conclusions about the actual prevalence [29]. Additionally, sampling bias in these cases may have affected the representativeness of the data. To address these limitations, this study synthesized multiple data sources by integrating survey data and epidemiological statistics at the theoretical level to enhance the coverage and reliability of the findings.

Patient data in the survey phase

During the survey phase, patient data were gathered from multiple sources, with administrative data primarily coming from hospitalized patients and additional data from hospital field research covering outpatient clinic attendees. Furthermore, considering that some patients sought cross-region or cross-province medical treatment, the study also gathered supplementary data from pharmaceutical companies and patient organizations. The specific survey program was detailed in supplementary file 2. The survey was approved by the Ethics Committee of Sichuan University, which obtained written informed consent from participants. The patient data were then matched to demographic characteristics such as date of birth and gender, and duplicated cases were excluded, resulting in survey-level data on patients who had been diagnosed.

Concretely speaking, the first approach of the survey phase was based on the administrative data, using The International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) codes to extract medical records of inpatients with 9 RDs in 2019 (The corresponding ICD-10 codes for each disease can be found in eTable 1 of the supplemental file). The second approach involved collecting information on rare disease patients from hospitals through two rounds of field surveys. The last two approaches involved collecting patient information from pharmaceutical companies and patient organizations with prior close contact with patients. Representatives from six pharmaceutical companies and staff from seven patient organizations participated in the survey.

Patient data at the theoretical level

Patient data at the theoretical level were further divided into two scenarios for analysis. The first scenario calculated the total number of possible patients with the disease based on prevalence indicators in the clinical guidelines. The second scenario considered the challenges of diagnosing yield diseases and calculated the number of patients likely to be successfully diagnosed in healthcare facilities, using diagnostic yield of rare diseases reported in the literature [30, 31]. All calculations were based on data regarding the population residing in Chengdu in 2019, the prevalence of RDs in China, and the average diagnostic yield of RDs. The demographic data were sourced from the Sichuan Statistical Yearbook, and the prevalence information was obtained from the official guidelines for RDs [32].

Price information regarding OMPs

There was potential competition between different treatments for rare diseases in the healthcare market, a factor that may have influenced price volatility. The main competitors for OMPs were alternative therapies. However, there was a lack of market information to discuss the impact of alternative medicines on orphan drug pricing. Therefore, this study’s discussion on the impact of orphan drug pricing on reimbursement budgets focused more on price fluctuations caused by factors such as payer-pharmaceutical company negotiations and philanthropic programs. Before healthcare payers planned to support high-priced OMPs for the treatments of RDs, a negotiation process was typically conducted with pharmaceutical companies to decide drug prices, which was an inevitable pricing procedure. Over the past three years, the National Healthcare Security Administration reduced the average price of drugs by 57.68% through negotiations to ensure the eligibility of drugs for inclusion in the reimbursement list [33,34,35]. Additionally, pharmaceutical companies often implemented charitable programs that reduced prices by one-third. Based on these practical evidences, OMP prices were assumed to follow the following reduction scenarios: i) original price; ii) 1/3 reduction; iii) 1/2 reduction; or iv) 2/3 reduction in the drug price.

Budget impact analysis

Cost of high-priced OMPs

From a societal perspective, the estimated cost of high-priced OMPs for RDs in Chengdu represented the overall budget planned for the entire region. To calculate this budget, the first step was to determine the drug expenditure for each individual patient based on the medicine specification and patient characteristics such as age and weight. Following this, the individual drug expenditures were aggregated to calculate the total estimated cost for high-priced OMPs intended for treating RDs in Chengdu.

Scenario analysis

To provide policymakers with projections of financial expenditures for OMPs and alternative policy designs, we presented a series of simulated policy scenarios, as shown in Fig. 2. These simulated policy scenarios referenced rare disease support policies in a few pilot regions and social health insurance reimbursement standards in China. The six assumed policy scenarios in the figure primarily consisted of variations in reimbursement ratio and per capita reimbursement ceiling [36, 37]. The reimbursement ratio was categorized into two types: uniform reimbursement rate and segmented reimbursement rate. The per capita reimbursement ceiling was categorized into three types: low, high, and no ceiling. A CNY 400,000 limit was set based on the upper reimbursement limit officially established for the social health insurance program. A CNY 850,000 limit was the sum of the upper reimbursement limit of social health insurance and critical illness insurance (CII).

Fig. 2
figure 2

Six assumed scenarios regarding the different policies

The budget for high-priced OMPs to treat RDs was estimated based on the calculated costs and policy simulations. Therefore, the budget for high-priced OMPs in Chengdu was expressed by the following formula:

$$B=\sum_{i}^{n}\frac{Dos{e}_{ij}}{Spe{c}_{j}}\times Pric{e}_{j}\times {R}_{ij}$$
(1)

where B was the budget for high-priced OMPs in Chengdu. Doseij denoted the annual projected dose for patient i using OMPj, Specj denoted the specification of OMPj, and Pricej denoted the unit price of OMPj, and Rij denoted the policy simulation for patient i using OMPj. There were n patients.

In the estimation process, the study generated 72 scenarios by combining the number of patients, drug prices, and simulated policies. From these scenarios, the range of variation in reimbursement budgets was calculated. In the simulated six policy scenarios, it was assumed that patient access to medicines remained constant across scenarios and that the medicine supply chain functioned normally, allowing patients to purchase the medicines they needed.

Budget estimation for the next three years

Further, to explore the budget sustainability of high-priced OMPs in Chengdu City, this analysis considered the fluctuation of the budget under the influence of three factors: i) An increase in the number of patients with rare diseases due to population growth; ii) An increase in the dosage of OMPs due to increased patient weight; and iii) The expanded range of reimbursement coverage due to newly released drugs. Based on the OMPs included in the list of multiple batches of new drugs under approval published by the National Medical Products Administration, this study estimated the budget growth in Chengdu for the years 2020 to 2022. Due to the uncertainty of drug approvals, it was challenging to forecast the list of drugs that would enter the market in the distant future. Therefore, this study analyzed the budget for the next three years as a short-term reference. The list of OMPs that had entered the approval process but were not yet available in China was presented in supplementary file 3. To provide policy recommendations under the optimal policy scenario previously assumed, the estimated budget under Scenario IV (i.e., segmented reimbursement rate, high ceiling) was selected, as shown in Fig. 2.

Results

Distribution characteristics of patients with RDs

Patient data obtained from the different sources were shown in Table 1. Overall, after excluding duplicate cases, a total of 98 patients with 9 target RD types were identified. The largest number of patients was found to have Spinal muscular atrophy (SMA), with 35 cases, while the number of patients with Fabry disease and Mucopolysaccharidosis IVA (MPS IVA) was relatively small, with 2 cases each. The estimated number of patients with the 9 target RD types based on prevalence and diagnostic yield in Chengdu, China, in 2019, were 957 and 381, respectively.

Table 1 The number of patients with rare diseases in Chengdu, China, 2019

The estimated costs for high-priced OMPs

Figure 3 showed the estimated costs for each individual patient and the total estimated costs of high-priced OMPs in Chengdu, China, in 2019. In Fig. 3A, the data collected during the survey phase on diagnosed patients indicated that the average annual drug expenditures for individual patients were extremely high. Most of the OMPs included in this study had an average annual out-of-pocket expenditure exceeding CNY 1 million per person. As for the total expenditure on high-priced OMPs in Chengdu, Fig. 3B provided relevant estimates based on varying drug prices and patient numbers. Based on the number of patients involved in the survey, the total costs of high-priced OMPs in Chengdu in 2019 were estimated to be CNY 179 million, CNY 120 million, CNY 90 million, and CNY 60 million under the conditions of the original price, 1/3 price reduction, 1/2 price reduction, and 2/3 price reduction, respectively. Based on the number of patients estimated by diagnostic yield, the total estimated costs were CNY 818 million, CNY 545 million, CNY 409 million, and CNY 272 million, respectively. Based on the number of patients estimated by prevalence, the total estimated costs were CNY 2.06 billion, CNY 1.37 billion, CNY 1.03 billion, and CNY 685 million, respectively.

Fig. 3
figure 3

The estimated costs on high-priced OMPs for rare diseases in Chengdu, China, 2019. A The estimated costs for each single patient, B The estimated total costs

The estimated budgets for high-priced OMPs for RDs based on scenario analysis

Figure 4 illustrated the fluctuation ranges of reimbursement budgets estimated by the study based on 72 simulated scenarios. The division of Fig. 4A-D was based on different scenarios of drug prices. Figure 4A demonstrated that when drug prices were controlled to remain at the original level, the reimbursement budget for OMPs ranged from CNY 36 million to CNY 1,794 million, depending on the number of patients and simulation policies. Figure 4B showed that with a one-third price reduction, the reimbursement budget for OMPs ranged from CNY 35 million to CNY 1,115 million under varying patient numbers and simulation policies. Figure 4C indicated that with a half price reduction, the reimbursement budget for OMPs ranged from CNY 34 million to CNY 791 million, again depending on the patient numbers and policies. Figure 4D demonstrated that with a two-thirds price reduction, the reimbursement budget for OMPs ranged from CNY 32 million to CNY 502 million, based on the number of patients and policies.

Fig. 4
figure 4

Budget of high-priced OMPs for rare diseases in different scenarios in Chengdu, China, 2019. A Original price, B The price was reduced by 1/3, C The price was reduced by 1/2, D The price was reduced by 2/3. (Policy scenario I) Uniform reimbursement rate, low ceiling; (Policy scenario II) Segmented reimbursement rate, low ceiling; (Policy scenario III) Uniform reimbursement rate, high ceiling; (Policy scenario IV) Segmented reimbursement rate, high ceiling; (Policy scenario V) Uniform reimbursement rate, no ceiling; (Policy scenario VI) Segmented reimbursement rate, no ceiling

The estimated budgets for high-priced OMPs for the next three years

Based on the resident population of Chengdu, the study estimated that the number of patients, according to prevalence, from 2020 to 2022 would be 972, 984, and 1,001, respectively; the number of patients estimated by diagnostic yield would be 390, 394, and 401, respectively. Additionally, 14 OMPs were projected to be newly marketed in the following three years. Figure 5A-D illustrated how reimbursement budgets fluctuated over the next three years under different combinations of drug price and patient volume scenarios. Figure 5A showed that the reimbursement budgets for OMPs in 2022, based on estimates of diagnosis and prevalence rates, were CNY 523 million and CNY 1.303 billion, respectively, assuming drug prices remained at their original levels. With a one-third reduction in drug prices, Fig. 5B showed that the reimbursement budgets for patients based on diagnosis yields and prevalence rates were CNY 513 million and CNY 1.28 billion, respectively, by the end of the three-year period. Figure 5C showed that, with a half reduction in drug prices, the estimated reimbursement budgets for 2022, depending on patient numbers, were CNY 491 million and CNY 1.225 billion, respectively. Finally, Fig. 5D showed that under a two-thirds reduction in drug prices, the reimbursement budget for high-priced OMPs ranged from CNY 359 million to CNY 894 million in 2022. Across all scenarios, particularly when patient numbers were estimated based on diagnosis yields, the four subfigures together indicated that the reimbursement budget for high-priced OMPs in Chengdu fluctuated between CNY 359 million and CNY 523 million in 2022.

Fig. 5
figure 5

Estimated budget of high-priced OMPs for rare diseases in Chengdu, China for the next three years. A Original price, B The price was reduced by 1/3, C The price was reduced by 1/2, D The price was reduced by 2/3

Discussions

In this paper, we assessed the budgetary impact of high-priced rare disease medications under various policy scenarios for a densely populated metropolitan city in China. First, to estimate the overall cost of OMPs, we selected relevant diseases based on the China Rare Disease Catalog and drug indications. Patient data for these diseases were then collected from multiple sources, including surveys and epidemiological information. Next, we gathered price data for each rare disease-related drug and combined it with patient data to estimate the total cost for all rare disease patients in Chengdu. Based on the current orphan drug reimbursement system in other regions of China, we designed several policy scenarios with different reimbursement ceilings and rates, ultimately calculating the reimbursement budgets required under each scenario. This study provides crucial budgetary insights for policymakers and supports the development of a rational reimbursement program for high-priced OMPs.

This study presents a new idea for budget impact analysis, addressing the challenges of data acquisition in estimating orphan drug reimbursement, particularly in the absence of actual prevalence data. Previous studies have often relied on a single data source, such as sample surveys or rare disease registry systems. However, the limited coverage of these data makes it difficult to provide robust evidence for estimating the budget required for rare disease medications. To address this issue, we integrated multiple data sources, including patient surveys, prevalence rates, and diagnostic yields, to form an interval estimate for the number of patients. This multi-source integration is more comprehensive and flexible, enhancing both the scientific rigor and accuracy of budget estimation. It also provides a practical methodological reference for countries and regions with incomplete data, making it particularly valuable for areas that have not yet established comprehensive rare disease information systems.

The budget estimates in this study show a lower diagnosis rate for rare disease patients compared with other related studies [38], and the reasons for this discrepancy may be multifaceted. Clinicians, especially in the case of ultra-rare diseases, tend to have limited experience in identifying and diagnosing these conditions, increasing the risk of misdiagnosis and delayed diagnosis [39]. Additionally, rare disease diagnoses are primarily concentrated in large tertiary hospitals where the studies were conducted, leading to regional imbalances in resources and a more complex, time-consuming diagnostic process for patients [27]. Although the diagnosis rates reported in this study are lower than those in some parts of the literature, this is not contradictory. The survey data used in this study provide a lower-bound estimate of the number of rare disease patients in Chengdu. Future studies should expand data sources and incorporate more field surveys and dynamic data tracking to improve the accuracy of these estimates.

The results of this study have important implications for policy design. By analyzing reimbursement budgets under different policy scenarios, we demonstrate the financial requirements in various scenarios. Given the differences in the capacity of health insurance funds across regions, this study provides a flexible framework for developing or adjusting reimbursement policies for high-cost OMPs. Regions can tailor policy parameters based on their economic capacity and the status of their health insurance funds, enabling more equitable and sustainable drug coverage for patients with rare diseases. The flexibility in budget impact analysis is especially important for regions with varying levels of economic development, enhancing the applicability of orphan drug reimbursement policies.

This study initially estimates the budgetary impact for one year, but it is important to note that as the survival time of patients with rare diseases increases, the associated costs will rise annually, placing greater pressure on health insurance funds. Additionally, the number of patients benefiting from reimbursement will expand as new OMPs and indications become available. Therefore, this study extends the analysis by projecting the reimbursement budget for the next three years. This projection provides policymakers with a forward-looking perspective, allowing them to anticipate future funding needs and incorporate longer-term financial planning into policy design. This analysis also offers a valuable reference for future studies on budgetary impact.

This study has several limitations. First, it did not account for the patients’ use of OMPs, such as medication adherence and adverse drug reactions, when estimating the reimbursement budget. This is because the number of patients with rare diseases is extremely small, leading to insufficient clinical trial evidence for OMPs. Future studies need to consider the impact of drug utilization to reinforce the feasibility of confirming budgets in the context of increased access to drug therapy for patients with rare diseases. Second, in the analysis of budget sustainability, the study only included the types of drugs that were most recently approved to enter the market and lacked consideration of the competitive relationships between pharmaceutical companies in the market. These factors can also directly affect drug pricing and patient affordability. Finally, although we set up diverse scenarios to discuss fluctuations in reimbursement budgets, other unobserved factors or complex mechanisms may affect the results. However, the study provides useful insights into the exploration of OMP budgets. Subsequently, more comprehensive studies can be conducted in the future based on practical data on OMP therapy.

Conclusions

This study found that the number of patients is a key factor in the budget estimation process by modeling various scenarios. Variations in patient estimates had a considerable impact on the final budget results, indicating that the inclusion of patient data should be handled with caution during budget estimation. When the data required for estimating the budget for OMPs are incomplete or uncertain, we recommend using interval estimation methods, which provide upper and lower budget ranges to improve the reliability of the estimates and offer policymakers more flexible and informative decision support.

Availability of data and materials

The data that support the findings of this study are available from health care providers but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the medical institution and the corresponding author.

Abbreviations

OMPs:

Orphan medicinal products

RDs:

Rare diseases

CII:

Critical illness insurance

SMA:

Spinal muscular atrophy

MPS IVA:

Mucopolysaccharidosis IVA

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Acknowledgements

We would thank Zhusheng Li and Jing Liao from Chengdu Healthcare Security Administration for their support in data collection.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 71874116 and 72074163), National Social Science Fund of China (Grant No. 21ZDA104), Taikang Yicai Public Health and Epidemic Control Fund, Bill & Melinda Gates Foundation, Sichuan Science and Technology Program (Grant No. 2022YFS0052, 22ZDYF0318 and 2021YFQ0060), and Sichuan University (Grant No. 2018hhf-27 and SKSYL201811).

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XZ, JP, conceptualization; XZ, TZ, JZ, DZ, data collecting; XZ, data analyzing and manuscript writing; all authors, manuscript reviewing. All authors read and approved the final manuscript.

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Correspondence to Jay Pan.

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Zhang, X., Zhou, T., Zhou, J. et al. Budget impact analysis of high-priced orphan medicinal products intended for the treatment of rare diseases in China: evidence from a densely populated metropolis of Chengdu. BMC Health Serv Res 24, 1123 (2024). https://doi.org/10.1186/s12913-024-11632-6

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