Intended for healthcare professionals

CCBYNC Open access
Analysis Health Technology Assessment in China

Use of real world data to improve drug coverage decisions in China

BMJ 2023; 381 doi: https://doi.org/10.1136/bmj-2021-068911 (Published 15 June 2023) Cite this as: BMJ 2023;381:e068911

Read the full collection: Health Technology Assessment in China

  1. Wen Wang, associate professor123,
  2. Jing Tan, associate professor123,
  3. Jing Wu, professor4,
  4. Shiyao Huang, research associate123,
  5. Yunxiang Huang, PhD candidate123,
  6. Feng Xie, professor of health economics5,
  7. Xin Sun, professor123
  1. 1Chinese Evidence-based Medicine Centre, and National Clinical Research Centre for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
  2. 2NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
  3. 3Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
  4. 4School of Pharmacology Science and Technology, Tianjin University, Tianjin, China
  5. 5Department of Clinical Epidemiology and Biostatistics, University of Macmaster, Hamilton, Canada
  1. Correspondence to: Xin Sun sunx79{at}hotmail.com

Wen Wang and colleagues discuss the rationale and propose a framework for using real world evidence to support coverage decisions in Chinese setting

Medical expenditure has substantially increased in China in the past decade with the introduction of new drugs with higher prices. Spending on basic medical insurance increased from ¥486bn (£58bn; €66bn; $70bn) in 2012 to ¥1760bn in 2018. The increased burdens posed great challenges for China’s healthcare system. In response, the National Healthcare Security Administration (NHSA) was established in 2018, and a dynamic adjustment mechanism was implemented for the national formulary, known as the national reimbursement drug list (NRDL). The list is reassessed regularly to add new drugs, remove obsolete drugs, or change reimbursement restrictions.

Applications for inclusion in the reimbursement list are open once a year. Renewal of coverage of existing drugs, which includes pricing renegotiation through reassessment of value, is also considered at the same time. The decisions about whether drugs are covered are based on multiple factors, including unmet needs, clinical benefit, and economic value. Since 2018, 507 drugs have been added to the list and 391 drugs removed.1

In the past few years, China has implemented expedited procedures to ensure faster access to new drugs. Within this context, the National Medical Products Administration (NMPA), which licenses products for use in China, adopted several policy amendments to speed up access to drugs newly approved for market. Upon approval, the NHSA would accelerate the inclusion of new drugs in the reimbursement list at the annual review. For instance, 74 drugs were included in the list in 2021, 66 of which were approved by NMPA in 2020.1 In particular, several oncology drugs were added within six months of regulatory approval.2 Orphan drugs are also prioritised for inclusion.3

Gaps in evidence for coverage decisions

Substantial challenges arise in assessing the value of drugs to inform coverage decisions. One problem is inadequate clinical evidence. Traditionally, the assessment process largely relies on evidence derived from randomised controlled trials, which are typically designed with strict patient selection criteria, implemented with reinforced compliance to treatments, and have relatively short follow-up. The drug performance in randomised trials may differ from that in the real world. Furthermore, the trials are often multicentre international trials in populations that might not fully represent the Chinese population.

Obtaining clinical evidence is particularly challenging for oncology and orphan drugs because they are often approved through expedited review pathways and use data from single arm trials. For instance, 52.5% of new drugs were approved through expedited pathways from 2017 to 2020 in China.4 Consequently, the effectiveness and comparative effectiveness was often uncertain at the time of approval. Moreover, these trials often had relatively short follow-up periods and evaluated short term treatment outcomes. This raises concerns about the clinical benefits of the drugs, especially in the long term.

Lack of clinical evidence is the tip of the iceberg. Some of the data required for economic modelling are not available from randomised trials, or the evidence is often weak or lacking. In many cases, for example, the data on disease burden and treatment patterns are primarily based on data from populations outside China,56 where the dose and course of treatments may differ from those in other countries.78

Economic models are commonly used to incorporate the data from multiple sources to produce cost effectiveness evidence to inform coverage decisions. However, the models are often criticised for lacking transparency and robustness, especially when locally relevant data are limited.9 Indeed, the lack of locally relevant data can substantially compromise the economic models as well as affecting estimations of budget impact. The expected usage, duration, and dosage of drugs in highly controlled trial settings are likely to differ from those in routine practice.

Use of real world data for coverage decisions

In the past several years, the extensive use of information technologies in healthcare and public health practices has boosted the generation of diverse real world data sources in China, including both national and local data sources.10 Electronic medical records, patient registries, and claims databases are now well established across the country. Together with sound epidemiological designs and robust analyses, these data can provide locally applicable evidence for assessing drug value.111213141516 Typically, these data are observational in nature and are used in observational study designs. However, randomised trials that are built on real world settings and make use of the strengths of these data systems are more able to control for different sources of biases than observational studies. Consequently, these randomised trials may offer better real world evidence about the value of drugs.17

In China, real world data may provide an array of local evidence to support decisions about whether a drug should be included in the reimbursement list and whether it should be renewed.

Use of real world data to support initial coverage decisions

Decisions about including a drug in the list for the first time require extensive and broad local evidence, ranging from disease epidemiology to treatment costs (table 1). Some of this evidence may be readily available from real world data, such as on treatment patterns, healthcare costs, and disease courses. These data are often embedded in electronic medical records and healthcare claims, and studies have shown the potential usefulness of these data for generating evidence.192526 However, when new drugs are approved under accelerated coverage review, they have not been used in the routine care. Thus, the evidence on efficacy, comparative effectiveness, and safety is often limited, particularly if only single arm trials are available.

Table 1

Framework for using real world data to supporting initial drug coverage decisions in China

View this table:

One solution is to generate evidence about comparative effectiveness before the initial coverage decision. This would require drug manufacturers to produce evidence from routine use of these drugs. However, this is unlikely to be feasible for all new drugs given the current healthcare security policy in China. We thus propose using real world evidence generated from the Hainan Boao Lecheng international medical tourism pilot zone, known as the Hainan model.27

In 2019, the Chinese government issued a policy to accelerate access to new and clinically demanding medical products. Under this policy, Chinese patients can receive new medical products that have been licensed by major regulatory authorities overseas in Boao’s healthcare institutions before the products are approved by the NMPA. The medicines are granted special approval for use in Boao for the indications approved overseas, and patients with a confirmed diagnosis are treated in the routine healthcare setting rather than as part of a trial. After treatment, they return home with discharge medications and are followed up at local medical institutions. Currently, the longest follow-up is three years. The data collected during routine use of the new medical products at Boao and follow-up information generated at local medical institutions are integrated through a patient registry.

Data from Boao can be used to assess the comparative effectiveness of new treatments through observational study designs or pragmatic randomised trials. Although randomised trials overcome the problem of confounding, observational designs may be more appropriate for rare diseases or when an urgent decision is needed.

Using data generated from Boao may have limitations, particularly that the travelling population receiving these new products may differ from general residents. As a result, the data may not be fully representative of the Chinese population. However, the special policy measures at Boao have created supportive conditions for new medical products to speed up their launch in the China market, and tens of thousands of people have received these new medical products at Boao.

So far real world data from Boao have been used to support regulatory approval without conducting a trial in China. This type of evidence could also be used to support reimbursement decisions to minimise delay to market entry. When no local trial is conducted, the real world data can be used to support regulatory approval and coverage decisions concurrently. In such a scenario, the main contribution of real world evidence is to provide evidence on effectiveness and safety of these drugs. If the registry contains information on clinical care and treatment outcomes of patients receiving standard care as well as those receiving the new drugs, statistical methods can be used to compare the two approaches. Additionally, these data can provide evidence on treatment patterns and costs.

When a local trial has been conducted for regulatory approval, real world data from Boao would be used primarily to support coverage decisions. In this case, real world data from Boao can provide an array of evidence for coverage decisions, including on real world effectiveness and safety, treatment patterns, and costs, which would inform economic modelling.

Use of real world data to support renewal

The reimbursement of new drugs often results in important changes in clinical practice, patient outcomes, and use of healthcare resources. In such cases, real world data can provide critical information on decisions about renewals (table 2). As new drugs are increasingly used over time a broader array of evidence may be accumulated. Various data sources are readily available under such circumstances and may cover all aspects of information for renewal. In particular, claims databases may offer unequivocal advantages over electronic medical records in the reassessment of treatment patterns, resources use, and budget impact. Other databases such as electronic medical records and patient registries can be important sources for assessing real world effectiveness and safety.23031 Notably, pragmatic randomised trials may offer stronger evidence on comparative effectiveness than observational studies, as well as providing data on economic impact to support coverage renewal.

Table 2

Framework for using real world data to support decisions to renew drug coverage in China

View this table:

Show me the data: improving real world data

Despite the great potential of real world data in informing coverage decisions in China, important challenges remain. Data relevance and quality are probably the primary obstacles. Available data may not be able to answer questions of interest for decisions, and data inaccuracy and incompleteness are not uncommon.323334 Databases from single institutions often have incomplete outpatient data and lack information on patient follow-up, limiting their usefulness for assessing long term effectiveness and safety. In addition, data sharing and access are also limited, making linkage of data from multiple medical institutions and subsequent use less likely. As a result, research questions for coverage decisions may not be well addressed.

Another challenge lies in the limited research capacity to produce trustworthy real world evidence. Data quality is often suboptimal because of lack of transparent and appropriate data collection. These are issues not only about data collection, but also about appropriate use of data for research purposes. Although several advanced methods have been proposed, selecting an appropriate method to adequately disentangle confounding is a challenge.3536 Currently, few research groups have received training in methodology. The process of handling data remains obscure. Development of quality standards is a high priority for the production of real world evidence, and transparency also needs to be improved.

A few important initiatives to address these problems have emerged in China. National policies on the development and application of “big” healthcare data have been issued, facilitating the production and application of real world data.37 Population based healthcare data systems have been increasingly established in large municipal cities, with data linkage put in place.2538 These linked data can provide important information for assessing the value of drugs. A working panel consisting of experts from academic institutions and authorities has also been formed to develop technical guidance documents to improve the transparency and quality of real world evidence. In the future, a real world evidence community should be developed that consists of policy makers, healthcare providers, patients, academic institutions, and industry. Such a community would be likely to result in better use of real world evidence to support coverage decisions in China.

Key messages

  • Important gaps exist in evidence to support decisions on which drugs should be reimbursed in China

  • Real world data collected in routine practice could provide useful information on the value of drugs both for initial coverage decisions and renewal

  • Data from use of new drugs in the Boao Lecheng pilot zone could also provide early information on Chinese context

  • Policy, academic, and technical forces are becoming available to improve the quality and relevance of data available to guide coverage decisions

Acknowledgments

We thank John Cairns for helpful suggestions and comments.

Footnotes

  • Contributors and sources: WW’s research focuses on real world data studies and clinical evaluation of drugs. JT has extensive expertise in real world data studies. SYH focuses on health policy. YXH researches real world data and health policy. FX’s research interests include health technology assessment and health economics. JW has expertise in pharmacoeconomics and health policy. XS is an expert in evidence based medicine and real world data research and is the guarantor of the article. WW drafted and critically revised the manuscript. JT, SYH, YXH, FX, and JW offered critical suggestions and revisions. XS conceptualised, drafted, and critically revised the manuscript.

  • Competing interests We have read and understood BMJ policy on declaration of interests and declare that the study was supported by the National Key R&D Program of China (Grant No 2017YFC1700406 and 2017YFC1700400), the National Natural Science Foundation of China (Grant No 82225049, 72104155), China Medical Broad (Grant No CMB 19-324), and 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (Grant No ZYYC08003). These funders had no role in the study design, collection, analysis, or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

  • Provenance and peer review: Commissioned; externally peer reviewed.

  • This article is part of a series (info to come)

http://creativecommons.org/licenses/by-nc/4.0/

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

References